title: Visuomotor Coupling Shapes the Functional Development of Mouse Visual Cortex

abstract: The emergence of sensory-guided behavior depends on sensorimotor coupling during development. How sensorimotor experience shapes neural processing is unclear. Here, we show that the coupling between motor output and visual feedback is necessary for the functional development of visual processing in layer 2/3 (L2/3) of primary visual cortex (V1) of the mouse. Using a virtual reality system, we reared mice in conditions of normal or random visuomotor coupling. We recorded the activity of identified excitatory and inhibitory L2/3 neurons in response to transient visuomotor mismatches in both groups of mice. Mismatch responses in excitatory neurons were strongly experience dependent and driven by a transient release from inhibition mediated by somatostatin-positive interneurons. These data are consistent with a model in which L2/3 of V1 computes a difference between an inhibitory visual input and an excitatory locomotion-related input, where the balance between these two inputs is finely tuned by visuomotor experience.


title: Function-Hiding Inner Product Encryption Is Practical

abstract: In a functional encryption scheme, secret keys are associated with functions and ciphertexts are associated with messages. Given a secret key for a function f, and a ciphertext for a message x, a decryptor learns f(x) and nothing else about x. Inner product encryption is a special case of functional encryption where both secret keys and ciphertext are associated with vectors. The combination of a secret key for a vector \({\mathbf {x}}\) and a ciphertext for a vector \(\mathbf {y}\) reveal \(\langle {\mathbf {x}}, \mathbf {y}\rangle \) and nothing more about \(\mathbf {y}\). An inner product encryption scheme is function-hiding if the keys and ciphertexts reveal no additional information about both \({\mathbf {x}}\) and \(\mathbf {y}\) beyond their inner product.


title: Impact of nutrition environmental interventions on point-of-purchase behavior in adults: a review.

abstract: Abstract Background . Nutrition interventions targeted to individuals are unlikely to significantly shift US dietary patterns as a whole. Environmental and policy interventions are more promising for shifting these patterns. We review interventions that influenced the environment through food availability, access, pricing, or information at the point-of-purchase in worksites, universities, grocery stores, and restaurants. Methods . Thirty-eight nutrition environmental intervention studies in adult populations, published between 1970 and June 2003, were reviewed and evaluated on quality of intervention design, methods, and description (e.g., sample size, randomization). No policy interventions that met inclusion criteria were found. Results . Many interventions were not thoroughly evaluated or lacked important evaluation information. Direct comparison of studies across settings was not possible, but available data suggest that worksite and university interventions have the most potential for success. Interventions in grocery stores appear to be the least effective. The dual concerns of health and taste of foods promoted were rarely considered. Sustainability of environmental change was never addressed. Conclusions . Interventions in “limited access” sites (i.e., where few other choices were available) had the greatest effect on food choices. Research is needed using consistent methods, better assessment tools, and longer durations; targeting diverse populations; and examining sustainability. Future interventions should influence access and availability, policies, and macroenvironments.


title: A unified optimization framework for auction and guaranteed delivery in online advertising

abstract: This paper proposes a new unified optimization framework combining pay-per-click auctions and guaranteed delivery in sponsored search. Advertisers usually have different (and sometimes mixed) marketing goals: brand awareness and direct response. Different mechanisms are good at addressing different goals, e.g., guaranteed delivery was often used to build brand awareness and pay-per-click auctions was widely used for direct marketing. Our new method accommodates both in a unified framework, with the search engine revenue as an optimization objective. In this way, we can target a guaranteed number of ad clicks (or impressions) per campaign for advertisers willing to pay a premium and enable keyword auctions for all others. Specifically, we formulate this joint optimization problem using linear programming and a column generation strategy for efficiency. To select the best column (a ranked list of ads) given a query, we propose a novel dynamic programming algorithm that takes the special structure of the ad allocation and pricing mechanisms into account. We have tested the proposed framework and the algorithms on real ad data obtained from a commercial search engine. The results demonstrate that our proposed approach can outperform several baselines in guaranteeing the number of clicks for the given advertisers, and in increasing the total revenue for the search engine.


title: ZOZZLE: Fast and Precise In-Browser JavaScript Malware Detection

abstract: JavaScript malware-based attacks account for a large fraction of successful mass-scale exploitation happening today. Attackers like JavaScript-based attacks because they can be mounted against an unsuspecting user visiting a seemingly innocent web page. While several techniques for addressing these types of exploits have been proposed, in-browser adoption has been slow, in part because of the performance overhead these methods incur. ::: ::: In this paper, we propose ZOZZLE, a low-overhead solution for detecting and preventing JavaScript malware that is fast enough to be deployed in the browser. ::: ::: Our approach uses Bayesian classification of hierarchical features of the JavaScript abstract syntax tree to identify syntax elements that are highly predictive of malware. Our experimental evaluation shows that ZOZZLE is able to detect JavaScript malware through mostly static code analysis effectively. ZOZZLE has an extremely low false positive rate of 0.0003%, which is less than one in a quarter million. Despite this high accuracy, the ZOZZLE classifier is fast, with a throughput of over one megabyte of JavaScript code per second.


title: Improved cosine similarity measures of simplified neutrosophic sets for medical diagnoses

abstract: We proposed improved cosine similarity measures of simplified neutrosophic sets (SNSs) based on cosine function, including single valued neutrosophic cosine similarity measures and interval neutrosophic cosine similarity measures, to overcome some disadvantages of existing cosine similarity measures of SNSs.We presented a medical diagnosis method based on the improved cosine similarity measures to solve medical diagnosis problems with simplified neutrosophic information.Two medical diagnosis problems were given to show the effectiveness and rationality of the diagnosis method using the improved cosine similarity measures. ObjectiveIn pattern recognition and medical diagnosis, similarity measure is an important mathematical tool. To overcome some disadvantages of existing cosine similarity measures of simplified neutrosophic sets (SNSs) in vector space, this paper proposed improved cosine similarity measures of SNSs based on cosine function, including single valued neutrosophic cosine similarity measures and interval neutrosophic cosine similarity measures. Then, weighted cosine similarity measures of SNSs were introduced by taking into account the importance of each element. Further, a medical diagnosis method using the improved cosine similarity measures was proposed to solve medical diagnosis problems with simplified neutrosophic information. Materials and methodsThe improved cosine similarity measures between SNSs were introduced based on cosine function. Then, we compared the improved cosine similarity measures of SNSs with existing cosine similarity measures of SNSs by numerical examples to demonstrate their effectiveness and rationality for overcoming some shortcomings of existing cosine similarity measures of SNSs in some cases. In the medical diagnosis method, we can find a proper diagnosis by the cosine similarity measures between the symptoms and considered diseases which are represented by SNSs. Then, the medical diagnosis method based on the improved cosine similarity measures was applied to two medical diagnosis problems to show the applications and effectiveness of the proposed method. ResultsTwo numerical examples all demonstrated that the improved cosine similarity measures of SNSs based on the cosine function can overcome the shortcomings of the existing cosine similarity measures between two vectors in some cases. By two medical diagnoses problems, the medical diagnoses using various similarity measures of SNSs indicated the identical diagnosis results and demonstrated the effectiveness and rationality of the diagnosis method proposed in this paper. ConclusionsThe improved cosine measures of SNSs based on cosine function can overcome some drawbacks of existing cosine similarity measures of SNSs in vector space, and then their diagnosis method is very suitable for handling the medical diagnosis problems with simplified neutrosophic information and demonstrates the effectiveness and rationality of medical diagnoses.


title: A Novel Self-Intersection Penalty Term for Statistical Body Shape Models and Its Applications in 3D Pose Estimation

abstract: Statistical body shape models are widely used in 3D pose estimation due to their low-dimensional parameters representation. However, it is difficult to avoid self-intersection between body parts accurately. Motivated by this fact, we proposed a novel self-intersection penalty term for statistical body shape models applied in 3D pose estimation. To avoid the trouble of computing self-intersection for complex surfaces like the body meshes, the gradient of our proposed self-intersection penalty term is manually derived from the perspective of geometry. First, the self-intersection penalty term is defined as the volume of the self-intersection region. To calculate the partial derivatives with respect to the coordinates of the vertices, we employed detection rays to divide vertices of statistical body shape models into different groups depending on whether the vertex is in the region of self-intersection. Second, the partial derivatives could be easily derived by the normal vectors of neighboring triangles of the vertices. Finally, this penalty term could be applied in gradient-based optimization algorithms to remove the self-intersection of triangular meshes without using any approximation. Qualitative and quantitative evaluations were conducted to demonstrate the effectiveness and generality of our proposed method compared with previous approaches. The experimental results show that our proposed penalty term can avoid self-intersection to exclude unreasonable predictions and improves the accuracy of 3D pose estimation indirectly. Further more, the proposed method could be employed universally in triangular mesh based 3D reconstruction.


title: The impact of IT-enabled manufacturing capabilities on plant profitability: New models and evidence

abstract: We propose and conceptualize a new measure of information technology-enabled manufacturing capability, based on a plant’s ability to use its mix of inputs to maximize its process outputs, and study the impact of these capabilities on manufacturing plant profitability. Our study extends the literature on IT-enablement of process capabilities using Data Envelopment Analysis, a non-parametric approach to estimate relative efficiency. We empirically test our models using data from U.S. plants in 2006 and 2007. Our results suggest that manufacturing capabilities and IT spending are positively associated with plant profitability. In addition, we observe that manufacturing process capabilities partially mediate the effect of IT spending on profitability. Our study makes two salient contributions. First, we conceptualize and test a new measure of organizational capability based on the relative efficiency of converting plant resource inputs into process outputs. Second, our findings reveal new pathways through which IT spending can impact plant profitability.


