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Disinformation, often known as fake news, is a major issue that has received a lot of attention lately. Many researchers have proposed effective means of detecting and addressing it. Current machine and deep learning based methodologies for classification/detection of fake news are content-based, network (propagation) based, or multimodal methods that combine both textual and visual information. We introduce here a framework, called FNACSPM, based on sequential pattern mining (SPM), for fake news analysis and classification. In this framework, six publicly available datasets, containing a diverse range of fake and real news, and their combination, are first transformed into a proper format. Then, algorithms for SPM are applied to the transformed datasets to extract frequent patterns (and rules) of words, phrases, or linguistic features. The obtained patterns capture distinctive characteristics associated with fake or real news content, providing valuable insights into the underlying structures and commonalities of misinformation. Subsequently, the discovered frequent patterns are used as features for fake news classification. This framework is evaluated with eight classifiers, and their performance is assessed with various metrics. Extensive experiments were performed and obtained results show that FNACSPM outperformed other state-of-the-art approaches for fake news classification, and that it expedites the classification task with high accuracy.

Top-k high utility pattern mining, which extracts the highest top-k patterns that the users want to find, has been actively studied. Most previous studies in this domain have focused on static databases, where data insertions do not occur. In the real world, however, various applications continuously generate new data, and existing top-k high utility pattern mining algorithms devised to process static databases cannot handle incremental databases. Although some methods can handle stream data, they have the limitation of processing a portion of the database rather than the entire accumulated database. In this paper, we suggest an efficient incremental mining method that discovers top-k high utility patterns from the entire accumulated database. The proposed approach utilizes a list structure that stores minimal utility information required for the mining process and does not generate candidate itemsets. The suggested algorithm processes the incremental data with a single database scan and restructures the list for efficient mining. Moreover, four efficient threshold raising techniques along with a restoring technique are utilized to calculate the optimal threshold value in an accumulated incremental environment. The results of the experiments on runtime, memory, and scalability show that the suggested method efficiently processes the entire incremental database.

This paper presents a dataset comprising 700 video sequences encoded in the two most popular video formats (codecs) of today, H.264 and H.265 (HEVC). Six reference sequences were encoded under different quality profiles, including several bitrates and resolutions, and were affected by various packet loss rates. Subsequently, the image quality of encoded video sequences was assessed by subjective, as well as objective, evaluation. Therefore, the enclosed spreadsheet contains results of both assessment approaches in a form of MOS (Mean Opinion Score) delivered by the absolute category ranking (ACR) procedure, SSIM (Structural Similarity Index Measure) and VMAF (Video Multimethod Assessment Fusion). All assessments are available for each test sequence. This allows a comprehensive evaluation of coding efficiency under different test scenarios without the necessity of real observers or a secure laboratory environment, as recommended by the ITU (International Telecommunication Union). As there is currently no standardized mapping function between the results of subjective and objective methods, this dataset can also be used to design and verify experimental machine learning algorithms that contribute to solving the relevant research issues.

Abstract In this study, an observation points‐based positive‐unlabeled learning algorithm (hence called OP‐PUL) is proposed to deal with positive‐unlabeled learning (PUL) tasks by judiciously assigning highly credible labels to unlabeled samples. The proposed OP‐PUL algorithm has three components. First, an observation point classifier ensemble (OPCE) algorithm is constructed to divide unlabeled samples into two categories, which are temporary positive and permanent negative samples. Second, a temporary OPC (TOPC) is trained based on the combination of original positive samples and permanent negative samples and then the permanent positive samples that are correctly classified with TOPC are retained from the temporary positive samples. Third, a permanent OPC (POPC) is finally trained based on the combination of original positive samples, permanent positive samples and permanent negative samples. An exhaustive experimental evaluation is conducted to validate the feasibility, rationality and effectiveness of the OP‐PUL algorithm, using 30 benchmark PU data sets. Results show that (1) the OP‐PUL algorithm is stable and robust as unlabeled samples and positive samples are increased in unlabeled data sets and (2) the permanent positive samples have a consistent probability distribution with the original positive samples. Moreover, a statistical analysis reveals that POPC in the OP‐PUL algorithm can yield better PUL performances on the 30 data sets in comparison with four well‐known PUL algorithms. This demonstrates that OP‐PUL is a viable algorithm to deal with PUL tasks.

Long-term urban traffic flow prediction is an important task in the field of intelligent transportation, as it can help optimize traffic management and improve travel efficiency. To improve prediction accuracy, a crucial issue is how to model spatiotemporal dependency in urban traffic data. In recent years, many studies have adopted spatiotemporal neural networks to extract key information from traffic data. However, most models ignore the semantic spatial similarity between long-distance areas when mining spatial dependency. They also ignore the impact of predicted time steps on the next unpredicted time step for making long-term predictions. Moreover, these models lack a comprehensive data embedding process to represent complex spatiotemporal dependency. This paper proposes a multi-scale persistent spatiotemporal transformer (MSPSTT) model to perform accurate long-term traffic flow prediction in cities. MSPSTT adopts an encoder-decoder structure and incorporates temporal, periodic, and spatial features to fully embed urban traffic data to address these issues. The model consists of a spatiotemporal encoder and a spatiotemporal decoder, which rely on temporal, geospatial, and semantic space multi-head attention modules to dynamically extract temporal, geospatial, and semantic characteristics. The spatiotemporal decoder combines the context information provided by the encoder, integrates the predicted time step information, and is iteratively updated to learn the correlation between different time steps in the broader time range to improve the model's accuracy for long-term prediction. Experiments on four public transportation datasets demonstrate that MSPSTT outperforms the existing models by up to 9.5% on three common metrics.

