title: Midlatency auditory evoked potentials and the startle response in the rat

abstract: The P13 midlatency auditory evoked potential in the rat is (i) sleep state dependent, (ii) undergoes rapid habituation and (iii) is blocked by the cholinergic antagonist scopolamine. As such, the P13 potential in the rat shows the same characteristics as the P1 (or P50) potential in the human. These potentials are thought to be mediated, at least in part, by the cholinergic arm of the reticular activating system. Previous studies have linked the reticular activating system with the startle response. The present study was undertaken to explore this relationship by simultaneously recording the P13 potential and the electromyographically recorded startle response using stimuli designed to elicit each response. Simultaneous recordings from the vertex and neck musculature following auditory click stimuli showed that: (i) the mean threshold of the P13 potential was 69.3 +/- 1.9 dB, while that for the startle response was 87.9 +/- 6.4 dB; (ii) the P13 potential was present during waking and paradoxical sleep, but absent during slow-wave sleep, while the startle response was present reliably only during waking; (iii) both responses habituated in response to paired stimuli, but the startle response was more habituated than the P13 potential; and (iv) both responses were facilitated by trains of stimuli in a similar manner. Recordings carried out from the auditory cortex verified that the primary cortical response had properties different from the P13 potential; i.e. it was present during all sleep-wake states, had a lower threshold and did not habituate rapidly. Finally, different patterns of startle responses were detected in the neck muscles. In every case, the P13 potential occurred during the middle, inhibitory phase of the startle response. These results suggest that the P13 potential and the startle response share response features, but the P13 potential appears to be more sensitive to auditory stimulation and to sleep-wake states. The startle response may be modulating descending systems by priming the spinal cord to respond in a "fight vs flight" fashion. On the other hand, the P13 response may be modulating ascending systems by triggering thalamocortical activity and resetting descending systems to allow novel motor strategies.


title: A Measurement-Driven Anti-Jamming System for 802.11 Networks

abstract: Dense, unmanaged IEEE 802.11 deployments tempt saboteurs into launching jamming attacks by injecting malicious interference. Nowadays, jammers can be portable devices that transmit intermittently at low power in order to conserve energy. In this paper, we first conduct extensive experiments on an indoor 802.11 network to assess the ability of two physical-layer functions, rate adaptation and power control, in mitigating jamming. In the presence of a jammer, we find that: 1) the use of popular rate adaptation algorithms can significantly degrade network performance; and 2) appropriate tuning of the carrier sensing threshold allows a transmitter to send packets even when being jammed and enables a receiver to capture the desired signal. Based on our findings, we build ARES, an Anti-jamming REinforcement System, which tunes the parameters of rate adaptation and power control to improve the performance in the presence of jammers. ARES ensures that operations under benign conditions are unaffected. To demonstrate the effectiveness and generality of ARES, we evaluate it in three wireless test-beds: 1) an 802.11n WLAN with MIMO nodes; 2) an 802.11a/g mesh network with mobile jammers; and 3) an 802.11a WLAN with TCP traffic. We observe that ARES improves the network throughput across all test-beds by up to 150%.


title: Design architecture of autonomous precision farming system

abstract: Precision farming technology is a valuable management concept that aids accurate soil parameter monitoring & control. Automation allows farmers to apply optimal amount of water and fertilizers at the field locations upon requirement. This paper describes the design and development of an Autonomous Precision Farming System (APFS) for agriculture automation. It is a low power, user friendly system which helps the farmers to plan irrigation and fertilization based on environmental and soil conditions. The system can intelligently operate pumps / valves based on the data available from the field and the preloaded programs available in the controller. APFS monitors and maintain the various farming parameters like soil moisture, pH level, atmospheric temperature, humidity, fertilizer concentration etc. and appraises the information to farmers. APFS features Wireless data acquisition through wireless motes. The graphical TFT touch screen provides responsive user interface. The farmers can configure the device for getting alerts of critical field parameters or hazardous conditions. These alerts can be conveyed to the farmer in the form of preprogrammed text Messages to his mobile phone through SMS. The system also incorporates mechanism for controlling the agricultural field equipment from a remote location through DTMF technique using mobile phones.


title: The CMA Evolution Strategy: A Comparing Review

abstract: Derived from the concept of self-adaptation in evolution strategies, the CMA (Covariance Matrix Adaptation) adapts the covariance matrix of a multi-variate normal search distribution. The CMA was originally designed to perform well with small populations. In this review, the argument starts out with large population sizes, reflecting recent extensions of the CMA algorithm. Commonalities and differences to continuous Estimation of Distribution Algorithms are analyzed. The aspects of reliability of the estimation, overall step size control, and independence from the coordinate system (invariance) become particularly important in small populations sizes. Consequently, performing the adaptation task with small populations is more intricate.


Preservation of raptor habitat on the Snake River: a unique use for arid wildlands


title: The causal relation between human papillomavirus and cervical cancer

abstract: The causal role of human papillomavirus infections in cervical cancer has been documented beyond reasonable doubt. The association is present in virtually all cervical cancer cases worldwide. It is the right time for medical societies and public health regulators to consider this evidence and to define its preventive and clinical implications. A comprehensive review of key studies and results is presented.


title: A handover optimization algorithm with mobility robustness for LTE systems

abstract: A large number of cells will be deployed to provide high speed services in any places using the Long-Term Evolution (LTE) system. The management of such a large number of cells increases the operating expenditure (OPEX). Self-organizing network (SON) attracts attention as an effective way to reduce OPEX. This paper proposes a self-optimization algorithm for handover (HO) parameters. In conventional studies, the HO optimization techniques are discussed in models with stationary mobility of user equipment (UE). On the other hand, the key feature of the proposed algorithm is the mobility robustness, which means that the HO performance is robust against the change in UE mobility. In order to realize the mobility robustness, the proposed algorithm adaptively adjusts the HO parameters considering cause of HO failures, which changes in UE mobility. We examine the performance of the proposed algorithm through the computer simulations and confirm the mobility robustness. The simulation study demonstrates the following; The HO parameters are initially set to the optimum values for UEs with 3 km/h based on the random walk. Then, when the mobility changes from 3 km/h random walk to 300 km/h linear motion, the HO failure rates increases to 19%. The proposed algorithm reduces such increasing HO failure rates less than 0.2%.


title: How Architects See Non-Functional Requirements: Beware of Modifiability (best paper)

abstract: This paper presents the analysis and key findings of a survey about dealing with non-functional requirements (NFRs) among architects. We find that, as long as the architect is aware of the importance of NFRs, they do not adversely affect project success, with one exception: highly business critical modifiability tends to be detrimental to project success, even when the architect is aware of it. IT projects where modifiability is perceived to have low business criticality lead to consistently high customer satisfaction. Our conclusion is that modifiability deserves more attention than it is getting now, especially because in general it is quantified and verified considerably less than other NFRs. Furthermore, IT projects that applied NFR verification techniques relatively early in development were more successful on average than IT projects that did not apply verification techniques (or applied it relatively late in development).


title: Using Topic Modelling to make sense of typhoon-related tweets

abstract: The Philippines is a country that is often plagued by typhoons. In times of this disaster, many people turn to social media such as Twitter for information, making it useful. We take advantage of data present in tweets to get some insights, through discovered topics, on how they reflect the behavior of Filipinos during typhoons. Thus, we present a framework that uses Biterm Topic Modelling (BTM) to make sense of typhoon-related tweets. We focused on tweets collected from February 2013 to November 2014 of Twitter users from Metro Manila. Data preprocessing was applied to remove noisy and irrelevant data, like stop words and punctuations. We then conducted experiments using BTM for topic modelling and open coding for evaluating the results. Results revealed different Filipino behaviors during a typhoon such as determination to rise up after the typhoon, voicing out concerns, and using word play. Future work could experiment on selecting the appropriate number of words per topic model.


title: Approximate Confidence and Prediction Intervals for Least Squares Support Vector Regression

abstract: Bias-corrected approximate 100(1-α)% pointwise and simultaneous confidence and prediction intervals for least squares support vector machines are proposed. A simple way of determining the bias without estimating higher order derivatives is formulated. A variance estimator is developed that works well in the homoscedastic and heteroscedastic case. In order to produce simultaneous confidence intervals, a simple Sidak correction and a more involved correction (based on upcrossing theory) are used. The obtained confidence intervals are compared to a state-of-the-art bootstrap-based method. Simulations show that the proposed method obtains similar intervals compared to the bootstrap at a lower computational cost.


title: Non-contact, automated cardiac pulse measurements using video imaging and blind source separation

abstract: Remote measurements of the cardiac pulse can provide comfortable physiological assessment without electrodes. However, attempts so far are non-automated, susceptible to motion artifacts and typically expensive. In this paper, we introduce a new methodology that overcomes these problems. This novel approach can be applied to color video recordings of the human face and is based on automatic face tracking along with blind source separation of the color channels into independent components. Using Bland-Altman and correlation analysis, we compared the cardiac pulse rate extracted from videos recorded by a basic webcam to an FDA-approved finger blood volume pulse (BVP) sensor and achieved high accuracy and correlation even in the presence of movement artifacts. Furthermore, we applied this technique to perform heart rate measurements from three participants simultaneously. This is the first demonstration of a low-cost accurate video-based method for contact-free heart rate measurements that is automated, motion-tolerant and capable of performing concomitant measurements on more than one person at a time.


