Search

We extend the prophet inequality problem to a competitive setting. At every period, a new realization of a random variable with a known distribution arrives, which is publicly observed. Then, two players simultaneously decide whether to pick an available value or to pass and wait until the next period (ties are broken uniformly at random). As soon as a player gets a value, he leaves the market and his payoff is the value of this realization. In the first variant, namely the "no recall" case, the agents can only bid at each period for the current value. In a second variant, the "full recall" case, the agents can also bid for any of the previous realizations which has not been already selected. For each variant, we study the subgame-perfect Nash equilibrium payoffs of the corresponding game. More specifically, we give a full characterization in the full recall case and show in particular that the expected payoffs of the players at any equilibrium are always equal, whereas in the no recall case the set of equilibrium payoffs typically has full dimension. Regarding the welfare at equilibrium, surprisingly the best equilibrium payoff a player can have may be strictly higher in the no recall case. However, the sum of equilibrium payoffs is weakly larger when the players have full recall. Finally, we show that in the case of 2 arrivals and arbitrary distributions, the prices of Anarchy and Stability in the no recall case are at most 4/3, and this bound is tight. [ABSTRACT FROM AUTHOR]

Drawing a referent of a to-be-remembered word often results in better recognition and recall of this word relative to a control task in which the word is written, a pattern dubbed the drawing effect. Although this effect is not always found in pure lists, we report three experiments in which the drawing effect emerged in both pure- and mixed-lists on recognition and recall tests, though the effect was larger in mixed lists. Our experiments then compared drawing effects on memory between pure- and mixed-list contexts to determine whether the larger mixed-list drawing effect reflected a benefit to draw items, a cost to write items, or a combination. In delayed recognition and free-recall tests, a mixed-list benefit emerged for draw items in which memory for mixed-list draw items was greater than pure-list draw items. This mixed-list drawing benefit was accompanied by a mixed-list writing cost compared to pure-list write items, indicating that the mixed-list drawing effect does not operate cost-free. Our findings of a pure-list drawing effect are consistent with a memory strength account, however, the larger drawing effect in mixed lists suggest that participants may also deploy a distinctiveness heuristic to aid retrieval of drawn items. [ABSTRACT FROM AUTHOR]

Intrusion Detection Model (IDM) is an essential device for network defence in current trend. Malicious users analyse the vulnerabilities of IDSs to capture unauthorized access. Furthermore, intrusion detection encompasses numerous numerical attributes and models, resulting in elevated detection errors and triggering false alarms. Hence, optimal computational intelligence shall be incorporated in IDM to achieve high detection rate and less number of false alarms. Considering the same, a new hybrid IDM framework is developed as the combination of Fuzzy Genetic Algorithm with Multi-Objective Particle Swarm Optimization that maximizes the detection accuracy, minimizes the false alarms and takes less computational complexity which will be explained first phase. The existing IDSs are constraint to the information trained incur into false positives based on user continuity for normal activity. The objective of this proposal is to extract optimal classification rules automatically from training data that helps to identify types of attacks correctly including the unknown attack types. For achieving this goal, Multi-Objective Particle Swarm Optimization (MOPSO) is used as classifier to enhance the identification of the rare attack classes within the IDM. The effectiveness of this method lies in its capacity to leverage information within an unfamiliar search space, guiding subsequent searches towards valuable subspaces. It provides better separability of various classes' i.e. normal behaviour and false alarms. In this FGA-MOPSO model, Principal Component Analysis (PCA) serves as the feature selection technique employed to identify pertinent features within the dataset, thereby enhancing the classifier's performance and Fuzzy Genetic Algorithm (FGA) is used to create new population for training the classifier with the help of three operations namely selection, crossover and mutation that helps to practice more patterns in training phase and to obtain better understanding of the proposed classifier. The simulation will illustrate that the system is competent to speed-up the training and testing process of intrusions detection is important for network applications.Please confirm if the author names are presented accurately and in the correct sequence (given name, middle name/initial, family name). Author 1 Given name: [Arun Kumar] Last name [Ramamoorthy]. Also, kindly confirm the details in the metadata are correct.Checked and Verified for Author 1. In Author 2 name, Given Name was [K.] and last name was[Karuppasamy], But its is just the opposite. Given Name is [Karuppasamy] and Last Name is [K.]. I have edited it. [ABSTRACT FROM AUTHOR]

Background: The causality assessment tool can be utilized to assist patients in identifying adverse drug reactions (ADRs). Aim: To evaluate the accuracy of the causality assessment tool for patients identifying ADRs compared to assessments made by pharmacists, and to explore how patients recall and recognize symptoms as ADRs. Method: Mixed methods study consisting of self-administered questionnaires (phase 1) and semi-structured, face-to-face interviews (phase 2) with patients who had experienced ADRs in the past year at a tertiary care hospital in Thailand. Results: Out of 769 questionnaires, 716 were returned and 622 of these were both valid and had at least one ADR (86.8%). Classification of patient-reported symptoms using the causality assessment tool found 12 (1.9%) highly-probable ADRs, 399 (64.1%) probable ADRs, 207 (33.3%) possible ADRs, and 4 (0.6%) that were not classified as ADRs. There was fair agreement between patient-assessed and pharmacist-assessed causality classifications using the Naranjo algorithm (K = 0.268) and the World Health Organization Uppsala Monitoring Centre (WHO-UMC) criteria (K = 0.373). The timing relationship between the occurrence of symptoms and administration of a suspected drug was the most frequently mentioned reason that patients gave for recalling and recognizing suspected ADRs. Conclusion: Promoting the causality assessment tool for use by patients in collaboration with healthcare professionals is likely to enhance patients' ability to correctly identify ADRs and ultimately contribute to increased medication safety. [ABSTRACT FROM AUTHOR]

Thunderstorms are natural disasters that impact people, animals, and the economy. Thunderstorms' detrimental repercussions can be avoided by identifying their occurrence in advance. The current work, in this respect, uses soft computing techniques such as K-Nearest Neighbour (KNN), Decision Tree (DT), Logistic Regression (LR), and Support Vector Machine (SVM) with various kernel functions to categorize the occurrence of thunderstorms over Ranchi, India. These techniques were trained and tested using two data sets: daily average and hourly meteorological datasets. The primary purpose of this study is to find which dataset-classifier combination is optimal for categorizing thunderstorm occurrence in Ranchi. No classifier was found to adequately classify either the Day Average Dataset or the Modified Day Average Dataset. On the other hand, the Hourly Dataset was found to be more balanced in terms of the number of thunderstorms that occurred than the Day Average and Modified Average datasets. The F-Score value of the incidence of thunderstorm incidents after using different classifiers was used to compare the outcomes of these datasets. The results reveal that using SVM with radial basis function. The Hourly Dataset is the best for thunderstorm day classification. For the overall and only incidence of thunderstorms classes, SVM-RBF gets 0.81 and 0.74 F-Scores, respectively. Other approaches, like grid search and Bagging, have been used to increase SVM-RBF performance. Grid search and Bagging are used on SVM-RBF to produce a hybrid Grid-Bag-SVM-RBF classifier with 82.04% accuracy and F-scores of 0.83 and 0.78 for overall and just thunderstorm occurrence, respectively. [ABSTRACT FROM AUTHOR]

