The computations involved in statistical learning have long been debated. Here, we build on work suggesting that a basic memory process, chunking, may account for the processing of statistical regularities into larger units. Drawing on methods from the memory literature, we developed a novel paradigm to test statistical learning by leveraging a robust phenomenon observed in serial recall tasks: that short‐term memory is fundamentally shaped by long‐term distributional learning. In the statistically induced chunking recall (SICR) (...) task, participants are exposed to an artificial language, using a standard statistical learning exposure phase. Afterward, they recall strings of syllables that either follow the statistics of the artificial language or comprise the same syllables presented in a random order. We hypothesized that if individuals had chunked the artificial language into word‐like units, then the statistically structured items would be more accurately recalled relative to the random controls. Our results demonstrate that SICR effectively captures learning in both the auditory and visual modalities, with participants displaying significantly improved recall of the statistically structured items, and even recall specific trigram chunks from the input. SICR also exhibits greater test–retest reliability in the auditory modality and sensitivity to individual differences in both modalities than the standard two‐alternative forced‐choice task. These results thereby provide key empirical support to the chunking account of statistical learning and contribute a valuable new tool to the literature. (shrink)

Supraspan verbal list-learning tests, such as the Rey Auditory Verbal Learning Test (RAVLT), are classic neuropsychological tests for assessing verbal memory. In this study, we investigated the impact of the meaning of the words to be learned on 3 memory stages (short-term recall, learning, and delayed recall) in a cohort of 447 healthy adults. First, we compared scores obtained from the RAVLT (word condition) to those of an alternative version of this test using phonologically similar (...) but meaningless items (pseudoword condition) and observed how each score varied as a function of age and sex. Then, we collected the participants’ self-reported strategies to retain the word and pseudoword lists and examined if these strategies mediated the age and sex effects on memory scores. The word condition resulted in higher memory scores than pseudoword condition at each memory stage and even canceled out, for the learning stage, the detrimental effect of age that was observed for the short-term and delayed recall. When taking sex into account, the word advantage was observed only in women for short-term recall. The self-reported strategies, which were similar for words and pseudowords, were based on the position of the item on the list (word: 53 %, pseudoword: 37%) or the meaning of the item (word: 64%, pseudoword: 58%) and were used alone or in combination. The best memory performance was associated with the meaning strategy in the word condition and with the combination of the meaning and position strategies in the pseudoword condition. Finally, we found that the word advantage observed in women for short-term recall was mediated by the use of the meaning strategy. The RAVLT scores were thus highly dependent on word meaning, notably because it allowed efficient semantic knowledge-based strategies. Within the framework of Tulving’s declarative memory model, these results are at odds with the depiction of the RAVLT as a verbal episodic memory test as it is increasingly referred to in the literature. (shrink)

Multiple-choice questions have an undeserved reputation for only being able to test student recall of basic facts. In fact, well-crafted mechanically gradable questions can measure very sophisticated cognitive skills, including those engaged at the highest level of Benjamin Bloom’s taxonomy of outcomes. In this article, I argue that multiple-choice questions should be a part of the diversified assessment portfolio for most philosophy courses. I present three arguments broadly related to fairness. First, multiple-choice questions allow one to consolidate subjective decision (...) making in a way that makes it easier to manage. Second, multiple-choice questions contribute to the diversity of an evaluation portfolio by balancing out problems with writing-based assessments. Third, by increasing the diversity of evaluations, multiple-choice questions increase the inclusiveness of the course. In the course of this argument I provide examples of multiple-choice questions that measure sophisticated learning and advice for how to write good multiple-choice questions. (shrink)

This paper reviews the fate of the central ideas behind the complementary learning systems (CLS) framework as originally articulated in McClelland, McNaughton, and O’Reilly (1995). This framework explains why the brain requires two differentially specialized learning and memory systems, and it nicely specifies their central properties (i.e., the hippocampus as a sparse, pattern-separated system for rapidly learning episodic memories, and the neocortex as a distributed, overlapping system for gradually integrating across episodes to extract latent semantic structure). We (...) review the application of the CLS framework to a range of important topics, including the following: the basic neural processes of hippocampal memory encoding and recall, conjunctive encoding, human recognition memory, consolidation of initial hippocampal learning in cortex, dynamic modulation of encoding versus recall, and the synergistic interactions between hippocampus and neocortex. Overall, the CLS framework remains a vital theoretical force in the field, with the empirical data over the past 15 years generally confirming its key principles. (shrink)