title: Density Sensitive Hashing

abstract: Nearest neighbors search is a fundamental problem in various research fields like machine learning, data mining and pattern recognition. Recently, hashing-based approaches, e.g., Locality Sensitive Hashing (LSH), are proved to be effective for scalable high dimensional nearest neighbors search. Many hashing algorithms found their theoretic root in random projection. Since these algorithms generate the hash tables (projections) randomly, a large number of hash tables (i.e., long codewords) are required in order to achieve both high precision and recall. To address this limitation, we propose a novel hashing algorithm called {\em Density Sensitive Hashing} (DSH) in this paper. DSH can be regarded as an extension of LSH. By exploring the geometric structure of the data, DSH avoids the purely random projections selection and uses those projective functions which best agree with the distribution of the data. Extensive experimental results on real-world data sets have shown that the proposed method achieves better performance compared to the state-of-the-art hashing approaches.


title: Representing Multilingual Data as Linked Data: the Case of BabelNet 2.0

abstract: Recent years have witnessed a surge in the amount of semantic information published on the Web. Indeed, the Web of Data, a subset of the Semantic Web, has been increasing steadily in both volume and variety, transforming the Web into a ‘global database’ in which resources are linked across sites. Linguistic fields – in a broad sense – have not been left behind, and we observe a similar trend with the growth of linguistic data collections on the so-called ‘Linguistic Linked Open Data (LLOD) cloud’. While both Semantic Web and Natural Language Processing communities can obviously take advantage of this growing and distributed linguistic knowledge base, they are today faced with a new challenge, i.e., that of facilitating multilingual access to the Web of data. In this paper we present the publication of BabelNet 2.0, a wide-coverage multilingual encyclopedic dictionary and ontology, as Linked Data. The conversion made use of lemon, a lexicon model for ontologies particularly well-suited for this enterprise. The result is an interlinked multilingual (lexical) resource which can not only be accessed on the LOD, but also be used to enrich existing datasets with linguistic information, or to support the process of mapping datasets across languages.


title: E-Learning Readiness Assessment of Medical Students in University of Fallujah

abstract: Learning in the medical school of universi-ty of Fallujah in Iraq is still by the classical face-to-face approach with minimal e-communication. Studies show that there are numerous models that have been devel-oped, however, they are used in developed countries whose e-Readiness is high hence not applicable in devel-oping countries. The aim of this paper is to assess medi-cal student’s perspectives of E-learning readiness. Stu-dents responded to a semi-structured self-administered questionnaire adapted from literature. Analysis was with SPSS version 19, using descriptive statistics, ANOVA, and independent samples T-test. The results obtained indicate that cultural readiness is the most important factor followed by technological readiness. In addition, the study results show that there are no significant rela-tionship between age, gender, and level of education, also it was reported in this study that the lowest mean for e-learn readiness is among students in a higher level of study. In conclusion, the students are not ready for eLearning, one of the reasons might be the ICT infra-structure is not adequate enough to support the use of e-learning. Further study with qualitative methodology will help in preparing for this evolution in learning.


title: Problematic Social Media Use: Results from a Large-Scale Nationally Representative Adolescent Sample

abstract: Despite social media use being one of the most popular activities among adolescents, prevalence estimates among teenage samples of social media (problematic) use are lacking in the field. The present study surveyed a nationally representative Hungarian sample comprising 5,961 adolescents as part of the European School Survey Project on Alcohol and Other Drugs (ESPAD). Using the Bergen Social Media Addiction Scale (BSMAS) and based on latent profile analysis, 4.5% of the adolescents belonged to the at-risk group, and reported low self-esteem, high level of depression symptoms, and elevated social media use. Results also demonstrated that BSMAS has appropriate psychometric properties. It is concluded that adolescents at-risk of problematic social media use should be targeted by school-based prevention and intervention programs.


Recommender Systems for Banking and Financial Services


title: Addressing the Broadband Crosstalk Challenges of Pogo Pin Type Interfaces for High-Density High-Speed Digital Applications

abstract: Input-output (I/O) cells in integrated circuit devices (ICs) are pushing data-rates to speeds of 5 Gb/s and above with an ever increasing number of pins. This is creating significant challenges for the automated test equipment (ATE) industry to develop appropriate interfaces that provide the needed performance. This article discusses crosstalk issues in high density, high performance ATE interconnects which usually include pogo pin type interfaces, mixed-dielectric printed circuit board (PCB) stack-ups with long differential traces, and innovative pogo/PCB via geometries. To demonstrate the crosstalk challenge and how to address it, a pogo pin assembly and pogo via design is analyzed as an example. This practical approach addresses aspects of creating the high performance interconnect such as pogo and PCB design issues, EM modeling, and measurements.


title: Feature-Aware Pixel Art Animation

abstract: Pixel art is a modern digital art in which high resolution images are abstracted into low resolution pixelated outputs using concise outlines and reduced color palettes. Creating pixel art is a labor intensive and skill-demanding process due to the challenge of using limited pixels to represent complicated shapes. Not surprisingly, generating pixel art animation is even harder given the additional constraints imposed in the temporal domain. Although many powerful editors have been designed to facilitate the creation of still pixel art images, the extension to pixel art animation remains an unexplored direction. Existing systems typically request users to craft individual pixels frame by frame, which is a tedious and error-prone process. In this work, we present a novel animation framework tailored to pixel art images. Our system bases on conventional key-frame animation framework and state-of-the-art image warping techniques to generate an initial animation sequence. The system then jointly optimizes the prominent feature lines of individual frames respecting three metrics that capture the quality of the animation sequence in both spatial and temporal domains. We demonstrate our system by generating visually pleasing animations on a variety of pixel art images, which would otherwise be difficult by applying state-of-the-art techniques due to severe artifacts.


title: Variable Inductor Based Bidirectional DC–DC Converter for Electric Vehicles

abstract: This paper presents the feasibility study of variable inductor (VI)-based bidirectional dc–dc converter for applications with a wide range of load variations such as electric vehicles. An additional winding is introduced to the conventional power inductor to inject a control current for adjusting the permeability of magnetic cores. This has significant merits in controlling the current ripple and enhancing the current handling capability of power inductors, thereby reducing the size of magnetic components and improving the performance. For current values, twice and three times the rated current, the current ripple is reduced by 40.90 % and 36.10 %, respectively. Nonetheless, this device requires a precisely controlled dc-current. As such, a small current controlled, low-power and low-cost buck converter is built to power up the auxiliary winding. To improve the reliability and robustness of the VI, an integrated closed loop control that enables the control of the main converter and the auxiliary converter is also implemented and tested in real time to test the viability of the VI.


title: Conceptual model of enterprise resource planning and business intelligence systems usage

abstract: Businesses have invested considerable resources in the usage of enterprise resource planning ERP and business intelligence BI systems. These systems were heavily studied in developed countries while there are a few and narrowly focused studies in developing ones. However, studies on the integration of ERP and BI have not been given enough attention hereafter ERPBI. There are many challenges facing the ERPBI usage in term of the steadily increasing speed with which new technologies are evolving. In addition, there are a number of factors that affecting this usage. Based on the finding of the literature, a model from a critical success factors CSFs perspective that examine the relationship between ERPBI usage and organisational performance is proposed. The conceptual model provides a foundation for more research in the future. The expected results of the study will improve the business outcome and help design strategies based on an investigation between ERPBI usage and organisational performance.


Active Fairness in Algorithmic Decision-Making


title: Temporal Difference Variational Auto-Encoder

abstract: To act and plan in complex environments, we posit that agents should have a mental simulator of the world with three characteristics: (a) it should build an abstract state representing the condition of the world; (b) it should form a belief which represents uncertainty on the world; (c) it should go beyond simple step-by-step simulation, and exhibit temporal abstraction. Motivated by the absence of a model satisfying all these requirements, we propose TD-VAE, a generative sequence model that learns representations containing explicit beliefs about states several steps into the future, and that can be rolled out directly without single-step transitions. TD-VAE is trained on pairs of temporally separated time points, using an analogue of temporal difference learning used in reinforcement learning.


title: Logics and Models of Real Time: A Survey

abstract: We survey logic-based and automata-based languages and techniques for the specification and verification of real-time systems. In particular, we discuss three syntactic extensions of temporal logic: time-bounded operators, freeze quantification, and time variables. We also discuss the extension of finite-state machines with clocks and the extension of transition systems with time bounds on the transitions. All of the resulting notations can be interpreted over a variety of different models of time and computation, including linear and branching time, interleaving and true concurrency, discrete and continuous time. For each choice of syntax and semantics, we summarize the results that are known about expressive power, algorithmic finite-state verification, and deductive verification.


title: ASCO 2013: bevacizumab and glioblastoma--a marriage dissolution?

abstract: The prognosis of patients affected by glioblastoma (GBM) is grim albeit the integrated therapeutic approaches now available. Standard surgery followed by chemoradiation median overall survival (OS) reaches 15 months in clinical trials. Despite primary treatment, recurrence is the rule in patients with GBM and for them OS ranges from 6 to 9 months. In recent years, the therapeutic scenario has been profoundly changed in view of the promising results obtained by bevacizumab (Avastin * *Avastin is a registered trade name of Bevacizumab, F. Hoffmann-La Roche Ltd, Basel, Switzerland. ), the most promising anti-angiogenesis agent, in two clinical trials. The results of both trials were presented at the last ASCO meeting in Chicago. Progression free survival was significantly improved with an acceptable safety and tolerability profile but surprisingly quality of life was preserved only in the AVAGlio trial. However, both studies showed that overall survival was not improved adding bevacizumab to temozolomide and radiotherapy.


