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Seiring dengan semakin berharganya nilai sebuah informasi dan semakin banyak sumber-sumber informasi, maka semakin banyak pula kebutuhan manusia untuk dapat menemukan informasi yang sesuai keperluannya dengan cepat. Information Retrieval (Perolehan Informasi) merupakan suatu pencarian informasi(biasanya berupa dokumen) yang didasarkan pada suatu query (inputan user) yang diharapkan dapat memenuhi keinginan user dari dokumen yang ada. Dua aspek penting dalam konsep Information Retrieval yang diterapkan dalam sebuah perancangan mesin pencari yaitu repesentasi dari informasi dan pengukuran yang akan mengukur nilai kesamaan antara dua obyek. Informasi yang dapat direpresentasikan menjadi sebuah obyek yang direpresentasikan dalam berbagai bentuk dan model (heterogeneous). Keadaan ini mengakibatkan bahwa pencarian untuk suatu obyek informasi yang diinginkan akan dapat dipetakan kepada beberapa obyek informati yang dinilai relevan. Relevansi dua informasi di-ukur dari keberadaan kata kunci (keyword) dan bobotnya. Konsekuensi logis atas keadaan ini adalah bahwa dalam melakukan pencarian obyek yang diinginkan, ditemukan terdapat ketidakpastian (uncertainly) terhadap penggunaan keyword pada query oleh pengguna dengan keberadaan keyword pada dokumen. Pada penelitian ini akan difokuskan pada studi efektifitas vektor model dengan menggunakan algoritma steming poter untuk membentuk kata-kata menjadi sebuah kata baku serta pembobotan frekuwensi term untuk menentukan tingkat kepentingan setiap indeks term dalam sebuah dokumen dan fungsi kesamaan cosine dalam mengukur kemiripan queri dengan dokumen. Hasil pengujian nilai kualitas rata-rata precesion dan recall bahwa untuk semua bentuk query pencarian dokumen mempunyai nilai precesion 100% artinya baik pencarian berdasarkan bentuk query isi, judul dan dokumen mempunyai precesion yang baik sedangkan hasil pengujian recall menunjukkan hasil yang berbeda, dimana pencarian berdasarkan bentuk queri isi dokumen menempati recall tertinggi sebesar 90%, sedangkan pencarian berdasarkan bentuk queri judul dokumen dengan nilai recall 70% dan pencarian berdasarkan bentuk query dokumen menempati posisi terendah dengan recall 33%.

Modern methods for network analytics provide an opportunity to revisit preconceived notions in the classification of diseases as “clusters of symptoms”. Curated collections which were subsequently modified, like the Diagnostic and Statistical Manuals of Mental Disorders “DSM-IV” and the most recent addition, DSM-5 allow us to introspect, using the solution provided by modern algorithms, if there exists a consensus between the clusters obtained via a data-driven approach, with the current classifications. In the case of mental disorders, the availability of a follow-up consensus collection (e.g. in this case the DSM-5), potentially allows investigating if the classification of disorders has moved closer (or away) to what a data-driven analytic approach would have unveiled by objectively inferring it from the data of DSM-IV. In this contribution, we present a new type of mathematical approach based on a global cohesion score which we introduce for the first time for the identification of communities of symptoms. Different from other approaches, this combinatorial optimization method is based on the identification of “triangles” in the network; these triads are the building block of feedback loops that can exist between groups of symptoms. We used a memetic algorithm to obtain a collection of highly connected-cohesive sets of symptoms and we compare the resulting community structure with the classification of disorders present in the DSM-IV.

Humanlike but not perfectly human agents frequently evoke feelings of eeriness, a phenomenon termed the Uncanny Valley (UV). The Categorical Perception Hypothesis proposes that effects associated with the UV are due to uncertainty as to whether to categorize agents falling into the valley as “human” or “nonhuman”. However, since UV studies have traditionally looked at agents of varying human-likeness, it remains unclear whether UV-related effects are due to categorical uncertainty in general or are specifically evoked by categorizations that require decisions regarding an agent’s human-likeness. Here, we used mouse tracking to determine whether agent spectra with (i.e., robot-human) and without (i.e., robot-animal and robot-stuffed animal) a human endpoint cause phenomena related to categorical perception to comparable extents. Specifically, we compared human and nonhuman agent spectra with respect to existence and location of a category boundary (H1-1 and H2-1), as well as the magnitude of cognitive conflict around the boundary (H1-2 and H2-2). The results show that human and nonhuman spectra exhibit category boundaries (H1-1) at which cognitive conflict is higher than for less ambiguous parts of the spectra (H1-2). However, in human agent spectra cognitive conflict maxima were more pronounced than for nonhuman agent spectra (H2-1) and category boundaries were shifted towards the human endpoint of the spectrum (H2-2). Overall, these results suggest a quantitatively, though not qualitatively, different categorization process for spectra containing human endpoints. Possible reasons and the impact for virtual and robotic agent design are discussed.

Variations in neural health along the cochlea can degrade the spectral and temporal representation of sounds conveyed by cochlear implants (CIs). We evaluated and compared one electrophysiological measure and two behavioural measures that have been proposed as estimates of neural health patterns, in order to explore the extent to which the different measures provide converging and consistent neural health estimates. All measures were obtained from the same 11 users of the Cochlear Corporation CI. The two behavioural measures were multipulse integration (MPI) and the polarity effect (PE), both measured on each of seven electrodes per subject. MPI was measured as the difference between thresholds at 80 pps and 1000 pps, and PE as the difference in thresholds between cathodic- and anodic-centred quadraphasic (QP) 80-pps pulse trains. It has been proposed that good neural health corresponds to a large MPI and to a large negative PE (lower thresholds for cathodic than anodic pulses). The electrophysiological measure was the effect of interphase gap (IPG) on the offset of the ECAP amplitude growth function (AGF), which has been correlated with spiral ganglion neuron density in guinea pigs. This ‘IPG offset’ was obtained on the same subset of electrodes used for the behavioural measures. Despite high test–retest reliability, there were no significant correlations between the neural health estimates for either within-subject comparisons across the electrode array, or between-subject comparisons of the means. A phenomenological model of a population of spiral ganglion neurons was then used to investigate physiological mechanisms that might underlie the different neural health estimates. The combined experimental and modelling results provide evidence that PE, MPI and IPG offset may reflect different characteristics of the electrode-neural interface.