IntroductionPsychoacoustic measures are used to quantify tinnitus perception in clinical and research settings. Residual inhibition (RI), for instance, is defined as a temporary reduction in tinnitus loudness after the presentation of an acoustic stimulus. A novel approach was developed recently to assess the minimum sound level producing RI, the minimum residual inhibition level (MRIL), after the repeated presentation of short sounds separated by short periods of silence. Two studies were performed: the objective of the study 1 was to assess the reliability and the reproducibility of this measures as well as others, including the minimum masking level (MML). The objective of study 2 was to assess whether a smoother envelope of the sound would produce similar levels of MML and MRIL and improve listening comfort.MethodsThirty participants with chronic tinnitus completed each study. For study 1, MML and MRIL were obtained at the seven standard audiometric frequencies at around 1-month interval. For study 2, MML and MRIL were obtained using triangular and trapezoidal-shaped NBN signals centered at three different frequencies.ResultsThe mean absolute test-retest difference was 3 dB for both MML and MRIL [Intraclass correlation, ICC (2,1) of 0.70 and 0.84, respectively] for the measures obtained in test and retest (86% of cases for MML and 21% of cases for complete MRIL). While the two types of envelopes provided comparable MRIL, the “triangular” envelope provided more listening comfort.DiscussionIn sum, the novel approach provides quick and reliable MML and MRIL measurement, with the potential benefit of subtyping participants.

Abstract In this paper, an Observation Points Classifier Ensemble (OPCE) algorithm is proposed to deal with High‐Dimensional Imbalanced Classification (HDIC) problems based on data processed using the Multi‐Dimensional Scaling (MDS) feature extraction technique. First, dimensionality of the original imbalanced data is reduced using MDS so that distances between any two different samples are preserved as well as possible. Second, a novel OPCE algorithm is applied to classify imbalanced samples by placing optimised observation points in a low‐dimensional data space. Third, optimization of the observation point mappings is carried out to obtain a reliable assessment of the unknown samples. Exhaustive experiments have been conducted to evaluate the feasibility, rationality, and effectiveness of the proposed OPCE algorithm using seven benchmark HDIC data sets. Experimental results show that (1) the OPCE algorithm can be trained faster on low‐dimensional imbalanced data than on high‐dimensional data; (2) the OPCE algorithm can correctly identify samples as the number of optimised observation points is increased; and (3) statistical analysis reveals that OPCE yields better HDIC performances on the selected data sets in comparison with eight other HDIC algorithms. This demonstrates that OPCE is a viable algorithm to deal with HDIC problems.

The Spike (S) protein of the SARS-CoV-2 virus that causes the COVID-19 disease is considered the most important target for vaccine, drug and therapeutic research as it attaches and binds to the ACE2 receptor of the host cells and allows the entry of this virus. Analysis and classification of newly determined S protein structures for SARS-CoV-2 are critical to properly understand their functional, evolutionary and architectural relatedness to already known protein structures. In this paper, first, the comparative analysis of SARS-CoV-2 S protein structures is performed. Through comparative analysis, the S protein structures in the PDB (protein data bank) database are compared and analyzed not only with each other but with the structures of other viruses for various parameters. Second, the S protein structures in PDB are classified into different variants, and the associated published literature is studied to investigate what kind of therapeutics (antibodies, T-cell receptors and small molecules) are used on the structures. This is the first study that classifies the S protein structures of the SARS-CoV-2 in PDB into various variants, and the obtained comparative analysis results could be beneficial to the research community, in general, and to crystallographers and health workers, in particular.

Tinnitus is prevalent among patients suffering from Single-Sided Deafness (SSD) and Asymmetrical Hearing Loss (AHL). In addition to bothersome tinnitus in the poorer ear, these patients also report issues with understanding speech in noise and sound localization. The conventional treatment options offered to these patients to improve auditory abilities are cochlear implantation, bone conduction devices or Contralateral Routing Of Signal (CROS) hearing aids. It was recently found that the benefit of cochlear implantation for tinnitus associated with AHL/SSD was greater than the other two approaches. It is conceivable that the lack of stimulation provided to the poorer ear in these last approaches explains their modest impact on tinnitus perception. A new technology that combines the ability to reroute the sound from the poorer ear to the good ear (CROS system) while still stimulating the poorer ear with conventional sound amplification has recently been developed: the StereoBiCROS system. The aim of this study was to investigate the effects of this new device on tinnitus. Twelve AHL and two SSD patients aged 70.7 ± 7.9 years with tinnitus were fitted with bilateral hearing aids that included 3 programs: Stereophonic, BiCROS and StereoBiCROS (CROS + bilateral amplification). The short-and long-term effect of the approach on tinnitus was assessed using a tinnitus Loudness Visual Analog Scale (VAS) and the Tinnitus Handicap Inventory (THI), respectively. Both the VAS and the THI were used before and one month after the hearing aid fitting. Of the 14 patients who used their hearing aids daily (12.6 ± 1.6 h per day) the StereoBiCROS program was the most used program (81.8 ± 20.5% of the time). The average THI total score decreased from 47 (± 22) to 15 (± 16) (p = 0.002) and the VAS-Loudness score decreased from 7 (± 1) to 2 (± 2) (p

