title: The Study of RFID Authentication Protocols and Security of Some Popular RFID Tags

abstract: A radio frequency identification (RFID) system consists of three components: radio frequency (RF) tags (or transponders), RF readers (or transceivers), and a backend server. Tag readers inquire tags of their contents by broadcasting an RF signal, without physical contact, at a rate of several hundred tags per second and from a range of several meters. The advancements of Silicon manufacturing also result in great cost reduction for RFID tags compared to barcodes, not to mention that the tags can carry more data and are more resistant to dust and twisting. Thanks to these excellent features, the world has seen many RFID systems already put to use by manufacturers and businesses of all kinds of goods for supply management and inventory control and such; in addition, many public facilities and parking lots have also brought in RFID systems to help them offer faster, easier and more user-friendly services. As a matter of fact, potential applications are everywhere [57]. Such features as great convenience, low cost, and wide applicability will soon make RFID systems the most pervasive microchips in history [57]. However, the wide distribution of RFID systems into modern society may very much likely get the security of both businesses and consumers exposed to threats and risks. For example, businesses may have malicious competitors on the market that collect unprotected RFIDs to gather information illegally, spread false tags to provide wrong information, or even launch denial of service (DOS) attacks against them. On the other hand, as a consumer, it is naturally preferred that the information of the purchase of RFID-tagged products be kept private from outsiders; however, a tag reader at a fixed location can read the content of an un-protected tag, tracing the RFID-tagged product or/and even identifying the person carrying the tagged product. Correlating data collected from multiple tag readers such as their locations and so on can also possibly be used to spy on an individual and track down his/her social interactions. Besides passive eavesdropping and tracking, a thief might use counterfeit tags to fool automated checkout or security systems into accepting wrong information like price, proof of presence or other information.


title: Security of signed elgamal encryption

abstract: Assuming a cryptographically strong cyclic group G of prime order q and a random hash function H, we show that ElGamal encryption with an added Schnorr signature is secure against the adaptive chosen ciphertext attack, in which an attacker can freely use a decryption oracle except for the target ciphertext. We also prove security against the novel one-more-decyption attack. Our security proofs are in a new model, corresponding to a combination of two previously introduced models, the Random Oracle model and the Generic model. The security extends to the distributed threshold version of the scheme. Moreover, we propose a very practical scheme for private information retrieval that is based on blind decryption of ElGamal ciphertexts.


title: Effect of Video-Game Experience and Position of Flight Stick Controller on Simulated-Flight Performance

abstract: AbstractThe purpose of this study was to determine the effects of video-game experience and flight-stick position on flying performance. The study divided participants into 2 groups; center- and side-stick groups, which were further divided into high and low level of video-game experience subgroups. The experiment consisted of 7 sessions of simulated flying, and in the last session, the flight stick controller was switched to the other position. Flight performance was measured in terms of the deviation of heading, altitude, and airspeed from their respective requirements. Participants with high experience in video games performed significantly better (p < .001) than the low-experienced group. Also, participants performed significantly better (p < .001) with the center-stick than the side-stick. When the side-stick controller was switched to the center-stick position, performance scores continued to increase (0.78 %). However, after switching from a center- to a side-stick controller, performance scores de...


title: BRAIN IMAGING NEUROREPORT Dissociation of reward anticipation and outcome with event-related fMRI

abstract: Reward processing involves both appetitive and consummatory phases. We sought to examine whether reward anticipation vs outcomes would recruit different regions of ventral forebrain circuitry using event-related fMRI. Nine healthy volunteers participated in a monetary incentive delays task in which they either responded to a cued target for monetary reward, responded to a cued target for no reward, or did not respond to a cued target during scanning. Multiple regression analyses indicated that while anticipation of reward vs non-reward activated foci in the ventral striatum, reward vs non-reward outcomes activated foci in the ventromedial frontal cortex. These findings suggest that reward anticipation and outcomes may differentially recruit distinct regions that lie along the trajectory of ascending dopamine projections. NeuroReport 12:3683‐3687 & 2001 Lippincott Williams & Wilkins.


title: Detection and classification of diseases of Grape plant using opposite colour Local Binary Pattern feature and machine learning for automated Decision Support System

abstract: Plant diseases cause major economic and production losses as well as curtailment in both quantity and quality of agricultural production. Now a day's, for supervising large field of crops there is been increased demand for plant leaf disease detection system. The critical issue here is to monitor the health of the plants and detection of the respective diseases. Studies show that most of the plant disease can be diagnosed from the properties of the leaf. Thus leaf based disease analysis for plants is an exciting new domain. The technique proposed for identification of plant disease through the leaf texture analysis and pattern recognition. In this work we focus on Grapes plant leaf disease detection system. The system takes a single leaf of a plant as an input and segmentation is performed after background removal. The segmented leaf image is then analyzed through high pass filter to detect the diseased part of the leaf. The segmented leaf texture is retrieved using unique fractal based texture feature. Fractal based features are locally invariant in nature and therefore provides a good texture model. The texture of every independent disease will be different. The extracted texture pattern is then classified using multiclass SVM. The work classifies focus on major diseases commonly observed in Grapes plant which are downy mildew & black rot. The proposed approach avails advice of agricultural experts easily to farmers with the accuracy of 96.6%.


title: Evaluating ICT Adoption in Rural Brazil: A Quantitative Analysis of Telecenters as Agents of Social Change

abstract: This quantitative study surveyed 538 adults in isolated rural settings in the state of Minas Gerais, Brazil, to examine whether telecenters operated by the non-profit organization Gems of the Earth improve digital literacy and promote social change. Using multivariate logistic regression, the study examined how individuals use information and communication technologies (ICTs) at the telecenter, and tested for predictors of their use. The findings confirm that these rural communities use ICTs for entertainment, to engage in civic participation, and to practice professional skills. The findings suggest that digital inclusion impacts these isolated communities by creating opportunities that may foster human development. This quantitative study surveyed 538 adults in isolated rural settings in the state of Minas Gerais, Brazil, in order to examine whether telecenters operated by the non-profit organization Gems of the Earth in the area improve digital literacy and promote social change in those remote mountain communities. Using multivariate logistic regression, the study examined how individuals use information and communication technologies (ICTs) at the telecenter, and tested for predictors of their use. The findings confirm that these rural communities use ICTs for entertainment, to engage in civic participation, and to practice professional skills. Results also indicate greater odds of ICT use among individuals aged 18 to 24 and among those who seek diversion. The findings suggest that digital inclusion impacts these isolated communities by creating opportunities for entertainment, civic engagement, professional development, and education in ways that may positively impact human development.


title: A design of multi-stage, multi-way microstrip power dividers with broadband properties

abstract: This paper presents a design method of multi-stage, multi-way microstrip power dividers with the aim of constructing a compact low-loss power divider with numbers of outputs. First, an integration design technique of power dividers composed of multi-step, multi-furcation and mitered bends is described. Since the analytical technique is founded on the planar circuit approach combined with the segmentation method, the optimization of the circuit patterns can be performed in a reasonable short computation time. Next, the present method is applied to the design of broadband N n -way power dividers such as 3 2 -way power divider consisting of 3-way dividers in two-stage structures, respectively. In addition, a 12-way power divider constructed from a series connection of a 3-way and three 4-way dividers is designed. The dividers equivalently contain a 3-section Chebyshev transformer to realize broadband properties. As a result, the fractional bandwidths of nearly 85% and 66.7% for the power-split imbalance less than 0.2 dB and the return loss better than -20 dB are obtained for the 9- and 12-way power dividers, respectively. The validity of these design results is confirmed by a commercial em-simulator (Ansoft HFSS) and experiments.


title: Low-Resource Neural Headline Generation

abstract: Recent neural headline generation models have shown great results, but are generally trained on very large datasets. We focus our efforts on improving headline quality on smaller datasets by the means of pretraining. We propose new methods that enable pre-training all the parameters of the model and utilize all available text, resulting in improvements by up to 32.4% relative in perplexity and 2.84 points in ROUGE.


title: A self-biased high performance folded cascode CMOS op-amp

abstract: Cascode CMOS op-amps use a large number of external bias voltages. This results in numerous drawbacks, namely, an area and power overhead, susceptibility of the bias lines to noise and cross-talk and high sensitivity of the bias point to process variations. In this paper we present a self-biasing technique for folded cascode CMOS op-amps that uses no additional devices and no bias voltages other than the two supply rails. The resulting self-biased op-amps are free from the above mentioned drawbacks and exhibit the same performance as existing folded casode op amps, except for a small reduction in slew rate. This is achieved by following transistor sizing constraints derived through detailed circuit analysis. The technique is applied to an existing high performance op amp. Simulation results show that the high performance is maintained while nine bias voltages are eliminated.


title: Clinical and Molecular Genetics of the Phosphodiesterases (PDEs)

abstract: Cyclic nucleotide phosphodiesterases (PDEs) are enzymes that have the unique function of terminating cyclic nucleotide signaling by catalyzing the hydrolysis of cAMP and GMP. They are critical regulators of the intracellular concentrations of cAMP and cGMP as well as of their signaling pathways and downstream biological effects. PDEs have been exploited pharmacologically for more than half a century, and some of the most successful drugs worldwide today affect PDE function. Recently, mutations in PDE genes have been identified as causative of certain human genetic diseases; even more recently, functional variants of PDE genes have been suggested to play a potential role in predisposition to tumors and/or cancer, especially in cAMP-sensitive tissues. Mouse models have been developed that point to wide developmental effects of PDEs from heart function to reproduction, to tumors, and beyond. This review brings together knowledge from a variety of disciplines (biochemistry and pharmacology, oncology, endocrinology, and reproductive sciences) with emphasis on recent research on PDEs, how PDEs affect cAMP and cGMP signaling in health and disease, and what pharmacological exploitations of PDEs may be useful in modulating cyclic nucleotide signaling in a way that prevents or treats certain human diseases.