I teach with water. It’s nothing very remarkable and I myself do not remember how I settled upon water as a most convenient introduction to what I have to teach, which is to say, to learn. Did not everything begin with water? My own beginnings, in any case, would border on the banal, if they did not signify so much about where I live (race and class) and how I teach (tradition, institution, location), the liberties I can responsibly take, or (...) the sheer length to which one might have to go to register and partake of a sense of wonder – and of outrage – on the impossible path toward a collective experience of learning. In this particular instalment, learning with water is very much about recalling what we know, knowing what we do with the knowledge that we have. I teach with water. I start my class by quietly, if ostensibly, depositing in front of the class, or at the center of the seminar table, a bottle of “spring water.” I then invite the students to attend to this classroom instance of the proverbial elephant, though not necessarily true to the desperate manner of the three blind men. (shrink)

Surprise has been explored as a cognitive-emotional phenomenon that impacts many aspects of mental life from creativity to learning to decision-making. In this paper, we specifically address the role of surprise in learning and memory. Although surprise has been cast as a basic emotion since Darwin's (1872) The Expression of the Emotions in Man and Animals, recently more emphasis has been placed on its cognitive aspects. One such view casts surprise as a process of “sense making” or “explanation (...) finding”: metacognitive explanation-based theory proposes that people's perception of surprise is a metacognitive assessment of the cognitive work done to explain a surprising outcome. Or, to put it more simply, surprise increases with the explanatory work required to resolve it. This theory predicts that some surprises should be more surprising than others because they are harder to explain. In the current paper, this theory is extended to consider the role of surprise in learning as evidenced by memorability. This theory is tested to determine how scenarios with differentially surprising outcomes impact the memorability of those outcomes. The results show that surprising outcomes (less-known outcomes) that are more difficult to explain are recalled more accurately than less-surprising outcomes that require little (known outcomes) or no explanation (normal). (shrink)

This article aims to help readers to learn about health care related cultural and religious beliefs and spiritual needs in Chinese communities. The recall diary of a severe acute respiratory syndrome (SARS)-infected intern working in Hoping Hospital in Taiwan during the 2003 SARS epidemic is presented and used to assist in understanding one patient’s spiritual activities when personally confronted with this newly emerging infectious disease. The article also gives an overview of the 2003 SARS epidemic in Taiwan, and discusses (...) people’s general perceptions towards infectious diseases, their coping strategies concerning disease, and their spiritual beliefs, the psychological impact of the 2003 SARS outbreak in Chinese communities, Chinese myths about infectious disease, and the religious activities of a SARS-infected intern in Taiwan. Recommendations are given on how to achieve quality holistic nursing care. (shrink)

Museum collection databases contain echoes of encounter between colonial collectors (broadly defined) and Indigenous people from around the world. The moment of acquisition—when an item passed out of a community and into the hands of the collector—often included multilingual acts of translation. An artist may have shared the Indigenous name of the object, or the terms associated with its origin and use. Late nineteenth and twemtieth century museum registrars would in turn transcribe this information from field logs into museum catalogs. (...) Over time, these catalog entries were transformed into digital records within collections managements systems (e.g., EMu, PastPerfect, etc.). As a result of this 150-year process, today’s museum collection databases are riddled with Indigenous words and descriptions, scattered across various metadata fields. They may include Native place-names, family names or vocabulary terms that, when translated, extend far beyond the categories ascribed by museum collection managers. These instances of Indigenous description may also serve as a crucial bridge for reconnecting source communities with items of particular interest to their cultural heritage and linguistic preservation efforts. Aiming to enhance the accessibility of Indigenous languages contained in the metadata of cultural heritage collections, this paper explores applications of machine learning methodologies to identify Indigenous terms present in museum catalogs. Specifically, we discuss methods that incorporate the Google Cloud Language Identification Service to detect A:shiwi (Pueblo of Zuni) language terms through a case study of metadata records from the two largest natural history museums in the USA. We utilize an elimination mechanism to exclude specific languages (e.g., English and Spanish) at the word and phrase levels to detect A:shiwi terms. Our approach outperforms conventional methods in terms of accuracy, recall, precision, and F1-scores. This method can be used to confront the “Digital Heap” of cultural heritage records across institutions to improve the discoverability of Indigenous languages in metadata descriptions and reconnect source communities with items of cultural patrimony. (shrink)