Educational materials for preschool children are often embedded with fantastical elements. Nevertheless, there is still little empirical evidence on their effectiveness, especially as concerns long-term retention. Virtual reality offers new ecological possibilities for investigating this type of learning, especially through the impact of immersion. In a between-design study, 168 children aged four to six years followed a virtual reality presentation on China presented by either a realistic young girl or an anthropomorphic animal. The level of immersion was manipulated, with half of the children following the presentation in immersive virtual reality (IVR) and the other half in desktop virtual reality (D-VR). Participants were asked to complete a new/old recognition task and a quiz task immediately after the presentation and once again one week later, with an additional transfer task being added for the second series. One week later, children performed significantly better on the new/old recognition task in the realistic condition when compared to the anthropomorphic condition. However, there were no differences observed in the quiz task and in the transfer task. It therefore seems that, under certain conditions, children remember a cultural presentation better when it is presented by a realistic avatar. The results further showed that the children performed significantly worse in the IVR conditions on all tasks. A possible explanation for this result is that IVR demands excessive cognitive resources from preschool children. Further studies should explore this unexpected result, as well as what could be done to make IVR effective for learning in preschool children. [ABSTRACT FROM AUTHOR]

Previous research established that readers acquire accurate and inaccurate information from fiction. The current study explored factors that might moderate these effects. Participants read fictional stories that each contained three assertions. The first two assertions in each story were either correct information or implausible misinformation, allowing a manipulation of the (implicit) credibility of the narrator. The last assertion in each story was the critical one, and was correct information, implausible misinformation, or plausible misinformation. After reading, participants answered general knowledge questions that were related to the critical assertions they encountered during reading. Encountering misinformation led to lower accuracy than being presented with correct information, and being presented with plausible misinformation led to higher production of that misinformation. The narrator credibility manipulation interacted with the type of critical assertion: When the critical assertion was presented accurately in a story, credible narrators presenting true critical assertions led to greater accuracy on the general knowledge test than when noncredible narrators presented this same information. These findings are discussed with respect to theories of validation during language comprehension. [ABSTRACT FROM AUTHOR]

This study examines the relationship between birth memory and recall and the perception of traumatic birth in women who were a postpartum one-year period and the affecting factors. This descriptive and correlational study was conducted with 285 participants in the pediatric department of a state university medical school. Data were collected using a participant information form, Birth Memories and Recall Questionnaire, and Perception of Traumatic Childbirth Scale. In the study, it was determined that the women had a moderate level of birth memories and recall, and the rate of those with a "high" and "very high" perception of traumatic childbirth was 45.9%. According to path analysis, Birth Memories and Recall Questionnaire score and educational status (primary secondary school) have a positive and significant effect on the perception of traumatic birth. The perception of traumatic birth was a predictor that explained 17.3% of birth memories and recall. Nearly half of the study participants perceived the experience of giving birth as traumatic, and birth memories and recall were at a moderate level. Improving women's perception of education and traumatic birth will contribute to positive birth memories and to create positive emotions when they remember their birth. [ABSTRACT FROM AUTHOR]

The continuous advancement of computer networks has given rise to grave concerns regarding security and susceptibility. Network administrators utilize intrusion detection systems (IDS) to deliver essential network security. Current IDS devices produce false alarms in response to routine user activities rather than detecting novel assaults. Neural networks may be used to overcome this problem and increase detection accuracy. In this paper, we propose a hybrid approach based on neural networks and correlation-based feature selection to detect anomalies. Experimental research is done on the standard dataset NSL-KDD for intrusion detection using current attacks. We introduce a novel hybrid crowd search analysis optimization with an artificial neural network (HCSAOANN). The findings demonstrate that it outperforms in high accuracy, precision, F1-Score, and recall. In the proposed HCSAOANN algorithm, to explore the feature space, we merged the crow search optimization (CSO), which can converge into the overall best solution in the search function, with the upgraded version of the crow search analysis method. Some performance criteria were applied in the studies using an artificial neural network (ANN) as a classifier. The HCSAOANN methodology outperformed as compared to the previous fuzzy technique and achieved 98% accuracy and a 98% precision rate, which is 2.2% better than the previous CSO-ANFIS technique and 8.87% superior to the FC-ANN technique. [ABSTRACT FROM AUTHOR]

The aim of this study is to compare substance use by forensic patients who have been formally recalled to hospital with those who remain conditionally discharged in the community. Data were collected from recalled patients (recalled group; RG) and those who remain conditionally discharged (discharged group; DG). A drug history was taken alongside wellbeing measures and screening of case notes. Cannabis use was significantly more prevalent in the RG post-discharge and subsequently in the community. Analysis comparing substance use in the community at different time points found that the DG was significantly less likely to be using cannabis post-discharge and subsequently in the community, compared to use at the time of their index offence. In contrast, the RG showed no change in cannabis use across time. Users of cannabis and alcohol post-discharge spent a significantly shorter amount of time in the community than non-users. Cannabis and alcohol appear associated with recall to hospital. [ABSTRACT FROM AUTHOR]

Recall deals with the encoding, storage, and retrieval of information. Bilinguals have greater flexibility for recall as the person will have multiple language choices to come out with the target word. In other words, a bilingual will have more lexical choices to retrieve the target word. The current study investigates cross-language recall abilities in balanced bilinguals. A cohort group of participants were divided into three subgroups. The first sub-group of participants was asked to recall in second language while the stimuli/ target words were presented in the first language. The second group of participants was asked to recall in their first language while the stimuli/target words were presented in the second language. The third group of participants was allowed to carry out a free recall task. The descriptive analysis backed by statistical analysis revealed no significant difference between three groups suggesting that balanced bilinguals would have greater cognitive flexibility resulting in superior cross-language recall abilities in this population. [ABSTRACT FROM AUTHOR]

Driven by Internet technology, the explosion of video content is growing exponentially. Hence, the need for video analysis and interpretation by human cognition is taken into account. Memorability is one of the cognitive measures that define the recall ability of watched visual content. Prior research works are done on image memorability, and video memorability modeling is least focused. Moreover, the computational features need to be robust to build a strong video memorability predictor model that is also not focused earlier. This paper proposes a robust, scalable and novel Stacked Bin- Convolutional Neural Network (SB_CNN) based Sparse Low-Rank Regressor (SLRR) model that classifies and predicts the interesting events of videos by performing a robust feature scaling process and intelligent memorability tasks. Inspired by the Low-Rank Representation (LRR) property in noise data handling, the proposed feature extraction process covered the low-rank issue on attributes such as objects, scenes, and annotated to the original feature space. The estimated sparse coefficient vector is fed into the Stacked Bin- Convolutional Neural Networks (SB-CNN) model that establishes the connections between video frames and their memorability scores by means of electing the best frames by following the objectives of Multi-Attribute Decision Making (MADM) technique. The selected frames are fed into the CNN classifier. With the estimated decision score, the proposed classifier has effectively predicted the events from relevant video with the better recall time 49. 9247. Experiments conducted on public datasets, SumMe and SUN database demonstrates the efficacy of the proposed technique. [ABSTRACT FROM AUTHOR]

Experiments comparing intuitive and reflective decisions provide insights into the cognitive foundations of human behavior. However, the relative strengths and weaknesses of the frequently used experimental techniques for activating intuition and reflection remain unknown. In a large-scale preregistered online experiment (N = 3667), we compared the effects of eight reflection, six intuition, and two within-subjects manipulations on actual and self-reported measures of cognitive performance. Compared to the overall control, the long debiasing training was the most effective technique for increasing actual reflection scores, and the emotion induction was the most effective technique for increasing actual intuition scores. In contrast, the reason and the intuition recall, the reason induction, and the brief time delay conditions failed to achieve the intended effects. We recommend using the debiasing training, the decision justification, or the monetary incentives technique to activate reflection, and the emotion induction, the cognitive load, or the time pressure technique to activate intuition. [ABSTRACT FROM AUTHOR]