Purpose: Student audiology training in tinnitus evaluation and management is heterogeneous and has been found to be insufficient. We designed a new clinical simulation laboratory for training students on psychoacoustic measurements of tinnitus: one student plays the role of the tinnitus patient, wearing a device producing a sound like tinnitus on one ear, while another student plays the role of the audiologist, evaluating their condition. The objective of the study was to test this new clinical simulation laboratory of tinnitus from the perspective of the students. Method: This study reports the findings from twenty-one audiology students (20 female and 1 male, mean age = 29, SD = 7.7) who participated in this laboratory for a mandatory audiology class at the Laval University of Quebec. Three students had hearing loss (one mild, two moderate). All students played the role of both the clinician and the patient, alternately. They also had to fill out a questionnaire about their overall experience of the laboratory. Results: The qualitative analysis revealed three main themes: “Benefits of the laboratory on future practice”, “Barriers and facilitators of the psychoacoustic assessment”, and “Awareness of living with tinnitus”. The participants reported that this experience would have a positive impact on their ability to manage tinnitus patients in their future career. Conclusion: This fast, cheap, and effective clinical simulation method could be used by audiology and other healthcare educators to strengthen students’ skills and confidence in tinnitus evaluation and management. The protocol is made available to all interested parties.

Abstract Misophonia is a condition where a strong arousal response is triggered when hearing specific human generated sounds, like chewing, and/or repetitive tapping noises, like pen clicking. It is diagnosed with clinical interviews and questionnaires since no psychoacoustic tools exist to assess its presence. The present study was aimed at developing and testing a new assessment tool for misophonia. The method was inspired by an approach we have recently developed for hyperacusis. It consisted of presenting subjects (n = 253) with misophonic, pleasant, and unpleasant sounds in an online experiment. The task was to rate them on a pleasant to unpleasant visual analog scale. Subjects were labeled as misophonics (n = 78) or controls (n = 55) by using self-report questions and a misophonia questionnaire, the MisoQuest. There was a significant difference between controls and misophonics in the median global rating of misophonic sounds. On the other hand, median global rating of unpleasant, and pleasant sounds did not differ significantly. We selected a subset of the misophonic sounds to form the core discriminant sounds of misophonia (CDSMiso). A metric: the CDS score, was used to quantitatively measure misophonia, both with a global score and with subscores. The latter could specifically quantify aversion towards different sound sources/events, i.e., mouth, breathing/nose, throat, and repetitive sounds. A receiver operating characteristic analysis showed that the method accurately classified subjects with and without misophonia (accuracy = 91%). The present study suggests that the psychoacoustic test we have developed can be used to assess misophonia reliably and quickly.

Utility-pattern mining in data has received a lot of attention from the knowledge discovery in database (KDD) community due to its high potential for many applications such as finance, biomedicine, manufacturing, e-commerce, and social media. Current research in utility-pattern mining primarily focuses on discovering patterns of high value (e.g., high profit) in large databases and analyzing/learning important factors (e.g., economic factors) in a data mining process. One of the most popular applications of utility mining is the analysis of large transactional databases to discover high-utility itemsets, which are sets of items that yield a high profit when purchased together.

High-utility sequential pattern mining (HUSPM) is an emerging topic in data mining, which considers both utility and sequence factors to derive the set of high-utility sequential patterns (HUSPs) from the quantitative databases. Several works have been presented to reduce the computational cost by variants of pruning strategies. In this paper, we present an efficient sequence-utility (SU)-chain structure, which can be used to store more relevant information to improve mining performance. Based on the SU-Chain structure, the existing pruning strategies can also be utilized here to early prune the unpromising candidates and obtain the satisfied HUSPs. Experiments are then compared with the state-of-the-art HUSPM algorithms and the results showed that the SU-Chain-based model can efficiently improve the efficiency performance than the existing HUSPM algorithms in terms of runtime and number of the determined candidates.

Interactive theorem provers (ITPs) are software tools that allow human users to write and verify formal proofs. In recent years, an emerging research area in ITPs is proof mining, which consists of identifying interesting proof patterns that can be used to guide the interactive proof process in ITPs. In previous studies, some data mining techniques, such as frequent pattern mining, have been used to analyze proofs to find frequent proof steps. Though useful, such models ignore the facts that not all proof steps are equally important. To address this issue, this paper proposes a novel proof mining approach based on finding not only frequent patterns but also high utility patterns to find proof steps of high importance (utility). A proof process learning approach is proposed based on high utility itemset mining (HUIM) for the PVS (Prototype Verification System) proof assistant. Proofs in PVS theories are first abstracted to a computer-processable corpus, where each line represents a proof sequence and proof commands in proof sequences are associated with utilities representing their weightage (importance). HUIM techniques are then applied on the corpus to discover frequent proof steps/high utility patterns and their relationships with each other. Experimental results suggest that combining frequent pattern mining techniques, such as sequential pattern mining and high utility itemset mining, with proof assistants, such as PVS, is useful to learn and guide the proof development process.

Analyzing customer transactions to discover high-utility itemsets is a popular task, which consists of finding the sets of items that are purchased together and yield a high profit. However, many studies assume that transactional data is static while in real-life, it changes over time. For example, the unit profits of items may vary from one week to another because sale prices and production costs may change. Many algorithms for mining high-utility itemsets (HUI) ignore this important property and thus are inapplicable or generate inaccurate results on real data. To address this issue, this paper proposes a novel algorithm named Multi-Core HUI Miner (MCH-Miner). It adapts techniques introduced in the iMEFIM algorithm to run on a parallel multi-core architecture to efficiently mine HUIs in dynamic transaction databases. An empirical evaluation shows that in most cases, MCH-Miner is significantly faster than iMEFIM, and that the cost of database scans is reduced.

