title: Internet Addiction: The Emergence of a New Clinical Disorder

abstract: Anecdotal reports indicated that some on-line users were becoming addicted to the Internet in much the same way that others became addicted to drugs or alcohol, which resulted in academic, social, and occupational impairment. However, research among sociologists, psychologists, or psychiatrists has not formally identified addictive use of the Internet as a problematic behavior. This study investigated the existence of Internet addiction and the extent of problems caused by such potential misuse. Of all the diagnoses referenced in the Diagnostic and Statistical Manual of Mental Disorders—Fourth Edition (DSM-IV; American Psychiatric Association, 1995), Pathological Gambling was viewed as most akin to the pathological nature of Internet use. By using Pathological Gambling as a model, addictive Internet use can be defined as an impulse-control disorder that does not involve an intoxicant. Therefore, this study developed a brief eight-item questionnaire referred to as a Diagnostic Questionnaire (DQ), which mod...


-‐ 11 December 2013 SENSOR NETWORKS FOR MONITORING AND CONTROL OF WATER DISTRIBUTION SYSTEMS


title: A Stock Market Forecasting Model Combining Two-Directional Two-Dimensional Principal Component Analysis and Radial Basis Function Neural Network

abstract: In this paper, we propose and implement a hybrid model combining two-directional two-dimensional principal component analysis ((2D)2PCA) and a Radial Basis Function Neural Network (RBFNN) to forecast stock market behavior. First, 36 stock market technical variables are selected as the input features, and a sliding window is used to obtain the input data of the model. Next, (2D)2PCA is utilized to reduce the dimension of the data and extract its intrinsic features. Finally, an RBFNN accepts the data processed by (2D)2PCA to forecast the next day's stock price or movement. The proposed model is used on the Shanghai stock market index, and the experiments show that the model achieves a good level of fitness. The proposed model is then compared with one that uses the traditional dimension reduction method principal component analysis (PCA) and independent component analysis (ICA). The empirical results show that the proposed model outperforms the PCA-based model, as well as alternative models based on ICA and on the multilayer perceptron.


title: Semantic (Web) Technology In Action: Ontology Driven Information Systems for Search, Integration and Analysis

abstract: Semantics is seen as the key ingredient in the next phase of the Web infrastructure as well as the next generation of information systems applications. In this context, we review some of the reservations expressed about the viability of the Semantic Web. We respond to these by identifying a Semantic Technology that supports the key capabilities also needed to realize the Semantic Web vision, namely representing, acquiring and utilizing knowledge. Given that scalability is a key challenge, we briefly review our observations from developing three classes of real world applications and corresponding technology components: search/browsing, integration, and analytics. We distinguish this proven technology from some parts of the Semantic Web approach and offer subjective remarks which we hope will foster additional debate.


title: Reduced-size dual band Wilkinson power dividers

abstract: In this paper, two new shaped dual band Wilkinson power dividers are presented. These configurations achieve significant size reduction and very good performance as compared to the conventional dual band Wilkinson power divider. Simulation and experimental results of two reduced size dividers at the receiving bands of the global system for mobile communication GSM demonstrate the realizablity and performance of the proposed configurations. The implementation of the proposed structures on GaAs (gallium arsenide) for X-band and Ku-band satellite down-link bands yields miniature configurations appropriate for MIC/MMIC applications.


title: A novel circular direction finding antenna array for unknown polarizations

abstract: In this paper a novel circular antenna array for direction finding in the VHF frequency range is introduced. This antenna array is capable of direction of arrival estimations, although the polarization of the incident wave is unknown, using a multi-polarization beamforming algorithm. The measured results of two different ruggedized prototypes, characterized on a free space antenna test site, correspond very well to the simulated results of 3-D models.


title: Declarative and Nondeclarative Memory: Multiple Brain Systems Supporting Learning and Memory

abstract: The topic of multiple forms of memory is considered from a biological point of view. Fact-and-event (declarative, explicit) memory is contrasted with a collection of non conscious (non-declarative, implicit) memory abilities including skills and habits, priming, and simple conditioning. Recent evidence is reviewed indicating that declarative and non declarative forms of memory have different operating characteristics and depend on separate brain systems. A brain-systems framework for understanding memory phenomena is developed in light of lesion studies involving rats, monkeys, and humans, as well as recent studies with normal humans using the divided visual field technique, event-related potentials, and positron emission tomography (PET).


title: Notice of RetractionOrientation-location distribution algorithm for the spatial effect evaluation of pipe-routing arrangent

abstract: The main character of pipe route arrangement of a ship is to search a feasible path that connects given locations and avoids all obstacles in an engine room environment scattered with equipments and components, at the same time, the selected route should meet special evaluation conditions. Spatial effect is an important factor in the evaluation of pipelines, but is ignored in most existing optimize algorithms or the given methods are too complicated. Orientation-location distribution algorithm is presented to evaluate the spatial effect of pipe-routing. According to the location and direction of pipeline, different spatial effect coefficients are given and used to cost modification of routes to get the optimum layout. The presented algorithm is simple and feasible.


title: Learning switched mode power supply design using MATLAB/SIMULINK

abstract: Switched Mode Power Supplies (SMPS) have become an indispensable part of modem electronic systems. In this paper, a generalized MATLAB/SIMULINK modeling techniques is presented. The proposed techniques have been successfully integrated into SMPS design course at Lunghua University, Taiwan. The main objective of the course is to teach final year undergraduate students and first year Master's students how to design an SMP in real life. Positive response from students indicates that the proposed technique is useful for SMPS design courses.


Perception Science in the Age of Deep Neural Networks


title: Choice of optimization routine for multi-agent models: A caseof viral video marketing campaign

abstract: Very few agent-base computational models are optimized because ::: the usually used optimization routine, the genetic algorithm, ::: is extremely time-consuming. This paper explores how much ::: precision is lost if a simpler optimization routine, mutational ::: hill climber, is used instead. It shows on the case of a ::: viral-video marketing model that even though the standard ::: genetic algorithm is slightly more precise, the mutation hill ::: climbing could be used as an approximate optimization routine ::: for robustness check and scenario analysis.


A SURVEY OF UNMANNED AERIAL VEHICLE ( UAV ) USAGE FOR IMAGERY


title: 2.4GHz patch antenna arrays suitable for Remotely Piloted Aircraft Systems

abstract: In this study, a low cost highly directive 1×4 end-fire dipole antenna array is designed and presented, providing 9.3dB gain. This array is going to be mounted on the wings of a Remotely Piloted Aircraft System (RPAS) to ensure reliable and secured links with the Ground Control Station (GCS) over large distances. Moreover a 2×4 low cost multi-layer patch antenna array with 14dB gain, placed on the side of the RPAS fuselage is designed and described, emitting the main lobe of radiation towards the horizon. In addition a 2×2 multi-layer antenna array with notched circular patches providing 10dB gain and producing circular polarization is presented, placed on the bottom of the RPAS, realizing the communication with the GCS over small distances. All antennas operate at Wi-Fi 2.4GHz frequency band.


title: Outer rotor high torque density PM motors with a peripheral position sensor

abstract: A survey of mainly outer rotor permanent magnet (PM) motors, suitable for a patented sensor technology sensing the leakage field from the permanent magnets, is performed with a specified target of high torque density. One motor, known to yield high torque density is used as a reference motor; an electric bike transverse flux motor. Its simulated and measured results are compared with other PM motors such as the axial flux motor, the Vernier motor and flux switching motors. Academic, simulated designs as well as industry developed and manufactured designs are considered. It is found that the reference motor is coming out as a good choice for motors with similar weight and size. Those outer rotor motors that have higher torque density than the reference motor are larger than the reference motor and/or need liquid cooling.


title: 135 – The Future of Clinical Engineering: The Challenge of Change

abstract: Clinical engineering is at a strategic inflection point. Technical, economic, regulatory, and cultural dynamics are at work shaping the future of healthcare delivery. As the nature of healthcare delivery is transformed by these forces, the types and mix of technology management and support services needed by the industry are changing significantly. Clinical engineering has a relatively short opportunity to adopt a service model that will meet these changing needs. Delay or failure to adopt an effective service model as we pass through the inflection point will result in a diminished role for clinical engineering in healthcare technology management as other technical professionals move in to fill the need. The question is: will clinical engineering rise to the challenge?


title: A Secure Routing Protocol Based on RPL for Internet of Things

abstract: Data transportation and routing in Internet of Things (IoT) is a challenging issue where massive data collection and gathering are predictable. The Routing Protocol for Low- power and Lossy Networks (RPL) is one of the best candidates to ensure routing in 6LoWPAN networks. However, RPL is vulnerable to a number of attacks related to exchanged control messages. In this paper, we propose a new secure routing protocol based on RPL referred to as Secure-RPL (SRPL). The main aim of SRPL is to prevent misbehaving nodes from maliciously changing control message values such as the rank of a node that may disturb a network by creating a fake topology. We introduce the concept of rank threshold along with hash chain authentication technique to deal with internal attacks like sinkhole, black hole, selective forwarding attacks etc. Simulation results show that SRPL is robust and resistant to this kind of attacks based on malicious manipulation of RPL metrics.


title: Raspberry Pi based interactive home automation system through E-mail

abstract: Home automation is becoming more and more popular day by day due to its numerous advantages. This can be achieved by local networking or by remote control. This paper aims at designing a basic home automation application on Raspberry Pi through reading the subject of E-mail and the algorithm for the same has been developed in python environment which is the default programming environment provided by Raspberry Pi. Results show the efficient implementation of proposed algorithm for home automation. LEDs were used to indicate the switching action.