With the rapid growth of informatics systems’ technology in this modern age, the Internet of Things (IoT) has become more valuable and vital to everyday life in many ways. IoT applications are now more popular than they used to be due to the availability of many gadgets that work as IoT enablers, including smartwatches, smartphones, security cameras, and smart sensors. However, the insecure nature of IoT devices has led to several difficulties, one of which is distributed denial-of-service (DDoS) attacks. IoT (...) systems have several security limitations due to their disreputability characteristics, like dynamic communication between IoT devices. The dynamic communications resulted from the limited resources of these devices, such as their data storage and processing units. Recently, many attempts have been made to develop intelligent models to protect IoT networks against DDoS attacks. The main ongoing research issue is developing a model capable of protecting the network from DDoS attacks that is sensitive to various classes of DDoS and can recognize legitimate traffic to avoid false alarms. Subsequently, this study proposes combining three deep learning algorithms, namely recurrent neural network (RNN), long short-term memory (LSTM)-RNN, and convolutional neural network (CNN), to build a bidirectional CNN-BiLSTM DDoS detection model. The RNN, CNN, LSTM, and CNN-BiLSTM are implemented and tested to determine the most effective model against DDoS attacks that can accurately detect and distinguish DDoS from legitimate traffic. The intrusion detection evaluation dataset (CICIDS2017) is used to provide more realistic detection. The CICIDS2017 dataset includes benign and up-to-date examples of typical attacks, closely matching real-world data of Packet Capture. The four models are tested and assessed using Confusion Metrix against four commonly used criteria: accuracy, precision, recall, and F-measure. The performance of the models is quite effective as they obtain an accuracy rate of around 99.00%, except for the CNN model, which achieves an accuracy of 98.82%. The CNN-BiLSTM achieves the best accuracy of 99.76% and precision of 98.90%. (shrink)

In this review article, Christiansen provides a historical perspective on the two research traditions, implicit learning and statistical learning, thus nicely setting the scene for this special issue of Topics in Cognitive Science. In this “tale of two literatures”, he first traces the history of both literatures before sketching a framework that provides a basis for understanding implicit learning and statistical learning as a unified phenomenon.

Seasonal outbreaks have several different periods that occur primarily during winter in temperate regions, while influenza may occur throughout the year in tropical regions, triggering outbreaks more irregularly. Similarly, dengue occurs in the star of the rainy season in early May and reaches its peak in late June. Dengue and flu brought an impact on various countries in the years 2017–2019 and streaming Twitter data reveals the status of dengue and flu outbreaks in the most affected regions. This research work (...) presents that Social Media Analysis can be used as a detector of the epidemic outbreak and to understand the sentiment of social media users regarding various diseases. Providing awareness about seasonal outbreaks through SMA is an effective approach for researchers and healthcare responders to detect the early outbreaks. The proposed model aims to find the sentiment about the disease in tweets, and the seasonal outbreaks-related tweets are classified into two classes as disease positive and disease negative. This work proposes a machine-learning-based approach to detect dengue and flu outbreaks in social media platform Twitter, using four machine learning algorithms: Random Forest, K-Nearest Neighbor, Support Vector Machine, and Decision Tree, with the help of Term Frequency and Inverse Document Frequency. For experimental analysis, two datasets are analyzed individually. The experimental results show that the RF classifier has outperformed the comparison models in terms of improved accuracy, precision, recall, F1-measure, and Receiver Operating Characteristic curve. The proposed work offers favorable performance with total precision, accuracy, recall, and F1-measure ranging from 84% to 88% for conventional machine learning techniques. (shrink)