title: Predictive coding as a model of cognition

abstract: Previous work has shown that predictive coding can provide a detailed explanation of a very wide range of low-level perceptual processes. It is also widely believed that predictive coding can account for high-level, cognitive, abilities. This article provides support for this view by showing that predictive coding can simulate phenomena such as categorisation, the influence of abstract knowledge on perception, recall and reasoning about conceptual knowledge, context-dependent behavioural control, and naive physics. The particular implementation of predictive coding used here (PC/BC-DIM) has previously been used to simulate low-level perceptual behaviour and the neural mechanisms that underlie them. This algorithm thus provides a single framework for modelling both perceptual and cognitive brain function.


title: Vehicle detection and classification based on convolutional neural network

abstract: Deep learning has emerged as a hot topic due to extensive application and high accuracy. In this paper this efficient method is used for vehicle detection and classification. We extract visual features from the activation of a deep convolutional network, large-scale sparse learning and other distinguishing features in order to compare their accuracy. When compared to the leading methods in the challenging ImageNet dataset, our deep learning approach obtains highly competitive results. Through the experiments with in short of training data, the features extracted by deep learning method outperform those generated by traditional approaches.


title: Fast learning for sentiment analysis on bullying

abstract: Bullying is a serious national health issue among adolescents. Social media offers a new opportunity to study bullying in both physical and cyber worlds. Sentiment analysis has the potential to identify victims who pose high risk to themselves or others, and to enhance the scientific understanding of bullying overall. We identify seven emotions common in bullying. While some of the emotions are well-studied before, others are non-standard in the sentiment analysis literature. We propose a fast training procedure to recognize these emotions without explicitly producing a conventional labeled training dataset. We apply our procedure to social media posts on bullying and discuss our findings.


title: Machine vision scheme for stain-release evaluation using Gabor filters with optimized coefficients

abstract: This paper presents an efficient and practical approach for automatic, unsupervised object detection and segmentation in two-texture images based on the concept of Gabor filter optimization. The entire process occurs within a hierarchical framework and consists of the steps of detection, coarse segmentation, and fine segmentation. In the object detection step, the image is first processed using a Gabor filter bank. Then, the histograms of the filtered responses are analyzed using the scale-space approach to predict the presence/absence of an object in the target image. If the presence of an object is reported, the proposed approach proceeds to the coarse segmentation stage, wherein the best Gabor filter (among the bank of filters) is automatically chosen, and used to segment the image into two distinct regions. Finally, in the fine segmentation step, the coefficients of the best Gabor filter (output from the previous stage) are iteratively refined in order to further fine-tune and improve the segmentation map produced by the coarse segmentation step. In the validation study, the proposed approach is applied as part of a machine vision scheme with the goal of quantifying the stain-release property of fabrics. To that end, the presented hierarchical scheme is used to detect and segment stains on a sizeable set of digitized fabric images, and the performance evaluation of the detection, coarse segmentation, and fine segmentation steps is conducted using appropriate metrics. The promising nature of these results bears testimony to the efficacy of the proposed approach.


title: Intellect as distinct from openness: Differences revealed by fMRI of working memory.

abstract: Relatively little is known about the neural bases of the Big Five personality trait Openness/Intellect. This trait is composed of two related but separable aspects, Openness to Experience and Intellect. On the basis of previous behavioral research (DeYoung, Peterson, & Higgins, 2005), we hypothesized that brain activity supporting working memory (WM) would be related to Intellect but not Openness. To test this hypothesis we used fMRI to scan a sample of 104 healthy adults, as they performed a difficult WM task. Intellect (and not Openness) was found to correlate with WM accuracy and with accuracy-related brain activity, in left lateral anterior prefrontal cortex and posterior medial frontal cortex. Neural activity in these regions mediated the association between Intellect and WM performance, implicating these regions in the neural substrate of Intellect. Intellect was also correlated significantly with scores on tests of intelligence and working memory capacity, but the association of Intellect with brain activity could not be entirely explained by cognitive ability.


title: Pseudo Label based Unsupervised Deep Discriminative Hashing for Image Retrieval

abstract: Hashing methods play an important role in large scale image retrieval. Traditional hashing methods use hand-crafted features to learn hash functions, which can not capture the high level semantic information. Deep hashing algorithms use deep neural networks to learn feature representation and hash functions simultaneously. Most of these algorithms exploit supervised information to train the deep network. However, supervised information is expensive to obtain. In this paper, we propose a pseudo label based unsupervised deep discriminative hashing algorithm. First, we cluster images via K-means and the cluster labels are treated as pseudo labels. Then we train a deep hashing network with pseudo labels by minimizing the classification loss and quantization loss. Experiments on two datasets demonstrate that our unsupervised deep discriminative hashing method outperforms the state-of-art unsupervised hashing methods.


title: DoShiCo challenge: Domain shift in control prediction

abstract: Training deep neural network policies end-to-end for real-world applications so far requires big demonstration datasets in the real world or big sets consisting of a large variety of realistic and closely related 3D CAD models. These real or virtual data should, moreover, have very similar characteristics to the conditions expected at test time. These stringent requirements and the time consuming data collection processes that they entail, are currently the most important impediment that keeps deep reinforcement learning from being deployed in real-world applications. Therefore, in this work we advocate an alternative approach, where instead of avoiding any domain shift by carefully selecting the training data, the goal is to learn a policy that can cope with it. To this end, we propose the DoShiCo challenge: to train a model in very basic synthetic environments, far from realistic, in a way that it can be applied in more realistic environments as well as take the control decisions on real-world data. In particular, we focus on the task of collision avoidance for drones. We created a set of simulated environments that can be used as benchmark and implemented a baseline method, exploiting depth prediction as an auxiliary task to help overcome the domain shift. Even though the policy is trained in very basic environments, it can learn to fly without collisions in a very different realistic simulated environment. Of course several benchmarks for reinforcement learning already exist - but they never include a large domain shift. On the other hand, several benchmarks in computer vision focus on the domain shift, but they take the form of a static datasets instead of simulated environments. In this work we claim that it is crucial to take the two challenges together in one benchmark.


title: A dynamic lithium-ion battery model considering the effects of temperature and capacity fading

abstract: Battery models capture the characteristics of real-life batteries, and can be used to predict their behavior under various operating conditions. In this paper, a dynamic model of lithium-ion battery has been developed with MATLAB/Simulink® in order to investigate the output characteristics of lithium-ion batteries. Dynamic simulations are carried out, including the observation of the changes in battery terminal output voltage under different charging/discharging, temperature and cycling conditions, and the simulation results are compared with the results obtained from several recent studies. The simulation studies are presented for manifesting that the model is effective and operational.


title: A Case Study: NLG meeting Weather Industry Demand for Quality and Quantity of Textual Weather Forecasts

abstract: In the highly competitive weather industry, demand for timely, accurate and personalized weather reports is always on the rise. In this paper we present a case study where Arria NLG and the UK national weather agency, the Met Office came together to test the hypothesis that NLG can meet the quality and quantity demands of a real-world use case.


title: Recognizing Detailed Human Context In-the-Wild from Smartphones and Smartwatches

abstract: We demonstrate usage of smartphones and smartwatches to automatically recognize a person's behavioral context in-the-wild. In our setup, subjects use their own personal phones and engage in regular behavior in their natural environments. Our system fuses complementary information from multi-modal sensors and simultaneously recognizes many contextual attributes from diverse behavioral domains, including work and leisure activities, body movement, transportation, and more. Our large scale labeled dataset is publicly available.


title: Digital Average Current-Mode Controller for DC–DC Converters in Physical Vapor Deposition Applications

abstract: A dc power supply in physical vapor deposition (PVD) applications operates as a constant power source during normal operation or as a constant current source during fast micro arcs in the plasma. A two-loop control is usually applied: an external (slower) loop controls the output power, while an internal (fast) loop controls the current. This paper introduces a digital average current mode controller for DC power supplies in PVD applications. The controller provides a current source response to fast disturbances, and enables equal current sharing among buck converter modules operating in parallel. Adaptive adjustments of the compensator gain in the digital current control loop enable operation over wide ranges of output power and voltage. The proposed control strategy is robust and noise insensitive. The current and power control algorithms are implemented on a digital signal processor/field-programmable gate array-based digital control platform using a low-cost 10-bit 500 kS/s analog-to-digital converter. The performance is verified on a 25 kW experimental prototype.


title: The Ethical Dangers of Deliberative Decision Making

abstract: Research on ethical decision making has been heavily influenced by normative decision theories that view intelligent choices as involving conscious deliberation and analysis. Recent developments in moral psychology, however, suggest that moral functions involved in ethical decision making are metaphorical and embodied. The research presented here suggests that deliberative decision making may actually increase unethical behaviors and reduce altruistic motives when it overshadows implicit, intuitive influences on moral judgments and decisions. Three lab experiments explored the potential ethical dangers of deliberative decision making. Experiments 1 and 2 showed that deliberative decision making, activated by a math problem-solving task or by simply framing the choice as a decision rather than an intuitive reaction, increased deception in a one-shot deception game. Experiment 3—which activated systematic thinking or intuitive feeling about the choice to donate to a charity—found that deliberative decision ...