title: Activity Recognition and Abnormal Behaviour Detection with Recurrent Neural Networks

abstract: In this paper, we study the problem of activity recognition and abnormal behaviour detection for elderly people with dementia. Very few studies have attempted to address this problem presumably because of the lack of experimental data in the context of dementia care. In particular, the paper investigates three variants of Recurrent Neural Networks (RNNs): Vanilla RNNs (VRNN), Long Short Term RNNs (LSTM) and Gated Recurrent Unit RNNs (GRU). Here activity recognition is considered as a sequence labelling problem, while abnormal behaviour is flagged based on the deviation from normal patterns. To provide an adequate discussion of the performance of RNNs in this context, we compare them against the state-of-art methods such as Support Vector Machines (SVMs), Na¨ive Bayes (NB), Hidden Markov Models (HMMs), Hidden Semi-Markov Models (HSMM) and Conditional Random Fields (CRFs). The results obtained indicate that RNNs are competitive with those state-of-art methods. Moreover, the paper presents a methodology for generating synthetic data reflecting on some behaviours of people with dementia given the difficulty of obtaining real-world data.


title: Toffee: enabling ad hoc, around-device interaction with acoustic time-of-arrival correlation

abstract: The simple fact that human fingers are large and mobile devices are small has led to the perennial issue of limited surface area for touch-based interactive tasks. In response, we have developed Toffee, a sensing approach that extends touch interaction beyond the small confines of a mobile device and onto ad hoc adjacent surfaces, most notably tabletops. This is achieved using a novel application of acoustic time differences of arrival (TDOA) correlation. Previous time-of-arrival based systems have required semi-permanent instrumentation of the surface and were too large for use in mobile devices. Our approach requires only a hard tabletop and gravity -- the latter acoustically couples mobile devices to surfaces. We conducted an evaluation, which shows that Toffee can accurately resolve the bearings of touch events (mean error of 4.3° with a laptop prototype). This enables radial interactions in an area many times larger than a mobile device; for example, virtual buttons that lie above, below and to the left and right.


title: AI buzzwords explained: multi-agent path finding (MAPF)

abstract: Kiva Systems was founded in 2003 to develop robot technology that automates the fetching of goods in order-fulfillment centers. It was acquired by Amazon in 2012 and changed its name to Amazon Robotics in 2014. Amazon order-fulfillment centers have inventory stations on the perimeter of the warehouse and storage locations in its center, see Figure 1. Each storage location can store one inventory pod. Each inventory pod holds one or more kinds of goods. A large number of warehouse robots operate autonomously in the warehouse. Each warehouse robot is able to pick up, carry and put down one inventory pod at a time. The warehouse robots move inventory pods from their storage locations to the inventory stations where the needed goods are removed from the inventory pods (to be boxed and eventually shipped to customers) and then back to the same or different empty storage locations (Wurman, D'Andrea, & Mountz, 2008).1


title: Properties of various plants and animals feedstocks for biodiesel production.

abstract: As an alternative fuel biodiesel is becoming increasingly important due to diminishing petroleum reserves and adverse environmental consequences of exhaust gases from petroleum-fuelled engines. Biodiesel, the non-toxic fuel, is mono alkyl esters of long chain fatty acids derived from renewable feedstock like vegetable oils, animal fats and residual oils. Choice of feedstocks depends on process chemistry, physical and chemical characteristics of virgin or used oils and economy of the process. Extensive research information is available on transesterification, the production technology and process optimization for various biomaterials. Consistent supply of feedstocks is being faced as a major challenge by the biodiesel production industry. This paper reviews physico-chemical properties of the plant and animal resources that are being used as feedstocks for biodiesel production. Efforts have also been made to review the potential resources that can be transformed into biodiesel successfully for meeting the ever increasing demand of biodiesel production.


title: Toward automatic role identification in unannotated folk tales

abstract: This paper presents an approach for automatically identifying high-level narrative structure information, particularly character roles, from unannotated folk tales. We introduce a new representation called action matrices to encode Propp's narrative theory on character role and their "sphere of action." We tested our approach in a fully automated system (Voz) using a corpus of 10 folk tales. Our experimental evaluation shows that action matrices capture useful information for role identification, provides insight into the error introduced by individual steps, and identifies the current bottlenecks.


title: Choice of intra-articular injection in treatment of knee osteoarthritis: platelet-rich plasma, hyaluronic acid or ozone options

abstract: PURPOSE ::: This study was performed to compare the efficacy of treatment in three groups of patients with knee osteoarthritis (OA) given an intra-articular injection of platelet-rich plasma (PRP), hyaluronic acid (HA) or ozone gas. ::: ::: ::: METHODS ::: A total of 102 patients with mild-moderate and moderate knee OA who presented at the polyclinic with at least a 1-year history of knee pain and VAS score ≥4 were randomly separated into three groups. Group 1 (PRP group) received intra-articular injection of PRP × 2 doses, Group 2 (HA group) received a single dose of HA, and Group 3 (Ozone group) received ozone × four doses. Weight-bearing anteroposterior-lateral and Merchant's radiographs of both knees were evaluated. WOMAC and VAS scores were applied to all patients on first presentation and at 1, 3, 6 and 12 months. ::: ::: ::: RESULTS ::: At the end of the 1st month after injection, significant improvements were seen in all groups. In the 3rd month, the improvements in WOMAC and VAS scores were similar in Groups 1 and 2, while those in Group 3 were lower (p < 0.001). At the 6th month, while the clinical efficacies of PRP and HA were similar and continued, the clinical effect of ozone had disappeared (p < 0.001). At the end of the 12th month, PRP was determined to be both statistically and clinically superior to HA (p < 0.001). ::: ::: ::: CONCLUSION ::: In the treatment of mild-moderate knee OA, PRP was more successful than HA and ozone injections, as the application alone was sufficient to provide at least 12 months of pain-free daily living activities. ::: ::: ::: LEVEL OF EVIDENCE ::: Therapeutic study, Level I.


title: Cloud Computing Security: A Survey

abstract: Cloud computing is an emerging technology paradigm that migrates current technological and computing concepts into utility-like solutions similar to electricity and water systems. Clouds bring out a wide range of benefits including configurable computing resources, economic savings, and service flexibility. However, security and privacy concerns are shown to be the primary obstacles to a wide adoption of clouds. The new concepts that clouds introduce, such as multi-tenancy, resource sharing and outsourcing, create new challenges to the security community. Addressing these challenges requires, in addition to the ability to cultivate and tune the security measures developed for traditional computing systems, proposing new security policies, models, and protocols to address the unique cloud security challenges. In this work, we provide a comprehensive study of cloud computing security and privacy concerns. We identify cloud vulnerabilities, classify known security threats and attacks, and present the state-of-the-art practices to control the vulnerabilities, neutralize the threats, and calibrate the attacks. Additionally, we investigate and identify the limitations of the current solutions and provide insights of the future security perspectives. Finally, we provide a cloud security framework in which we present the various lines of defense and identify the dependency levels among them. We identify 28 cloud security threats which we classify into five categories. We also present nine general cloud attacks along with various attack incidents, and provide effectiveness analysis of the proposed countermeasures.


title: A Novel Method to Reconstruct Overhead High-Voltage Power Lines Using Cable Inspection Robot LiDAR Data

abstract: Overhead high-voltage power lines are key components of power transmission and their monitoring has a very significant influence on security and reliability of power system. Advanced laser scanning techniques have been widely used to capture three-dimensional (3D) point clouds of power system scenes. Nevertheless, power line corridors are found in increasingly complex environments (e.g., mountains and forests), and the multi-loop structure on the same power line tower raises great challenges to process light detection and ranging (LiDAR) data. This paper addresses these challenges by constructing a new collection mode of LiDAR data for power lines using cable inspection robot (CIR). A novel method is proposed to extract and reconstruct power line using CIR LiDAR data, which has two advantages: (1) rapidly extracts power line point by position and orientation system (POS) extraction model; and (2) better solves pseudo-line during reconstruction of power line by structured partition. The proposed method mainly includes four steps: CIR LiDAR data generation, POS-based crude extraction, voxel-based accurate extraction and power line reconstruction. The feasibility and validity of the proposed method are verified by test site experiment and actual line experiment, demonstrating a fast and reliable solution to accurately reconstruct power line.


title: Prevalence of cardiovascular morbidities in Myanmar

abstract: BackgroundCardiovascular diseases (CVDs) are now in a rising trend in South East Asia including Myanmar due to increase in major cardiovascular risk factors in both urban and rural areas, such as smoking, obesity and diabetes mellitus. It is necessary to determine CVD morbidities in Myanmar for planning of prevention and control activities for CVDs. The cross-sectional household survey was conducted in 2012 with 600 people aged 40 years and above in four townships (Kyauk-Tan, Mawlamyaing, Pathein and Pyay) and used face-to-face interview with standard questionnaire [Rose Angina Questionnaire and Questionnaire by European Cardiovascular Indicators Surveillance Set (EUROCISS) Research Group] to determine the level of reported CVD morbidities in adult population.ResultsAge of the study population ranged from 40 to 99 years with the mean age of 56 years. Seventy-one percent of the study population was women. Nine percent of the study population have suffered from angina according to Rose Angina Questionnaire. Prevalence of possible heart attack, stroke and heart failure was 7.5, 1.5 and 2.8%. Prevalence of hypertension was 51%.ConclusionThe CVD morbidities are high. There is a need for strengthening prevention and control activities of CVDs.


title: Characterizing the Internet hierarchy from multiple vantage points

abstract: The delivery of IP traffic through the Internet depends on the complex interactions between thousands of autonomous systems (AS) that exchange routing information using the border gateway protocol (BGP). This paper investigates the topological structure of the Internet in terms of customer-provider and peer-peer relationships between autonomous systems, as manifested in BGP routing policies. We describe a technique for inferring AS relationships by exploiting partial views of the AS graph available from different vantage points. Next we apply the technique to a collection of ten BGP routing tables to infer the relationships between neighboring autonomous systems. Based on these results, we analyze the hierarchical structure of the Internet and propose a five-level classification of AS. Our characterization differs from previous studies by focusing on the commercial relationships between autonomous systems rather than simply the connectivity between the nodes.


