Search

Contribution: Prior studies on goal congruity show that students are more motivated to pursue careers that allow them to work with and help others and give back to their community (i.e., careers that afford prosocial value). This paper discovers this same pattern in electrical engineering (EE) and discovers that prosocial affordance beliefs are significantly associated with intensions to persist, while agency beliefs are not. Background: Goal congruity theory finds that people are more motivated to pursue a career if it aligns with values they endorse. This theory can shed light on why some students do not persist in EE because of the stereotype that the profession does not allow working with and helping others. Research Questions: This paper seeks to answer whether EE students perceive the profession as affording prosocial value, and to test associations between prosocial perceptions and motivation to persist in the field. Methodology: The first study in this paper was conducted on students in an introductory EE course ($n\,\,= 79$) that measured affordance beliefs about the EE profession and tested associations with intensions to persist. The second study compared affordance beliefs and trait endorsements held by students in the introductory level course with those in an advanced EE courses ($n\,\,=51$). Findings: Mediation analysis revealed that the more novice students believe that EE allows them to fulfill prosocial goals, the greater their persistence intentions (95% CI: 0.01 to 0.34). This analysis also showed that agency beliefs were not strongly associated with persistence intensions. [ABSTRACT FROM AUTHOR]

Numerous research efforts have been conducted to simulate the crowd movements, while relatively few of them are specifically focused on multihazard situations. In this paper, we propose a novel crowd simulation method by modeling the generation and contagion of panic emotion under multihazard circumstances. In order to depict the effect from hazards and other agents to crowd movement, we first classify hazards into different types (transient and persistent, concurrent and nonconcurrent, and static and dynamic) based on their inherent characteristics. Second, we introduce the concept of perilous field for each hazard and further transform the critical level of the field to its invoked-panic emotion. After that, we propose an emotional contagion model to simulate the evolving process of panic emotion caused by multiple hazards. Finally, we introduce an emotional reciprocal velocity obstacles (RVOs) model to simulate the crowd behaviors by augmenting the traditional RVO model with emotional contagion, which for the first time combines the emotional impact and local avoidance together. Our experimental results demonstrate that the overall approach is robust, can better generate realistic crowds and the panic emotion dynamics in a crowd. Furthermore, it is recommended that our method can be applied to various complex multihazard environments. [ABSTRACT FROM AUTHOR]

The social presence theory in social psychology suggests that computer-mediated online interactions are inferior to face-to-face, in-person interactions. Thus, it's important to organize social activities for online social network users to meet in person. In this paper, we consider the scenarios of organizing in person friend-making social activities via online social networks (OSNs) and formulate a new research problem, namely, Hop-bounded Maximum Group Friending (HMGF), that takes into consideration both existing friendships and the likelihood of new friend making in organization of the targeted in person friend-making social activities. To find a set of attendees for such social activities, HMGF is unique and challenging due to the interplay of the group size, the constraint on existing friendships, and the objective of maximizing the likelihood of friend making. We prove that HMGF is NP-Hard, and there exists no approximation algorithm for it unless $P=NP$ P = N P . We also provide an Integer Linear Programming (ILP) formulation for the HMGF problem. The ILP formulation, which can be solved efficiently by a commercial solver to obtain the optimal solution for small HMGF instances, acts as a baseline approach for comparison in the evaluation of the proposed algorithm. We further propose an error-bounded approximation algorithm, MaxGF, to efficiently obtain the solutions very close to the optimal solutions. To boost the performance, we devise two graph-theoretical pruning strategies, namely Neighbor Pruning and Core Pruning, which can effectively avoid redundant graph explorations to improve the performance of HMGF. We also study HMGF on a class of special graphs, threshold graphs, which have properties very similar to many online social networks. We prove that MaxGF can obtain the optimal solution to HMGF on threshold graphs in polynomial time. We conduct a user study to validate our problem formulation and perform extensive experiments on real datasets to demonstrate the efficiency and effectiveness of our proposed algorithm. The experimental results manifest that our proposed algorithms outperform the baselines, including the ILP formulation. [ABSTRACT FROM AUTHOR]

The advancements in computer systems and networks have created a new environment for criminal acts, widely known as cybercrime. Cybercrime incidents are occurrences of particular criminal offences that pose a serious threat to the global economy, safety, and well-being of society. This paper offers a comprehensive understanding of cybercrime incidents and their corresponding offences combining a series of approaches reported in relevant literature. Initially, this paper reviews and identifies the features of cybercrime incidents, their respective elements and proposes a combinatorial incident description schema. The schema provides the opportunity to systematically combine various elements—or cybercrime characteristics. Additionally, a comprehensive list of cybercrime-related offences is put forward. The offences are ordered in a two-level classification system based on specific criteria to assist in better classification and correlation of their respective incidents. This enables a thorough understanding of the repeating and underlying criminal activities. The proposed system can serve as a common reference overtaking obstacles deriving from misconceptions for cybercrimes with cross-border activities. The proposed schema can be extended with a list of recommended actions, corresponding measures and effective policies that match with the offence type and subsequently with a particular incident. This matching will enable better monitoring, handling and moderate cybercrime incident occurrences. The ultimate objective is to incorporate the schema-based description of cybercrime elements to a complete incident management system with standard operating procedures and protocols. [ABSTRACT FROM AUTHOR]

Since the all-day and all-weather working characteristics, polarimetric synthetic aperture radar (PolSAR) has been widely used in many application for earth observation. PolSAR system can acquire targets scattering information under different polarization patterns and obtain detailed information, such as physical characteristics, spatial distribution and direction of the targets, etc. However, in high-resolution PolSAR images, the scattering characteristics of targets are very sophisticated, and different parts of the same target may present different scattering mechanisms. As a result, traditional interpretation technologies are sometimes not applicable in a high-resolution PolSAR scenario. Recently, many artificial intelligence algorithms that simulate the scheme of human cognition have been developed and they have great significance for efficient, intelligent, and accurate images interpretation. In this paper, based on human cognition theory and taking into account the characteristics of targets in PolSAR images, an algorithm for the interpretation of various targets in high-resolution PolSAR images is presented. In this paper, a “perception–reasoning–decision” cognition model is built following the cognition process of professional PolSAR image interpreters. In addition, with the help of theories such as multilevel image segmentation, fuzzy logic, neural network, and the context semantic characteristics, the mathematical model is established and implemented. The proposed method is performed on several sets of PolSAR images obtained under different imaging conditions and is validated by comparing with existing classification methods. The results show that the proposed method can effectively improve the identification of targets from PolSAR images. [ABSTRACT FROM AUTHOR]

