title: Smart phone application development for monitoring epilepsy seizure detection based on EEG signal classification

abstract: Automated epilepsy seizure detection is the solution to the limitation and time consuming of manual epilepsy monitoring and detection using EEG signals. In this paper we developed a technique for epilepsy seizure detection using EEG signals. The signal will be pre-processed and filtered using multiple filters. Then, the filtered signal will be decomposed into sub-bands. Furthermore, feature extraction is applied; we developed a combined feature consists of combining three features into one. Finally, we used well-known classifiers such as Support Vector Machine (SVM), Artificial Neural Network (ANN), and K-Nears Neighbor (KNN) to differentiate between epileptic and non-epileptic signals, and we achieved an accuracy of 97%. Furthermore, we developed an Android-based smartphone application for monitoring epilepsy detection based on the classification results of the EEG signal. A notification will be sent to the patient, doctors, and family members when an epilepsy seizure occurs. Once the EEG signal is classified as epileptic, the App will display a visual notification indicating that Epileptic Seizure has been detected. Moreover, it will trigger an alarm and send a message notification to all associated phone numbers, and the EEG signal will display on the App. Although we are using an EEG signal from a dataset, we have generated both normal and epileptic EEG signals using a waveform generator, and we have displayed those signals on the spectrum analyzer for future real time detection using our Android App.


title: Simulation-based optimization of Markov reward processes

abstract: We propose a simulation-based algorithm for optimizing the average reward in a Markov reward process that depends on a set of parameters. As a special case, the method applies to Markov decision processes where optimization takes place within a parametrized set of policies. The algorithm involves the simulation of a single sample path, and can be implemented online. A convergence result (with probability 1) is provided.


title: Human Age Prediction and Classification Using Facial Image

abstract: Human face is one of the most important sources of the information, which can be utilized for personal verification and identification. This paper discusses the method of finding the human age using the facial image of a person. It has many real world applications like human computer interaction, internet security, multimedia communication, vending machines etc. During growth, aging is affected in two main forms, one is the size and shape variation and the other is the textural variation. In this paper, we use the textural variation of the face during the growth, which appear more in the adulthood in the form of wrinkles. Areas of the face where these textural variations occur more are identified, like forehead, cheeks, regions around eyes etc. Studying these areas, the human age is identified and classified. Here we use the method of Hough Transform for feature extraction and Polynomial Regression for age classification.


title: Analyzing knowledge communities using foreground and background clusters

abstract: Insight into the growth (or shrinkage) of “knowledge communities” of authors that build on each other's work can be gained by studying the evolution over time of clusters of documents. We cluster documents based on the documents they cite in common using the Streemer clustering method, which finds cohesive foreground clusters (the knowledge communities) embedded in a diffuse background. We build predictive models with features based on the citation structure, the vocabulary of the papers, and the affiliations and prestige of the authors and use these models to study the drivers of community growth and the predictors of how widely a paper will be cited. We find that scientific knowledge communities tend to grow more rapidly if their publications build on diverse information and use narrow vocabulary and that papers that lie on the periphery of a community have the highest impact, while those not in any community have the lowest impact.


title: Modulation of the brain activity in outcome evaluation by interpersonal relationship: An ERP study

abstract: Abstract Previous event-related potential (ERP) studies employing monetary gambling tasks have demonstrated that the brain responds differentially not only to one's own gain and loss but also to the others’ gambling outcomes. Empathy and motivational significance are implicated in the processes of outcome evaluation. This study is to explore to what extent the brain activity is modulated by the interpersonal relationship between the individual and the other agent, who can be a friend or a stranger. Brain potentials were recorded while the participant observed reward feedback to his/her own, his/her friend's, or a stranger's performance in a gambling task. The magnitude and latency of the effect on an early ERP component, the FRN, did not differ between the friend- and the stranger-observation conditions, whereas a late component, the P300, was modulated not only by reward valence but also by the interpersonal relationship between the observer and the other agent. These findings suggest that brain responses in outcome evaluation may be divided into an earlier semi-automatic process and a later cognitive appraisal process and that the interpersonal relationship comes into play mostly in the late attention-sensitive stage.


title: Intimate partner violence in Pakistan: a systematic review

abstract: Intimate partner violence (IPV) is a major social and public health problem affecting people in various cultures and societies. Though the issue of IPV in Pakistan has been researched since the 1990s, no attempt has been made systematically to review the available evidence on IPV in Pakistan. This article presents findings of a systematic review of available empirical literature related to IPV in Pakistan. Using various key words, MEDLINE, CINAHL, and PsycINFO were searched to identify relevant studies. This resulted in the identification of 55 potential studies for inclusion. After application of exclusion criteria 23 studies were identified, 20 of which used quantitative research designs, two used qualitative designs and one adopted a mixed method case study approach. All studies reported men as the perpetrators of IPV and women as its victims. FINDINGS are presented and discussed for IPV for its forms, predictors, effects and victims' responses. Pakistani peoples' perceived reasons for and their attitudes towards IPV are also presented and discussed. Language: en


title: On the Convergence of Decentralized Gradient Descent

abstract: Consider the consensus problem of minimizing $f(x)=\sum_{i=1}^n f_i(x)$ where each $f_i$ is only known to one individual agent $i$ out of a connected network of $n$ agents. All the agents shall collaboratively solve this problem and obtain the solution subject to data exchanges restricted to between neighboring agents. Such algorithms avoid the need of a fusion center, offer better network load balance, and improve data privacy. We study the decentralized gradient descent method in which each agent $i$ updates its variable $x_{(i)}$, which is a local approximate to the unknown variable $x$, by combining the average of its neighbors' with the negative gradient step $-\alpha \nabla f_i(x_{(i)})$. The iteration is $$x_{(i)}(k+1) \gets \sum_{\text{neighbor} j \text{of} i} w_{ij} x_{(j)}(k) - \alpha \nabla f_i(x_{(i)}(k)),\quad\text{for each agent} i,$$ where the averaging coefficients form a symmetric doubly stochastic matrix $W=[w_{ij}] \in \mathbb{R}^{n \times n}$. We analyze the convergence of this iteration and derive its converge rate, assuming that each $f_i$ is proper closed convex and lower bounded, $\nabla f_i$ is Lipschitz continuous with constant $L_{f_i}$, and stepsize $\alpha$ is fixed. Provided that $\alpha<O(1/L_h)$ where $L_h=\max_i\{L_{f_i}\}$, the objective error at the averaged solution, $f(\frac{1}{n}\sum_i x_{(i)}(k))-f^*$, reduces at a speed of $O(1/k)$ until it reaches $O(\alpha)$. If $f_i$ are further (restricted) strongly convex, then both $\frac{1}{n}\sum_i x_{(i)}(k)$ and each $x_{(i)}(k)$ converge to the global minimizer $x^*$ at a linear rate until reaching an $O(\alpha)$-neighborhood of $x^*$. We also develop an iteration for decentralized basis pursuit and establish its linear convergence to an $O(\alpha)$-neighborhood of the true unknown sparse signal.


title: A low voltage high output impedance bulk driven regulated cascode current mirror

abstract: This work proposes the design of a new low voltage high output impedance CMOS current mirror that offers enhanced output voltage compliance using bulk-driven technique. The input/output characteristics of the proposed current mirror are discussed. Designed circuit is simulated in a proprietary 180 nm CMOS process, using Cadence Spectre and BSIM3v3 models. Simulated results with 0.8 V power supply and 50 uA input current reveal that the proposed implementation requires a minimum input and output voltages of 0.4 V and 0.39 V respectively. It yields an increase of the output impedance compared with that of existing bulk driven current mirrors, thus offering a potential solution to mitigate the effect of ultra-deep submicron CMOS transistors used in sub 1-V current mirrors and current sources. Compared to high output impedance gate driven regulated cascode current mirror (GDRCCM), low voltage high output impedance body driven regulated cascode current mirror (BDRCCM) supports higher output voltage swing. Thus, the proposed design finds wide acceptability in low voltage and low power CMOS analog integrated circuits.


title: A compact high isolation MIMO antenna for 2.4-GHz WLAN application

abstract: A new method to improve isolation of MIMO antenna is presented in this letter. The main radiator is a monopole antenna. To reduce the mutual coupling among antenna ports, slots are inserted on the ground plane and the radiator is surrounded by four L-shaped parasitic strips. The simulation results show that the isolation of adjacent ports can reach below −20dB with the envelope correlation coefficient less than 0.1 at 2.4GHz. The results indicate that the MIMO antenna is suitable for 2.4–2.4835GHZ (WLAN) applications.


title: Probabilistic Programming in Python using PyMC3

abstract: Probabilistic Programming allows for automatic Bayesian inference on user-defined probabilistic models. Recent advances in Markov chain Monte Carlo (MCMC) sampling allow inference on increasingly complex models. This class of MCMC, known as Hamiltonian Monte Carlo, requires gradient information which is often not readily available. PyMC3 is a new open source Probabilistic Programming framework written in Python that uses Theano to compute gradients via automatic dierentiation as well as compile probabilistic programs on-the-fly to C for increased speed. Contrary to other Probabilistic Programming languages, PyMC3 allows model specification directly in Python code. The lack of a domain specific language allows for great flexibility and direct interaction with the model. This paper is a tutorial-style introduction to this software package.