title: A Scalable Model-Based Learning Algorithm With Application to UAVs

abstract: Unmanned aerial vehicles (UAVs) play essential roles in many areas including search and rescue, monitoring, and exploration. As such, fast and accurate trajectory tracking is crucial for UAVs especially in emergency situations or cluttered environment. This letter proposes a novel learning algorithm that improves UAVs’ tracking performance through learning without human intervention. This learning algorithm, while possessing self-learning capacity, falls into the model-based learning methodology and therefore inherits the advantages of control techniques. This algorithm is particularly pursued and developed for scenarios that the reference trajectory is either too aggressive to follow or not compatible with system dynamics. Numerical study is conducted to validate the effectiveness and efficiency of the proposed learning algorithm and demonstrate the enhanced tracking and learning performance.


title: A Joint Decoding Algorithm for Named Entity Recognition

abstract: In the past year, two new models for naming entity recognition have been proposed, namely: Leveraging Linguistic Structures for Named Entity Recognition with Bidirectional Recursive Neural Networks; A FOFE-based Local Detection Approach for Named Entity Recognition and Mention Detection. In this article, we design a co-decoding scheme that can combine the outputs (views) of the two systems to produce an output that is more accurate than the outputs of individual systems. The proposed method has been evaluated in ontonotes 5.0. Experiments show this method surpasses current state-of-the-art on OntoNotes 5.0.


title: Aadhaar Based Biometric Attendance System Using Wireless Fingerprint Terminals

abstract: In every educational institution, attendance for students is compulsory to obtain good knowledge of the lessons taught in the classrooms and also for gaining wisdom as well as the personality development. A biometric attendance system is designed and implemented for efficient real time monitoring as well as transparency in the management of student's genuine attendance using GSM based wireless fingerprint terminals (WFTs). This system can be suitable to deploy at the educational institutions. Each individual has a unique pattern of fingerprints motivates the use of them for biometric authentication and are verified for finding the presence of the students in the institute. In this paper, two different approaches are discussed to authenticate the captured fingerprint in the verification process. The first approach uses data base created by the organization itself and the second approach uses the Aadhaar Central Identification Repository (CIDR). Wireless fingerprint terminals are responsible to capture and store the attendance records of the students in the device data base and updating them to the server data base. SMS Alerts to students and their parents are sent in case of their irregularity, absence or shortage of attendance. The same system can be extended for the use of avoiding malpractice in the examinations as a part of examination monitoring system.


Inferring Social Network Structure Using Mobile Phone Data


title: Corpus-Oriented Grammar Development for Acquiring a Head-Driven Phrase Structure Grammar from the Penn Treebank

abstract: This paper describes a method of semi-automatically acquiring an English HPSG grammar from the Penn Treebank. First, heuristic rules are employed to annotate the treebank with partially-specified derivation trees of HPSG. Lexical entries are automatically extracted from the annotated corpus by inversely applying HPSG schemata to partially-specified derivation trees. Predefined HPSG schemata assure the acquired lexicon to conform to the theoretical formulation of HPSG. Experimental results revealed that this approach enabled us to develop an HPSG grammar with significant robustness at small cost.


title: The promise and pitfalls of intranasally administering psychopharmacological agents for the treatment of psychiatric disorders

abstract: Accumulating research demonstrates the potential of intranasal delivery of psychopharmacological agents to treat a range of psychiatric disorders and symptoms. It is believed that intranasal administration offers both direct and indirect pathways to deliver psychopharmacological agents to the central nervous system. This administration route provides a unique opportunity to repurpose both old drugs for new uses and improve currently approved drugs that are indicated for other administration routes. Despite this promise, however, the physiology of intranasal delivery and related assumptions behind the bypassing of the blood brain barrier is seldom considered in detail in clinical trials and translational research. In this review, we describe the current state of the art in intranasal psychopharmacological agent delivery research and current challenges using this administration route, and discuss important aspects of nose-to-brain delivery that may improve the efficacy of these new therapies in future research. We also highlight current gaps in the literature and suggest how research can directly examine the assumptions of nose-to-brain delivery of psychopharmacological agents in humans.


title: O-POCO: Online point cloud compression mapping for visual odometry and SLAM

abstract: This paper presents O-POCO, a visual odometry and SLAM system that makes online decisions regarding what to map and what to ignore. It takes a point cloud from classical SfM and aims to sample it on-line by selecting map features useful for future 6D relocalisation. We use the camera's traveled trajectory to compartamentalize the point cloud, along with visual and spatial information to sample and compress the map. We propose and evaluate a number of different information layers such as the descriptor information's relative entropy, map-feature occupancy grid, and the point cloud's geometry error. We compare our proposed system against both SfM, and online and offline ORB-SLAM using publicly available datasets in addition to our own. Results show that our online compression strategy is capable of outperforming the baseline even for conditions when the number of features per key-frame used for mapping is four times less.


Fast Sorted-Set Intersection using SIMD Instructions


title: Forecasting Fine-Grained Air Quality Based on Big Data

abstract: In this paper, we forecast the reading of an air quality monitoring station over the next 48 hours, using a data-driven method that considers current meteorological data, weather forecasts, and air quality data of the station and that of other stations within a few hundred kilometers. Our predictive model is comprised of four major components: 1) a linear regression-based temporal predictor to model the local factors of air quality, 2) a neural network-based spatial predictor to model global factors, 3) a dynamic aggregator combining the predictions of the spatial and temporal predictors according to meteorological data, and 4) an inflection predictor to capture sudden changes in air quality. We evaluate our model with data from 43 cities in China, surpassing the results of multiple baseline methods. We have deployed a system with the Chinese Ministry of Environmental Protection, providing 48-hour fine-grained air quality forecasts for four major Chinese cities every hour. The forecast function is also enabled on Microsoft Bing Map and MS cloud platform Azure. Our technology is general and can be applied globally for other cities.


title: Pedestrian Detection and Tracking for Counting Applications in Crowded Situations

abstract: This paper describes a vision based pedestrian detection and tracking system which is able to count people in very crowded situations like escalator entrances in underground stations. The proposed system uses motion to compute regions of interest and prediction of movements, extracts shape information from the video frames to detect individuals, and applies texture features to recognize people. A search strategy creates trajectories and new pedestrian hypotheses and then filters and combines those into accurate counting events. We show that counting accuracies up to 98 % can be achieved.


title: 5G: Evolution of a secure mobile technology

abstract: The mobile technology is an ever evolving concept. The world has seen various generations of mobile technology be it 1G, 2G, 3G or 4G. The fifth generation of mobile technology i.e. 5G is seen as a futuristic notion that would help in solving the issues that are pertaining in the 4G. In this paper we have discussed various security issues of 4G with respect to Wi-max and long term evolution. These issues are discussed at MAC and physical layer level. The security issues are seen in terms of possible attacks, system vulnerabilities and privacy concerns. We have also highlighted how the notions of 5G can be tailored to provide a more secure mobile computing environment. We have considered the futuristic architectural framework for 5G networks in our discussion. The basic concepts and features of the fifth generation technology are explained here. We have also analyzed five pillars of strength for the 5G network security which would work in collaboration with each other to provide a secure mobile computing environment to the user.


title: Complex Wishart Distribution Based Analysis of Polarimetric Synthetic Aperture Radar Data

abstract: Multi-look, polarimetric synthetic aperture radar (SAR) data are often worked with in the so-called covariance matrix representation. For each pixel this representation gives a 3 times 3 Hermitian, positive definite matrix which follows a complex Wishart distribution. Based on this distribution a test statistic for equality of two such matrices and an associated asymptotic probability for obtaining a smaller value of the test statistic are given and applied to change detection, edge detection and segmentation in polarimetric SAR data. In a case study EMISAR L-band data from 17 April 1998 and 20 May 1998 covering agricultural fields near Foulum, Denmark, are used. Soon the Japanese ALOS, the German TerraSAR-X and the Canadian RADARSAT-2 will acquire space-borne, polarimetric data making analysis based on these methods important.


title: Constructing identities in cyberspace : The case of eating disorders

abstract: This paper consists of a discourse analysis of data collected from websites that have been created by and for people who wish to share experiences of eating disorders in a positive and supportive environment. These sites have earned the broad description ‘pro-ana’ (where ‘ana’ is short for ‘anorexia’). Site users have come to see themselves as a broad on-line community of like-minded individuals, but within this community there are many subgroups, and the boundaries between these subgroups are fiercely contested. In addition, frequent attacks on such websites in the media (charged with ‘promoting eating disorders’), and by occasional hostile site visitors, have often forced the community into a defensive mode. The result is a rich tapestry of identity work. The analysis examines several ‘pro-ana’ sites and explores the way in which the identity is used to police the boundaries of the community, and ultimately, what it means ‘to be ana’ rather than ‘mia’ (bulimic), ‘a normal’, ‘a faker’, or even ‘a hater’.


title: Review of Recent Phased Arrays for Millimeter-Wave Wireless Communication

abstract: Owing to the rapid growth in wireless data traffic, millimeter-wave (mm-wave) communications have shown tremendous promise and are considered an attractive technique in fifth-generation (5G) wireless communication systems. However, to design robust communication systems, it is important to understand the channel dynamics with respect to space and time at these frequencies. Millimeter-wave signals are highly susceptible to blocking, and they have communication limitations owing to their poor signal attenuation compared with microwave signals. Therefore, by employing highly directional antennas, co-channel interference to or from other systems can be alleviated using line-of-sight (LOS) propagation. Because of the ability to shape, switch, or scan the propagating beam, phased arrays play an important role in advanced wireless communication systems. Beam-switching, beam-scanning, and multibeam arrays can be realized at mm-wave frequencies using analog or digital system architectures. This review article presents state-of-the-art phased arrays for mm-wave mobile terminals (MSs) and base stations (BSs), with an emphasis on beamforming arrays. We also discuss challenges and strategies used to address unfavorable path loss and blockage issues related to mm-wave applications, which sets future directions.


