Search

This article examines the role of technology firms in computerizing psychological tests from the 1960s to 1980s. It focuses on National Computer Systems (NCS)'s development of computer software to interpret the Minnesota Multiphasic Personality Inventory. NCS trumpeted their computerized interpretation as a way to free up clerical labor and mitigate human bias, even as psychologists cautioned that proprietary algorithms risked obscuring decision rules. Clinics, courtrooms, and businesses all had competing interests in the use of computerized personality tests. I argue that test developers promoted computerized psychological tests as technical fixes for bias, even as courts and psychologists pointed to the complex layers of technological and social mediation embedded in software programs for psychological tests. This article contributes to histories of computing emphasizing the importance of intellectual property law in software development; to the relationship between labor, technology, and expertise; and to scholarship on the history and politics of algorithms. [ABSTRACT FROM AUTHOR]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

The edge and curve of an image surface are crucial visual cues in vision psychology. Studies show that human beings can effectively process curvature information, such as distinguishing the concavity and convexity of an image. This finding indicates that curvature is essential for a desired image to be felt authentic and real. In this paper, a novel few-view computed tomography (CT) image reconstruction model is proposed based on mean curvature (MC). Similar to the total variation model, the MC employs the $L_{1}$ -norm to utilize the sparse prior information. Constructing efficient numerical algorithms for minimizing the MC model is significant due to the associated high-order Euler–Lagrange equations. A two-step numerical method, including curvature smoothing and surface fitting, is presented to solve the proposed model, which can be stably and efficiently solved by the alternating direction minimization. By applying the variable splitting method, the explicit solutions of the corresponding subproblems can be efficiently and quickly approximated by fast Fourier transform and the proximal point method. The accuracy and efficiency of the simulated and real data are qualitatively and quantitatively evaluated to verify the efficiency and feasibility of the proposed method. Comparisons with conventional algorithms demonstrate that the proposed approach has considerable advantages in few-view CT reconstruction problems. [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]

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]

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]

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]

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]

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]

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]

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]

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]

Groups are the primary entities that make up a crowd. Understanding group-level dynamics and properties is thus scientifically important and practically useful in a wide range of applications, especially for crowd understanding. In this paper, we show that fundamental group-level properties, such as intra-group stability and inter-group conflict, can be systematically quantified by visual descriptors. This is made possible through learning a novel collective transition prior, which leads to a robust approach for group segregation in public spaces. From the former, we further devise a rich set of group-property visual descriptors. These descriptors are scene-independent and can be effectively applied to public scenes with a variety of crowd densities and distributions. Extensive experiments on hundreds of public scene video clips demonstrate that such property descriptors are complementary to each other, scene-independent, and they convey critical information on physical states of a crowd. The proposed group-level descriptors show promising results and potentials in multiple applications, including crowd dynamic monitoring, crowd video classification, and crowd video retrieval. [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]

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]

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]

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]

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]

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]

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]

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]

Exponential growth of information generated by online social networks demands effective and scalable recommender systems to give useful results. Traditional techniques become unqualified because they ignore social relation data; existing social recommendation approaches consider social network structure, but social contextual information has not been fully considered. It is significant and challenging to fuse social contextual factors which are derived from users’ motivation of social behaviors into social recommendation. In this paper, we investigate the social recommendation problem on the basis of psychology and sociology studies, which exhibit two important factors: individual preference and interpersonal influence. We first present the particular importance of these two factors in online behavior prediction. Then we propose a novel probabilistic matrix factorization method to fuse them in latent space. We further provide a scalable algorithm which can incrementally process the large scale data. We conduct experiments on both Facebook style bidirectional and Twitter style unidirectional social network data sets. The empirical results and analysis on these two large data sets demonstrate that our method significantly outperforms the existing approaches. [ABSTRACT FROM AUTHOR]

Information systems (IS) investments fail largely because a newly implemented system is not fully infused into its users’ everyday life at work. IS infusion beyond an organization's mandated usage requires that users are actively motivated to use it and that they hold authority over usage of the system. Psychological empowerment represents one's motivational orientation, as well as one's authority to tap into a system's maximum potential. This empowerment is likely an important mechanism underlying successful IS infusion. The main objectives of this study are to: 1) understand the antecedents of empowerment; and 2) examine the effect of empowerment on IS infusion. We developed a theoretical model that was empirically tested through a field study, which focused upon individuals’ use of a customer relationship management system. The results strongly support our theoretical model and reveal that the design of a work environment (i.e., IS, job, and social structure) affects the development of user empowerment. User empowerment in-turn plays a prominent role in regulating various aspects of IS infusion (i.e., extended use, integrative use, and emergent use). Overall, this paper contributes to postadoption research by introducing the concept of user empowerment and demonstrating its critical role in regulating IS infusion. It also helps managers promote user empowerment and eventually achieve IS infusion in organizations. [ABSTRACT FROM PUBLISHER]