title: Survey on various door lock access control mechanisms

abstract: In day to day life, security of an object or property plays a major role. Nowadays, security is the major threat faced by most of the organizations; hence security is gaining more importance in these days. This paper gives a survey on various automatic identification and access control mechanisms that have been used over the years to prevent unauthorized access. In olden days, for high security zones like locker rooms for banks, military sites etc, traditional lock systems or passwords were employed. But this solution was not secure. Due to the advancements in technology RFID cards were used, but this was not useful for the user due to the chance of getting lost, forgotten or stolen. Later various door lock security systems based on biometrics, GSM, OTP, cryptography etc were developed. A lot of research is going on various automatic door lock systems and can expect more secure systems in the upcoming years.


title: Data Augmentation of Spectral Data for Convolutional Neural Network (CNN) Based Deep Chemometrics

abstract: Deep learning methods are used on spectroscopic data to predict drug content in tablets from near infrared (NIR) spectra. Using convolutional neural networks (CNNs), features are ex- tracted from the spectroscopic data. Extended multiplicative scatter correction (EMSC) and a novel spectral data augmentation method are benchmarked as preprocessing steps. The learned models perform better or on par with hypothetical optimal partial least squares (PLS) models for all combinations of preprocessing. Data augmentation with subsequent EMSC in combination gave the best results. The deep learning model CNNs also outperform the PLS models in an extrapolation chal- lenge created using data from a second instrument and from an analyte concentration not covered by the training data. Qualitative investigations of the CNNs kernel activations show their resemblance to wellknown data processing methods such as smoothing, slope/derivative, thresholds and spectral region selection.


title: Designing a GDPR-Compliant and Usable Privacy Dashboard

abstract: The role of personal data gained significance across all business domains in past decades. Despite strict legal restrictions that processing personal data is subject to, users tend to respond to the extensive collection of data by service providers with distrust. Legal battles between data subjects and processors emphasized the need of adaptations by the current law to face today’s challenges. The European Union has taken action by introducing the General Data Protection Regulation (GDPR), which was adopted in April 2016 and will inure in May 2018. The GDPR extends existing data privacy rights of EU citizens and simultaneously puts pressure on controllers and processors by defining high penalties in case of non-compliance. Uncertainties remain to which extent controllers and processors need to adjust their existing technologies in order to conform to the new law. This work designs, implements, and evaluates a privacy dashboard for data subjects intending to enable and ease the execution of data privacy rights granted by the GDPR.


title: Search and tracking algorithms for swarms of robots

abstract: Target search and tracking is a classical but difficult problem in many research domains, including computer vision, wireless sensor networks and robotics. We review the seminal works that addressed this problem in the area of swarm robotics, which is the application of swarm intelligence principles to the control of multi-robot systems. Robustness, scalability and flexibility, as well as distributed sensing, make swarm robotic systems well suited for the problem of target search and tracking in real-world applications. We classify the works we review according to the variations and aspects of the search and tracking problems they addressed. As this is a particularly application-driven research area, the adopted taxonomy makes this review serve as a quick reference guide to our readers in identifying related works and approaches according to their problem at hand. By no means is this an exhaustive review, but an overview for researchers who are new to the swarm robotics field, to help them easily start off their research. Surveys algorithms applicable to swarm robotic systems for target search and tracking.Identifies variations of the search and tracking problem addressed in the literature.Discusses desired capabilities of search and tracking algorithms for robot swarms.


title: xLED: Covert Data Exfiltration from Air-Gapped Networks via Router LEDs

abstract: In this paper we show how attackers can covertly leak data (e.g., encryption keys, passwords and files) from highly secure or air-gapped networks via the row of status LEDs that exists in networking equipment such as LAN switches and routers. Although it is known that some network equipment emanates optical signals correlated with the information being processed by the device ('side-channel'), intentionally controlling the status LEDs to carry any type of data ('covert-channel') has never studied before. A malicious code is executed on the LAN switch or router, allowing full control of the status LEDs. Sensitive data can be encoded and modulated over the blinking of the LEDs. The generated signals can then be recorded by various types of remote cameras and optical sensors. We provide the technical background on the internal architecture of switches and routers (at both the hardware and software level) which enables this type of attack. We also present amplitude and frequency based modulation and encoding schemas, along with a simple transmission protocol. We implement a prototype of an exfiltration malware and discuss its design and implementation. We evaluate this method with a few routers and different types of LEDs. In addition, we tested various receivers including remote cameras, security cameras, smartphone cameras, and optical sensors, and also discuss different detection and prevention countermeasures. Our experiment shows that sensitive data can be covertly leaked via the status LEDs of switches and routers at a bit rates of 10 bit/sec to more than 1Kbit/sec per LED.


title: Applying the Ethics of Care to Your Nursing Practice

abstract: Use of the theory of care ethics is discussed to help nurses determine if they are applying this theory effectively in their practice. After a basic definition of caring, including Watson’s caring theory, the evolution of the theory of ethics of care will be delineated briefly. A case will be used to illustrate Tronto’s (1993) four phases of caring and her four elements of care.


title: Image Quality Assessment for Fake Biometric Detection: Application to Iris, Fingerprint, and Face Recognition

abstract: To ensure the actual presence of a real legitimate trait in contrast to a fake self-manufactured synthetic or reconstructed sample is a significant problem in biometric authentication, which requires the development of new and efficient protection measures. In this paper, we present a novel software-based fake detection method that can be used in multiple biometric systems to detect different types of fraudulent access attempts. The objective of the proposed system is to enhance the security of biometric recognition frameworks, by adding liveness assessment in a fast, user-friendly, and non-intrusive manner, through the use of image quality assessment. The proposed approach presents a very low degree of complexity, which makes it suitable for real-time applications, using 25 general image quality features extracted from one image (i.e., the same acquired for authentication purposes) to distinguish between legitimate and impostor samples. The experimental results, obtained on publicly available data sets of fingerprint, iris, and 2D face, show that the proposed method is highly competitive compared with other state-of-the-art approaches and that the analysis of the general image quality of real biometric samples reveals highly valuable information that may be very efficiently used to discriminate them from fake traits.


title: SIFT-Bag kernel for video event analysis

abstract: In this work, we present a SIFT-Bag based generative-to-discriminative framework for addressing the problem of video event recognition in unconstrained news videos. In the generative stage, each video clip is encoded as a bag of SIFT feature vectors, the distribution of which is described by a Gaussian Mixture Models (GMM). In the discriminative stage, the SIFT-Bag Kernel is designed for characterizing the property of Kullback-Leibler divergence between the specialized GMMs of any two video clips, and then this kernel is utilized for supervised learning in two ways. On one hand, this kernel is further refined in discriminating power for centroid-based video event classification by using the Within-Class Covariance Normalization approach, which depresses the kernel components with high-variability for video clips of the same event. On the other hand, the SIFT-Bag Kernel is used in a Support Vector Machine for margin-based video event classification. Finally, the outputs from these two classifiers are fused together for final decision. The experiments on the TRECVID 2005 corpus demonstrate that the mean average precision is boosted from the best reported 38.2% in [36] to 60.4% based on our new framework.


title: Reward Shaping via Meta-Learning

abstract: Reward shaping is one of the most effective methods to tackle the crucial yet challenging problem of credit assignment in Reinforcement Learning (RL). However, designing shaping functions usually requires much expert knowledge and hand-engineering, and the difficulties are further exacerbated given multiple similar tasks to solve. In this paper, we consider reward shaping on a distribution of tasks, and propose a general meta-learning framework to automatically learn the efficient reward shaping on newly sampled tasks, assuming only shared state space but not necessarily action space. We first derive the theoretically optimal reward shaping in terms of credit assignment in model-free RL. We then propose a value-based meta-learning algorithm to extract an effective prior over the optimal reward shaping. The prior can be applied directly to new tasks, or provably adapted to the task-posterior while solving the task within few gradient updates. We demonstrate the effectiveness of our shaping through significantly improved learning efficiency and interpretable visualizations across various settings, including notably a successful transfer from DQN to DDPG.


Context-Aware Smoothing for Neural Machine Translation


title: Building and Using Comparable Corpora

abstract: The 1990s saw a paradigm change in the use of corpus-driven methods in NLP. In the field of multilingual NLP (such as machine translation and terminology mining) this implied the use of parallel corpora. However, parallel resources are relatively scarce: many more texts are produced daily by native speakers of any given language than translated. This situation resulted in a natural drive towards the use of comparable corpora, i.e. non-parallel texts in the same domain or genre. Nevertheless, this research direction has not produced a single authoritative source suitable for researchers and students coming to the field. The proposed volume providesa reference source, identifying the state of the art in the field as well as future trends. The book is intended for specialists and students in natural language processing, machine translation and computer-assisted translation.


title: Intrusion Detection in 802.11 Networks: Empirical Evaluation of Threats and a Public Dataset

abstract: WiFi has become the de facto wireless technology for achieving short- to medium-range device connectivity. While early attempts to secure this technology have been proved inadequate in several respects, the current more robust security amendments will inevitably get outperformed in the future, too. In any case, several security vulnerabilities have been spotted in virtually any version of the protocol rendering the integration of external protection mechanisms a necessity. In this context, the contribution of this paper is multifold. First, it gathers, categorizes, thoroughly evaluates the most popular attacks on 802.11 and analyzes their signatures. Second, it offers a publicly available dataset containing a rich blend of normal and attack traffic against 802.11 networks. A quite extensive first-hand evaluation of this dataset using several machine learning algorithms and data features is also provided. Given that to the best of our knowledge the literature lacks such a rich and well-tailored dataset, it is anticipated that the results of the work at hand will offer a solid basis for intrusion detection in the current as well as next-generation wireless networks.