In sport, perceptual skill training software is intended to assist tactical training in the field. The aim of this field study was to test whether ‘laboratory-based’ pattern recall training would augment tactical skill training performed on the field. Twenty-six soccer players between 14-16 years of age from a single team participated in this study and were divided into three groups. The first received field training on a specific tactical skill plus cognitive training sessions on the pattern recall task. (...) The second performed only the field training while the third group served as a control group and had field training on other topics. The task on the pre-, post- and retention tests was to recall specific soccer patterns displayed on a computer screen. Results showed significant changes between pre- to post-test performance. There was no significant interaction between groups and tests but the effect size was large. From pre- to retention-test, there was a significant difference between tests and an interaction between groups and tests, but no main effect difference between groups. On the basis of significance testing only retention was affected by the additional training, however, descriptive results and effect sizes from pre- to post-test were as expected and suggested there were learning benefits. Together these results indicate that augmented perceptual-cognitive training might be beneficial, but some limitations in our study design (e.g., missing field test, missing placebo group, etc.) need to be improved in future work. (shrink)

Floods are one of the most common natural disasters in the world that affect all aspects of life, including human beings, agriculture, industry, and education. Research for developing models of flood predictions has been ongoing for the past few years. These models are proposed and built-in proportion for risk reduction, policy proposition, loss of human lives, and property damages associated with floods. However, flood status prediction is a complex process and demands extensive analyses on the factors leading to the occurrence (...) of flooding. Consequently, this research proposes an Internet of Things-based flood status prediction (IoT-FSP) model that is used to facilitate the prediction of the rivers flood situation. The IoT-FSP model applies the Internet of Things architecture to facilitate the flood data acquisition process and three machine learning (ML) algorithms, which are Decision Tree (DT), Decision Jungle, and Random Forest, for the flood prediction process. The IoT-FSP model is implemented in MATLAB and Simulink as development platforms. The results show that the IoT-FSP model successfully performs the data acquisition and prediction tasks and achieves an average accuracy of 85.72% for the three-fold cross-validation results. The research finding shows that the DT scores the highest accuracy of 93.22%, precision of 92.85, and recall of 92.81 among the three ML algorithms. The ability of the ML algorithm to handle multivariate outputs of 13 different flood textual statuses provides the means of manifesting explainable artificial intelligence and enables the IoT-FSP model to act as an early warning and flood monitoring system. (shrink)

Associative learning is an essential feature of human cognition, accounting for the influence of priming and interference effects on memory recall. Here, we extend our account of associative learning that learns asymmetric item-to-item associations over time via experience by including link maturation to balance associations between longer-term stability while still accounting for short-term variability. This account, combined with an existing account of activation strengthening and decay, predicts both human response times and error rates for the fan effect (...) for both target and foil stimuli. (shrink)

Pervasive usage and the development of social media networks have provided the platform for the fake news to spread fast among people. Fake news often misleads people and creates wrong society perceptions. The spread of low-quality news in social media has negatively affected individuals and society. In this study, we proposed an ensemble-based deep learning model to classify news as fake or real using LIAR dataset. Due to the nature of the dataset attributes, two deep learning models were (...) used. For the textual attribute “statement,” Bi-LSTM-GRU-dense deep learning model was used, while for the remaining attributes, dense deep learning model was used. Experimental results showed that the proposed study achieved an accuracy of 0.898, recall of 0.916, precision of 0.913, and F-score of 0.914, respectively, using only statement attribute. Moreover, the outcome of the proposed models is remarkable when compared with that of the previous studies for fake news detection using LIAR dataset. (shrink)

