title: UNS: A Portable, Mobile, and Exchangeable Namespace for Supporting Fetch-from-Anywhere Big Data Eco-Systems

abstract: Modern global file systems such as parallel file systems or distributed file systems normally use a global namespace (GNS) to support viewing and accessing files that are independent of their physical storage locations. Using GNS support, we can seamlessly modify and reconfigure physical data storage without affecting how users view and access it. However, with the rapid growth of data size, the current design and implementations of the GNS system is experiencing problems of manageability and scalability. In this paper, we present a new namespace architecture called the Universal Namespace (UNS). UNS enhances both the local and global namespace with portable, parallel data mobile, and exchangeable properties and supports fetch-from-anywhere storage systems without requiring remote mounting. UNS exploits the extended file attributes of file systems and enhances file system metadata with unified remote data access information and methods. We design the proposed UNS to support big data computing on multiple geolocation-based shared storage systems. We describe the innovative concepts of the proposed UNS's architecture and demonstrate UNS's early performance results from various test cases. Finally, we summarize the benefits and advantages of using UNS in supporting remote data sharing for extreme scale computing and big data computing. Furthermore, we present the future design and developmental direction of UNS.


title: A primer on spatial modeling and analysis in wireless networks

abstract: The performance of wireless networks depends critically on their spatial configuration, because received signal power and interference depend critically on the distances between numerous transmitters and receivers. This is particularly true in emerging network paradigms that may include femtocells, hotspots, relays, white space harvesters, and meshing approaches, which are often overlaid with traditional cellular networks. These heterogeneous approaches to providing high-capacity network access are characterized by randomly located nodes, irregularly deployed infrastructure, and uncertain spatial configurations due to factors like mobility and unplanned user-installed access points. This major shift is just beginning, and it requires new design approaches that are robust to spatial randomness, just as wireless links have long been designed to be robust to fading. The objective of this article is to illustrate the power of spatial models and analytical techniques in the design of wireless networks, and to provide an entry-level tutorial.


title: EEG Signal Compression Based on Adaptive Arithmetic Coding and First-Order Markov Model for an Ambulatory Monitoring System

abstract: Compression of EEG signals have a basic role in the consumption power reduction of an ambulatory EEG system. This paper outlines a scheme for EEG compression based on adaptive model arithmetic coding (AMAC) and First order Markov (FM) model. In this scheme, signals are stored within an L-second buffer and quantized to some levels. Then, the AMAC-FM compression algorithm is applied to encode the symbols sequence for wireless transmission. In order to achieve the optimal entropy, this algorithm changes dynamically probability distribution of symbols based on current encoded symbols between encoder and decoder. Finally, the proposed algorithm is established to compression of Freiburg University epilepsy EEG dataset and compression ratio (CR) is obtained. The results indicate that our algorithm can achieve a high CR in relation to other EEG compression methods such as JPEG2000.


title: Autonomous reinforcement learning on raw visual input data in a real world application

abstract: We propose a learning architecture, that is able to do reinforcement learning based on raw visual input data. In contrast to previous approaches, not only the control policy is learned. In order to be successful, the system must also autonomously learn, how to extract relevant information out of a high-dimensional stream of input information, for which the semantics are not provided to the learning system. We give a first proof-of-concept of this novel learning architecture on a challenging benchmark, namely visual control of a racing slot car. The resulting policy, learned only by success or failure, is hardly beaten by an experienced human player.


title: Data analysis and optimization for (citi)bike sharing

abstract: Bike-sharing systems are becoming increasingly prevalent in urban environments. They provide a low-cost, environmentally-friendly transportation alternative for cities. The management of these systems gives rise to many optimization problems. Chief among these problems is the issue of bicycle rebalancing. Users imbalance the system by creating demand in an asymmetric pattern. This necessitates action to put the system back in balance with the requisite levels of bicycles at each station to facilitate future use. In this paper, we tackle the problem of maintaing system balance during peak rush-hour usage as well as rebalancing overnight to prepare the system for rush-hour usage. We provide novel problem formulations that have been motivated by both a close collaboration with the New York City bike share (Citibike) and a careful analysis of system usage data. We analyze system data to discover the best placement of bikes to facilitate usage. We solve routing problems for overnight shifts as well as clustering problems for handling mid rush-hour usage. The tools developed from this research are currently in daily use at NYC Bike Share LLC, operators of Citibike.


Machine Learning for Semantic Parsing in Review


title: Coupled Detection and Trajectory Estimation for Multi-Object Tracking

abstract: We present a novel approach for multi-object tracking which considers object detection and spacetime trajectory estimation as a coupled optimization problem. It is formulated in a hypothesis selection framework and builds upon a state-of-the-art pedestrian detector. At each time instant, it searches for the globally optimal set of spacetime trajectories which provides the best explanation for the current image and for all evidence collected so far, while satisfying the constraints that no two objects may occupy the same physical space, nor explain the same image pixels at any point in time. Successful trajectory hypotheses are fed back to guide object detection in future frames. The optimization procedure is kept efficient through incremental computation and conservative hypothesis pruning. The resulting approach can initialize automatically and track a large and varying number of persons over long periods and through complex scenes with clutter, occlusions, and large-scale background changes. Also, the global optimization framework allows our system to recover from mismatches and temporarily lost tracks. We demonstrate the feasibility of the proposed approach on several challenging video sequences.


title: UML-based approach to generate testing sequence of polymorphic software

abstract: Polymorphism is one of the three most important characteristics of object-oriented technology.It not only enhances the flexibility of programming and reuse of codes,but also brings new challenges to software testing.The cause of its appearance and its formalization expressing were analyzed.Under the guidance of control-flow graph,the information of class diagram was integrated with collaboration diagram,traditional Interprocedural Restricted Control-Flow Graph(IRCFG) was extended with polymorphic information and class diagram information.After analyzing the coverage criteria,an approach to generate testing sequence with polymorphic information was given.


title: A Comparative Study Between the Capabilities of MySQl Vs. MongoDB as a Back-End for an Online Platform

abstract: In this article we present a comparative study between the usage capabilities of MongoDB, a non-relational database, and MySQL’s usage capabilities, a relational database, as a back-end for an online platform. We will also present the advantages of using a non-relational database, namely MongoDB, compared to a relational database, namely MySQL, integrated in an online platform, which allows users to publish different articles, books, magazines and so on, and also gives them the possibility to share online their items with other people. Nowadays, most applications have thousands of users that perform operations simultaneously thus, it takes more than one operation to be executed at a time, to really see the differences between the two databases. This paper aims to highlight the differences between MySQL and MongoDB, integrated in an online platform, when various operations were executed in parallel by many users.


title: Combining Vertex-Centric Graph Processing with SPARQL for Large-Scale RDF Data Analytics

abstract: Modern applications require sophisticated analytics on RDF graphs that combine structural queries with generic graph computations. Existing systems support either declarative SPARQL queries, or generic graph processing, but not both. We bridge the gap by introducing Spartex, a versatile framework for complex RDF analytics. Spartex extends SPARQL to combine seamlessly generic graph algorithms (e.g., PageRank, Shortest Paths, etc.) with SPARQL queries. Spartex builds on existing vertex-centric graph processing frameworks, such as Graphlab or Pregel. It implements a generic SPARQL operator as a vertex-centric program that interprets SPARQL queries and executes them efficiently using a built-in optimizer. In addition, any graph algorithm implemented in the underlying vertex-centric framework, can be executed in Spartex. We present various scenarios where our framework simplifies significantly the implementation of complex RDF data analytics programs. We demonstrate that Spartex scales to datasets with billions of edges, and show that our core SPARQL engine is at least as fast as the state-of-the-art specialized RDF engines. For complex analytical tasks that combine generic graph processing with SPARQL, Spartex is at least an order of magnitude faster than existing alternatives.


title: Wireless Industrial Monitoring and Control Networks: The Journey So Far and the Road Ahead

abstract: While traditional wired communication technologies have played a crucial role in industrial monitoring and control networks over the past few decades, they are increasingly proving to be inadequate to meet the highly dynamic and stringent demands of today’s industrial applications, primarily due to the very rigid nature of wired infrastructures. Wireless technology, however, through its increased pervasiveness, has the potential to revolutionize the industry, not only by mitigating the problems faced by wired solutions, but also by introducing a completely new class of applications. While present day wireless technologies made some preliminary inroads in the monitoring domain, they still have severe limitations especially when real-time, reliable distributed control operations are concerned. This article provides the reader with an overview of existing wireless technologies commonly used in the monitoring and control industry. It highlights the pros and cons of each technology and assesses the degree to which each technology is able to meet the stringent demands of industrial monitoring and control networks. Additionally, it summarizes mechanisms proposed by academia, especially serving critical applications by addressing the real-time and reliability requirements of industrial process automation. The article also describes certain key research problems from the physical layer communication for sensor networks and the wireless networking perspective that have yet to be addressed to allow the successful use of wireless technologies in industrial monitoring and control networks.


title: Dynamics of Boltzmann Q-Learning in Two-Player Two-Action Games

abstract: We consider the dynamics of Q learning in two-player two-action games with a Boltzmann exploration mechanism. For any nonzero exploration rate the dynamics is dissipative, which guarantees that agent strategies converge to rest points that are generally different from the game's Nash equlibria (NEs). We provide a comprehensive characterization of the rest point structure for different games and examine the sensitivity of this structure with respect to the noise due to exploration. Our results indicate that for a class of games with multiple NEs the asymptotic behavior of learning dynamics can undergo drastic changes at critical exploration rates. Furthermore, we demonstrate that, for certain games with a single NE, it is possible to have additional rest points (not corresponding to any NE) that persist for a finite range of the exploration rates and disappear when the exploration rates of both players tend to zero.