Many social media (SM) platforms have emerged as a result of the online social network's (OSN) rapid expansion. SM has become important in day-to-day life, and spammers have turned their attention to SM. Spam detection (SD) is done in two different ways, such as machine learning (ML) and expert-based detection. The expert-based detection technique's accuracy depends on expert knowledge, and it takes huge time to detect the spams. Thus, ML-based spam detection is preferred in OSN. Spam identification on social networks is a difficult operation involving a variety of factors, and spam and ham have resulted in an imbalanced data distribution, which gives flexibility to spammers for corrupting our devices. SD based on ML algorithms like logistic regression (LR), K-nearest neighbor (KNN), decision trees (DT), random forest (RF), support vector machine (SVM) and eXtreme gradient boosting (XGB), voting classifier (VC) and extra tree classifier (ETC) are used to design the address balance and to attain high assessment accuracy in an imbalanced datasets. ETC method minimizes the bias through the original sampling process. For reducing processing complexity, the ETC method uses a smaller size constant factor instead of a larger one. Thus, the ETC technique produces better data splitting than DT and RF techniques. Text is vectorized by vectorizers, and all the relative results are stored in it. The VC is an ensemble method that integrates predictions form several methods to forecast an output class depending on which predictions have the highest probability. The multi-class results are aggregated and forecast for the majority voted class. The experimental result shows that, as compared to KN, NB, ETC, RF, SVC, LR, XGB and DT, the proposed VC provides a higher classification accuracy rate of 97.96%, 97.56% of precision, 89.95% of recall and 91.96% of F1-measures. Similarly, ETC provides 97.77% accuracy, 98.31% of precision, 84.78% of recall and 91.05% of F1-measures. Compared to conventional ML algorithms, VC and ETC provide higher accuracy, precision, recall and F1-measures. Thus, ETC and VC are preferable for spam detection. The website has been designed to detect messages as spam or not. [ABSTRACT FROM AUTHOR]

A large body of research in the study of memory has accumulated to date on the part-list cuing impairment in recall. This phenomenon refers to the lower recall of studied information in the presence of some studied words provided as retrieval cues compared to when no cues are provided. We review the current literature on the part-list cuing impairment in recall and report a meta-analysis utilizing the procedural and statistical information obtained from 109 samples (N = 5,605). In each experiment, participants studied a list of words and subsequently performed a recall task either in the presence or absence of part-list cues. The meta-analysis shows that the part-list cuing impairment is a robust, medium-sized impairment (Cohen, 1988). This recall impairment was not significantly sensitive to the number of study items provided, the relationship among study items, the number of part-list cues provided, the amount of time provided for recall, or certain other factors of interest. Our analyses also demonstrate that longer retention periods between study and retrieval mitigate the part-list cuing impairment in recall. We discuss the implications of meta-analysis results for elements of experimental design, the findings of past literature, as well as the underlying theoretical mechanisms proposed to account for this impairment in recall and the applied consequences of this recall impairment. [ABSTRACT FROM AUTHOR]

Retrieval-induced forgetting (RIF) is typically observed in verbal memory tasks, although a few studies have observed RIF in visual spatial tasks. This leaves an open question as to whether RIF depends on semantic identity to link across semantic properties of objects, or whether RIF depends on access to the perceptual features of objects. To explore RIF of spatial positions, we report three experiments utilizing a continuous measure of the accessibility and precision for objects that were distinguished by their shape, color, and spatial region. After a study phase, half of the objects in a single-color category were selectively practiced for their spatial position, by requiring the object to be placed in the exact spatial position seen previously. Finally, all objects were probed for their spatial position at test. No RIF occurred for objects that shared only one color feature but were located within the same spatial region (in Experiment 1) or when objects shared the same color, but were located within different spatial regions (in Experiment 3). However, RIF did occur when objects shared the same spatial region and the same color (Experiment 2). Overall, the interim recall of the spatial positions of cue-objects impairs access to the position of other cue-objects within the same color category, but only when these groups had sufficient overlapping and competing features. The finding that RIF only occurs to the accessibility of spatial positions, not the precision of visual spatial memory, was interpreted as consistent with inhibitory theories of forgetting. [ABSTRACT FROM AUTHOR]

Constructive episodic retrieval processes play an adaptive role in supporting divergent thinking (i.e., creatively combining diverse bits of information) and means-end problem solving (i.e., generating steps to solve a social problem). However, the constructive nature of episodic memory that supports these adaptive functions also leads to memory error. In three experiments we aimed to identify a direct link between divergent thinking and means-end problem solving – as assessed in the Alternative Uses Task (AUT) and Means-End Problem Solving (MEPS) task – with the generation of false memories in the Deese-Roediger-McDermott paradigm. In Experiment 1, we replicated prior findings where false memory was positively correlated with performance on the AUT, and also showed for the first time that increased performance in the MEPS task is associated with increased false recall. In Experiment 2, we demonstrated that the link between false recall and performance on the MEPS task did not extend to other forms of problem solving, as assessed with the Everyday Descriptions Task (EDT). In Experiment 3, we showed that when the EDT was preceded by the MEPS task in an attempt to influence participants to engage in a similar episodic-problem solving strategy, performance in both tasks was correlated with false memory. These findings provide evidence for a direct link between the adaptive benefits of constructive episodic processes, in the form of enhanced divergent creative thinking and problem solving, and costs, in the form of increased memory error. [ABSTRACT FROM AUTHOR]

Panoramic views offer information on heading direction and on location to visually navigating animals. This review covers the properties of panoramic views and the information they provide to navigating animals, irrespective of image representation. Heading direction can be retrieved by alignment matching between memorized and currently experienced views, and a gradient descent in image differences can lead back to the location at which a view was memorized (positional image matching). Central place foraging insects, such as ants, bees and wasps, conduct distinctly choreographed learning walks and learning flights upon first leaving their nest that are likely to be designed to systematically collect scene memories tagged with information provided by path integration on the direction of and the distance to the nest. Equally, traveling along routes, ants have been shown to engage in scanning movements, in particular when routes are unfamiliar, again suggesting a systematic process of acquiring and comparing views. The review discusses what we know and do not know about how view memories are represented in the brain of insects, how they are acquired and how they are subsequently used for traveling along routes and for pinpointing places. [ABSTRACT FROM AUTHOR]

Human health issues require estimation of heart rhythm anomalies, brain wave pattern abnormalities, blood parameter outliers, social media analysis, and more. Researchers propose many deep learning models to estimate these issues, and each uses neural optimizations to classify input parameters into disease types. But most of the existing models are either highly complex, or are not capable of capturing multiple body parameters, in order to perform comprehensive disease diagnosis. To overcome the challenges of feature representation and classification, this text proposes design of a high-density feature representation model for multidomain analysis of human health issues. The proposed model collects patient-specific datasets from multidomain sources like blood reports, electrocardiograms (ECGs), electroencephalograms (EEGs), social media posts, and physiological parameters to generate comprehensive health feature vectors. Fourier, Wavelet, Convolutional, and Gabor components transform vectors into multimodal feature sets. Representing the input vector into these components improves feature density, which helps identify multiple disease types. Bee Colony Optimization Models estimate high-variance feature sets using these features. A 1D CNN Model classifies these sets to help identify disease classes. The model performed well in accuracy, precision, recall, and AUC (area under the curve) tests for heart, brain, and psychological issues, making it suitable for clinical use. Compared to standard disease classification models, it improved accuracy, precision, recall, and fMeasure by 3.2%, 3.5%, 2.9%, and 2.5%, making it useful for real-time deployments. [ABSTRACT FROM AUTHOR]