In lieu of an abstract, here is a brief excerpt of the content:The Contemplative Classroom, or Learning by Heart in the Age of GoogleBarbara NewmanIn his provocative essay “Slow Knowledge,” David Orr outlines the countervailing assumptions of what he calls “the culture of fast knowledge.” Among these are the widely shared, though rarely examined, beliefs that “only that which can be measured is true knowledge; the more knowledge we have, the better; there are no significant distinctions between information and (...) knowledge; and wisdom is an undefinable, hence unimportant category.”1 If all this were true, it would follow that computers are fast overtaking humans as the next intelligent species. Or, to put it differently, the two species have been colluding for some time to produce smarter machines and dumber people, as we humans abdicate more and more of our mental tasks. Moreover, when it comes time to weigh values—to ask not how quickly or efficiently some task can be done, but whether it ought to be done at all—we are strangely disinclined to challenge digital fatalism, which has become the default logic of late capitalism. Whenever a new digital option appears, we assume that if it can be done and someone somewhere is doing it, then it should be done and we ought to do it too. So even the local hardware store has to be on Facebook so customers can “like” it, and the AAR needs a Twitter account to send weekly tweets. We seldom pause to ask questions about means and ends, unintended consequences, or the sheer mindless clutter of our lives—let alone the implications of this or that new app for our descendants down to the seventh generation, or the enlightenment of all sentient beings. Surface obliterates depth; instant stimulation trumps mature reflection; short-term profit overrules the long-range good.Computers themselves are of course morally neutral. Nonetheless, it is useful to recall why contemplation is the antithesis of the fast knowledge they promote. Contemplative practice is grounded in such values as presence, mindfulness, inwardness, and the integration of mind and body. The last point is worth stressing, for all knowledge, even the most spiritual, is embodied; we are still biological beings, not cyborgs. Though contemplatives of the past have often been fierce ascetics, even the most emaciated hermit could scarcely despise the body to the degree that we find in [End Page 3] some contemporary Internet addicts. Consider the following, reported in July 2012 in Newsweek:When the new DSM [Diagnostic and Statistical Manual of Mental Disorders] is released next year, Internet Addiction Disorder will be included for the first time, albeit in an appendix tagged for “further study.” China, Taiwan, and Korea recently accepted the diagnosis, and began treating problematic Web use as a grave national health crisis. In those countries,... the story is sensational front-page news. One young couple neglected its infant to death while nourishing a virtual baby online. A young man fatally bludgeoned his mother for suggesting he log off (and then used her credit card to rack up more hours). At least 10 ultra-Web users, serviced by one-click noodle delivery, have died of blood clots from sitting too long.2Even short of such extremes, numerous studies have linked excessive Internet use in the United States with increased rates of depression, anxiety, suicidal thoughts, and obsessive-compulsive behavior. Teens admit to being exhausted by the constant need to update their Facebook status and answer countless texts, yet they are afraid to log off for fear that some excitement may pass them by. There is even a new acronym, FOMO, for the fear of missing out. If a central aim of contemplative practice is to dismantle and disengage from the “false self,” there is no better tool for constructing false selves than social media. In her book Alone Together, the social psychologist Sherry Turkle quotes a young man who told her, “What I learned in high school was profiles, profiles, profiles, how to make a me.”3 Another student said he maintained four avatars onscreen at all times, along with his email, video games, and—yes—coursework. His “real life,” he said, was “just one more window,... not usually my best one.”4But social... (shrink)

The purpose of this study is to propose and develop an intelligent e-learning system based on advanced document management techniques at Al-Quds Open University. In this article, we focus on a case using e-mail contents as supplement educational materials at QOU. We describe how the interactive classification system based on concept hierarchy can simplify this task. This system provides the functions to index, classify, and retrieve a collection of e-mail messages based on user profiles. By automatically indexing e-mail messages (...) using our E-mail Classification System for e-Learning, instructors and/or students can easily find their messages and find the topics. The test results of our system evaluation showed that a good classification quality has been achieved, with a precision of 77.4%, recall of 90%, and F-measure of 82.8. (shrink)

Ability to recall the timing of events is a crucial aspect of associative learning. Yet, traditional theories of associative learning have often overlooked the role of time in learning association and shaping the behavioral outcome. They address temporal learning as an independent and parallel process. Temporal Coding Hypothesis is an attempt to bringing together the associative and non-associative aspects of learning. This account proposes temporal maps, a representation that encodes several aspects of a learned (...) association, but attach considerable importance to the temporal aspect. A temporal map helps an agent to make inferences about missing information by applying an integration mechanism over a common element present in independently acquired temporal maps. We review the empirical evidence demonstrating the construct of temporal maps and discuss the importance of this concept in clinical and behavioral interventions. (shrink)