title: 1.3 μm EML TOSA for Serial 40 Gbps Ethernet solution

abstract: First 40 Gbps operation of a 1.3 μm EML TOSA employing a cost-effective flexible printed circuit as an electrical interface is demonstrated with the extinction ratio of 7.3 dB for serial 40 Gbps Ethernet solution.


title: An Assessment of Open Relation Extraction Systems for the Semantic Web

abstract: Abstract Open relation extraction has been a growing field of research in the last few years. This paper compares some of the most prominent open relation extractors and explores their strength and weaknesses on standard datasets. In particular, we highlight the lack of formal guidelines that define a valid relation and state that open relation extractors must be situated in particular tasks and contexts. In this respect, we briefly analyze the role of open relation extractors for the semantic Web and linked data context.


title: Same Stats, Different Graphs: Generating Datasets with Varied Appearance and Identical Statistics through Simulated Annealing

abstract: Datasets which are identical over a number of statistical properties, yet produce dissimilar graphs, are frequently used to illustrate the importance of graphical representations when exploring data. This paper presents a novel method for generating such datasets, along with several examples. Our technique varies from previous approaches in that new datasets are iteratively generated from a seed dataset through random perturbations of individual data points, and can be directed towards a desired outcome through a simulated annealing optimization strategy. Our method has the benefit of being agnostic to the particular statistical properties that are to remain constant between the datasets, and allows for control over the graphical appearance of resulting output.


title: A Linear Programming Approach for Multiple Object Tracking

abstract: We propose a linear programming relaxation scheme for the class of multiple object tracking problems where the inter-object interaction metric is convex and the intra-object term quantifying object state continuity may use any metric. The proposed scheme models object tracking as a multi-path searching problem. It explicitly models track interaction, such as object spatial layout consistency or mutual occlusion, and optimizes multiple object tracks simultaneously. The proposed scheme does not rely on track initialization and complex heuristics. It has much less average complexity than previous efficient exhaustive search methods such as extended dynamic programming and is found to be able to find the global optimum with high probability. We have successfully applied the proposed method to multiple object tracking in video streams.


title: Short version of the Smartphone Addiction Scale adapted to Spanish and French: Towards a cross-cultural research in problematic mobile phone use

abstract: Abstract Research into smartphone addiction has followed the scientific literature on problematic mobile phone use developed during the last decade, with valid screening scales being developed to identify maladaptive behaviour associated with this technology, usually in adolescent populations. This study adapts the short version of the Smartphone Addiction Scale [SAS-SV] into Spanish and into French. The aim of the study was to (i) examine the scale's psychometric properties in both languages, (ii) estimate the prevalence of potential excessive smartphone use among Spanish and Belgian adults, and (iii) compare the addictive symptomatology measured by the SAS-SV between potentially excessive users from both countries. Data were collected via online surveys administered to 281 and 144 voluntary participants from both countries respectively, aged over 18 years and recruited from academic environments. Results indicated that the reliability was excellent (i.e., Cronbach alphas: Spain: .88 and Belgium: .90), and the validity was very good (e.g., unifactoriality with a 49% and 54% of variance explained through explorative factor analysis, respectively). Findings showed that the prevalence of potential excessive smartphone use 12.5% for Spanish and 21.5% for francophone Belgians. The scale showed that at least 60% of excessive users endorsed withdrawal and tolerance symptoms in both countries, although the proposed addictive symptomatology did not cover the entire group of estimated excessive users and cultural differences appeared. This first cross-cultural study discusses the smartphone excessive use construct from its addictive pathway.


title: Hough Transform and Active Contour for Enhanced Iris Segmentation

abstract: Iris segmentation is considered as the most difficult and fundamental step in an iris recognition system. While iris boundaries are largely approximated by two circles or ellipses, other methods define more accurately the iris resulting in better recognition results. In this paper we propose an iris segmentation method using Hough transform and active contour to detect a circular approximation of the outer iris boundary and to accurately segment the inner boundary in its real shape motivated by the fact that richer iris textures are closer to the pupil than to the sclera. Normalization, encoding and matching are implemented according to Daugman‟s method. The method, tested on CASIA-V3 iris images database is compared to Daugman‟s iris recognition system. Recognition performance is measured in terms of decidability, accuracy at the equal error rate and ROC curves. Improved recognition performance is obtained using our segmentation model proposing its use for better iris


title: The dark side of elderly acceptance of preventive mobile health services in China

abstract: Mobile health services have become increasingly important for people, especially for the elderly. Despite the potential benefits, there are challenges and barriers for the elderly in adopting mobile health services. Drawing upon the dual factor model, we investigate the enablers and the inhibitors of the elderly mobile health service adoption behaviour. We also address two typical characteristics of elderly users—technology anxiety and dispositional resistance to change—to understand the antecedents of the enablers and the inhibitors. The hypothesized model is empirically tested using data collected from a field survey of 204 customers of a large elderly service providing company in China. The key findings include: (1) resistance to change influences perceived usefulness but does not influence perceived ease of use and adoption intention; (2) technology anxiety is negatively associated with perceived ease of use but positively associated with resistance to change; (3) dispositional resistance to change is negatively associated with perceived ease of use but positively associated with resistance to change. Implications for research and practice are discussed.


title: Design of Axial Flux PM Synchronous Machines Through 3-D Coupled Electromagnetic Thermal and Fluid-Dynamical Finite-Element Analysis

abstract: This paper investigates the thermal behavior of axial flux synchronous permanent-magnet machines (AFSPMMs) through a 3-D thermal-magnetic finite-element analysis (FEA). This paper offers a systematic approach to magnetothermal FEA. The considered axial flux machine is wound on a soft magnetic compound core. The computation of thermal field is performed via a coupled thermal and fluid-dynamical model based on FEA, where the thermal sources are obtained from a DC current flow model and a magnetostatic model. This paper includes an overview of the finite-element method (FEM) model implementation. Simulation results are presented, and, to validate the model, the simulated surface temperature rise of the motor parts is compared by experimental data.


title: The Strategic Role of the Board

abstract: This article reports the results of a survey of 101 UK company boards designed to obtain information on the extent of their strategic involvement, the style of strategy making, the planning process and the methods used to monitor and control the implementation of strategy. There is much prescriptive work on the need for boards to be involved in the strategy making process, but little empirical evidence has been gathered about current practices. This article makes a contribution to this debate. With Pettigrew (1993) we aim to avoid placing this research within any one research tradition, but attempt to provide descriptive results which increase our understanding of the board's role in strategy based on initial findings from an on-going study.


title: SCL: simplifying distributed SDN control planes

abstract: We consider the following question: what consistency model is appropriate for coordinating the actions of a replicated set of SDN controllers? We first argue that the conventional requirement of strong consistency, typically achieved through the use of Paxos or other consensus algorithms, is conceptually unnecessary to handle unplanned network updates. We present an alternate approach, based on the weaker notion of eventual correctness, and describe the design of a simple coordination layer (SCL) that can seamlessly turn a set of singleimage SDN controllers (that obey certain properties) into a distributed SDN system that achieves this goal (whereas traditional consensus mechanisms do not). We then show through analysis and simulation that our approach provides faster responses to network events. While our primary focus is on handling unplanned network updates, our coordination layer also handles policy updates and other situations where consistency is warranted. Thus, contrary to the prevailing wisdom, we argue that distributed SDN control planes need only be slightly more complicated than single-image controllers.


title: Robust Angle Invariant GAS Meter Reading

abstract: In this work we propose a novel method for automatic gas meter reading from real world images. In a wide range of countries all over the world, the existing automatic technology is not adopted, usually the reading is manually done on site, and a picture is taken through a mobile device as a proof of reading. In order to confirm the reading, a tedious work of checking the proof images is commonly done offline by an operator. With this contribution we aim to supply an effective system, able to provide a real support to the validation process reducing the human effort and the time consumed. We exploit both region-based and Maximally Stable Extremal Regions techniques, during the phase involving the localization of the meter area and to detect the meter counter digits in the detection step respectively. The evaluation has been carried out on every step of our approach, as well as on the overall assessment; although the problem is complex, the proposed method leads to good results even when applied to degraded images, it represents an effective solution to the gas meter reading problem and it can be utilized in real applications.


title: A sequentially Markov conditional sampling distribution for structured populations with migration and recombination

abstract: Conditional sampling distributions (CSDs), sometimes referred to as copying models, underlie numerous practical tools in population genomic analyses. Though an important application that has received much attention is the inference of population structure, the explicit exchange of migrants at specified rates has not hitherto been incorporated into the CSD in a principled framework. Recently, in the case of a single panmictic population, a sequentially Markov CSD has been developed as an accurate, efficient approximation to a principled CSD derived from the diffusion process dual to the coalescent with recombination. In this paper, the sequentially Markov CSD framework is extended to incorporate subdivided population structure, thus providing an efficiently computable CSD that admits a genealogical interpretation related to the structured coalescent with migration and recombination. As a concrete application, it is demonstrated empirically that the CSD developed here can be employed to yield accurate estimation of a wide range of migration rates.