Consumers ’ attitude towards online shopping : Factors influencing employees of crazy domains to shop online


title: A weakly informative default prior distribution for logistic and other regression models

abstract: We propose a new prior distribution for classical (nonhierarchical) logistic regression models, constructed by first scaling all nonbinary variables to have mean 0 and standard deviation 0.5, and then placing independent Student-t prior distributions on the coefficients. As a default choice, we recommend the Cauchy distribution with center 0 and scale 2.5, which in the simplest setting is a longer-tailed version of the distribution attained by assuming one-half additional success and one-half additional failure in a logistic regression. Cross-validation on a corpus of datasets shows the Cauchy class of prior distributions to outperform existing implementations of Gaussian and Laplace priors. We recommend this prior distribution as a default choice for routine applied use. It has the advantage of always giving answers, even when there is complete separation in logistic regression (a common problem, even when the sample size is large and the number of predictors is small), and also automatically applying more shrinkage to higher-order interactions. This can be useful in routine data analysis as well as in automated procedures such as chained equations for missing-data imputation. We implement a procedure to fit generalized linear models in R with the Student-t prior distribution by incorporating an approximate EM algorithm into the usual iteratively weighted least squares. We illustrate with several applications, including a series of logistic regressions predicting voting preferences, a small bioassay experiment, and an imputation model for a public health data set.


title: A Single-Stage Single-Phase Transformer-Less Doubly Grounded Grid-Connected PV Interface

abstract: A transformer provides galvanic isolation and grounding of the photovoltaic (PV) array in a PV-fed grid-connected inverter. Inclusion of the transformer, however, may increase the cost and/or bulk of the system. To overcome this drawback, a single-phase, single-stage [no extra converter for voltage boost or maximum power point tracking (MPPT)], doubly grounded, transformer-less PV interface, based on the buck-boost principle, is presented. The configuration is compact and uses lesser components. Only one (undivided) PV source and one buck-boost inductor are used and shared between the two half cycles, which prevents asymmetrical operation and parameter mismatch problems. Total harmonic distortion and DC component of the current supplied to the grid is low, compared to existing topologies and conform to standards like IEEE 1547. A brief review of the existing, transformer-less, grid-connected inverter topologies is also included. It is demonstrated that, as compared to the split PV source topology, the proposed configuration is more effective in MPPT and array utilization. Design and analysis of the inverter in discontinuous conduction mode is carried out. Simulation and experimental results are presented.


title: GVDB: raytracing sparse voxel database structures on the GPU

abstract: Simulation and rendering of sparse volumetric data have different constraints and solutions depending on the application area. Generating precise simulations and understanding very large data are problems in scientific visualization, whereas convincing simulations and realistic visuals are challenges in motion pictures. Both require volumes with dynamic topology, very large domains, and efficient high quality rendering. We present the GPU voxel database structure, GVDB, based on the voxel database topology of Museth [Mus13], as a method for efficient GPU-based compute and raytracing on a sparse hierarchy of grids. GVDB introduces an indexed memory pooling design for dynamic topology, and a novel hierarchical traversal for efficient raytracing on the GPU. Examples are provided for ray sampling of volumetric data, rendering of isosurfaces with multiple scattering, and raytracing of level sets. We demonstrate that GVDB can give large performance improvements over CPU methods with identical quality.


title: Exploring the educational potential of robotics in schools

abstract: This study reviews recently published scientific literature on the use of robotics in schools, in order to: (a) identify the potential contribution of the incorporation of robotics as educational tool in schools, (b) present a synthesis of the available empirical evidence on the educational effectiveness of robotics as an educational tool in schools, and (c) define future research perspectives concerning educational robotics. After systematically searching online bibliographic databases, ten relevant articles were located and included in the study. For each article, we analyze the purpose of the study, the content to be taught with the aid of robotics, the type of robot used, the research method used, and the sample characteristics (sample size, age range of students and/or level of education) and the results observed. The articles reviewed suggest that educational robotics usually acts as an element that enhances learning, however, this is not always the case, as there are studies that have reported situations in which there was no improvement in learning. The outcomes of the literature review are discussed in terms of their implications for future research, and can provide useful guidance for educators, practitioners and researchers in the area. Highlights? We performed a systematic review of studies with quantitative evidence of the use of robotics in schools. ? Studies indicate positive outcomes for teaching concepts related to the STEM areas. ? Nine important factors to increase the success of robotics as a teaching tool are presented. ? More research is needed about how to use robotics to develop new skills in students.


title: Integrated energy and spectrum harvesting for 5G wireless communications

abstract: Two important issues have arisen in 5G wireless communications: spectral efficiency and energy efficiency. The existing wireless communication architectures and technologies may not be able to address these two issues at the same time for 5G multi-tier networks where users in different tiers have different priorities for channel access. In this article, we first present the motivation for our proposal of a new paradigm of integrated spectrum and energy harvesting for 5G wireless networks, in which spectrum and energy resources are efficiently collected and intelligently managed. Then we present an integrated spectrum and energy harvesting 5G architecture for dealing with current challenges. After that, based on the proposed architecture, we provide integrated spectrum and energy control mechanisms for typical 5G networks. Finally, we propose a cooperative medium access control scheme for heterogeneous 5G networks that attracts cooperative sensing participants with heterogeneous energy harvesting capabilities. Illustrative results demonstrate significant energy and spectrum efficiency for 5G wireless networks.


Recognition and treatment of suicidal adults-A SWOT analysis of the present status of our knowledge


title: Algorithmic trading using intelligent agents

abstract: A thermistor element for temperature measurement has a thermistor chip in wafer form and thin film electrodes formed on respective principal surfaces of the thermistor chip. The thin film electrodes are formed by evaporation. Lead wires are adhered to the thin film electrodes with heat-resisting conductive material. The assembled members are sealed with glass. The electrodes may be made of two-layer films.


title: Role of glucose in chewing gum-related facilitation of cognitive function

abstract: This study tests the hypothesis that chewing gum leads to cognitive benefits through improved delivery of glucose to the brain, by comparing the cognitive performance effects of gum and glucose administered separately and together. Participants completed a battery of cognitive tests in a fully related 2!2 design, where one factor was Chewing Gum (gum vs. mint sweet) and the other factor was Glucose Co-administration (consuming a 25 g glucose drink vs. consuming water). For four tests (AVLT Immediate Recall, Digit Span, Spatial Span and Grammatical Transformation), beneficial effects of chewing and glucose were found, supporting the study hypothesis. However, on AVLT Delayed Recall, enhancement due to chewing gum was not paralleled by glucose enhancement, suggesting an alternative mechanism. The glucose delivery model is supported with respect to the cognitive domains: working memory, immediate episodic long-term memory and language-based attention and processing speed. However, some other mechanism is more likely to underlie the facilitatory effect of chewing gum on delayed episodic long-term memory. q 2004 Elsevier Ltd. All rights reserved.


title: Psychopaths know right from wrong but don’t care

abstract: Adult psychopaths have deficits in emotional processing and inhibitory control, engage in morally inappropriate behavior, and generally fail to distinguish moral from conventional violations. These observations, together with a dominant tradition in the discipline which sees emotional processes as causally necessary for moral judgment, have led to the conclusion that psychopaths lack an understanding of moral rights and wrongs. We test an alternative explanation: psychopaths have normal understanding of right and wrong, but abnormal regulation of morally appropriate behavior. We presented psychopaths with moral dilemmas, contrasting their judgments with age- and sex-matched (i) healthy subjects and (ii) non-psychopathic, delinquents. Subjects in each group judged cases of personal harms (i.e. requiring physical contact) as less permissible than impersonal harms, even though both types of harms led to utilitarian gains. Importantly, however, psychopaths’ pattern of judgments on different dilemmas was the same as those of the other subjects. These results force a rejection of the strong hypothesis that emotional processes are causally necessary for judgments of moral dilemmas, suggesting instead that psychopaths understand the distinction between right and wrong, but do not care about such knowledge, or the consequences that ensue from their morally inappropriate behavior.


title: An IoT gateway centric architecture to provide novel M2M services

abstract: This paper proposes an innovative Internet of Things (IoT) architecture that allows real time interaction between mobile clients and smart/legacy things (sensors and actuators) via a wireless gateway. The novel services provided are: (i) dynamic discovery of M2M device and endpoints by the clients, (ii) managing connection with non-smart things connected over modbus, (iii) associate metadata to sensor and actuator measurements using Sensor Markup Language (SenML) representation and (iv) extending the current capabilities of SenML to support actuator control from mobile clients. These clients are equipped with an end-user application that initiates the discovery phase to learn about the devices and endpoints (sensors and actuators) connected to the wireless gateway. Then the user can select desired sensors to receive and display sensor metadata and control actuators from the mobile device. Prototypes of the mobile application and the wireless gateway have been implemented to validate the entire architecture. The gateway is implemented using RESTful web services and currently runs in a Google App Engine. Two real life scenarios are discussed that can be implemented using the architecture. Finally overall contributions and future research scopes are summarized.