Although we frequently acquire knowledge and skills through social interactions, the focus of most research on learning is on individual learning. Here we characterize Interaction Based Learning, which represents the acquisition of knowledge or skill through social interactions, and compare it to Observational Learning —learning by observation. To that end, we designed a movement synchronization paradigm whereby participants learned Tai-Chi inspired movement sequences from trained teachers in two separated sessions. We used a motion capture system (...) to track the movement of 40 dyads comprised of a teacher and learner, who were randomly divided into OL or IBL groups, and calculated time-varying synchrony of three-dimensional movement velocity. While in the IBL group both the learner and the teacher could see each other through a transparent glass, in the OL group dyads interacted through a one-way mirror, such that the learners observed the teacher, but the teacher could not see the learners. Results show that although the number of movements recalled was not different between groups, we found improved movement smoothness in the IBL compared to the OL group, indicating movement acquisition was better in the IBL group. In addition, we found that motor synchronization levels in dyads improved over time, indicating that movement synchronization can be learned and retained. In the first session, the IBL group, but not the OL group, showed a significant improvement in synchronization. This suggests that dyadic interaction is important for learning movement sequences, and that bidirectional communication of signals and mutual feedback are essential for the consolidation of motor learning. (shrink)

Software defects prediction at the initial period of the software development life cycle remains a critical and important assignment. Defect prediction and correctness leads to the assurance of the quality of software systems and has remained integral to study in the previous years. The quick forecast of imperfect or defective modules in software development can serve the development squad to use the existing assets competently and effectively to provide remarkable software products in a given short timeline. Hitherto, several researchers have (...) industrialized defect prediction models by utilizing statistical and machine learning techniques that are operative and effective approaches to pinpoint the defective modules. Tree family machine learning techniques are well-thought-out to be one of the finest and ordinarily used supervised learning methods. In this study, different tree family machine learning techniques are employed for software defect prediction using ten benchmark datasets. These techniques include Credal Decision Tree, Cost-Sensitive Decision Forest, Decision Stump, Forest by Penalizing Attributes, Hoeffding Tree, Decision Tree, Logistic Model Tree, Random Forest, Random Tree, and REP-Tree. Performance of each technique is evaluated using different measures, i.e., mean absolute error, relative absolute error, root mean squared error, root relative squared error, specificity, precision, recall, F-measure, G-measure, Matthew’s correlation coefficient, and accuracy. The overall outcomes of this paper suggested RF technique by producing best results in terms of reducing error rates as well as increasing accuracy on five datasets, i.e., AR3, PC1, PC2, PC3, and PC4. The average accuracy achieved by RF is 90.2238%. The comprehensive outcomes of this study can be used as a reference point for other researchers. Any assertion concerning the enhancement in prediction through any new model, technique, or framework can be benchmarked and verified. (shrink)

Word learning is basic to foreign language acquisition, however time consuming and not always successful. Empirical studies have shown that traditional (visual) word learning can be enhanced by gestures. The gesture benefit has been attributed to depth of encoding. Gestures can lead to depth of encoding because they trigger semantic processing and sensorimotor enrichment of the novel word. However, the neural underpinning of depth of encoding is still unclear. Here, we combined an fMRI and a behavioral study to (...) investigate word encoding online. In the scanner, participants encoded 30 novel words of an artificial language created for experimental purposes and their translation into the subjects’ native language. Participants encoded the words three times visually, audiovisually, and by additionally observing semantically related gestures performed by an actress. Hemodynamic activity during word encoding revealed the recruitment of cortical areas involved in stimulus processing. In this study, depth of encoding can be spelt out in terms of sensorimotor brain networks that grow larger the more sensory modalities are linked to the novel word. Word retention outside the scanner documented a positive effect of gestures in a free recall test in the short-term. (shrink)

Curiosity motivates the search for missing information, driving learning, scientific discovery, and innovation. Yet, identifying that there is a gap in one's knowledge is itself a critical step, and may demand that one formulate a question to precisely express what is missing. Our work captures the integral role of self‐generated questions during the acquisition of new information, which we refer to as active‐curiosity‐driven learning. We tested active‐curiosity‐driven learning using our “Curiosity Question & Answer Task” paradigm, where participants (...) (N=135) were asked to generate questions in response to novel, incomplete factual statements and provided the opportunity to forage for answers. We also introduce new measures of question quality that express how well questions capture stimulus and foraging information. We hypothesized that active question asking should influence behavior across the stages of our task by increasing the probability that participants express curiosity, forage for answers, and remember what they had thereby discovered. We found that individuals who asked a high number of quality questions experienced elevated curiosity, were more likely to pursue missing information that was semantically related to their questions, and more likely to retain the information on a later cued recall test. Additional analyses revealed that curiosity played a predominant role in motivating participants to forage for missing information, and that both curiosity and satisfaction with the acquired information boosted memory recall. Overall, our results suggest that asking questions enhances the value of missing information, with important implications for learning and discovery of all forms. (shrink)