Contour completion plays an important role in visual perception, where the goal is to group fragmented low-level edge elements into perceptually coherent and salient contours. Most existing methods for contour completion have focused on pixelwise detection accuracy. In contrast, fewer methods have addressed the global contour closure effect, despite psychological evidences for its importance. This paper proposes a purely contour-based higher order CRF model to achieve contour closure, through local connectedness approximation. This leads to a simplified problem structure, where our higher order inference problem can be transformed into an integer linear program and be solved efficiently. Compared with the methods based on the same bottom–up edge detector, our method achieves a superior contour grouping ability (measured by Rand index), a comparable precision–recall performance, and more visually pleasing results. Our results suggest that contour closure can be effectively achieved in contour domain, in contrast to a popular view that segmentation is essential for this purpose. [ABSTRACT FROM PUBLISHER]

This retrospective describes the overall research project that gave rise to the authors’ paper “Neuronlike adaptive elements that can solve difficult learning control problems” that was published in the 1983 Neural and Sensory Information Processing special issue of the IEEE Transactions on Systems, Man, and Cybernetics. This look back explains how this project came about, presents the ideas and previous publications that influenced it, and describes our most closely related subsequent research. It concludes by pointing out some noteworthy aspects of this article that have been eclipsed by its main contributions, followed by commenting on some of the directions and cautions that should inform future research. [ABSTRACT FROM AUTHOR]

This paper investigated the psychological effects of viewing ultra-high-resolution images. Many subjective evaluation experiments assessing the quality of images have been conducted. However, the psychological effects of viewing ultra-high-resolution images have not been well investigated, especially under non-standard viewing conditions. Although a higher image resolution has been reported to affect the sense of realness, more impressive factors that appeal to the general viewers have not been examined. This paper conducted subjective evaluation experiments, in which images with different resolutions using familiar subjects were presented to viewers with practical viewing distances, and their ratings of impressions were obtained. In addition, we examined the relationship between higher- and lower-order impressions. We found an enhancement of the impressions of “beautiful” or “delicious” with an increase in the resolutions of the presented images. Furthermore, the tendency of this impression enhancement was observed even when viewing it as far as four times the design viewing distance. The results of multiple regression analyses provide insight on the production and processing of ultra-high-resolution images for impression enhancement. [ABSTRACT FROM AUTHOR]

This paper presents a novel self-injection-locked radar with two antennas to transmit and retransmit a continuous wave to opposite sides of a human body for monitoring vital signs in real time with large body movements. The system can prevent the nonlinear effects of large Doppler phase shifts on the cancellation of body motion artifacts, to extract accurate vital signs from the received Doppler-shifted signal. However, the effectiveness of the cancellation is limited by the received clutter echoes in this system. Therefore, the system also has a clutter effect elimination circuit to improve substantially the detection of vital signs in an examinee at various positions between the two antennas even when the examinee moves often by more than one wavelength. [ABSTRACT FROM PUBLISHER]

This article is an extension of a work in progress paper originally presented at the conference FiE 2020, Frontiers in Education by (Schirichian et al., 2020), as a result of Schirichian’s master’s in science dissertation. It studies the relations between academic performance, protection factors, life at university, and personal characteristics to understand how they influence students’ academic success in undergraduate engineering programs in Brazil. In this challenging environment, several students overcome these issues and graduate, which indicates that some protection structures allow students to face challenging situations and be successful in their studies. Current research studied the relationships between academic performance, self-efficacy, causal attributions, personal characteristics, and life at university, with a group of 30 students. Models were studied by combining the different variables and aspects evaluated in the research, and the findings show relationship between academic performance and: 1) self-efficacy, causal attributions, and personal characteristics (such as leisure activities, living with their parents, or college accommodations) and those who attended private schools during basic education; 2 protection factors self-efficacy and causal attributions with advancing in the program and the time of enrolment in the university; and 3) the dimensions initiation and persistence, success/internality and failure/internality, indicating that those who perceive themselves as playing a major role in their academic journey tend to have better outcomes. Further results show the influence of social inequalities, particularly for those students who declared themselves brown and who conducted their basic education in public schools (which are generally of lower quality than private ones in Brazil). [ABSTRACT FROM AUTHOR]

Contribution: This paper has demonstrated that the use of blended learning in an electrical machines course has increased student engagement, pass rate, and scores across the GPA spectrum. Background: Lack of engagement by students in electric machine courses have been reported across the world. Interventions however, usually take the form of laboratory and simulation assignments. There is little information on, however, the use of blended learning and flipped classroom techniques to combat these problems. Intended Outcomes: Increased engagement in the course evidenced by exam performance. In particular, for the transformer, dc motor, induction motor, and synchronous motor the students should be able to explain the construction and principle of operation, and compute voltages, currents and power for various operating conditions. Application Design: The approach involved online lectures, tutorial submissions, and peer feedback forums followed by face to face tutorial sessions. These were chosen because online activities would allow students to access material on devices with which they are accustomed to engaging deeply and because peer feedback has been shown to build community, and increase the quality of discourse for both students and lecturer. Findings: There was a significant improvement in exam performance when compared to previous years. The pass rate of the course moved from approximately 60% in previous years to 86%. The class average mark also increased from approximately 50% in previous years to 63%. [ABSTRACT FROM AUTHOR]