Text mining


PHARA: a Personal Health Augmented Reality Assistant to Support Decision-Making at Grocery Stores


title: A review of communication-oriented optical wireless systems

abstract: This article presents an overview of optical wireless (OW) communication systems that operate both in the short- (personal and indoor systems) and the long-range (outdoor and hybrid) regimes. Each of these areas is discussed in terms of (a) key requirements, (b) their application framework, (c) major impairments and applicable mitigation techniques, and (d) current and/or future trends. Personal communication systems are discussed within the context of point-to-point ultra-high speed data transfer. The most relevant application framework and related standards are presented, including the next generation Giga-IR standard that extends personal communication speeds to over 1 Gb/s. As far as indoor systems are concerned, emphasis is given on modeling the dispersive nature of indoor OW channels, on the limitations that dispersion imposes on user mobility and dispersion mitigation techniques. Visible light communication systems, which provide both illumination and communication over visible or hybrid visible/infrared LEDs, are presented as the most important representative of future indoor OW systems. The discussion on outdoor systems focuses on the impact of atmospheric effects on the optical channel and associated mitigation techniques that extend the realizable link lengths and transfer rates. Currently, outdoor OW is commercially available at 10 Gb/s Ethernet speeds for Metro networks and Local-Area-Network interconnections and speeds are expected to increase as faster and more reliable optical components become available. This article concludes with hybrid optical wireless/radio-frequency (OW/RF) systems that employ an additional RF link to improve the overall system reliability. Emphasis is given on cooperation techniques between the reliable RF subsystem and the broadband OW system.


title: Bistatic backscatter radio for power-limited sensor networks

abstract: For applications that require large numbers of wireless sensors spread in a field, backscatter radio can be utilized to minimize the monetary and energy cost of each sensor. Commercial backscatter systems such as those in radio frequency identification (RFID), utilize modulation designed for the bandwidth limited regime, and require medium access control (MAC) protocols for multiple access. High tag/sensor bitrate and monostatic reader architectures result in communication range reduction. In sharp contrast, sensing applications typically require the opposite: extended communication ranges that could be achieved with bitrate reduction and bistatic reader architectures. This work presents non-coherent frequency shift keying (FSK) for bistatic backscatter radio; FSK is appropriate for the power limited regime and also allows many RF tags/sensors to convey information to a central reader simultaneously with simple frequency division multiplexing (FDM). However, classic non-coherent FSK receivers are not directly applicable in bistatic backscatter radio. This work a) carefully derives the complete signal model for bistatic backscatter radio, b) describes the details of backscatter modulation with emphasis on FSK and its corresponding receiver, c) proposes techniques to overcome the difficulties introduced by the utilization of bistatic architectures, such as the carrier frequency offset (CFO), and d) presents bit error rate (BER) performance for the proposed receiver and carrier recovery techniques.


title: Multiband Printed Monopole Slot Antenna for WWAN Operation in the Laptop Computer

abstract: A multiband printed monopole slot antenna promising for operating as an internal antenna in the thin-profile laptop computer for wireless wide area network (WWAN) operation is presented. The proposed antenna is formed by three monopole slots operated at their quarter-wavelength modes and arranged in a compact planar configuration. A step-shaped microstrip feedline is applied to excite the three monopole slots at their respective optimal feeding position, and two wide operating bands at about 900 and 1900 MHz are obtained for the antenna to cover all the five operating bands of GSM850/900/1800/1900/UMTS for WWAN operation. The antenna is easily printed on a small-size FR4 substrate and shows a length of 60 mm only and a height of 12 mm when mounted at the top edge of the system ground plane or supporting metal frame of the laptop display. Details of the proposed antenna are presented and studied.


title: Kafka and Its Using in High-throughput and Reliable Message Distribution

abstract: Nowadays, with the rapid development of distributed computing, message distribution has been more and more important to distributed systems. Kafka was developed to collect and distribute massive messages for distributed systems. It is a high-throughput distributed messaging system. Kafka can always keep stable performance even if it processes millions of messages per second. Messages are persistent in Kafka, and Kafka is able to scale out easily. Besides, it is suitable for both online and offline processing simultaneously. Existing messaging systems all have some disadvantages. In comparison with these messaging systems, Kafka has superior performance. Therefore, Kafka has already been successfully used in production by some companies for a certain time. Though Kafka is still able to be improved in some aspects, it's expected to have broad development prospects.


title: Factors mediating disclosure in social network sites

abstract: In this paper, we explore how privacy settings and privacy policy consumption (reading the privacy policy) affect the relationship between privacy attitudes and disclosure behaviors. We present results from a survey completed by 122 users of Facebook regarding their information disclosure practices and their attitudes about privacy. Based on our data, we develop and evaluate a model for understanding factors that affect how privacy attitudes influence disclosure and discuss implications for social network sites. Our analysis shows that the relationship between privacy attitudes and certain types of disclosures (those furthering contact) are controlled by privacy policy consumption and privacy behaviors. This provides evidence that social network sites could help mitigate concerns about disclosure by providing transparent privacy policies and privacy controls.


Learning Methods for Sequential Decision Making with Imperfect Representations


title: Visual precis generation using coresets

abstract: Given an image stream, our on-line algorithm will select the semantically-important images that summarize the visual experience of a mobile robot. Our approach consists of data pre-clustering using coresets followed by a graph based incremental clustering procedure using a topic based image representation. A coreset for an image stream is a set of representative images that semantically compresses the data corpus, in the sense that every frame has a similar representative image in the coreset. We prove that our algorithm e ciently computes the smallest possible coreset under natural well-defined similarity metric and up to provably small approximation factor. The output visual summary is computed via a hierarchical tree of coresets for di↵erent parts of the image stream. This allows multi-resolution summarization (or a video summary of specified duration) in the batch setting and a memory-e cient incremental summary for the streaming case.


THE IMPORTANCE OF TACIT KNOWLEDGE WITHIN THE ORGANIZATION Orzea Ivona


title: Levels of γ-aminobutyric acid-benzodiazepine receptors in abstinent, alcohol-dependent women : Preliminary findings from an 123I-iomazenil single photon emission tomography study

abstract: Background: Although alcohol dependence in women is an increasing problem, little is known about the effects of alcohol on the female brain. Evidence from a few structural and functional neuroimaging studies suggests that the female brain may be more susceptible than the male brain to the harmful effects of alcohol. However, no in vivo studies of the neuropharmacology of alcohol dependence in women have been carried out. The aim of this preliminary study was to test the hypothesis that alcohol dependence in women is associated with greater reduction in γ-aminobutyric acid (GABA)-benzodiazepine receptor levels than in men with an equivalent drinking history. ::: ::: ::: ::: Methods: We used single photon emission tomography and 123I-iomazenil to label the central GABA-benzodiazepine receptor and to compare semiquantified levels in 9 abstinent alcohol-dependent and 13 control women. These groups were further compared with equivalent male groups from a previous study. ::: ::: ::: ::: Results: There was a trend toward a reduction in GABA-benzodiazepine receptor levels in alcohol-dependent women, but this did not reach significance. These lower levels were seen primarily in the cerebellum, occipital lobes, and parietal cortex (left > right). This was in marked contrast with the pattern of reduction seen in the previous study of male dependence, where significant reductions were seen primarily in the frontal cortex. ::: ::: ::: ::: Conclusions: Due to the semiquantitative analysis performed and the relatively small number of subjects in this study, which resulted in a nonsignificant trend, we can only comment on the differences in the pattern of lower levels of GABA-benzodiazepine receptors seen in alcohol dependence in men and women. Although we are not able to ascertain whether the female brain is more susceptible to the effects of alcohol, it appears that alcohol has a differential effect on the central GABA-benzodiazepine receptors in men and women. Recent animal evidence supports this hypothesis. Future studies should explore whether other neuropharmacological differences exist between men and women in alcohol dependence that could have implications for pharmacotherapy.


title: Single Side Band Direct Detection for Data Center Interconnect and Digital Radio Over Fiber

abstract: Distributed data-centers have emerged as a key architecture for future optical networks to satisfy the ever-growing demand for high speed connectivity and cloud services. This network architecture relies on high speed optical connections with interface rates beyond 100 Gb/s over 80 km. The conventional low-cost IM/DD transmission scheme cannot meet this performance requirement while the full coherent detection solution is still too expensive. As a result, alternative self-coherent detection (SCD) schemes such as single-side-band (SSB) transmission have attracted a lot of attention recently for high speed data center interconnect (DCI) applications. This invited paper provides a thorough overview about the progress of SSB transmission over the last five years. In addition, it also discusses an emerging application of SSB transmission scheme for high capacity digital radio-over-fiber (RoF) systems.


title: AUTOMATIC SHAPE-BASED TARGET EXTRACTION FOR CLOSE-RANGE PHOTOGRAMMETRY

abstract: In order to perform precise identification and location of artificial coded targets in natural scenes, a novel design of circle-based coded target and the corresponding coarse-fine extraction algorithm are presented. The designed target separates the target box and coding box totally and owns an advantage of rotation invariance. Based on the original target, templates are prepared by three geometric transformations and are used as the input of shape-based template matching. Finally, region growing and parity check methods are used to extract the coded targets as final results. No human involvement is required except for the preparation of templates and adjustment of thresholds in the beginning, which is conducive to the automation of close-range photogrammetry. The experimental results show that the proposed recognition method for the designed coded target is robust and accurate.