title: Smoke Screener or Straight Shooter: Detecting Elite Sybil Attacks in User-Review Social Networks

abstract: Popular User-Review Social Networks (URSNs)---such as Dianping, Yelp, and Amazon---are often the targets of reputation attacks in which fake reviews are posted in order to boost or diminish the ratings of listed products and services. These attacks often emanate from a collection of accounts, called Sybils, which are collectively managed by a group of real users. A new advanced scheme, which we term elite Sybil attacks, recruits organically highly-rated accounts to generate seemingly-trustworthy and realistic-looking reviews. These elite Sybil accounts taken together form a large-scale sparsely-knit Sybil network for which existing Sybil fake-review defense systems are unlikely to succeed. In this paper, we conduct the first study to define, characterize, and detect elite Sybil attacks. We show that contemporary elite Sybil attacks have a hybrid architecture, with the first tier recruiting elite Sybil workers and distributing tasks by Sybil organizers, and with the second tier posting fake reviews for profit by elite Sybil workers. We design ElsieDet, a three-stage Sybil detection scheme, which first separates out suspicious groups of users, then identifies the campaign windows, and finally identifies elite Sybil users participating in the campaigns. We perform a large-scale empirical study on ten million reviews from Dianping, by far the most popular URSN service in China. Our results show that reviews from elite Sybil users are more spread out temporally, craft more convincing reviews, and have higher filter bypass rates. We also measure the impact of Sybil campaigns on various industries (such as cinemas, hotels, restaurants) as well as chain stores, and demonstrate that monitoring elite Sybil users over time can provide valuable early alerts against Sybil campaigns.


title: Adimen-SUMO: Reengineering an Ontology for First-Order Reasoning

abstract: In this paper, the authors present Adimen-SUMO, an operational ontology to be used by first-order theorem provers in intelligent systems that require sophisticated reasoning capabilities e.g. Natural Language Processing, Knowledge Engineering, Semantic Web infrastructure, etc.. Adimen-SUMO has been obtained by automatically translating around 88% of the original axioms of SUMO Suggested Upper Merged Ontology. Their main interest is to present in a practical way the advantages of using first-order theorem provers during the design and development of first-order ontologies. First-order theorem provers are applied as inference engines for reengineering a large and complex ontology in order to allow for formal reasoning. In particular, the authors' study focuses on providing first-order reasoning support to SUMO. During the process, they detect, explain and repair several important design flaws and problems of the SUMO axiomatization. As a by-product, they also provide general design decisions and good practices for creating operational first-order ontologies of any kind.


title: Theatre in Southeast Asia

abstract: 1. Introduction Part I: A Historical Background 2. The Cultural Setting 3. The Development of Theatre Genres 4. Traditions of Theatre Part II: Theatre as Art 5. Drama 6. Music and Dance 7. Production 8. Transmission of Theatre Art Part III: Theatre as an Institution 9. Extent and Distribution 10. Theatre and the Social Contract 11. The Troupe 12. Economics 13. The Audience 14. Social Status of Troupes Part IV: Theatre as Communication 15. Past and Present 16. Theatre and Other Mass Media 17. Theatre and the Communication Process Appendix: Major Theatre Genres as Performed in 1964 Bibiliography Notes Glossary Index


title: Wireless Access in Ultra-Reliable Low-Latency Communication (URLLC)

abstract: The future connectivity landscape and, notably, the 5G wireless systems will feature Ultra-Reliable Low Latency Communication (URLLC). The coupling of high reliability and low latency requirements in URLLC use cases makes the wireless access design very challenging, in terms of both the protocol design and of the associated transmission techniques. This paper aims to provide a broad perspective on the fundamental tradeoffs in URLLC as well as the principles used in building access protocols. Two specific technologies are considered in the context of URLLC: massive MIMO and multi-connectivity, also termed interface diversity. The paper also touches upon the important question of the proper statistical methodology for designing and assessing extremely high reliability levels.


title: Programming VLIW architectures with super operations

abstract: The length of a statically created instruction schedule determines to a great extent the performance of program executions on VLIW architectures. In this paper we present a simple, yet effective, method to reduce the length of a static instruction schedule by introducing new hardware operations, referred to as super operations. A super operation replaces a number of operations, while maintaining functionality, hence decreasing the total number of operations to be executed and thereby eliminating the dependencies between them. In order to replace a number of operations, super operations must often process more operands and produce more results than traditional operations. The Philips TM-1000 is a VLIW based architecture. Its CPU is a 5-issue machine with 27 functional units, each connected to one issue-slot. To support super operations, we extend the hardware with special functional units which are connected to more than one issue-slot. In this paper we discuss the modifications that were made to the compiler in order to support super operations and we demonstrate the ease with which super operations can be applied by the application programmer. To a lesser extent, we address consequences of super operations concerning the hardware. Furthermore, we demonstrate the benefit of super operations by showing the performance improvement for some multimedia applications.


title: Nonlinear model & controller design for magnetic levitation system

abstract: This paper aims at development of non linear dynamic model for Magnetic Levitation System and proposed linear and nonlinear state space controllers. The linear controller was designed by linearizing the model around equilibrium point, while nonlinear controller was based on feedback linearization where a nonlinear state-space transformation is used to linearize the system exactly. Relative degree of the system was determined and conditions were found that ensure relative degree be well defined. Magnetic Levitation system considered in this study is taken as a ferromagnetic ball suspended in a voltage controlled magnetic field. Dynamic behaviour of the system was modeled by the study of electromagnetic and mechanical subsystems. State space model was derived from the system equations. Linear full state feedback controller along with linear observer was designed and was compared with nonlinear full state feedback with nonlinear observer. Both linear and nonlinear controllers were simulated using matlab and results are presented.


title: A Framework for Understanding the Critical Success Factors of Enterprise Business Intelligence Implementation

abstract: Implementation of a business intelligence (BI) system requires considerable resources. Although, the BI market appears vibrant and the importance of BI is widely accepted, few studies have investigated the Critical Success Factors (CSF) that affects its successful implementation. There is an imperative for a CSF approach to enable BI stakeholders to focus on the key issues that lead to successful BI systems implementation. This paper investigates the success factors and their contextual issues that may help organizations achieve a successful BI system implementation.


title: Correlation Matrix Memories

abstract: A new model for associative memory, based on a correlation matrix, is suggested. In this model information is accumulated on memory elements as products of component data. Denoting a key vector by q(p), and the data associated with it by another vector x(p), the pairs (q(p), x(p)) are memorized in the form of a matrix {see the Equation in PDF File} where c is a constant. A randomly selected subset of the elements of M xq can also be used for memorizing. The recalling of a particular datum x(r) is made by a transformation x(r)=M xq q(r). This model is failure tolerant and facilitates associative search of information; these are properties that are usually assigned to holographic memories. Two classes of memories are discussed: a complete correlation matrix memory (CCMM), and randomly organized incomplete correlation matrix memories (ICMM). The data recalled from the latter are stochastic variables but the fidelity of recall is shown to have a deterministic limit if the number of memory elements grows without limits. A special case of correlation matrix memories is the auto-associative memory in which any part of the memorized information can be used as a key. The memories are selective with respect to accumulated data. The ICMM exhibits adaptive improvement under certain circumstances. It is also suggested that correlation matrix memories could be applied for the classification of data.


title: A Decision Tree Algorithm Pertaining to the Student Performance Analysis and Prediction

abstract: of an educational institute can be measured in terms of successful students of the institute. The analysis related to the prediction of students academic performance in higher education seems an essential requirement for the improvement in quality education. Data mining techniques play an important role in data analysis. For the construction of a classification model which could predict performance of students, particularly for engineering branches, a decision tree algorithm associated with the data mining techniques have been used in the research. A number of factors may affect the performance of students. Here some significant factors have been considered while constructing the decision tree for classifying students according to their attributes (grades). In this paper four different decision tree algorithms J48, NBtree, Reptree and Simple cart were compared and J48 decision tree algorithm is found to be the best suitable algorithm for model construction. Cross validation method and percentage split method were used to evaluate the efficiency of the different algorithms. The traditional KDD process has been used as a methodology. The WEKA (Waikato Environment for Knowledge Analysis) tool was used for analysis and prediction. . Results obtained in the present study may be helpful for identifying the weak students so that management could take appropriate actions, and success rate of students could be increased sufficiently.


title: Online Learning to Rank with Feedback at the Top

abstract: We consider an online learning to rank setting in which, at each round, an oblivious adversary generates a list of $m$ documents, pertaining to a query, and the learner produces scores to rank the documents. The adversary then generates a relevance vector and the learner updates its ranker according to the feedback received. We consider the setting where the feedback is restricted to be the relevance levels of only the top $k$ documents in the ranked list for $k \ll m$. However, the performance of learner is judged based on the unrevealed full relevance vectors, using an appropriate learning to rank loss function. We develop efficient algorithms for well known losses in the pointwise, pairwise and listwise families. We also prove that no online algorithm can have sublinear regret, with top-1 feedback, for any loss that is calibrated with respect to NDCG. We apply our algorithms on benchmark datasets demonstrating efficient online learning of a ranking function from highly restricted feedback.


title: Contrasts in student engagement, meaning-making, dislikes, and challenges in a discovery-based program of game design learning

abstract: This implementation study explores middle school, high school and community college student experiences in Globaloria, an educational pilot program of game design offered in schools within the U.S. state of West Virginia, supported by a non-profit organization based in New York City called the World Wide Workshop Foundation. This study reports on student engagement, meaning making and critique of the program, in their own words. The study’s data source was a mid-program student feedback survey implemented in Pilot Year 2 (2008/2009) of the 5 year design-based research initiative, in which the researchers posed a set of open-ended questions in an online survey questionnaire answered by 199 students. Responses were analyzed using inductive textual analysis. While the initial purpose for data collection was to elicit actionable program improvements as part of a design-based research process, several themes emergent in the data tie into recent debates in the education literature around discovery-based learning. In this paper, we draw linkages from the categories of findings that emerged in student feedback to this literature, and identify new scholarly research questions that can be addressed in the ongoing pilot, the investigation of which might contribute new empirical insights related to recent critiques of discovery based learning, self-determination theory, and the productive failure phenomenon.