There are multiple distinct sources through which numerous news contents that occur in digital medium tend to converge. Web contents constitute massive number of features. Complete coverage of all kinds of news is absolutely vital to retain customer confidence and to have a competitive edge over other news agencies. Aggregating such massive news content from different heterogeneous sources requires an integration of convergent computing. Classification of these online news is a challenging task in the age of Internet where news keeps flowing from several heterogeneous sources. Due to constant rise in manipulation of web contents, accurate classification of digital news is the need of the hour. Precise detection of specific news into their respective class is a major challenge in recent times. In this scenario, the need of an automated predictive-based approach can be of great use in effective organization and classification of news in a pool of web portals. This research study comprises the application of Bernoulli model to determine the effectiveness of multi-class digital news categorization that arrive in real time. The system model presented in this analysis was evaluated using python, and the result was demonstrated using six distinct classes of news with a 6000 feature size dataset from TagMyNews dataset. The classification accuracy using the Bernoulli model was computed to be 98.4%, while the evaluated precision metric was 92.7%, and recall value was 90.6%. The F-Score metric generated an optimum value of 91.4%. The execution time for Bernoulli model was only 12 s. The computed result using Bernoulli model was compared with some other related renowned existing works and the results generated by Bernoulli model gave optimum performance and the news classification efficiency is highly enhanced. [ABSTRACT FROM AUTHOR]

The recall of an episodic narrative, as a function of presentation modality (audio-visual sketch, audio-only), was examined across three cumulative experiments. Experiment 1: participants (N = 84) were asked to recall a narrative delivered via audio, or audio accompanied by a sketch. Experiment 2: participants (N = 116) were asked to recall a narrative delivered via sketch or audio-only format, both of which varied in duration/number of details to recall. Experiment 3: participants (N = 173) recalled a narrative that varied by modality (audio-only, non-concurrent sketch, hybrid sketch, concurrent sketch) and access/no access to the sketch during recall. Aggregating the data across the three experiments (mini meta-analysis) revealed that participants provided with sketch-based narratives recalled more information and had higher recall accuracy (Md = 0.80 [95% CI = 0.56, 1.04], Z = 6.51, p < 0.001) than those provided with audio-only narratives. The results also revealed that concurrent sketches led to the best recall of a narrative, and having access to the sketch during recall had minimal impact on recall. The potential implications of the results for recalling information in various contexts are discussed. [ABSTRACT FROM AUTHOR]

The importance of infrared or thermal imaging can be seen in various applications based on Machine Vision. The real-time based application in surveillance systems like detection and tracking of moving objects has shown strong potential. According to available research, there exist some significant issues in colored video frames due to sudden environmental change or other external effects like varying backgrounds. In contrast, the thermal video frames are less affected by sudden light intensity and varying backgrounds. The suggested work utilizes a combined approach of Pearson's correlation and Background Subtraction (BGS) technique over thermal video frame sequences for detection of moving objects. The automatically generated threshold value boosts the efficacy of the algorithm in differentiating between background and foreground frames. The quantitative analysis shows the higher value of different performance parameters like Accuracy, F-measure, Recall, etc. Moreover, the qualitative analysis of obtained results strongly indicates that the proposed method outperforms when compared with the existing methods. [ABSTRACT FROM AUTHOR]

While items learned immediately before testing are generally remembered better than prior items in a study list, in delayed testing this relationship is reversed, yielding a negative recency effect. To adjudicate between the strategic rehearsal and spacing accounts of this phenomenon, we examined performance of 169 participants on a delayed recognition test following multiple sessions requiring the study and immediate free recall testing of 16 lists of 16 words. This revealed a strong effect of the amount of spacing between initial study position and initial free recall position on the degree of negative recency, supporting the spacing account. Furthermore, these spacing effects were nonmonotonic, suggesting that they are mediated by consolidation processes. Additional analyses indicate that strategies and rehearsal opportunities may also contribute to the effects of within-list encoding position on subsequent long-term memory, but for recall more than for recognition. [ABSTRACT FROM AUTHOR]

Individuals can take on various roles in conversation. Some roles are more active, with the participant responsible for guiding that conversation in pursuit of the group’s goals. Other roles are more passive, like when one is an overhearer. Classic accounts posit that overhearers do not form conversational common ground because they do not actively participate in the communication process. Indeed, empirical findings demonstrate that overhearers do not comprehend conversation as well as active participants. Little is known, however, about long-term memory for conversations in overhearers. Overhearers play an important role in legal settings and dispute resolution, and it is critical to understand how their memory differs in quality and content from active participants in conversation. Here we examine – for the first time – the impact of one’s conversational role as a speaker, addressee, or overhearer on subsequent memory for conversation. Data from 60 participants recalling 60 conversations reveal that after a brief delay, overhearers recall significantly less content from conversation compared to both speakers and addressees, and that the content they do recall is less accurately sourced to its actual contributor. Mnemonic similarity is higher between active conversational participants than between active participants and overhearers. These findings provide key support for the hypothesis that the process of forming common ground in interactive conversation shapes and supports memory for that conversation. [ABSTRACT FROM AUTHOR]

Motivational and emotional influences on memory have been studied extensively; however, despite the link between these constructs, they have been studied in separate lines of research, with very little work examining their effects concurrently. The current study takes a novel approach to manipulate motivational and emotional influences orthogonally, and within the same task, to test their interplay on intentional memory formation. If emotion and reward motivation are tightly linked, they may rely on overlapping cognitive mechanisms, thus we would not expect emotion and reward to interact in memory. Alternatively, they could be distinct constructs and therefore would boost memory when both are included in the same experimental trial, above and beyond additive effects. To test these competing predictions, in Experiment 1, participants (n = 180) completed an old/new recognition memory task with emotional (negative, positive) and neutral words intentionally encoded with high or low reward anticipation cues. In Experiment 2, participants (n = 159) encoded emotional and neutral words with a high or low reward cue, but memory was tested with free recall using study–test blocks. The findings from both experiments converged. There were main effects of emotion and reward in generally hypothesized directions, but no evidence of an interaction between these factors. This is in line with the prediction that emotion and reward motivation are similar constructs. Their combination within a trial does not boost memory above and beyond either of these factors alone perhaps indicating these constructs have similar cognitive mechanisms. [ABSTRACT FROM AUTHOR]

Almost 20% of all male employees in Germany who become unemployed return to their previous employers. Such temporary layoffs and the subsequent recalls are often used by firms to shift their labor costs onto society and the unemployment benefit system, which has led to various legislation aimed at prohibiting or reducing this undesired instrument in Germany. I analyze the interplay between fixed-term contracts, which can be used to undermine legal regulations, and temporary layoffs for men. For this purpose, I use comprehensive administrative data at individual level, complemented by various firm characteristics. My results show that unemployed workers who had previously worked on fixed-term contracts are more often recalled by their previous firms than workers who had permanent contracts. Moreover, older and low-skilled employees as well as migrants are particularly affected by the interplay between fixed-term contracts and temporary layoffs. This is also confirmed for women in an additional robustness analysis. [ABSTRACT FROM AUTHOR]

Challenges to verbal encoding may affect the ability of autistic individuals to express their ideas. Therefore, visuospatial expression may represent a person's knowledge and skills more accurately than spoken language. To test this hypothesis, we asked seven autistic adults to linguistically retell and visuospatially reenact several animated clips. On average, visuospatial responses contained more correct elements than spoken responses. The level of intention of the three stimulus categories did not systematically affect response accuracy. Participants who produced visuospatial responses before spoken responses and those who had watched a greater number of stimuli assigned higher intentionality to shapes in the animations that were designed to elicit mentalizing. The modality used for expression may affect accuracy of responses by autistic individuals. [ABSTRACT FROM AUTHOR]