Human factors studies are becoming more and more crucial in the automotive sector due to the need to evaluate the driver.s reactions to the increasingly sophisticated driving-assistant technologies. Driving simulators allow performing this kind of study in a controlled and safe environment. However, the driving simulation.s Level of Detail (LOD) can affect the users. perception of driving scenarios and make an experimental campaign.s outcomes unreliable. This paper proposes a study investigating possible correlations between driver.s behaviors and emotions, and simulated driving scenarios. Four scenarios replicating the same real area were built with four LODs from LOD0 (only the road is drawn) to LOD3 (all buildings with real textures for facades and roofs are inserted together with items visible from the road). 32 participants drove in all the four scenarios on a fixed-base driving simulator; their performance relating to the vehicle control (i.e., speed, trajectory, brake and gas pedal use, and steering wheel), their physiological data (electrodermal activity, and eye movements), their subjective perceptions, opinions and emotional state were measured. The results showed that drivers. behavior changes in a very complex way. Geometrical features of the route and environmental elements constrain much more driving behavior than LOD does Emotions are not affected by LODs. Generally, different signals showed different correlations with the LOD level, suggesting that future studies should consider their measures while modeling the virtual scenario. It is hypothesized that scenario realism is more relevant during leisurely environmental interaction, whilst simulator fidelity is crucial in task-driven interactions. [ABSTRACT FROM AUTHOR]

Teaching electrical and computer software engineers how to configure network services normally requires the detailed presentation of many configuration commands and their numerous parameters. Students tend to find it difficult to maintain acceptable levels of motivation. In many cases, this results in their not attending classes and not dedicating enough time to acquire the required competencies. This paper describes a successful experiment that was able to improve student class attendance, performance, and motivational patterns by using mobile devices in class to provide students with contextualized learning pills. A learning pill is a simple exercise that summarizes some of the key concepts explained in class and promotes reflection and self-study. Each student attending a particular lecture or laboratory session automatically receives a learning pill on his or her mobile phone just after the related concept has been presented. This paper details the scenario used in the experiment and analyzes the performance and motivational patterns of 170 students at the Higher Polytechnic School, Carlos III University of Madrid, Leganes, Spain. [ABSTRACT FROM PUBLISHER]

Existing studies have shown functional brain networks in patients with major depressive disorder (MDD) have abnormal network topology structure. But the methods to construct brain network still exist some issues to be solved. This paper is to explore reliable and robust construction methods of functional brain network using different coupling methods and binarization approaches, based on high-density 128-channel resting state EEG recordings from 16 MDD patients and 16 normal controls (NC). It was found that the combination of imaginary part of coherence and cluster-span threshold outperformed other methods. Based on this combination, right hemisphere function deficiency, symmetry breaking and randomized network structure were found in MDD, which confirmed that MDD had aberrant cognitive processing. Furthermore, clustering coefficient in left central region in theta band and node betweenness centrality in right temporal region in alpha band were significantly negatively correlated with depressive level. And these network metrics had the ability to discriminate MDD from NC, which indicated that these network metrics might be served as the electrophysiological characteristics for probable MDD identification. Hence, this paper may provide reliable methods to construct functional brain network and offer potential biomarkers in MDD. [ABSTRACT FROM AUTHOR]

Emotion is an integral part of cognition. There is significant evidence of mutual, bi-directional influence between cognitive and emotional processes. Also, more and more research works propose an integrative view of emotion and cognition. In this paper, we review a large literature on emotion–cognition interactions in psychology, neuroscience, and computational modeling. Then, we introduce eMODUL, which consolidates this literature into a conceptual model. In particular, this model stresses the importance of emotional modulations and the roles they play with respect to the system autonomy depending on the targeted computational/cognitive processes (e.g., allocation of resources, organization of behavior). To illustrate these aspects and support our theoretical model, we review two robotic experiments where eMODUL is instantiated. The results demonstrate the interest of our approach for the development of interaction/communication and autonomy/adaptation capabilities in cognitive robots. In terms of natural cognition understanding they give additional insights into the emergence of emotion, the construction of multilevel appraisal, and the link between emotion and cognition in task-related emotions. [ABSTRACT FROM AUTHOR]

In this paper, we present a novel aperiodic directional structure measurement (ADSM) toward road network extraction. Based on the observations from Cognitive Psychology regarding the aperiodicity and local directionality, ADSM can well characterize roadlike structures independent of the spectral character and contrast. By exploiting such measurement as guidance, we construct a mask to denote potential road regions. Then, by combining with some common morphology operators, our approach is able to provide robust road centerlines efficiently. We evaluate our approach with data from various satellite sensors and make comprehensive comparisons with previous state-of-the-art methods. Experimental results demonstrate the merit using our ADSM as a metric to identify potential road structures, as well as the effectiveness and efficiency of our road network extraction system. [ABSTRACT FROM AUTHOR]

Contribution: This paper examined student effort and performance in an introductory programming course with respect to student-held implicit theories and self-efficacy. Background: Implicit theories and self-efficacy help in understanding academic success, which must be considered when developing effective learning strategies for programming. [ABSTRACT FROM AUTHOR]

With the advent of the Internet of Things (IoT) and a rapid deployment of smart devices and wireless sensor networks (WSNs), humans interact extensively with machine data. These human decision makers use sensors that provide information through a sociotechnical network. The sensors can be other human users or they can be IoT devices. The decision makers themselves are also part of the network, and there is a need to understand how they will behave. In this paper, the decision fusion behavior of humans is analyzed on the basis of behavioral experiments. The data collected from these experiments demonstrate that people perform decision fusion in a stochastic manner dependent on various factors, unlike machines that perform this task in a deterministic manner. A Bayesian hierarchical model is developed to characterize the observed stochastic human behavior. This hierarchical model captures the differences observed in people at individual, crowd, and population levels. The implications of such a model on designing large-scale inference systems are presented by developing optimal decision fusion trees with both human and machine agents. [ABSTRACT FROM AUTHOR]