PD1/PD-L1 inhibition as a potential radiosensitizer in head and neck squamous cell carcinoma: a case report


title: Legal Semantic Web- A Recommendation System

abstract: The project Legal Semantic WebA Recommendation System makes use of the Semantic Web and it is used for the proactive legal decision making. With the help of web semantics, a lawyer handling a new case can filter out similar cases from the court case repository implemented using RDF (Resource Description Framework), and from here he can extract the judgments done on those similar cases. In this way he can better prepare himself with similar judgments in his hands which will guide him to an improved argumentation. The role of web semantics here is that it introduces intelligent matching of the court case details. The search is not only thorough but also accurate and precise to the maximum level of attainment with the use of ontology designed exclusively for this purpose.


title: SeVeN: Augmenting Word Embeddings with Unsupervised Relation Vectors

abstract: We present SeVeN (Semantic Vector Networks), a hybrid resource that encodes relationships between words in the form of a graph. Different from traditional semantic networks, these relations are represented as vectors in a continuous vector space. We propose a simple pipeline for learning such relation vectors, which is based on word vector averaging in combination with an ad hoc autoencoder. We show that by explicitly encoding relational information in a dedicated vector space we can capture aspects of word meaning that are complementary to what is captured by word embeddings. For example, by examining clusters of relation vectors, we observe that relational similarities can be identified at a more abstract level than with traditional word vector differences. Finally, we test the effectiveness of semantic vector networks in two tasks: measuring word similarity and neural text categorization. SeVeN is available at bitbucket.org/luisespinosa/seven.


title: Staphylococcus aureus RN6390 Replicates and Induces Apoptosis in a Pulmonary Epithelial Cell Line

abstract: Staphylococcus aureus frequently colonizes the airways of patients with compromised airway defenses (e.g., cystic fibrosis [CF] patients) for extended periods. Persistent and relapsing infections may be related to live S. aureus bacteria actively residing inside epithelial cells. In this study, we infected a respiratory epithelial cell line, which was derived from a CF patient, with S. aureus RN6390. Internalization of S. aureus was found to be time and dose dependent and could be blocked by cytochalasin D. Transmission electron microscopy revealed that internalized bacteria resided within endocytic vacuoles without any evidence of lysosomal fusion in a 24-h period. The results of internalization experiments and time-lapse fluorescence microscopy of epithelial cells infected with green fluorescent S. aureus indicate that, after an initial lag period of 7 to 9 h, intracellular bacteria began to replicate, with three to five divisions in a 24-h period, leading to apoptosis of infected cells. Induction of apoptosis required bacterial internalization and is associated with intracellular replication. The slow and gradual replication of S. aureus inside epithelial cells hints at the role of host factors or signals in bacterial growth and further suggests possible cross talk between host cells and S. aureus.


title: Computer-aided detection of lung nodules: a review

abstract: We present an in-depth review and analysis of salient methods for computer-aided detection of lung nodules. We evaluate the current methods for detecting lung nodules using literature searches with selection criteria based on validation dataset types, nodule sizes, numbers of cases, types of nodules, extracted features in traditional feature-based classifiers, sensitivity, and false positives (FP)/scans. Our review shows that current detection systems are often optimized for particular datasets and can detect only one or two types of nodules. We conclude that, in addition to achieving high sensitivity and reduced FP/scans, strategies for detecting lung nodules must detect a variety of nodules with high precision to improve the performances of the radiologists. To the best of our knowledge, ours is the first review of the effectiveness of feature extraction using traditional feature-based classifiers. Moreover, we discuss deep-learning methods in detail and conclude that features must be appropriately selected to improve the overall accuracy of the system. We present an analysis of current schemes and highlight constraints and future research areas.


title: A multi-modal parcellation of human cerebral cortex

abstract: Understanding the amazingly complex human cerebral cortex requires a map (or parcellation) of its major subdivisions, known as cortical areas. Making an accurate areal map has been a century-old objective in neuroscience. Using multi-modal magnetic resonance images from the Human Connectome Project (HCP) and an objective semi-automated neuroanatomical approach, we delineated 180 areas per hemisphere bounded by sharp changes in cortical architecture, function, connectivity, and/or topography in a precisely aligned group average of 210 healthy young adults. We characterized 97 new areas and 83 areas previously reported using post-mortem microscopy or other specialized study-specific approaches. To enable automated delineation and identification of these areas in new HCP subjects and in future studies, we trained a machine-learning classifier to recognize the multi-modal ‘fingerprint’ of each cortical area. This classifier detected the presence of 96.6% of the cortical areas in new subjects, replicated the group parcellation, and could correctly locate areas in individuals with atypical parcellations. The freely available parcellation and classifier will enable substantially improved neuroanatomical precision for studies of the structural and functional organization of human cerebral cortex and its variation across individuals and in development, aging, and disease.


title: Policing cyber-neighbourhoods: tension monitoring and social media networks

abstract: We propose that late modern policing practices, that rely on neighbourhood intelligence, the monitoring of tensions, surveillance and policing by accommo- dation, need to be augmented in light of emerging ‘cyber-neighbourhoods’, namely social media networks. The 2011 riots in England were the first to evidence the widespread use of social media platforms to organise and respond to disorder. The police were ill-equipped to make use of the intelligence emerging from these non-terrestrial networks and were found to be at a disadvantage to the more tech-savvy rioters and the general public. In this paper, we outline the development of the ‘tension engine’ component of the Cardiff Online Social Media ObServatroy (COSMOS). This engine affords users with the ability to monitor social media data streams for signs of high tension which can be analysed in order to identify deviations from the ‘norm’ (levels of cohesion/low tension). This analysis can be overlaid onto a palimpsest of curated data, such as official statistics about neighbourhood crime, deprivation and demography, to provide a multidimensional picture of the ‘terrestrial’ and ‘cyber’ streets. As a consequence, this ‘neighbourhood informatics’ enables a means of questioning official constructions of civil unrest through reference to the user-generated accounts of social media and their relationship to other, curated, social and economic data.


title: Wiggling through complex traffic: Planning trajectories constrained by predictions

abstract: The vision of autonomous driving is piecewise becoming reality. Still the problem of executing the driving task in a safe and comfortable way in all possible environments, for instance highway, city or rural road scenarios is a challenging task. In this paper we present a novel approach to planning trajectories for autonomous vehicles. Hereby we focus on the problem of planning a trajectory, given a specific behavior option, e.g. merging into a specific gap at a highway entrance or a roundabout. Therefore we explicitly take arbitrary road geometry and prediction information of other traffic participants into account. We extend former contributions in this field by providing a flexible problem description and a trajectory planner without specialization to distinct classes of maneuvers beforehand. Using a carefully chosen representation of the dynamic free space, the method is capable of considering multiple lanes including the predicted dynamics of other traffic participants, while being real-time capable at the same time. The combination of those properties in one general planning method represents the novelty of the proposed method. We demonstrate the capability of our algorithm to plan safe trajectories in simulation and in real traffic in real-time.


title: A Pilot Beta-Thalassaemia Screening Program in the Albanian Population for a Health Planning Program

abstract: In Albania, no definite national screening programme of beta-thalassaemia has yet been developed for carrier detection. Only limited information about the occurrence and the types of haemoglobin abnormalities is available. Thus, an educational and screening programme was carried out in one high school with a total of 217 young students from the coastal province of Lushnja in Albania. The pilot programme included a systematic sampling of whole saliva, DNA genomic extraction and the determination of defective beta-thalassaemia genes by reverse dot-blot hybridization with 22 probes specific for the Mediterranean populations.Of the 201 subjects tested, 17 (8.4%) students turned out to be carriers of beta-thalassaemia mutations and haemoglobin variants. The most common mutation is HbS (c.20A-->T) with a frequency of 3.2%, followed by IVS-I-110 (G-->A) (c.93-21G-->A) substitution identified in 4 out of 402 chromosomes (1%). In the province of Lushnja, the frequency of beta-thalassaemia carriers was high. As expected, the results show that identified mutations in this population are similar to those found in the east Mediterranean area, suggesting the same origin for mutant alleles during migratory streams. Implementation of a routine carrier-screening programme is significantly facilitated by the presence of only two mutations and would be a wise approach to prevent beta-thalassaemia in the region.