title: The top eight issues Queensland Australia's aged-care nurses and assistants-in-nursing worried about outside their workplace: A qualitative snapshot

abstract: The attainment of a work-life balance is an important issue for recruitment, retention and workforce planning. This paper aims to report on the free text data provided by the aged-care sector nurses around perceptions of important work-life issues. Data were written responses of aged-care nurses to the open-ended request at the end of a survey, which asked them to list up to five political/social/environmental issues concerning them outside of their work. For aged-care nurses, when asked to list political/social/environmental issues they were concerned about outside of work in late 2007, there emerged considered issues around work and life. Among the top eight themes there is an intriguing balance between the themes work, industrial relations, aged care/elder care and health-care services compared with the themes environment, water, societal values and housing. Qualitative insights into the political/social/environmental issues aged-care nurses are concerned about outside of your work suggest their desire for a labour/life or work/life harmony. Aged-care nurses place an equal importance on the nature of labour and the basics of life. The findings provide information for aged-care sector managers and workforce planners on areas in need of consideration to recruit and retain a workforce within aged care.


title: A Novel Glove Monitoring System Used to Quantify Neurological Symptoms During Deep-Brain Stimulation Surgery

abstract: Deep brain stimulation (DBS) surgery is most effective in reducing the symptoms of Parkinson's disease and essential tremor. At present, there is no designated instrumental method for measuring the immediate effects of DBS. This paper presents the concept of a glove monitoring system for DBS. With the benefits of microelectromechanical systems, inertial measurement unit, and force sensitive resistor (FSR), the system is portable and can be integrated into a textile glove. Tremors, bradykinesia, and rigidity assessments are performed by the system. Several test tasks are chosen to be performed during DBS surgery to evaluate the electrode's position and stimulation intensity. Each quantified symptom severity of the patient is added to a list shown in the graphical user interface for comparison. Comparative experiments between the prototype and an electromagnetic motion tracking system were presented. The FSR boxes were validated with weights. Experimental results show that this system is reliable for tremor amplitude determination, movement angles measurement, and resistance measurement to a passive movement. In addition, it can be found that inconsistent tremor movements have an influence on the tremor amplitude calculation done with power spectral density estimation.


title: Action Organization to Intention Understanding

abstract: Inferior parietal lobule (IPL) neurons were studied when monkeys performed motor acts embedded in different actions and when they observed similar acts done by an experimenter. Most motor IPL neurons coding a specific act (e.g., grasping) showed markedly different activations when this act was part of different actions (e.g., for eating or for placing). Many motor IPL neurons also discharged during the observation of acts done by others. Most responded differentially when the same observed act was embedded in a specific action. These neurons fired during the observation of an act, before the beginning of the subsequent acts specifying the action. Thus, these neurons not only code the observed motor act but also allow the observer to understand the agent's intentions.


title: Evaluation And Analysis Of Eye Gaze Interaction

abstract: Abstract : Eye gaze interaction can provide a convenient and natural addition to user-computer dialogues. We have previously reported on our interaction techniques Using eye gaze. In this report, we assess the effectiveness of our approach in a controlled setting. We present two experiments that compare our eye gaze object selection technique with conventional selection by mouse. The results show that for a simple task, it takes 60% less time to select an object with our eye gaze technique than with a mouse. We also use Fitts' Law to investigate the speed and quality differences between eye gaze selection and mouse selection. Our eye gaze selection technique is shown to produce a low slope, more like pure eye movement, which suggests that the technique preserves the inherent speed advantage of the eye over the hand. We find that eye gaze interaction is at least as fast as the mouse and it is convenient in situations where it is important to use the hands for other tasks. It is particularly beneficial for the larger screens, workspaces, and virtual environment of the future, and will become increasingly practical as eye tracker technology matures.


title: Pneumatic Servo Ball & Plate System Based on Touch Screen and Oscillating Cylinder

abstract: The pneumatic servo ball & plate system is a typical nonlinear system. A state observer is designed by using a simplified mathematical model of the system to observe the velocity of the ball, and a PD-fuzzy controller is designed to implement the real-time control of the system. Simulation results show that the PD-fuzzy control has better dynamic and static characteristics than that of the PID control, which shows the advantage of applying PD-fuzzy controller to nonlinear system.


title: Expected stock returns and volatility

abstract: This paper examines the relation between stock returns and stock market volatility. We find evidence that the expected market risk premium (the expected return on a stock portfolio minus the Treasury bill yield) is positively related to the predictable volatility of stock returns. There is also evidence that unexpected stock market returns are negatively related to the unexpected change in the volatility of stock returns. This negative relation provides indirect evidence of a positive relation between expected risk premiums and volatility.


title: Underwater-Drone With Panoramic Camera for Automatic Fish Recognition Based on Deep Learning

abstract: Highly developed drone technology enables the use of drones in a wide variety of areas. However, those drones are mainly used in the unmanned aerial vehicles. We believe that underwater drones will become a big research topic and find a market in the near future. We developed an underwater drone with a 360° panoramic camera acting as the “eye” of the drone. The designs are based on the open-source hardware and will be shared as an open-source for contributing to the innovation of manufacturing including drone. The function of the 360° panoramic camera is generated by correcting the images taken by two fisheye lenses. The underwater drone was designed by extending the Raspberry Pi compute module, the frame was designed by OpenSCAD, and the printed circuit board was designed by MakePro. As for the application of the underwater drone, we focused on fish recognition for investigating fish species in a natural lake to help protect the original environment. Fish recognition is based on deep learning, which is the biggest topic in the artificial intelligence research field today. Experimental results show that the function of the underwater drone achieved at diving in the leak automatically. The 360° panoramic images were generated correctly. Fish recognition achieved 87% accuracy by deep learning.


title: A digital detecting method for synchronous rectification based on dual-verification for LLC resonant converter

abstract: GaN HEMT has a higher voltage drop than conventional silicon based power MOSFETs during the time of reverse conduction, which is an unneglectable interval in LLC converter with synchronous rectification (SR). To further improve the efficiency of the LLC resonant converter, this paper presents a SR detecting scheme base on an adaptive drain-source voltage sensing strategy to eliminate both the body diode conduction and reverse current from the load to the source. Compared to the current SR detecting methods, the turning off time of the SR can be stabilized by the proposed dual-verification technique instead of switching between the body diode conduction and the reverse current from the load to the source. The principle and the algorithm are discussed and optimized based on the operating modes and the turning off transition of the SR. A prototype based on GaN HEMT is built and tested to verify the proposed method. The experimental results show that the proposed SR detecting scheme eliminates the current from the load to the power source, thus, the reliability of the converter can be improved. Moreover, compared to the current best technique, the efficiency of the converter is further improved by 0.2 %.


title: Blockchain as an Audit-Able Communication Channel

abstract: Applications requiring strict access control, such as medical record query, often require auditing of the query. The current typical design relies on server side logging. However, logging on server-side do not provide strict means of auditing, since the server can be tampered with attackers, and also anybody who has permission to write can modify the log. We propose a scheme using blockchain technology, as a request-response channel for a client-server system, to record both client request and server reply in an audi-table manner. We have implemented a proof-of-concept system on top of a publicly available blockchain testbed. By using a blockchain as a client-server request-response channel, the request-response sequence can be verified by anybody who has access to the blockchain, providing a way to implement audit log for strictly controlled resources.


title: Purification and Characterization of a Glutathione Peroxidase from the Aloe vera Plant

abstract: Extracts from the parenchymous leaf-gel of the Aloe vera plant (Aloe barbadensis Miller) were shown to contain glutathione peroxidase (GSHPx) activity. The activity was purified to homogeneity by ion exchange and gel filtration (FPLC) chromatography in the presence of 0.5 mM glutathione. The native enzyme has an apparent molecular weight of 62 kD as determined by gel filtration. In the presence of sodium dodecylsulfate (SDS), the molecular weight was estimated to be about 16 kD as determined by polyacrylamide-gel electrophoresis (SDS-PAGE). The native enzyme is proposed to be constituted of four identical subunits; it also contains one atom of selenium per subunit, as found with most glutathione peroxidases from animal sources. The Km values were determined to be 3.2 mM for glutathione and 0.26 mM for the hydroperoxide substrate, cumene hydroperoxide. The enzyme is competitively inhibited by N, S, bis-FMOC glutathione (Ki = 0.32 mM), a potent inhibitor of glyoxalase II. Inhibitors of glyoxalase I (e.g. S-octylglutathione) have no effect on the peroxidase activity.