This paper describes the development of an aero control experiment apparatus (ACEA) for use in aerospace control practical courses. The ACEA incorporates a systematic multihierarchy learning and teaching method, and was designed to improve aerospace engineering students' understanding of unmanned aerial vehicle (UAV) control systems. It offers a number of standard experimental modules at three levels: component, system, and simulation and evaluation. The software and hardware design methods used in the modules were chosen to allow students to conduct experiments in a simple, more hands-on manner. As a result, they were able to select or reconfigure their experiments and so continuously improve their skills and extend the ACEA itself. The practical curriculum modules—grouped in three skill levels of basic experiments, case study, and independent design—were applied in a 3-year M.Sc. course, UAV Control Systems. The marking criteria created to evaluate students' achievements are presented, along with some results of this evaluation. [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 rapid development of social media sharing has created a huge demand for automatic media classification and annotation techniques. Attribute learning has emerged as a promising paradigm for bridging the semantic gap and addressing data sparsity via transferring attribute knowledge in object recognition and relatively simple action classification. In this paper, we address the task of attribute learning for understanding multimedia data with sparse and incomplete labels. In particular, we focus on videos of social group activities, which are particularly challenging and topical examples of this task because of their multimodal content and complex and unstructured nature relative to the density of annotations. To solve this problem, we 1) introduce a concept of semilatent attribute space, expressing user-defined and latent attributes in a unified framework, and 2) propose a novel scalable probabilistic topic model for learning multimodal semilatent attributes, which dramatically reduces requirements for an exhaustive accurate attribute ontology and expensive annotation effort. We show that our framework is able to exploit latent attributes to outperform contemporary approaches for addressing a variety of realistic multimedia sparse data learning tasks including: multitask learning, learning with label noise, N-shot transfer learning, and importantly zero-shot learning. [ABSTRACT FROM PUBLISHER]

The drive toward sustainable wastewater management is challenging the conventional paradigm of linear end-of-pipe solutions. A shift toward more sustainable solutions requires that information about new ideas, systems, and technologies be more readily accessible for addressing wastewater problems. It is commonly argued that decision-making needs to involve engineers and other community representatives to define values and brainstorm solutions. This paper describes a decision support system (DSS) prototype that is designed to help community planners identify solutions which balance environmental, economic, and social goals. The system is designed to be scalable, adaptable, and flexible to allow fair assessment of new ideas and technologies. It supports the exploration of consequences of various alternatives and visualizes the tradeoffs between them. Our DSS takes in modular descriptions of components and a description of a community context, automates the design of alternative wastewater systems, and facilitates evaluating how well each design satisfies the given context. It provides an adaptable platform from which new solutions can be designed without having to predefine how a single component fits within a specific system. Our DSS facilitates the exploration of alternative solutions by visualizing the effect of various tradeoffs and their consequences in relation to the community's sustainability goals. [ABSTRACT FROM AUTHOR]

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]

A novel decision theory is emerging out of sparse findings in economics, mathematics and, most importantly, psychology and computational cognitive science. It rejects a fundamental assumption of the theory of rational decision making, namely, that uncertain belief rests on independent assessments of utility and probability, and includes envisioning possibilities within its scope. Several researchers working with these premises, independently of one another, have remarked that when decision is made, the positive features of the alternative that will be chosen are highlighted, and that this alternative is opposed to a loosing alternative, whose unpleasant aspects are stressed. By doing so, decision makers construct a coherent framework that provides them with a sense of direction in spite of an uncertain future. This paper frames together contributions from different disciplines, often unknown to one another, with the hope of improving the coordination of research efforts. Furthermore, it discusses the status of this emerging theory with respect to our current idea of rationality. This collection might be useful in order to develop theories and models of decision making in uncertain situations, where consequences are unknown and possibilities must be conceived. It does not provide a simple solution, but it may lay a base for future developments. [ABSTRACT FROM PUBLISHER]

In building emergency evacuation, the perception of hazards can stress crowds, evoke their competitive behaviors, and trigger disorder and blocking as they pass through narrow passages (e.g., a small exit). This is a serious concern threatening evacuees' survivability and egress efficiency. How to optimize crowd guidance while considering such effects is an important problem. Based on advanced microscopic pedestrian models and simulations, this paper establishes a new macroscopic network-flow model where fire, smoke, and psychological factors can evoke a crowd's desire to escape—the desired flow rate. Disorder and blocking occur when the desired flow rate exceeds the passage capacity, resulting in a drastic decrease of crowd movement in a nonlinear and random fashion. To effectively guide crowds, a divide-and-conquer approach is developed based on groups to reduce computational complexity and to reflect psychological findings. Egress routes for individual groups are optimized by using a novel combination of stochastic dynamic programming and the rollout scheme. These routes are then coordinated so that limited passage capacities are shared to meet the total need for joint movement. Numerical testing and simulation demonstrate that, compared with a strategy of merely using nearest exits, our solution can evacuate more people more rapidly by preventing or mitigating potential disorder and blocking at bottleneck passages. [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]

In response to demands from industry and the profession for more graduates, first-year engineering numbers have grown considerably over the last decade, matched by an increasing diversity of academic backgrounds. In order to support first-year students effectively, and ensure the courses they take remain appropriately pitched, the academic preparedness of these students must be determined. Since 2007, the lecturers in the compulsory first-year Electrical and Digital Systems course at the University of Auckland (UoA), Auckland, New Zealand, have administered a short diagnostic test to determine the level of conceptual understanding of electricity and electromagnetics possessed by the incoming students. This paper presents and discusses student understanding of dc circuit theory as revealed by the diagnostic test and subsequent investigations. The evidence is indicative of both flawed conceptual models and context-triggered misapplication of fundamental rules. Parallels are drawn with the results of research conducted elsewhere, indicating the misconceptions are robust and pervasive, crossing institutional and national boundaries. Not only are concepts such as current and voltage poorly understood, but even more basic concepts such as series and parallel connections are confusing for a significant number of students. Understanding the incorrect models that underlie these basic misconceptions is the first step to correcting them. Only then can students proceed to the more advanced concepts that engineering graduates are required to master. [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]