title: Color reduction based on ant colony

abstract: In this article a method for color reduction based on ant colony algorithm is presented. Generally color reduction involves two steps: choosing a proper palette and mapping colors to this palette. This article is about the first step. Using ant colony algorithm, pixel clusters are formed based on their colors and neighborhood information to make the final palette. A comparison between the results of the proposed method and some other methods is presented. There are some parameters in the proposed method which can be set based on user needs and priorities. This increases the flexibility of the method.


title: Decoupled Algorithm for MRI Reconstruction Using Nonlocal Block Matching Model: BM3D-MRI

abstract: The block matching 3D (BM3D) is an efficient image model, which has found few applications other than its niche area of denoising. We will develop a magnetic resonance imaging (MRI) reconstruction algorithm, which uses decoupled iterations alternating over a denoising step realized by the BM3D algorithm and a reconstruction step through an optimization formulation. The decoupling of the two steps allows the adoption of a strategy with a varying regularization parameter, which contributes to the reconstruction performance. This new iterative algorithm efficiently harnesses the power of the nonlocal, image-dependent BM3D model. The MRI reconstruction performance of the proposed algorithm is superior to state-of-the-art algorithms from the literature. A convergence analysis of the algorithm is also presented.


title: Inflatable penile prosthesis (IPP): diagnosis of complications

abstract: Inflatable penile prostheses are a common treatment for erectile dysfunction that is unresponsive to less-invasive measures. Complications can arise at the time of the placement of the prosthesis or at a later date. Complications may be related to infection and/or mechanical failure of one or more of the prosthesis components including cylinder, reservoir, pump, and tubing. Mechanical failure includes kinking, migration, and aneurysmal dilation of the cylinder, erosion of the tunica albuginea of the corpora cavernosum by the cylinder, disconnection of the tubing, and migration of the reservoir. MRI of the entire components with the cylinders in both flaccid and inflated states is the best imaging modality available to image patients with suspected implant complications.


title: Implementation of 85V High Side LDMOS with n-layer in a 0.35um BCD Process

abstract: This paper report 85 V high-side LDMOS which is implemented in a conventional 0.3 5 um BCDMOS process using one additional mask. The process has no thermal budget modification but use simple additional implant step. Also it is completely compatible with the conventional BCDMOS process and has similar performances with 80 V SOI LDMOS.


The Theory of Branching Processes


title: Can brain state be manipulated to emphasize individual differences in functional connectivity?☆

abstract: While neuroimaging studies typically collapse data from many subjects, brain functional organization varies between individuals, and characterizing this variability is crucial for relating brain activity to behavioral phenotypes. Rest has become the default state for probing individual differences, chiefly because it is easy to acquire and a supposed neutral backdrop. However, the assumption that rest is the optimal condition for individual differences research is largely untested. In fact, other brain states may afford a better ratio of within- to between-subject variability, facilitating biomarker discovery. Depending on the trait or behavior under study, certain tasks may bring out meaningful idiosyncrasies across subjects, essentially enhancing the individual signal in networks of interest beyond what can be measured at rest. Here, we review theoretical considerations and existing work on how brain state influences individual differences in functional connectivity, present some preliminary analyses of within- and between-subject variability across conditions using data from the Human Connectome Project, and outline questions for future study.


title: Towards Accurate Marker-Less Human Shape and Pose Estimation over Time

abstract: Existing marker-less motion capture methods often assume known backgrounds, static cameras, and sequence specific motion priors, which narrows its application scenarios. Here we propose a fully automatic method that given multi-view video, estimates 3D human motion and body shape. We take recent SMPLify \cite{bogo2016keep} as the base method, and extend it in several ways. First we fit the body to 2D features detected in multi-view images. Second, we use a CNN method to segment the person in each image and fit the 3D body model to the contours to further improves accuracy. Third we utilize a generic and robust DCT temporal prior to handle the left and right side swapping issue sometimes introduced by the 2D pose estimator. Validation on standard benchmarks shows our results are comparable to the state of the art and also provide a realistic 3D shape avatar. We also demonstrate accurate results on HumanEva and on challenging dance sequences from YouTube in monocular case.


title: A software engineering perspective on environmental modeling framework design

abstract: The environmental modeling community has historically been concerned with the proliferation of models and the effort associated with collective model development tasks (e.g., code generation, data transformation, etc.). Environmental modeling frameworks (EMFs) have been developed to address this problem, but much work remains before EMFs are adopted as mainstream modeling tools. Environmental model development requires both scientific understanding of environmental phenomena and software developer proficiency. EMFs support the modeling process through streamlining model code development, allowing seamless access to data, and supporting data analysis and visualization. EMFs also support aggregation of model components into functional units, component interaction and communication, temporal-spatial stepping, scaling of spatial data, multi-threading/multi-processor support, and cross-language interoperability. Some EMFs additionally focus on high-performance computing and are tailored for particular modeling domains such as ecosystem, socio-economic, or climate change research. The Object Modeling System Version 3 (OMS3) EMF employs new advances in software framework design to better support the environmental model development process. This paper discusses key EMF design goals/constraints and addresses software engineering aspects that have made OMS3 framework development efficacious and its application practical, as demonstrated by leveraging software engineering efforts outside of the modeling community and lessons learned from over a decade of EMF development. Software engineering approaches employed in OMS3 are highlighted including a non-invasive lightweight framework design supporting component-based model development, use of implicit parallelism in system design, use of domain specific language design patterns, and cloud-based support for computational scalability. The key advancements in EMF design presented herein may be applicable and beneficial for other EMF developers seeking to better support environmental model development through improved framework design.


title: Maximizing Semantic Relatedness to Perform Word Sense Disambiguation

abstract: This article presents a method of word sense disambiguation that assigns a target word the sense that is most related to the senses of its neighboring words. We explore the use of measures of similarity and relatedness that are based on finding paths in a concept network, information content derived from a large corpus, and word sense glosses. We observe that measures of relatedness are useful sources of information for disambiguation, and in particular we find that two gloss based measures that we have developed are particularly flexible and effective measures for word sense disambiguation.


title: A Survey on Resource Scheduling in Cloud Computing: Issues and Challenges

abstract: Resource scheduling in cloud is a challenging job and the scheduling of appropriate resources to cloud workloads depends on the QoS requirements of cloud applications. In cloud environment, heterogeneity, uncertainty and dispersion of resources encounters problems of allocation of resources, which cannot be addressed with existing resource allocation policies. Researchers still face troubles to select the efficient and appropriate resource scheduling algorithm for a specific workload from the existing literature of resource scheduling algorithms. This research depicts a broad methodical literature analysis of resource management in the area of cloud in general and cloud resource scheduling in specific. In this survey, standard methodical literature analysis technique is used based on a complete collection of 110 research papers out of large collection of 1206 research papers published in 19 foremost workshops, symposiums and conferences and 11 prominent journals. The current status of resource scheduling in cloud computing is distributed into various categories. Methodical analysis of resource scheduling in cloud computing is presented, resource scheduling algorithms and management, its types and benefits with tools, resource scheduling aspects and resource distribution policies are described. The literature concerning to thirteen types of resource scheduling algorithms has also been stated. Further, eight types of resource distribution policies are described. Methodical analysis of this research work will help researchers to find the important characteristics of resource scheduling algorithms and also will help to select most suitable algorithm for scheduling a specific workload. Future research directions have also been suggested in this research work.


title: One-pass clustering superpixels

abstract: A superpixel is an image patch which is better aligned with intensity edges than a rectangular patch. Superpixels are perceptually consistent units which carry more information than pixels and adhere well to image boundaries. Nowadays superpixels are widely used for segmentation in computer vision and biomedicai applications. There are many approaches to generate superpixels such as SLIC, QuickShift, Turbopixels and Normalized cuts algorithms, each with its own advantages and drawbacks that may be better suited to a particular application. In this paper we propose a one-pass clustering (OPC) technique to efficiently generate superpixels in the combined five-dimensional feature space of CIELAB colour and XY image plane. The Berkeley segmentation dataset (BSDS500) is used to quantitatively compare the performance of OPC with SLIC superpixels method as measured by boundary recall and under-segmentation error. OPC superpixels achieve comparable performance to previously reported results which allow a trade-off between a less regular space superpixels but accurate boundaries or better efficiency.


title: SymDroid: Symbolic Execution for Dalvik Bytecode

abstract: Apps on Google’s Android mobile device platform are written in Java, but are compiled to a special bytecode language called Dalvik. In this paper, we introduce SymDroid, a symbolic executor that operates directly on Dalvik bytecode. SymDroid begins by first translating Dalvik into μ-Dalvik, a simpler language that has only 16 instructions, in contrast to Dalvik’s more than 200 instructions. We present a formalism for SymDroid’s symbolic executor, which can be described with a small number of operational semantics rules; this semantics may be of independent interest. In addition to modeling bytecode instructions, SymDroid also contains models of some key portions of the Android platform, including libraries and the platform’s lifecycle control code. We evaluated SymDroid in two ways. First, we ran it on the Android Compatibility Test Suite, and found it passed all tests except ones that used library or system routines we have not yet implemented. On this test suite, SymDroid runs about twice as slow as the Dalvik VM, and about twice as fast as the Java VM. Second, we used SymDroid to discover the (path) conditions under which contacts may be accessed on an Android app, and found it was able to do so successfully. These results suggest that SymDroid, while still a prototype, is a promising first step in enabling direct, precise analysis of Android apps.