Discovering frequent itemsets is a data analysis task used in numerous domains. It consists of finding sets of items (itemsets) that frequently appear in a set of database records (also called transactions). Though discovering frequent itemsets is useful, it can produce a large amount of spurious patterns. As a result, the user may spend a great amount of time to analyze the itemsets found by a frequent itemset mining algorithm to find truly interesting patterns. Hence, in recent years, a key research topic has emerged which is to discover statistically significant patterns in databases. The most popular model for identifying itemsets that are statistically significant is to discover non-redundant productive itemsets. The state-of-the-art algorithm to extract this set of patterns is OPUS-Miner. A key drawback of that algorithm is that it is designed to be applied to a static database. Moreover, a second drawback of OPUS-Miner is that it discovers all patterns in a database. In other words, the user cannot search for itemsets containing some specific items. This paper addresses these issues by defining the novel problem of discovering targeted non redundant productive itemsets in dynamic databases. An algorithm named IDPI+ (Interactive Discovery of Productive Itemsets) is presented, storing transactions in a tree structure, which can then be interactively queried to identify productive and non redundant itemsets containing specific items. A structure named Query-Tree is also introduced to process many queries at the same time. Moreover, to handle dynamic databases, efficient transaction insertion and deletion algorithms are provided to update the tree. It was observed in an experimental evaluation on benchmark datasets containing various types of data that IDPI+ can handle thousands of queries per second on a desktop computer. Moreover, it was found that IPDI+ is more than an order of magnitude faster than a baseline algorithm.

The growing demand for eliciting useful knowledge from data calls for techniques that can discover insights (in the form of patterns) that users need. Methodologies for describing intrinsic and relevant properties of data through the extraction of useful patterns, however, work on fixed input data, and the data representation, therefore, constrains the discovered insights. In this regard, this paper aims at providing foundations to make the descriptive knowledge that is extracted by pattern mining more user-centric by relying on flexible data structures defined on two different perspectives: concepts and data records. In this sense, items in data can be grouped into abstract terms through subjective hierarchies of concepts, whereas data records can also be organized based on the users' subjective perspective. A series of easy-to-follow toy examples are considered for each of the two perspectives to demonstrate the usefulness and necessity of the proposed foundations in pattern mining. Finally, aiming at experimentally testing whether classical pattern mining algorithms can be adapted to such flexible data structures, the experimental analysis comprises different methodologies, including exhaustive search, random search, and evolutionary approaches. All these approaches are based on well-known and widely recognized techniques to demonstrate the usefulness of the provided foundations for future research works and more efficient and specifically designed algorithms. Obtained insights demonstrate the importance of working with subjectivity: an item is a type of soda but belongs to a pack, including two or more soda types.

The Naive Bayesian classifier (NBC) is a well-known classification model that has a simple structure, low training complexity, excellent scalability, and good classification performances. However, the NBC has two key limitations: (1) it is built upon the strong assumption that condition attributes are independent, which often does not hold in real-life, and (2) the NBC does not handle continuous attributes well. To overcome these limitations, this paper presents a novel approach for NBC construction, called mixed-attribute fusion-based NBC (MAF-NBC). It alleviates the two aforementioned limitations by relying on a mixed-attribute fusion mechanism with an improved autoencoder neural network for NBC construction. MAF-NBC transforms the original mixed attributes of a data set into a series of encoded attributes with maximum independence as a pre-processing step. To guarantee the generation of useful encoded attributes, an efficient objective function is designed to optimize the weights of the autoencoder neural network by considering both the encoding error and the attribute’s dependence. A series of persuasive experiments was conducted to validate the feasibility, rationality, and effectiveness of the designed MAF-NBC approach. Results demonstrate that MAF-NBC has superior classification performance than eight state-of-the-art Bayesian algorithms, namely the discretization-based NBC (Dis-NBC), flexible naive Bayes (FNB), tree-augmented naive (TAN) Bayes, averaged one-dependent estimator (AODE), hidden naive Bayes (HNB), deep feature weighting for NBC (DFW-NBC), correlation-based feature weighting filter for NBC (CFW-NBC), and independent component analysis-based NBC (ICA-NBC).

Privacy-preserving data mining has become an interesting and emerging issue in recent years since it can, not only hide the sensitive information but still mine the meaningful knowledge at the same time. Since privacy-preserving data mining is a non-trivial task, which is also concerned as a NP-hard problem, several evolutionary algorithms were presented to find the optimized solutions but most of them focus on considering a single-objective function with the pre-defined weight values of three side effects (hiding failure, missing cost, and artificial cost). In this paper, we aim at designing a multiple objective particle swarm optimization method for hiding the sensitive information based on the density clustering approach (named CMPSO). The presented CMPSO is more flexible to select the most appropriate solutions for hiding the sensitive information based on user's preference. Extensive experiments are carried on two datasets to show that the designed CMPSO algorithm has good performance than the traditional single-objective evolutionary approaches in terms of three side effects.

Internet of Things (IoT) supports high flexibility and convenience in several applications because the IoT devices continuously transfer, share, and exchange data without human intervention. During shared or exchanged progress of data, security and privacy threats result because the published or shared data mainly corresponds to a raw dataset, and an attacker can easily obtain details on the shared data in an IoT environment. In the paper, we present a sanitization approach by adopting the hierarchical-cluster method to hide confidential information while still discovering useful and meaningful information in the sanitized dataset. The multi-objective particle swarm optimization framework and an algorithm termed as HCMPSO are utilized to balance four side effects, namely, hiding failure, missing cost, artificial cost, and database dissimilarity (Dis), and thereby provide optimized solutions for data sanitization. The experiments are performed to compare the performance of the designed HCMPSO with that of the single-objective cpGA2DT and multi-objective NSGA-II-based approaches. As shown in the results, the designed HCMPSO exhibits good performance in terms of hiding failure, and thus the most confidential information is hidden after the sanitization process. The shared or published data in IoT is secured. Furthermore, the designed sanitization algorithm achieves reasonable results in terms of missing cost, artificial cost, and Dis.