Intelligence-Driven Computer Network Defense Informed by Analysis of Adversary Campaigns and Intrusion Kill Chains


Using Inquiry-based Bluetooth RSSI Probability Distributions for Indoor Positioning


title: Exploring the Core Concepts of Media Richness Theory: The Impact of Cue Multiplicity and Feedback Immediacy on Decision Quality

abstract: Employing media richness theory, a model is developed to open the black box surrounding the impact of computer-mediated communication systems on decision quality. The effects on decision quality of two important communication system factors, cue multiplicity and feedback immediacy, are examined in light of three important mediating constructs: social perceptions, message clarity, and ability to evaluate others. A laboratory experiment examining two tasks and employing face-to-face, electronic meeting, electronic conferencing, and electronic mail communication systems is used to assess the model's validity. Results provide consistent support for the research model as well as media richness theory. ::: ::: Richer media facilitate social perceptions (total socio-emotional communication and positive socio-emotional climate) and perceived ability to evaluate others' deception and expertise. Leaner media (electronic mail and electronic conferencing) facilitate communication clarity when participants have less task-relevant knowledge. The impacts of these mediating constructs on decision quality were found to depend on the levels of participant expertise and deception. In general, it was found that richer media can have significantly positive impacts on decision quality when participants' task-relevant knowledge is high. Moreover, effects of participant deception can be mitigated by employing richer media.


title: A Hybrid Model for Linking Multiple Social Identities Across Heterogeneous Online Social Networks

abstract: Automated online profiling consists of the accurate identification and linking of multiple online identities across heterogeneous online social networks that correspond to the same entity in the physical world. The paper proposes a hybrid profile correlation model which relies on a diversity of techniques from different application domains, such as record linkage and data integration, image and text similarity, and machine learning. It involves distance-based comparison methods and the exploitation of information produced by a social network identification process for use as external knowledge towards searches on other social networks; thus, the remaining identification tasks for the same individual are optimized. The experimental study shows that, even with limited resources, the proposed method collects and combines accurate information effectively from different online sources in a fully-automated way. The mined knowledge then becomes a powerful toolkit to carry out social engineering and other attacks, or for profit and decision-making data mining purposes.


title: What Is Spontaneous Pain and Who Has It

abstract: Abstract Spontaneous pain is often discussed in the context of both chronic inflammatory and neuropathic pain conditions, and it has been suggested that spontaneous pain, rather than stimulus-evoked pain, may be the more significant clinical problem. The following issues are discussed here. First, it is suggested that the concept of spontaneous pain makes no sense when the pain is the result of an ongoing inflammatory reaction. Evidence is reviewed that indicates that spontaneous pain is present in patients with neuropathic pain, but perhaps only in a subset of such patients. Second, it is suggested that in the presence of allodynia and hyperalgesia, stimulation from the activities of daily life occurs very many times a day and that these stimulus-evoked pains may summate to give a fluctuating level of daily pain that both patients and investigators mistake for spontaneous pain. Perspective Which is more important—stimulus-evoked pain or spontaneous pain? This review suggests that to answer the question we will need to distinguish neuropathic spontaneous pain from inflammatory ongoing pain and to differentiate both from summated allodynic and hyperalgesic pains caused by the stimuli of daily life.


title: Far-Field Prediction Using Only Magnetic Near-Field Scanning for EMI Test

abstract: Far-field prediction for electromagnetic interference (EMI) testing is achieved using only magnetic near-field on a Huygens's surface. The electrical field on the Huygens's surface is calculated from the magnetic near-field using the finite element method (FEM). Two examples are used to verify the proposed method. The first example uses the field radiated by an infinitesimal electric dipole. The calculated results are compared with the analytical solution. In the second example, the calculated results are compared with full-wave simulation results for the radiation of a print circuit board (PCB). The validity of this method when the near-field is high-impedance field is verified as well. Sensitivity of the far field to noise in both magnitude and phase in the near-field data is also investigated. The results indicate that the proposed method is very robust to the random variation of both. The effect of using only four sides of the Huygens's box is investigated as well, revealing that, in some instances, the incomplete Huygens's box can be used to predict the far field well. The proposed method is validated using near-field measurement data taken from a sleeve dipole antenna. The error for the maximum far-field value is in only 1.3 dB.


title: Quantification of time in Digital Libraries: Temporal Zipf's law

abstract: The temporal dimension of a text document defines the temporal scope of its narrated event. This temporal dimension acquires more importance in corpora created along several years of production, such as digital libraries. Temporal aspects of text have been the subject of many researches with specific tasks, notably information retrieval and event detection, while no studies have been conducted to quantify and analyze the richness of the temporal dimension of different text collections. Analysing thirteen text collections we show how the extent and characteristics of the time presence in text varies among collections that have different scopes, although time intervals are mentioned in almost all the text units analyzed. We found that unique intervals follow the same distribution, given by the Zipf's law, that holds for single words.


title: Dust particle detection in traffic surveillance video using motion singularity analysis

abstract: Dust particle detection in video aims to automatically determine whether the video is degraded by dust particle or not. Dust particles are usually stuck on the camera lends and typically temporally static in the images of a video sequence captured from a dynamic scene. The moving objects in the scene can be occluded by the dusts; consequently, the motion information of moving objects tends to yield singularity. Motivated by this, a dust detection approach is proposed in this paper by exploiting motion singularity analysis in the video. First, the optical model of dust particle is theoretically studied in by simulating optical density of artifacts produced by dust particles. Then, the optical flow is exploited to perform motion singularity analysis for blind dust detection in the video without the need for ground truth dust-free video. More specifically, a singularity model of optical flow is proposed in this paper using the direction of the motion flow field, instead of the amplitude of the motion flow field. The proposed motion singularity model is further incorporated into a temporal voting mechanism to develop an automatic dust particle detection in the video. Experiments are conducted using both artificially-simulated dust-degraded video and real-world dust-degraded video to demonstrate that the proposed approach outperforms conventional approaches to achieve more accurate dust detection.


title: An End-to-End Goal-Oriented Dialog System with a Generative Natural Language Response Generation

abstract: Recently advancements in deep learning allowed the development of end-to-end trained goal-oriented dialog systems. Although these systems already achieve good performance, some simplifications limit their usage in real-life scenarios. ::: In this work, we address two of these limitations: ignoring positional information and a fixed number of possible response candidates. We propose to use positional encodings in the input to model the word order of the user utterances. Furthermore, by using a feedforward neural network, we are able to generate the output word by word and are no longer restricted to a fixed number of possible response candidates. Using the positional encoding, we were able to achieve better accuracies in the Dialog bAbI Tasks and using the feedforward neural network for generating the response, we were able to save computation time and space consumption.


title: 3-D Histogram-Based Segmentation and Leaf Detection for Rosette Plants

abstract: Recognition and segmentation of plant organs like leaves is one of the major challenges in digital plant phenotyping. Here we present a 3-D histogram-based segmentation and recognition approach for top view images of rosette plants such as Arabidopsis thaliana and tobacco. Furthermore a euclidean-distance-map-based method for the detection of leaves and the corresponding plant leaf segmentation method were developed. An approach for the detection of optimal leaf split points for the separation of overlapping leaf segments was created. We tested and tuned our algorithms for the Leaf Segmentation Challenge (LSC). The results demonstrate that our method is robust and handles demanding imaging situations and different species with high accuracy.


title: Information needs for software development analytics

abstract: Software development is a data rich activity with many sophisticated metrics. Yet engineers often lack the tools and techniques necessary to leverage these potentially powerful information resources toward decision making. In this paper, we present the data and analysis needs of professional software engineers, which we identified among 110 developers and managers in a survey. We asked about their decision making process, their needs for artifacts and indicators, and scenarios in which they would use analytics. The survey responses lead us to propose several guidelines for analytics tools in software development including: Engineers do not necessarily have much expertise in data analysis; thus tools should be easy to use, fast, and produce concise output. Engineers have diverse analysis needs and consider most indicators to be important; thus tools should at the same time support many different types of artifacts and many indicators. In addition, engineers want to drill down into data based on time, organizational structure, and system architecture.


Robust Anomaly Detection Using Support Vector Machines


title: USB: Ultrashort Binary Descriptor for Fast Visual Matching and Retrieval

abstract: Currently, many local descriptors have been proposed to tackle a basic issue in computer vision: duplicate visual content matching. These descriptors either are represented as high-dimensional vectors relatively expensive to extract and compare or are binary codes limited in robustness. Bag-of-visual words (BoWs) model compresses local features into a compact representation that allows for fast matching and scalable indexing. However, the codebook training, high-dimensional feature extraction, and quantization significantly degrade the flexibility and efficiency of BoWs model. In this paper, we study an alternative to current local descriptors and BoWs model by extracting the ultrashort binary descriptor (USB) and a compact auxiliary spatial feature from each keypoint detected in images. A typical USB is a 24-bit binary descriptor, hence it directly quantizes visual clues of image keypoints to about 16 million unique IDs. USB allows fast image matching and indexing and avoids the expensive codebook training and feature quantization in BoWs model. The spatial feature complementarily captures the spatial configuration in neighbor region of each keypoint, hence is used to filter mismatched USBs in a cascade verification. In image matching task, USB shows promising accuracy and nearly one-order faster speed than SIFT. We also test USB in retrieval tasks on UKbench, Oxford5K, and 1.2 million distractor images. Comparisons with recent retrieval methods manifest the competitive accuracy, memory consumption, and significantly better efficiency of our approach.


title: Tupleware: Redefining Modern Analytics

abstract: There is a fundamental discrepancy between the targeted and actual users of current analytics frameworks. Most systems are designed for the data and infrastructure of the Googles and Facebooks of the world---petabytes of data distributed across large cloud deployments consisting of thousands of cheap commodity machines. Yet, the vast majority of users operate clusters ranging from a few to a few dozen nodes, analyze relatively small datasets of up to a few terabytes, and perform primarily compute-intensive operations. Targeting these users fundamentally changes the way we should build analytics systems. ::: This paper describes the design of Tupleware, a new system specifically aimed at the challenges faced by the typical user. Tupleware's architecture brings together ideas from the database, compiler, and programming languages communities to create a powerful end-to-end solution for data analysis. We propose novel techniques that consider the data, computations, and hardware together to achieve maximum performance on a case-by-case basis. Our experimental evaluation quantifies the impact of our novel techniques and shows orders of magnitude performance improvement over alternative systems.