A new Design Criteria for Hash-Functions


title: Big data and cloud computing: current state and future opportunities

abstract: Scalable database management systems (DBMS)---both for update intensive application workloads as well as decision support systems for descriptive and deep analytics---are a critical part of the cloud infrastructure and play an important role in ensuring the smooth transition of applications from the traditional enterprise infrastructures to next generation cloud infrastructures. Though scalable data management has been a vision for more than three decades and much research has focussed on large scale data management in traditional enterprise setting, cloud computing brings its own set of novel challenges that must be addressed to ensure the success of data management solutions in the cloud environment. This tutorial presents an organized picture of the challenges faced by application developers and DBMS designers in developing and deploying internet scale applications. Our background study encompasses both classes of systems: (i) for supporting update heavy applications, and (ii) for ad-hoc analytics and decision support. We then focus on providing an in-depth analysis of systems for supporting update intensive web-applications and provide a survey of the state-of-the-art in this domain. We crystallize the design choices made by some successful systems large scale database management systems, analyze the application demands and access patterns, and enumerate the desiderata for a cloud-bound DBMS.


title: Wave Optics Theory and 3-D Deconvolution for the Light Field Microscope

abstract: Light field microscopy is a new technique for high-speed volumetric imaging of weakly scattering or fluorescent specimens. It employs an array of microlenses to trade off spatial resolution against angular resolution, thereby allowing a 4-D light field to be captured using a single photographic exposure without the need for scanning. The recorded light field can then be used to computationally reconstruct a full volume. In this paper, we present an optical model for light field microscopy based on wave optics, instead of previously reported ray optics models. We also present a 3-D deconvolution method for light field microscopy that is able to reconstruct volumes at higher spatial resolution, and with better optical sectioning, than previously reported. To accomplish this, we take advantage of the dense spatio-angular sampling provided by a microlens array at axial positions away from the native object plane. This dense sampling permits us to decode aliasing present in the light field to reconstruct high-frequency information. We formulate our method as an inverse problem for reconstructing the 3-D volume, which we solve using a GPU-accelerated iterative algorithm. Theoretical limits on the depth-dependent lateral resolution of the reconstructed volumes are derived. We show that these limits are in good agreement with experimental results on a standard USAF 1951 resolution target. Finally, we present 3-D reconstructions of pollen grains that demonstrate the improvements in fidelity made possible by our method.


title: Convolutional Deep Maxout Networks for Phone Recognition

abstract: Convolutional neural networks have recently been shown to outperform fully connected deep neural networks on several speech recognition tasks. Their superior performance is due to their convolutional structure that processes several, slightly shifted versions of the input window using the same weights, and then pools the resulting neural activations. This pooling operation makes the network less sensitive to translations. The convolutional network results published up till now used sigmoid or rectified linear neurons. However, quite recently a new type of activation function called the maxout activation has been proposed. Its operation is closely related to convolutional networks, as it applies a similar pooling step, but over different neurons evaluated on the same input. Here, we combine the two technologies, and experiment with deep convolutional neural networks built from maxout neurons. Phone recognition tests on the TIMIT database show that switching to maxout units from rectifier units decreases the phone error rate for each network configuration studied, and yields relative error rate reductions of between 2% and 6%.


title: powerlaw: A Python Package for Analysis of Heavy-Tailed Distributions

abstract: Power laws are theoretically interesting probability distributions that are also frequently used to describe empirical data. In recent years, effective statistical methods for fitting power laws have been developed, but appropriate use of these techniques requires significant programming and statistical insight. In order to greatly decrease the barriers to using good statistical methods for fitting power law distributions, we developed the powerlaw Python package. This software package provides easy commands for basic fitting and statistical analysis of distributions. Notably, it also seeks to support a variety of user needs by being exhaustive in the options available to the user. The source code is publicly available and easily extensible.


title: A guided tour to approximate string matching

abstract: We survey the current techniques to cope with the problem of string matching that allows errors. This is becoming a more and more relevant issue for many fast growing areas such as information retrieval and computational biology. We focus on online searching and mostly on edit distance, explaining the problem and its relevance, its statistical behavior, its history and current developments, and the central ideas of the algorithms and their complexities. We present a number of experiments to compare the performance of the different algorithms and show which are the best choices. We conclude with some directions for future work and open problems.


title: Distributed Stochastic ADMM for Matrix Factorization

abstract: Matrix factorization (MF) has become the most popular technique for recommender systems due to its promising performance. Recently, distributed (parallel) MF models have received much attention from researchers of big data community. In this paper, we propose a novel model, called distributed stochastic alternating direction methods of multipliers (DS-ADMM), for large-scale MF problems. DS-ADMM is a distributed stochastic variant of ADMM. In particular, we first devise a new data split strategy to make the distributed MF problem fit for the ADMM framework. Then, a stochastic ADMM scheme is designed for learning. Finally, we implement DS-ADMM based on message passing interface (MPI), which can run on clusters with multiple machines (nodes). Experiments on several data sets from recommendation applications show that our DS-ADMM model can outperform other state-of-the-art distributed MF models in terms of both efficiency and accuracy.


ASMOV : Ontology Alignment with Semantic Validation


title: Smart tourism: foundations and developments

abstract: Smart tourism is a new buzzword applied to describe the increasing reliance of tourism destinations, their industries and their tourists on emerging forms of ICT that allow for massive amounts of data to be transformed into value propositions. However, it remains ill-defined as a concept, which hinders its theoretical development. The paper defines smart tourism, sheds light on current smart tourism trends, and then lays out its technological and business foundations. This is followed by a brief discussion on the prospects and drawbacks of smart tourism. The paper further draws attention to the great need for research to inform smart tourism development and management.


title: Concept-Based Automatic Amharic Document Categorization

abstract: Along with the continuously growing volume of information resources, there is a growing interest toward better solutions for finding, filtering and organizing these resources. Automatic text categorization can play an important role in a wide variety of flexible, dynamic, and personalized information management tasks. The aim of this research work is to make use of concepts as a way of improving the categorization process for Amharic 1 documents. In recent years, ontology-based document categorization method is introduced to solve the problem of document classification. Previous works on keyword-based document categorization miss some important issues of considering semantic relationships between words. In order to resolve the existing problems, this research proposed a framework that automatically categorizes Amharic documents into predefined categories using concepts. The research shows that the use of concepts for an Amharic document categorizer results in 92.9% accuracy.


title: Large-Scale Named Entity Disambiguation Based on Wikipedia Data

abstract: This paper presents a large-scale system for the recognition and semantic disambiguation of named entities based on information extracted from a large encyclopedic collection and Web search results. It describes in detail the disambiguation paradigm employed and the information extraction process from Wikipedia. Through a process of maximizing the agreement between the contextual information extracted from Wikipedia and the context of a document, as well as the agreement among the category tags associated with the candidate entities, the implemented system shows high disambiguation accuracy on both news stories and Wikipedia articles.


title: Data-driven analytics for cyber-threat intelligence and information sharing

abstract: Efficient analysis of shared Cyber Threat Intelligence (CTI) information is crucial for network risk assessment and security hardening. There is a growing interest in implementing a proactive line of defense through threat profiling. However, determining the resiliency of a particular network with respect to relevant threats reported in CTI shared data remains a challenge, largely due to the lack of semantics and contextual information present in textual representations of the threat knowledge. To overcome the limitations of existing CTI frameworks, we devise a threat analytics framework based on Web Ontology Language (OWL) for formal specification, semantic reasoning, and contextual analysis, allowing the derivation of network associated threats from large volumes of shared threat feeds. Our ontology represents constructs of Structured Threat Information eXpression (STIX) with the additional concepts of Cyber Observable eXpression (CybOX), network configurations, and Common Vulnerabilities and Exposure (CVE) for risk analysis and threat actor profiling. The framework provides an automated mechanism to investigate cyber threats targeting the network under question by classifying the threat relevance, determining threat likelihood, identifying the affected and exposed assets through formulated rules and inferences. We perform a comprehensive structural and conceptual evaluation of critical advanced persistent threats (APTs) collected from credible sources and determine their relevance and risk posed to realistic network case studies. Finally we show that the proposed framework is novel in the type of analytics it provides and outperforms other competing approaches in terms of efficiency and effectiveness.