title: Impact of drone swarm formations in 3D scene reconstruction

abstract: Swarms of unmanned autonomous vehicles adopt effective algorithms to control their localization and formation. These strategies grant safe and collision-free navigation, control the positioning of drones, and automatize their motion according to the final task. This paper investigates how formations affect the reconstruction accuracy of a swarm of drones patrolling a 3D environment. Considering that the swarm configuration is significantly constrained by the geometry of the scene, experimental results show that it is possible to overcome the limitations of an ineffective configuration by exploiting camera-in-view side information.


title: A new trend for knowledge-based decision support systems design

abstract: Knowledge-based decision support systems (KBDSS) have evolved greatly over the last few decades. The key technologies underpinning the development of KBDSS can be classified into three categories: technologies for knowledge modelling and representation, technologies for reasoning and inference and web-based technologies. In the meantime, service systems have emerged and become increasingly important to value adding activities in the current knowledge economy. This paper provides a review on the recent advances in the three types of technologies, as well as the main application domains of KBDSS as service systems. Based on the examination of literature, future research directions are recommended for the development of KBDSS in general and in particular to support decision-making in service industry.


title: Transformational and transactional leadership and innovative behavior: The moderating role of psychological empowerment

abstract: Innovative behavior is increasingly important for organizations' survival. Transformational leadership, in contrast to transactional leadership, has been argued to be particularly effective in engendering follower innovative behavior. However, empirical evidence for this relationship is scarce and inconsistent. Addressing this issue, we propose that follower psychological empowerment moderates the relationship of transformational and transactional leadership with follower innovative behavior. In a field study with 230 employees of a government agency in the Netherlands combining multisource ratings, we show that transformational leadership is positively related to innovative behavior only when psychological empowerment is high, whereas transactional leadership has a negative relationship with innovative behavior only under these conditions. Copyright (C) 2009 John Wiley & Sons, Ltd.


title: A Novel Architecture of a Parallel Web Crawler

abstract: ABSTRACT Due to the explosion in the size of the WWW[1,4,5] it becomes essential to make the crawling process parallel. In this paper we present an architecture for a parallel crawler that consists of multiple crawling processes called as C-procs which can run on network of workstations. The proposed crawler is scalable, is resilient against system crashes and other event. The aim of this architecture is to efficiently and effectively crawl the current set of publically indexable web pages so that we can maximize the download rate while minimizing the overhead from parallelization Keywords WWW, Search Engines, Crawlers, Parallel Crawlers. 1. INTRODUCTION The World-Wide Web has undergone explosive, exponential growth. As a consequence, users find themselves unable to browse the ever-changing, distributed hyperlink structure of the web. Furthermore, they are subjected to information overload – information is too abundant . With the increasing number of information resources on the Web, it is often more difficult to locate the resources that are relevant to a given need. Henceforth, Search Engines[2,3] are becoming equally important tool in locating relevant information.


title: Deep learning for detecting inappropriate content in text

abstract: Today, there are a large number of online discussion fora on the internet which are meant for users to express, discuss and exchange their views and opinions on various topics. For example, news portals, blogs, social media channels such as youtube. typically allow users to express their views through comments. In such fora, it has been often observed that user conversations sometimes quickly derail and become inappropriate such as hurling abuses, passing rude and discourteous comments on individuals or certain groups/communities. Similarly, some virtual agents or bots have also been found to respond back to users with inappropriate messages. As a result, inappropriate messages or comments are turning into an online menace slowly degrading the effectiveness of user experiences. Hence, automatic detection and filtering of such inappropriate language has become an important problem for improving the quality of conversations with users as well as virtual agents. In this paper, we propose a novel deep learning-based technique for automatically identifying such inappropriate language. We especially focus on solving this problem in two application scenarios—(a) Query completion suggestions in search engines and (b) Users conversations in messengers. Detecting inappropriate language is challenging due to various natural language phenomenon such as spelling mistakes and variations, polysemy, contextual ambiguity and semantic variations. For identifying inappropriate query suggestions, we propose a novel deep learning architecture called “Convolutional Bi-Directional LSTM (C-BiLSTM)" which combines the strengths of both Convolution Neural Networks (CNN) and Bi-directional LSTMs (BLSTM). For filtering inappropriate conversations, we use LSTM and Bi-directional LSTM (BLSTM) sequential models. The proposed models do not rely on hand-crafted features, are trained end-end as a single model, and effectively capture both local features as well as their global semantics. Evaluating C-BiLSTM, LSTM and BLSTM models on real-world search queries and conversations reveals that they significantly outperform both pattern-based and other hand-crafted feature-based baselines.


title: Fault Handling in FPGAs and Microcontrollers in Safety-Critical Embedded Applications: A Comparative Survey

abstract: Safety-critical nowadays include more and more embedded computer systems, based on different hardware platforms. These hardware platforms, reaching from microcontrollers to programmable logic devices, lead to fundamental differences in design. Major differences result from different hardware architectures and their robustness and reliability as well as from differences in the corresponding software design. This paper gives an overview on how these hardware platforms differ with respect to fault handling possibilities as fault avoidance and fault tolerance and the resulting influence on the safety of the overall system.


title: Nomophobia: Dependency on virtual environments or social phobia?

abstract: Background: The increasing use of new technologies and virtual communication involving personal computers (PCs), tablets and mobile phones are causing changes in individuals' daily habits and behavior. We report the case of an individual with social phobia who developed a dependency on communication through virtual environments (i.e., nomophobia), and used a PC as a form of relating to the outside world to reduce stress and to avoid direct social relations. Nomophobia refers to the discomfort or anxiety caused by the non-availability of a mobile phone, PC or any another virtual communication device. Social phobia is described as an anxiety disorder of chronic evolution. Objective: To study nomophobia as a manifest behavior that might serve as an indication of a possible anxiety disorder. Methodology: The treatment consisted of the use of medication, cognitive-behavioral therapy (CBT) and the application of evaluation tools (interviews, scales, inventories and questionnaires). Results: The individual responded satisfactorily to medication and CBT treatment, which reduced his time using the PC and increased his exposure to real-life situations. Conclusion: Nomophobic behavior produces changes in daily habits and can reveal other aspects to be investigated, such as the presence of comorbid mental disorders.


Rapid Development of TTS Corpora for Four South African Languages


title: Ricco's areas for S- and L-cone mechanisms across the retina

abstract: The purposes of this study were to measure areas of complete spatial summation (i.e., Ricco's area) for S- and L-cone mechanisms and to evaluate whether the sizes of Ricco's area could be explained in terms of either the densities of photoreceptors or ganglion cells. Increment thresholds were measured at the fovea and at 1.5°, 4°, 8°, and 20° in the superior retina using a temporal two-alternative forced-choice procedure. Test stimuli ranging from -0.36 to 4.61 log area (min(2)) were presented on concentric 12.3° adapting and auxiliary fields, which isolated either an S- or L-cone mechanism on the plateau of the respective threshold vs. intensity function. The data indicate that from 0-20° retinal eccentricity, the size of Ricco's area is larger for the S-cone mechanism than the L-cone mechanism, increases monotonically for the L-cone mechanism, and, for both cone mechanisms, increases between 8-20° retinal eccentricity. This latter finding suggests that ganglion cell density rather than cone density defines the size of Ricco's area in the parafoveal and peripheral retina.


title: Securing Data Analytics on SGX with Randomization

abstract: Protection of data privacy and prevention of unwarranted information disclosure is an enduring challenge in cloud computing when data analytics is performed on an untrusted third-party resource. Recent advances in trusted processor technology, such as Intel SGX, have rejuvenated the efforts of performing data analytics on a shared platform where data security and trustworthiness of computations are ensured by the hardware. However, a powerful adversary may still be able to infer private information in this setting from side channels such as cache access, CPU usage and other timing channels, thereby threatening data and user privacy. Though studies have proposed techniques to hide such information leaks through carefully designed data-independent access paths, such techniques can be prohibitively slow on models with large number of parameters, especially when employed in a real-time analytics application. In this paper, we introduce a defense strategy that can achieve higher computational efficiency with a small trade-off in privacy protection. In particular, we study a strategy that adds noise to traces of memory access observed by an adversary, with the use of dummy data instances. We quantitatively measure privacy guarantee, and empirically demonstrate the effectiveness and limitation of this randomization strategy, using classification and clustering algorithms. Our results show significant reduction in execution time overhead on real-world data sets, when compared to a defense strategy using only data-oblivious mechanisms.