Funder: Oticon Centre of Excellence for Hearing and Speech Sciences (CHeSS), It has been suggested that a specialized high-temporal-acuity brainstem pathway can be activated by stimulating more apically in the cochlea than is achieved by cochlear implants (CIs) when programmed with contemporary clinical settings. We performed multiple experiments to test the effect on pitch perception of phantom stimulation and asymmetric current pulses, both supposedly stimulating beyond the most apical electrode of a CI. The two stimulus types were generated using a bipolar electrode pair, composed of the most apical electrode of the array and a neighboring, more basal electrode. Experiment 1 used a pitch-ranking procedure where neural excitation was shifted apically or basally using so-called phantom stimulation. No benefit of apical phantom stimulation was found on the highest rate up to which pitch ranks increased (upper limit), nor on the slopes of the pitch-ranking function above 300 pulses per second (pps). Experiment 2 used the same procedure to study the effects of apical pseudomonophasic pulses, where the locus of excitation was manipulated by changing stimulus polarity. A benefit of apical stimulation was obtained for the slopes above 300 pps. Experiment 3 used an adaptive rate discrimination procedure and found a small but significant benefit of both types of apical stimulation. Overall, the results show some benefit for apical stimulation on temporal pitch processing at high pulse rates but reveal that the effect is smaller and more variable across listeners than suggested by previous research. The results also provide some indication that the benefit of apical stimulation may decline over time since implantation.

Rapid advances in technology for highly automated vehicles (HAVs) have raised concerns about coexistence of HAVs and human road users. Although there is a long tradition of research into human road user interactions, there is a lack of shared models and terminology to support cross-disciplinary research and development towards safe and acceptable interaction-capable HAVs. Here, we review the main themes and findings in previous theoretical and empirical interaction research, and find large variability in perspectives and terminologies. We unify these perspectives in a structured, cross-theoretical conceptual framework, describing what road traffic interactions are, how they arise, and how they get resolved. Two key contributions are: (1) a stringent definition of “interaction”, as “a situation where the behaviour of at least two road users can be interpreted as being influenced by the possibility that they are both intending to occupy the same region of space at the same time in the near future”, and (2) a taxonomy of the types of behaviours that road users exhibit in interactions. We hope that this conceptual framework will be useful in the development of improved empirical methodology, theoretical models, and technical requirements on vehicle automation.

Abstract. Language comprehenders have been arguing to mentally represent the implied orientation of objects. However, compared to the effects of shape, size, and color, the effect of orientation is rather small. We examined a potential explanation for the relatively low magnitude of the orientation effect: Object size moderates the orientation effect. Theoretical considerations led us to predict a smaller orientation effect for small objects than for large objects in a sentence–picture verification task. We furthermore investigated whether this pattern generalizes across languages (Chinese, Dutch, and English) and tasks (picture-naming task). The results of the verification task show an orientation effect overall, which is not moderated by object size (contrary to our hypothesis) and language (consistent with our hypothesis). Meanwhile, the preregistered picture–picture verification task showed the predicted interaction between object size and orientation effect. We conducted exploratory analyses to address additional questions.

The uncanny valley (UV) hypothesis suggests that increasingly human-like robots or virtual characters elicit more familiarity in their observers (positive affinity) with the exception of near-human characters that elicit strong feelings of eeriness (negative affinity). We studied this hypothesis in three experiments with carefully matched images of virtual faces varying from artificial to realistic. We investigated both painted and computer-generated (CG) faces to tap a broad range of human-likeness and to test whether CG faces would be particularly sensitive to the UV effect. Overall, we observed a linear relationship with a slight upward curvature between human-likeness and affinity. In other words, less realistic faces triggered greater eeriness in an accelerating manner. We also observed a weak UV effect for CG faces; however, least human-like faces elicited much more negative affinity in comparison. We conclude that although CG faces elicit a weak UV effect, this effect is not fully analogous to the original UV hypothesis. Instead, the subjective evaluation curve for face images resembles an uncanny slope more than a UV. Based on our results, we also argue that subjective affinity should be contrasted against subjective rather than objective measures of human-likeness when testing UV.

Machine learning (ML) has been introduced into the medical field as a means to provide diagnostic tools capable of enhancing accuracy and precision while minimizing laborious tasks that require human intervention. There is mounting evidence that the technology fueled by ML has the potential to detect and substantially improve treatment of complex mental disorders such as depression. We developed a framework capable of detecting depression with minimal human intervention: artificial intelligence mental evaluation (AiME). This framework consists of a short human-computer interactive evaluation that utilizes artificial intelligence, namely deep learning, and can predict whether the participant is depressed or not with satisfactory performance. Because of its ease of use, this technology can offer a viable tool for mental health professionals to identify symptoms of depression, thus enabling a faster preventative intervention. Furthermore, it may alleviate the challenge of observing and interpreting highly nuanced physiological and behavioral biomarkers of depression by providing a more objective evaluation. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

There is a body of research on how driver behaviour might change in response to bicyclists’ appearance. In 2007, Walker published a study suggesting motorists drove closer on average when passing a bicyclist if the rider wore a helmet, potentially increasing the risk of a collision. Olivier and Walter re-analysed the same data in 2013 and claimed helmet wearing was not associated with close vehicle passing. Here we show how Olivier and Walter’s analysis addressed a subtly, but importantly, different question than Walker’s. Their conclusion was based on omitting information about variability in driver behaviour and instead dividing overtakes into two binary categories of ‘close’ and ‘not close’; we demonstrate that they did not justify or address the implications of this choice, did not have sufficient statistical power for their approach, and moreover show that slightly adjusting their definition of ‘close’ would reverse their conclusions. We then present a new analysis of the original dataset, measuring directly the extent to which drivers changed their behaviour in response to helmet wearing. This analysis confirms that drivers did, overall, get closer when the rider wore a helmet. The distribution of overtaking events shifted just over one-fifth of a standard deviation closer to the rider – a potentially important behaviour if, as theoretical frameworks suggest, near-misses and collisions lie on a continuum. The paper ends by considering wider issues surrounding this topic and suggests public health research might be best served by shifting focus to risk elimination rather than harm mitigation.