title: An improvement for semantics-based recommender systems grounded on attaching temporal information to ontologies and user profiles

abstract: Recommender systems in online shopping automatically select the most appropriate items to each user, thus shortening his/her product searching time in the shops and adapting the selection as his/her particular preferences evolve over time. This adaptation process typically considers that a user's interest in a given type of product always decreases with time from the moment of the last purchase. However, the necessity of a product for a user depends on both the nature of the own item and the personal preferences of the user, being even possible that his/her interest increases over time from the purchase. Some existing approaches focus only on the first factor, missing the point that the influence of time can be very different for different users. To solve this limitation, we present a filtering strategy that exploits the semantics formalized in an ontology in order to link items (and their features) to time functions. The novelty lies within the fact that the shapes of these functions are corrected by temporal curves built from the consumption stereotypes into which each user fits best. Our preliminary experiments involving real users have revealed significant improvements of recommendation precision with regard to previous time-driven filtering approaches.


title: An investigation of classification algorithms for intrusion detection system — A quantitative approach

abstract: Nowadays security issues are growing in a tremendous rate. So it is expedient to have a mechanism to keep track of its security issues in the network or host. The Intrusion Detection System (IDS) has a critical part for supervising the networks. The false alarm rate and accuracy are the two important factors to be considered in the design of competent IDS. The role of classification algorithms is indispensable in the decision making of IDS. The redundant and irrelevant features of dataset also affects the performance of classifiers which in turn degrading the evaluation of anomaly detection. The proposed work focuses on the detailed study of different classifiers on two feature selection techniques using NSL-KDD dataset, where Random Forest on Principal Component Analysis (PCA) gives the accuracy rate of 99.52% and false alarm rate is 0.48%.


title: Enhancing Bitcoin Security and Performance with Strong Consistency via Collective Signing

abstract: While showing great promise, Bitcoin requires users to wait tens of minutes for transactions to commit, and even then, offering only probabilistic guarantees. This paper introduces ByzCoin, a novel Byzantine consensus protocol that leverages scalable collective signing to commit Bitcoin transactions irreversibly within seconds. ByzCoin achieves Byzantine consensus while preserving Bitcoin's open membership by dynamically forming hash power-proportionate consensus groups that represent recently-successful block miners. ByzCoin employs communication trees to optimize transaction commitment and verification under normal operation while guaranteeing safety and liveness under Byzantine faults, up to a near-optimal tolerance of f faulty group members among 3f + 2 total. ByzCoin mitigates double spending and selfish mining attacks by producing collectively signed transaction blocks within one minute of transaction submission. Tree-structured communication further reduces this latency to less than 30 seconds. Due to these optimizations, ByzCoin achieves a throughput higher than PayPal currently handles, with a confirmation latency of 15-20 seconds.


title: Dynamics of facial expression: recognition of facial actions and their temporal segments from face profile image sequences

abstract: Automatic analysis of human facial expression is a challenging problem with many applications. Most of the existing automated systems for facial expression analysis attempt to recognize a few prototypic emotional expressions, such as anger and happiness. Instead of representing another approach to machine analysis of prototypic facial expressions of emotion, the method presented in this paper attempts to handle a large range of human facial behavior by recognizing facial muscle actions that produce expressions. Virtually all of the existing vision systems for facial muscle action detection deal only with frontal-view face images and cannot handle temporal dynamics of facial actions. In this paper, we present a system for automatic recognition of facial action units (AUs) and their temporal models from long, profile-view face image sequences. We exploit particle filtering to track 15 facial points in an input face-profile sequence, and we introduce facial-action-dynamics recognition from continuous video input using temporal rules. The algorithm performs both automatic segmentation of an input video into facial expressions pictured and recognition of temporal segments (i.e., onset, apex, offset) of 27 AUs occurring alone or in a combination in the input face-profile video. A recognition rate of 87% is achieved.


title: A 2D and 3D Indoor Mapping Approach for Virtual Navigation Services

abstract: Specific navigation systems are necessary to navigate in complex buildings and these should provide a realistic virtual indoor environment. A key issue is creation and management of building documentation. Map information is typically proprietary, sometimes recorded on heterogeneous media, and it is even volatile, such as layouts in a shopping mall, where change is continuous. In order to create a navigation system with a realistic indoor virtual environment, we propose a 2D and 3D indoor mapping approach, which is a key module of indoor services life cycle. Our approach is based on a 3D game engine, which 1) can build an effective 2D SVG map that updates selectively, 2) converts it into a 3D virtual scene automatically at back-end, 3) includes an information management system at back-end, 4) renders and interacts efficiently and effectively on a smart phone, and 5) provides a vivid virtual indoor visualization for users' navigation at front-end. Our solution of back-end includes 2D SVG indoor map management and 3D indoor model creation. Front-end includes indoor 3D modeling and navigation scene implementation in Unity3D. Such indoor mapping approach provides a solid foundation for further indoor management and navigation services.


title: Code Consistent Hashing Based on Information-Theoretic Criterion

abstract: Learning based hashing techniques have attracted broad research interests in the Big Media research area. They aim to learn compact binary codes which can preserve semantic similarity in the Hamming embedding. However, the discrete constraints imposed on binary codes typically make hashing optimizations very challenging. In this paper, we present a code consistent hashing ( CCH ) algorithm to learn discrete binary hash codes. To form a simple yet efficient hashing objective function, we introduce a new code consistency constraint to leverage discriminative information and propose to utilize the Hadamard code which favors an information-theoretic criterion as the class prototype. By keeping the discrete constraint and introducing an orthogonal constraint, our objective function can be minimized efficiently. Experimental results on three benchmark datasets demonstrate that the proposed CCH outperforms state-of-the-art hashing methods in both image retrieval and classification tasks, especially with short binary codes.


title: Violent video games and the Supreme Court: Lessons for the scientific community in the wake of Brown v. Entertainment Merchants Association.

abstract: In June 2011 the U.S. Supreme Court ruled that video games enjoy full free speech protections and that the regulation of violent game sales to minors is unconstitutional. The Supreme Court also referred to psychological research on violent video games as "unpersuasive" and noted that such research contains many methodological flaws. Recent reviews in many scholarly journals have come to similar conclusions, although much debate continues. Given past statements by the American Psychological Association linking video game and media violence with aggression, the Supreme Court ruling, particularly its critique of the science, is likely to be shocking and disappointing to some psychologists. One possible outcome is that the psychological community may increase the conclusiveness of their statements linking violent games to harm as a form of defensive reaction. However, in this article the author argues that the psychological community would be better served by reflecting on this research and considering whether the scientific process failed by permitting and even encouraging statements about video game violence that exceeded the data or ignored conflicting data. Although it is likely that debates on this issue will continue, a move toward caution and conservatism as well as increased dialogue between scholars on opposing sides of this debate will be necessary to restore scientific credibility. The current article reviews the involvement of the psychological science community in the Brown v. Entertainment Merchants Association case and suggests that it might learn from some of the errors in this case for the future. (PsycINFO Database Record (c) 2013 APA, all rights reserved). Language: en


title: Gender stereotypes about intellectual ability emerge early and influence children’s interests

abstract: Common stereotypes associate high-level intellectual ability (brilliance, genius, etc.) with men more than women. These stereotypes discourage women’s pursuit of many prestigious careers; that is, women are underrepresented in fields whose members cherish brilliance (such as physics and philosophy). Here we show that these stereotypes are endorsed by, and influence the interests of, children as young as 6. Specifically, 6-year-old girls are less likely than boys to believe that members of their gender are “really, really smart.” Also at age 6, girls begin to avoid activities said to be for children who are “really, really smart.” These findings suggest that gendered notions of brilliance are acquired early and have an immediate effect on children’s interests.


title: Facilitating software refactoring with appropriate resolution order of bad smells

abstract: Bad smell is a key concept in software refactoring. We have a bunch of bad smells, refactoring rules, and refactoring tools, but we do not know which kind of bad smells should be resolved first. The resolution of one kind of bad smells may have impact on the resolution of other bad smells. Consequently, different resolution orders of the same set of bad smells may require different effort, and/or lead to different quality improvement. In order to ease the work and maximize the effect of refactoring, we try to analyze the relationships among different kinds of bad smells, and their impact on resolution orders of these bad smells. With the analysis, we recommend a resolution order of common bad smells. The main contribution of this paper is to motivate the necessity to arrange resolution orders of bad smells, and recommend a resolution order of common bad smells.


title: Where are the hard knapsack problems

abstract: The knapsack problem is believed to be one of the "easier" NP-hard problems. Not only can it be solved in pseudo-polynomial time, but also decades of algorithmic improvements have made it possible to solve nearly all standard instances from the literature. The purpose of this paper is to give an overview of all recent exact solution approaches, and to show that the knapsack problem still is hard to solve for these algorithms for a variety of new test problems. These problems are constructed either by using standard benchmark instances with larger coefficients, or by introducing new classes of instances for which most upper bounds perform badly. The first group of problems challenge the dynamic programming algorithms while the other group of problems are focused towards branch-and-bound algorithms. Numerous computational experiments with all recent state-of-the-art codes are used to show that (KP) is still difficult to solve for a wide number of problems. One could say that the previous benchmark tests were limited to a few highly structured instances, which do not show the full characteristics of knapsack problems.


title: A 9.2–12.7 GHz wideband fractional-N subsampling PLL in 28nm CMOS with 280fs RMS jitter

abstract: This paper describes a fractional-N subsampling PLL in 28nm CMOS. Fractional lock is achieved by using a 10bit digital-to-time converter (DTC) that generates a delayed sampling clock with minimal impact on PLL performance. Background calibration guarantees appropriate DTC gain, reducing spurs. The system achieves -38 dBc of integrated phase noise (280fs RMS jitter) at 10GHz when a worst-case fractional spur of -43 dBc is present. In-band phase noise is at the level of -104 dBc/Hz. The class-B VCO used can be tuned from 9.2 GHz to 12.7 GHz (32%). The total power consumption of the synthesizer, including the VCO, is 13 mW from 0.9V and 1.8V supplies.