title: Key infection: smart trust for smart dust

abstract: Future distributed systems may include large self-organizing networks of locally communicating sensor nodes, any small number of which may be subvened by an adversary. Providing security for these sensor networks is important, but the problem is complicated by the fact that managing cryptographic key material is hard: low-cost nodes are neither tamper-proof nor capable of performing public key cryptography efficiently. We show how the key distribution problem can be dealt with in environments with a partially present, passive adversary: a node wishing to communicate securely with other nodes simply generates a symmetric key and sends it in the clear to its neighbours. Despite the apparent insecurity of this primitive, we can use mechanisms for key updating, multipath secrecy amplification and multihop key propagation to build up extremely resilient trust networks where at most a fixed proportion of communications links can be eavesdropped. We discuss applications in which this assumption is sensible. Many systems must perforce cope with principals who are authenticated weakly, if at all; the resulting issues have often been left in the 'too hard' tray. One particular interest of sensor networks is that they present a sufficiently compact and tractable version of this problem. We can perform quantitative analyses and simulations of alternative strategies, some of which we present here. We also hope that This work may start to challenge the common belief that authentication is substantially about bootstrapping trust. We argue that, in distributed systems where the opponent can subvert any small proportion of nodes, it is more economic to invest in resilience than in bootstrapping.


title: Review of Relation Extraction Methods: What Is New Out There?

abstract: Relation extraction is a part of Information Extraction and an established task in Natural Language Processing. This paper presents an overview of the main directions of research and recent advances in the field. It reviews various techniques used for relation extraction including knowledge-based, supervised and self-supervised methods. We also men- tion applications of relation extraction and identify current trends in the way the field is developing.


title: Neuroscience-Inspired Artificial Intelligence

abstract: The fields of neuroscience and artificial intelligence (AI) have a long and intertwined history. In more recent times, however, communication and collaboration between the two fields has become less commonplace. In this article, we argue that better understanding biological brains could play a vital role in building intelligent machines. We survey historical interactions between the AI and neuroscience fields and emphasize current advances in AI that have been inspired by the study of neural computation in humans and other animals. We conclude by highlighting shared themes that may be key for advancing future research in both fields.


ZigBee Exploited The good , the bad and the ugly


title: Learning intentions for improved human motion prediction

abstract: For many tasks, robots need to operate in human populated environments. Human motion prediction therefore is gaining importance. The concept of social forces defines virtual repelling and attracting forces from and to obstacles and points of interest. These social forces can then be used to model typical human movements given an environment and a person's intention. This work shows how such models can exploit typical motion patterns summarized by growing hidden Markov models (GHMMs) that can be learned from data online and without human intervention. An extensive series of experiments shows that exploiting the intended position estimated using a GHMM within a social forces based motion model yields a significant performance gain in comparison with the standard constant velocity-based models.


title: An empirical study on developer interactions in StackOverflow

abstract: StackOverflow provides a popular platform where developers post and answer questions. Recently, Treude et al. manually label 385 questions in StackOverflow and group them into 10 categories based on their contents. They also analyze how tags are used in StackOverflow. In this study, we extend their work to obtain a deeper understanding on how developers interact with one another on such a question and answer web site. First, we analyze the distributions of developers who ask and answer questions. We also investigate if there is a segregation of the StackOverflow community into questioners and answerers. We also perform automated text mining to find the various kinds of topics asked by developers. We use Latent Dirichlet Allocation (LDA), a well known topic modeling approach, to analyze the contents of tens of thousands of questions and answers, and produce five topics. Our topic modeling strategy provides an alternative perspective different from that of Treude et al. for categorizing StackOverflow questions. Each question can now be categorized into several topics with different probabilities, and the learned topic model could automatically assign a new question to several categories with varying probabilities. Last but not least, we show the distributions of questions and developers belonging to various topics generated by LDA.


title: NSH: Normality Sensitive Hashing for Anomaly Detection

abstract: Locality sensitive hashing (LSH) is a computationally efficient alternative to the distance based anomaly detection. The main advantages of LSH lie in constant detection time, low memory requirement, and simple implementation. However, since the metric of distance in LSHs does not consider the property of normal training data, a naive use of existing LSHs would not perform well. In this paper, we propose a new hashing scheme so that hash functions are selected dependently on the properties of the normal training data for reliable anomaly detection. The distance metric of the proposed method, called NSH (Normality Sensitive Hashing) is theoretically interpreted in terms of the region of normal training data and its effectiveness is demonstrated through experiments on real-world data. Our results are favorably comparable to state-of-the arts with the low-level features.


title: Extracting Support Data for a Given Task

abstract: We report a novel possibility for extracting a small subset of a data base which contains all the information necessary to solve a given classification task: using the Support Vector Algorithm to train three different types of handwritten digit classifiers, we observed that these types of classifiers construct their decision surface from strongly overlapping small (≈ 4%) subsets of the data base. This finding opens up the possibility of compressing data bases significantly by disposing of the data which is not important for the solution of a given task. ::: ::: In addition, we show that the theory allows us to predict the classifier that will have the best generalization ability, based solely on performance on the training set and characteristics of the learning machines. This finding is important for cases where the amount of available data is limited.


title: MIN-MAX: A Counter-Based Algorithm for Regular Expression Matching

abstract: We propose an NFA-based algorithm called MIN-MAX to support matching of regular expressions (regexp) composed of Character Classes with Constraint Repetitions (CCR). MIN-MAX is well suited for massive parallel processing architectures, such as FPGAs, yet it is effective on any other computing platform. In MIN-MAX, each active CCR engine (to implement one CCR term) evaluates input characters, updates (MIN, MAX) counters, and asserts control signals, and all the CCR engines implemented in the FPGA run simultaneously. Unlike traditional designs, (MIN, MAX) counters contain dynamically updated lower and upper bounds of possible matching counts, instead of actual matching counts, so that feasible matching lengths are compactly enclosed in the counter value. The counter-based design can support constraint repetitions of n using O({\rm log} n) memory bits rather than that of O(n) in existing solutions. MIN-MAX can resolve character class ambiguity between adjacent CCR terms and support overlapped matching when matching collisions are absent. We developed a set of heuristic rules to assess the absence of collision for CCR-based regexps, and tested them on Snort and SpamAssassin rule sets. The results show that the vast majority of rules are immune from collisions, so that MIN-MAX can cost effectively support overlapped matching. As a bonus, the new architecture also supports fast reconfiguration via ordinary memory writes rather than resynthesis of the entire design, which is critical for time-sensitive regexp deployment scenarios.


Machine Learning in Statistical Arbitrage


title: SLAM-Loop Closing with Visually Salient Features

abstract: Within the context of Simultaneous Localisation and Mapping (SLAM), “loop closing” is the task of deciding whether or not a vehicle has, after an excursion of arbitrary length, returned to a previously visited area. Reliable loop closing is both essential and hard. It is without doubt one of the greatest impediments to long term, robust SLAM. This paper illustrates how visual features, used in conjunction with scanning laser data, can be used to a great advantage. We use the notion of visual saliency to focus the selection of suitable (affine invariant) image-feature descriptors for storage in a database. When queried with a recently taken image the database returns the capture time of matching images. This time information is used to discover loop closing events. Crucially this is achieved independently of estimated map and vehicle location. We integrate the above technique into a SLAM algorithm using delayed vehicle states and scan matching to form interpose geometric constraints. We present initial results using this system to close loops (around 100m) in an indoor environment.


title: Leveraging semantic signatures for bug search in binary programs

abstract: Software vulnerabilities still constitute a high security risk and there is an ongoing race to patch known bugs. However, especially in closed-source software, there is no straightforward way (in contrast to source code analysis) to find buggy code parts, even if the bug was publicly disclosed. To tackle this problem, we propose a method called Tree Edit Distance Based Equational Matching (TEDEM) to automatically identify binary code regions that are "similar" to code regions containing a reference bug. We aim to find bugs both in the same binary as the reference bug and in completely unrelated binaries (even compiled for different operating systems). Our method even works on proprietary software systems, which lack source code and symbols. The analysis task is split into two phases. In a preprocessing phase, we condense the semantics of a given binary executable by symbolic simplification to make our approach robust against syntactic changes across different binaries. Second, we use tree edit distances as a basic block-centric metric for code similarity. This allows us to find instances of the same bug in different binaries and even spotting its variants (a concept called vulnerability extrapolation). To demonstrate the practical feasibility of the proposed method, we implemented a prototype of TEDEM that can find real-world security bugs across binaries and even across OS boundaries, such as in MS Word and the popular messengers Pidgin (Linux) and Adium (Mac OS).