Discovering periodic patterns in a customer transaction database is the task of identifying itemsets (sets of items or values) that periodically appear in a sequence of transactions. Numerous methods can identify patterns exhibiting a periodic behavior. Nonetheless, a problem of these traditional approaches is that the concept of periodic behavior is defined very strictly. Indeed, a pattern is considered to be periodic if the amount of time or number of transactions between all pairs of its consecutive occurrences is less than a fixed maxPer (maximum periodicity) threshold. As a result, a pattern can be eliminated by a traditional algorithm for mining periodic patterns even if all of its periods but one respect the maxPer constraint. Consequently, many patterns that are almost always periodic are not presented to the user. But these patterns could be considered as interesting as they generally appear periodically. To address this issue, this paper suggests to use three measures to identify periodic patterns. These measures are named average, maximum and minimum periodicity, respectively. They are each designed to evaluate a different aspect of the periodic behavior of patterns. By using them together in a novel algorithm called Periodic Frequent Pattern Miner, more flexibility is given to users to select patterns meeting specific periodic requirements. The designed algorithm has been evaluated on several datasets. Results show that the proposed solution is scalable, efficient, and can identify a small sets of patterns compared to the Eclat algorithm for mining all frequent patterns in a database.

High average-utility itemsets mining (HAUIM) is an emerging topic in data mining. Compared to traditional high utility itemset mining, HAUIM more fairly measures the utility of itemsets by considering their lengths (number of items). Many previous studies have presented algorithms to efficiently mine high average-utility itemsets (HAUIs). Most of these algorithms, however, only mine HAUIs using a single minimum high average-utility threshold, which limits their usefulness to analyze real data. This is a problem because different items are not equally important to the user. The importance of an item can be expressed for example in terms of weights, interestingness or unit profit. In the past, a baseline algorithm called HAUIM-MMAU was presented to mine HAUIs using multiple minimum high average-utility thresholds. However, it uses a generate-and-test approach to mine HAUIs using a level-wise approach, which is time consuming. In this paper, we propose an efficient algorithm to discover HAUIs based on the average-utility list structure. A tighter upper-bound model is used to reduce the search space instead of the one used in traditional HAUIM, which is called the auub model. Three pruning strategies are also respectively developed to increase the performance HAUIs. Experiments show that the proposed algorithm outperforms the state-of-the-art HAUIM-MMAU algorithm in terms of runtime, memory usage, number of candidates, and scalability.

High-utility itemset mining (HUIM) has been gaining popularity in the field of data mining. Frequent itemset mining used to be the main tool to reveal high-frequency patterns but failed to consider the concept of profit. HUIM, on the other hand, obtains the itemsets and is practical in commercial applications. A main challenge in HUIM is that HUIM should handle the exponential search space for HUIM when the number of distinct items and the size of the database are both too large. The other challenge is that existing HUIM methods overlook the length of high-utility itemsets; hence, a large itemset gets an unreasonable estimated profit as opposed to the actual value. Therefore, several algorithms were proposed to mine high average-utility itemsets. High average-utility itemset mining (HAUIM) is an extension for the traditional HUIM, which provides a different measure with HUIM. It discovers utility patterns by considering both their utilities and lengths. To reduce the searching space in HAUIM, average-utility upper-bound, looser upper-bound utility, and a revised tighter upper-bound model are proposed to prune the searching graph in HAUIM. These three upper-bounds for high average-utility itemsets decrease the number of candidate patterns efficiently. However, they still overestimate a high average-utility itemset and waste on assessing the unnecessary patterns. Two new tighter upper-bounds, maximum following utility upper-bound and top-k transaction-maximum utility upper-bound, are proposed in this paper to further contract the size of candidate pattern set. Experiments conducted on several benchmark data sets show that the proposed method outperforms the previous HAUIM algorithms in terms of runtime, the number of join operations, and scalability.

High-utility itemset mining (HUIM) has become a popular data mining task, as it can reveal patterns that have a high-utility, contrarily to frequent pattern mining, which focuses on discovering frequent patterns. High average-utility itemset mining (HAUIM) is a variation of HUIM that provides an alternative measure, called the average utility, to select patterns by considering both their utilities and lengths. In the last decades, several algorithms have been developed to mine high average-utility itemsets (HAUIs). But most of them consume large amounts of memory and have long execution times, since they generally utilize the average-utility upper-bound (auub) model to overestimate the average utilities of itemsets. To improve the performance of HAUIM, this paper proposes two novel tighter upper-bound models as alternative to the traditional auub model for mining HAUIs. The looser upper-bound model considers the remaining-maximum utility in transactions to reduce the upper bound on the utilities of itemsets. The second upper-bound model ignores irrelevant items in transactions to further tighten the upper bound. Three pruning strategies are also designed to reduce the search space for mining HAUIs by a greater amount compared with the state-of-the-art HAUI-Miner algorithm. Experiments conducted on several benchmark data sets show that the designed algorithm integrating the two novel upper-bound models outperforms the traditional HAUI-Miner algorithm in terms of runtime, memory usage, number of join operations, and scalability.

In this paper, we propose an novel interactive outlier detection system called feature-rich interactive outlier detection (FRIOD), which features a deep integration of human interaction to improve detection performance and greatly streamline the detection process. A user-friendly interactive mechanism is developed to allow easy and intuitive user interaction in all the major stages of the underlying outlier detection algorithm which includes dense cell selection, location-aware distance thresholding, and final top outlier validation. By doing so, we can mitigate the major difficulty of the competitive outlier detection methods in specifying the key parameter values, such as the density and distance thresholds. An innovative optimization approach is also proposed to optimize the grid-based space partitioning, which is a critical step of FRIOD. Such optimization fully considers the high-quality outliers it detects with the aid of human interaction. The experimental evaluation demonstrates that FRIOD can improve the quality of the detected outliers and make the detection process more intuitive, effective, and efficient.