WHERE HAVE I HEARD THIS STORY BEFORE?: IDENTIFYING NARRATIVE SIMILARITY IN MOVIE SUMMARIES


title: Information-theoretic compression of pose graphs for laser-based SLAM

abstract: In graph-based simultaneous localization and mapping (SLAM), the pose graph grows over time as the robot gathers information about the environment. An ever growing pose graph, however, prevents long-term mapping with mobile robots. In this paper, we address the problem of efficient information-theoretic compression of pose graphs. Our approach estimates the mutual information between the laser measurements and the map to discard the measurements that are expected to provide only a small amount of information. Our method subsequently marginalizes out the nodes from the pose graph that correspond to the discarded laser measurements. To maintain a sparse pose graph that allows for efficient map optimization, our approach applies an approximate marginalization technique that is based on Chow-Liu trees. Our contributions allow the robot to effectively restrict the size of the pose graph. Alternatively, the robot is able to maintain a pose graph that does not grow unless the robot explores previously unobserved parts of the environment. Real-world experiments demonstrate that our approach to pose graph compression is well suited for long-term mobile robot mapping.


title: Bounded matrix factorization for recommender system

abstract: Matrix factorization has been widely utilized as a latent factor model for solving the recommender system problem using collaborative filtering. For a recommender system, all the ratings in the rating matrix are bounded within a pre-determined range. In this paper, we propose a new improved matrix factorization approach for such a rating matrix, called Bounded Matrix Factorization (BMF), which imposes a lower and an upper bound on every estimated missing element of the rating matrix. We present an efficient algorithm to solve BMF based on the block coordinate descent method. We show that our algorithm is scalable for large matrices with missing elements on multicore systems with low memory. We present substantial experimental results illustrating that the proposed method outperforms the state of the art algorithms for recommender system such as stochastic gradient descent, alternating least squares with regularization, SVD++ and Bias-SVD on real-world datasets such as Jester, Movielens, Book crossing, Online dating and Netflix.


title: Optimal negative current control for four-phase interleaved bi-directional buck/boost converters to achieve ZVS and ZCS

abstract: This paper presents an optimal negative current control for four-phase interleaved bi-directional converters to achieve soft-switching features. Four-phase buck/boost converters with bi-directional operation and in discontinuous conduction mode (DCM) are designed and implemented. The optimal negative current is determined and controlled to reduce reactive power loss, while achieves ZVS and ZCS turn-on for the active switches in the bi-directional converters. Variable frequency and PWM control are also introduced to fit load requirements and insure the DCM operation. Simulation and experimental results obtained from a four-phase interleaved prototype have verified its performance and feasibility.


title: Collaborative Topic Regression with Social Matrix Factorization for Recommendation Systems

abstract: Social network websites, such as Facebook, YouTube, Lastfm etc, have become a popular platform for users to connect with each other and share content or opinions. They provide rich information for us to study the influence of user's social circle in their decision process. In this paper, we are interested in examining the effectiveness of social network information to predict the user's ratings of items. We propose a novel hierarchical Bayesian model which jointly incorporates topic modeling and probabilistic matrix factorization of social networks. A major advantage of our model is to automatically infer useful latent topics and social information as well as their importance to collaborative filtering from the training data. Empirical experiments on two large-scale datasets show that our algorithm provides a more effective recommendation system than the state-of-the art approaches. Our results reveal interesting insight that the social circles have more influence on people's decisions about the usefulness of information (e.g., bookmarking preference on Delicious) than personal taste (e.g., music preference on Lastfm). We also examine and discuss solutions on potential information leak in many recommendation systems that utilize social information.


title: Model-based food volume estimation using 3D pose

abstract: We are developing a dietary assessment system to automatically identify and quantify foods and beverages consumed by analyzing meal images captured with a mobile device. After food items are segmented and identified, accurately estimating the volume of the food in the image is important for determining the nutrient content of the food. In this paper, we proposed a novel food portion size estimation method for rigid food items using a single image. First, we create a 3D graphical model during the training step using 3D reconstruction from multiple views. Then, for each food image, we determine the translation and elevation parameters of each of the food items, which are relative to the camera coordinate through camera calibration. Using these geometric parameters we project the pre-built 3D model of each food item back to the image plane. Subsequently, the remaining degrees-of-freedom (DOF) for the final pose is estimated by image similarity measure. The experimental results of our volume estimation method for four food categories validate the accuracy and reliability of our model-based approach.


title: Vector Control of an Induction Motor based on a DSP

abstract: In this thesis project, a vector control system for an induction motor is implemented on an evaluation board. By comparing the pros and cons of eight candidates of evaluation boards, the TMS320F28335 DSP Experimenter Kit is selected as the digital controller of the vector control system. Necessary peripheral and interface circuits are built for the signal measurement, the three-phase inverter control and the system protection. These circuits work appropriately except that the conditioning circuit for analog-to-digital conversion contains too much noise. At the stage of the control algorithm design, the designed vector control system is simulated in Matlab/Simulink with both S-function and Simulink blocks. The simulation results meet the design specifications well. When the control system is verified by simulations, the DSP evaluation board is programmed and the system is tested. The test results show that the current regulator and the speed regulator are able to regulate the stator currents and the machine speed fast and precisely. With the initial values of the motor parameters there is a 12.5% overshoot in the current step response. By adjusting the stator resistance and the inductance this overshoot could be removed and only minor difference remains between the simulated and measured current step responses.


Learning Effective Embeddings from Medical Notes


title: Effect of Control-Loops Interactions on Power Stability Limits of VSC Integrated to AC System

abstract: This paper investigates the effect of control-loops interactions on power stability limits of the voltage-source converter (VSC) as connected to an ac system. The focus is put on the physical mechanism of the control-loops interactions in the VSC, revealing that interactions among the control loops result in the production of an additional loop. The open-loop gain of the additional loop is employed to quantify the severity of the control-loop interactions. Furthermore, the power current sensitivity, closely related to control-loops interactions, is applied to estimate the maximum transferrable power of the VSC connected to an ac grid. On that basis, stability analysis results show that interactions between dc-link voltage control and phase-locked loop restrict the power angle to about 51° for stable operation with no dynamic reactive power supported. Conversely, the system is capable of approaching the ac-side maximum power transfer limits with alternating voltage control included. Simulations in MATLAB/Simulink are conducted to validate the stability analysis.


title: Learning Neural Models for End-to-End Clustering

abstract: We propose a novel end-to-end neural network architecture that, once trained, directly outputs a probabilistic clustering of a batch of input examples in one pass. It estimates a distribution over the number of clusters $k$, and for each $1 \leq k \leq k_\mathrm{max}$, a distribution over the individual cluster assignment for each data point. The network is trained in advance in a supervised fashion on separate data to learn grouping by any perceptual similarity criterion based on pairwise labels (same/different group). It can then be applied to different data containing different groups. We demonstrate promising performance on high-dimensional data like images (COIL-100) and speech (TIMIT). We call this ``learning to cluster'' and show its conceptual difference to deep metric learning, semi-supervise clustering and other related approaches while having the advantage of performing learnable clustering fully end-to-end.


title: Web of Things: Description, Discovery and Integration

abstract: The vision for the "Web of Things" (WoT) aims at bringing physical objects of the world into the World Wide Web. The Web is constantly evolving and has changed over the last couple of decades and the changes have spurted new areas of growth. The primary focus of the WoT is to bridge the gap between physical and digital worlds over a common and widely used platform, which is the Web. Everyday physical "things", which are not Web-enabled, and have limited or zero computing capability, can be accommodated within the Web. As a step towards this direction, this work focuses on the specification of a thing, its descriptors and functions that could participate in the process of its discovery and operations. Besides, in this model for the WoT, we also propose a semantic Web-based architecture to integrate these things as Web resources to further demystify the realization of the WoT vision.