title: The Neural Career of Sensory-motor Metaphors

abstract: The role of sensory-motor systems in conceptual understanding has been controversial. It has been proposed that many abstract concepts are understood metaphorically through concrete sensory-motor domains such as actions. Using fMRI, we compared neural responses with literal action (Lit; The daughter grasped the flowers), metaphoric action (Met; The public grasped the idea), and abstract (Abs; The public understood the idea) sentences of varying familiarity. Both Lit and Met sentences activated the left anterior inferior parietal lobule, an area involved in action planning, with Met sentences also activating a homologous area in the right hemisphere, relative to Abs sentences. Both Met and Abs sentences activated the left superior temporal regions associated with abstract language. Importantly, activation in primary motor and biological motion perception regions was inversely correlated with Lit and Met familiarity. These results support the view that the understanding of metaphoric action retains a link to sensory-motor systems involved in action performance. However, the involvement of sensory-motor systems in metaphor understanding changes through a gradual abstraction process whereby relatively detailed simulations are used for understanding unfamiliar metaphors, and these simulations become less detailed and involve only secondary motor regions as familiarity increases. Consistent with these data, we propose that anterior inferior parietal lobule serves as an interface between sensory-motor and conceptual systems and plays an important role in both domains. The similarity of abstract and metaphoric sentences in the activation of left superior temporal regions suggests that action metaphor understanding is not completely based on sensory-motor simulations but relies also on abstract lexical-semantic codes.


title: Leading Edge Perspective The Heroes of CRISPR

abstract: Three years ago, scientists reported that CRISPR technology can enable precise and efficient genome editing in living eukaryotic cells. Since then, the method has taken the scientific community by storm, with thousands of labs using it for applications from biomedicine to agriculture. Yet, the preceding 20-year journey--the discovery of a strange microbial repeat sequence; its recognition as an adaptive immune system; its biological characterization; and its repurposing for genome engineering--remains little known. This Perspective aims to fill in this backstory--the history of ideas and the stories of pioneers--and draw lessons about the remarkable ecosystem underlying scientific discovery.


title: MilkyWay-2 supercomputer: system and application

abstract: On June 17, 2013, MilkyWay-2 (Tianhe-2) supercomputer was crowned as the fastest supercomputer in the world on the 41th TOP500 list. This paper provides an overview of the MilkyWay-2 project and describes the design of hardware and software systems. The key architecture features of MilkyWay-2 are highlighted, including neo-heterogeneous compute nodes integrating commodity-off-the-shelf processors and accelerators that share similar instruction set architecture, powerful networks that employ proprietary interconnection chips to support the massively parallel message-passing communications, proprietary 16-core processor designed for scientific computing, efficient software stacks that provide high performance file system, emerging programming model for heterogeneous systems, and intelligent system administration. We perform extensive evaluation with wide-ranging applications from LINPACK and Graph500 benchmarks to massively parallel software deployed in the system.


title: A Survey of Enabling Technologies of Low Power and Long Range Machine-to-Machine Communications

abstract: Low power and long range machine-to-machine (M2M) communication techniques are expected to provide ubiquitous connections for the wireless devices. In this paper, three major low power and long range M2M solutions are surveyed. The first type of solutions is referred to as the low power wide area (LPWA) network. The design of the LPWA techniques features low cost, low data rate, long communication range, and low power consumption. The second type of solutions is the IEEE 802.11ah which features higher data rates using a wider bandwidth than the LPWA-based solutions. The third type of solutions is operated under the cellular network infrastructure. Based on the analysis of the pros and cons of the enabling technologies of the surveyed M2M solutions, as well as the corresponding deployment strategies, the gaps in knowledge are identified. The paper also presents a summary of the research directions for improving the performance of the surveyed low power and long range M2M communication technologies.


title: Beyond Temporal Pooling: Recurrence and Temporal Convolutions for Gesture Recognition in Video

abstract: Recent studies have demonstrated the power of recurrent neural networks for machine translation, image captioning and speech recognition. For the task of capturing temporal structure in video, however, there still remain numerous open research questions. Current research suggests using a simple temporal feature pooling strategy to take into account the temporal aspect of video. We demonstrate that this method is not sufficient for gesture recognition, where temporal information is more discriminative compared to general video classification tasks. We explore deep architectures for gesture recognition in video and propose a new end-to-end trainable neural network architecture incorporating temporal convolutions and bidirectional recurrence. Our main contributions are twofold; first, we show that recurrence is crucial for this task; second, we show that adding temporal convolutions leads to significant improvements. We evaluate the different approaches on the Montalbano gesture recognition dataset, where we achieve state-of-the-art results.


title: A developmental study of the structural integrity of white matter in autism

abstract: Diffusion tensor imaging was used to examine developmental changes in the organization of white matter in a large sample of male participants with autism and controls between the ages of 10 and 35 years. Participants with autism had lower fractional anisotropy in areas within and near the corpus callosum and in the right retrolenticular portion of the internal capsule. Only one area, in the posterior limb of the right internal capsule, showed an interaction between age and group. The findings suggest that reductions in the structural integrity of white matter in autism persist into adulthood. These reductions may underlie the behavioral pattern observed in autism, as well as findings of reduced functional connectivity in functional magnetic resonance imaging signal between activating cortical areas.


title: Neural pathways link social support to attenuated neuroendocrine stress responses

abstract: Abstract It is well established that a lack of social support constitutes a major risk factor for morbidity and mortality, comparable to risk factors such as smoking, obesity, and high blood pressure. Although it has been hypothesized that social support may benefit health by reducing physiological reactivity to stressors, the mechanisms underlying this process remain unclear. Moreover, to date, no studies have investigated the neurocognitive mechanisms that translate experiences of social support into the health outcomes that follow. To investigate these processes, thirty participants completed three tasks in which daily social support, neurocognitive reactivity to a social stressor, and neuroendocrine responses to a social stressor were assessed. Individuals who interacted regularly with supportive individuals across a 10-day period showed diminished cortisol reactivity to a social stressor. Moreover, greater social support and diminished cortisol responses were associated with diminished activity in the dorsal anterior cingulate cortex (dACC) and Brodmann’s area (BA) 8, regions previously associated with the distress of social separation. Lastly, individual differences in dACC and BA 8 reactivity mediated the relationship between high daily social support and low cortisol reactivity, such that supported individuals showed reduced neurocognitive reactivity to social stressors, which in turn was associated with reduced neuroendocrine stress responses. This study is the first to investigate the neural underpinnings of the social support-health relationship and provides evidence that social support may ultimately benefit health by diminishing neural and physiological reactivity to social stressors.


title: Automatic classification of documents by formality

abstract: This paper addresses the task of classifying documents into formal or informal style. We studied the main characteristics of each style in order to choose features that allowed us to train classifiers that can distinguish between the two styles. We built our data set by collecting documents for both styles, from different sources. We tested several classification algorithms, namely Decision Trees, Naive Bayes, and Support Vector Machines, to choose the classifier that leads to the best classification results. We performed attribute selection in order to determine the contribution of each feature to our model.


title: Phase-Only Transmit Beam Broadening for Improved Radar Search Performance

abstract: The operational value of applying transmit beam broadening in radar search is considered. Test cases are established to evaluate search radar performance with beam broadening and without beam broadening, and also for different beam broadening arrangements. The test cases are evaluated according to search occupancy and probability of detection performance. The advantage of beam broadening for a horizon search fence is demonstrated; this approach extends to generic search fence design, and is particularly advantageous for broadening factors of 3:1 or higher.


A Correctness Proof for a Practical Byzantine-Fault-Tolerant Replication Algorithm


title: SphereFlow: 6 DoF Scene Flow from RGB-D Pairs

abstract: We take a new approach to computing dense scene flow between a pair of consecutive RGB-D frames. We exploit the availability of depth data by seeking correspondences with respect to patches specified not as the pixels inside square windows, but as the 3D points that are the inliers of spheres in world space. Our primary contribution is to show that by reasoning in terms of such patches under 6 DoF rigid body motions in 3D, we succeed in obtaining compelling results at displacements large and small without relying on either of two simplifying assumptions that pervade much of the earlier literature: brightness constancy or local surface planarity. As a consequence of our approach, our output is a dense field of 3D rigid body motions, in contrast to the 3D translations that are the norm in scene flow. Reasoning in our manner additionally allows us to carry out occlusion handling using a 6 DoF consistency check for the flow computed in both directions and a patchwise silhouette check to help reason about alignments in occlusion areas, and to promote smoothness of the flow fields using an intuitive local rigidity prior. We carry out our optimization in two steps, obtaining a first correspondence field using an adaptation of PatchMatch, and subsequently using alpha-expansion to jointly handle occlusions and perform regularization. We show attractive flow results on challenging synthetic and real-world scenes that push the practical limits of the aforementioned assumptions.


title: Ankle Syndesmosis A Qualitative and Quantitative Anatomic Analysis

abstract: Background:Syndesmosis sprains can contribute to chronic pain and instability, which are often indications for surgical intervention. The literature lacks sufficient objective data detailing the complex anatomy and localized osseous landmarks essential for current surgical techniques.Purpose:To qualitatively and quantitatively analyze the anatomy of the 3 syndesmotic ligaments with respect to surgically identifiable bony landmarks.Study Design:Descriptive laboratory study.Methods:Sixteen ankle specimens were dissected to identify the anterior inferior tibiofibular ligament (AITFL), posterior inferior tibiofibular ligament (PITFL), interosseous tibiofibular ligament (ITFL), and bony anatomy. Ligament lengths, footprints, and orientations were measured in reference to bony landmarks by use of an anatomically based coordinate system and a 3-dimensional coordinate measuring device.Results:The syndesmotic ligaments were identified in all specimens. The pyramidal-shaped ITFL was the broadest, originating from t...