Although autism spectrum disorders (ASD) are commonly characterized by diminished episodic memory, the literature in this area is mixed. We address these inconsistent findings by employing multilevel Bayesian meta-analysis to quantify episodic memory differences between individuals with ASD and typically developing (TD) controls. We used meta-regression to evaluate the effects of test modality (e.g., word list, story recall), delay interval (immediate vs. delayed), retrieval demands (recognition vs. recall), and sensory modality (auditory vs. visual) on episodic memory in ASD. A total of 338 effect sizes from 113 empirical articles, including 5,632 unique participants (ASD = 2,777, TD = 2,855), were included. Results show that the memory deficits associated with ASD were larger for recall (g = -0.52, se = 0.04, 95% CrI [-0.60, -0.43]) compared to recognition (g = -0.25, se = 0.05, 95% CrI [-0.35, -0.14]) and differed based on the testing modality. For example, effect sizes were smallest for words (g = -0.28, se = 0.05, 95% CrI [-0.38, -0.18]), pictures (g = -0.38, se = 0.07, 95% CrI [-0.52, -0.24]), and figure reproduction (g = -0.49, se = 0.11, 95% CrI [-0.70, -0.27]). However, effect sizes for sentences (g = -0.59, se = 0.20, 95% CrI [-1.00, -0.21]), stories (Hedges' g = -0.54, se = 0.08, 95% CrI [-0.69, -0.38]) and staged events (g = -0.75, se = 0.10, 95% CrI [-0.95, -0.55]) were much larger. These findings suggest that ASD is associated with a small to medium reduction in scores on episodic memory tests relative to TD controls. [ABSTRACT FROM AUTHOR]

Relying on external memory aids is a common memory strategy that has long allowed us to "remember" vast amounts of information more reliably than with our internal memory alone. However, recent work has provided evidence consistent with the idea that offloading memory demands encourages a reduced engagement in intentional or top-down memory strategies/efforts, leading to lower memory performance in general. Evidence for this view comes from results demonstrating a reduced primacy effect but intact recency and isolation effects when individuals could offload memory demands (but had to unexpectedly rely on their internal memory at test). In the present investigation, we attempt a replication of these critical results, given some inconsistencies in the findings between studies. In addition, we extend the examination of offloading's impact on memory via examining individual differences in reliance on the external store (when available) and different strategies for the use of that store. Results of the replication are generally consistent with previous research. An individual differences analysis yielded results consistent with the notion that increased reliance on an external store can compromise internal/biological memory in the absence of that store. Finally, a verbal model of offloading memory demands within a framework of effort and study time allocation is presented. Together, the results both reinforce extant research and extend it in new directions. [ABSTRACT FROM AUTHOR]

Many studies suggest that information about past experience, or episodic memory, is divided into discrete units called "events." Yet we can often remember experiences that span multiple events. Events that occur in close succession might simply be linked because of their proximity to one another, but we can also build links between events that occur farther apart in time. Intuitively, some kind of organizing principle should enable temporally distant events to become bridged in memory. We tested the hypothesis that episodic memory exhibits a narrative-level organization, enabling temporally distant events to be better remembered if they form a coherent narrative. Furthermore, we tested whether post-encoding memory consolidation is necessary to integrate temporally distant events. In three experiments, participants learned and subsequently recalled events from fictional stories, in which pairs of temporally distant events involving side characters ("sideplots") either formed one coherent narrative or two unrelated narratives. Across participants, we varied whether recall was assessed immediately after learning, or after a delay: 24 hours, 12 hours between morning and evening ("wake"), or 12 hours between evening and morning ("sleep"). Participants recalled more information about coherent than unrelated narrative events, in most delay conditions, including immediate recall and wake conditions, suggesting that post-encoding consolidation was not necessary to integrate temporally distant events into a larger narrative. Furthermore, post hoc modeling across experiments suggested that narrative coherence facilitated recall over and above any effects of sentence-level semantic similarity. This reliable memory benefit for coherent narrative events supports theoretical accounts which propose that narratives provide a high-level architecture for episodic memory. [ABSTRACT FROM AUTHOR]

Purpose: We evaluated whether recalling personal achievements would result in an increase in feelings of pride, and if so, whether social anxiety (SA) moderated this relationship.Community samples were recruited in 2022 via the online platform Prolific. On average, participants in both studies were aged 32–33, had some post-secondary education, were married/cohabitating and self-identified as White (60%), Asian (10%) or Black (7%). Participants completed measures of state pride and affect before and after different types of writing tasks. Study 1 participants (N = 398) recalled and wrote about either one or three personal achievement experiences. To control for the general effects of self-reflection, Study 2 participants (N = 396) wrote about either achievement or non-achievement-oriented events.Mixed-model Time X Condition ANCOVAs were conducted with pride as the dependent variable and social anxiety as covariate. Both studies revealed significant increases in pride and positive affect following recall of achievement events, and (Study 2) no significant change following recall of non-achievement events. SA had no significant effect on change in pride, suggesting that SA did not suppress the benefits of achievement recall.Facilitating recall of personal achievements may help to heighten pride and positive affect regardless of social anxiety level. The results support further research on the role of pride in social anxiety.Methods: We evaluated whether recalling personal achievements would result in an increase in feelings of pride, and if so, whether social anxiety (SA) moderated this relationship.Community samples were recruited in 2022 via the online platform Prolific. On average, participants in both studies were aged 32–33, had some post-secondary education, were married/cohabitating and self-identified as White (60%), Asian (10%) or Black (7%). Participants completed measures of state pride and affect before and after different types of writing tasks. Study 1 participants (N = 398) recalled and wrote about either one or three personal achievement experiences. To control for the general effects of self-reflection, Study 2 participants (N = 396) wrote about either achievement or non-achievement-oriented events.Mixed-model Time X Condition ANCOVAs were conducted with pride as the dependent variable and social anxiety as covariate. Both studies revealed significant increases in pride and positive affect following recall of achievement events, and (Study 2) no significant change following recall of non-achievement events. SA had no significant effect on change in pride, suggesting that SA did not suppress the benefits of achievement recall.Facilitating recall of personal achievements may help to heighten pride and positive affect regardless of social anxiety level. The results support further research on the role of pride in social anxiety.Results: We evaluated whether recalling personal achievements would result in an increase in feelings of pride, and if so, whether social anxiety (SA) moderated this relationship.Community samples were recruited in 2022 via the online platform Prolific. On average, participants in both studies were aged 32–33, had some post-secondary education, were married/cohabitating and self-identified as White (60%), Asian (10%) or Black (7%). Participants completed measures of state pride and affect before and after different types of writing tasks. Study 1 participants (N = 398) recalled and wrote about either one or three personal achievement experiences. To control for the general effects of self-reflection, Study 2 participants (N = 396) wrote about either achievement or non-achievement-oriented events.Mixed-model Time X Condition ANCOVAs were conducted with pride as the dependent variable and social anxiety as covariate. Both studies revealed significant increases in pride and positive affect following recall of achievement events, and (Study 2) no significant change following recall of non-achievement events. SA had no significant effect on change in pride, suggesting that SA did not suppress the benefits of achievement recall.Facilitating recall of personal achievements may help to heighten pride and positive affect regardless of social anxiety level. The results support further research on the role of pride in social anxiety.Conclusions: We evaluated whether recalling personal achievements would result in an increase in feelings of pride, and if so, whether social anxiety (SA) moderated this relationship.Community samples were recruited in 2022 via the online platform Prolific. On average, participants in both studies were aged 32–33, had some post-secondary education, were married/cohabitating and self-identified as White (60%), Asian (10%) or Black (7%). Participants completed measures of state pride and affect before and after different types of writing tasks. Study 1 participants (N = 398) recalled and wrote about either one or three personal achievement experiences. To control for the general effects of self-reflection, Study 2 participants (N = 396) wrote about either achievement or non-achievement-oriented events.Mixed-model Time X Condition ANCOVAs were conducted with pride as the dependent variable and social anxiety as covariate. Both studies revealed significant increases in pride and positive affect following recall of achievement events, and (Study 2) no significant change following recall of non-achievement events. SA had no significant effect on change in pride, suggesting that SA did not suppress the benefits of achievement recall.Facilitating recall of personal achievements may help to heighten pride and positive affect regardless of social anxiety level. The results support further research on the role of pride in social anxiety. [ABSTRACT FROM AUTHOR]