title: Phishing for Phools in the Internet of Things: Modeling One-to-Many Deception using Poisson Signaling Games

abstract: Strategic interactions ranging from politics and pharmaceuticals to e-commerce and social networks support equilibria in which agents with private information manipulate others which are vulnerable to deception. Especially in cyberspace and the Internet of things, deception is difficult to detect and trust is complicated to establish. For this reason, effective policy-making, profitable entrepreneurship, and optimal technological design demand quantitative models of deception. In this paper, we use game theory to model specifically one-to-many deception. We combine a signaling game with a model called a Poisson game. The resulting Poisson signaling game extends traditional signaling games to include 1) exogenous evidence of deception, 2) an unknown number of receivers, and 3) receivers of multiple types. We find closed-form equilibrium solutions for a subset of Poisson signaling games, and characterize the rates of deception that they support. We show that receivers with higher abilities to detect deception can use crowd-defense tactics to mitigate deception for receivers with lower abilities to detect deception. Finally, we discuss how Poisson signaling games could be used to defend against the process by which the Mirai botnet recruits IoT devices in preparation for a distributed denial-of-service attack.


title: Anxiety Disorders and Sensory Over-Responsivity in Children with Autism Spectrum Disorders: Is There a Causal Relationship?

abstract: Anxiety disorders and sensory over-responsivity (SOR) are common in children with autism spectrum disorders (ASD), and there is evidence for an association between these two conditions. Currently, it is unclear what causal mechanisms may exist between SOR and anxiety. We propose three possible theories to explain the association between anxiety and SOR: (a) SOR is caused by anxiety; (b) Anxiety is caused by SOR; or (c) SOR and anxiety are causally unrelated but are associated through a common risk factor or diagnostic overlap. In this paper, we examine support for each theory in the existing anxiety, autism, and neuroscience literature, and discuss how each theory informs choice of interventions and implications for future studies.


title: DBOD-DS: distance based outlier detection for data

abstract: Data stream is a newly emerging data model for applications like environment monitoring, Web click stream, network traffic monitoring, etc. It consists of an infinite sequence of data points accompanied with timestamp coming from external data source. Typically data sources are located onsite and very vulnerable to external attacks and natural calamities, thus outliers are very common in the datasets. Existing techniques for outlier detection are inadequate for data streams because of its metamorphic data distribution and uncertainty. In this paper we propose an outlier detection technique, called Distance-Based Outline Detection for Data Streams (DBOD-DS) based on a novel continuously adaptive probability density function that addresses all the new issues of data streams. Extensive experiments on a real dataset for meteorology applications show the supremacy of DBOD-DS over existing techniques in terms of accuracy.


title: Cohesion-based Online Actor-Critic Reinforcement Learning for mHealth Intervention

abstract: In the wake of the vast population of smart device users worldwide, mobile health (mHealth) technologies are hopeful to generate positive and wide influence on people's health. They are able to provide flexible, affordable and portable health guides to device users. Current online decision-making methods for mHealth assume that the users are completely heterogeneous. They share no information among users and learn a separate policy for each user. However, data for each user is very limited in size to support the separate online learning, leading to unstable policies that contain lots of variances. Besides, we find the truth that a user may be similar with some, but not all, users, and connected users tend to have similar behaviors. In this paper, we propose a network cohesion constrained (actor-critic) Reinforcement Learning (RL) method for mHealth. The goal is to explore how to share information among similar users to better convert the limited user information into sharper learned policies. To the best of our knowledge, this is the first online actor-critic RL for mHealth and first network cohesion constrained (actor-critic) RL method in all applications. The network cohesion is important to derive effective policies. We come up with a novel method to learn the network by using the warm start trajectory, which directly reflects the users' property. The optimization of our model is difficult and very different from the general supervised learning due to the indirect observation of values. As a contribution, we propose two algorithms for the proposed online RLs. Apart from mHealth, the proposed methods can be easily applied or adapted to other health-related tasks. Extensive experiment results on the HeartSteps dataset demonstrates that in a variety of parameter settings, the proposed two methods obtain obvious improvements over the state-of-the-art methods.


title: Visualization in Ontology Tools

abstract: With a literature study we found an enormous number of ontology visualization tools. Many of them apply graph visualization but there are other approaches as well. We have identified interesting solutions for dealing with the complexity of large ontologies. Ontology engineering, ontology mapping and alignment can benefit from Information Visualization. Our collection is a starting point to demonstrate the usefulness of Information Visualization techniques, however, a detailed evaluation would be the next step to consolidate this research area and help to boost the adoption of ontologies in common Web applications.


title: CARMA: A robust motion artifact reduction algorithm for heart rate monitoring from PPG signals

abstract: Photoplethysmography (PPG) is a non invasive measurement of the blood flow, that can be used instead of electrocardiography to estimate heart rate (HR). Most existing techniques used for HR monitoring in fitness with PPG focus on slowly running alone, while those suitable for intensive physical exercise need an initialization stage in which wearers are required to stand still for several seconds. This paper present a novel algorithm for HR estimation from PPG signal based on motion artifact removal (MAR) and adaptive tracking (AT) that overcomes limitations of the previous techniques. Experimental evaluations performed on datasets recorded from several subjects during running show an average absolute error of HR estimation of 2.26 beats per minute, demonstrating the validity of the presented technique to monitor HR using wearable devices which use PPG signals.


title: Structural XML Classification in Concept Drifting Data Streams

abstract: Classification of large, static collections of XML data has been intensively studied in the last several years. Recently however, the data processing paradigm is shifting from static to streaming data, where documents have to be processed online using limited memory and class definitions can change with time in an event called concept drift. As most existing XML classifiers are capable of processing only static data, there is a need to develop new approaches dedicated for streaming environments. In this paper, we propose a new classification algorithm for XML data streams called XSC. The algorithm uses incrementally mined frequent subtrees and a tree-subtree similarity measure to classify new documents in an associative manner. The proposed approach is experimentally evaluated against eight state-of-the-art stream classifiers on real and synthetic data. The results show that XSC performs significantly better than competitive algorithms in terms of accuracy and memory usage.


Play like a man, win like a woman : what men know about success that women need to learn


title: Philippine Frogs of the Genus Leptobrachium (Anura; Megophryidae): Phylogeny-based Species Delimitation, Taxonomic Review, and Descriptions of Three New Species

abstract: Abstract We review the Philippine frogs of the genus Leptobrachuim. All previous treatments have referred Philippine populations to L. hasseltii, a species we restrict to Java and Bali, Indonesia. We use external morphology, body proportions, color pattern, advertisement calls, and phylogenetic analysis of molecular sequence data to show that Philippine populations of Leptobrachium represent three distinct and formerly unrecognized evolutionary lineages, and we describe each (populations on Mindoro, Palawan, and Mindanao Island groups) as new species. Our findings accentuate the degree to which the biodiversity of Philippine amphibians is currently underestimated and in need of comprehensive review with new and varied types of data. Lagom: Pinagbalik aralan namin ang mga palaka sa Pilipinas mula sa genus Leptobrachium. Ang nakaraang mga palathala ay tumutukoy sa populasyon ng L. hasseltii, ang uri ng palaka na aming tinakda lamang sa Java at Bali, Indonesia. Ginamit namin ang panglabas na morpolohiya, pro...


title: Adaptive Nonlinear Model Inversion Control of a Twin Rotor System Using Artificial Intelligence

abstract: The paper investigates the development of an adaptive dynamic nonlinear model inversion control law for a twin rotor MIMO system (TRMS) utilizing artificial neural networks and genetic algorithms. The TRMS is an aerodynamic test rig representing the control challenges of modern air vehicles. A highly nonlinear 1DOF mathematical model of the TRMS is considered in this study and a nonlinear inverse model is developed for the pitch channel. In the absence of model inversion errors, a genetic algorithm-tuned PD controller is used to enhance the tracking characteristics of the system. An adaptive neural network element is integrated thereafter with the feedback control system to compensate for model inversion errors. In order to show the effectiveness of the proposed method in the simulation environment an inversion error has deliberately been provided as an uncertainty in the real situation. Square and sinusoidal reference command signals are used to test the control system performance, and it is noted that an excellent tracking response is exhibited in the presence of inversion errors caused by model uncertainty.


title: CASME II: An Improved Spontaneous Micro-Expression Database and the Baseline Evaluation

abstract: A robust automatic micro-expression recognition system would have broad applications in national safety, police interrogation, and clinical diagnosis. Developing such a system requires high quality databases with sufficient training samples which are currently not available. We reviewed the previously developed micro-expression databases and built an improved one (CASME II), with higher temporal resolution (200 fps) and spatial resolution (about 280x340 pixels on facial area). We elicited participants' facial expressions in a well-controlled laboratory environment and proper illumination (such as removing light flickering). Among nearly 3000 facial movements, 247 micro-expressions were selected for the database with action units (AUs) and emotions labeled. For baseline evaluation, LBP-TOP and SVM were employed respectively for feature extraction and classifier with the leave-one-subject-out cross-validation method. The best performance is 63.41% for 5-class classification.


title: Cultivating Computational Thinking Practices and Mathematical Habits of Mind in Lattice Land

abstract: ABSTRACTThere is a great deal of overlap between the set of practices collected under the term “computational thinking” and the mathematical habits of mind that are the focus of much mathematics instruction. Despite this overlap, the links between these two desirable educational outcomes are rarely made explicit, either in classrooms or in the literature. This paper presents Lattice Land, a computational learning environment and accompanying curriculum designed to support the development of mathematical habits of mind and promote computational thinking practices in high-school mathematics classrooms. Lattice Land is a mathematical microworld where learners explore geometrical concepts by manipulating polygons drawn with discrete points on a plane. Using data from an implementation in a low-income, urban public high school, we show how the design of Lattice Land provides an opportunity for learners to use computational thinking practices and develop mathematical habits of mind, including tinkering, experim...