title: Emotion Generation and Emotion Regulation: One or Two Depends on Your Point of View.

abstract: Emotion regulation has the odd distinction of being a wildly popular construct whose scientific existence is in considerable doubt. In this article, we discuss the confusion about whether emotion generation and emotion regulation can and should be distinguished from one another. We describe a continuum of perspectives on emotion, and highlight how different (often mutually incompatible) perspectives on emotion lead to different views about whether emotion generation and emotion regulation can be usefully distinguished. We argue that making differences in perspective explicit serves the function of allowing researchers with different theoretical commitments to collaborate productively despite seemingly insurmountable differences in terminology and methods.


title: Criteria for maximal oxygen uptake: review and commentary.

abstract: Historically, the achievement of maximal oxygen uptake (VO2max) has been based on objective criteria such as a leveling off of oxygen uptake with an increase in work rate, high levels of lactic acid in the blood in the minutes following the exercise test, elevated respiratory exchange ratio, and achievement of some percentage of an age-adjusted estimate of maximal heart rate. These criteria are reviewed relative to their history, the degree to which they have been achieved in published research, and how investigators and reviewers follow them in current practice. The majority of the criteria were based on discontinuous protocols, often carried out over several days. Questions are raised about the applicability of these criteria to modern continuous graded exercise test protocols, and our lack of consistency in the terminology we use relative to the measurement of maximal oxygen uptake.


title: Towards a Meta-Reasoning Framework for Reasoning about Vagueness in OWL Ontologies

abstract: When developing ontologies, knowledge engineers and domain experts often use predicates that are vague, i.e., predicates that lack clear applicability conditions and boundaries such as High, Expert or Bad. In previous works, we have shown how such predicates within ontologies can hamper the latter's shareability and meaning explicitness and we have proposed Vagueness Ontology (VO), an OWL metaontology for representing vagueness-aware ontologies, i.e., ontologies whose (vague) elements are annotated by explicit descriptions of the nature and characteristics of their vagueness. A limitation of VO is that it does not model the way vagueness and its characteristics propagate when defining more complex OWL axioms (such as conjunctive classes), neither does it enforce any kind of vagueness-related consistency. For that, in this paper, we expand VO by means of formal inference rules and constraints that model the way vagueness descriptions of complex ontology elements can be automatically derived. More importantly, we enable the efficient execution of these rules by means of a novel meta-reasoning framework.


title: Flipping the CMOS Switch

abstract: As CMOS technology is scaled down and adopted for many RF and millimeter-wave radio systems, design of T/R switches in CMOS has received considerable attention. Many T/R switches designed in 0.5 ?m 65 nm CMOS processes have been reported. Table 4 summarizes these T/R switches. Some of them have become great candidates for WLAN and UWB radios. However, none of them met the requirements of mobile cellular and WPAN 60-GHz radios. CMOS device innovations and novel ideas such as artificial dielectric strips and bandgap structures may provide a comprehensive solution to the challenges of design of T/R switches for mobile cellular and 60-GHz radios.


title: Analysis of template aging in iris biometrics

abstract: It has been widely believed that biometric template aging does not occur for iris biometrics. We compare the match score distribution for short time-lapse iris image pairs, with a mean of approximately one month between the enrollment image and the verification image, to the match score distributions for image pairs with one, two and three years of time lapse. We find clear and consistent evidence of a template aging effect that is noticeable at one year and that increases with increasing time lapse. For a state-of-the-art iris matcher, and three years of time lapse, at a decision threshold corresponding to a one in two million false match rate, we observe an 153% increase in the false non-match rate, with a bootstrap estimated 95% confidence interval of 85% to 307%.


Split-Ordered Lists : Lock-Free Extensible Hash Tables


title: Strong is the new skinny: A content analysis of fitspiration websites.

abstract: Abstract “Fitspiration” websites are media that aim to inspire people to live healthy and fit lifestyles through motivating images and text related to exercise and diet. Given the link between similar Internet content (i.e., healthy living blogs) and problematic messages, we hypothesized that content on these sites would over-emphasize appearance and promote problematic messages regarding exercise and diet. Keywords “fitspo” and “fitspiration” were entered into search engines. The first 10 images and text from 51 individual websites were rated on a variety of characteristics. Results indicated that a majority of messages found on fitspiration websites focused on appearance. Other common themes included content promoting exercise for appearance-motivated reasons and content promoting dietary restraint. “Fitspiration” websites are a source of messages that reinforce over-valuation of physical appearance, eating concerns, and excessive exercise. Further research is needed to examine the impact viewing such content has on participants’ psychological health.


title: Social Categorization and the Perception of Groups and Group Differences

abstract: The classification of human beings into distinct groups is a fundamental feature of social perception. Problematic phenomena, such as prejudice, discrimination, and intergroup conflict, are commonly traced back to categorization. We explore the minimal conditions under which categorization occurs and the basic mechanisms by which it affects cognition and behavior. We show that comparisons between groups are not necessary for categorization, reveal the conditions under which people overestimate or underestimate differences between groups, and sketch a model showing how social categorization gives rise to differences in the evaluation of ingroups and outgroups and to differences in the accuracy of judgments of ingroups and outgroups. We conclude with reflections on intergroup conflict and the role of moral judgment in such conflicts.


AI System Designs for the First RTS-Game AI Competition


title: Efficient locking for concurrent operations on B-trees

abstract: The B-tree and its variants have been found to be highly useful (both theoretically and in practice) for storing large amounts of information, especially on secondary storage devices. We examine the problem of overcoming the inherent difficulty of concurrent operations on such structures, using a practical storage model. A single additional “link” pointer in each node allows a process to easily recover from tree modifications performed by other concurrent processes. Our solution compares favorably with earlier solutions in that the locking scheme is simpler (no read-locks are used) and only a (small) constant number of nodes are locked by any update process at any given time. An informal correctness proof for our system is given.


Superstitious behavior as a byproduct of intelligent adaptation


title: Will they like this?: evaluating code contributions with language models

abstract: Popular open-source software projects receive and review contributions from a diverse array of developers, many of whom have little to no prior involvement with the project. A recent survey reported that reviewers consider conformance to the project's code style to be one of the top priorities when evaluating code contributions on Github. We propose to quantitatively evaluate the existence and effects of this phenomenon. To this aim we use language models, which were shown to accurately capture stylistic aspects of code. We find that rejected change sets do contain code significantly less similar to the project than accepted ones, furthermore, the less similar change sets are more likely to be subject to thorough review. Armed with these results we further investigate whether new contributors learn to conform to the project style and find that experience is positively correlated with conformance to the project's code style.


title: Quantum Nearest-Neighbor Algorithms for Machine Learning

abstract: We present several quantum algorithms for performing nearest-neighbor learning. At the core of our algorithms are fast and coherent quantum methods for computing distance metrics such as the inner product and Euclidean distance. We prove upper bounds on the number of queries to the input data required to compute these metrics. In the worst case, our quantum algorithms lead to polynomial reductions in query complexity relative to the corresponding classical algorithm. In certain cases, we show exponential or even super-exponential reductions over the classical analog. We study the performance of our quantum nearest-neighbor algorithms on several real-world binary classification tasks and find that the classification accuracy is competitive with classical methods.


title: Pufferfish: A framework for mathematical privacy definitions

abstract: In this article, we introduce a new and general privacy framework called Pufferfish. The Pufferfish framework can be used to create new privacy definitions that are customized to the needs of a given application. The goal of Pufferfish is to allow experts in an application domain, who frequently do not have expertise in privacy, to develop rigorous privacy definitions for their data sharing needs. In addition to this, the Pufferfish framework can also be used to study existing privacy definitions. We illustrate the benefits with several applications of this privacy framework: we use it to analyze differential privacy and formalize a connection to attackers who believe that the data records are independent; we use it to create a privacy definition called hedging privacy, which can be used to rule out attackers whose prior beliefs are inconsistent with the data; we use the framework to define and study the notion of composition in a broader context than before; we show how to apply the framework to protect unbounded continuous attributes and aggregate information; and we show how to use the framework to rigorously account for prior data releases.


title: Linguistic complexity: locality of syntactic dependencies

abstract: Abstract This paper proposes a new theory of the relationship between the sentence processing mechanism and the available computational resources. This theory – the Syntactic Prediction Locality Theory (SPLT) – has two components: an integration cost component and a component for the memory cost associated with keeping track of obligatory syntactic requirements. Memory cost is hypothesized to be quantified in terms of the number of syntactic categories that are necessary to complete the current input string as a grammatical sentence. Furthermore, in accordance with results from the working memory literature both memory cost and integration cost are hypothesized to be heavily influenced by locality (1) the longer a predicted category must be kept in memory before the prediction is satisfied, the greater is the cost for maintaining that prediction; and (2) the greater the distance between an incoming word and the most local head or dependent to which it attaches, the greater the integration cost. The SPLT is shown to explain a wide range of processing complexity phenomena not previously accounted for under a single theory, including (1) the lower complexity of subject-extracted relative clauses compared to object-extracted relative clauses, (2) numerous processing overload effects across languages, including the unacceptability of multiply center-embedded structures, (3) the lower complexity of cross-serial dependencies relative to center-embedded dependencies, (4) heaviness effects, such that sentences are easier to understand when larger phrases are placed later and (5) numerous ambiguity effects, such as those which have been argued to be evidence for the Active Filler Hypothesis.