title: A 1 MHz Half-Bridge Resonant DC/DC Converter Based on GaN FETs and Planar Magnetics

abstract: A 1 MHz half-bridge resonant dc/dc converter based on GaN FETs and planar magnetics is proposed in this paper, which improves the system efficiency and power density. The resonant network can achieve satisfactory soft-switching characteristics based on a small impedance angle, which greatly reduces the losses of switches and diodes. The losses characteristics during the turn-on and turn-off transitions are analyzed in detail. The calculation results show that the GaN FETs with low output capacitance and on resistance can achieve fast switching speed and low losses in high-frequency conditions. To reduce the profile and increase the power density of the system, planar magnetics are used in this paper. The response surface methodology (RSM) and modular layer model (MLM) are adopted to help design the planar inductor and transformer, respectively. Both of the methods offer clearer and more effective ways to design the planar magnetics. A 25-W prototype is built to verify the feasibility of the proposed high-frequency converter.


title: Stacked Sparse Autoencoder (SSAE) for Nuclei Detection on Breast Cancer Histopathology Images

abstract: Automated nuclear detection is a critical step for a number of computer assisted pathology related image analysis algorithms such as for automated grading of breast cancer tissue specimens. The Nottingham Histologic Score system is highly correlated with the shape and appearance of breast cancer nuclei in histopathological images. However, automated nucleus detection is complicated by 1) the large number of nuclei and the size of high resolution digitized pathology images, and 2) the variability in size, shape, appearance, and texture of the individual nuclei. Recently there has been interest in the application of "Deep Learning" strategies for classification and analysis of big image data. Histopathology, given its size and complexity, represents an excellent use case for application of deep learning strategies. In this paper, a Stacked Sparse Autoencoder (SSAE), an instance of a deep learning strategy, is presented for efficient nuclei detection on high-resolution histopathological images of breast cancer. The SSAE learns high-level features from just pixel intensities alone in order to identify distinguishing features of nuclei. A sliding window operation is applied to each image in order to represent image patches via high-level features obtained via the auto-encoder, which are then subsequently fed to a classifier which categorizes each image patch as nuclear or non-nuclear. Across a cohort of 500 histopathological images (2200 × 2200) and approximately 3500 manually segmented individual nuclei serving as the groundtruth, SSAE was shown to have an improved F-measure 84.49% and an average area under Precision-Recall curve (AveP) 78.83%. The SSAE approach also out-performed nine other state of the art nuclear detection strategies.


title: Vision-only approach and landing

abstract: Landing an aircraft in a remote landing zone autonomously presents several challenges. Firstly, the exact location, orientation, and elevation of the landing zone is not always known; secondly, the accuracy of the aircrafts navigation solution is not always su‐cient for this type of precision maneuver. This paper explores a method for estimating the relative position and attitude of the aircraft to marked landing area using only the images from a single camera. The corners of the landing zone are marked with red beacons. The position of these beacons in the camera image are extracted using simple and e‐cient image processing techniques. Then the location of the corners are used as the measurements for an extended Kalman fllter, which is designed to estimate the relative position, velocity, attitude and angular rates of the aircraft. The performance of this navigation algorithm is demonstrated using simulation.


title: MEASURING ECONOMIC BENEFITS OF INTERMODAL TRANSPORTATION

abstract: Intermodal transportation is an example of the general purpose technologies that are characterized by statistically significant spillover effects. Diffusion, secondary innovations, and increased demand for specific human capital are basic features of general purpose technologies. Eventually these features affect major macroeconomic variables, especially productivity. Micro and macro evidence should be combined to establish a better understanding of the connecting mechanisms from the micro level to the overall performance of an economy or the macro level. This study analyses these issues with respect to intermodal transportation. The goal is to understand the basic micro and macro mechanisms behind intermodal transportation in order to develop a rigorous framework for evaluation of benefits from intermodal transportation. Usually benefits for transportation investments are analyzed within a framework of cost-benefit analysis or its related techniques such a financial analysis, cost-effectiveness analysis, life-cycle costing, economic impact analysis, and others. While these tools are valid techniques in general, their application to intermodal transportation would underestimate the overall economic impact by missing important aspects of productivity enhancement.


title: Spectral Quasi-Equilibrium Manifold for Chemical Kinetics.

abstract: The Spectral Quasi-Equilibrium Manifold (SQEM) method is a model reduction technique for chemical kinetics based on entropy maximization under constraints built by the slowest eigenvectors at equilibrium. The method is revisited here and discussed and validated through the Michaelis-Menten kinetic scheme, and the quality of the reduction is related to the temporal evolution and the gap between eigenvalues. SQEM is then applied to detailed reaction mechanisms for the homogeneous combustion of hydrogen, syngas, and methane mixtures with air in adiabatic constant pressure reactors. The system states computed using SQEM are compared with those obtained by direct integration of the detailed mechanism, and good agreement between the reduced and the detailed descriptions is demonstrated. The SQEM reduced model of hydrogen/air combustion is also compared with another similar technique, the Rate-Controlled Constrained-Equilibrium (RCCE). For the same number of representative variables, SQEM is found to provide a more accurate description.


title: The Berlin Brain–Computer Interface: Non-Medical Uses of BCI Technology

abstract: Brain–computer interfacing (BCI) is a steadily growing area of research. While initially BCI research was focused on applications for paralyzed patients, increasingly more alternative applications in healthy human subjects are proposed and investigated. In particular, monitoring of mental states and decoding of covert user states have seen a strong rise of interest. Here, we present some examples of such novel applications which provide evidence for the promising potential of BCI technology for non-medical uses. Furthermore, we discuss distinct methodological improvements required to bring non-medical applications of BCI technology to a diversity of layperson target groups, e.g., ease of use, minimal training, general usability, short control latencies.


title: In Defense of the Internet: The Relationship between Internet Communication and Depression, Loneliness, Self-Esteem, and Perceived Social Support

abstract: As more people connect to the Internet, researchers are beginning to examine the effects of Internet use on users' psychological health. Due in part to a study released by Kraut and colleagues in 1998, which concluded that Internet use is positively correlated with depression, loneliness, and stress, public opinion about the Internet has been decidedly negative. In contrast, the present study was designed to test the hypothesis that Internet usage can affect users beneficially. Participants engaged in five chat sessions with an anonymous partner. At three different intervals they were administered scales measuring depression, loneliness, self-esteem, and social support. Changes in their scores were tracked over time. Internet use was found to decrease loneliness and depression significantly, while perceived social support and self-esteem increased significantly.


title: Beyond Bandwidth: Dimensions of Connection in Interpersonal Communication

abstract: Computer-mediated communication (CMC) is a keystone of computer-supported collaborative work. Current CMC theory utilizes an information channel metaphor in which media vary according to how well they afford the transfer of messages in the channel, i.e., bandwidth. This paper draws attention to a different aspect of communication argued to be equally important: a relation between people that defines a state of communicative readiness in which fruitful communication is likely. Drawing on research on instant messaging (Nardi et al., 2000) and face to face communication (Nardi et al., 2002; Nardi and Whittaker, 2003), as well as related literature, three dimensions of connection that activate readiness are proposed: affinity, commitment, and attention. These dimensions comprise a field of connection between dyads. A field of connection is conceptualized as a labile, multidimensional space in which the values of the dimensions vary according to the history of communicative activity. Affinity, commitment, and attention are constantly monitored, negotiated, and managed through social bonding, expression of commitment, and capture of attention. The management of fields of connection requires significant interactional work to sustain communication over time.


title: MADMAC: Multiple Attribute Decision Methodology for Adoption of Clouds

abstract: Cloud Adoption decisions tend to involve multiple, conflicting criteria (attributes) with incommensurable units of measurements, which must be compared among multiple alternatives using imprecise and incomplete available information. Multi-attribute Decision Making (MADM) has been shown to provide a rational basis to aid decision making in such scenarios. We present a MADMAC framework for cloud adoption, consisting of 3 Decision Areas (DA) referred to as the Cloud Switch, Cloud Type and Vendor Choice. It requires the definition of Attributes, Alternatives and Attribute Weights, to construct a Decision Matrix and arrive at a relative ranking to identify the optimal alternative. We also present a taxonomy organized in a two level hierarchy: Server-centric clouds, Client-centric clouds and Mobile-centric clouds, which further map to detailed, specific applications or workloads. DSS presented showing algorithms derived from MADMAC can compute and optimize CA decisions separately for the three stages, where the attributes differently influence CA decisions. A modified Wide-band Delphi method is proposed for assessing the relative weights for each attribute, by workload. Relative ranks are calculated using these weights, and the Simple Additive Weighting (SAW) method is used to generate value functions for all the alternatives, and rank the alternatives by their value to finally choose the best alternative. Results from application of the method to four different types of workloads show that the method converges on reasonable cloud adoption decisions. MADMAC's key advantage is its fully quantitative and iterative convergence approach based on proven multi-attribute decision methods, which enables decision makers to comparatively assess the relative robustness of alternative cloud adoption decisions in a defensible manner. Being amenable to automation, it can respond well to even complex arrays of decision criteria inputs, unlike human decision makers. It can be implemented as a web-based DSS to readily support cloud decision making world-wide, and improved further using fuzzy TOPSIS methods, to address concerns about preferential inter-dependence of attributes, insufficient input data or judgment expertise.