Several approaches have been proposed to anonymize relational databases using the criterion of k-anonymity, to avoid the disclosure of sensitive information by re-identification attacks. A relational database is said to meet the criterion of k-anonymity if each record is identical to at least (k - 1) other records in terms of quasi-identifier attribute values. To anonymize a transactional database and satisfy the constraint of k-anonymity, each item must successively be considered as a quasi-identifier attribute. But this process greatly increases dimensionality, and thus also the computational complexity of anonymization, and information loss. In this paper, a novel efficient anonymization system called PTA is proposed to not only anonymize transactional data with a small information loss but also to reduce the computational complexity of the anonymization process. The PTA system consists of three modules, which are the Pre-processing module, the TSP module, and the Anonymity model, to anonymize transactional data and guarantees that at least k-anonymity is achieved: a pre-processing module, a traveling salesman problem module, and an anonymization module. Extensive experiments have been carried to compare the efficiency of the designed approach with the state-of-the-art anonymization algorithms in terms of scalability, runtime, and information loss. Results indicate that the proposed PTA system outperforms the compared algorithms in all respects.

Acoustic shocks and traumas sometimes result in a cluster of debilitating symptoms, including tinnitus, hyperacusis, ear fullness and tension, dizziness, and pain in and outside the ear. The mechanisms underlying this large variety of symptoms remain elusive. In this article, we elaborate on the hypothesis that the tensor tympani muscle (TTM), the trigeminal nerve (TGN), and the trigeminal cervical complex (TCC) play a central role in generating these symptoms. We argue that TTM overuse (due to the acoustic shock), TTM overload (due to muscle tension), and ultimately, TTM injury (due to hypoxia and “energy crisis”) lead to inflammation, thereby activating the TGN, TCC, and cortex. The TCC is a crossroad structure integrating sensory inputs coming from the head–neck complex (including the middle ear) and projecting back to it. The multimodal integration of the TCC may then account for referred pain outside the ear when the middle ear is inflamed and activates the TGN. We believe that our model proposes a synthetic and explanatory framework to explain the phenomena occurring postacoustic shock and potentially also after other nonauditory causes. Indeed, due to the bidirectional properties of the TCC, musculoskeletal disorders in the region of the head–neck complex, including neck injury due to whiplash or temporomandibular disorders, may impact the middle ear, thereby leading to otic symptoms. This previously unavailable model type is experimentally testable and must be taken as a starting point for identifying the mechanisms responsible for this particular subtype of tinnitus and its associated symptoms.

Tinnitus masking and residual inhibition (RI) are two well-known psychoacoustic measures of tinnitus. While it has long been suggested that they may provide diagnostic and prognostic information, these measures are still rarely performed in clinics, as they are too time consuming. Given this issue, the main goal of the present study was to validate a new method for assessing these measures. An acoustic sequence made of pulsed stimuli, which included a fixed stimulus duration and interstimulus interval, was applied to 68 tinnitus patients at two testing sites. First, the minimum masking level (MML) was measured by raising the stimulus intensity until the tinnitus was unheard during the stimulus presentation. Second, the level of the stimulus was further increased until the tinnitus was suppressed during the silence interval between the acoustic pulses. This level was called the minimum residual inhibition level (MRIL). The sequential measurement of MML and MRIL from the same stimulus condition offers several advantages such as time efficiency and the ability to compare results between the MRIL and MML. Our study confirms that, from this new approach, MML and MRIL can be easily and quickly obtained from a wide variety of patients displaying either normal hearing or different hearing loss configurations. Indeed, MML was obtained in all patients except one (98.5%), and some level of MRIL was found on 59 patients (86.7%). More so, this approach allows the categorization of tinnitus patients into different subgroups based on the properties of their MRIL.

Le point de départ de ma proposition est que la responsabilité est un terme plus approprié que la vertu pour désigner les exhortations au devoir civique dans l’ère contemporaine. De même, à défaut de voir l’implication citoyenne comme l’expression de la rationalité individuelle ou de la conscience morale dans la sphère publique, je propose de comprendre la responsabilité comme une matrice discursive et gouvernementale qui perpétue des modèles comportementaux bien spécifiques. J’entends ainsi démontrer que la responsabilité est devenue une modalité indispensable du « gouvernement de la conduite » au sein d’une rationalité néolibérale. En plus de constituer une série de dispositions morales qui remédierait aux failles et aux lacunes de l’individualisme (néo)libéral, la responsabilité se matérialise dans les incitations à une citoyenneté active et participe de ce fait à la rationalisation du retranchement de l’État de la sphère sociale. Comme l’illustrent des courants aussi divers que le néoconservatisme, le communautarisme et l’économie sociale, la responsabilité se présente moins comme une solution de rechange aux créneaux du néolibéralisme qu’une série de techniques et de standards comportementaux visant à compléter et renforcer l’application élargie d’une logique micro-économique. Je montrerai que le recours à la notion de responsabilité est particulièrement visible dans les politiques d’aide sociale et au sein des nouveaux partenariats entre l’État, le milieu communautaire et les communautés locales. En effet, l’aide étatique est de plus en plus conditionnelle à la démonstration de certaines dispositions morales et psychologiques comme la volonté, la ténacité et la probité. L’article se divise en quatre parties. La première s’attarde à définir brièvement la « gouvernementalité », concept initialement développé par Michel Foucault. La deuxième se penche sur des travaux plus contemporains, notamment ceux du sociologue britannique Nikolas Rose qui se préoccupe de la responsabilité en tant que modalité gouvernementale au sein du néolibéralisme. La troisième partie met en relief les particularités du néolibéralisme américain. En vue de cerner les pratiques et discours associés à la responsabilité de façon plus précise, la dernière partie examine l’un des principaux programmes d’aide sociale aux États-Unis, l’Aid to Families with Dependent Children (AFDC), devenu le Temporary Assistance for Needy Families (TANF) en 1996, des années 1980 jusqu’à aujourd’hui.