Secret sharing schemes allow a dealer to distribute a secret piece of information among several parties such that only qualified subsets of parties can reconstruct the secret. The collection of qualified subsets is called an access structure. The best known example is the k -threshold access structure, where the qualified subsets are those of size at least k . When k=2 and there are n parties, there are schemes for sharing an \ell bits, and this is tight even for secrets of 1 b. In these schemes, the number of parties n must be given in advance to the dealer. In this paper, we consider the case where the set of parties is not known in advance and could potentially be infinite. Our goal is to give the {t} {^{\mathrm{ th}}} party arriving the smallest possible share as a function of t$ . Our main result is such a scheme for the k$ -threshold access structure and 1-bit secrets where the share size of party t$ is . For k=2$ we observe an equivalence to prefix codes and present matching upper and lower bounds of the form \log t + \log \log t + \log \log \log t + O(1)$ . Finally, we show that for any access structure there exists such a secret sharing scheme with shares of size . [ABSTRACT FROM AUTHOR]

Measurement is customarily performed to acquire information useful to support decisions. More and more frequently, the complexity of analyzed systems and problems requires to measure various heterogeneous properties and to process the returned results in order to obtain information directly exploitable to support decision-making activities. However, despite a high relevance for many engineering applications, methods for rigorously processing data about heterogeneous empirical properties have not been properly analyzed yet. As a result, decision makers attain their conclusions by subjectively fusing together the available measurement results without following a formal procedure, which has a negative impact on the verifiability and the reliability of the whole evaluation process. This paper proposes a simple and effective multicriteria decision-making method that can be applied whenever measurement results are available for the considered decision criteria. Moreover, unlike most of the scientific literature on decision making, the proposed method provides not only the most desirable decision outcome, but it also returns information on the outcome reliability. This is achieved by leveraging on measurement fundamentals. In particular, by expressing measurement uncertainty using interval values, the related interval priorities of the considered decision alternatives can be determined. The proposed methodology is finally applied to a dependability-benchmarking case study. [ABSTRACT FROM AUTHOR]

In this paper, we describe a tool coined as artificial intelligence-based student learning evaluation tool (AISLE). The main purpose of this tool is to improve the use of artificial intelligence techniques in evaluating a student's understanding of a particular topic of study using concept maps. Here, we calculate the probability distribution of the concepts identified in the concept map developed by the student. The evaluation of a student's understanding of the topic is assessed by analyzing the curve of the graph generated by this tool. This technique makes extensive use of XML parsing to perform the required evaluation. The tool was successfully tested with students from two undergraduate courses and the results of testing are described in this paper. [ABSTRACT FROM PUBLISHER]

Artificial Intelligence (AI) based Speech Emotion Recognition (SER) has been widely used in the consumer field for control of smart home personal assistants, with many such devices on the market. However, with the increase in computational power, connectivity, and the need to enable people to live in the home for longer though the use of technology, then smart home assistants that could detect human emotion will improve the communication between a user and the assistant enabling the assistant of offer more productive feedback. Thus, the aim of this work is to analyze emotional states in speech and propose a suitable method considering performance verses complexity for deployment in Consumer Electronics home products, and to present a practical live demonstration of the research. In this article, a comprehensive approach has been introduced for the human speech-based emotion analysis. The 1-D convolutional neural network (CNN) has been implemented to learn and classify the emotions associated with human speech. The paper has been implemented on the standard datasets (emotion classification) Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) and Toronto Emotional Speech Set database (TESS) (Young and Old). The proposed approach gives 90.48%, 95.79% and 94.47% classification accuracies in the aforementioned datasets. We conclude that the 1-D CNN classification models used in speaker-independent experiments are highly effective in the automatic prediction of emotion and are ideal for deployment in smart home assistants to detect emotion. [ABSTRACT FROM AUTHOR]

Research problem: During humanitarian crises, communities of people face various types of dangers. To counter the dangers, they need information in a short period. Such need creates the opportunity for misinformation. Such misinformation can result in information harms that can generate short- or long-term consequences. Literature review: Prior researchers have tackled the situation by using technical or behavioral approaches. Research question: What are the harms from misinformation? We propose a taxonomy of 15 information harms grouped in 8 categories and assess the perception of risk regarding the harms through a survey of respondents who have experienced crisis response situations. Methodology: This paper examines two scenarios, the 2013 Boston Marathon bombing and the 2017 Oroville Dam evacuation order crises, through two dimensions: Likelihood of occurrence and Level of impacts of the harms. Results and conclusions: Findings are presented through visualization and test results for significant differences of harms between scenarios. Similar groups of harms are identified with different severity levels based on post hoc analyses: those with 1. high likelihood and low impact (psychological and confusion harms), 2. low likelihood and low impact (reputation and privacy harms), and 3. low likelihood and high impact (physical, financial, safety, and social harms). In addition to establishing the taxonomy of misinformation harms, findings will have practical value in emergency response and recovery activities to effectively prioritize resources to minimize specific harms from misinformation in crises. Further research directions are also discussed. [ABSTRACT FROM AUTHOR]

Emergency evacuation from airports is an important consideration given the continuing occurrence of both natural and human caused disasters. Unfortunately, the traditional evacuation-drill approach to prepare for emergency situations presents several practical challenges at the scale and magnitude required for airports. In this paper, we present an agent-based model (ABM) called exitus which is capable of determining the extent to which collective behavior and overall evacuation time of passenger groups is affected by changes in the built environment for large, complex structures. The model is unique because it explicitly considers the physical and psychological characteristics of individuals with disabilities. In our first experiment, several bomb simulation scenarios were conducted at an international airport using exitus. Several important findings were revealed including: 1) the importance of stairway and exit configurations; 2) the inherent weaknesses of the pier airport design in affecting timely evacuations; 3) who the most vulnerable groups of people are; 4) the particular risk engendered from crowded or complex building interiors for individuals with disabilities; and 5) the potential problems caused by locating explosive detection system machines near passenger processing areas. The results of a second exploratory experiment also revealed the importance of realistically modeling psychological attributes of individuals with disabilities and their potential impact on collective evacuation performance. Overall the findings demonstrated the model’s ability to generate a common operational picture capable of guiding preparedness efforts for both public and private organizations encompassing a wide variety of professional endeavor. [ABSTRACT FROM PUBLISHER]