title: Phoneypot: Data-driven Understanding of Telephony Threats

abstract: Cyber criminals are increasingly using robocalling, voice phishing and caller ID spoofing to craft attacks that are being used to scam unsuspecting users who have traditionally trusted the telephone. It is necessary to better understand telephony threats to effectively combat them. Although there exist crowd sourced complaint datasets about telephony abuse, such complaints are often filed after a user receives multiple calls over a period of time, and sometimes they lack important information. We believe honeypot technologies can be used to augment telephony abuse intelligence and improve its quality. However, a telephony honeypot presents several new challenges that do not arise in other traditional honeypot settings. We present Phoneypot, a first large scale telephony honeypot, that allowed us to explore ways to address these challenges. By presenting a concrete implementation of Phoneypot using a cloud infrastructure and 39,696 phone numbers (phoneytokens), we provide evidence of the benefits of telephony honeypots. Phoneypot received 1.3 million calls from 250K unique sources over a period of seven weeks. We detected several debt collectors and telemarketers calling patterns and an instance of a telephony denial-of-service attack. This provides us with new insights into telephony abuse and attack patterns.


title: Evaluation and selection of Medical Tourism sites : A rough AHP based MABAC approach

abstract: In this paper, a novel multiple criteria decision making (MCDM) methodology is presented for assessing and prioritizing medical tourism destinations in uncertain environment. A systematic evaluation and assessment method is proposed by integrating rough number based AHP (Analytic Hierarchy Process) and rough number based MABAC (Multi-Attributive Border Approximation area Comparison). Rough number is used to aggregate individual judgments and preferences to deal with vagueness in decision making due to limited data. Rough AHP analyzes the relative importance of criteria based on their preferences given by experts. Rough MABAC evaluates the alternative sites based on the criteria weights. The proposed methodology is explained through a case study considering different cities for healthcare service in India. The validity of the obtained ranking for the given decision making problem is established by testing criteria proposed by Wang and Triantaphyllou (2008) along with further analysis and discussion.


IBAL: A Probabilistic Rational Programming Language


title: Depression Assessment by Fusing High and Low Level Features from Audio, Video, and Text

abstract: Depression is a major cause of disability world-wide. The present paper reports on the results of our participation to the depression sub-challenge of the sixth Audio/Visual Emotion Challenge (AVEC 2016), which was designed to compare feature modalities (audio, visual, interview transcript-based) in gender-based and gender-independent modes using a variety of classification algorithms. In our approach, both high and low level features were assessed in each modality. Audio features were extracted from the low-level descriptors provided by the challenge organizers. Several visual features were extracted and assessed including dynamic characteristics of facial elements (using Landmark Motion History Histograms and Landmark Motion Magnitude), global head motion, and eye blinks. These features were combined with statistically derived features from pre-extracted features (emotions, action units, gaze, and pose). Both speech rate and word-level semantic content were also evaluated. Classification results are reported using four different classification schemes: i) gender-based models for each individual modality, ii) the feature fusion model, ii) the decision fusion model, and iv) the posterior probability classification model. Proposed approaches outperforming the reference classification accuracy include the one utilizing statistical descriptors of low-level audio features. This approach achieved f1-scores of 0.59 for identifying depressed and 0.87 for identifying not-depressed individuals on the development set and 0.52/0.81, respectively for the test set.


title: Brazing defect inspection using three-dimensional visualization

abstract: The typical defects identified at brazing of the heat exchanger are void defects and incomplete brazing defects. Brazing defects of the heat exchanger by CT(Computerized tomography) were inspected by defect inspection system. Then, the raw data was stored through after classified by the types and locations. Data retrieved was external image data, defect image data and defect size data and was visualized by 3-D(three-dimensional). The system applied by openGL(Open Graphics Library), MFC and API(Application Program Interface) enabled us to try to confirm not only overall defect visualization but partial defect visualization. Through this experiment, it could get the 3-D images of the defects.


title: Antioxidative effect of rhizome of Zinziber officinale on paraben induced lipid peroxidation: an in vitro study.

abstract: Antioxidative effect of aqueous extract of rhizome of Ginger (Zinziber officinale) was examined on p-hydroxybenzoic acid (paraben) induced lipid peroxidation. Addition of paraben (25-150 microg/mL) to liver and kidney homogenates significantly increases H2O2 induced lipid peroxidation in vitro. Effect was dose dependent up to 100 microg/mL concentration. An addition of aqueous extract of ginger significantly reduced paraben (100 microg/mL) induced lipid peroxidation in liver and kidney homogenates. The effect was concentration dependent.


Supporting Situationally Aware Cybersecurity Systems 30 th September 2015


Aliasing Artifacts and Accidental Algorithmic Art


title: Joint Semantic and Latent Attribute Modelling for Cross-Class Transfer Learning

abstract: A number of vision problems such as zero-shot learning and person re-identification can be considered as cross-class transfer learning problems. As mid-level semantic properties shared cross different object classes, attributes have been studied extensively for knowledge transfer across classes. Most previous attribute learning methods focus only on human-defined/nameable semantic attributes, whilst ignoring the fact there also exist undefined/latent shareable visual properties, or latent attributes. These latent attributes can be either discriminative or non-discriminative parts depending on whether they can contribute to an object recognition task. In this work, we argue that learning the latent attributes jointly with user-defined semantic attributes not only leads to better representation but also helps semantic attribute prediction. A novel dictionary learning model is proposed which decomposes the dictionary space into three parts corresponding to semantic, latent discriminative and latent background attributes respectively. Such a joint attribute learning model is then extended by following a multi-task transfer learning framework to address a more challenging unsupervised domain adaptation problem, where annotations are only available on an auxiliary dataset and the target dataset is completely unlabelled. Extensive experiments show that the proposed models, though being linear and thus extremely efficient to compute, produce state-of-the-art results on both zero-shot learning and person re-identification.


title: Online Self-Tracking Groups to Increase Fruit and Vegetable Intake: A Small-Scale Study on Mechanisms of Group Effect on Behavior Change

abstract: BACKGROUND ::: Web-based interventions with a self-tracking component have been found to be effective in promoting adults' fruit and vegetable consumption. However, these interventions primarily focus on individual- rather than group-based self-tracking. The rise of social media technologies enables sharing and comparing self-tracking records in a group context. Therefore, we developed an online group-based self-tracking program to promote fruit and vegetable consumption. ::: ::: ::: OBJECTIVE ::: This study aims to examine (1) the effectiveness of online group-based self-tracking on fruit and vegetable consumption and (2) characteristics of online self-tracking groups that make the group more effective in promoting fruit and vegetable consumption in early young adults. ::: ::: ::: METHODS ::: During a 4-week Web-based experiment, 111 college students self-tracked their fruit and vegetable consumption either individually (ie, the control group) or in an online group characterized by a 2 (demographic similarity: demographically similar vs demographically diverse) × 2 (social modeling: incremental change vs ideal change) experimental design. Each online group consisted of one focal participant and three confederates as group members or peers, who had their demographics and fruit and vegetable consumption manipulated to create the four intervention groups. Self-reported fruit and vegetable consumption were assessed using the Food Frequency Questionnaire at baseline and after the 4-week experiment. ::: ::: ::: RESULTS ::: Participants who self-tracked their fruit and vegetable consumption collectively with other group members consumed more fruits and vegetables than participants who self-tracked individually (P=.01). The results did not show significant main effects of demographic similarity (P=.32) or types of social modeling (P=.48) in making self-tracking groups more effective in promoting fruit and vegetable consumption. However, additional analyses revealed the main effect of performance discrepancy (ie, difference in fruit and vegetable consumption between a focal participant and his/her group members during the experiment), such that participants who had a low performance discrepancy from other group members had greater fruit and vegetable consumption than participants who had a high performance discrepancy from other group members (P=.002). A mediation test showed that low performance discrepancy led to greater downward contrast (b=-0.78, 95% CI -2.44 to -0.15), which in turn led to greater fruit and vegetable consumption. ::: ::: ::: CONCLUSIONS ::: Online self-tracking groups were more effective than self-tracking alone in promoting fruit and vegetable consumption for early young adults. Low performance discrepancy from other group members lead to downward contrast, which in turn increased participants' fruit and vegetable consumption over time. The study highlighted social comparison processes in online groups that allow for sharing personal health information. Lastly, given the small scale of this study, nonsignificant results with small effect sizes might be subject to bias.


title: Physical and circuit modeling of HfO2 based ferroelectric memories and devices

abstract: The discovery of ferroelectric properties in polycrystalline HfO2 has revived the interest in ferroelectric (FE) memories, which shows scaling feasibility allowing targeting high-density storage applications. In order to provide engineering guidelines for FE memory devices it is crucial to establish a correlation between the electrical device performances and the underlying physical mechanisms. In this work, we will discuss physical and circuit modeling approaches for FE memories connecting the FE HfO2 materials properties to the electrical performances of memory cells, artificial synapse for neuromorphic and in memory computing applications.


title: Approaches to text mining arguments from legal cases

abstract: This paper describes recent approaches using text-mining to automatically profile and extract arguments from legal cases. We outline some of the background context and motivations. We then turn to consider issues related to the construction and composition of corpora of legal cases. We show how a Context-Free Grammar can be used to extract arguments, and how ontologies and Natural Language Processing can identify complex information such as case factors and participant roles. Together the results bring us closer to automatic identification of legal arguments.