Personality and demographics are important variables in social sciences, while in NLP they can aid in interpretability and removal of societal biases. However, datasets with both personality and demographic labels are scarce. To address this, we present PANDORA, the first large-scale dataset of Reddit comments labeled with three personality models (including the well-established Big 5 model) and demographics (age, gender, and location) for more than 10k users. We showcase the usefulness of this dataset on three experiments, where we leverage the more readily available data from other personality models to predict the Big 5 traits, analyze gender classification biases arising from psycho-demographic variables, and carry out a confirmatory and exploratory analysis based on psychological theories. Finally, we present benchmark prediction models for all personality and demographic variables., Proceedings of the Ninth International Workshop on Natural Language Processing for Social Media, NAACL 2021, https://www.aclweb.org/anthology/2021.socialnlp-1.12

The COVID-19 pandemic has caused uncertainty and disruptions in daily life. It has mandated social distancing and online education. Teens are spending a significant amount of time online and less time on extracurricular activities including team sports, choir/orchestra, and school socials. The cancellation of SAT, the switch to online AP exams, and the Credit/No Credit policy for 2nd-semester all contribute to the uncertainty in students regarding their future. Our project aims to create a survey that seeks opinions from teens about how they are managing with online socialization, the effectiveness of the online school, and stress levels. Using convenience sampling, adolescents (n = 168) were invited to participate in an anonymous online survey. Participants were asked about the effectiveness of online socializing, online education, hobbies, and extracurriculars to determine stress levels. We looked at models with two dependent stress variables: “low energy, insomnia and headache” and “forgetfulness and disorganization”. We used descriptive, regression, and correlation analysis to assess what the predictors of anxiety and stress are. Results show that stress levels are highly correlated with online exposure, online schooling, the credit/no credit, and home environment. The purpose of this study is to help school communities and leaders understand the effects on teens during the shelter in place order and identify areas of improvement in socio-academic life. Further studies need to be conducted to follow up with the findings of this project.

The global digital transformation enables computational audiology for advanced clinical applications that have the potential to impact the global burden of hearing loss. In this paper we describe emerging hearing-related artificial intelligence applications and argue for their potential to improve access, precision and efficiency of hearing health care services. In addition, we raise awareness of risks that must be addressed to enable a safe digital transformation in audiology. We envision a future where computational audiology is implemented via open-source models using interoperable shared data and where health care providers adopt new roles within a network of distributed expertise. All of this should take place in a health care system where privacy, the responsibility of each stakeholder and, most importantly, the safety and autonomy of patients are all guarded by design.

Approximationism as an art form is defined in a deep definition of human nature, as a model maker, in correlations and autocorrelations with varying degrees of naturalness, human cognition, is defined as symbolic dynamism and abstractionism, in a spectrum ,with algorithms and data mining, defined as approximationism in visual declaration, symbolic mathematics, natural language and metaphors, an ontological API and Taxonomy in cognition. Various metrics of formal elements of approximationism for art therapy are the topic of a future publication.Keywords: approximationism, ontology API, taxonomy, complexity theory, metrics, visual dynamism, visual declaration, graphics calculators, docking stations.What:Mathematics is a universal language, but Godel's incompleteness theorem proves it’s incompleteness, not even closed form expressions for natural language or for consciousness are possible, so approximations are the solution, every model we make and data we collect are mere approximations. Is art too an approximation and a visual description of a model? This is approximationism, the art of approximations of life, art as an approximation.We explore the use of approximation art in art therapy and data mining or sketch to coding, for the formal definitions and the programming of approximationist art therapy.

This study develops a new way to model the emotional impact on a particular individual (person, group,...) that may have feelings, to any event or any information. The significance of an event or an information depends on the person undergoing it. Each person reacts emotionally differently to a same situation in terms of intensity and/or type of emotion. A particular same event is therefore perceived subjectively different by each individual. The reason is that each person has his own personality, his own experience, his own history, his own education and tradition ... In this study, we assume that all the Events / Information have common interpretable points relative to each individual: Who is in the event ?, What is done? When it is done? Where it is done? How often/much it is done? Why it is done? and How it is done? Each element of the vector [Who; What; When; Where; How Much; Why; How] will be called as an emotional stimulus and will be mathematically modeled relatively to the studied individual. These stimuli, interpreted differently by each individual, will be the starting point for the creation of emotions at different intensities. The objective of this study is to mathematically link these different stimuli to the intensity of the resulting emotions following an event relatively to an individual.

Domestic demand-side response (DSR) involves having householders shift energy-consumptive behaviours forwards or backwards in time to avoid peak-demand periods. Time of Use tariffs, which vary the cost of energy depending on when it is used, are a proposed mechanism for DSR. However, we do not know which behaviours, if any, are amenable to being brought forward or postponed, nor what information people would need to do this. Here we presented people with hypothetical variable financial costs and, for the first time, carbon costs for future energy consumption. People's think-aloud decisions about when they would perform consumptive activities were qualitatively analysed. We show that non-daily high-consumption activities (e.g., ironing, vacuuming) are perceived as moveable in response to price changes or, notably, information about environmental consequences; but barriers to moving other activities, even in the face of higher costs or environmental harm, include household dynamics (difficulties negotiating consumption across cohabitants), social roles, community living (not wishing to disturb neighbours), lack of energy literacy and the routine nature of many tasks. This study provides a framework for understanding which behaviours might or might not be shifted and what guidance and information may be needed by householders if tariffs are to provide effective DSR.