Correspondence analysis is a statistical method that allows researchers to explore relationships among complex categorical variables. This paper will provide researchers with the theoretical and practical foundations for understanding and applying correspondence analysis to their own research agendas. Problem: Technical communicators use a variety of research methods and collect a variety of types of data. Of particular interest to technical communicators is categorical data, or data that are not traditionally quantitative. For instance, technical communicators often collect and analyze language data from a variety of texts. Analyzing this type of data can be difficult using traditional statistical methods. Key concepts: Variable types, a priori versus exploratory research designs, contingency tables, and data visualization are central to understanding the foundations of correspondence analysis. Key lessons: To conduct correspondence analysis, a researcher must walk through a series of steps including: (1) determining whether correspondence analysis is appropriate, (2) choosing a statistical software package, (3) running the correspondence analysis, and (4) interpreting and applying the results. Implications for practice: While correspondence analysis provides many useful insights into categorical data, a researcher must consider several things when deciding to use correspondence analysis. These include the potential to misinterpret and misapply the results of a correspondence analysis. [ABSTRACT FROM AUTHOR]

The inspiration for this paper comes from a successful experiment conducted with students in the “Mobile Robots” course in the fifth year of the integrated Master's program in the Department of Electrical and Computer Engineering, Faculty of Engineering, University of Porto (FEUP), Porto, Portugal. One of the topics in this Mobile Robots course is “ Localization of Mobile Robots using the Extended Kalman Filter in a LEGO NXT,” which gives the students the opportunity to study the concepts of localization. This experiment comes within the framework of teaching localization concepts in mobile robotics and focuses primarily on explaining the Kalman filter concept. It involves a specific tool developed by the authors and based on LEGO NXT technology. The work presented here could be a helpful guide for teaching concepts related to localization in mobile robotics to ensure adequate understanding of the concept and of the use of the extended Kalman filter (EKF). The LegoFeup robot described here was built using a LEGO Mindstorms NXT and tested both in simulation and in real scenarios. Based on the results obtained, the authors concluded that the developed tool is effective in motivating students. The implementation of the tool, the structure of the Mobile Robots course, and the criteria for student assessment are described in this paper. [ABSTRACT FROM PUBLISHER]

Nonnegative matrix factorization (NMF) has become a popular data-representation method and has been widely used in image processing and pattern-recognition problems. This is because the learned bases can be interpreted as a natural parts-based representation of data and this interpretation is consistent with the psychological intuition of combining parts to form a whole. For practical classification tasks, however, NMF ignores both the local geometry of data and the discriminative information of different classes. In addition, existing research results show that the learned basis is unnecessarily parts-based because there is neither explicit nor implicit constraint to ensure the representation parts-based. In this paper, we introduce the manifold regularization and the margin maximization to NMF and obtain the manifold regularized discriminative NMF (MD-NMF) to overcome the aforementioned problems. The multiplicative update rule (MUR) can be applied to optimizing MD-NMF, but it converges slowly. In this paper, we propose a fast gradient descent (FGD) to optimize MD-NMF. FGD contains a Newton method that searches the optimal step length, and thus, FGD converges much faster than MUR. In addition, FGD includes MUR as a special case and can be applied to optimizing NMF and its variants. For a problem with 165 samples in R^1600, FGD converges in 28 s, while MUR requires 282 s. We also apply FGD in a variant of MD-NMF and experimental results confirm its efficiency. Experimental results on several face image datasets suggest the effectiveness of MD-NMF. [ABSTRACT FROM AUTHOR]

The impact of leader characteristics on international activities has been identified in the literature, but leader narcissism, as a psychological feature, has long been neglected, especially in the field of international business. Based on an empirical study of Chinese construction firms over the period between 2006 and 2016, this paper discusses the positive relationships between leaders’ narcissism and firms’ pace of internationalization, as well as the tendency to choose developing countries as destinations. The data also raise strong support for the argument that firm internal factor-foreign listing and external factor-institutional environment (anticorruption) weaken the effects of leader narcissism on international patterns. These research results indicate the effects of leaders’ personality on international management under different internal and external environments and contribute to upper echelons theory. Meanwhile, it is emphasized that leaders’ personal decision-making plays an important role in the strategy and performance of construction firms. [ABSTRACT FROM AUTHOR]

Many existing methods formulate the action prediction task as recognizing early parts of actions in trimmed videos. In this paper, we focus on predicting actions from ongoing untrimmed videos where actions might not happen at the very beginning of videos. It is extremely challenging to predict actions in such untrimmed videos due to ambiguous or even no information of actions in the early parts of videos. To address this problem, we propose a prediction confidence that assesses the decision quality of a prediction model. Guided by the confidence, the model continuously refines the prediction results by itself with the increasing observed video frames. Specifically, we build a Self Prediction Refining Network (SPR-Net) which incrementally learns the confidence for action prediction. SPR-Net consists of three modules: a temporal hybrid network, an incremental confidence learner, and a self-refining Gumbel softmax sampler. The temporal hybrid network generates the action category distributions by integrating static scene and dynamic motion information. The incremental confidence learner calculates the confidence in an incremental manner, judging the extent to which the temporal hybrid network should believe its prediction result. The self-refining Gumbel softmax sampler models the mutual relationship between the prediction confidence and the category distribution, which enables them to be jointly learned in an end-to-end fashion. We also present a sparse self-attention mechanism to encode local spatio-temporal features into the frame-level motion representation to further improve the prediction performance. Extensive experiments on five datasets (i.e., UT-Interaction, BIT-Interaction, UCF101, THUMOS14, and ActivityNet) validate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