title: Microsurgical Anatomy of the Temporal Lobe and Its Implications on Temporal Lobe Epilepsy Surgery

abstract: Objective. We review the neuroanatomical aspects of the temporal lobe related to the temporal lobe epilepsy. The neuronal, the ventricular, and the vascular structures are demonstrated. Methods. The previous articles published from the laboratory of the senior author are reviewed. Results. The temporal lobe has four surfaces. The medial surface has a complicated microanatomy showing close relation to the intraventricular structures, such as the amygdala or the hippocampus. There are many white matter bundles in the temporal lobe showing relation to the extra- and intraventricular structures. The surgical approaches commonly performed to treat temporal lobe epilepsy are discussed under the light of these data. Conclusion. A thorough knowledge of the microanatomy is necessary in cortical, subcortical, and intraventricular structures of the temporal lobe to achieve better results.


title: Non-photorealistic rendering in context: an observational study

abstract: Pen-and-ink line drawing techniques are frequently used to depict form, tone, and texture in artistic, technical, and scientific illustration. In non-photorealistic rendering (NPR), considerable progress has been made towards reproducing traditional pen-and-ink techniques for rendering 3D objects. However, formal evaluation and validation of these NPR images remain an important open research problem. In this paper we present an observational study with three groups of users to examine their understanding and assessment of hand-drawn pen-and-ink illustrations of objects in comparison with NPR renditions of the same 3D objects. The results show that people perceive differences between those two types of illustration but that those that look computer-generated are still highly valued as scientific illustrations.


title: Extreme Adaptation for Personalized Neural Machine Translation

abstract: Every person speaks or writes their own flavor of their native language, influenced by a number of factors: the content they tend to talk about, their gender, their social status, or their geographical origin. When attempting to perform Machine Translation (MT), these variations have a significant effect on how the system should perform translation, but this is not captured well by standard one-size-fits-all models. In this paper, we propose a simple and parameter-efficient adaptation technique that only requires adapting the bias of the output softmax to each particular user of the MT system, either directly or through a factored approximation. Experiments on TED talks in three languages demonstrate improvements in translation accuracy, and better reflection of speaker traits in the target text.


title: Predicting online grocery buying intention: a comparison of the theory of reasoned action and the theory of planned behavior

abstract: This paper tests the ability of two consumer theories-the theory of reasoned action and the theory of planned behavior-in predicting consumer online grocery buying intention. In addition, a comparison of the two theories is conducted. Data were collected from two web-based surveys of Danish (n=1222) and Swedish (n=1038) consumers using self-administered questionnaires. These results suggest that the theory of planned behavior (with the inclusion of a path from subjective norm to attitude) provides the best fit to the data and explains the highest proportion of variation in online grocery buying intention.


title: When Deep Learning Meets Metric Learning: Remote Sensing Image Scene Classification via Learning Discriminative CNNs

abstract: Remote sensing image scene classification is an active and challenging task driven by many applications. More recently, with the advances of deep learning models especially convolutional neural networks (CNNs), the performance of remote sensing image scene classification has been significantly improved due to the powerful feature representations learnt through CNNs. Although great success has been obtained so far, the problems of within-class diversity and between-class similarity are still two big challenges. To address these problems, in this paper, we propose a simple but effective method to learn discriminative CNNs (D-CNNs) to boost the performance of remote sensing image scene classification. Different from the traditional CNN models that minimize only the cross entropy loss, our proposed D-CNN models are trained by optimizing a new discriminative objective function. To this end, apart from minimizing the classification error, we also explicitly impose a metric learning regularization term on the CNN features. The metric learning regularization enforces the D-CNN models to be more discriminative so that, in the new D-CNN feature spaces, the images from the same scene class are mapped closely to each other and the images of different classes are mapped as farther apart as possible. In the experiments, we comprehensively evaluate the proposed method on three publicly available benchmark data sets using three off-the-shelf CNN models. Experimental results demonstrate that our proposed D-CNN methods outperform the existing baseline methods and achieve state-of-the-art results on all three data sets.


Towards a framework for automatic geographic feature extraction from Twitter


title: Segmentation of skin cancer images

abstract: An automatic method for segmentation of images of skin cancer and other pigmented lesions is presented. This method first reduces a color image into an intensity image and approximately segments the image by intensity thresholding. Then, it refines the segmentation using image edges. Double thresholding is used to focus on an image area where a lesion boundary potentially exists. Image edges are then used to localize the boundary in that area. A closed elastic curve is fitted to the initial boundary, and is locally shrunk or expanded to approximate edges in its neighborhood in the area of focus. Segmentation results from 20 randomly selected images show an average error that is about the same as that obtained by four experts manually segmenting the images.


title: The Medical Management of Rhinosinusitis

abstract: The management of rhinosinusitis depends on a number of variables related to the duration and severity of symptoms in the individual patient. Furthermore acute rhinosinusitis is managed differently than chronic rhinosinusitis. Because a variety of conservative and pharmacologic interventions are available, the physician can find it difficult to develop a cohesive and logical approach to treatment. An understanding of the pathophysiology, microbiology, and natural history of rhinosinusitis is necessary to formulate the best treatment plan for the individual patient.


title: Building Rome on a Cloudless Day

abstract: This paper introduces an approach for dense 3D reconstruction from unregistered Internet-scale photo collections with about 3 million images within the span of a day on a single PC ("cloudless"). Our method advances image clustering, stereo, stereo fusion and structure from motion to achieve high computational performance. We leverage geometric and appearance constraints to obtain a highly parallel implementation on modern graphics processors and multi-core architectures. This leads to two orders of magnitude higher performance on an order of magnitude larger dataset than competing state-of-the-art approaches.


title: Direct and Indirect Human Computer Interaction Based Biometrics

abstract: In this paper we survey the state of the art in direct and indirect human computer interaction based biometrics. Direct HCI biometrics are based on abilities, style, preference, knowledge, or strategy used by people while working with a computer. The indirect HCI-based biometrics are events that can be obtained by monitoring users’ HCI behavior indirectly via observable low-level actions of computer software. We examine current research and analyze the types of features used to describe HCI behavior. After comparing accuracy rates for verification of users using different HCI- based biometric approaches we address privacy issues which arise with the use of HCI dependant biometrics. Finally, we present results of our experiments with direct and indirect HCI-based behavioral biometrics employed as a part of an intrusion detection system.


title: Occupancy grid computation from dense stereo and sparse structure and motion points for automotive applications

abstract: We present a complete processing chain for computing 2D occupancy grids from image sequences. A multi layer grid is introduced which serves several purposes. First the 3D points reconstructed from the images are distributed onto the underlying grid. Thereafter a virtual measurement is computed for each cell thus reducing computational complexity and rejecting potential outliers. Subsequently a height profile is updated from which the current measurement is partitioned into ground and obstacle pixels. Different height profile update strategies are tested and compared yielding a stable height profile estimation. Lastly the occupancy layer of the grid is updated. To asses the algorithm we evaluate it quantitatively by comparing the output of it to ground truth data illustrating its accuracy. We show the applicability of the algorithm by using both, dense stereo reconstructed and sparse structure and motion points. The algorithm was implemented and run online on one of our test vehicles in real time.


title: Personality and Prejudice: From Big Five Personality Factors to Facets

abstract: Extending our previous research on personality and prejudice, we tested the predictive power of Big Five facet compared with factor scores in three studies. Study 1 (N=170) examined the predictive power of factors and facets when explaining generalized prejudice, a composite of four prejudice types. Study 2 (N=158) focused on sexism and Study 3 (N=80) examined the impact of personality and experimentally manipulated social norm against expressing sexism. Multiple regression analyses showed the strongest facets (Tender-Mindedness and Values) to outperform the strongest factors (Agreeableness and Openness to Experience) in predicting prejudice in all three studies. We discuss the outcome against the background of previous empirical findings and the two major approaches - the personality and the social psychological - to explaining individual differences in prejudice.


title: A tertiary study: experiences of conducting systematic literature reviews in software engineering

abstract: Context: The use of Systematic Literature Review (SLR) requires expertise and poses many challenges for novice researchers. The experiences of those who have used this research methodology can benefit novice researchers in effectively dealing with these challenges. Objective: The aim of this study is to record the reported experiences of conducting Systematic Literature Reviews, for the benefit of new researchers. Such a review will greatly benefit the researchers wanting to conduct SLR for the very first time. Method: We conducted a tertiary study to gather the experiences published by researchers. Studies that have used the SLR research methodology in software engineering and have implicitly or explicitly reported their experiences are included in this review. Results: Our research has revealed 116 studies relevant to the theme. The data has been extracted by two researchers working independently and conflicts resolved after discussion with third researcher. Findings from these studies highlight Search Strategy, Online Databases, Planning and Data Extraction as the most challenging phases of SLR. Lack of standard terminology in software engineering papers, poor quality of abstracts and problems with search engines are some of the most cited challenges. Conclusion: Further research and guidelines is required to facilitate novice researchers in conducting these phases properly.