When speech is presented in noise, listeners must recruit cognitive resources to resolve the mismatch between the noisy input and representations in memory. A consequence of this effortful listening is impaired memory for content presented earlier. In the first study on effortful listening, Rabbitt, The Quarterly Journal of Experimental Psychology, 20, 241–248 (1968; Experiment 2) found that recall for a list of digits was poorer when subsequent digits were presented with masking noise than without. Experiment 3 of that study extended this effect to more naturalistic, passage-length materials. Although the findings of Rabbitt’s Experiment 2 have been replicated multiple times, no work has assessed the robustness of Experiment 3. We conducted a replication attempt of Rabbitt’s Experiment 3 at three signal-to-noise ratios (SNRs). Results at one of the SNRs (Experiment 1a of the current study) were in the opposite direction from what Rabbitt, The Quarterly Journal of Experimental Psychology, 20, 241–248, (1968) reported – that is, speech was recalled more accurately when it was followed by speech presented in noise rather than in the clear – and results at the other two SNRs showed no effect of noise (Experiments 1b and 1c). In addition, reanalysis of a replication of Rabbitt’s seminal finding in his second experiment showed that the effect of effortful listening on previously presented information is transient. Thus, effortful listening caused by noise appears to only impair memory for information presented immediately before the noise, which may account for our finding that noise in the second-half of a long passage did not impair recall of information presented in the first half of the passage. [ABSTRACT FROM AUTHOR]

A binary classification problem is common in medical field, and we often use sensitivity, specificity, accuracy, negative and positive predictive values as measures of performance of a binary predictor. In computer science, a classifier is usually evaluated with precision (positive predictive value) and recall (sensitivity). As a single summary measure of a classifier's performance, F1 score, defined as the harmonic mean of precision and recall, is widely used in the context of information retrieval and information extraction evaluation since it possesses favorable characteristics, especially when the prevalence is low. Some statistical methods for inference have been developed for the F1 score in binary classification problems; however, they have not been extended to the problem of multi-class classification. There are three types of F1 scores, and statistical properties of these F1 scores have hardly ever been discussed. We propose methods based on the large sample multivariate central limit theorem for estimating F1 scores with confidence intervals. [ABSTRACT FROM AUTHOR]

Urban dynamics refers to a phenomenon wherein certain factors contribute to imparting changes to an urban area. These factors can aid in either growth or deterioration of the city. One important factor that acts as a threat to the urban environment is rapid urbanization. To monitor and control such urban expansion, prediction is a necessity. It will throw some light on how the city grows and how it will affect the environment and living conditions. A precise and accurate dataset on land use/land cover (LULC) is a must for such analysis. Several methods exist to classify satellite data based on spectral reflectance with advancements in satellite technology and the means to process it. One problem is that there is no single classifier that can produce accurate results on LULC predictions. Each classifier varies in its performance based on several factors. Hence this research was carried out by choosing three classifiers, namely maximum likelihood (MLC), random forest algorithm (RFA) as well as support vector machines (SVM), which are commonly used. Its performance is then evaluated for a pan-sharpened Landsat 8 data of the study area, i.e., Salem and its surrounding urban agglomerations. From the results, it was inferred that the overall accuracies of MLC, SVM and RFA are 0.885, 0.930 and 0.945, respectively. Though the MLC accuracy is acceptable, it had many misclassifications of other classes into built-up classes. SVM and RFA performances were found good overall, but SVM had fewer misclassifications compared to RFA. SVM produced results close to reality and was concluded as the best classification method for pan-sharpened Landsat 8 data for Salem and its surrounding area. [ABSTRACT FROM AUTHOR]

In the classic view of verbal short-term memory, immediate recall is achieved by maintaining phonological representations, while the influence of other linguistic information is negligible. According to language-based accounts, short-term retention of verbal material is inherently bound to language production and comprehension, thus also influenced by semantic or syntactic factors. In line with this, serial recall is better when lists are presented in a canonical word order for English rather than in a noncanonical order (e.g., when adjectives precede nouns rather than vice versa; Perham et al., 2009, Quarterly Journal of Experimental Psychology, 62[7], 1285–1293). However, in many languages, grammaticality is not exclusively determined by word order. In German, an adjective–noun sequence is grammatical only if the adjective is inflected in congruence with the noun's person, number, and grammatical gender. Therefore, we investigated whether similar effects of syntactic word order occur in German. In two modified replications of Perham et al.'s study, we presented lists of three pairs of adjectives and nouns, presented in adjective–noun or in noun–adjective order. In addition, we manipulated morphosyntactic congruence between nouns and adjectives within pairs (Exp. 1: congruently inflected vs. uninflected adjectives; Exp. 2: congruently inflected vs. incongruently inflected adjectives). Both experiments show an interaction: Word order affected recall performance only when adjectives were inflected in congruence with the corresponding noun. These findings are in line with language-based models and indicate that, in a language that determines grammaticality in an interplay of syntactic and morphosyntactic factors, word order alone is not sufficient to improve verbal short-term memory. [ABSTRACT FROM AUTHOR]

Ironically, the presentation of a subset of studied material as retrieval cues at test often impairs recall of the remaining (target) material—an effect known as part-list cuing impairment. Part-list cues are typically provided at the beginning of the recall period, a time when nearly all individuals would be able to recall at least some studied items on their own. Across two experiments, we examined the effects of part-list cuing when student participants could decide on their own when the cues were presented during the recall period. Results showed that participants activated the cues relatively late in the recall period, when recall was already close to asymptote. Critically, such delayed cuing no longer impaired recall performance. The detrimental effect of part-list cuing, as it has been demonstrated numerous times in the memory literature, thus seems to depend on presentating the cue items (too) early in the recall period. [ABSTRACT FROM AUTHOR]

People can purposefully forget information that has become irrelevant, as is demonstrated in list-method directed forgetting (LMDF). In this task, participants are cued to intentionally forget an already studied list (list 1) before encoding a second list (list 2); this induces forgetting of the first-list items. Most research on LMDF has been conducted with short retention intervals, but very recent studies indicate that such directed forgetting can be lasting. We examined in two experiments whether core findings in the LMDF literature generalize from short to long retention intervals. The focus of Experiment 1 was on the previous finding that, with short retention interval, list-2 encoding is necessary for list-1 forgetting to arise. Experiment 1 replicated the finding after a short delay of 3 min between study and test and extended it to a longer delay of 20 min. The focus of Experiment 1 was on the absence of list-1 forgetting in item recognition, previously observed after short retention interval. Experiment 1 replicated the finding after a short delay of 3 min between study and test and extended it to longer delays of 20 min and 24 h. Implications of the results for theoretical explanations of LMDF are discussed. [ABSTRACT FROM AUTHOR]