The relationship of similarity may be the most universal relationship that exists between every two objects in either the material world or the mental world. Although similarity modeling has been the focus of cognitive science for decades, many theoretical and realistic issues are still under controversy. In this paper, a new theoretical framework that conforms to the nature of similarity and incorporates the current similarity models into a universal model is presented. The new model, i.e., the systematic similarity model, which is inspired by the contrast model of similarity and structure mapping theory in cognitive psychology, is the universal similarity measurement that has many potential applications in text, image, or video retrieval. The text relevance ranking experiments undertaken in this research tentatively show the validity of the new model. [ABSTRACT FROM AUTHOR]

In this paper, two psychological functions, intuition and attention, are modeled by a newly proposed hierarchically arranged generalized regression neural network (HA-GRNN). The main contribution of the paper is two-fold: 1) to provide an engineering basis for a macroscopic representation of psychology-oriented functions by means of artificial neural networks; 2) to propose a concrete model for the two functions, intuition and attention, in terms of the associated interactive and evolutionary processes within an HA-GRNN. In the simulation study, the effectiveness of an HA-GRNN is justified within the context of pattern classification tasks. [ABSTRACT FROM AUTHOR]

Recent research has unearthed that blink rate variability (BRV) can be employed as a psychophysiological measure. However, its efficiency for mental state recognition (MSR) has not been investigated yet. Because BRV can indicate dynamics inherent in eye blinks, we conjectured that BRV might exhibit stronger abilities for the MSR if compared with blink rate (BR), known as the leading indicator derived from eye blinks for MSR. Therefore, in this paper, we attempted to differentiate between high and low cognitive loads of an individual through the analyses of BR and BRV, respectively, which could be viewed as a preliminary study for comparing their MSR abilities. First, an $n$ -back experiment was performed to collect data. Then, in order to characterize the phenomenon of BRV, the features were extracted from its time and frequency domains, respectively. Finally, the area under the curve (AUC) values of BRV and BR for MSR were estimated by the ten commonly used classifiers, respectively. The results indicated that BRV achieves significantly higher AUC values than BR, which suggests its strong potentiality for MSR. In sum, the BRV may prove to be a promising method for the MSR, which should be considered in the future. [ABSTRACT FROM AUTHOR]

To avoid inconvenient vehicle stops at charging stations, on-road wireless charging of electric vehicles (EVs) is a promising application in the future smart grid. In this paper, we study a critical yet open problem for this application, i.e., the impact of wireless charging and mobility of EVs on the wholesale electricity market based on locational marginal price (LMP), which is mainly determined by the EV mobility patterns. To capture the dynamics in vehicle traffic flow and state of charge of EV batteries, we model the EV mobility as a queuing network based on the statistics obtained via traffic information systems. Then, the load on each power system bus with respect to EV wireless charging is obtained using the stationary distribution of the queuing network. An economic dispatch problem is formulated to incorporate the EV wireless charging demand, and the LMP of each power system bus is obtained. Furthermore, a pricing mechanism based on the LMP variations of power system buses is investigated to enhance the social welfare. The performance of our proposed analytical model is verified by a realistic road traffic simulator (SUMO) based on a 3-bus test system and an IEEE 30-bus test system, respectively. Simulation results indicate that our proposed analytical model can accurately provide an estimation of the LMP variations due to EV wireless charging. [ABSTRACT FROM PUBLISHER]

Indicators for a decrement in vigilance are a slowdown in reaction times and an increase in alpha power in the electroencephalogram in posterior regions of the brain. Transcranial alternating current stimulation (tACS) is a neuropsychological technique that has been found to interact with intrinsic brain oscillations and is able to enhance cognitive and behavioral performance. Recent studies show that tACS in the gamma frequency range (30–80 Hz) is able to downregulate amplitudes in the alpha frequency range (8–12 Hz), in accordance to the effect referred to as cross-frequency coupling, where intrinsic alpha and gamma waves modulate each other. We applied 40 Hz gamma-tACS to the visual cortex during a vigilance experiment and investigated if stimulation improves reaction times and error rates with time-on-task. In our sham controlled experiment, participants completed two blocks of 30 minutes duration while performing the same visual two-choice task. The first block was used as BASELINE. A statistical analysis with a linear mixed model revealed a significantly lower increase of modeled reaction times over time in the INTERVENTION-block of the tACS-group as compared with their BASELINE-block whereas there was no significant change between the BASELINE- and INTERVENTION-block for the SHAM-group. Error rates did not differ between groups. This paper indicates that gamma-tACS can enhance performance in vigilance tasks as it significantly decreased the slowdown of reaction times in our study. [ABSTRACT FROM AUTHOR]

Electrical lighting has seen many advancements since Edison first patented his version of the incandescent lamp. From those early days, lighting technology eventually changed with the introduction of mercury vapor, fluorescent, metal halide, and high-pressure sodium lamps. While each of these new light sources offered tremendous benefits over the incandescent lamp, their acceptance and any further advancement happened over a number of decades. Light-emitting diodes (LEDs) as a method of general lighting entered the market in the early 2000s. They were expensive and not very energy efficient. Within a few years, these lighting LEDs had dramatically improved. By 2006, they became trendy for residential and commercial applications, crossing over into the roadway lighting market a couple of years later. By 2010, they had become very “popular” as an industrial light source. In 2011, LEDs became mainstream and more affordable. Moving forward, LEDs are poised to dominate. Over the next decade, it is expected that LEDs will render most other light sources obsolete. The dilemma is that just about every evaluation method used for the past 140 years for every other light source cannot be applied directly to LED light sources. This paper will examine the LED revolution and what you need to know to survive. [ABSTRACT FROM PUBLISHER]