title: Combining Confocal Imaging and Descattering

abstract: In translucent objects, light paths are affected by multiple scattering, which is polluting any observation. Confocal imaging reduces the influence of such global illumination effects by carefully focusing illumination and viewing rays from a large aperture to a specific location within the object volume. The selected light paths still contain some global scattering contributions, though. Descattering based on high frequency illumination serves the same purpose. It removes the global component from observed light paths. We demonstrate that confocal imaging and descattering are orthogonal and propose a novel descattering protocol that analyzes the light transport in a neighborhood of light transport paths. In combination with confocal imaging, our descattering method achieves optical sectioning in translucent media with higher contrast and better resolution.


title: Power-Extraction Circuits for Piezoelectric Energy Harvesters in Miniature and Low-Power Applications

abstract: When a piezoelectric energy harvester is connected to a simple load circuit, the damping force which the piezoelectric element is able to generate is often below the optimal value to maximize electrical power generation. Circuits that aim to increase the power output of a piezoelectric energy harvester do so by modifying the voltage onto which the piezoelectric current source drives its charge. This paper presents a systematic analysis and comparison of all the principal types of power extraction circuit that allow this damping force to be increased, under both ideal and realistic constraints. Particular emphasis is placed on low-amplitude operation. A circuit called single-supply prebiasing is shown to harvest more power than previous approaches. Most of the analyzed circuits able to increase the power output do so by synchronously inverting or charging the piezoelectric capacitance through an inductor. For inductor Q factors greater than around only 2, the single-supply prebiasing circuit has the highest power density of the analyzed circuits. The absence of diodes in conduction paths, achievable with a minimum number of synchronous rectifiers, means that the input excitation amplitude is not required to overcome a minimum value before power can be extracted, making it particularly suitable for microscale applications or those with a wide variation in amplitude.


title: Fast computation of min-Hash signatures for image collections

abstract: A new method for highly efficient min-Hash generation for document collections is proposed. It exploits the inverted file structure which is available in many applications based on a bag or a set of words. Fast min-Hash generation is important in applications such as image clustering where good recall and precision requires a large number of min-Hash signatures. Using the set of words represenation, the novel exact min-Hash generation algorithm achieves approximately a 50-fold speed-up on two dataset with 105 and 106 images respectively. We also propose an approximate min-Hash assignment process which reaches a more than 200-fold speed-up at the cost of missing about 2–3% of matches. We also experimentally show that the method generalizes to other modalities with significantly different statistics.


title: Leveraging LSTM Networks for Attack Detection in Fog-to-Things Communications

abstract: The evolution and sophistication of cyber-attacks need resilient and evolving cybersecurity schemes. As an emerging technology, the Internet of Things (IoT) inherits cyber-attacks and threats from the IT environment despite the existence of a layered defensive security mechanism. The extension of the digital world to the physical environment of IoT brings unseen attacks that require a novel lightweight and distributed attack detection mechanism due to their architecture and resource constraints. Architecturally, fog nodes can be leveraged to offload security functions from IoT and the cloud to mitigate the resource limitation issues of IoT and scalability bottlenecks of the cloud. Classical machine learning algorithms have been extensively used for intrusion detection, although scalability, feature engineering efforts, and accuracy have hindered their penetration into the security market. These shortcomings could be mitigated using the deep learning approach as it has been successful in big data fields. Apart from eliminating the need to craft features manually, deep learning is resilient against morphing attacks with high detection accuracy. This article proposes an LSTM network for distributed cyber-attack detection in fog-to-things communication. We identify and analyze critical attacks and threats targeting IoT devices, especially attacks exploiting vulnerabilities of wireless communications. The conducted experiments on two scenarios demonstrate the effectiveness and efficiency of deeper models over traditional machine learning models.


title: A Line Feature Extraction Method for Finger-Knuckle-Print Verification

abstract: Due to its mobility and reliability, the outer finger-knuckle-print (FKP) possesses several advantages over other biometric traits of the hand. However, most existing state-of-the-art methods utilize either local features alone or together with global features for FKP verification. These methods often demand high computational cost despite their high verification accuracy. In this paper, we propose a novel and fast matrix projection method for extracting line features from the finger-knuckle-print for person verification. Essentially, both the horizontal and the vertical knuckle lines are extracted by projecting the knuckle print image onto a shift-and-difference matrix. Such a matrix enables directional image shifting and subtraction within a single matrix multiplication. The resultant difference image then goes through a sigmoidal activation for contrast enhancement. Subsequently, the Fourier spectrum of the contrast enhanced image is adopted as the holistic features of the given finger-knuckle-print image. The entire process of extracting the proposed features is expressed in an analytic form to facilitate a fast vectorized implementation. For cognition performance enhancement, the two directional line features are subsequently fused at the score level by minimizing the error counts of the extreme learning machine kernel. Extensive experiments are performed to compare the proposed method with competing methods using three public finger-knuckle-print databases. Our experimental results show encouraging performance in terms of verification accuracy and computational efficiency.


Romantic Love Is Associated with Enhanced Inhibitory Control in an Emotional Stop-Signal Task


title: Novel Gaussianized vector representation for improved natural scene categorization

abstract: We present a novel Gaussianized vector representation for scene images by an unsupervised approach. Each image is first encoded as an ensemble of orderless bag of features. A global Gaussian Mixture Model (GMM) learned from all images is then used to randomly distribute each feature into one Gaussian component by a multinomial trial. The posteriors of the feature on all the Gaussian components serve as the parameters of the multinomial distribution. Finally, the normalized means of the features distributed in every Gaussian component are concatenated to form a supervector, which is a compact representation for each scene image. We prove that these supervectors observe the standard normal distribution. The Gaussianized vector representation is a more generalized form of the widely used histogram representation. Our experiments on scene categorization tasks using this vector representation show significantly improved performance compared with the histogram-of-features representation. This paper is an extended version of our work that won the IBM Best Student Paper Award at the 2008 International Conference on Pattern Recognition (ICPR 2008) (Zhou et al., 2008).


title: Optimizing JMS Performance for Cloud-Based Application Servers

abstract: Many business-oriented services will be graduallyoffered in the Cloud. Java Message Service (JMS) is a criticalmessaging technology in Java-based business applications, particularlyto those that are based on the Java Enterprise Edition(Java EE) open standard. Maintaining high performance inthe horizontally scaled, and elastic, cloud environment iscritical to the success of the business applications. In thispaper, we present practical considerations in optimizing JMSperformance for the cloud deployment, where some of thefindings may also serve to improve the design of JMS containerso it adapts well to cloud computing. Our work also includesperformance evaluation on the proposed strategies.


title: Extended HMM and Ranking Models for Chinese Spelling Correction

abstract: Spelling correction has been studied for many decades, which can be classified into two categories: (1) regular text spelling correction, (2) query spelling correction. Although the two tasks share many common techniques, they have different concerns. This paper presents our work on the CLP-2014 bake-off. The task focuses on spelling checking on foreigner Chinese essays. Compared to online search query spelling checking task, more complicated techniques can be applied for better performance. Therefore, we proposed a unified framework for Chinese essays spelling correction based on extended HMM and ranker-based models, together with a rule-based model for further polishing. Our system showed better performance on the test dataset.


title: Sense of Community on Twitter and Instagram: Exploring the Roles of Motives and Parasocial Relationships

abstract: Abstract Although research has explored the ways in which people form virtual communities to converse about media figures, television shows, and similar topics, little research has examined the link between virtual communities and the parasocial relationships (PSRs) that are often the focus of these conversations and users' experiences in those virtual communities. We examined sense of community (SOC) on Twitter and Instagram as a function of users' motives for use and users' PSR on the sites. In addition to examining the relative importance of different motives for using Twitter and Instagram, we predicted that PSR would mediate the association between motives for use and SOC. Results of an online survey revealed that Instagram users (n = 276) reported stronger social interaction motives than did Twitter users (n = 223). Social interaction and expressive information sharing motives were directly positively associated with SOC for users of both sites. Instagram users also exhibited indirect effects of exp...


title: Survey of optical indoor positioning systems

abstract: Recent advances in CCD technologies, processing speed and image understanding have been driving the development of the camera-based positioning systems. An improved performance of optical systems has triggered image based positioning methods to become an attractive alternative for applications in industrial metrology as well as for robot- and pedestrian navigation. This paper provides a survey of current optical indoor positioning approaches. Different systems are briefly described and categorized based on how the images are referenced to the environment.


title: Designing intelligent energy harvesting communication systems

abstract: From being a scientific curiosity only a few years ago, energy harvesting (EH) is well on its way to becoming a game-changing technology in the field of autonomous wireless networked systems. The promise of long-term, uninterrupted and self-sustainable operation in a diverse array of applications has captured the interest of academia and industry alike. Yet the road to the ultimate network of perpetual communicating devices is plagued with potholes: ambient energy is intermittent and scarce, energy storage capacity is limited, and devices are constrained in size and complexity. In dealing with these challenges, this article will cover recent developments in the design of intelligent energy management policies for EH wireless devices and discuss pressing research questions in this rapidly growing field.