Context Sensitive Lemmatization Using Two Successive Bidirectional Gated Recurrent Networks


title: Real-time automatic 3D scene generation from natural language voice and text descriptions

abstract: Automatic scene generation using voice and text offers a unique multimedia approach to classic storytelling and human computer interaction with 3D graphics. In this paper, we present a newly developed system that generates 3D scenes from voice and text natural language input. Our system is intended to benefit non-graphics domain users and applications by providing advanced scene production through an automatic system. Scene descriptions are constructed in real-time using a method for depicting spatial relationships between and among different objects. Only the polygon representations of the objects are required for object placement. In addition, our system is robust. The system supports different quality polygon models such as those widely available on the Internet.


title: Social and Personality Psychology Compass 5/1 (2011): 88–103, 10.1111/j.1751-9004.2010.00334.x Emotional Intelligence: Implications for Personal, Social, Academic, and Workplace Success

abstract: This article presents an overview of the ability model of emotional intelligence and includes a discussion about how and why the concept became useful in both educational and workplace settings. We review the four underlying emotional abilities comprising emotional intelligence and the assessment tools that that have been developed to measure the construct. A primary goal is to provide a review of the research describing the correlates of emotional intelligence. We describe what is known about how emotionally intelligent people function both intra- and interpersonally and in both academic and workplace settings. The facts point in one direction: The job offer you have in hand is perfect – great salary, ideal location, and tremendous growth opportunities. Yet, there is something that makes you feel uneasy about resigning from your current position and moving on. What will you do? Ignore the feeling and choose what appears to be the logical path, or go with your gut and risk disappointing your family? Or, might you consider both your thoughts and feelings about the job in order to make the decision? Solving problems and making wise decisions using both thoughts and feelings or logic and intuition is a part of what we refer to as emotional intelligence (Mayer & Salovey, 1997; Salovey & Mayer, 1990). Linking emotions and intelligence was relatively novel when first introduced in a theoretical model about twenty years ago (Salovey & Mayer, 1990; but see Gardner, 1983 ⁄ 1993). Among the many questions posed by both researchers and laypersons alike were: Is emotional intelligence an innate, nonmalleable mental ability? Can it be acquired with instruction and training? Is it a new intelligence or just the repackaging of existing constructs? How can it be measured reliably and validly? What does the existence of an emotional intelligence mean in everyday life? In what ways does emotional intelligence affect mental health, relationships, daily decisions, and academic and workplace performance? In this article, we provide an overview of the theory of emotional intelligence, including a brief discussion about how and why the concept has been used in both educational and workplace settings. Because the field is now replete with articles, books, and training manuals on the topic – and because the definitions, claims, and measures of emotional intelligence have become extremely diverse – we also clarify definitional and measurement issues. A final goal is to provide an up-to-date review of the research describing what the lives of emotionally intelligent people ‘look like’ personally, socially, academically, and in the workplace.


title: A One-Class NIDS for SDN-Based SCADA Systems

abstract: Power systems are undergoing an intense process of modernization, and becoming highly dependent on networked systems used to monitor and manage system components. These so-called Smart Grids comprise energy generation, transmission, and distribution subsystems, which are monitored and managed by Supervisory Control and Data Acquisition (SCADA) systems. In this paper, we discuss the benefits of using Software-Defined Networking (SDN) to assist in the deployment of next generation SCADA systems. We also present a specific Network-Based Intrusion Detection System (NIDS) for SDN-based SCADA systems, which uses SDN to capture network information and is responsible for monitoring the communication between power grid components. Our approach relies on SDN to periodically gather statistics from network devices, which are then processed by One-Class Classification (OCC) algorithms. Given that attack traces in SCADA networks are scarce and not publicly disclosed by utility companies, the main advantage of using OCC algorithms is that they do not depend on known attack signatures to detect possible malicious traffic. Our results indicate that OCC algorithms achieve an approximate accuracy of 98% and can be effectively used to detect cyber-attacks targeted against SCADA systems.


title: The psychology of whistleblowing

abstract: Whistleblowing — reporting another person's unethical behavior to a third party — represents an ethical quandary. In some cases whistleblowing appears heroic whereas in other cases it appears reprehensible. This article describes how the decision to blow the whistle rests on the tradeoff that people make between fairness and loyalty. When fairness increases in value, whistleblowing is more likely whereas when loyalty increases in value, whistleblowing is less likely. Furthermore, we describe systematic personal, situational, and cultural factors stemming from the fairness-loyalty tradeoff that drive whistleblowing. Finally, we describe how minimizing this tradeoff and prioritizing constructive dissent can encourage whistleblowing and strengthen collectives.


title: Analysis on Center-Tap Rectifier Voltage Oscillation of LLC Resonant Converter

abstract: The LLC resonant converter employing a center-tap rectifier can suffer from a high voltage oscillation across rectifier diodes owing to a leakage inductance of a transformer secondary. The amplitude of this voltage oscillation is varied according to design parameters, parasitic components, and operation regions, i.e., below-resonant region and above-resonant region. To reduce the diode voltage stress, this paper analyzes the voltage oscillation mechanism and presents the design consideration.


Receiver operating characteristic - Wikipedia, the free encyclopedia


title: The Impact of Information Quality on Quality of Life: An Information Quality Oriented Framework

abstract: Information affects almost all aspects of life, and thus the Quality of Information (IQ) plays a critical role in businesses and societies; It can have significant positive and negative impacts on the quality of life of citizens, employees and organizations. Over many years aspects and challenges of IQ have been studied within various contexts. As a result, the general approach to the study of IQ has offered numerous management and measurement approaches, IQ frameworks and list of IQ criteria. As the volume of data and information increases, IQ problems become pervasive. Whereas earlier studies investigated specific aspects of IQ, the next phase of IQ research will need to examine IQ in a wider context, thus its impact on the quality of life and societies. In this paper we apply an IQ oriented framework to two cases, cloud computing and lifelogging, illustrating the impact of IQ on the quality of life. The paper demonstrates the value of the framework, the impact IQ can have on the quality of life and in summary provides a foundation for further research. key words: Information Quality, ageing, cloud, social, lifelogging


title: Homomorphisms are a good basis for counting small subgraphs

abstract: We introduce graph motif parameters, a class of graph parameters that depend only on the frequencies of constant-size induced subgraphs. Classical works by Lovasz show that many interesting quantities have this form, including, for fixed graphs H, the number of H-copies (induced or not) in an input graph G, and the number of homomorphisms from H to G. We use the framework of graph motif parameters to obtain faster algorithms for counting subgraph copies of fixed graphs H in host graphs G. More precisely, for graphs H on k edges, we show how to count subgraph copies of H in time kO(k)· n0.174k + o(k) by a surprisingly simple algorithm. This improves upon previously known running times, such as O(n0.91k + c) time for k-edge matchings or O(n0.46k + c) time for k-cycles. Furthermore, we prove a general complexity dichotomy for evaluating graph motif parameters: Given a class C of such parameters, we consider the problem of evaluating f e C on input graphs G, parameterized by the number of induced subgraphs that f depends upon. For every recursively enumerable class C, we prove the above problem to be either FPT or #W[1]-hard, with an explicit dichotomy criterion. This allows us to recover known dichotomies for counting subgraphs, induced subgraphs, and homomorphisms in a uniform and simplified way, together with improved lower bounds. Finally, we extend graph motif parameters to colored subgraphs and prove a complexity trichotomy: For vertex-colored graphs H and G, where H is from a fixed class of graphs, we want to count color-preserving H-copies in G. We show that this problem is either polynomial-time solvable or FPT or #W[1]-hard, and that the FPT cases indeed need FPT time under reasonable assumptions.


title: Text Localization in Real-World Images Using Efficiently Pruned Exhaustive Search

abstract: An efficient method for text localization and recognition in real-world images is proposed. Thanks to effective pruning, it is able to exhaustively search the space of all character sequences in real time (200ms on a 640x480 image). The method exploits higher-order properties of text such as word text lines. We demonstrate that the grouping stage plays a key role in the text localization performance and that a robust and precise grouping stage is able to compensate errors of the character detector. The method includes a novel selector of Maximally Stable Extremal Regions (MSER) which exploits region topology. Experimental validation shows that 95.7% characters in the ICDAR dataset are detected using the novel selector of MSERs with a low sensitivity threshold. The proposed method was evaluated on the standard ICDAR 2003 dataset where it achieved state-of-the-art results in both text localization and recognition.


title: Warehousing and OLAP Analysis of Bibliographic Data

abstract: In this paper, the system bgMath/OLAP for warehousing and online analytical processing bibliographic data is proposed. The implemented system can be useful for the users maintaining their electronic libraries with publications in order to monitoring, evaluating and comparing the scientific development of particular researchers, entire research groups, certain scientific fields and problems.