For college students, it turns out the thesis task is not an easy task. These difficulties make college students often despair to stress and cause their to not be able to complete their studies on time. Stress is the second most common problem felt by students. Dance and Movement Therapy is a therapy that uses movement and dance to support the body's intellectual, emotional, and motor functions. Emotionally dance enables the individual to have self awareness, reduce stress and is the best means that can be done to express emotional feelings. This research swants to see the effect of Dance and Movement Therapy assisted by AudioVisual-based educational media on the handling of stress thesis final level students. So this research aims to overcome the stress of thesis on students with the educational media used in this study. This research used research and development. This research was conducted on 25 college students who were undergoing a thesis period. The results of this study as a whole, are less effective because researchers cannot ascertain whether the subject really follows the video from start to finish or not.

In many workplace contexts, accurate predictions of a human’s fatigue state can drastically improve system safety. Biomathematical models of fatigue (BMMs) are a family of dynamic phenomenological models that predict the neurobehavioural outcomes of fatigue (e.g., sleepiness, performance impairment) based on sleep/wake history (Dawson, Darwent, & Roach, 2017). However, to-date there are no open source implementations of BMMs, and this presents a significant barrier to their broadscale adoption by researchers and industry practitioners. FIPS is an open source R package (R Core Team, 2020) to facilitate BMM research and simulation. FIPS has implementations of several published bio-mathematical models and includes functions for easily manipulating sleep history data into the required data structures. FIPS also includes default plot and summary methods to aid model interpretation. Model objects follow tidy data conventions (Wickham, 2014), enabling FIPS to be integrated into existing research workflows of R users.

As social robots are increasingly introduced into health interventions, one potential area where they might prove valuable is in supporting people’s psychological health through conversation. Given the importance of self-disclosure for psychological health, this study assessed the viability of using social robots for eliciting rich disclosures that identify needs and emotional states in human interaction partners. Three within-subject experiments were conducted with participants interacting with another person, a humanoid social robot, and a disembodied conversational agent (voice assistant). We performed a number of objective evaluations of disclosures to these three agents via speech content and voice analyses and also probed participants’ subjective evaluations of their disclosures to three agents. Our findings suggest that participants overall disclose more to humans than artificial agents, that agents’ embodiment influences disclosure quantity and quality, and that people are generally aware of differences in their personal disclosures to three agents studied here. Together, the findings set the stage for further investigation into the psychological underpinnings of self-disclosures to artificial agents and their potential role in eliciting disclosures as part of mental and physical health interventions.

In this paper we shall review the approaches to mathematically formulate cognitive thought processes or consciousness made by Yinsheng(2008) and Bhushan(2018). In the first section of this paper, we will provide a brief insight on both the discrete approach to general consciousness made by Yinsheng and the quantum mechanical approach towards information consciousness made by Bhushan. In the following section, we will thoroughly discuss the implication of the above theories as well as some other theories like Orch-OR theory of consciousness by Hameroff and Penrose(2013).

States of apprehension and tension resulting from the covid19 pandemic process, showed in health technicians states similar to the symptoms of anxiety disorder, in addition to depressive states in nurses, nursing technicians and doctors in an emergency unit. The adoption of hygiene procedures minimized risk and states of tension; however, the subjective questions show the emotional fragility and the psychological vulnerability of health professionals. Interventions combining brief psychotherapy and Positive Psychology practices, helped the resilience of these professionals.

Augmented-reality (AR) displays render virtual content overlaid on the real world. AR offers great potential for Soldiers to immediately perceive relevant tactical information without the need to consult a physical map or other reference material, or divert their eyes away from the immediate environment. However, realizing the potential benefits of using AR requires that the conformal subset of virtual content is perceived in the correct physical location. Conformal virtual content must be correctly associated with the physical location, object, or person intended to be augmented. In this report, we provide emerging recommendations aimed at improving spatial perception of virtual content.

Research on individual variation has received increased attention. The bulk of the models discussed in psychological research so far, focus mainly on the temporal development of the mean structure. We expand the view on within-person residual variability and present a new model parameterization derived from classic multivariate GARCH models used to predict and forecast volatility in financial time-series. We propose a new pdBEKK and a modified dynamic conditional correlation (DCC) model that accommodate external time-varying predictors for the within-person variance. The main goal of this work is to evaluate the potential usefulness of MGARCH models for research in within-person variability. MGARCH models partition the within-person variance into, at least, 3 components: An overall constant and unconditional baseline variance, a process that introduces variance conditional on previous innovations, or random shocks, and a process that governs the carry-over effects of previous conditional variance, similar to an AR model. These models allow for variance spillover effects from one time-series to another. We illustrate the pdBEKK- and the DCC-MGARCH on two individuals who have rated their daily positive and negative affect over 100 consecutive days. The full models comprised a multivariate ARMA(1,1) model for the means and included physical activity as moderator of the overall baseline variance. Overall, the pdBEKK seems to result in a more straightforward psychological interpretation, but the DCC is generally easier to estimate and can accommodate more simultaneous time-series. Both models require rather large amounts of datapoints to detect nonzero parameters. We provide an R-package bmgarch that facilitates the estimation of these types of models. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Tujuan penulisan artikel ini adalah untuk (1) mengetahui adanya miskonsepsi peserta didik pada pembelajaran fisika modulus young (2) penggunaan pendekatan konflik kognitif untuk mereduksi miskonsepsi peserta didik dalam pokok pembahasan modulus young. Metode penelitian yang digunakan adalah literasi (studi pustaka). Topik penelitian ini merupakan mata pelajaran fisika dengan pokok pembahasan modulus young kelas XI. Miskonsepsi merupakan hal yang sering terjadi pada materi fisika, salah satunya pada pokok pembahasan modulus young. Kemampuan Kognitif adalah suatu proses berfikir untuk menghubungkan, menilai dan mempertimbangkan suatu kejadian atau peristiwa.

The sheep-goat effect cannot be explained without considering mind of specific states. This work speculates mind-matter interaction, as was inferred by many including Erwin Schrödinger, for entertaining analytically-flavored “phenomenological” interpretation of the sheep-goat effect. A fundamental construct of this theory is the operation of mind-matter events in a spatial-temporal-attentional domain, which reduces to the spatial-temporal domain wherein purely objective events are registered, at the vanishing of the effect of attention. Time-dependent Schrodinger’s equation is then treated as a lower-dimensional case of a higher-dimensional mind-matter-event equation elaborated in the spatial-temporal-attentional complex. The domain-coupling when modulated by the phase difference between the temporal and attentional dimensions, can enhance or inhibit the deviation of the event registration in the spatial-temporal domain from its baseline probability. The outcome is akin the sheep-goat effect.