title: Proving the Incompatibility of Efficiency and Strategyproofness via SMT Solving

abstract: Two important requirements when aggregating the preferences of multiple agents are that the outcome should be economically efficient and the aggregation mechanism should not be manipulable. In this paper, we provide a computer-aided proof of a sweeping impossibility using these two conditions for randomized aggregation mechanisms. More precisely, we show that every efficient aggregation mechanism can be manipulated for all expected utility representations of the agents' preferences. This settles an open problem and strengthens a number of existing theorems, including statements that were shown within the special domain of assignment. Our proof is obtained by formulating the claim as a satisfiability problem over predicates from real-valued arithmetic, which is then checked using an SMT (satisfiability modulo theories) solver. In order to verify the correctness of the result, a minimal unsatisfiable set of constraints returned by the SMT solver was translated back into a proof in higher-order logic, which was automatically verified by an interactive theorem prover. To the best of our knowledge, this is the first application of SMT solvers in computational social choice.


title: Randomized rumor spreading

abstract: Investigates the class of epidemic algorithms that are commonly used for the lazy transmission of updates to distributed copies of a database. These algorithms use a simple randomized communication mechanism to ensure robustness. Suppose n players communicate in parallel rounds in each of which every player calls a randomly selected communication partner. In every round, players can generate rumors (updates) that are to be distributed among all players. Whenever communication is established between two players, each one must decide which of the rumors to transmit. The major problem is that players might not know which rumors their partners have already received. For example, a standard algorithm forwarding each rumor form the calling to the called players for /spl Theta/(ln n) rounds needs to transmit the rumor /spl Theta/(n ln n) times in order to ensure that every player finally receives the rumor with high probability. We investigate whether such a large communication overhead is inherent to epidemic algorithms. On the positive side, we show that the communication overhead can be reduced significantly. We give an algorithm using only O(n ln ln n) transmissions and O(ln n) rounds. In addition, we prove the robustness of this algorithm. On the negative side, we show that any address-oblivious algorithm needs to send /spl Omega/(n ln ln n) messages for each rumor, regardless of the number of rounds. Furthermore, we give a general lower bound showing that time and communication optimality cannot be achieved simultaneously using random phone calls, i.e. every algorithm that distributes a rumor in O(ln n) rounds needs /spl omega/(n) transmissions.


title: New approaches in selection and evaluation of electronic resources

abstract: In the early years of 2000, academic libraries used to transfer nearly 16% of their purchase budget to electronic resources which have been begun to be used commonly from the middle of the 1990’s whereas today it is clear that they transfer more than 90% of their budget. The selection and evaluation process of electronic resources have become much more important either due to the increasing budget spared for electronic resources or because of the tightening budgets of the libraries. In the light of the facts mentioned above, within the scope of the study, the issues to be taken into consideration throughout subscription process of the electronic resources, which has begun to gain speed since the beginning of 2000’s in our country, are specified. Within this framework, as well as focusing particularly on access models for the electronic resources, issues to be paid attention such as scope, content, functionality, cost, publisher support, licensing and technical requirements are also addressed. Primary requirements and approaches regarding the evaluation process are specified, as well.


title: Modulating early visual processing by language

abstract: It is commonly assumed that language refers to high-level visual concepts while leaving low-level visual processing unaffected. This view dominates the current literature in computational models for language-vision tasks, where visual and linguistic input are mostly processed independently before being fused into a single representation. In this paper, we deviate from this classic pipeline and propose to modulate the \emph{entire visual processing} by linguistic input. Specifically, we condition the batch normalization parameters of a pretrained residual network (ResNet) on a language embedding. This approach, which we call MOdulated RESnet (\MRN), significantly improves strong baselines on two visual question answering tasks. Our ablation study shows that modulating from the early stages of the visual processing is beneficial.


title: Transfer Learning in Multi-Armed Bandit: A Causal Approach

abstract: We leverage causal inference tools to support a principled and more robust transfer of knowledge in reinforcement learning (RL) settings. In particular, we tackle the problem of transferring knowledge across bandit agents in settings where causal effects cannot be identified by Pearl's {do-calculus} nor standard off-policy learning techniques. Our new identification strategy combines two steps -- first, deriving bounds over the arm's distribution based on structural knowledge; second, incorporating these bounds in a novel bandit algorithm, B-kl-UCB. Simulations demonstrate that our strategy is consistently more efficient than the current (non-causal) state-of-the-art methods.


title: An obstruction to Delaunay triangulations in Riemannian manifolds

abstract: Delaunay has shown that the Delaunay complex of a finite set of points \(P\) of Euclidean space \(\mathbb {R}^m\) triangulates the convex hull of \(P,\) provided that \(P\) satisfies a mild genericity property. Voronoi diagrams and Delaunay complexes can be defined for arbitrary Riemannian manifolds. However, Delaunay’s genericity assumption no longer guarantees that the Delaunay complex will yield a triangulation; stronger assumptions on \(P\) are required. A natural one is to assume that \(P\) is sufficiently dense. Although results in this direction have been claimed, we show that sample density alone is insufficient to ensure that the Delaunay complex triangulates a manifold of dimension greater than 2.


title: Lockless multi-core high-throughput buffering scheme for kernel tracing

abstract: Studying execution of concurrent real-time online systems, to identify far-reaching and hard to reproduce latency and performance problems, requires a mechanism able to cope with voluminous information extracted from execution traces. Furthermore, the workload must not be disturbed by tracing, thereby causing the problematic behavior to become unreproducible. In order to satisfy this low-disturbance constraint, we created the LTTng kernel tracer. It is designed to enable safe and race-free attachment of probes virtually anywhere in the operating system, including sites executed in non-maskable interrupt context. In addition to being reentrant with respect to all kernel execution contexts, LTTng offers good performance and scalability, mainly due to its use of per-CPU data structures, local atomic operations as main buffer synchronization primitive, and RCU (Read-Copy Update) mechanism to control tracing. Given that kernel infrastructure used by the tracer could lead to infinite recursion if traced, and typically requires non-atomic synchronization, this paper proposes an asynchronous mechanism to inform the kernel that a buffer is ready to read. This ensures that tracing sites do not require any kernel primitive, and therefore protects from infinite recursion. This paper presents the core of LTTng's buffering algorithms and measures its performance.


title: How we teach impacts student learning: peer instruction vs. lecture in CS0

abstract: In this paper we look at the impact on student learning of how a class is taught. We compare 2 sections of a non-majors CS0 course offered in the same term, by the same instructor, covering the same content and utilizing the same book, labs and exams. One section was taught using standard lecture practices including lecture from slides, live coding and weekly quizzes. The other section was taught using the Peer Instruction (PI) method that actively engages students in constructing their own learning, instead of absorbing understanding from the instructor's explanations. Using a factorial analysis of variance, we find a main effect of instructional method on final exam grade (F (1,200) = 5.87, p = 0.016) with students in the Peer Instruction section scoring an average 5.7% higher than in the standard lecture practices section. We find no significant interactions among gender and grade or class status (lower or upper division) and grade. In a separate analysis, we also find the interaction of instructional method and high school background to be significant (F (1,147) = 7.48, p = 0.007). In discussion we consider the meaning of these results for educators and describe questions for future work.


title: Observing the Permanent-Magnet Temperature of Synchronous Motors Based on Electrical Fundamental Wave Model Quantities

abstract: Estimating the magnet temperature of permanent-magnet synchronous motors on a model basis is the key for both the highest device utilization and preventing irreversible demagnetization. Using an exact flux linkage observer in the fundamental wave domain is an elegant way to indirectly retrieve the magnet temperature without requiring any additional sensors or signal injection. This contribution addresses several important issues for the real-world applicability of a permanent magnet temperature observer: thermal expansion affecting the radial air gap thickness, operation at low speeds or even standstill, and estimating the fundamental content of the motor's terminal voltage within one switching cycle. The proposed observer is comprehensively validated in the entire electrical and thermal operation range achieving a satisfying worst case error less than 10 K.


title: HEIGHT DETERMINATION OF EXTENDED OBJECTS USING SHADOWS IN SPOT IMAGES

abstract: Building heights were estimated with relatively high accuracy using shadows in a set of single-look SPOT panchromatic and multispectral images taken from the same satellite simultaneously. Shadows cast by rows of trees in SPOT images were first used to estimate mean heights of trees. Calibration lines were then constructed to relate the actual mean heights of rows of trees to the estimated heights. Using these calibration lines, heights of some industrial buildings in the image were estimated using their shadows with sub-pixel accuracy. The accuracy achieved is better than$hree metres, or one-third the pixel size of the SPOT panchromatic image. One of the important challenges involved in the process was to determine an appropriate threshold for delineating shadow zones in the images. A technique is provided for this problem. The technique is useful for estimating heights of extended objects situated in pat terrains. The type of resampling used for overlaying a multispectral image over a panchromatic image changes the accuracy of height estimation. However, the change is tolerable if the heights to be estimated ore within the ground-truth data range used for deriving calibration lines.


title: New space-time metaphors foster new mental representations of time.

abstract: Can learning new linguistic metaphors foster new nonlinguistic representations? We describe a set of studies in which we trained English-speaking participants to talk about time using vertical spatial metaphors that are novel to English. One group learned a mapping that placed earlier events above and the other a mapping that placed earlier events below. After mastering the new metaphors, participants were tested in a non-linguistic space-time implicit association task – the Orly task. This task has been used previously to document cross-linguistic differences in representations of time (Boroditsky et. al 2010; Fuhrman et al 2011). Some participants completed temporal judgments in the Orly task without any other secondary task, while others did so under either verbal or visual interference. Finally, we report data from a serendipitous sample of Chinese-English bilinguals on the same task.