title: Behavioral and neural correlates of emotional intelligence: An Event-Related Potentials (ERP) study

abstract: Abstract The present study was aimed at identifying potential behavioral and neural correlates of emotional intelligence (EI) by using scalp-recorded Event-Related Potentials (ERPs). EI levels were defined according to both self-report questionnaire and a performance-based test. We identified ERP correlates of emotional processing by comparing ERPs elicited in trials using pleasant, neutral and unpleasant pictures. The effects of these emotion-inducing pictures were then compared across groups with low and high EI levels. Behavioral results revealed a significant valence×EI group interaction effect since valence ratings were lower for unpleasant pictures and higher for pleasant pictures in the high EI group compared with the low EI group. The groups did not differ with respect to neutral picture ratings. The ERP results indicate that participants with high EI exhibited significantly greater mean amplitudes of the P2 (200–300 ms post-stimulus) and P3 (310–450 ms post-stimulus) ERP components in response to emotional and neutral pictures, at posterior-parietal as well as at frontal scalp locations. This may suggest greater recruitment of resources to process all emotional and non-emotional stimuli at early and late processing stages among individuals with higher EI. The present study also underscores the usefulness of ERP methodology as a sensitive measure for the study of emotional stimuli processing in the research field of EI.


title: Towards wearable sensing-based assessment of fluid intake

abstract: Fluid intake is an important information for many health and assisted living applications. At the same time it is inherently difficult to monitor. Existing reliable solutions require augmented drinking containers, which severely limits the applicability of such systems. In this paper we investigate two key components of an unobtrusive, wearable solution that is independent of a particular drinking container or environment.


title: A Failure Detection and Prediction Mechanism for Enhancing Dependability of Data Centers

abstract:  Abstract—Modern data centers continue to grow in their scale and complexity. They are changing dynamically as well due to the addition and removal of system components, changing execution environments, frequent updates and upgrades, online repairs and more. Classical reliability theory and conventional methods do rarely consider the actual state of a system and are therefore not capable to reflect the dynamics of runtime systems and failure processes. In this paper, we present an unsupervised failure detection and prediction method using an ensemble of Bayesian models. It characterizes normal execution states of the system and detects anomalous behaviors. We implement a prototype of our failure detection and prediction mechanism and evaluate its performance on a data center test platform. Experimental results show that our proposed method can forecast failure dynamics with high accuracy.


title: Towards a Reusable Infrastructure for Crowdsourcing

abstract: In the course of the last few years crowd sourcing has received growing research focus due to its conception of solving complex tasks with the help of a flexible group of contributors of whom each needs to only contribute a simpler task part. Hence, the crowd can contribute by collecting data from distributed locations, completing map information, or voting on product ideas, et cetera. However, even though it is a necessary conceptual feature, the participation of large numbers of users with heterogeneous devices, generic infrastructures for crowd sourcing can hardly be found. For example, the management of users, mobile devices and contributed data has to be repetitively implemented in new projects. To ease the development of crowd sourcing applications, in this paper we propose a generic platform for simplified crowd sourcing deployment while supporting diverse crowd sourcing scenarios, the ability to handle large numbers of users and the involvement of heterogeneous mobile devices. The focus therein is put on the deployment process. Hence, the evaluation is based on an actual deployment, namely the migration of Cyface, an existing crowd sourcing project build from scratch, into using our proposed infrastructure.


title: Analysis of traffic accidents on rural highways using Latent Class Clustering and Bayesian Networks

abstract: Abstract One of the principal objectives of traffic accident analyses is to identify key factors that affect the severity of an accident. However, with the presence of heterogeneity in the raw data used, the analysis of traffic accidents becomes difficult. In this paper, Latent Class Cluster (LCC) is used as a preliminary tool for segmentation of 3229 accidents on rural highways in Granada (Spain) between 2005 and 2008. Next, Bayesian Networks (BNs) are used to identify the main factors involved in accident severity for both, the entire database (EDB) and the clusters previously obtained by LCC. The results of these cluster-based analyses are compared with the results of a full-data analysis. The results show that the combined use of both techniques is very interesting as it reveals further information that would not have been obtained without prior segmentation of the data. BN inference is used to obtain the variables that best identify accidents with killed or seriously injured. Accident type and sight distance have been identify in all the cases analysed; other variables such as time, occupant involved or age are identified in EDB and only in one cluster; whereas variables vehicles involved, number of injuries, atmospheric factors, pavement markings and pavement width are identified only in one cluster.


title: Sharing Spectrum Through Spectrum Policy Reform and Cognitive Radio

abstract: Traditionally, interference protection is guaranteed through a policy of spectrum licensing, whereby wireless systems get exclusive access to spectrum. This is an effective way to prevent interference, but it leads to highly inefficient use of spectrum. Cognitive radio along with software radio, spectrum sensors, mesh networks, and other emerging technologies can facilitate new forms of spectrum sharing that greatly improve spectral efficiency and alleviate scarcity, if policies are in place that support these forms of sharing. On the other hand, new technology that is inconsistent with spectrum policy will have little impact. This paper discusses policies that can enable or facilitate use of many spectrum-sharing arrangements, where the arrangements are categorized as being based on coexistence or cooperation and as sharing among equals or primary-secondary sharing. A shared spectrum band may be managed directly by the regulator, or this responsibility may be delegated in large part to a license-holder. The type of sharing arrangement and the entity that manages it have a great impact on which technical approaches are viable and effective. The most efficient and cost-effective form of spectrum sharing will depend on the type of systems involved, where systems under current consideration are as diverse as television broadcasters, cellular carriers, public safety systems, point-to-point links, and personal and local-area networks. In addition, while cognitive radio offers policy-makers the opportunity to improve spectral efficiency, cognitive radio also provides new challenges for policy enforcement. A responsible regulator will not allow a device into the marketplace that might harm other systems. Thus, designers must seek innovative ways to assure regulators that new devices will comply with policy requirements and will not cause harmful interference.


title: Alzheimer’s Disease in Social Media: Content Analysis of YouTube Videos

abstract: Background: Approximately 5.5 million Americans are living with Alzheimer’s disease (AD) in 2017. YouTube is a popular platform for disseminating health information; however, little is known about messages specifically regarding AD that are being communicated through YouTube. Objective: This study aims to examine video characteristics, content, speaker characteristics, and mobilizing information (cues to action) of YouTube videos focused on AD. Methods: Videos uploaded to YouTube from 2013 to 2015 were searched with the term “Alzheimer’s disease” on April 30th, 2016. Two coders viewed the videos and coded video characteristics (the date when a video was posted, Uniform Resource Locator, video length, audience engagement, format, author), content, speaker characteristics (sex, race, age), and mobilizing information. Descriptive statistics were used to examine video characteristics, content, audience engagement (number of views), speaker appearances in the video, and mobilizing information. Associations between variables were examined using Chi-square and Fisher’s exact tests. Results: Among the 271 videos retrieved, 25.5% (69/271) were posted by nonprofit organizations or universities. Informal presentations comprised 25.8% (70/271) of all videos. Although AD symptoms (83/271, 30.6%), causes of AD (80/271, 29.5%), and treatment (76/271, 28.0%) were commonly addressed, quality of life of people with AD (34/271, 12.5%) had more views than those more commonly-covered content areas. Most videos featured white speakers (168/187, 89.8%) who were adults aged 20 years to their early 60s (164/187, 87.7%). Only 36.9% (100/271) of videos included mobilizing information. Videos about AD symptoms were significantly less likely to include mobilizing information compared to videos without AD symptoms (23/83, 27.7% vs 77/188, 41.0% respectively; P=.03). Conclusions: This study contributes new knowledge regarding AD messages delivered through YouTube. Findings of the current study highlight a potential gap between available information and viewers’ interests. YouTube videos on AD could be beneficial if the messages delivered meet users’ needs and provide mobilizing information for further resources. Study findings will be useful to government agencies, researchers, nonprofit organizations that promote information about AD, and those responsible for social media to provide useful and accurate health information for the public. [Interact J Med Res 2017;6(2):e19]


title: An outpatient program in behavioral medicine for chronic pain patients based on the practice of mindfulness meditation: Theoretical considerations and preliminary results

abstract: Abstract The practice of mindfulness meditation was used in a 10-week Stress Reduction and Relaxation Program to train chronic pain patients in self-regulation. The meditation facilitates an attentional stance towards proprioception known as detached observation. This appears to cause an “uncoupling” of the sensory dimension of the pain experience from the affectivel evaluative alarm reaction and reduce the experience of suffering via cognitive reappraisal. Data are presented on 51 chronic pain patients who had not improved with traditional medical care. The dominant pain categories were low back, neck and shoulder, and headache. Facial pain, angina pectoris, noncoronary chest pain, and GI pain were also represented. At 10 weeks, 65% of the patients showed a reduction of ≥33% in the mean total Pain Rating Index (Melzack) and 50% showed a reduction of ≥50%. Similar decreases were recorded on other pain indices and in the number of medical symptoms reported. Large and significant reductions in mood disturbance and psychiatric symptomatology accompanied these changes and were relatively stable on follow-up. These improvements were independent of the pain category. We conclude that this form of meditation can be used as the basis for an effective behavioral program in self-regulation for chronic pain patients. Key features of the program structure, and the limitations of the present uncontrolled study are discussed.


title: Combining full-text analysis and bibliometric indicators. A pilot study

abstract: In the present study full-text analysis and traditional bibliometric methods are combined to improve the efficiency of the individual methods in the mapping of science. The methodology is applied to map research papers from a special issue of Scientometrics. The outcomes substantiate that such hybrid methodology can be applied to both research evaluation and information retrieval. The subject classification given by the guest-editors of the special issue is used for validation purposes. Because of the limited number of papers underlying the study the paper is considered a pilot study that will be extended in a later study on the basis of a larger corpus.