Driver models help improve and evaluate systems for road crash mitigation and avoidance. As systems develop and address increasingly complex scenarios. Driver models also need to be developed to be able to account for the interactions among these road users. Even as we improve driver modeling with control-theory models and actual data-driven implementations, existing driver models fail to sufficiently take interaction among road users into consideration. This paper addresses this insufficiency by proposing a new operational framework to computationally model interactions among road users. For this purpose, we introduce a definition for interaction among road users. The modeling framework is demonstrated by a specific driving scenario: the overtaking of a cyclist when an oncoming vehicle may be present. In this scenario, modeling driver interaction using Unified modeling language within our framework can lead to improved crash mitigation and avoidance through tailored system activation of automated emergency braking

When faced with an imminent collision threat, human vehicle drivers respond with braking in a manner which is stereotypical, yet modulated in complex ways by many factors, including the specific traffic situation and past driver eye movements. A computational model capturing these phenomena would have high applied value, for example in virtual vehicle safety testing methods, but existing models are either simplistic or not sufficiently validated. This paper extends an existing quantitative driver model for initiation and modulation of pre-crash brake response, to handle off-road glance behavior. The resulting models are fitted to time-series data from real-world naturalistic rear-end crashes and near-crashes. A stringent parameterization and model selection procedure is presented, based on particle swarm optimization and maximum likelihood estimation. A major contribution of this paper is the resulting first-ever fit of a computational model of human braking to real near-crash and crash behavior data. The model selection results also permit novel conclusions regarding behavior and accident causation: Firstly, the results indicate that drivers have partial visual looming perception during off-road glances; that is, evidence for braking is collected, albeit at a slower pace, while the driver is looking away from the forward roadway. Secondly, the results suggest that an important causation factor in crashes without off-road glances may be a reduced responsiveness to visual looming, possibly associated with cognitive driver state (e.g., drowsiness or erroneous driver expectations). It is also demonstrated that a model parameterized on less-critical data, such as near-crashes, may also accurately reproduce driver behavior in highly critical situations, such as crashes.

We conducted a comprehensive meta-analysis for evaluating the effectiveness of Virtual Reality (VR) on the rehabilitation and training of individuals diagnosed with an autism spectrum disorder. After a systematic search and targeted screening, 33 articles were analyzed. Results revealed that individuals undergoing VR training have remarkable improvements with a large effect size (g= 0.76). Furthermore, the results of the analysis of different skills indicated diverse effectiveness. The strongest effect was observed for daily living skills (g=1.23). This effect was moderate for other skills: g=0.45 for Cognitive skills, g=0.47 for Emotion Recognition skills, and g=0.67 for Social and Communication skills. In conclusion, the application of VR-based settings in clinical practice is highly encouraged, though their standardization and customization need more research.

This report details the performance of professional and expert facial comparison practitioners working in the Australian policing and national security context. As part of a joint University of Adelaide - Defence Science and Technology Group project, known as the Human Operator Capability Project, a comprehensive survey of 149 facial comparison practitioners within Australian State and Federal Government agencies was conducted in late 2010. The survey collected data in a range of categories including: demographics, training and work history, facial comparison tools and techniques, attitudes to the facial comparison task and use of facial recognition systems. This report outlines the key findings from the survey, including implications for the consideration of participating agencies at the time. An epilogue reflects on what has changed in the time since the survey was conducted.

We present an online platform, called BeeMe, designed to test the current boundaries of Internet collective action and problem solving. BeeMe allows a scalable internet crowd of online users to collectively control the actions of a human surrogate acting in physical space. BeeMe demonstrates how intelligent goal-oriented decision-making can emerge from large crowds in quasi real-time.We analyzed data collected from a global BeeMe live performance that involved thousands of individuals, collectively solving a sci-fi Internet mystery. We study simple heuristic algorithms that read in users' chat messages and output human actionable commands representing majority preferences, and compare their performance to the behavior of a human operator solving the same task. Results show that simple heuristics can achieve near-human performance in interpreting the democratic consensus. When human and machine's output differ, the discrepancy is often due to human bias favoring non-representative views. We discuss our results in light of previous work and the contemporary debate on democratic digital systems.

Intensive repeated measurement designs are frequently used to investigate within-person variation over relatively brief intervals of time. The majority of research utilizing these designs rely on unit-weighted scale scores, which assume that the constructs are measured without error. An alternative approach makes use of multilevel structural equation models (MSEM), which permit the specification of latent variables at both within-person and between-person levels. These models disaggregate measurement error from systematic variance, which should result in less biased within-person estimates and larger effect sizes. Differences in power, precision, and bias between multilevel unit-weighted and MSEM models were compared through a series of Monte Carlo simulations. Results based on simulated data revealed that precision was consistently poorer in the MSEM models than the unit-weighted models, particularly when reliability was low. However, the degree of bias was considerably greater in the unit-weighted model than the latent variable model. Although the unit-weighted model consistently underestimated the effect of a covariate, it generally had similar power relative to the MSEM model due to the greater precision. Considerations for scale development and the impact of within-person reliability are highlighted.

Human cognition is characterized by its complexity and multidimensionality. Adaptive and appropriate behavior in the here and now requires a system which integrates sensorimotor information from multiple modalities, the individual’s personal experience, and possible future events. Nevertheless, measuring such complexity has proven difficult, at best. Classical laboratory experiments have been crucial to informing models of brain functioning. These types of experiments, nevertheless, generally do not capture the experiences of the real world and therefore lack ecological validity. This is mostly a methodological -not necessarily epistemic- limitation. The first section of this article will address the theoretical assumptions underlying the Mobile Brain/Body Imaging approach, which -we argue- are best understood from the Embodied, Embedded, Extended and Enactive approach to cognition. Later, we briefly review the technological advances that have enabled MoBI implementation. Finally, a discussion of MoBI’s strengths and limitations for the study of cognition within the 4E framework is presented with an emphasis in the Chilean context.