title: Attention-Gated Networks for Improving Ultrasound Scan Plane Detection

abstract: In this work, we apply an attention-gated network to real-time automated scan plane detection for fetal ultrasound screening. Scan plane detection in fetal ultrasound is a challenging problem due the poor image quality resulting in low interpretability for both clinicians and automated algorithms. To solve this, we propose incorporating self-gated soft-attention mechanisms. A soft-attention mechanism generates a gating signal that is end-to-end trainable, which allows the network to contextualise local information useful for prediction. The proposed attention mechanism is generic and it can be easily incorporated into any existing classification architectures, while only requiring a few additional parameters. We show that, when the base network has a high capacity, the incorporated attention mechanism can provide efficient object localisation while improving the overall performance. When the base network has a low capacity, the method greatly outperforms the baseline approach and significantly reduces false positives. Lastly, the generated attention maps allow us to understand the model's reasoning process, which can also be used for weakly supervised object localisation.


title: Self-Presentation 2.0: Narcissism and Self-Esteem on Facebook

abstract: Abstract Online social networking sites have revealed an entirely new method of self-presentation. This cyber social tool provides a new site of analysis to examine personality and identity. The current study examines how narcissism and self-esteem are manifested on the social networking Web site Facebook.com. Self-esteem and narcissistic personality self-reports were collected from 100 Facebook users at York University. Participant Web pages were also coded based on self-promotional content features. Correlation analyses revealed that individuals higher in narcissism and lower in self-esteem were related to greater online activity as well as some self-promotional content. Gender differences were found to influence the type of self-promotional content presented by individual Facebook users. Implications and future research directions of narcissism and self-esteem on social networking Web sites are discussed.


title: A method to detect Internet of Things botnets

abstract: The main security problems, typical for the Internet of Things (IoT), as well as the purpose of gaining unauthorized access to the IoT, are considered in this paper. Common characteristics of the most widespread botnets are provided. A method to detect compromised IoT devices included into a botnet is proposed. The method is based on a model of logistic regression. The article describes a developed model of logistic regression which allows to estimate the probability that a device initiating a connection is running a bot. A list of network protocols, used to gain unauthorized access to a device and to receive instructions from common and control (C&C) server, is provided too.


Multi-source transformer with combined losses for automatic post editing


title: Adaptive Task Allocation for Mobile Edge Learning

abstract: This paper aims to establish a new optimization paradigm to efficiently execute distributed learning tasks on wireless edge nodes with heterogeneous computing and communication capacities. We will refer to this new paradigm as “Mobile Edge Learning (MEL)”. The problem of adaptive task allocation for MEL is considered in this paper with the aim to maximize the learning accuracy, while guaranteeing that the total times of data distribution/aggregation over heterogeneous channels, and local computation on heterogeneous nodes, are bounded by a preset duration. The problem is first formulated as a quadratically-constrained integer linear problem. Being NP-hard, the paper relaxes it into a non-convex problem over real variables. We then propose a solution based on deriving analytical upper bounds on the optimal solution of this relaxed problem using KKT conditions. The merits of this proposed solution is exhibited by comparing its performances to both numerical approaches and the equal task allocation approach.


title: Robust Evaluation of RoboCup Soccer Strategies by Using Match History

abstract: In this paper we improve the performance of an evolutionary method for obtaining team strategies in a simulated robot soccer domain. In the previous method each team strategy was evaluated based on the goals and the goals against of a single game. It is possible for a good team strategy to be eliminated from the population in the evolutionary method as there is a high degree of uncertainty in the simulated soccer game. In order to tackle the problem of uncertainty, we propose a robust evaluation method using match history. The performance of team strategies in the proposed method is measured by the average goals and average goals against. Through a series of computational experiments, we show the effectiveness of our robust evaluation method.


title: Low Side-Lobe Substrate-Integrated-Waveguide Antenna Array Using Broadband Unequal Feeding Network for Millimeter-Wave Handset Device

abstract: A low side-lobe substrate-integrated-waveguide (SIW) antenna array is presented at the 28-GHz band using broadband unequal feeding network for millimeter-wave (mm-wave) handset devices. The ground-plane size of the proposed antenna is fixed to half of the size of the Samsung Galaxy Note 4. The antenna array has been implemented with a multilayer structure created by stacking three substrates and a copper plate. An 8-way SIW feeding network with broadband 4-stage T-junction dividers and a cavity-backed antenna are investigated to obtain broadband performance. The proposed unequal T-junction dividers with phase compensation are introduced and designed for various output ratios. Applying Taylor beam-pattern synthesis in the 8-way SIW feeding network, low side-lobe performance is achieved. The measured result of the fabricated antenna has 2.3 GHz bandwidth within $\text{S}_11\,{ . The fabricated antenna can be performed with a gain up to 13.97 dBi with a low cross-polarization and symmetrical fan beam radiation patterns with low side-lobe levels. Most of the measured results are validated with the simulated results. The proposed antenna array provides low cost, broadband performance, and good radiation performances with low side-lobe levels for mm-wave handset devices.


title: Can recurrent neural networks warp time?

abstract: Successful recurrent models such as long short-term memories (LSTMs) and gated recurrent units (GRUs) use ad hoc gating mechanisms. Empirically these models have been found to improve the learning of medium to long term temporal dependencies and to help with vanishing gradient issues. We prove that learnable gates in a recurrent model formally provide quasi- invariance to general time transformations in the input data. We recover part of the LSTM architecture from a simple axiomatic approach. This result leads to a new way of initializing gate biases in LSTMs and GRUs. Ex- perimentally, this new chrono initialization is shown to greatly improve learning of long term dependencies, with minimal implementation effort.


title: Hot-Carrier Degradation in Power LDMOS: Selective LOCOS- Versus STI-Based Architecture

abstract: In this paper, we present an analysis of the degradation induced by hot-carrier stress in new generation power lateral double-diffused MOS (LDMOS) transistors. Two architectures with the same nominal voltage and comparable performance featuring a selective LOCOS and a shallow-trench isolation are investigated by means of constant voltage stress measurements and TCAD simulations. In particular, the on-resistance degradation in linear regime is experimentally extracted and numerically reproduced under different stress conditions. A similar amount of degradation has been reached by the two architectures, although different physical mechanisms contribute to the creation of the interface states. By using a recently developed physics-based degradation model, it has been possible to distinguish the damage due to collisions of single high-energetic electrons (single-particle events) and the contribution of colder electrons impinging on the silicon/oxide interface (multiple-particle events). A clear dominance of the single-electron collisions has been found in the case of LOCOS structure, whereas the multiple-particle effect plays a clear role in STI-based device at larger gate-voltage stress.


title: Neural Ordinary Differential Equations

abstract: We introduce a new family of deep neural network models. Instead of specifying a discrete sequence of hidden layers, we parameterize the derivative of the hidden state using a neural network. The output of the network is computed using a black-box differential equation solver. These continuous-depth models have constant memory cost, adapt their evaluation strategy to each input, and can explicitly trade numerical precision for speed. We demonstrate these properties in continuous-depth residual networks and continuous-time latent variable models. We also construct continuous normalizing flows, a generative model that can train by maximum likelihood, without partitioning or ordering the data dimensions. For training, we show how to scalably backpropagate through any ODE solver, without access to its internal operations. This allows end-to-end training of ODEs within larger models.


title: Caregiver burden: A concept analysis

abstract: Abstract The development of the concept of burden for use in research lacks consistent conceptualization and operational definitions. The purpose of this article is to analyze the concept of burden in an effort to promote conceptual clarity. The technique advocated by Walker and Avant is used to analyze this concept. Critical attributes of burden include subjective perception, multidimensional phenomena, dynamic change, and overload. Predisposing factors are caregiver's characteristics, the demands of caregivers, and the involvement in caregiving. The consequences of burden generate problems in care-receiver, caregiver, family, and health care system. Overall, this article enables us to advance this concept, identify the different sources of burden, and provide directions for nursing intervention. Copyright © 2000 by W.B. Saunders Company


title: Random sampling technique for overfitting control in genetic programming

abstract: One of the areas of Genetic Programming (GP) that, in comparison to other Machine Learning methods, has seen fewer research efforts is that of generalization. Generalization is the ability of a solution to perform well on unseen cases. It is one of the most important goals of any Machine Learning method, although in GP only recently has this issue started to receive more attention. In this work we perform a comparative analysis of a particularly interesting configuration of the Random Sampling Technique (RST) against the Standard GP approach. Experiments are conducted on three multidimensional symbolic regression real world datasets, the first two on the pharmacokinetics domain and the third one on the forestry domain. The results show that the RST decreases overfitting on all datasets. This technique also improves testing fitness on two of the three datasets. Furthermore, it does so while producing considerably smaller and less complex solutions. We discuss the possible reasons for the good performance of the RST, as well as its possible limitations.


title: Scalable statistical bug isolation

abstract: We present a statistical debugging algorithm that isolates bugs in programs containing multiple undiagnosed bugs. Earlier statistical algorithms that focus solely on identifying predictors that correlate with program failure perform poorly when there are multiple bugs. Our new technique separates the effects of different bugs and identifies predictors that are associated with individual bugs. These predictors reveal both the circumstances under which bugs occur as well as the frequencies of failure modes, making it easier to prioritize debugging efforts. Our algorithm is validated using several case studies, including examples in which the algorithm identified previously unknown, significant crashing bugs in widely used systems.