What influences the extent to which perceptual information interferes with the contents of visual working memory? In two experiments using a combination of change detection and continuous reproduction tasks, I show that binding novelty is a key factor in producing interference. In Experiment 2, participants viewed arrays of colored circles, then completed consecutive change detection and recall tests of their memory for stochastically independent items from the same array. When the probe used in the change detection test was novel (i.e., required a "change" response), subsequent recall performance was worse than in trials with matching (i.e., "no change") probes, irrespective of whether or not the same item was tested in both phases. In Experiment 2, participants viewed arrays of oriented arrows, then completed a change detection (requiring memory) or direction judgement (not requiring memory) test, followed by recalling a stochastically independent item. Again, novel probes in the first phase led to worse recall, irrespective of whether the initial task required memory. This effect held whether the probe was wholly novel (i.e., a new feature presented at any location) or simply involved a novel binding (i.e., an old feature presented at a new location). These findings highlight the role of novelty in visual interference, consistent with the assumptions of computational models of WM, and suggest that new bindings of old information are sufficient to produce such interference. [ABSTRACT FROM AUTHOR]

Context: A hairy requirements engineering (RE) task involving natural language (NL) documents is (1) a non-algorithmic task to find all relevant answers in a set of documents, that is (2) not inherently difficult for NL-understanding humans on a small scale, but is (3) unmanageable in the large scale. In performing a hairy RE task, humans need more help finding all the relevant answers than they do in recognizing that an answer is irrelevant. Therefore, a hairy RE task demands the assistance of a tool that focuses more on achieving high recall, i.e., finding all relevant answers, than on achieving high precision, i.e., finding only relevant answers. As close to 100% recall as possible is needed, particularly when the task is applied to the development of a high-dependability system. In this case, a hairy-RE-task tool that falls short of close to 100% recall may even be useless, because to find the missing information, a human has to do the entire task manually anyway. On the other hand, too much imprecision, too many irrelevant answers in the tool's output, means that manually vetting the tool's output to eliminate the irrelevant answers may be too burdensome. The reality is that all that can be realistically expected and validated is that the recall of a hairy-RE-task tool is higher than the recall of a human doing the task manually. Objective: Therefore, the evaluation of any hairy-RE-task tool must consider the context in which the tool is used, and it must compare the performance of a human applying the tool to do the task with the performance of a human doing the task entirely manually, in the same context. The context of a hairy-RE-task tool includes characteristics of the documents being subjected to the task and the purposes of subjecting the documents to the task. However, traditionally, many a hairy-RE-task tool has been evaluated by considering only (1) how high is its precision, or (2) how high is its F-measure, which weights recall and precision equally, ignoring the context, and possibly leading to incorrect — often underestimated — conclusions about how effective it is. Method: To evaluate a hairy-RE-task tool, this article offers an empirical procedure that takes into account not only (1) the performance of the tool, but also (2) the context in which the task is being done, (3) the performance of humans doing the task manually, and (4) the performance of those vetting the tool's output. The empirical procedure uses (I) on one hand, (1) the recall and precision of the tool, (2) a contextually weighted F-measure for the tool, (3) a new measure called summarization of the tool, and (4) the time required for vetting the tool's output, and (II) on the other hand, (1) the recall and precision achievable by and (2) the time required by a human doing the task. Results: The use of the procedure is shown for a variety of different contexts, including that of successive attempts to improve the recall of an imagined hairy-RE-task tool. The procedure is shown to be context dependent, in that the actual next step of the procedure followed in any context depends on the values that have emerged in previous steps. Conclusion: Any recommendation for a hairy-RE-task tool to achieve close to 100% recall comes with caveats and may be required only in specific high-dependability contexts. Appendix C applies parts of this procedure to several hairy-RE-task tools reported in the literature using data published about them. The surprising finding is that some of the previously evaluated tools are actually better than they were thought to be when they were evaluated using mainly precision or an unweighted F-measure. [ABSTRACT FROM AUTHOR]

Exemplar models are often used in research on multiple-cue judgments to describe the underlying process of participants' responses. In these experiments, participants are repeatedly presented with the same exemplars (e.g., poisonous bugs) and instructed to memorize these exemplars and their corresponding criterion values (e.g., the toxicity of a bug). We propose that there are two possible outcomes when participants judge one of the already learned exemplars in some later block of the experiment. They either have memorized the exemplar and their respective criterion value and are thus able to recall the exact value, or they have not learned the exemplar and thus have to judge its criterion value, as if it was a new stimulus. We argue that psychologically, the judgments of participants in a multiple-cue judgment experiment are a mixture of these two qualitatively distinct cognitive processes: judgment and recall. However, the cognitive modeling procedure usually applied does not make any distinction between these processes and the data generated by them. We investigated potential effects of disregarding the distinction between these two processes on the parameter recovery and the model fit of one exemplar model. We present results of a simulation as well as the reanalysis of five experimental data sets showing that the current combination of experimental design and modeling procedure can bias parameter estimates, impair their validity, and negatively affect the fit and predictive performance of the model. We also present a latent-mixture extension of the original model as a possible solution to these issues. [ABSTRACT FROM AUTHOR]

According to the item/order hypothesis, high-frequency words are processed more efficiently and therefore order information can be readily encoded. In contrast, low-frequency words are processed less efficiently and the focus on item-specific processing compromises order information. Most experiments testing this account use free recall, which has led to two problems: First, the role of order information is difficult to evaluate in free recall, and second, the data from free recall show all three possible patterns of results: memory for high-frequency words can be better than, the same as, or worse than that for low-frequency words. A series of experiments tested the item/order hypothesis using tests where the role of order information is less ambiguous. The item/order hypothesis predicts better performance for high- than low-frequency words when pure lists are used in both immediate serial recall (ISR) and serial reconstruction of order (SRO) tests. In contrast, when mixed (alternating) lists are used, it predicts better performance for low- than for high-frequency words with ISR tests, but equivalent performance with SRO tests. The experiments generally confirm these predictions, with the notable exception of a block order effect in SRO tasks: When a block of low-frequency lists preceded a block of high-frequency lists, a high-frequency advantage was observed but when a block of high-frequency lists preceded a block of low-frequency lists, no frequency effect was observed. A final experiment provides evidence that this block order effect is due to metacognitive factors. [ABSTRACT FROM AUTHOR]

In this study, a method for a robot to recall multiple grasping methods for a given object is proposed. The aim of this study was for robots to learn grasping methods for new objects by observing the grasping activities of humans in daily life without special instructions. For this setting, only one grasping motion was observed for an object at a time, and it was never known whether other grasping methods were possible for the object, although supervised learning generally requires all possible answers for each training input. The proposed method gives a solution for that learning situations by employing a convolutional neural network with automatic clustering of the observed grasping method. In the proposed method, the grasping methods are clustered during the process of learning of the grasping position. The method first recalls grasping positions and the network estimates the multi-channel heatmap such that each channel heatmap indicates one grasping position, then checks the graspability for each estimated position. Finally, the method recalls the hand shapes based on the estimated grasping position and the object's shape. This paper describes the results of recalling multiple grasping methods and demonstrates the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

The article is devoted to the study of the effectiveness of the convolutional neural networks (CNNs) application for solving the problem of tennis players face recognition. For ease of analysis, two players were selected: Roger Federer (Switzerland) and Rafael Nadal (Spain). To isolate faces from the publicly available images of the players, it is proposed to use the Haar cascades and the Viola–Jones method. These images are used to train and test convolutional networks with various parameters: architecture, including the number of layers; epochs of learning; optimization methods; and also when applying various regularization methods, including drop out and data augmentation. The use of regularization made it possible to reduce the effect of overfitting. In addition, the efficiency of networks with pretrained layers based on transfer learning methods is investigated. The VGG-16 convolutional network is chosen for the transfer learning. For a large number of different combinations of convolutional networks, metrics are calculated for precision, recall, and accuracy. The average gain for these parameters is 25% with the best set of characteristics for convolutional networks and training. It is also shown in the study that the patterns of applying certain modifications are universal for optical images. In particular, similar architectures and training approaches are also tested for the problem of recognizing cats and dogs on a much larger dataset. The study confirms the average increase in recognition metrics of 26%. [ABSTRACT FROM AUTHOR]