Malware is a sophisticated, malicious, and sometimes unidentifiable application on the network. The classifying network traffic method using machine learning shows to perform well in detecting malware. In the literature, it is reported that this good performance can depend on a reduced set of network features. This study presents an empirical evaluation of two statistical methods of reduction and selection of features in an Android network traffic dataset using six supervised algorithms: Naïve Bayes, support vector machine, multilayer perceptron neural (...) network, decision tree, random forest, and K-nearest neighbors. The principal component analysis and logistic regression methods with p value were applied to select the most representative features related to the time properties of flows and features of bidirectional packets. The selected features were used to train the algorithms using binary and multiclass classification. For performance evaluation and comparison metrics, precision, recall, F-measure, accuracy, and area under the curve were used. The empirical results show that random forest obtains an average accuracy of 96% and an AUC-ROC of 0.98 in binary classification. For the case of multiclass classification, again random forest achieves an average accuracy of 87% and an AUC-ROC over 95%, exhibiting better performance than the other machine learning algorithms. In both experiments, the 13 most representative features of a mixed set of flow time properties and bidirectional network packets selected by LR were used. In the case of the other five classifiers, their results in terms of precision, recall, and accuracy, are competitive with those obtained in related works, which used a greater number of input features. Therefore, it is empirically evidenced that the proposed method for the selection of features, based on statistical techniques of reduction and extraction of attributes, allows improving the identification performance of malware traffic, discriminating it from the benign traffic of Android applications. (shrink)

The purpose is to provide researchers with reliable Scientific Research Data from the massive amounts of research data to establish a sustainable Scientific Research environment. Specifically, the present work proposes establishing an Intelligent Recommendation System based on Machine Learning algorithm and SRD. Firstly, the IRS is established over ML technology. Then, based on user Psychology and Collaborative Filtering recommendation algorithm, a hybrid algorithm [namely, Content-Based Recommendation-Collaborative Filtering ] is established to improve the utilization efficiency of SRD and Sustainable Development (...) of SR. Consequently, the present work designs literature and SRD-targeted IRS using the hybrid recommendation under the background of SD. The proposed system’s feasibility is analyzed through experiments. Additionally, the system performance is analyzed and verified from accuracy, diversity, coverage, novelty, and recommendation efficiency. The results show that the hybrid algorithm can make up for the shortcomings of a single algorithm and improve the recommendation efficiency. Experiments show that the accuracy of the proposed CBR-CF algorithm is the highest. In particular, the recommendation accuracy for the single-user system can reach 82–93%, and the recall of all recommended algorithms falls between 60 and 91%. The recall of the hybrid algorithm is higher than that of a single algorithm, and the highest recall is 91%. Meanwhile, the hybrid algorithm has comprehensive coverage, good applicability, and diversity. Therefore, SD-oriented SRD-targeted IRS is of great significance to improve the SRD utilization and the accuracy of IRS, and expand the achievement value of SR. The research content provides a reference for establishing a sustainable SR environment and improving SR efficiency. (shrink)

Research has shown that gesture production supports learning across a number of tasks. It is unclear, however, whether gesture production during encoding can support narrative recall, who gesture production benefits most, and whether certain types of gestures are more beneficial than others. This study, therefore, investigated the effect of gesture production during the encoding of a narrative on subsequent narrative recall, and whether individuals’ levels of verbal and nonverbal memory moderated this effect. Additionally, this study investigated whether (...) producing certain types of gestures during encoding was more beneficial than others. Participants (N = 90, Mage = 20.43) read aloud a narrative while under instruction to produce gestures, under no specific instruction to produce gestures, or were required to keep their hands behind their back to prevent them from gesturing. Participants completed measures assessing verbal and nonverbal memory. While gesture production during encoding benefitted narrative recall (as measured through specific questions), verbal memory moderated the effect, such that gesture production was more beneficial for individuals with higher than lower verbal memory. Furthermore, producing representational gestures during encoding benefitted recall of points in the narrative at which those gestures were produced, while beat gestures had no effect. Findings have implications for understanding the mechanisms underlying the links between gesture and learning, as well as practical implications in instructional settings. (shrink)