High-utility itemset mining (HUIM) is a critical issue in recent years since it can be used to reveal the profitable products by considering both the quantity and profit factors instead of frequent itemset mining (FIM) of association rules (ARs). In this paper, an evolutionary algorithm is presented to efficiently mine high-utility itemsets (HUIs) based on the binary particle swarm optimization. A maximal pattern (MP)-tree strcutrue is further designed to solve the combinational problem in the evolution process. Substantial experiments on real-life datasets show that the proposed binary PSO-based algorithm has better results compared to the state-of-the-art GA-based algorithm.

This study reports the case of an acoustic shock injury (ASI), which did not result in a significant hearing loss, but was followed by manifold chronic symptoms both within (tinnitus, otalgia, tingling in the ear, tension in the ear, and red tympanum) and outside the ears (blocked nose, pain in the neck/temporal region). We suggest that these symptoms may result from a loop involving injury to middle ear muscles, peripheral inflammatory processes, activation and sensitization of the trigeminal nerve, the autonomic nervous system, and central feedbacks. The pathophysiology of this ASI is reminiscent of that observed in post-traumatic trigeminal-autonomic cephalalgia. This framework opens new and promising perspectives on the understanding and medical management of ASI.

The auditory brainstem response (ABR) in tinnitus subjects has been extensively investigated over the last decade with the hopes of finding possible abnormalities related to the pathology. Despite this effort, the use of the ABR for tinnitus diagnosis or as an outcome measure is under debate. The present study reviewed published literature on ABR and tinnitus. The authors searched PubMed, MedLine, Embase, PsycINFO, and CINAHL, and identified additional records through manually searching reference lists and gray literature. There were 4,566 articles identified through database searching and 151 additional studies through the manual search (4,717 total): 2,128 articles were removed as duplicates, and 2,567 records did not meet eligibility criteria. From the final 22 articles that were included, ABR results from 1,240 tinnitus subjects and 664 control subjects were compiled and summarized with a focus on three main areas: the participant characteristics, the methodology used, and the outcome measures of amplitude and/or latency of waves I, III, and V. The results indicate a high level of heterogeneity between the studies for all the assessed areas. Amplitude and latency differences between tinnitus and controls were not consistent between studies. Nevertheless, the longer latency and reduced amplitude of wave I for the tinnitus group with normal hearing compared to matched controls was the most consistent finding across studies. These results support the need for greater stratification of the tinnitus population and the importance of a standardized ABR method to make comparisons between studies possible.

High-utility sequential pattern mining (HUSPM) has become an important issue in the field of data mining. Several HUSPM algorithms have been designed to mine high-utility sequential patterns (HUPSPs). They have been applied in several real-life situations such as for consumer behavior analysis and event detection in sensor networks. Nonetheless, most studies on HUSPM have focused on mining HUPSPs in precise data. But in real-life, uncertainty is an important factor as data is collected using various types of sensors that are more or less accurate. Hence, data collected in a real-life database can be annotated with existing probabilities. This paper presents a novel pattern mining framework called high utility-probability sequential pattern mining (HUPSPM) for mining high utility-probability sequential patterns (HUPSPs) in uncertain sequence databases. A baseline algorithm with three optional pruning strategies is presented to mine HUPSPs. Moroever, to speed up the mining process, a projection mechanism is designed to create a database projection for each processed sequence, which is smaller than the original database. Thus, the number of unpromising candidates can be greatly reduced, as well as the execution time for mining HUPSPs. Substantial experiments both on real-life and synthetic datasets show that the designed algorithm performs well in terms of runtime, number of candidates, memory usage, and scalability for different minimum utility and minimum probability thresholds.

Oncolytic viruses (OVs) replicate selectively in tumor cells and exert anti-tumor cytotoxic activity. Among them, Newcastle Disease Virus (NDV), a bird RNA virus of the paramyxovirus family, appears outstanding. Its anti-tumor effect is based on: (i) oncolytic activity and (ii) immunostimulation. Together these activities facilitate the induction of post-oncolytic adaptive immunity. We will present milestones during the last 60 years of clinical evaluation of this virus. Two main strategies of clinical application were followed using the virus (i) as a virotherapeutic agent, which is applied systemically or (ii) as an immunostimulatory agent combined with tumor cells for vaccination of cancer patients. More recently, a third strategy evolved. It combines the strategies (i) and (ii) and includes also dendritic cells (DCs). The first step involves systemic application of NDV to condition the patient. The second step involves intradermal application of a special DC vaccine pulsed with viral oncolysate. This strategy, called NDV/DC, combines anti-cancer activity (oncolytic virotherapy) and immune-stimulatory properties (oncolytic immunotherapy) with the high potential of DCs (DC therapy) to prime naive T cells. The aim of such treatment is to first prepare the cancer-bearing host for immunocompetence and then to instruct the patient’s immune system with information about tumor-associated antigens (TAAs) of its own tumor together with danger signals derived from virus infection. This multimodal concept should optimize the generation of strong polyclonal T cell reactivity targeted against the patient’s TAAs and lead to the establishment of a long-lasting memory T cell repertoire.