On the universality of target‐enrichment baits for phylogenomic research


DOMAIN-INVARIANT REPRESENTATION LEARNING


title: Heavy Hitter Estimation over Set-Valued Data with Local Differential Privacy

abstract: In local differential privacy (LDP), each user perturbs her data locally before sending the noisy data to a data collector. The latter then analyzes the data to obtain useful statistics. Unlike the setting of centralized differential privacy, in LDP the data collector never gains access to the exact values of sensitive data, which protects not only the privacy of data contributors but also the collector itself against the risk of potential data leakage. Existing LDP solutions in the literature are mostly limited to the case that each user possesses a tuple of numeric or categorical values, and the data collector computes basic statistics such as counts or mean values. To the best of our knowledge, no existing work tackles more complex data mining tasks such as heavy hitter discovery over set-valued data. In this paper, we present a systematic study of heavy hitter mining under LDP. We first review existing solutions, extend them to the heavy hitter estimation, and explain why their effectiveness is limited. We then propose LDPMiner, a two-phase mechanism for obtaining accurate heavy hitters with LDP. The main idea is to first gather a candidate set of heavy hitters using a portion of the privacy budget, and focus the remaining budget on refining the candidate set in a second phase, which is much more efficient budget-wise than obtaining the heavy hitters directly from the whole dataset. We provide both in-depth theoretical analysis and extensive experiments to compare LDPMiner against adaptations of previous solutions. The results show that LDPMiner significantly improves over existing methods. More importantly, LDPMiner successfully identifies the majority true heavy hitters in practical settings.


title: Hard- and soft-tissue contributions to the esthetics of the posed smile in growing patients seeking orthodontic treatment

abstract: Introduction: The purpose of this investigation was to broaden the understanding of how various skeletal, dental, and soft-tissue relationships are related to the esthetics of the smile in patients with malocclusions before orthodontic treatment. Methods: Images of the posed smile were captured from digital video clips of 60 growing patients (33 girls, 27 boys) seeking orthodontic treatment; they were judged by panels of laypersons and orthodontists. Discriminant analysis identified determinants of the “pleasing smile” from the results of a visual analog scale. Quantitative measurements of the soft and hard tissues were made by using the smile images, cephalometric radiographs, and study models. The esthetics of the smile were correlated with specific skeletal, dental, and soft-tissue structures in the anteroposterior, vertical, and transverse dimensions (Pearson test on nontopographic correlations). Results: The esthetic smile judgments of orthodontists agreed with those of laypersons (r >0.93). The vertical thicknesses of the lips were the most significant component of a pleasant smile, for both the orthodontists (upper lip) and laypersons (lower lip) (discriminant power: 75%). The vertical thickness of the upper lip had a significant positive correlation with the position of the maxillary incisor. Conclusions: Vertical lip thickness proved to be the most influential variable in smile esthetics. The significant relationship of incisor protrusion with the vertical thickness of the vermilion border of the upper lip must be considered when planning orthodontic treatment.


title: A C/ka dual frequency dual Layer circularly polarized reflectarray antenna with microstrip ring elements

abstract: This paper reports a dual frequency dual layer circularly polarized reflectarray operating in the C and Ka bands. A 0.5-m right-hand circularly polarized planar reflectarray antenna is designed using microstrip ring elements of variable rotations to achieve a cophasal beam at broadside. The microstrip ring elements are more compact than the traditional reflectarray elements and can minimize blockage for the multilayer multifrequency applications. The highest efficiencies measured are 46% at 7.3 GHz and 38% at 31.75 GHz. The tested cross-polarization levels are -21 dB at 7.3 GHz and -29.2 dB at 31.75 GHz at the broadside direction. The tested results show that the designed ring element is suitable for both the single and dual layer applications with good bandwidth and circularly polarized performance.


title: On-body localization of wearable devices: An investigation of position-aware activity recognition

abstract: Human activity recognition using mobile device sensors is an active area of research in pervasive computing. In our work, we aim at implementing activity recognition approaches that are suitable for real life situations. This paper focuses on the problem of recognizing the on-body position of the mobile device which in a real world setting is not known a priori. We present a new real world data set that has been collected from 15 participants for 8 common activities were they carried 7 wearable devices in different positions. Further, we introduce a device localization method that uses random forest classifiers to predict the device position based on acceleration data. We perform the most complete experiment in on-body device location that includes all relevant device positions for the recognition of a variety of different activities. We show that the method outperforms other approaches achieving an F-Measure of 89% across different positions. We also show that the detection of the device position consistently improves the result of activity recognition for common activities.


title: Everyday dwelling with WhatsApp

abstract: In this paper, we present a study of WhatsApp, an instant messaging smartphone application. Through our interviews with participants, we develop anthopologist Tim Ingold's notion of dwelling, and discuss how use of WhatsApp is constitutive of a felt-life of being together with those close by. We focus on the relationship "doings" in WhatsApp and how this togetherness and intimacy are enacted through small, continuous traces of narrative, of tellings and tidbits, noticings and thoughts, shared images and lingering pauses; this is constitutive of dwelling. Further, we discuss how an intimate knowing of others in these relationships, through past encounters and knowledge of coming together in the future, pertain to the particular forms of relationship engagements manifest through the possibilities presented in WhatsApp. We suggest that this form of sociality is likely to be manifest in other smartphone IM-like applications.


title: A Benchmark for Iris Location and a Deep Learning Detector Evaluation

abstract: The iris is considered as the biometric trait with the highest unique probability. The iris location is an important task for biometrics systems, affecting directly the results obtained in specific applications such as iris recognition, spoofing and contact lenses detection, among others. This work defines the iris location problem as the delimitation of the smallest squared window that encompasses the iris region. In order to build a benchmark for iris location we annotate (iris squared bounding boxes) four databases from different biometric applications and make them publicly available to the community. Besides these 4 annotated databases, we include 2 others from the literature. We perform experiments on these six databases, five obtained with near infra-red sensors and one with visible light sensor. We compare the classical and outstanding Daugman iris location approach with two window based detectors: 1) a sliding window detector based on features from Histogram of Oriented Gradients (HOG) and a linear Support Vector Machines (SVM) classifier; 2) a deep learning based detector fine-tuned from YOLO object detector. Experimental results showed that the deep learning based detector outperforms the other ones in terms of accuracy and runtime (GPUs version) and should be chosen whenever possible.


title: The IIoT/IoT device control model based on narrow-band IoT (NB-IoT)

abstract: The article discusses the topical issue of creating a technologically integrated network of data centers built on a single virtualization platform to support the functioning of IoT infrastructures. A comprehensive solution for the interaction of all IIoT / IoT components is proposed, based on three types of platforms. The role of Data centers of each type is substantiated depending on geographical, economic and branch peculiarities of the Russian Federation. An analysis of wireless technologies for the transmission of small amounts of data over long distances in the context of their current and projected use for IIoT / IoT in Russia is being conducted. In particular, the draft of the Russian communication standard for the Internet of things NB-FI is being analyzed. The structural scheme (model) of the decision on the organization of the Control Center IIoT / IoT is offered.


title: Question Answering over Knowledge Base with Neural Attention Combining Global Knowledge Information

abstract: With the rapid growth of knowledge bases (KBs) on the web, how to take full advantage of them becomes increasingly important. Knowledge base-based question answering (KB-QA) is one of the most promising approaches to access the substantial knowledge. Meantime, as the neural network-based (NN-based) methods develop, NN-based KB-QA has already achieved impressive results. However, previous work did not put emphasis on question representation, and the question is converted into a fixed vector regardless of its candidate answers. This simple representation strategy is unable to express the proper information of the question. Hence, we present a neural attention-based model to represent the questions dynamically according to the different focuses of various candidate answer aspects. In addition, we leverage the global knowledge inside the underlying KB, aiming at integrating the rich KB information into the representation of the answers. And it also alleviates the out of vocabulary (OOV) problem, which helps the attention model to represent the question more precisely. The experimental results on WEBQUESTIONS demonstrate the effectiveness of the proposed approach.


title: Exploiting shaper context to improve performance bounds of Ethernet AVB networks

abstract: New hard real-time Advanced Driver Assistance Systems such as the Collision-Avoidance System push the bandwidth requirements of the communication infrastructure to a new level. Controller Area Network (CAN) and FlexRay are reaching their limits. Ethernet-based automotive networks such as Ethernet AVB are capable of addressing these requirements. However, designing predictable Ethernet networks is more complex than the design of a traditional CAN bus. Formal real-time performance characteristics are key to a successful Ethernet integration. In this paper we present an improved Ethernet AVB performance analysis which exploits traffic-stream correlations. The results are significantly tighter compared to related work.


title: On the Wisdom of Experts vs. Crowds: Discovering Trustworthy Topical News in Microblogs

abstract: Extracting news on specific topics from the Twitter microblogging site poses formidable challenges, which include handling millions of tweets posted daily, judging topicality and importance of tweets, and ensuring trustworthiness of results in the face of spam. To date, all scalable approaches have relied on crowd wisdom, i.e., keyword-matching on the global tweet stream to gather relevant tweets, and crowd- endorsements to judge the importance of tweets. We propose a fundamentally different methodology -- for a given topic, we identify trustworthy experts on the topic, and extract news-stories that are most popular among the experts. Comparing the crowd-based and expert-based methodologies, we demonstrate that the news-stories obtained by our methodology (i) have higher relevance for a wide variety of topics, (ii) achieve very high coverage of important news-stories posted globally in Twitter, and (iii) are far more trustworthy. Using our methodology, we implemented and publicly deployed a topical news system for Twitter, which can extract news-stories on thousands of topics.