title: VEWS: A Wikipedia Vandal Early Warning System

abstract: We study the problem of detecting vandals on Wikipedia before any human or known vandalism detection system reports flagging potential vandals so that such users can be presented early to Wikipedia administrators. We leverage multiple classical ML approaches, but develop 3 novel sets of features. Our Wikipedia Vandal Behavior (WVB) approach uses a novel set of user editing patterns as features to classify some users as vandals. Our Wikipedia Transition Probability Matrix (WTPM) approach uses a set of features derived from a transition probability matrix and then reduces it via a neural net auto-encoder to classify some users as vandals. The VEWS approach merges the previous two approaches. Without using any information (e.g. reverts) provided by other users, these algorithms each have over 85% classification accuracy. Moreover, when temporal recency is considered, accuracy goes to almost 90%. We carry out detailed experiments on a new data set we have created consisting of about 33K Wikipedia users (including both a black list and a white list of editors) and containing 770K edits. We describe specific behaviors that distinguish between vandals and non-vandals. We show that VEWS beats ClueBot NG and STiki, the best known algorithms today for vandalism detection. Moreover, VEWS detects far more vandals than ClueBot NG and on average, detects them 2.39 edits before ClueBot NG when both detect the vandal. However, we show that the combination of VEWS and ClueBot NG can give a fully automated vandal early warning system with even higher accuracy.


title: DOI 10.3758/s13421-012-0274-5 Metacognition of the testing effect: Guiding learners to predict the benefits of retrieval

abstract: If the mnemonic benefits of testing are to be widely realized in real-world learning circumstances, people must appreciate the value of testing and choose to utilize testing during self-guided learning. Yet metacognitive judgments do not appear to reflect the enhancement provided by testing Karpicke & Roediger (Science 319:966–968, 2008). In this article, we show that under judicious conditions, learners can indeed reveal an understanding of the beneficial effects of testing, as well as the interaction of that effect with delay (Experiment 1). In that experiment, subjects made judgments of learning (JOLs) for previously studied or previously tested items in either a cue-only or a cue–target context, and either immediately or after a 1-day delay. When subjects made judgments in a cue-only context, their JOLs accurately reflected the effects of testing, both immediately and at a delay. To evaluate the potential of exposure to such conditions for promoting generalized appreciation of testing effects, three further experiments elicited global predictions about restudied and tested items across two study/test cycles (Experiments 2, 3, and 4). The results indicated that learners’ global naive metacognitive beliefs increasingly reflect the beneficial effects of testing when learners experience these benefits with increasing external support. If queried under facilitative circumstances, learners appreciate the mnemonic enhancement that testing provides on both an item-by-item and global basis but generalize that knowledge to future learning only with considerable guidance.


title: Word sense disambiguation in Bengali: A knowledge based approach using Bengali WordNet

abstract: In this paper, a knowledge based approach for Word Sense Disambiguation (WSD) in Bengali language has been presented. Bengali WordNet, developed at ISI Kolkata has been used as a knowledge base and the input data set is prepared from the Bengali Text Corpus developed in the TDIL (Technology Development for Indian Language) project of the Government of India. The proposed approach resolute the exact sense of a Bengali ambiguous word based on the maximum overlap among the dictionary definitions of the ambiguous word, with its collocating words in that sentence and the synonymous words of these collocating words. The algorithm is tested on 9 (nine) mostly used Bengali ambiguous words. The accuracy of the output is achieved 75% which is verified by an expert. The challenges and the pitfalls of this approach are discussed in this report in detail.


title: Online learned discriminative part-based appearance models for multi-human tracking

abstract: We introduce an online learning approach to produce discriminative part-based appearance models (DPAMs) for tracking multiple humans in real scenes by incorporating association based and category free tracking methods. Detection responses are gradually associated into tracklets in multiple levels to produce final tracks. Unlike most previous multi-target tracking approaches which do not explicitly consider occlusions in appearance modeling, we introduce a part based model that explicitly finds unoccluded parts by occlusion reasoning in each frame, so that occluded parts are removed in appearance modeling. Then DPAMs for each tracklet is online learned to distinguish a tracklet with others as well as the background, and is further used in a conservative category free tracking approach to partially overcome the missed detection problem as well as to reduce difficulties in tracklet associations under long gaps. We evaluate our approach on three public data sets, and show significant improvements compared with state-of-art methods.


title: PGSP: a protocol for secure communication in peer-to-peer system

abstract: The Internet today is seeing the evolution of peer-to-peer (P2P) applications and interaction. Such interaction implies a direct communication between two end nodes of the Internet. P2P applications currently are facilitated by a central server, largely to ensure the authenticity of users. However; we foresee two issues with such a system - time/bandwidth usage for facilitation and non availability of a central facilitation server for P2P networks. We propose a security protocol called PGSP (peer group security protocol), relying on tamperproof hardware, to authenticate two peer nodes mutually. PGSP also establishes a secure channel between any two nodes without any central facilitation and, thus, allows for the two nodes to exchange a public-private key pair at the time of interaction. PGSP fits well with the resurrecting duckling security paradigm (Stajano, F. and Anderson, R., Proc. 3rd AT&T Software Symp., 1999). Once the hardware is imprinted for authentication, PGSP is robust against man-in-the-middle attack, passive eavesdropping and active impersonation attacks, ensuring source authentication, data confidentiality and data integrity. The proposed scheme is scalable to the addition of both new nodes and new P2P applications/groups to every node, and is cost-effective.


title: Coordinated multipoint: Concepts, performance, and field trial results

abstract: Coordinated multipoint or cooperative MIMO is one of the promising concepts to improve cell edge user data rate and spectral efficiency beyond what is possible with MIMOOFDM in the first versions of LTE or WiMAX. Interference can be exploited or mitigated by cooperation between sectors or different sites. Significant gains can be shown for both the uplink and downlink. A range of technical challenges were identified and partially addressed, such as backhaul traffic, synchronization and feedback design. This article also shows the principal feasibility of COMP in two field testbeds with multiple sites and different backhaul solutions between the sites. These activities have been carried out by a powerful consortium consisting of universities, chip manufacturers, equipment vendors, and network operators.


title: Learning in the Model Space for Fault Diagnosis

abstract: The emergence of large scaled sensor networks facilitates the collection of large amounts of real-time data to monitor and control complex engineering systems. However, in many cases the collected data may be incomplete or inconsistent, while the underlying environment may be time-varying or un-formulated. In this paper, we have developed an innovative cognitive fault diagnosis framework that tackles the above challenges. This framework investigates fault diagnosis in the model space instead of in the signal space. Learning in the model space is implemented by fitting a series of models using a series of signal segments selected with a rolling window. By investigating the learning techniques in the fitted model space, faulty models can be discriminated from healthy models using one-class learning algorithm. The framework enables us to construct fault library when unknown faults occur, which can be regarded as cognitive fault isolation. This paper also theoretically investigates how to measure the pairwise distance between two models in the model space and incorporates the model distance into the learning algorithm in the model space. The results on three benchmark applications and one simulated model for the Barcelona water distribution network have confirmed the effectiveness of the proposed framework.


title: A smart metering system for wireless power measurement with mobile application

abstract: At present, advanced meter enabled with wireless machine-to-machine (M2M) communication can play a pivotal role for energy measurement in smart grid. However, the traditional metering and billing system implemented in developing countries (e.g. Bangladesh) even some developed countries is too non-automated and complex that leads a wake-up call to establish a robust and smart metering system to measure energy for user convenience. In this paper, an automated smart metering system is presented to measure energy consumption wirelessly for residential/ industrial areas. The measurement system is an ARDUINO based master-slave wireless technology that incorporates with smart billing payment scheme via Android mobile application. This wireless technology requires a pair of RF transmitter and receiver for transmitting energy consumption data, and for subsequent billing and payment system. Here the authors also measure the performance of the proposed system analytically.


title: Speaker-independent phone recognition using hidden Markov models

abstract: Hidden Markov modeling is extended to speaker-independent phone recognition. Using multiple codebooks of various linear-predictive-coding (LPC) parameters and discrete hidden Markov models (HMMs) the authors obtain a speaker-independent phone recognition accuracy of 58.8-73.8% on the TIMIT database, depending on the type of acoustic and language models used. In comparison, the performance of expert spectrogram readers is only 69% without use of higher level knowledge. The authors introduce the co-occurrence smoothing algorithm, which enables accurate recognition even with very limited training data. Since the results were evaluated on a standard database, they can be used as benchmarks to evaluate future systems. >


title: Resistance to change: a literature review and empirical study

abstract: This paper examines organizational change, focusing on the distinction of changes according to their scope and presenting a typology of evolutionary and strategic changes. It also offers an in‐depth study of resistance to change. Through empirical research, the paper analyzes the importance of the sources of resistance to change defined theoretically, and considers both types of changes. The article shows which sources of resistance differ most, according to the scope of change, offering hints about where organizations should pay special attention when initiating a change process.


title: Negative Capacitance beyond Ferroelectric Switches.

abstract: Negative capacitance transistors are a unique class of switches capable of operation beyond the Boltzmann limit to realize subthermionic switching. To date, the negative capacitance effect has been predominantly attributed to devices employing an unstable insulator with ferroelectric properties, exhibiting a two-well energy landscape, in accordance with the Landau theory. The theory and operation of a solid electrolyte field effect transistor (SE-FET) of subthreshold swing less than 60 mV/dec in the absence of a ferroelectric gate dielectric are demonstrated in this work. Unlike ferroelectric FETs that rely on a sudden switching of dipoles to achieve negative capacitance, we demonstrate a distinctive mechanism that relies on the accumulation and dispersion of ions at the interfaces of the oxide, leading to a subthreshold slope (SS) as low as 26 mV/dec in these samples. The frequency of operation of these unscaled devices lies in a few millihertz because at higher or lower frequencies, the ions in the insulator are either too fast or too slow to produce voltage amplification. This is unlike Landau switches, where the SS remains below 60 mV/dec even under quasi-static sweep of the gate bias. The proposed FETs show a higher on-current with a thicker oxide in the entire range of gate voltage, clearly distinguishing their scaling laws from those of ferroelectric FETs. Our theory, validated with experiment, demonstrates a new class of devices capable of negative capacitance that opens up alternate methods of steep switching beyond the traditional approach of ferroelectric or memristive FETs.