Les deux dernières décennies ont été marquées par une amélioration sensible de la production de statistiques agricoles en tenant compte du fait que la demande en information géographique est toujours croissante et de plus en plus diversifiée tant à l'échelle nationale qu'internationale. Cet article présente quelques aspects méthodologiques (les méthodes aréolaires conventionnelles) de production d'information géographique à travers les statistiques spatiales agricoles en utilisant les cartes topographiques à échelle fine et les images satellites à haute résolution spatiale comme base de sondage pour l'application des enquêtes agricoles en Roumanie post-communiste. L'article présente l'application du projet MARS en Roumanie en s'appuyant sur des exemples du Plateau de Falticeni, au nord-est du pays, et en focalisant sur l'adaptation de la méthodologie et de la collecte des données à une situation foncière et environnementale spécifique à un pays de l'Europe Centrale et de l'Est.

The diagnosis of tinnitus relies on self-report. Psychoacoustic measurements of tinnitus pitch and loudness are essential for assessing claims and discriminating true from false ones. For this reason, the quantification of tinnitus remains a challenging research goal. We aimed to: (1) assess the precision of a new tinnitus likeness rating procedure with a continuous-pitch presentation method, controlling for music training, and (2) test whether tinnitus psychoacoustic measurements have the sensitivity and specificity required to detect people faking tinnitus. Musicians and non-musicians with tinnitus, as well as simulated malingerers without tinnitus, were tested. Most were retested several weeks later. Tinnitus pitch matching was first assessed using the likeness rating method: pure tones from 0.25 to 16 kHz were presented randomly to participants, who had to rate the likeness of each tone to their tinnitus, and to adjust its level from 0 to 100 dB SPL. Tinnitus pitch matching was then assessed with a continuous-pitch method: participants had to match the pitch of their tinnitus to an external tone by moving their finger across a touch-sensitive strip, which generated a continuous pure tone from 0.5 to 20 kHz in 1-Hz steps. The predominant tinnitus pitch was consistent across both methods for both musicians and non-musicians, although musicians displayed better external tone pitch matching abilities. Simulated malingerers rated loudness much higher than did the other groups with a high degree of specificity (94.4%) and were unreliable in loudness (not pitch) matching from one session to the other. Retest data showed similar pitch matching responses for both methods for all participants. In conclusion, tinnitus pitch and loudness reliably correspond to the tinnitus percept, and psychoacoustic loudness matches are sensitive and specific to the presence of tinnitus.

This article aims to expose the main governmental shifts in recent American history (1961-2000) by examining two programs: the Assistance to Families with Dependent Children (AFDC) and the Agency for International development (US-AID). Through the exploration of primary and secondary sources, we analyse the production, organisation and circulation of governmental practices in the realms of both domestic and foreign policy. In the American context, practices of government typically revolve around freedom, efficiency models and individual responsibility. Throughout the analysis, we find that the general critiques which have guided reforms and experiments in both areas converge around the same elements. This testifies to the fact that the reflexions and technical models directed at the optimal management of populations are more far-reaching than they first appear. Moreover, the historical transformations in welfare and foreign aid practices bear out the increasingly disciplinary nature of the administration and objectification of the poor, both within the United States and internationally.

Repeat-associated small interfering RNAs (rasiRNAs) are derived from various genomic repetitive elements and ensure genomic stability by silencing endogenous transposable elements. Here we describe a novel subset of 46 rasiRNAs named LNCR rasiRNAs due to their homology with one long non-coding RNA (LNCR) of Spodoptera frugiperda. LNCR operates as the intermediate of an unclassified transposable element (TE-LNCR). TE-LNCR is a very invasive transposable element, present in high copy numbers in the S. frugiperda genome. LNCR rasiRNAs are single-stranded RNAs without a prominent nucleotide motif, which are organized in two distinct, strand-specific clusters. The expression of LNCR and LNCR rasiRNAs is developmentally regulated. Formation of heterochromatin in the genomic region where three copies of the TE-LNCR are embedded was followed by chromatin immunoprecipitation (ChIP) and we observed this chromatin undergo dynamic changes during development. In summary, increased LNCR expression in certain developmental stages is followed by the appearance of a variety of LNCR rasiRNAs which appears to correlate with subsequent accumulation of a heterochromatic histone mark and silencing of the genomic region with TE-LNCR. These results support the notion that a repeat-associated small interfering RNA pathway is linked to heterochromatin formation and/or maintenance during development to establish repression of the TE-LNCR transposable element. This study provides insights into the rasiRNA silencing pathway and its role in the formation of fluctuating heterochromatin during the development of one holocentric organism.

New approaches of therapeutic cancer vaccination are needed to improve the antitumor activity of T cells from cancer patients. We studied over the last years the activation of human T cells for tumor attack. To this end, we combined the personalized therapeutic tumor vaccine ATV-NDV—which is obtained by isolation, short in vitro culture, irradiation, and infection of patient's tumor cells by Newcastle Disease Virus (NDV)—with bispecific antibodies (bsAbs) binding to this vaccine and introducing anti-CD3 (signal 1) and anti-CD28 (signal 2) antibody activities. This vaccine called ATV-NDV/bsAb showed the unique ability to reactivate a preexisting potentially anergized antitumor memory T cell repertoire. But it also activated naive T cells to have antitumor properties in vitro and in vivo. This innovative concept of direct activation of cancer patients' T cells via cognate and noncognate interactions provides potential for inducing strong antitumor activities aiming at overriding T cell anergy and tumor immune escape mechanisms.