The explosive growth in popularity of social networking leads to the problematic usage. An increasing number of social network mental disorders (SNMDs), such as Cyber-Relationship Addiction, Information Overload, and Net Compulsion, have been recently noted. Symptoms of these mental disorders are usually observed passively today, resulting in delayed clinical intervention. In this paper, we argue that mining online social behavior provides an opportunity to actively identify SNMDs at an early stage. It is challenging to detect SNMDs because the mental status cannot be directly observed from online social activity logs. Our approach, new and innovative to the practice of SNMD detection, does not rely on self-revealing of those mental factors via questionnaires in Psychology. Instead, we propose a machine learning framework, namely, Social Network Mental Disorder Detection (SNMDD), that exploits features extracted from social network data to accurately identify potential cases of SNMDs. We also exploit multi-source learning in SNMDD and propose a new SNMD-based Tensor Model (STM) to improve the accuracy. To increase the scalability of STM, we further improve the efficiency with performance guarantee. Our framework is evaluated via a user study with 3,126 online social network users. We conduct a feature analysis, and also apply SNMDD on large-scale datasets and analyze the characteristics of the three SNMD types. The results manifest that SNMDD is promising for identifying online social network users with potential SNMDs. [ABSTRACT FROM AUTHOR]

In this paper, the authors seek to answer one fundamental question - what brings people together to form a community? In this article, they explore the personalities (psychological) and values (sociological) of individuals in social network communities in order to understand such natural selection. [ABSTRACT FROM AUTHOR]

Focal vibration is an effective intervention for the management of spasticity. However, its neuromechanical effects, particularly how tonic vibration reflex is induced explicitly, remain implicit. In this paper, we utilize a high-speed camera and a method of image processing to quantify the muscle vibration rigorously and disclose the neuromechanical mechanism of focal vibration. The vibration of 75 Hz is applied on the muscle belly of the biceps brachii and muscle responses are captured by a high-speed camera in profile. The muscle silhouettes are identified by the Canny edge detector to represent the stretch of muscle fibers, and the consistency between the muscle stretch and profile deformation has been confirmed by the magnetic resonance imaging in advance. Oscillations of muscle points discretized by pixels are identified by the fast Fourier transformation, respectively, and results demonstrate that focal vibration stretches muscle by producing muscle waves. Specifically, each point vibrates harmonically, and, given the linear phase modulation with transverse position, the muscle vibration propagates as traveling waves. The propagation of muscle waves is associated with muscle stretch, whose frequency is the same with the vibrator due to the curved baseline, and thus induces the tonic vibration reflex via spinal circuits. [ABSTRACT FROM PUBLISHER]

In this paper, we propose a novel text representation paradigm and a set of follow-up text representation models based on cognitive psychology theories. The intuition of our study is that the knowledge implied in a large collection of documents may improve the understanding of single documents. Based on cognitive psychology theories, we propose a general text enrichment framework, study the key factors to enable activation of implicit information, and develop new text representation methods to enrich text with the implicit information. Our study aims to mimic some aspects of human cognitive procedure in which given stimulant words serve to activate understanding implicit concepts. By incorporating human cognition into text representation, the proposed models advance existing studies by mining implicit information from given text and coordinating with most existing text representation approaches at the same time, which essentially bridges the gap between explicit and implicit information. Experiments on multiple tasks show that the implicit information activated by our proposed models matches human intuition and significantly improves the performance of the text mining tasks as well. [ABSTRACT FROM PUBLISHER]

Due to the timeliness of emergency response and much unknown information in emergency situations, this paper proposes a method to deal with the emergency decision making, which can comprehensively reflect the emergency decision making process. By utilizing the hesitant fuzzy elements to represent the fuzziness of the objects and the hesitant thought of the experts, this paper introduces the negative exponential function into the prospect theory so as to portray the psychological behaviors of the experts, which transforms the hesitant fuzzy decision matrix into the hesitant fuzzy prospect decision matrix (HFPDM) according to the expectation-levels. Then, this paper applies the energy and the entropy in thermodynamics to take the quantity and the quality of the decision values into account, and defines the thermodynamic decision making parameters based on the HFPDM. Accordingly, a whole procedure for emergency decision making is conducted. What is more, some experiments are designed to demonstrate and improve the validation of the emergency decision making procedure. Last but not the least, this paper makes a case study about the emergency decision making in the firing and exploding at Port Group in Tianjin Binhai New Area, which manifests the effectiveness and practicability of the proposed method. [ABSTRACT FROM PUBLISHER]

Psychological stress is threatening people’s health. It is non-trivial to detect stress timely for proactive care. With the popularity of social media, people are used to sharing their daily activities and interacting with friends on social media platforms, making it feasible to leverage online social network data for stress detection. In this paper, we find that users stress state is closely related to that of his/her friends in social media, and we employ a large-scale dataset from real-world social platforms to systematically study the correlation of users’ stress states and social interactions. We first define a set of stress-related textual, visual, and social attributes from various aspects, and then propose a novel hybrid model - a factor graph model combined with Convolutional Neural Network to leverage tweet content and social interaction information for stress detection. Experimental results show that the proposed model can improve the detection performance by 6-9 percent in F1-score. By further analyzing the social interaction data, we also discover several intriguing phenomena, i.e., the number of social structures of sparse connections (i.e., with no delta connections) of stressed users is around 14 percent higher than that of non-stressed users, indicating that the social structure of stressed users’ friends tend to be less connected and less complicated than that of non-stressed users. [ABSTRACT FROM PUBLISHER]

Over the past 10 years, the amount of usergenerated content has increased at phenomenal rates. However, due to a lack of common content descriptors, reliable website searches and access to this content is becoming increasingly difficult. There are third party products available that allow metadata descriptors to be created automatically but the formatting may not be compatible with the website database. With so many different standards for metadata, the whole area is becoming, effectively, de-standardized. This paper will present a cross-standard formatting platform that will allow compatibility between standards. [ABSTRACT FROM PUBLISHER]