title: Equivalence between priority queues and sorting

abstract: We present a general deterministic linear space reduction from priority queues to sorting implying that if we can sort up to n keys in S(n) time per key, then there is a priority queue supporting delete and insert in O(S(n)) time and find-min in constant time. Conversely, a priority queue can trivially be used for sorting: first insert all keys to be sorted, then extract them in sorted order by repeatedly deleting the minimum. Asymptotically, this settles the complexity of priority queues in terms of that of sorting. Previously, at SODA'96, such a result was presented by the author for the special case of monotone priority queues where the minimum is not allowed to decrease. Besides nailing down the complexity of priority queues to that of sorting, and vice versa, our result yields several improved bounds for linear space integer priority queues with find-min in constant time: Deterministically. We get O(log log n) update time using a sorting algorithm of Han from STOC'02. This improves the O((log log n)(log log log n)) update time of Han from SODA'01. Randomized. We get O(slog log n) expected update time using a randomized sorting algorithm of Han and Thorup from FOCS'02. This improves the O(log log n) expected update time of Thorup from SODA'96. Deterministically in AC0 (without multiplication). For any e > 0, we get O((log log n)1pe) update time using an AC0 sorting algorithm of Han and Thorup from FOCS'02. This improves the O((log log n)2) update time of Thorup from SODA'98. Randomized in AC0. We get O(log log n) expected update time using a randomized AC0 sorting algorithm of Thorup from SODA'97. This improves the O((log log n)1pe) expected update time of Thorup also from SODA'97. The above bounds assume that each integer is stored in a single word and that word operations take unit time as in the word RAM.


Medico-legal issues in cardiopulmonary resuscitation and defibrillation.


title: CS 0.5: a better approach to introductory computer science for majors

abstract: There are often problems when students enter a course with widely different experience levels with key course topics. If the material is covered too slowly, those with greater experience get bored and lose interest. If the material is covered too quickly, those with less experience get lost and feel incompetent. This problem with incoming students of our Computer Science Major led us to create CS 0.5: an introductory Computer Science course to target those CS majors who have little or no background with programming. Our goal is to provide these students with an engaging curriculum and prepare them to keep pace in future courses with those students who enter with a stronger background. Following the lead of Mark Guzdial's work on using media computation for non-majors at Georgia Tech, we use media computation as the tool to provide this engaging curriculum. We report here on our experience to date using the CS 0.5 approach with a media computation course.


title: Reinforcement Learning-SLAM for finding minimum cost path and mapping

abstract: In this work, we propose the integration of two of the most widely used approaches for the implementation of autonomous navigation systems: the reinforcement learning for path finding, along with SLAM (Simultaneous Localization and Mapping) type algorithms for localization and mapping of the environment. These two approaches are integrated to address the problem of how a robot should explore an unknown and dynamic environment while it collects perception features in order to locate itself and, at the same time, to obtain information clues about cost traversability of an area. So, when a robot is exploring and mapping with a SLAM algorithm it is also learning to associate perception features with costs and actions to find optimal paths from the starting point to the goal point in dynamical environments.


title: Inquire: Large-scale Early Insight Discovery for Qualitative Research

abstract: We introduce Inquire, a tool designed to enable qualitative exploration of utterances in social media and large-scale texts. As opposed to keyword search, Inquire allows the effective use of sentences as queries to quickly explore millions of documents to retrieve semantically-similar sentences. We apply Inquire to LiveJournal.com (LJ) database, which contains millions of personal diaries, and we use semantic embeddings trained in LJ or Google News (GN) datasets. We present the system design through iterative evaluations with qualitative researchers. We show how queries become a part of the inductive process, enabling researchers to try multiple ideas while gaining intuition and discovering less-obvious insights. We discuss the choice of LJ as a rich source of public posts, the preference for GN embeddings which link formal language (e.g. "reminiscence triggers") with colloquial expressions (e.g. "music brings back memories"), the interplay between tool and user, and potential qualitative and social research opportunities.


title: Improving Elevator Performance Using Reinforcement Learning

abstract: This paper describes the application of reinforcement learning (RL) to the difficult real world problem of elevator dispatching. The elevator domain poses a combination of challenges not seen in most RL research to date. Elevator systems operate in continuous state spaces and in continuous time as discrete event dynamic systems. Their states are not fully observable and they are nonstationary due to changing passenger arrival rates. In addition, we use a team of RL agents, each of which is responsible for controlling one elevator car. The team receives a global reinforcement signal which appears noisy to each agent due to the effects of the actions of the other agents, the random nature of the arrivals and the incomplete observation of the state. In spite of these complications, we show results that in simulation surpass the best of the heuristic elevator control algorithms of which we are aware. These results demonstrate the power of RL on a very large scale stochastic dynamic optimization problem of practical utility.


title: Adaptive Behavior Generation for Autonomous Driving using Deep Reinforcement Learning with Compact Semantic States

abstract: Making the right decision in traffic is a challenging task that is highly dependent on individual preferences as well as the surrounding environment. Therefore it is hard to model solely based on expert knowledge. In this work we use Deep Reinforcement Learning to learn maneuver decisions based on a compact semantic state representation. This ensures a consistent model of the environment across scenarios as well as a behavior adaptation function, enabling on-line changes of desired behaviors without re-training. The input for the neural network is a simulated object list similar to that of Radar or Lidar sensors, superimposed by a relational semantic scene description. The state as well as the reward are extended by a behavior adaptation function and a parameterization respectively. With little expert knowledge and a set of mid-level actions, it can be seen that the agent is capable to adhere to traffic rules and learns to drive safely in a variety of situations.


title: The Development and Implementation of Scaffolding-Based Self-Regulated Learning System for e/m-Learning

abstract: This paper proposes a self-regulated learning (SRL) system with scaffolding support in order to develop independent learning skills among students. The SRL system uses self-regulated learning and scaffolding theories to appeal to both instructors and learners. On the part of the instructors, a Content Accessibility Subsystem is provided to easily organize learning materials and to dynamically provide different levels of support for their learners. As for the learners, many subsystems are proposed that provide a conducive mobile learning environment for them. With the application of the scaffolding theory, the system can easily adjust to provide help to the learners, facilitating SRL processes anytime and anywhere, and establishing the learners’ SRL patterns gradually. The learners in the experiment deemed that that the proposed system could provide them selfregulatory attributes. The experiment results show that the average SRL score of learners increases, though the improvement is not significant. However, the result also showed that the SR skills of students in the group of Low SR significantly improved.


title: Infection, Incest, and Iniquity: Investigating the Neural Correlates of Disgust and Morality

abstract: Disgust, an emotion related to avoiding harmful substances, has been linked to moral judgments in many behavioral studies. However, the fact that participants report feelings of disgust when thinking about feces and a heinous crime does not necessarily indicate that the same mechanisms mediate these reactions. Humans might instead have separate neural and physiological systems guiding aversive behaviors and judgments across different domains. The present interdisciplinary study used functional magnetic resonance imaging (n = 50) and behavioral assessment to investigate the biological homology of pathogen-related and moral disgust. We provide evidence that pathogen-related and sociomoral acts entrain many common as well as unique brain networks. We also investigated whether morality itself is composed of distinct neural and behavioral subdomains. We provide evidence that, despite their tendency to elicit similar ratings of moral wrongness, incestuous and nonsexual immoral acts entrain dramatically separate, while still overlapping, brain networks. These results (i) provide support for the view that the biological response of disgust is intimately tied to immorality, (ii) demonstrate that there are at least three separate domains of disgust, and (iii) suggest strongly that morality, like disgust, is not a unified psychological or neurological phenomenon.


Exploration of Digital Game Players ’ Superpower Behavior from the Perspective of a Desirous Subject


title: Post-Quantum Cryptography on FPGA Based on Isogenies on Elliptic Curves

abstract: To the best of our knowledge, we present the first hardware implementation of isogeny-based cryptography available in the literature. Particularly, we present the first implementation of the supersingular isogeny Diffie-Hellman (SIDH) key exchange, which features quantum-resistance. We optimize this design for speed by creating a high throughput multiplier unit, taking advantage of parallelization of arithmetic in $\mathbb {F}_{p^{2}}$ , and minimizing pipeline stalls with optimal scheduling. Consequently, our results are also faster than software libraries running affine SIDH even on Intel Haswell processors. For our implementation at 85-bit quantum security and 128-bit classical security, we generate ephemeral public keys in 1.655 million cycles for Alice and 1.490 million cycles for Bob. We generate the shared secret in an additional 1.510 million cycles for Alice and 1.312 million cycles for Bob. On a Virtex-7, these results are approximately 1.5 times faster than known software implementations running the same 512-bit SIDH. Our results and observations show that the isogeny-based schemes can be implemented with high efficiency on reconfigurable hardware.