Fast and Accurate Decision Trees for Natural Language Processing Tasks


title: Interactive scene prediction for automotive applications

abstract: In this work, a framework for motion prediction of vehicles and safety assessment of traffic scenes is presented. The developed framework can be used for driver assistant systems as well as for autonomous driving applications. In order to assess the safety of the future trajectories of the vehicle, these systems require a prediction of the future motion of all traffic participants. As the traffic participants have a mutual influence on each other, the interaction of them is explicitly considered in this framework, which is inspired by an optimization problem. Taking the mutual influence of traffic participants into account, this framework differs from the existing approaches which consider the interaction only insufficiently, suffering reliability in real traffic scenes. For motion prediction, the collision probability of a vehicle performing a certain maneuver, is computed. Based on the safety evaluation and the assumption that drivers avoid collisions, the prediction is realized. Simulation scenarios and real-world results show the functionality.


title: Data quality assessment in context: A cognitive perspective

abstract: In organizations today, the risk of poor information quality is becoming increasingly high as larger and more complex information resources are being collected and managed. To mitigate this risk, decision makers assess the quality of the information provided by their IS systems in order to make effective decisions based on it. To do so, they may rely on quality metadata: objective quality measurements tagged by data managers onto the information used by decision makers. Decision makers may also gauge information quality on their own, subjectively and contextually assessing the usefulness of the information for solving the specific task at hand. Although information quality has been defined as fitness for use, models of information quality assessment have thus far tended to ignore the impact of contextual quality on information use and decision outcomes. Contextual assessments can be as important as objective quality indicators because they can affect which information gets used for decision making tasks. This research offers a theoretical model for understanding users' contextual information quality assessment processes. The model is grounded in dual-process theories of human cognition, which enable simultaneous evaluation of both objective and contextual information quality attributes. Findings of an exploratory laboratory experiment suggest that the theoretical model provides an avenue for understanding contextual aspects of information quality assessment in concert with objective ones. The model offers guidance for the design of information environments that can improve performance by integrating both objective and subjective aspect of users' quality assessments.


title: Sensorless Control of Doubly-Fed Induction Generators Using a Rotor-Current-Based MRAS Observer

abstract: This paper presents a new sensorless method for the vector control of Doubly-Fed Induction Machines (DFIMs) without using speed sensors or rotor position measurements. The proposed sensorless method is based on the Model Reference Adaptive System (MRAS) estimating the rotor position and speed from the machine rotor currents. The method is appropriate for both stand-alone and grid-connected operation of variable speed DFIMs. To design the MRAS observer with the appropriate dy- namic response, a small signal model is derived. The sensitivity of the method for variation in the machine parameters is also analyzed. Speed catching on the fly and synchronization of the Doubly-Fed Induction Generator with the utility are also ad- dressed. Experimental results obtained from a 3.5-kW prototype are presented and fully analyzed.


title: Understanding Sustained Participation in Open Source Software Projects

abstract: Prior research into open source software (OSS) developer participation has emphasized individuals' motivations for joining these volunteer communities, but it has failed to explain why people stay or leave in the long run. Building upon Lave and Wenger's theory of legitimate peripheral participation (LPP), this paper offers a longitudinal investigation of one OSS community in which sustained participation is hypothesized to be associated with the coevolution of two major elements of LPP theory: "situated learning" (the process of acting knowledgeably and purposefully in the world) and "identity construction" (the process of being identified within the community). To test this hypothesis, data were collected from multiple sources, including online public project documents, electronic mail messages, tracker messages, and log files. Results from qualitative analyses revealed that initial conditions to participate did not effectively predict long-term participation, but that situated learning and identity construction behaviors were positively linked to sustained participation. Furthermore, this study reveals that sustained participants distinguished themselves by consistently engaging in situated learning that both made conceptual (advising others) and practical contributions (improving the code). Implications and future research are discussed.


title: Distributed hierarchical processing in the primate cerebral cortex

abstract: In recent years, many new cortical areas have been identified in the macaque monkey. The number of identified connections between areas has increased even more dramatically. We report here on (1) a summary of the layout of cortical areas associated with vision and with other modalities, (2) a computerized database for storing and representing large amounts of information on connectivity patterns, and (3) the application of these data to the analysis of hierarchical organization of the cerebral cortex. Our analysis concentrates on the visual system, which includes 25 neocortical areas that are predominantly or exclusively visual in function, plus an additional 7 areas that we regard as visual-association areas on the basis of their extensive visual inputs. A total of 305 connections among these 32 visual and visual-association areas have been reported. This represents 31% of the possible number of pathways it each area were connected with all others. The actual degree of connectivity is likely to be closer to 40%. The great majority of pathways involve reciprocal connections between areas. There are also extensive connections with cortical areas outside the visual system proper, including the somatosensory cortex, as well as neocortical, transitional, and archicortical regions in the temporal and frontal lobes. In the somatosensory/motor system, there are 62 identified pathways linking 13 cortical areas, suggesting an overall connectivity of about 40%. Based on the laminar patterns of connections between areas, we propose a hierarchy of visual areas and of somato sensory/motor areas that is more comprehensive than those suggested in other recent studies. The current version of the visual hierarchy includes 10 levels of cortical processing. Altogether, it contains 14 levels if one includes the retina and lateral geniculate nucleus at the bottom as well as the entorhinal cortex and hippocampus at the top. Within this hierarchy, there are multiple, intertwined processing streams, which, at a low level, are related to the compartmental organization of areas V1 and V2 and, at a high level, are related to the distinction between processing centers in the temporal and parietal lobes. However, there are some pathways and relationships (about 10% of the total) whose descriptions do not fit cleanly into this hierarchical scheme for one reason or another. In most instances, though, it is unclear whether these represent genuine exceptions to a strict hierarchy rather than inaccuracies or uncertainties in the reported assignment.


A Chi-Square-Based Decision for Real-Time Malware Detection Using PE-File Features.


title: Representations to go: learning robotics, learning by robotics

abstract: We present the first steps of a pioneering didactic approach addressed to primary and secondary school: usually detached in the Educational Robotics literature, the learning of robotics and through robotics are here rejoined. This approach is rooted in constructivism and invites children to become authors of their own learning tools rather than merely users. Visual representations of didactic content and mobile robots are the items to be constructed by the supervised children. A methodological analogy is set between the construction and manipulation of these two items: they are both subjects of a problem solving procedure whose results are supposed to affect each other. A scenario is proposed to assess our approach and to set the basis of an educational program and tool-artifact. Future works will focus on the validity of this approach and to ultimately verify the impact of technology on children skills improvements with relation to engagement, visually content organization, and aware interaction with technology.


title: Enabling the IoT Paradigm in E-health Solutions through the VIRTUS Middleware

abstract: In Europe, in a context of growing population and decreasing resources, ageing related diseases represent one of the most relevant challenges in terms of healthcare organization complexity, plus care levels and system financing balancing. Nowadays there are several researches that are studying the application of the IoT (Internet of Things) paradigm in the e-health field. This paper explains how a solution, built on the top of the VIRTUS IoT middleware, provides a valid alternative to current IoT solutions, which are mainly based on SOA (Service Oriented Architecture). VIRTUS leverage an Instant Messaging protocol (XMPP) to guarantee a (near) real-time, secure and reliable communication channel among heterogeneous devices. The presented development has been exploited in a healthcare case study: an implementation of a cost-savvy remote body movement monitoring system, aimed at classify daily patients' activities, designed as a modular architecture and deployed in a large scale scenario. The paper analyzes the features offered by the VIRTUS middleware, if used within a e-health solution, providing a comparison with other well-known systems.


title: Design Team Perception of Development Team Composition: Implications for Conway's Law

abstract: Conway's law, the idea that a software system reflects the structure of the organization that built it, is one of the most well-known "laws" in software engineering. However, the seemingly straightforward phenomenon described by Conway appears to be subject to nuances of personal and organizational dynamics as well as contextual factors, most of which are neither well-understood nor well-studied. As a pilot study intended to foster discussion within the RESER community, we performed a small and somewhat informal qualitative study designed to elucidate some of these nuances. We posited that the designers' perception of the ultimate composition of the development team would affect the resultant system architecture more so than would the actual composition of the design team. The results of the pilot study support this hypothesis and are intended as a motivator for on-going discussion, as well as a catalyst for more thorough and formal differentiated replications, to explore and elucidate the nuances of Conway's law.