title: Adaptive and Online One-Class Support Vector Machine-Based Outlier Detection Techniques for Wireless Sensor Networks

abstract: Outlier detection in wireless sensor networks is essential to ensure data quality, secure monitoring and reliable detection of interesting and critical events. A key challenge for outlier detection in wireless sensor networks is to adaptively identify outliers in an online manner with a high accuracy while maintaining the resource consumption of the network to a minimum. In this paper, we propose one-class support vector machine-based outlier detection techniques that sequentially update the model representing normal behavior of the sensed data and take advantage of spatial and temporal correlations that exist between sensor data to cooperatively identify outliers. Experiments with both synthetic and real data show that our online outlier detection techniques achieve high detection accuracy and low false alarm rate.


title: Localization of Access Points Based on Signal Strength Measured by a Mobile User Node

abstract: The issue of locating access points (APs) in a building is investigated in detail in this paper. The signal strength of beacon frames from a selected AP is measured at multiple positions, and the location of the selected AP is estimated based on the distance information inferred from the measured signal strength and a modified version of the Hata-Okumara model. A strategy is proposed to select three measurement points to improve localization accuracy for a selected target AP when signal strength is measured at multiple positions. The accuracy of the proposed scheme is evaluated through experiment.


title: Near-/Sub-Vth process, voltage, and temperature (PVT) sensors with dynamic voltage selection

abstract: A process, voltage and temperature (PVT) sensors with dynamic voltage selection are proposed for environmental management in the ultra-low voltage dynamic voltage and frequency scaling (DVFS) system. The process and voltage (PV) sensors initially monitor the process variation. With known process information, PV sensors can real-time provide voltage variation status. The temperature sensor has six temperature sensitive ring oscillators (TSROs) generating frequency proportional to temperature. It dynamically selects the proper TSRO to convert the frequency into digital readings according to voltage status provided by PV sensors. With known process and voltage information from PV sensors, a pure temperature measurement result can be obtained. The proposed PVT sensors are designed in TSMC 65nm CMOS technology. This work can be dynamically operated over an ultra-low voltage range from 0.25V to 0.5V. Only 2.3μW is consumed at 0.25V. They can achieve 0.15 C resolution and 50k samples/sec conversion rate.


title: Average and small-signal modeling of self-oscillating flyback converter with applied switching delay

abstract: This paper presents a consistent methodology to assess the effect of variable-frequency operation on the dynamics of a flyback converter used in low-cost, high-volume, battery-charger applications. The self-oscillation is typically implemented using peak-current control in boundary conduction mode with a deterministic delay before switching on the next cycle. The modeling is based on the modified state-space-averaging method, where the effect of the varying cycle time and peak-current-mode control is included using special cycle-time and on-time constraints derived from the inductor-current waveforms. The method leads to accurate full-order models, where the effect of the circuit parasitics and the switching delay may also be taken into account. In most of the cases, the effect of parasitics and switching delay is, however, minimal, and therefore, simple small-signal models may be used. The applied switching delay affects the dc operating point in terms of duty ratio and switching frequency, and shall be, therefore, carefully considered.


LOW LEVELS OF PLATELET GLUTATHIONE IN EALES'DISEASE


title: Simultaneous Traffic Sign Detection and Boundary Estimation using Convolutional Neural Network

abstract: We propose a novel traffic sign detection system that simultaneously estimates the location and precise boundary of traffic signs using convolutional neural network (CNN). Estimating the precise boundary of traffic signs is important in navigation systems for intelligent vehicles where traffic signs can be used as 3D landmarks for road environment. Previous traffic sign detection systems, including recent methods based on CNN, only provide bounding boxes of traffic signs as output, and thus requires additional processes such as contour estimation or image segmentation to obtain the precise sign boundary. In this work, the boundary estimation of traffic signs is formulated as a 2D pose and shape class prediction problem, and this is effectively solved by a single CNN. With the predicted 2D pose and the shape class of a target traffic sign in an input image, we estimate the actual boundary of the target sign by projecting the boundary of a corresponding template sign image into the input image plane. By formulating the boundary estimation problem as a CNN-based pose and shape prediction task, our method is end-to-end trainable, and more robust to occlusion and small targets than other boundary estimation methods that rely on contour estimation or image segmentation. The proposed method with architectural optimization provides an accurate traffic sign boundary estimation which is also efficient in compute, showing a detection frame rate higher than 7 frames per second on low-power mobile platforms.


title: What Are You Known For?: Learning User Topical Profiles with Implicit and Explicit Footprints

abstract: User interests and expertise are valuable but often hidden resources on social media. For example, Twitter Lists and LinkedIn's Skill Tags provide a partial perspective on what users are known for (by aggregating crowd tagging knowledge), but the vast majority of users are untagged; their interests and expertise are essentially hidden from important applications such as personalized recommendation, community detection, and expert mining. A natural approach to overcome these limitations is to intelligently learn user topical profiles by exploiting information from multiple, heterogeneous footprints: for instance, Twitter users who post similar hashtags may have similar interests, and YouTube users who upvote the same videos may have similar preferences. And yet identifying "similar" users by exploiting similarity in such a footprint space often provides conflicting evidence, leading to poor-quality user profiles. In this paper, we propose a unified model for learning user topical profiles that simultaneously considers multiple footprints. We show how these footprints can be embedded in a generalized optimization framework that takes into account pairwise relations among all footprints for robustly learning user profiles. Through extensive experiments, we find the proposed model is capable of learning high-quality user topical profiles, and leads to a 10-15% improvement in precision and mean average error versus a cross-triadic factorization state-of-the-art baseline.


title: Leadership styles in nursing

abstract: Nurses are often asked to think about leadership, particularly in times of rapid change in healthcare, and where questions have been raised about whether leaders and managers have adequate insight into the requirements of care. This article discusses several leadership styles relevant to contemporary healthcare and nursing practice. Nurses who are aware of leadership styles may find this knowledge useful in maintaining a cohesive working environment. Leadership knowledge and skills can be improved through training, where, rather than having to undertake formal leadership roles without adequate preparation, nurses are able to learn, nurture, model and develop effective leadership behaviours, ultimately improving nursing staff retention and enhancing the delivery of safe and effective care.


title: Using Ontology-Based Traffic Models for More Efficient Decision Making of Autonomous Vehicles

abstract: The paper describes how a high-level abstract world model can be used to support the decision-making process of an autonomous driving system. The approach uses a hierarchical world model and distinguishes between a low-level model for the trajectory planning and a high-level model for solving the traffic coordination problem. The abstract world model used in the CyberCars-2 project is presented. It is based on a topological lane segmentation and introduces relations to represent the semantic context of the traffic scenario. This makes it much easier to realize a consistent and complete driving control system, and to analyze, evaluate and simulate such a system.


title: Knowledge-based question answering using the semantic embedding space

abstract: We extract semantic links of words and logical properties from unstructured data.We jointly encode semantics of words and logical properties into an embedding space.Embedding space provides semantic similarities between word and logical properties.Questions and potential answers can be represented on the embedding space.Potential answers are ranked based on semantic similarities with a given question. Semantic transformation of a natural language question into its corresponding logical form is crucial for knowledge-based question answering systems. Most previous methods have tried to achieve this goal by using syntax-based grammar formalisms and rule-based logical inference. However, these approaches are usually limited in terms of the coverage of the lexical trigger, which performs a mapping task from words to the logical properties of the knowledge base, and thus it is easy to ignore implicit and broken relations between properties by not interpreting the full knowledge base. In this study, our goal is to answer questions in any domains by using the semantic embedding space in which the embeddings encode the semantics of words and logical properties. In the latent space, the semantic associations between existing features can be exploited based on their embeddings without using a manually produced lexicon and rules. This embedding-based inference approach for question answering allows the mapping of factoid questions posed in a natural language onto logical representations of the correct answers guided by the knowledge base. In terms of the overall question answering performance, our experimental results and examples demonstrate that the proposed method outperforms previous knowledge-based question answering baseline methods with a publicly released question answering evaluation dataset: WebQuestions.


title: Visual Analysis of Dynamic Networks Using Change Centrality

abstract: The visualization and analysis of dynamic social networks are challenging problems, demanding the simultaneous consideration of relational and temporal aspects. In order to follow the evolution of a network over time, we need to detect not only which nodes and which links change and when these changes occur, but also the impact they have on their neighbourhood and on the overall relational structure. Aiming to enhance the perception of structural changes at both the micro and the macro level, we introduce the change centrality metric. This novel metric, as well as a set of further metrics we derive from it, enable the pair wise comparison of subsequent states of an evolving network in a discrete-time domain. Demonstrating their exploitation to enrich visualizations, we show how these change metrics support the visual analysis of network dynamics.


title: DGA Botnet detection using Collaborative Filtering and Density-based Clustering

abstract: In recent years, the botnet phenomenon is one of the most dangerous threat to Internet security, which supports a wide range of criminal activities, including distributed denial of service (DDoS) attacks, click fraud, phishing, malware distribution, spam emails, etc. An increasing number of botnets use Domain Generation Algorithms (DGAs) to avoid detection and exclusion by the traditional methods. By dynamically and frequently generating a large number of random domain names for candidate command and control (C&C) server, botnet can be still survive even when a C&C server domain is identified and taken down. This paper presents a novel method to detect DGA botnets using Collaborative Filtering and Density-Based Clustering. We propose a combination of clustering and classification algorithm that relies on the similarity in characteristic distribution of domain names to remove noise and group similar domains. Collaborative Filtering (CF) technique is applied to find out bots in each botnet, help finding out offline malwares infected-machine. We implemented our prototype system, carried out the analysis of a huge amount of DNS traffic log of Viettel Group and obtain positive results.