title: 11-Level cascaded H-bridge grid-tied inverter interface with solar panels

abstract: This paper presents a single-phase 11-level (5 H-bridges) cascade multilevel DC-AC grid-tied inverter. Each inverter bridge is connected to a 200 W solar panel. OPAL-RT lab was used as the hardware in the loop (HIL) real-time control system platform where a Maximum Power Point Tracking (MPPT) algorithm was implemented based on the inverter output power to assure optimal operation of the inverter when connected to the power grid as well as a Phase Locked Loop (PLL) for phase and frequency match. A novel SPWM scheme is proposed in this paper to be used with the solar panels that can account for voltage profile fluctuations among the panels during the day. Simulation and experimental results are shown for voltage and current during synchronization mode and power transferring mode to validate the methodology for grid connection of renewable resources.


title: Relational completeness of data base sublanguages

abstract: In the near future, we can expect a great variety of languages to be proposed for interrogating and updating data bases. This paper attempts to provide a theoretical basis which may be used to determine how complete a selection capability is provided in a proposed data sublanguage independently of any host language in which the sublanguage may be embedded. A relational algebra and a relational calculus are defined. Then, an algorithm is presented for reducing an arbitrary relation-defining expression (based on the calculus) into a semantically equivalent expression of the relational algebra. Finally, some opinions are stated regarding the relative merits of calculusoriented versus algebra-oriented data sublanguages from the standpoint of optimal search and highly discriminating authorization schemes. RJ 987 Hl7041) March 6, 1972 Computer Sciences


title: Progress on smart focal plane technologies for extremely large telescopes

abstract: A key instrument for an Extremely Large Telescope (ELT) is likely to be multi-object spectrometer which observes at least 100 discrete sources with diffraction limited spatial resolution and moderate spectral resolution in the wavelength region from 1.0 to 2.5 μm. Such an instrument has been chosen as the principal driver for the Smart Focal Planes technology development project which has brought together 14 companies and institutes in Europe and Australia. An overview of a new ELT instrument concept based upon beam manipulators (including novel 'starbug' miniature robots) is presented; supported by a summary of scientific goals and systems requirements. Progress made on specific support technology studies is also presented, including work on image slicer replication and cryogenic reconfigurable slits.


title: Fast Ego-motion Estimation with Multi-rate Fusion of Inertial and Vision

abstract: This paper presents a tracking system for ego-motion estimation which fuses vision and inertial measurements using EKF and UKF (Extended and Unscented Kalman Filters), where a comparison of their performance has been done. It also considers the multi-rate nature of the sensors: inertial sensing is sampled at a fast sampling frequency while the sampling frequency of vision is lower. the proposed approach uses a constant linear acceleration model and constant angular velocity model based on quaternions, which yields a non-linear model for states and a linear model in measurement equations. Results show that a significant improvement is obtained on the estimation when fusing both measurements with respect to just vision or just inertial measurements. It is also shown that the proposed system can estimate fast-motions even when vision system fails. Moreover, a study of the influence of the noise covariance is also performed, which aims to select their appropriate values at the tuning process. The setup is an end-effector mounted camera, which allow us to pre-define basic rotational and translational motions for validating results.


title: Key points in dermoscopic differentiation between early acral melanoma and acral nevus

abstract: Acral skin is the most prevalent site of malignant melanoma in non-Caucasian populations. On acral skin, other various kinds of pigmented lesions are also detected. Particularly, melanocytic nevus is commonly seen on acral volar skin; approximately 10% of Japanese have a nevus on their soles. Prognosis of acral melanoma is still generally poor because of delayed detection in the advanced stages. To improve the prognosis, early detection is essential. Early acral melanoma is seen as a brownish macule, which is clinically quite similar to acral nevus. Therefore, clinicians often face a dilemma when they see a pigmented macule on acral volar skin. Introduction of dermoscopy was a great epoch in this field. Pigmentation pattern on dermoscopy is completely opposite between early acral melanoma and acral nevus; pigmentation on the ridges of the surface skin markings is detected in early acral melanoma, whereas pigmentation along the furrows of the skin markings is seen in acral nevus. We termed these dermoscopic patterns the parallel ridge pattern and the parallel furrow pattern, respectively. These features are highly helpful in the differentiation between the two biologically distinct entities. The sensitivity and specificity of the parallel ridge pattern in diagnosing early acral melanoma is 86% and 99%, respectively. However, we must be aware that dermoscopic features in acral nevus sometimes mimic the parallel ridge pattern and that other conditions also could show dermoscopic features similar to the parallel ridge pattern. In this review article, we summarize key points of the dermoscopic diagnosis of early acral melanoma and then describe the three-step algorithm for the management of acral melanocytic lesions, which surely aids us in effectively detecting early acral melanoma and in reducing unnecessary resection of benign nevus.


title: Towards practical, high-capacity, low-maintenance information storage in synthesized DNA

abstract: Digital production, transmission and storage have revolutionized how we access and use information but have also made archiving an increasingly complex task that requires active, continuing maintenance of digital media. This challenge has focused some interest on DNA as an attractive target for information storage because of its capacity for high-density information encoding, longevity under easily achieved conditions and proven track record as an information bearer. Previous DNA-based information storage approaches have encoded only trivial amounts of information or were not amenable to scaling-up, and used no robust error-correction and lacked examination of their cost-efficiency for large-scale information archival. Here we describe a scalable method that can reliably store more information than has been handled before. We encoded computer files totalling 739 kilobytes of hard-disk storage and with an estimated Shannon information of 5.2 × 10(6) bits into a DNA code, synthesized this DNA, sequenced it and reconstructed the original files with 100% accuracy. Theoretical analysis indicates that our DNA-based storage scheme could be scaled far beyond current global information volumes and offers a realistic technology for large-scale, long-term and infrequently accessed digital archiving. In fact, current trends in technological advances are reducing DNA synthesis costs at a pace that should make our scheme cost-effective for sub-50-year archiving within a decade.


title: Energy efficiency analysis with circuit power consumption in massive MIMO systems

abstract: It was shown that the required transmit power to support a target achievable rate is inversely proportional to the number of antennas in massive multiple-input multiple-output (MIMO) systems [1]. However, the consumed power of the massive MIMO systems should include not only transmit power but also the fundamental power for operating the circuit at the transmitter, because the effect of circuit power consumption is more serious when the transmitter is equipped with massive number of antennas. Hence, to analyze the exact power consumption of massive MIMO systems, we investigate the energy efficiency for multiple cellular systems with large-scale antenna arrays under a consideration of circuit power consumption of each antenna. In particular, we propose a new power consumption model that considers not only transmit power on the power amplifier but also circuit power dissipated by analog devices and residually lossy factors in base stations (BSs). Through new energy efficiency formulation based on the proposed power consumption model, we analyze the tendency of the energy efficiency as the number of antennas increases and can see that the energy efficiency becomes a quasi-concave function with respect to the number of antennas. Finally, from the derived function of the energy efficiency, we determine the number of antennas to provide the maximum energy efficiency.


title: Large Scale Text Classification Using Map Reduce and Naive Bayes Algorithm for Domain Specified Ontology Building

abstract: Internet that covers a large information gives an opportunity to obtain knowledge from it. Internet contains large unstructured and unorganized data such as text, video, and image. Problems arise on how to organize large amount of data and obtain a useful information from it. This information can be used as knowledge in the intelligent computer system. Ontology as one of knowledge representation covers a large area topic. For constructing domain specified ontology, we use large text dataset on Internet and organize it into specified domain before ontology building process is done. We try to implement naive bayes text classifier using map reduce programming model in our research for organizing our large text dataset. In this experiment, we use animal and plant domain article in Wikipedia online encyclopedia as our dataset. Our proposed method can achieve highest accuracy with score about 98.8%. This experiment shows that our proposed method provides a robust system and good accuracy for classifying document into specified domain in preprocessing phase for domain specified ontology building.


title: The next information architecture evolution: the data lake wave

abstract: Data warehouses and data marts have long been considered as the unique solution for providing end-users with decisional information. More recently, data lakes have been proposed in order to govern data swamps. However, no formal definition has been proposed in the literature. Existing works are not complete and miss important parts of the topic. In particular, they do not focus on the influence of the data gravity, the infrastructure role of those solutions and of course are proposing divergent definitions and positioning regarding the usage and the interaction with existing decision support system. In this paper, we propose a novel definition of data lakes, together with a comparison with other over several criteria as the way to populate them, how to use, what is the Data Lake end user profile. We claim that data lakes are complementary components in decisional information systems and we discuss their position and interactions regarding the other components by proposing an interaction model.


title: A computational framework for context-aware adaptation of user interfaces

abstract: In order to address challenges posed by different users conducting their interactive tasks on heterogeneous platforms and devices in various environments, this paper provides a computational framework to support the adaptation of the user interface of interactive systems. This framework consists of: a meta-model for understanding fundamental concepts required by adaptation, a reference framework for characterizing seven dimensions for conducting adaptation based on the meta-model, and a design space for consistently assessing the adaptation coverage. In this way, development phases are considered with a standard approach, a unified terminology, and an extensive catalog of techniques.


title: Photoacoustic Resonance Imaging

abstract: In this paper, a new imaging modality, named photoacoustic resonance imaging (PARI), is proposed and experimentally demonstrated. Being distinct from conventional single nanosecond laser pulse induced wideband PA signal, the proposed PARI method utilizes multiburst modulated laser source to induce PA resonant signal with enhanced signal strength and narrower bandwidth. Moreover, imaging contrast could be clearly improved than conventional single-pulse laser based PA imaging by selecting optimum modulation frequency of the laser source, which originates from physical properties of different materials beyond the optical absorption coefficient. Experimental validation on phantoms and ex vivo tissues have been performed to show the merits of the proposed PARI method with much improved image contrast.