Generating a sufficient number of regions with high accuracy is an important objective in the region proposal generation techniques. This paper presents a new, robust, and effective approach, which is based on the bottom-up segmentation, to produce a pool of well-quality regions. After image segmentation, the segmented candidates are expanded into the surrounding regions. The suggested algorithm produces some enlarged regions, which better cover objects and stuff. The proposed process can be applied in three different modes, namely fixed_mode, all_mode, and efficient_mode. The fixed_mode extends each region into parts of all the adjacent regions using an extension controller, which considers adjacent sequential pixels for each point on the region boundary. In all_mode, the current region is merged with all the adjacent regions to generate a larger region. The efficient_mode is then implemented using the accumulation of the results from both the fixed_mode and all_mode. Besides, the algorithm can be repeated in the fixed_mode and all_mode by considering a variety of values for the extension controller factor. No features are required to be extracted in the proposed algorithm, except for the image segmentation stage. In this study, four challenging datasets known as MSRC, VOC2007, VOC2012, and COCO 2017 are used to compare the proposed algorithm with other segmentation and region proposal algorithms. As a significant advantage compared to well-known region proposal algorithms, our approach achieves a greater Recall with the desirable number of regions. Furthermore, the algorithm shows a good improvementin extraction of small, medium, and large objects. [ABSTRACT FROM AUTHOR]

We acquire a lot of information about the world through texts, which can be categorized at the broadest level into two primary genres: narratives and exposition. Stories and essays differ across a variety of dimensions, including structure and content, with numerous theories hypothesizing that stories are easier to understand and recall than essays. However, empirical work in this area has yielded mixed results. To synthesize research in this area, we conducted a meta-analysis of experiments in which memory and/or comprehension of narrative and expository texts was investigated. Based on over 75 unique samples and data from more than 33,000 participants, we found that stories were more easily understood and better recalled than essays. Moreover, this result was robust, not influenced by the inclusion of a single effect-size or single study, and not moderated by various study characteristics. This finding has implications for any domain in which acquiring and retaining information is important. [ABSTRACT FROM AUTHOR]

One of the major challenges related to face recognition is the effective face representation. In this paper, a new rotation algorithm is proposed to make the slanting face to upright position. Gabor features have been identified to be effective for face recognition. To investigate the prospective of Gabor phase and its combination with magnitude for face recognition, Rotated Local Gabor XOR patterns (RLGXP), which encodes the Rotated Gabor phase by using the local XOR pattern (LXP) operator is first proposed, which increases the effectiveness of face representation. Then, Cell-based Fishers linear discriminant (CFLD) to reduce the dimensionality of the proposed descriptor is introduced. Finally, by using CFLD, local patterns of Rotated Gabor magnitude and phase for face recognition are combined. This proposed approach is evaluated on MIT, GTAVE, PIE, CMU and Home databases. Moreover, to enhance the effectiveness of face representation through feature design (RLGXP), the Gabor filter parameters like mask size, scale and orientation are very important. The effectiveness of the rotating local Gabor model + CFLD method was verified for upslopes and uprights of different data sets. The analysis of face feature representation using the RLGXP + CFLD method basically depends on the phase range, the appropriate choice of the number of sub-cells per pattern map and size of neasret region. Here, the sensitivity analysis used for the above three parameters is best selected and executed. This paper also investigates the effect of different Gabor filter parameters on face recognition accuracy. Parameter Selection Investigation result proves that Gabor wavelets comprising 5 scales and 8 orientations have been chosen to extract Facial Feature Points (FFP). The final result shows that Rotated Local Gabor Features provides the best face recognition accuracy. [ABSTRACT FROM AUTHOR]

The convolutional neural network (CNN) has brought about a drastic change in the field of image processing and pattern recognition. The filters of CNN model correspond to the activation maps that extract features from the input images. Thus, the number of filters and filter size are of significant importance to learning and recognition accuracy of CNN model-based systems such as the biometric-based person authentication system. The present paper proposes to analyze the impact of varying the number of filters of CNN models on the accuracy of the biometric-based single classifiers using human face, fingerprint and iris for person identification, and also biometric-based super-classification using both bagging- and programming-based boosting methods. The present paper gives an insight to the optimal set of filters in CNN model that gives the maximum overall accuracy of the classifier system. [ABSTRACT FROM AUTHOR]

With enormous development in diverse kinds of images through electronic communication networks, it becomes a demanding chore to retrieve the efficient result from a wide collection of images. CBIR i.e. content based image retrieval answers the problem as it selects visual image contents or features to deal with images. Since images are represented by certain features to facilitate accurate retrieval of the required images here this article proposed a method to extract certain features from the image based on color, texture, and shape. This paper has proposed an approach named as weighted edge matching information retrieval (WEMIR) to perform content based image retrieval. It is a fusion approach to extract the color, texture and shape features from images. With the single feature extraction, acceptable outcomes are not formed. Hence multi-feature extraction is developed to perform retrieval of images. To extract the color feature, the higher order of confined mean is used to improve the lower contrast to gain high contrast. To extract the texture features multi optimization techniques are used and for shape feature extraction weighted edge matching technique is used. Pixel content is extracted from each image present in the database as well as for the test image provided. Based on the proposed method WEMIR the optimal features are obtained for query and image database. By using image distance measure corresponding images are retrieved from the database. The competence of the proposed WEMIR is measured using precision and recall. The proposed method shows better retrieval results while compared with the traditional methods. [ABSTRACT FROM AUTHOR]

Although several nonhuman animals have the ability to recognize and match templates in computerized tasks, we know little about their ability to recall and then physically manufacture specific features of mental templates. Across three experiments, Goffin cockatoos (Cacatua goffiniana), a species that can use tools in captivity, were exposed to two pre-made template objects, varying in either colour, size (long or short) or shape (I or L-shaped), where only one template was rewarded. Birds were then given the opportunity to manufacture versions of these objects themselves. We found that all birds carved paper strips from the same colour material as the rewarded template, and half were also able to match the size of a template (long and short). This occurred despite the template being absent at test and birds being rewarded at random. However, we found no evidence that cockatoos could carve L-shaped pieces after learning that L-shaped templates were rewarded, though their manufactured strips were wider than in previous tests. Overall, our results show that Goffin cockatoos possess the ability to physically adjust at least the size dimension of manufactured objects relative to a mental template. This ability has previously only been shown in New Caledonian crows, where template matching was suggested as a potential mechanism allowing for the cumulative cultural transmission of tool designs. Our results show that within avian tool users, the ability to recreate a physical template from memory does not seem to be restricted to species that have cumulative tool cultures. [ABSTRACT FROM AUTHOR]

Correlated quality metrics extracted from a source code repository can be utilized to design a model to automatically predict defects in a software system. It is obvious that the extracted metrics will result in a highly unbalanced data, since the number of defects in a good quality software system should be far less than the number of normal instances. It is also a fact that the selection of the best discriminating features significantly improves the robustness and accuracy of a prediction model. Therefore, the contribution of this paper is twofold, first it selects the best discriminating features that help in accurately predicting a defect in a software component. Secondly, a cost-sensitive logistic regression and decision tree ensemble-based prediction models are applied to the best discriminating features for precisely predicting a defect in a software component. The proposed models are compared with the most recent schemes in the literature in terms of accuracy, area under the curve, and recall. The models are evaluated using 11 datasets and it is evident from the results and analysis that the performance of the proposed prediction models outperforms the schemes in the literature. [ABSTRACT FROM AUTHOR]