Massive MIMO Technologies and Challenges towards 5G


title: Evaluation and improvement of VoIP capacity for LTE

abstract: LTE-Advanced (LTE-A) proposed by the 3rd Generation Partnership Project (3GPP) has been accepted by the International Telecommunication Union (ITU) in 2010 as an IMT-Advanced (IMT-A) compliant 4G mobile radio system. Besides high spectral efficiency for data services, LTE-A had to prove its capability to support a large number of voice calls through Voice over IP (VoIP).


title: A sparse sampling strategy for angular superresolution of real beam scanning radar

abstract: This paper investigates techniques for angular superresolution using limited data of real beam scanning radar (RBSR). In order to improve the angular resolution of RBSR, many algorithms have been proposed. However, for most algorithms, large amounts of sampling data is necessary. The requirement of data increases the burden of the radar system. Fortunately, the sparse signal reconstruction techniques provide a new train of thought for us. It has been proved in array signal processing and image processing that the techniques only need limited sampling data to realize DOA estimation and image superresolution. This paper describes the sparse sampling model of RBSR as an underdetermined equation-solving problem, the received signals are sparsely recovered in target domain. Two algorithms, including smooth approximation algorithm and focal underdetermined system solver (FOCUSS), based on different optimization ideas, are adopted to solve the problem. Simulation results show that compressive sampling methods can recover the target domain accurately, especially under the condition of high signal-to-noise ratio (SNR).


title: Leveraging Long and Short-term Information in Content-aware Movie Recommendation

abstract: Movie recommendation systems provide users with ranked lists of movies based on individual's preferences and constraints. Two types of models are commonly used to generate ranking results: long-term models and session-based models. While long-term models represent the interactions between users and movies that are supposed to change slowly across time, session-based models encode the information of users' interests and changing dynamics of movies' attributes in short terms. In this paper, we propose an LSIC model, leveraging Long and Short-term Information in Content-aware movie recommendation using adversarial training. In the adversarial process, we train a generator as an agent of reinforcement learning which recommends the next movie to a user sequentially. We also train a discriminator which attempts to distinguish the generated list of movies from the real records. The poster information of movies is integrated to further improve the performance of movie recommendation, which is specifically essential when few ratings are available. The experiments demonstrate that the proposed model has robust superiority over competitors and sets the state-of-the-art. We will release the source code of this work after publication.


title: A post-wall waveguide (SIW) matched load with thin-film resistor

abstract: A matched load for post-wall waveguide (SIW; Substrate Integrated Waveguide) is presented. It consists of an electrically-shorted post-wall waveguide and a rectangular thin-film resistor sheet on the surface of the waveguide, resulting in a quite compact structure without three-dimensional bulky absorber as in conventional waveguide matched loads. A fabricated X-band matched load has achieved less than −20 dB reflection in more than 20 % relative bandwidth.


title: Free radical activity of synthetic vitreous fibers: iron chelation inhibits hydroxyl radical generation by refractory ceramic fiber.

abstract: Synthetic vitreous fibers are in widespread use but the parameters that dictate their carcinogenicity are still a matter of scientific debate. The free radical activities of a panel comprising an asbestos sample and five different respirable synthetic vitreous fiber samples were determined, to address the hypothesis that carcinogenic fibers have greater free radical activity than noncarcinogenic fibers. On the basis of recent inhalation studies, the six samples were divided into three carcinogenic fibers-amphibole asbestos, silicon carbide, and refractory ceramic fiber 1 (designated with the abbreviation RCF 1)-and three noncarcinogenic fibers--man-made vitreous fiber 10 (a glass fiber sample designated with the abbreviation MMVF 10), Code 100/475 glass fiber, and RCF4. All experiments were carried out with equal fiber numbers. Of the two assays of free radical activity used, the plasmid assay of DNA scission showed only amosite asbestos to have free radical activity, while the salicylate assay of hydroxyl activity showed that both amosite asbestos and RCF1 release hydroxyl radicals; silicon carbide fibers had no free radical activity in either of the assays. None of the noncarcinogenic fibers demonstrated free radical activity in either of the assays. The differences in the two assays in demonstrating free radical activity with RCF1 may be due to increased release of Fe from RCF1 under the more acid conditions of the salicylate assay, which was confirmed by the fact that soluble iron caused hydroxylation of salicylate. Presence of an iron chelator inhibited the ability of the RCF1 fibers to cause hydroxylation of salicylate, demonstrating that RCF1 generates hydroxyl radical by Fenton chemical reaction in the same way as amphibole asbestos.


title: User modeling for point-of-interest recommendations in location-based social networks: the state-of-the-art

abstract: The rapid growth of location-based services(LBSs)has greatly enriched people's urban lives and attracted millions of users in recent years. Location-based social networks(LBSNs)allow users to check-in at a physical location and share daily tips on points-of-interest (POIs) with their friends anytime and anywhere. Such check-in behavior can make daily real-life experiences spread quickly through the Internet. Moreover, such check-in data in LBSNs can be fully exploited to understand the basic laws of human daily movement and mobility. This paper focuses on reviewing the taxonomy of user modeling for POI recommendations through the data analysis of LBSNs. First, we briefly introduce the structure and data characteristics of LBSNs,then we present a formalization of user modeling for POI recommendations in LBSNs. Depending on which type of LBSNs data was fully utilized in user modeling approaches for POI recommendations, we divide user modeling algorithms into four categories: pure check-in data-based user modeling, geographical information-based user modeling, spatio-temporal information-based user modeling, and geo-social information-based user modeling. Finally,summarizing the existing works, we point out the future challenges and new directions in five possible aspects


Metaphors We Live By


title: On the Security and Privacy of Internet of Things Architectures and Systems

abstract: The Internet of Things (IoT) brings together a multitude of technologies, with a vision of creating an interconnected world. This will benefit both corporations as well as the end-users. However, a plethora of security and privacy challengesneed to be addressed for the IoT to be fully realized. In thispaper, we identify and discuss the properties that constitutethe uniqueness of the IoT in terms of the upcoming securityand privacy challenges. Furthermore, we construct requirementsinduced by the aforementioned properties. We survey the fourmost dominant IoT architectures and analyze their securityand privacy components with respect to the requirements. Ouranalysis shows a mediocre coverage of security and privacyrequirements. Finally, through our survey we identify a number ofresearch gaps that constitute the steps ahead for future research.


title: Feature Selection with Selective Sampling

abstract: Feature selection, as a preprocessing step to machine learning, has been very effective in reducing dimensionality, removing irrelevant data, increasing learning accuracy, and improving result comprehensibility. Traditional feature selection methods resort to random sampling in dealing with data sets with a huge number of instances. In this paper, we introduce the concept of active feature selection, and investigate a selective sampling approach to active feature selection in a filter model setting. We present a formalism of selective sampling based on data variance, and apply it to a widely used feature selection algorithm Relief. Further, we show how it realizes active feature selection and reduces the required number of training instances to achieve time savings without performance deterioration. We design objective evaluation measures of performance, conduct extensive experiments using both synthetic and benchmark data sets, and observe consistent and significant improvement. We suggest some further work based on our study and experiments.


title: Recent Advances in Zero-Shot Recognition

abstract: With the recent renaissance of deep convolutional neural networks (CNNs), encouraging breakthroughs have been achieved on the supervised recognition tasks, where each class has sufficient and fully annotated training data. However, to scale the recognition to a large number of classes with few or no training samples for each class remains an unsolved problem. One approach is to develop models capable of recognizing unseen categories without any training instances, or zero-shot recognition/learning. This article provides a comprehensive review of existing zero-shot recognition techniques covering various aspects ranging from representations of models, data sets, and evaluation settings. We also overview related recognition tasks including one-shot and open-set recognition, which can be used as natural extensions of zero-shot recognition when a limited number of class samples become available or when zero-shot recognition is implemented in a real-world setting. We highlight the limitations of existing approaches and point out future research directions in this existing new research area.


title: Reducing Redundant Computations with Flexible Attention

abstract: Recently, attention mechanism plays a key role to achieve high performance for Neural Machine Translation models. It applies a score function to the encoder states to obtain alignment weights. However, as this computation is done for all positions in each decoding step, the attention mechanism greatly increases the computational complexity. In this paper we propose a novel attention model which can reduce redundant attentional computations in a flexible manner. The proposed mechanism tracks the center of attention in each decoding step, and computes position-based penalties. In the test time, the computations of the score function for heavily penalized positions are skipped. In our experiments, we found that the computations in the attention model can be reduced by 54% in average with almost no loss of accuracy.


title: Deterministic Annealing for Clustering, Compression, Classification, Regression, and Related Optimization Problems

abstract: The deterministic annealing approach to clustering and its extensions has demonstrated substantial performance improvement over standard supervised and unsupervised learning methods in a variety of important applications including compression, estimation, pattern recognition and classification, and statistical regression. The application-specific cost is minimized subject to a constraint on the randomness of the solution, which is gradually lowered. We emphasize the intuition gained from analogy to statistical physics. Alternatively the method is derived within rate-distortion theory, where the annealing process is equivalent to computation of Shannon's rate-distortion function, and the annealing temperature is inversely proportional to the slope of the curve. The basic algorithm is extended by incorporating structural constraints to allow optimization of numerous popular structures including vector quantizers, decision trees, multilayer perceptrons, radial basis functions, and mixtures of experts.