title: Bike Angels: An Analysis of Citi Bike's Incentive Program

abstract: Bike-sharing systems provide a sustainable and affordable transportation alternative in many American cities. However, they also face intricate challenges due to imbalance, caused by asymmetric traffic demand. That imbalance often-times leads to bike-sharing stations being empty (full), causing out-of-stock events for customers that want to rent (return) bikes at such stations. In recent years, the study of data-driven methods to help support the operation of such system, has developed as a popular research area. In this paper, we study the impact of Bike Angels, an incentive program New York City's Citi Bike system set up in 2015 to crowd-source some of its operational challenges related to imbalance. We develop a performance metric for both online- and offline-policies to set incentives within the system; our results indicate that though Citi Bike's original offline policy performed well in a regime in which incentives given to customers are not associated to costs, there is ample space for improvement when the costs of the incentives are taken into consideration. Motivated by these findings, we develop several online- and offline- policies to investigate the trade-offs between real-time and offline decision-making; one of our online policies has since been adopted by Citi Bike.


title: To Belong Is to Matter Sense of Belonging Enhances Meaning in Life

abstract: In four methodologically diverse studies (N = 644), we found correlational (Study 1), longitudinal (Study 2), and experimental (Studies 3 and 4) evidence that a sense of belonging predicts how meaningful life is perceived to be. In Study 1 (n = 126), we found a strong positive correlation between sense of belonging and meaningfulness. In Study 2 (n = 248), we found that initial levels of sense of belonging predicted perceived meaningfulness of life, obtained 3 weeks later. Furthermore, initial sense of belonging predicted independent evaluations of participants essays on meaning in life. In Studies 3 (n = 105) and 4 (n = 165), we primed participants with belongingness, social support, or social value and found that those primed with belongingness (Study 3) or who increased in belongingness (Study 4) reported the highest levels of perceived meaning. In Study 4, belonging mediated the relationship between experimental condition and meaning.


title: On traveling path and related problems for a mobile station in a rechargeable sensor network

abstract: Wireless power transfer is a promising technology to fundamentally address energy problems in a wireless sensor network. To make such a technology work effectively, a vehicle is needed to carry a charger to travel inside the network. On the other hand, it has been well recognized that a mobile base station offers significant advantages over a fixed one. In this paper, we investigate an interesting problem of co-locating the mobile base station on the wireless charging vehicle. We study an optimization problem that jointly optimizes traveling path, stopping points, charging schedule, and flow routing. Our study is carried out in two steps. First, we study an idealized problem that assumes zero traveling time, and develop a provably near-optimal solution to this idealized problem. In the second step, we show how to develop a practical solution with non-zero traveling time and quantify the performance gap between this solution and the unknown optimal solution to the original problem.


title: Self-Learning Power Control in Wireless Sensor Networks

abstract: Current trends in interconnecting myriad smart objects to monetize on Internet of Things applications have led to high-density communications in wireless sensor networks. This aggravates the already over-congested unlicensed radio bands, calling for new mechanisms to improve spectrum management and energy efficiency, such as transmission power control. Existing protocols are based on simplistic heuristics that often approach interference problems (i.e., packet loss, delay and energy waste) by increasing power, leading to detrimental results. The scope of this work is to investigate how machine learning may be used to bring wireless nodes to the lowest possible transmission power level and, in turn, to respect the quality requirements of the overall network. Lowering transmission power has benefits in terms of both energy consumption and interference. We propose a protocol of transmission power control through a reinforcement learning process that we have set in a multi-agent system. The agents are independent learners using the same exploration strategy and reward structure, leading to an overall cooperative network. The simulation results show that the system converges to an equilibrium where each node transmits at the minimum power while respecting high packet reception ratio constraints. Consequently, the system benefits from low energy consumption and packet delay.


title: A Culture–Behavior–Brain Loop Model of Human Development

abstract: Increasing evidence suggests that cultural influences on brain activity are associated with multiple cognitive and affective processes. These findings prompt an integrative framework to account for dynamic interactions between culture, behavior, and the brain. We put forward a culture–behavior–brain (CBB) loop model of human development that proposes that culture shapes the brain by contextualizing behavior, and the brain fits and modifies culture via behavioral influences. Genes provide a fundamental basis for, and interact with, the CBB loop at both individual and population levels. The CBB loop model advances our understanding of the dynamic relationships between culture, behavior, and the brain, which are crucial for human phylogeny and ontogeny. Future brain changes due to cultural influences are discussed based on the CBB loop model.


title: Intuitive IoT-based H2U healthcare system for elderly people

abstract: The development on newer healthcare services for the elderly citizens has become an immediate necessity today. There have been distinctive health challenges focused in the society through technical innovations. Most of the elderly people today experiences loneliness and psychological depressions, either as a result of living alone/ abandonment or due to reduced connection with their children and relatives. To enhance the quality of services in the elderly healthcare system we have developed a ubiquitous intuitive IoT-based (Internet of Things) Help to You (H2U) healthcare system to integrate various technologies of wearable devices, biosensors and wireless sensor networks in order to provide an intensive service management platform. This method would support the real time activity and monitor the healthcare system for the elderly citizens. In this method the information collected by various wearable devices in real time are stored in the central database which thereby connects people, doctors, and practitioner at the time of an emergency for the right information. This way the system could increase accessibility, efficiency, and also lower the health expenses to improve the comfort and safety as well as management of daily routines of an elderly life.


title: Using data mining techniques for diagnosis and prognosis of cancer disease

abstract: Breast cancer is one of the leading cancers for women in developed countries including India. It is the second most common cause of cancer death in women. The high incidence of breast cancer in women has increased significantly in the last years. In this paper we have discussed various data mining approaches that have been utilized for breast cancer diagnosis and prognosis. Breast Cancer Diagnosis is distinguishing of benign from malignant breast lumps and Breast Cancer Prognosis predicts when Breast Cancer is to recur in patients that have had their cancers excised. This study paper summarizes various review and technical articles on breast cancer diagnosis and prognosis also we focus on current research being carried out using the data mining techniques to enhance the breast cancer diagnosis and prognosis.


title: A review on electromagnetic suspension systems for passenger vehicle

abstract: This paper discussing all the design literature review for electromagnetic suspension systems for passenger vehicle. Electromagnetic suspension is the alternative for existing conventional suspension system that uses passive suspension system. Generally, linear motor is used in the design of the suspension. This is due to the behavior of the motor that can exert linear force directly to the attached load. In addition, the linear force from linear motor is controllable. This paperreview with the effects of all types of electromagnetic suspension systems to the passenger's comfort. The reliability of fully active suspension system for vehicle also highlighted in this paper. A quarter-car model is used to assess the vehicle body vibration. This paper also deals with regenerative properties of linear motor that can improve the performance of lightweight vehicle.


title: Compact query term selection using topically related text

abstract: Many recent and highly effective retrieval models for long queries use query reformulation methods that jointly optimize term weights and term selection. These methods learn using word context and global context but typically fail to capture query context. In this paper, we present a novel term ranking algorithm, PhRank, that extends work on Markov chain frameworks for query expansion to select compact and focused terms from within a query itself. This focuses queries so that one to five terms in an unweighted model achieve better retrieval effectiveness than weighted term selection models that use up to 30 terms. PhRank terms are also typically compact and contain 1-2 words compared to competing models that use query subsets up to 7 words long. PhRank captures query context with an affinity graph constructed using word co-occurrence in pseudo-relevant documents. A random walk of the graph is used for term ranking in combination with discrimination weights. Empirical evaluation using newswire and web collections demonstrates that performance of reformulated queries is significantly improved for long queries and at least as good for short, keyword queries compared to highly competitive information retrieval (IR) models.


title: LateralPaD: A surface-haptic device that produces lateral forces on a bare finger

abstract: The LateralPaD is a surface haptic device that generates lateral (shear) force on a bare finger, by vibrating the touch surface simultaneously in both out-of-plane (normal) and in-plane (lateral) directions. A prototype LateralPaD has been developed in which the touch surface is glass, and piezoelectric actuators drive normal and lateral resonances at the same ultrasonic frequency (∼22.3 KHz). The force that develops on the finger can be controlled by modulating the relative phase of the two resonances. A 2DOF load cell setup is used to characterize the dependence of induced lateral force on vibration amplitude, relative phase, and applied normal force. A Laser Doppler Vibrometer (LDV) is used to measure the motion of glass surface as well as of the fingertip. Together, these measurements yield insight into the mechanism of lateral force generation. We show evidence for a mechanism dependent on tilted impacts between the LateralPaD and fingertip.


title: Templateless Non-Rigid Reconstruction and Motion Tracking With a Single RGB-D Camera

abstract: We present a novel templateless approach for nonrigid reconstruction and motion tracking using a single RGB-D camera. Without any template prior, our system achieves accurate reconstruction and tracking for considerably deformable objects. To robustly register the input sequence of partial depth scans with dynamic motion, we propose an efficient local-to-global hierarchical optimization framework inspired by the idea of traditional structure-from-motion. Our proposed framework mainly consists of two stages, local nonrigid bundle adjustment and global optimization. To eliminate error accumulation during the nonrigid registration of loop motion sequences, we split the full sequence into several segments and apply local nonrigid bundle adjustment to align each segment locally. Global optimization is then adopted to combine all segments and handle the drift problem through loop-closure constraint. By fitting to the input partial data, a deforming 3D model sequence of dynamic objects is finally generated. Experiments on both synthetic and real test data sets and comparisons with state of the art demonstrate that our approach can handle considerable motions robustly and efficiently, and reconstruct high-quality 3D model sequences without drift.


title: Automatic Segmentation of Brain Tumor using K-Means Clustering and its Area Calculation

abstract: In this paper, a simple algorithm for detecting the range and shape of tumor in brain MR Images is described. Generally, CT scan or MRI that is directed into intracranial cavity produces a complete image of brain. This image is visually examined by the physician for detection and diagnosis of brain tumor. To avoid that, this project uses computer aided method for segmentation (detection) of brain tumor based on the combination of two algorithms. This method allows the segmentation of tumor tissue with accuracy and reproducibility comparable to manual segmentation. In addition, it also reduces the time for analysis. At the end of the process the tumor is extracted from the MR image and its exact position and the shape also determined. The stage of the tumor is displayed based on the amount of area calculated from the cluster. physician gives the treatment for the strokes rather than the treatment for tumor. So detection of tumor is important for that treatment. They are mass and malignant. The detection of the malignant tumor is somewhat difficult to mass tumor. For the accurate detection of the malignant tumor that needs a 3-D representation of brain and 3-D analyzer tool. The developing platform for the detection is mat lab. Because it is easy to developed and execute. At the end, we are providing systems that detect the tumor and its shape.