We had previously discussed that hMT+ should have two distinct regions, one is connected to the central retina, and another is connected to the retinal peripheries; and both of these distinct regions generate different perceived speed. Namely, in the central region, the stimulus appears to move much slower, see references 1 & 2; in the peripheral region however, it appears to move faster and even with reversed direction, see reference 4. This article, however, provides evidence that repeated symmetric patterns are perceived to move much slower than repeated asymmetric patterns (RAP); additionally, asymmetric but not symmetric patterns tends to generate illusory motion reversals. Evidences which conclude that symmetric, but not asymmetric patterns are preferred to be processed by the central retina; an additional characteristic that adds further distinction between the central and the peripheral hMT+ regions.

Research on social judgments of faces often investigates relationships between specific measures of face shape taken from face images (facial metrics) and either perceptual ratings of the faces on various traits (e.g., attractiveness) or characteristics of the photographed individual (e.g., their health). A barrier to carrying out this research using large numbers of face images is the substantial time it takes to manually position the landmarks from which these facial metrics are derived. Although research on face recognition has led to the development of software that can automatically position landmarks on face images, the utility of such methods for deriving facial metrics commonly used in research on social judgments of faces has not yet been established. Thus, we investigated the correlations between four facial metrics commonly used in social perception research (sexual dimorphism, distinctiveness, bilateral asymmetry, and fWHR) when measured from manually and automatically placed landmarks. In two independent sets of open access face images, we found that facial metrics derived from manually and automatically placed landmarks were typically highly correlated. These data validate the use of automatically placed landmarks for calculating facial metrics to use in research on social judgments of faces. We also provide a tutorial for the automatic placement of landmarks on face images.

Due to its increasing ease-of-use and ability to quickly collect large samples, online behavioral research is currently booming. With this increasing popularity, it is important that researchers are aware of who online participants are, and what devices and software they use to access experiments. While it is somewhat obvious that these factors can impact data quality, it remains unclear how big this problem is. To understand how these characteristics impact experiment presentation and data quality, we performed a battery of automated tests on a number of representative setups. We investigated how different web-building platforms (Gorilla, jsPsych, Lab.js, and psychoJS/PsychoPy3), browsers (Chrome, Edge, Firefox, and Safari), and operating systems (mac OS and Windows 10) impact display time across 30 different frame durations for each software combination. In addition, we employed a robot actuator in representative setups to measure response recording across aforementioned platforms, and between different keyboard types (desktop and integrated laptop). We then surveyed over 200 000 participants on their demographics, technology, and software to provide context to our findings. We found that modern web-platforms provide a reasonable accuracy and precision for display duration and manual response time, but also identify specific combinations that produce unexpected variance and delays. While no single platform stands out as the best in all features and conditions, our findings can help researchers make informed decisions about which experiment building platform is most appropriate in their situation, and what equipment their participants are likely to have.

The ability to automatically assess emotional responses via contact-free video recording taps into a rapidly growing market aimed at predicting consumer choices. If consumer attention and engagement are measurable in a reliable and accessible manner, relevant marketing decisions could be informed by objective data. Although significant advances have been made in automatic affect recognition, several practical and theoretical issues remain largely unresolved. These concern the lack of cross-system validation, a historical emphasis of posed over spontaneous expressions, as well as more fundamental issues regarding the weak association between subjective experience and facial expressions. To address these limitations, the present paper argues that extant commercial and free facial expression classifiers should be rigorously validated in cross-system research. Furthermore, academics and practitioners must better leverage fine-grained emotional response dynamics, with stronger emphasis on understanding naturally occurring spontaneous expressions. We posit that applied consumer research might be better situated to examine facial behavior in socio-emotional contexts rather than decontextualized, laboratory studies, and highlight how AHAA can be successfully employed in this context. Also, facial activity should be considered less as a single outcome variable, and more as promising input for two-step machine learning in combination with other (multimodal) features. We illustrate this point in a case study using facial activity as input features to predict crying behavior in response to sad movies. Implications of this approach and potential obstacles that need to be overcome are discussed within the context of consumer research.

SA is a widely used cognitive construct in human factors, summarized as “knowing what is going on.” Generally, SA is theoretically posited to be a critical causal factor and/or construct for performance. However, some researchers have raised concerns that SA may be circular and also that SA may lack the appropriate psychological mechanisms relevant to performance. We address these conflicting perspectives using meta-analysis to evaluate the specific and general patterns of associations among SA-performance effect sizes. Specifically, we focus on the validity of SA for performance—the degree to which SA represents or captures the relevant psychological processes and mechanisms related to task performance. From the empirical literature, we coded associations of eight unique measures of SA with (task) performance: 492 effects from 38 papers met the systematic review inclusion criteria. In contrast to SA’s broadly theorized fundamental link with performance, the magnitude of most meta-analytic mean effect sizes for SA measures was limited to medium or lower effects. Although there was a significant overall mean effect, its magnitude was also limited (r = 0.24). In addition, there was high unexplained systematic variation with an enormous plausible range for individual effects (r = -0.20 to 0.60). The meta-analytic results are inconsistent with theories postulating SA is fundamental to performance. Instead, SA’s validity for performance tends to be, on average, weak with large variations among effects. Therefore, theories may need to be revised. Furthermore, even presuming SA is causally linked to performance as generally theorized, improvements in SA (such as SA-based design and training) may not correspond to meaningful increases in task performance.