title: ParaGraphE: A Library for Parallel Knowledge Graph Embedding

abstract: Knowledge graph embedding aims at translating the knowledge graph into numerical representations by transforming the entities and relations into continuous low-dimensional vectors. Recently, many methods [1, 5, 3, 2, 6] have been proposed to deal with this problem, but existing single-thread implementations of them are time-consuming for large-scale knowledge graphs. Here, we design a unified parallel framework to parallelize these methods, which achieves a significant time reduction without influencing the accuracy. We name our framework as ParaGraphE, which provides a library for parallel knowledge graph embedding. The source code can be downloaded from this https URL .


title: Optimal Control of the Gearshift Command for Hybrid Electric Vehicles

abstract: This paper proposes a design method for the energy management strategy to explore the potential fuel saving of a hybrid electric vehicle (HEV) equipped with an automated manual transmission. The control algorithm is developed based on the combination of dynamic programming (DP) and Pontryagin's minimum principle (PMP) to optimally control the discrete gearshift command, in addition to the continuous power split between the internal combustion engine and the electric machine. The proposed method outperforms DP in terms of computational efficiency, being 171 times faster, without loss of accuracy. Simulation results for a middle-sized HEV on the New European Drive Cycle show that, to further optimize the gearshift strategy, an additional fuel saving of 20.3% can be reached. Furthermore, with the start-stop functionality available, it is shown that the two-point boundary-value problem following from PMP cannot be solved with sufficient accuracy without loss of optimality. This means that the finding of a constant value for the Lagrange multiplier while satisfying the battery state-of-energy (SOE) at the terminal time is not always guaranteed. Therefore, an alternative approach of SOE feedback control to adapt the Lagrange multiplier is adopted. The obtained results are very close to the globally optimal solution from DP. Simulation results, including the start-stop functionality, show that the relative fuel saving can be up to 26.8% compared with the case of a standard gearshift strategy.


title: Radar and Lidar Target Signatures of Various Object Types and Evaluation of Extended Object Tracking Methods for Autonomous Driving Applications

abstract: Ahstract- This paper presents a novel discussion on comparison of common extended object tracking methods for estimating target-extension ellipses based on real world road level traffic data and a unique presentation on different target level signatures obtained for various object types such as cars, pedestrians and bicyclist obtained using automotive multi-mode radar network and 4x-Iayer automotive Iidar. The most commonly used extended object tracking methods are briefly introduced. In addition, measurement profiles of road users, car, cyclists, and pedestrians are investigated to compare each model and its assumptions with the real data. The obtained information can be further used to define the appropriate object specific and sensor specific measurement model. The measurement distribution is discussed in detail and compared with the models' assumptions. Extended object tracking is performed on the obtained data and performance analysis is carried out using high resolution ground truth. The evaluation is carried out separately on the radar and lidar data on exemplary traffic scenes. As a final step, a track-to-track intersection fusion approach is evaluated on the same data set to find out the different information gains. The relation between measurements and track behavior as well as other influences are discussed.


title: Deep Relevance Ranking Using Enhanced Document-Query Interactions

abstract: We explore several new models for document relevance ranking, building upon the Deep Relevance Matching Model (DRMM) of Guo et al. (2016). Unlike DRMM, which uses context-insensitive encodings of terms and query-document term interactions, we inject rich context-sensitive encodings throughout our models, inspired by PACRR's (Hui et al., 2017) convolutional n-gram matching features, but extended in several ways including multiple views of query and document inputs. We test our models on datasets from the BIOASQ question answering challenge (Tsatsaronis et al., 2015) and TREC ROBUST 2004 (Voorhees, 2005), showing they outperform BM25-based baselines, DRMM, and PACRR.


title: An Evolutionary Algorithm for Column Generation in Integer Programming: An Effective Approach for 2D Bin Packing

abstract: We consider the 3-stage two-dimensional bin packing problem, which occurs in real-world problems such as glass cutting. For it, we present a new integer linear programming formulation and a branch and price algorithm. Column generation is performed by applying either a greedy heuristic or an Evolutionary Algorithm (EA). Computational experiments show the benefits of the EA-based approach. The higher computational effort of the EA pays off in terms of better final solutions; furthermore more instances can be solved to provable optimality.


title: TOWARDS THE SENSORY MARKETING: STIMULATING THE FIVE SENSES (SIGHT, HEARING, SMELL, TOUCH AND TASTE) AND ITS IMPACT ON CONSUMER BEHAVIOR

abstract: Current development of sensory marketing indicates the emergence of a new era in the field of marketing : an era in which the five senses of consumers will be the focus of marketing strategies and techniques. That's why marketers, free from mass marketing and advertising practices, use new, stimulating and innovative procedures to impact and influence the customers. This paper introduces sensory marketing as a new paradigm in the field of marketing; attention to the five senses (sight, smell, hearing, taste, touch) and the effect of these senses on consumer behavior are studied. The statistical population consisted of 234 customers in a multi-purpose shopping center in Tabriz and they were chosen by random sampling. For collecting data, we used a questionnaire whose reliability was 0.838. Our findings indicate that the five senses simultaneously affect consumer behavior. Stimulating the senses of sight, smell, hearing, touch and taste also affect consumer behavior. Thus, the results indicate that strategic stimulation of the senses of the consumer in marketing process can be one of the practical methods of companies and retailers to attract customers and increase their loyalty.


title: Alzheimer's disease diagnosis on structural MR images using circular harmonic functions descriptors on hippocampus and posterior cingulate cortex

abstract: Abstract Recently, several pattern recognition methods have been proposed to automatically discriminate between patients with and without Alzheimer's disease using different imaging modalities: sMRI, fMRI, PET and SPECT. Classical approaches in visual information retrieval have been successfully used for analysis of structural MRI brain images. In this paper, we use the visual indexing framework and pattern recognition analysis based on structural MRI data to discriminate three classes of subjects: normal controls (NC), mild cognitive impairment (MCI) and Alzheimer's disease (AD). The approach uses the circular harmonic functions (CHFs) to extract local features from the most involved areas in the disease: hippocampus and posterior cingulate cortex (PCC) in each slice in all three brain projections. The features are quantized using the Bag-of-Visual-Words approach to build one signature by brain (subject). This yields a transformation of a full 3D image of brain ROIs into a 1D signature, a histogram of quantized features. To reduce the dimensionality of the signature, we use the PCA technique. Support vector machines classifiers are then applied to classify groups. The experiments were conducted on a subset of ADNI dataset and applied to the “Bordeaux-3City” dataset. The results showed that our approach achieves respectively for ADNI dataset and “Bordeaux-3City” dataset; for AD vs NC classification, an accuracy of 83.77% and 78%, a specificity of 88.2% and 80.4% and a sensitivity of 79.09% and 74.7%. For NC vs MCI classification we achieved for the ADNI datasets an accuracy of 69.45%, a specificity of 74.8% and a sensitivity of 62.52%. For the most challenging classification task (AD vs MCI), we reached an accuracy of 62.07%, a specificity of 75.15% and a sensitivity of 49.02%. The use of PCC visual features description improves classification results by more than 5% compared to the use of hippocampus features only. Our approach is automatic, less time-consuming and does not require the intervention of the clinician during the disease diagnosis.


title: Blockchain and Smart Contracts for Insurance: Is the Technology Mature Enough?

abstract: Blockchain is receiving increasing attention from academy and industry, since it is considered a breakthrough technology that could bring huge benefits to many different sectors. In 2017, Gartner positioned blockchain close to the peak of inflated expectations, acknowledging the enthusiasm for this technology that is now largely discussed by media. In this scenario, the risk to adopt it in the wake of enthusiasm, without objectively judging its actual added value is rather high. Insurance is one the sectors that, among others, started to carefully investigate the possibilities of blockchain. For this specific sector, however, the hype cycle shows that the technology is still in the innovation trigger phase, meaning that the spectrum of possible applications has not been fully explored yet. Insurers, as with many other companies not necessarily active only in the financial sector, are currently requested to make a hard decision, that is, whether to adopt blockchain or not, and they will only know if they were right in 3–5 years. The objective of this paper is to support actors involved in this decision process by illustrating what a blockchain is, analyzing its advantages and disadvantages, as well as discussing several use cases taken from the insurance sector, which could easily be extended to other domains.


title: Brief Mindfulness Meditation Training Reduces Mind Wandering: The Critical Role of Acceptance

abstract: Mindfulness meditation programs, which train individuals to monitor their present-moment experience in an open or accepting way, have been shown to reduce mind wandering on standardized tasks in several studies. Here we test 2 competing accounts for how mindfulness training reduces mind wandering, evaluating whether the attention-monitoring component of mindfulness training alone reduces mind wandering or whether the acceptance training component is necessary for reducing mind wandering. Healthy young adults (N = 147) were randomized to either a 3-day brief mindfulness training condition incorporating instruction in both attention monitoring and acceptance, a mindfulness training condition incorporating attention monitoring instruction only, a relaxation training condition, or an active reading-control condition. Participants completed measures of dispositional mindfulness and treatment expectancies before the training session on Day 1 and then completed a 6-min Sustained Attention to Response Task (SART) measuring mind wandering after the training session on Day 3. Acceptance training was important for reducing mind wandering, such that the attention-monitoring plus acceptance mindfulness training condition had the lowest mind wandering relative to the other conditions, including significantly lower mind wandering than the attention-monitoring only mindfulness training condition. In one of the first experimental mindfulness training dismantling studies to-date, we show that training in acceptance is a critical driver of mindfulness-training reductions in mind wandering. This effect suggests that acceptance skills may facilitate emotion regulation on boring and frustrating sustained attention tasks that foster mind wandering, such as the SART. (PsycINFO Database Record