Relaxed lasso


Dynamics of Blockchain Implementation - A Case Study from the Energy Sector


title: Real-time simulation as a way to improve daily operations in an emergency room

abstract: Emergency Rooms (ER) are resource constrained systems that demand efficient management in order to fulfill their care and service objectives. This article explores the use of real-time simulation to improve daily operations at an ER where unplanned events may occur. Such a simulation model must adequately portray the current state of the system; however ER workflow management systems often provide only limited information for this purpose. This paper studies the impact of the model's ability to predict ER performance with a limited amount of input information, such that what-if questions may be asked to guide decision making. Towards this end, two input data scenarios are compared to a case of perfect information. One scenario considers only patient arrival times and the other assumes additional knowledge of patient care pathways. Although both generate similar performance measures, the case with least information yields a slightly worse estimate of patient care pathway composition.


title: Learning Deconvolution Network for Semantic Segmentation

abstract: We propose a novel semantic segmentation algorithm by learning a deep deconvolution network. We learn the network on top of the convolutional layers adopted from VGG 16-layer net. The deconvolution network is composed of deconvolution and unpooling layers, which identify pixelwise class labels and predict segmentation masks. We apply the trained network to each proposal in an input image, and construct the final semantic segmentation map by combining the results from all proposals in a simple manner. The proposed algorithm mitigates the limitations of the existing methods based on fully convolutional networks by integrating deep deconvolution network and proposal-wise prediction, our segmentation method typically identifies detailed structures and handles objects in multiple scales naturally. Our network demonstrates outstanding performance in PASCAL VOC 2012 dataset, and we achieve the best accuracy (72.5%) among the methods trained without using Microsoft COCO dataset through ensemble with the fully convolutional network.


title: Using a Knowledge Graph to Combat Human Trafficking

abstract: There is a huge amount of data spread across the web and stored in databases that we can use to build knowledge graphs. However, exploiting this data to build knowledge graphs is difficult due to the heterogeneity of the sources, scale of the amount of data, and noise in the data. In this paper we present an approach to building knowledge graphs by exploiting semantic technologies to reconcile the data continuously crawled from diverse sources, to scale to billions of triples extracted from the crawled content, and to support interactive queries on the data. We applied our approach, implemented in the DIG system, to the problem of combating human trafficking and deployed it to six law enforcement agencies and several non-governmental organizations to assist them with finding traffickers and helping victims.


title: Suggestibility of the child witness: A historical review and synthesis

abstract: The field of children's testimony is in turmoil, but a resolution to seemingly intractable debates now appears attainable. In this review, we place the current disagreement in historical context and describe psychological and legal views of child witnesses held by scholars since the turn of the 20th century. Although there has been consistent interest in children's suggestibility over the past century, the past 15 years have been the most active in terms of the number of published studies and novel theorizing about the causal mechanisms that underpin the observed findings. A synthesis of this research posits three "families" of factors--cognitive, social, and biological--that must be considered if one is to understand seemingly contradictory interpretations of the findings. We conclude that there are reliable age differences in suggestibility but that even very young children are capable of recalling much that is forensically relevant. Findings are discussed in terms of the role of expert witnesses. Language: en


title: A tactile sensor sheet using pressure conductive rubber with electrical-wires stitched method

abstract: A new type of tactile sensor using pressure-conductive rubber with stitched electrical wires is presented. The sensor is thin and flexible and can cover three-dimensional objects. Since the sensor adopts a single-layer composite structure, the sensor is durable with respect to external force. In order to verify the effectiveness of this tactile sensor, we performed an experiment in which a four-fingered robot hand equipped with tactile sensors grasped sphere and column. The sensor structure, electrical circuit, and characteristics are described. The sensor control system and experimental results are also described.


title: Measurement of pitch in speech: An implementation of Goldstein’s theory of pitch perception

abstract: Recent developments in hearing theory have resulted in the rather general acceptance of the idea that the perception of pitch of complex sounds is the result of the psychological pattern recognition process. The pitch is supposedly mediated by the fundamental of the harmonic spectrum which fits the spectrum of the complex sound optimally. The problem of finding the pitch is then equivalent to finding the best harmonic match. Goldstein [J. Acoust. Soc. Am. 54, 1496–1516 (1973)] has described an objective procedure for finding the best fit for stimuli containing relatively few spectral components. He uses a maximum likelihood criterion. Application of this procedure to various data on the pitch of complex sounds yielded good results. This motivated our efforts to apply the pattern recognition theory of pitch to the problem of measuring pitch in speech. Although we were able to follow the main line of Goldstein’s procedure, some essential changes had to be made. The most important is that in our implementati...


title: Online Learning to Rank with Top-k Feedback

abstract: We consider two settings of online learning to rank where feedback is restricted to top ranked items. The problem is cast as an online game between a learner and sequence of users, over $T$ rounds. In both settings, the learners objective is to present ranked list of items to the users. The learner's performance is judged on the entire ranked list and true relevances of the items. However, the learner receives highly restricted feedback at end of each round, in form of relevances of only the top $k$ ranked items, where $k \ll m$. The first setting is \emph{non-contextual}, where the list of items to be ranked is fixed. The second setting is \emph{contextual}, where lists of items vary, in form of traditional query-document lists. No stochastic assumption is made on the generation process of relevances of items and contexts. We provide efficient ranking strategies for both the settings. The strategies achieve $O(T^{2/3})$ regret, where regret is based on popular ranking measures in first setting and ranking surrogates in second setting. We also provide impossibility results for certain ranking measures and a certain class of surrogates, when feedback is restricted to the top ranked item, i.e. $k=1$. We empirically demonstrate the performance of our algorithms on simulated and real world datasets.


title: Applying Data Mining Classification Techniques for Employee's Performance Prediction

abstract: The valuable knowledge can be discovered through data mining process.In data mining, classification is one of the major tasks to impart knowledge from huge amount of data. This technique is widely used in various fields, but it has not attracted much attention in Human Resource Management (HRM). This article presents a study on the implementation of data mining approach for employee development regarding to their future performance.By using this approach, the performance patterns can be discovered from the existing database and will be used for future performance prediction in their career development. ::: In the experimental phase, we have used selected classification techniques to propose the appropriate technique for the dataset. An experiment is carried out to demonstrate the feasibility of the suggested classification techniques using employee’s performance data. Thus, the experiment results, we suggest the potential classification techniques and the ::: possible prediction model for employee’s performance forecasting.


title: Mapping, Planning, and Sample Detection Strategies for Autonomous Exploration

abstract: This paper presents algorithmic advances and field trial results for autonomous exploration and proposes a solution to perform simultaneous localization and mapping (SLAM), complete coverage, and object detection without relying on GPS or magnetometer data. We demonstrate an integrated approach to the exploration problem, and we make specific contributions in terms of mapping, planning, and sample detection strategies that run in real-time on our custom platform. Field tests demonstrate reliable performance for each of these three main components of the system individually, and high-fidelity simulation based on recorded data playback demonstrates the viability of the complete solution as applied to the 2013 NASA Sample Return Robot Challenge.


title: Urban probes: encountering our emerging urban atmospheres

abstract: Urban Atmospheres captures a unique, synergistic moment - expanding urban populations, rapid adoption of Bluetooth mobile devices, tiny ad hoc sensor networks, and the widespread influence of wireless technologies across our growing urban landscapes. The United Nations recently reported that 48 percent of the world's population current live in urban areas and that this number is expected to exceed the 50 percent mark world wide by 2007 [1]. In developed nations the number of urban dwellers is even more dramatic - expected to exceed 75%. Current studies project Bluetooth-enabled devices to reach 5.4 billion units by 2005 - five times the number of mobile phones or Internet connections [2]. Mobile phone penetration already exceeds 80% of the population in places like the European Union (EU) and parts of Asia [3]. WiFi hardware is being deployed at the astonishing rate of one every 4 seconds globally [4]. We argue that now is the time to initiate inspirational research into the very essence of these newly emerging technological urban spaces. We desire to move towards an improved understanding of the emotional experience of urban life. This paper describes Urban Probes - a lightweight, provocative, intervention methodology designed to rapidly deconstruct urban situations, reveal new opportunities for technology in urban spaces, and guide future long term research in urban computing. We also describe a completed Urban Probe exploring urban trash.


title: The Truth, The Whole Truth, and Nothing But the Truth: A Pragmatic Guide to Assessing Empirical Evaluations

abstract: An unsound claim can misdirect a field, encouraging the pursuit of unworthy ideas and the abandonment of promising ideas. An inadequate description of a claim can make it difficult to reason about the claim, for example, to determine whether the claim is sound. Many practitioners will acknowledge the threat of unsound claims or inadequate descriptions of claims to their field. We believe that this situation is exacerbated, and even encouraged, by the lack of a systematic approach to exploring, exposing, and addressing the source of unsound claims and poor exposition. ::: This article proposes a framework that identifies three sins of reasoning that lead to unsound claims and two sins of exposition that lead to poorly described claims and evaluations. Sins of exposition obfuscate the objective of determining whether or not a claim is sound, while sins of reasoning lead directly to unsound claims. ::: Our framework provides practitioners with a principled way of critiquing the integrity of their own work and the work of others. We hope that this will help individuals conduct better science and encourage a cultural shift in our research community to identify and promulgate sound claims.


title: Modeling Review Argumentation for Robust Sentiment Analysis

abstract: Most text classification approaches model text at the lexical and syntactic level only, lacking domain robustness and explainability. In tasks like sentiment analysis, such approaches can result in limited effectiveness if the texts to be classified consist of a series of arguments. In this paper, we claim that even a shallow model of the argumentation of a text allows for an effective and more robust classification, while providing intuitive explanations of the classification results. Here, we apply this idea to the supervised prediction of sentiment scores for reviews. We combine existing approaches from sentiment analysis with novel features that compare the overall argumentation structure of the given review text to a learned set of common sentiment flow patterns. Our evaluation in two domains demonstrates the benefit of modeling argumentation for text classification in terms of effectiveness and robustness.


title: ULA-OP: an advanced open platform for ultrasound research

abstract: The experimental test of novel ultrasound (US) investigation methods can be made difficult by the lack of flexibility of commercial US machines. In the best options, these only provide beamformed radiofrequency or demodulated echo-signals for acquisition by an external PC. More flexibility is achieved in high-level research platforms, but these are typically characterized by high cost and large size. This paper presents a powerful but portable US system, specifically developed for research purposes. The system design has been based on high-level commercial integrated circuits to obtain the maximum flexibility and wide data access with minimum of electronics. Preliminary applications involving nonstandard imaging transmit/receive strategies and simultaneous B-mode and multigate spectral Doppler mode are discussed.


title: A +25-dBm IIP3 1.7–2.1-GHz FDD Receiver Front End With Integrated Hybrid Transformer in 28-nm CMOS

abstract: A highly linear receiver (RX) front end with integrated hybrid transformer (HT) for frequency-division duplexing mobile communications is reported. The HT, implemented with a three-winding coplanar transformer, is used to interface the RX front end with the antenna and the power amplifier. The primary is driven at its center tap with the transmitted signal and at one input with the antenna signal, while the other input is connected to an on-chip programmable balancing impedance. The two secondary drives a differential push–pull common-gate low-noise amplifier (LNA). Assuming a perfectly linear hybrid only 45 dB of transmitter (TX)–RX isolation and 35 dB of common-mode rejection are required to meet the intermodulation specs thanks to the +25-dBm receiver IIP3. This would drastically simplify hybrid balancing and adaptation loop. Cascaded noise figure of duplexer, LNA, and baseband is below 6.7 dB and TX insertion loss below 4.3 dB from 1.7 to 2.1 GHz. The implemented prototype in 28-nm CMOS has an active area of 0.7 mm2 and requires only 26 mW.