Along with the Coronavirus pandemic, another crisis has manifested itself in the form of mass fear and panic phenomena, fueled by incomplete and often inaccurate information. There is therefore a tremendous need to address and better understand COVID-19&rsquo, s informational crisis and gauge public sentiment, so that appropriate messaging and policy decisions can be implemented. In this research article, we identify public sentiment associated with the pandemic using Coronavirus specific Tweets and R statistical software, along with its sentiment analysis packages. We demonstrate insights into the progress of fear-sentiment over time as COVID-19 approached peak levels in the United States, using descriptive textual analytics supported by necessary textual data visualizations. Furthermore, we provide a methodological overview of two essential machine learning (ML) classification methods, in the context of textual analytics, and compare their effectiveness in classifying Coronavirus Tweets of varying lengths. We observe a strong classification accuracy of 91% for short Tweets, with the Na&iuml, ve Bayes method. We also observe that the logistic regression classification method provides a reasonable accuracy of 74% with shorter Tweets, and both methods showed relatively weaker performance for longer Tweets. This research provides insights into Coronavirus fear sentiment progression, and outlines associated methods, implications, limitations and opportunities.

This paper is about describing the features of a software that was developed for its user safety which we called SecureIT is a android based soft ware using Android SDK along with Firebase and Google map SDK along with Node.Js. The aim of developing this project was to make sure and taking its users safety to a next level. Actually now a days some crime incidents like rapes, fire accidents and snatchings are very common and we believe many of those can be prevented if victim got support at the right time. According to the well known daily news paper of Bangladesh The Daily Star there were about 1413 women was rapped where 76 women were dead in 2019. On the same pa-per it also said that in 2018 and 2017 the number of rapes were 732 and 818 where we can easily get that the number increases to almost double in 2019. Where we get a point that if those girls get support or get people known about their location at the right time they might get rid of the situation and the number of rapes could be reduced a lot because we all know that now a days using mo-bile smartphone is too easy for people. Although leading Chinese mobile phone company Xiaomi introduced a new feature Emergency SOS service in their mobile phones at the end of 2018 with a MIUI 10 update but this feature is only limited to the Xiaomi phones and it was well advertised as well. This paper will briefly describe all the features of our software and its usages., Comment: 17 pages

Designing artificial agents that can closely imitate human behavior, might influence humans in perceiving them as intentional agents. Nonetheless, the factors that are crucial for an artificial agent to be perceived as an animated and anthropomorphic being still need to be addressed. In the current study, we investigated some of the factors that might affect the perception of a robot's behavior as human-like or intentional. To meet this aim, seventy-nine participants were exposed to two different behaviors of a humanoid robot under two different instructions. Before the experiment, participants' biases towards robotics as well as their personality traits were assessed. Our results suggest that participants’ sensitivity to human-likeness relies more on their expectations rather than on perceptual cues.

Bisnis Online menjadi bisnis yang diburu banyak orang karena banyak kelebihan-kelebihan yang diperoleh melalui bisnis ini seperti modal yang tidak terlalu besar tetapi bisa menghasilkan uang yang besar, Salah satu bisnis Online yang sangat marak saat ini adalah perdagangan elektronik (E-commerce). PrestaShop adalah salah satu CMS Open source E-commerce yang bersifat gratis yang dapat digunakan untuk membuat sebuah website E-commerce. Seiring dengan meningkatnya pemanfaatan aplikasi CMS E-commerce yang bersifat Open Source, akan semakin banyak pihak-pihak yang berusaha mencari kelemahan-kelemahan yang ada. Celah keamanan ini dapat dijadikan sebuah bentuk serangan yang beraneka jenis dan tingkat kecangihannya. Dikarenakan aplikasi E-commerce yang digunakan yaitu Prestashop, berupa aplikasi Open Source maka keamanannya belum sepenuhnya terjamin. Oleh karena itu, perlu dilakukan penelitian untuk menganalisis kebutuhan keamanan informasi dengan menggunakan metodologi SQUARE, sehingga rekomendasi bisa diberikan sejak tahap awal sebelum aplikasi ini dipergunakan dan dipublish. Metodologi yang digunakan dalam penelitian ini adalah metodologi SQUARE yang terdiri dari sembilan langkah proses yang dikembangkan untuk membantu menganalisis kebutuhan keamanan aplikasi E-commerce. Hasil penelitian ini, dapat disimpulkan bahwa metodologi SQUARE sangat membantu dalam melakukan analisis kebutuhan keamanan sistem dalam pengembangan sistem berupa aplikasi E-commerce yang menggunakan CMS Prestashop.

In the presence of others, sense of agency (SoA), i.e. the perceived relationship between our own actions and external events, is reduced. The present study aimed at investigating whether the phenomenon of reduced SoA is observed in human-robot interaction, similarly to human-human interaction. To this end, we tested SoA when people interacted with a robot (Experiment 1), with a passive, non-agentic air pump (Experiment 2), or when they interacted with both a robot and a human being (Experiment 3). Participants were asked to rate the perceived control they felt on the outcome of their action while performing a diffusion of responsibility task. Results showed that the intentional agency attributed to the artificial entity differently affect the performance and the perceived SoA on the outcome of the task. Experiment 1 showed that, when participants successfully performed an action, they rated SoA over the outcome as lower in trials in which the robot was also able to act (but did not), compared to when they were performing the task alone. However, this did not occur in Experiment 2, where the artificial entity was an air pump, which had the same influence on the task as the robot, but in a passive manner and thus lacked intentional agency. Results of Experiment 3 showed that SoA was reduced similarly for the human and robot agents, threby indicating that attribution of intentional agency plays a crucial role in reduction of SoA. Together, our results suggest that interacting with robotic agents affects SoA, similarly to interacting with other humans, but differently from interacting with non-agentic mechanical devices. This has important implications for the applied of social robotics, where a subjective decrease in SoA could have negative consequences, such as in robot-assisted care in hospitals.