Personalized Natural Language Understanding


title: Graph Laplacian Regularization for Inverse Imaging: Analysis in the Continuous Domain

abstract: Inverse imaging problems are inherently under-determined, and hence it is important to employ appropriate image priors for regularization. One recent popular prior---the graph Laplacian regularizer---assumes that the target pixel patch is smooth with respect to an appropriately chosen graph. However, the mechanisms and implications of imposing the graph Laplacian regularizer on the original inverse problem are not well understood. To address this problem, in this paper we interpret neighborhood graphs of pixel patches as discrete counterparts of Riemannian manifolds and perform analysis in the continuous domain, providing insights into several fundamental aspects of graph Laplacian regularization. Specifically, we first show the convergence of the graph Laplacian regularizer to a continuous-domain functional, integrating a norm measured in a locally adaptive metric space. Focusing on image denoising, we derive an optimal metric space assuming nonlocal self-similarity of pixel patches, leading to an optimal graph Laplacian regularizer for denoising in the discrete domain. We then interpret graph Laplacian regularization as an anisotropic diffusion scheme to explain its behavior during iterations, e.g., its tendency to promote piecewise smooth signals under certain settings. To verify our analysis, an iterative image denoising algorithm is developed. Experimental results show that our algorithm performs competitively with state-of-the-art denoising methods such as BM3D for natural images, and outperforms them significantly for piecewise smooth images.


title: A 2.4-GHz 0.18-/spl mu/m CMOS self-biased cascode power amplifier

abstract: A two-stage self-biased cascode power amplifier in 0.18-/spl mu/m CMOS process for Class-1 Bluetooth application is presented. The power amplifier provides 23-dBm output power with a power-added efficiency (PAE) of 42% at 2.4 GHz. It has a small signal gain of 38 dB and a large signal gain of 31 dB at saturation. This is the highest gain reported for a two-stage design in CMOS at the 0.8-2.4-GHz frequency range. A novel self-biasing and bootstrapping technique is presented that relaxes the restriction due to hot carrier degradation in power amplifiers and alleviates the need to use thick-oxide transistors that have poor RF performance compared with the standard transistors available in the same process. The power amplifier shows no performance degradation after ten days of continuous operation under maximum output power at 2.4-V supply. It is demonstrated that a sliding bias technique can be used to both significantly improve the PAE at mid-power range and linearize the power amplifier. By using the sliding bias technique, the PAE at 16 dBm is increased from 6% to 19%, and the gain variation over the entire power range is reduced from 7 to 0.6 dB.


title: A sensorless force control in twin drive system

abstract: This article describes the method of a sensorless force control in a twin drive system. The twin drive system is composed of two motors connected by a differential mechanism. Its generated output is the difference of torque between two motors. Since its two motors can continue to drive even when the system makes a stationary motion, the effective static friction of the system is drastically reduced. The disturbance torque is so small, this system is able to estimate the external disturbance torque more than usual motor. By using this system, it is possible to construct the force sensorless controller more exactly.


title: Toward mapping surface deformation in three dimensions using InSAR

abstract: [1] One of the limitations of deformation measurements made with interferometric synthetic aperture radar (InSAR) is that an interferogram only measures one component of the surface deformation — in the satellite's line of sight. We investigate strategies for mapping surface deformation in three dimensions by using multiple interferograms, with different imaging geometries. Geometries for both current and future missions are evaluated, and their abilities to resolve the displacement vector are compared. The north component is always the most difficult to determine using data from near-polar orbiting satellites. However, a satellite with an inclination of about 60°/120° would enable all three components to be well resolved. We attempt to resolve the 3D displacements for the 23 October 2002 Nenana Mountain (Alaska) Earthquake. The north component's error is much larger than the signal, but proxies for eastward and vertical motion can be determined if the north component is assumed negligible. Inversions of hypothetical coseismic interferograms demonstrate that earthquake model parameters can be well recovered from two interferograms, acquired on ascending and descending tracks.


title: Defining Creativity: Finding Keywords for Creativity Using Corpus Linguistics Techniques

abstract: A computational system that evaluates creativity needs guidance on what creativity actually is. It is by no means straightforward to provide a computer with a formal definition of creativity; no such definition yet exists and viewpoints in creativity literature vary as to what the key components of creativity are considered to be. This work combines several viewpoints for a more general consensus of how we define creativity, using a corpus linguistics approach. 30 academic papers from various academic disciplines were analysed to extract the most frequently used words and their frequencies in the papers. This data was statistically compared with general word usage in written English. The results form a list of words that are significantly more likely to appear when talking about creativity in academic texts. Such words can be considered keywords for creativity, guiding us in uncovering key sub-components of creativity which can be used for computational assessment of creativity.


title: Fiber-Based All-Solid-State Flexible Supercapacitors for Self-Powered Systems

abstract: All-solid-state flexible supercapacitors based on a carbon/MnO2 (C/M) core–shell fiber structure were fabricated with high electrochemical performance such as high rate capability with a scan rate up to 20 V s–1, high volume capacitance of 2.5 F cm–3, and an energy density of 2.2 × 10–4 Wh cm–3. By integrating with a triboelectric generator, supercapacitors could be charged and power commercial electronic devices, such as a liquid crystal display or a light-emitting-diode, demonstrating feasibility as an efficient storage component and self-powered micro/nanosystems.


title: Pricing data center demand response

abstract: Demand response is crucial for the incorporation of renewable energy into the grid. In this paper, we focus on a particularly promising industry for demand response: data centers. We use simulations to show that, not only are data centers large loads, but they can provide as much (or possibly more) flexibility as large-scale storage if given the proper incentives. However, due to the market power most data centers maintain, it is difficult to design programs that are efficient for data center demand response. To that end, we propose that prediction-based pricing is an appealing market design, and show that it outperforms more traditional supply function bidding mechanisms in situations where market power is an issue. However, prediction-based pricing may be inefficient when predictions are inaccurate, and so we provide analytic, worst-case bounds on the impact of prediction error on the efficiency of prediction-based pricing. These bounds hold even when network constraints are considered, and highlight that prediction-based pricing is surprisingly robust to prediction error.


title: Wan2tlk?: everyday text messaging

abstract: Texting--using a mobile phone to send text messages--has become a form of mass communication. Building on studies that described how British teenagers have incorporated text messaging into their lives, we examine the purposes and nature of the conversations themselves. We also present findings that suggest that teenagers do not have many simultaneous multiple conversations via text messaging; end most text messaging conversations by switching to another medium; and, that, despite popular beliefs, communicate with surprisingly few friends via their mobile phones. Finally we describe how and what words they shorten in their text messages.


title: Compact, Dual-Polarized UWB-Antenna, Embedded in a Dielectric

abstract: A compact dual-polarized antenna is described for ultrawideband (UWB) applications. The main features of the antenna, besides an ultrawidebandwidth are low cross-polarization and small dimensions. The feed of the antenna is based on the tapered slot antennas, enclosed in a dielectric. The new antenna concept, including the feed of the antenna for dual-polarization and its integration, is described in detail. Prototypes are shown and their performances are demonstrated, based on simulation and measurement results in frequency and time domain. The antenna prototype input match is better than -10 dB, a maximal gain of 10.5 dBi and a mean polarization decoupling of approximately 17 dB in the main beam direction in the 3.1-10.6 GHz UWB band are achieved. In the time domain a peak value of 0.4 m/ns, a full width at half maximum of 100 ps and a ringing of 145 ps are measured. With a maximum antenna diameter of 35 mm and 53 mm length this new antenna is also suited for UWB antenna arrays.


title: Learning to Predict Lane Changes in Highway Scenarios Using Dynamic Filters On a Generic Traffic Representation

abstract: In highway driving scenarios it is important for highly automated driving systems to be able to recognize and predict the intended maneuvers of other drivers in order to make robust and informed decisions. Many methods utilize the current kinematics of vehicles to make these predictions, but it is possible to examine the relations between vehicles as well to gain more information about the traffic scene and make more accurate predictions. The work presented in this paper proposes a novel method of predicting lane change maneuvers in highway scenarios using deep learning and a generic visual representation of the traffic scene. Experimental results suggest that by operating on the visual representation, the spacial relations between arbitrary vehicles can be captured by our method and used for more informed predictions without the need for explicit dynamic or driver interaction models. The proposed method is evaluated on highway driving scenarios using the Interstate-80 dataset and compared to a kinematics based prediction model, with results showing that the proposed method produces more robust predictions across the prediction horizon than the comparison model.