Benchmarks for Evaluating Anomaly Based Intrusion Detection Solutions


title: A Comparative Performance Analysis of OFDM Using MATLAB Simulation with M-PSK and M-QAM Mapping

abstract: In wireless communication, concept of parallel transmission of symbols is applied to achieve high throughput and better transmission quality. Orthogonal Frequency Division Multiplexing (OFDM) is one of the techniques for parallel transmission. The idea of OFDM is to split the total transmission bandwidth into a number of orthogonal subcarriers in order to transmit the symbols using these subcarriers in parallel. In this paper, proposed OFDM system design is simulated using MATLAB simulink toolbox. The digital modulation schemes such as M-PSK (M-ary Phase Shift Keying) and M-QAM (M-ary Quadrature Amplitude Modulation), which provide way of parallel transmission, are compared to analyze the BER performance of designed OFDM system. Mentioned schemes used in OFDM system can be selected on the basis of the requirement of power or spectrum efficiency and BER analysis.


title: SupportingTrust in Autonomous Driving

abstract: Autonomous cars will likely hit the market soon, but trust into such a technology is one of the big discussion points in the public debate. Drivers who have always been in complete control of their car are expected to willingly hand over control and blindly trust a technology that could kill them. We argue that trust in autonomous driving can be increased by means of a driver interface that visualizes the car's interpretation of the current situation and its corresponding actions. To verify this, we compared different visualizations in a user study, overlaid to a driving scene: (1) a chauffeur avatar, (2) a world in miniature, and (3) a display of the car's indicators as the baseline. The world in miniature visualization increased trust the most. The human-like chauffeur avatar can also increase trust, however, we did not find a significant difference between the chauffeur and the baseline.


title: The labor economics of paid crowdsourcing

abstract: We present a model of workers supplying labor to paid crowdsourcing projects. We also introduce a novel method for estimating a worker's reservation wage - the key parameter in our labor supply model. We tested our model by presenting experimental subjects with real-effort work scenarios that varied in the offered payment and difficulty. As predicted, subjects worked less when the pay was lower. However, they did not work less when the task was more time-consuming. Interestingly, at least some subjects appear to be "target earners," contrary to the assumptions of the rational model. The strongest evidence for target earning is an observed preference for earning total amounts evenly divisible by 5, presumably because these amounts make good targets. Despite its predictive failures, we calibrate our model with data pooled from both experiments. We find that the reservation wages of our sample are approximately log normally distributed, with a median wage of $1.38/hour. We discuss how to use our calibrated model in applications.


title: Dynamic eccentricity fault diagnosis in switched reluctance motor

abstract: In this paper, a novel view of switched reluctance motor analysis under eccentricity fault for providing the precise and reliable model is presented. It then continues with describing the performance characteristics and comparison results of a six by four switched reluctance motor with dynamic rotor eccentricity utilizing three-dimensional finite element analysis. The results of the three-dimensional finite element analysis for a switched reluctance motor including flux-linkages, terminal inductance per phase, and mutual inductances for various eccentric motor conditions by considering the end effects and axial fringing fields for simulating reliable model are obtained and presented. Finally, these results present useful information regarding to the detection of dynamic eccentricity.


title: A bidirectional TX/RX four element phased-array at 60GHz with RF-IF conversion block in 90nm CMOS process

abstract: A 60 GHz 4 element bidirectional phased-array TX/RX chip with a 2 bit phase shifter and IF converter to/from 12GHz, using 90nm CMOS process, is described. The array features 7 dB gain, measured noise figure (NF) of 9 dB, IP1dB of −19dbm for RX, and output Psat of +3.5dBm for TX , drawing 60 mA from a 1.3-V supply. The RMS amplitude and phase error of the phase shifter is 0.7dB and 2° max respectively from 57 to 66 GHz. This new architecture together with the compact layout decreases chip size to about half compared to a separate TX and RX design. The use of passive phase shifters and combiners reduce the current consumption. Total die area is 1.6×1 mm2 with half of the area being the IF converter block. First pass success was achieved by the closed ground environment design methodology of the passive structures and by proper layout. To our knowledge, this the first report on a bidirectional 60 GHz array, with the lowest reported chip power consumption and size.


title: Research using qualitative, quantitative or mixed methods and choice based on the research

abstract: Research is fundamental to the advancement of medicine and critical to identifying the most optimal therapies unique to particular societies. This is easily observed through the dynamics associated with pharmacology, surgical technique and the medical equipment used today versus short years ago. Advancements in knowledge synthesis and reporting guidelines enhance the quality, scope and applicability of results; thus, improving health science and clinical practice and advancing health policy. While advancements are critical to the progression of optimal health care, the high cost associated with these endeavors cannot be ignored. Research fundamentally needs to be evaluated to identify the most efficient methods of evaluation. The primary objective of this paper is to look at a specific research methodology when applied to the area of clinical research, especially extracorporeal circulation and its prognosis for the future.


title: Energy-Efficient Nonvolatile Reconfigurable Logic Using Spin Hall Effect-Based Lookup Tables

abstract: In this paper, we leverage magnetic tunnel junction (MTJ) devices to design an energy-efficient nonvolatile lookup table (LUT), which utilizes a spin Hall effect (SHE) assisted switching approach for MTJ storage cells. SHE–MTJ characteristics are modeled in Verilog-A based on precise physical equations. Functionality of the proposed SHE–MTJ-based LUT is validated using SPICE simulation. Our proposed SHE—MTJ-based LUT (SHE–LUT) is compared with the most energy-efficient MTJ-based LUT circuits. The obtained results show more than 6%, 37%, and 67% improvement over three previous MTJ-based designs in term of read energy consumption. Moreover, the reconfiguration delay and energy of the proposed design is compared with that of the MTJ-based LUTs which utilize the spin transfer torque (STT) switching approach for reconfiguration. The results exhibit that SHE–LUT can operate at 78% higher clock frequency while achieving at least 21% improvement in terms of reconfiguration energy consumption. The operation-specific clocking mechanisms for managing the SHE–LUT operations are introduced along with detailed analyses concerning tradeoffs. Results are extended to design a 6-input fracturable LUT using SHE–MTJs.


title: Internal fraud risk reduction: results of a data mining case study

abstract: Corporate fraud represents a huge cost to the current economy. Academic literature has demonstrated how data mining techniques can be of value in the fight against fraud. This research has focused on fraud detection, mostly in a context of external fraud. In this paper, we discuss the use of a data mining approach to reduce the risk of internal fraud. Reducing fraud risk involves both detection and prevention. Accordingly, a descriptive data mining strategy is applied as opposed to the widely used prediction data mining techniques in the literature. The results of using a multivariate latent class clustering algorithm to a case company's procurement data suggest that applying this technique in a descriptive data mining approach is useful in assessing the current risk of internal fraud. The same results could not be obtained by applying a univariate analysis.


title: Probabilistic Recursive Reasoning for Multi-Agent Reinforcement Learning

abstract: Humans are capable of attributing latent mental contents such as beliefs, or intentions to others. The social skill is critical in everyday life to reason about the potential consequences of their behaviors so as to plan ahead. It is known that humans use this reasoning ability recursively, i.e. considering what others believe about their own beliefs. In this paper, we start from level-1 recursion and introduce a probabilistic recursive reasoning (PR2) framework for multi-agent reinforcement learning. Our hypothesis is that it is beneficial for each agent to account for how the opponents would react to its future behaviors. Under the PR2 framework, we adopt variational Bayes methods to approximate the opponents' conditional policy, to which each agent finds the best response and then improve their own policy. We develop decentralized-training-decentralized-execution algorithms, PR2-Q and PR2-Actor-Critic, that are proved to converge in the self-play scenario when there is one Nash equilibrium. Our methods are tested on both the matrix game and the differential game, which have a non-trivial equilibrium where common gradient-based methods fail to converge. Our experiments show that it is critical to reason about how the opponents believe about what the agent believes. We expect our work to contribute a new idea of modeling the opponents to the multi-agent reinforcement learning community.