Eccles’s expectancy-value theory (EVT) has been used to understand students’ achievement-related beliefs in certain STEM domains such as math and science. Studies have started to explore the role that parents and teachers EVT beliefs have in influencing students STEM-related career interest. However, a clear picture of the magnitude of this effect is unclear because studies exploring this association often only focus on one specific STEM domain and on one age group (e.g. lower elementary, upper elementary, middle school, or high school). Given that parents and teachers play a large role in the lives of students, it is important to understand how their beliefs are related to the education and career decision of students. As such, the goal of this systematic review and meta-analysis is to examine the magnitude of the association between the STEM-related expectancy and task-value beliefs of parents/teachers about their children/students with their child/student’s STEM-related career aspirations and academic achievement. The findings of this systematic review and meta-analysis will be discussed in terms of the implications for STEM-related education

We previously found that the retinal peripheries may not signal the human visual awareness when ‘spatial wrapping’ stimulus has very low contrast, or when the human subjects perform deep inhalation, see reference 19 and 20. In this study, we found that those techniques are able to significantly reduce the motion aftereffect (MAE) too. Namely, when we reduce the contrast of the MAE stimulator, the MAE is significantly reduced. Similarly, when we ask the subjects to perform deep inhalation in the end of viewing a ‘high contrast’ MAE stimulator, the MAE is drastically attenuated. The neurophysiological processes of the previous techniques are vastly different, as explained in our previous work. Namely, significant contrast reduction deactivates the retinal peripheries due to the following reasons; first, extremely low contrast stimulus constricts the pupil that disallows the retinal peripheries from receiving enough light rays to signal the brain actively, second, for extremely low contrast conditions, the center-surround antagonism process in the retinal peripheries might not signal the brain at all. Deep inhalation, however, may cause idle links between the retinal peripheries and their corresponding neurological pathways that eventually signal the visual awareness, a process that is also found to weaken the MAE.

The search for the elusive “killer app” of sonification has been a recurring theme in sonification research. In this comment, I argue that the killer-app criterion of success stems from interdisciplinary tensions about how to evaluate sonifications. Using auditory graphs as an example, I argue that the auditory display community has produced successful examples of sonic information design that accomplish the human factors goal of improving human interactions with systems. Still, barriers to using sonifications in interfaces remain, and reducing those barriers could result in more widespread use of audio in systems.

Trust in automation is a key determinant for the adoption of automated systems and their appropriate use. Therefore, it constitutes an essential research area for the introduction of automated vehicles to road traffic. In this study, we investigated the influence of trust promoting (Trust promoted group) and trust lowering (Trust lowered group) introductory information on reported trust, reliance behavior and take-over performance. Forty participants encountered three situations in a 17-minute highway drive in a conditionally automated vehicle (SAE Level 3). Situation 1 and Situation 3 were non-critical situations where a take-over was optional. Situation 2 represented a critical situation where a take-over was necessary to avoid a collision. A non-driving-related task (NDRT) was presented between the situations to record the allocation of visual attention. Participants reporting a higher trust level spent less time looking at the road or instrument cluster and more time looking at the NDRT. The manipulation of introductory information resulted in medium differences in reported trust and influenced participants’ reliance behavior. Participants of the Trust promoted group looked less at the road or instrument cluster and more at the NDRT. The odds of participants of the Trust promoted group to overrule the automated driving system in the non-critical situations were 3.65 times (Situation 1) to 5 times (Situation 3) higher. In Situation 2, the Trust promoted group’s mean take-over time was extended by 1154 ms and the mean minimum time-to-collision was 933 ms shorter. Six participants from the Trust promoted group compared to no participant of the Trust lowered group collided with the obstacle. The results demonstrate that the individual trust level influences how much drivers monitor the environment while performing an NDRT. Introductory information influences this trust level, reliance on an automated driving system, and if a critical take-over situation can be successfully solved.

This article provides evidence that motion has the ability to stably fuse two different colored ‘spatially separated’ stimuli without impairments against the shape of those separated stimuli. Based on our observations, dilated pupils promote colours fusion; and the optimal fusion happens when the stimuli are moving in opposite direction of the actual physical motion, namely, when there is illusory motion reversal. The previous observations suggested that retinal peripheries have main contributions on the present illusion which we called; motion induced colours fusion ‘MICF’ illusion.

Depth, colour, and thermal images contain practical and actionable information for the visually-impaired. Conveying this information through alternative modalities such as audition creates new interaction possibilities for users as well as opportunities to study neuroplasticity. The ‘SoundSight’ App (www.SoundSight.co.uk) provides a smartphone platform that allows 3D position, colour, and thermal information to directly control thousands of high-quality sounds in real-time to create completely unique and responsive soundscapes for the user. These sounds could be anything - tones, rainfall, speech, instruments, or even full musical tracks. Users have a fine degree of control over how these sounds are presented through controlling the timing and selection of sounds played at a given moment. Through utilising smartphone technology with a novel approach to sonification, the SoundSight App provides a cheap, widely-accessible, scalable, and flexible sensory tool. In this paper we discuss common problems encountered with assistive sensory tools reaching long-term adoption, how our device seeks to address these problems, its theoretical background, its technical implementation, and finally we showcase a range of use case scenarios for scientists, artists, and the blind community.

The accurate and objective measurement of cognitive workload is important in many aspects of psychological research. The Detection Response Task (DRT) is a well-validated method for measuring cognitive workload that has been used extensively in applied tasks, for example to investigate the effects of fatigue and phone usage on driving. Given its success in applied tasks, we investigated whether the DRT could be used to measure cognitive workload in cognitive tasks more commonly used in experimental cognitive psychology, and whether this application could be extended to online environments. We had participants perform a multiple object tracking task while simultaneously performing a DRT. We manipulated the cognitive load of the multiple object tracking task by changing the number of dots to be tracked. Measurements from the DRT were sensitive to changes in the cognitive load, establishing the efficacy of the DRT for experimental cognitive tasks in lab-based situations. This sensitivity continued when applied to an online environment (with our code for the online DRT implementation being freely available at \url{https://osf.io/dc39s/}), though to a reduced extent compared to the in-lab situation, opening up the potential use of the DRT to a much greater range of tasks and situations, but suggesting that in-lab applications are best when possible.

This article provides evidence that high-speed high-information visual stimulus is able to indefinitely dominate the visual awareness; at speed of 21 degrees/second. Greater speeds emphasize greater dominance duration for the motion stimulus, however, at 21 degrees/second the moving stimulus dominates the visual awareness exclusively and indefinitely. Astonishingly, no room for mixed precepts had been reported by the human subjects at that speed, as if, it’s the ultimate domination.