This paper investigates the use of conversational agents to scaffold online collaborative learning discussions through an approach called academically productive talk (APT). In contrast to past work on dynamic support for collaborative learning, which has involved using agents to elevate the conceptual depth of collaborative discussion by leading students in groups through directed lines of reasoning, this APT-based approach lets students follow their own lines of reasoning and promotes productive practices such as explanation of reasoning and refinement of ideas. Two forms of support are contrasted, namely, Revoicing support and Feedback support. The study provides evidence that Revoicing support resulted in significantly more intensive reasoning exchange between students in the chat and significantly more learning during the chat than when that form of support was absent. Another form of support, namely, Feedback support increased expression of reasoning while marginally decreasing the intensity of the interaction between students and did not affect learning. [ABSTRACT FROM PUBLISHER]

In the intelligence community, the existence of a malicious insider poses a severe threat to information, to the actual analytic process, and, ultimately, to any decision-making process relying on such information and analyses. An analyst with malicious intent can create irreversible short-term, as well as long-term, damage that is hard to detect. In this paper, we propose a novel methodology that detects malicious analysts who attempt to manipulate decision makers' perceptions through their intelligence reports. This detection method relies on each analyst's working style, which we assume to be consistent from task to task. In order to measure an analyst's degree of consistency, we employ a user-modeling technique that automatically builds a computational model of each analyst based on observation of their activities. We hypothesize that inconsistency is mainly caused by malicious actions. Therefore, the detection method evaluates how consistent an analyst is across different tasks and raises an alert if any significantly large inconsistency is detected. A normalization procedure is employed which allows us to compare across a group of analysts and is shown to reduce noise and amplify inconsistency that is due to malicious actions. We show that this improves detection performance. Our experiments demonstrate the effectiveness of our approach in detecting malicious insiders. In the experiments, the percentage of malicious insiders grouped with legitimate ones is varied, and results are collected with and without normalization in order to provide a comprehensive analysis of our approach. [ABSTRACT FROM PUBLISHER]

This paper presents a significant advance in a reactive blended learning methodology applied to an introductory control engineering course. This proposal was based on the inclusion of a reactive element (a fuzzy-logic-based controller) designed to regulate the workload for each student according to his/her activity and performance. The contribution of this proposal stands on the inclusion of elements related to motivational factors in the students. Student motivation has been widely identified as a key factor for the academic success of every teaching–learning activity. [ABSTRACT FROM AUTHOR]

A stress-detection system is proposed based on physiological signals. Concretely, galvanic skin response (GSR) and heart rate (HR) are proposed to provide information on the state of mind of an individual, due to their nonintrusiveness and noninvasiveness. Furthermore, specific psychological experiments were designed to induce properly stress on individuals in order to acquire a database for training, validating, and testing the proposed system. Such system is based on fuzzy logic, and it described the behavior of an individual under stressing stimuli in terms of HR and GSR. The stress-detection accuracy obtained is 99.5% by acquiring HR and GSR during a period of 10 s, and what is more, rates over 90% of success are achieved by decreasing that acquisition period to 3–5 s. Finally, this paper comes up with a proposal that an accurate stress detection only requires two physiological signals, namely, HR and GSR, and the fact that the proposed stress-detection system is suitable for real-time applications. [ABSTRACT FROM AUTHOR]

This paper deals with the evaluation of Japanese traditional crafts, in which product items are assessed according to the so-called “Kansei” features by means of the semantic differential method. For traditional crafts, decisions on which items to buy or use are usually influenced by personal feelings/characteristics; therefore, we shall propose a consumer-oriented evaluation model targeting these specific requests by consumers. Particularly, given a consumer's request, the proposed model aims to define an evaluation function that quantifies how well a product item meets the consumer's feeling preferences. An application to evaluating patterns of Kutani porcelain is conducted to illustrate how the proposed evaluation model works, in practice. [ABSTRACT FROM PUBLISHER]

In order to assess the compliance of ingested wireless device (IWD) with related international safety guidelines, the studies on the biological effects and the signal intensity of an IWD in two realistic human body models using the finite-difference time-domain (FDTD) method are reported in this paper. Simulation studies are carried out in 21 scenarios where the IWD is placed at seven positions with three orientations, for each of the two human models. Specific absorption rate (SAR), temperature rise, near fields and far fields are analyzed in the 21 scenarios at four selected operation frequencies of 430 and 800 MHz, and 1.2 and 2.4 GHz, respectively. Our study indicates that the radiation intensity outside of the human body decreases with the increase of operation frequency. Furthermore, the radiation characteristics of the IWD are orientation and position dependent. The polarization of electric field outside of the human body is similar to that of the Radio Frequency (RF) source. As far as the compliance of safety is concerned, among all the simulated scenarios the maxima of the averaged-1g and averaged-10g SARs can reach 3.71 W/kg and 1.37 W/kg at the input power of 25 mW, respectively. [ABSTRACT FROM AUTHOR]

The article presents information about various studies related to health care. "Messy Details" reports that technical work in health-care presents tensions between the universal cognitive works. "Technical Work in Healthcare" covers a wide range of topics, use different methods of investigation, pursue different lines of inquiry and yield different sorts of insights. The papers show some of the many ways that human factors researchers can study technical work in healthcare.

Objective: Conventional mammogram enhancement methods use transform-domain filtering, which possibly produce some artifacts or not well highlight all local details in images. This paper presents a new enhancement method based on intuitionistic fuzzy sets. Methods: The presented algorithm initially separates a mammogram via a global threshold and then fuzzifies the image utilizing the intuitionistic fuzzy membership function that adopts restricted equivalence functions. After that, the presented scheme hyperbolizes membership degrees of foreground and background areas, defuzzifies the fuzzy plane, and achieves a filtered image via normalization. Finally, an enhanced mammogram is obtained by fusing the original image with filtered one. These implementations can be processed in parallel. Results: This algorithm can improve the contrast and visual quality of regions of interest. Conclusion: Real data experiments demonstrate that our method has better performance regarding the improvement of contrast and visual quality of abnormalities in mammograms (such as masses and/or microcalcifications), compared with classical baseline methods. Significance: This algorithm has potential for understanding and determining abnormalities. [ABSTRACT FROM PUBLISHER]