title: Spatiotemporal Stereo and Scene Flow via Stequel Matching

abstract: This paper is concerned with the recovery of temporally coherent estimates of 3D structure and motion of a dynamic scene from a sequence of binocular stereo images. A novel approach is presented based on matching of spatiotemporal quadric elements (stequels) between views, as this primitive encapsulates both spatial and temporal image structure for 3D estimation. Match constraints are developed for bringing stequels into correspondence across binocular views. With correspondence established, temporally coherent disparity estimates are obtained without explicit motion recovery. Further, the matched stequels also will be shown to support direct recovery of scene flow estimates. Extensive algorithmic evaluation with ground truth data incorporated in both local and global correspondence paradigms shows the considerable benefit of using stequels as a matching primitive and its advantages in comparison to alternative methods of enforcing temporal coherence in disparity estimation. Additional experiments document the usefulness of stequel matching for 3D scene flow estimation.


title: Minority Language Twitter: Part-of-Speech Tagging and Analysis of Irish Tweets

abstract: Noisy user-generated text poses problems for natural language processing. In this paper, we show that this statement also holds true for the Irish language. Irish is regarded as a low-resourced language, with limited annotated corpora available to NLP researchers and linguists to fully analyse the linguistic patterns in language use in social media. We contribute to recent advances in this area of research by reporting on the development of part-ofspeech annotation scheme and annotated corpus for Irish language tweets. We also report on state-of-the-art tagging results of training and testing three existing POStaggers on our new dataset.


title: Effects of feedback in a computer-based assessment for learning

abstract: The effects of written feedback in a computer-based assessment for learning on students' learning outcomes were investigated in an experiment at a Higher Education institute in the Netherlands. Students were randomly assigned to three groups, and were subjected to an assessment for learning with different kinds of feedback. These are immediate knowledge of correct response (KCR) + elaborated feedback (EF), delayed KCR + EF, and delayed knowledge of results (KR). A summative assessment was used as a post-test. No significant effect was found for the feedback condition on student achievement on the post-test. Results suggest that students paid more attention to immediate than to delayed feedback. Furthermore, the time spent reading feedback was positively influenced by students' attitude and motivation. Students perceived immediate KCR + EF feedback to be more useful for learning than KR. Students also had a more positive attitude towards feedback in a CBA when they received KCR + EF rather than KR only.


title: Gamification design frameworks: a systematic mapping study

abstract: The purpose of this article is to present a systematic mapping study conducted to discover the current state of the art of the Gamification Design Framework. This study explores more than 992 documents from several digital libraries, that were published from 2009 until the end of 2017. Only 58 candidate documents were selected for a critical comparative analysis based on key patterns specified in this work. The analysis of a multiple and diverse Framework has led to some quality aspects that can be used for the new Gamification Design Framework proposals.


title: A Domain Independent Technique to Generate Feature Opinion Pairs for Opinion Mining

abstract: The rapid growth of e-commerce and social media services in recent times have opened up many ::: interesting Opinion mining research problems. Among others, Aspect/Feature based Opinion extraction have ::: managed to grab researcher’s attention. Towards this direction, many researchers have proposed techniques ::: that are supervised and domain dependent for extracting opinions. In this paper, a novel technique that is ::: unsupervised and domain independent is proposed for generating relevant Feature Opinion pairs with good ::: accuracy. Technique employs grammatical relationships obtained by using typed dependency parsers to ::: further refine the pairs extracted by Part of Speech taggers. It also focusses on words that are verbs and nouns ::: which in some cases imply opinions, unlike other existing work which mainly focusses on adjectives and ::: adverb expressions for Opinion analysis. The proposed technique was tested on 9 data sets of different ::: domains. The result demonstrated a good percentage reduction in number of irrelevant Feature Opinion pairs ::: and the relevancy of retained pairs was found to be considerably high.


title: Collaborative filtering meets next check-in location prediction

abstract: With the increasing popularity of Location-based Social Networks, a vast amount of location check-ins have been accumulated. Though location prediction in terms of check-ins has been recently studied, the phenomena that users often check in novel locations has not been addressed. To this end, in this paper, we leveraged collaborative filtering techniques for check-in location prediction and proposed a short- and long-term preference model. We extensively evaluated it on two large-scale check-in datasets from Gowalla and Dianping with 6M and 1M check-ins, respectively, and showed that the proposed model can outperform the competing baselines.


title: Machinery equipment early fault detection using Artificial Neural Network based Autoencoder

abstract: Machinery equipment early fault detection is still in an open challenge. The objective of this paper is to introduce a parametric method Artificial Neural Network based Autoencoder implemented to perform early fault detection of a machinery equipment. The performance of this method is then compared to one of the industry state of the art nonparametric methods called Similarity Based Modeling. The comparison is done by analyzing the implementation result on both artificial and real case dataset. Root Mean Square Error (RMSE) is applied to measure the performance. Based on the result of the research, both of these methods are effective to do pattern recognition and able to identify data anomaly or in this case is fault identification.


title: Automated essay evaluation: the criterion online writing service

abstract: In this article, we describe a deployed educational technology application: the Criterion Online Essay Evaluation Service, a web-based system that provides automated scoring and evaluation of student essays. Criterion has two complementary applications: (1) Critique Writing Analysis Tools, a suite of programs that detect errors in grammar, usage, and mechanics, that identify discourse elements in the essay, and that recognize potentially undesirable elements of style, and (2) e-rater version 2.0, an automated essay scoring system. Critique and e-rater provide students with feedback that is specific to their writing in order to help them improve their writing skills and is intended to be used under the instruction of a classroom teacher. Both applications employ natural language processing and machine learning techniques. All of these capabilities outperform baseline algorithms, and some of the tools agree with human judges in their evaluations as often as two judges agree with each other.


An Evil Copy: How the Loader Betrays You.


title: Learning deep physiological models of affect

abstract: More than 15 years after the early studies in Affective Computing (AC), [1] the problem of detecting and modeling emotions in the context of human-computer interaction (HCI) remains complex and largely unexplored. The detection and modeling of emotion is, primarily, the study and use of artificial intelligence (AI) techniques for the construction of computational models of emotion. The key challenges one faces when attempting to model emotion [2] are inherent in the vague definitions and fuzzy boundaries of emotion, and in the modeling methodology followed. In this context, open research questions are still present in all key components of the modeling process. These include, first, the appropriateness of the modeling tool employed to map emotional manifestations and responses to annotated affective states; second, the processing of signals that express these manifestations (i.e., model input); and third, the way affective annotation (i.e., model output) is handled. This paper touches upon all three key components of an affective model (i.e., input, model, output) and introduces the use of deep learning (DL) [3], [4], [5] methodologies for affective modeling from multiple physiological signals.


title: EMBER: An Open Dataset for Training Static PE Malware Machine Learning Models

abstract: This paper describes EMBER: a labeled benchmark dataset for training machine learning models to statically detect malicious Windows portable executable files. The dataset includes features extracted from 1.1M binary files: 900K training samples (300K malicious, 300K benign, 300K unlabeled) and 200K test samples (100K malicious, 100K benign). To accompany the dataset, we also release open source code for extracting features from additional binaries so that additional sample features can be appended to the dataset. This dataset fills a void in the information security machine learning community: a benign/malicious dataset that is large, open and general enough to cover several interesting use cases. We enumerate several use cases that we considered when structuring the dataset. Additionally, we demonstrate one use case wherein we compare a baseline gradient boosted decision tree model trained using LightGBM with default settings to MalConv, a recently published end-to-end (featureless) deep learning model for malware detection. Results show that even without hyper-parameter optimization, the baseline EMBER model outperforms MalConv. The authors hope that the dataset, code and baseline model provided by EMBER will help invigorate machine learning research for malware detection, in much the same way that benchmark datasets have advanced computer vision research.


title: DCAN: Dual Channel-wise Alignment Networks for Unsupervised Scene Adaptation

abstract: Harvesting dense pixel-level annotations to train deep neural networks for semantic segmentation is extremely expensive and unwieldy at scale. While learning from synthetic data where labels are readily available sounds promising, performance degrades significantly when testing on novel realistic data due to domain discrepancies. We present Dual Channel-wise Alignment Networks (DCAN), a simple yet effective approach to reduce domain shift at both pixel-level and feature-level. Exploring statistics in each channel of CNN feature maps, our framework performs channel-wise feature alignment, which preserves spatial structures and semantic information, in both an image generator and a segmentation network. In particular, given an image from the source domain and unlabeled samples from the target domain, the generator synthesizes new images on-the-fly to resemble samples from the target domain in appearance and the segmentation network further refines high-level features before predicting semantic maps, both of which leverage feature statistics of sampled images from the target domain. Unlike much recent and concurrent work relying on adversarial training, our framework is lightweight and easy to train. Extensive experiments on adapting models trained on synthetic segmentation benchmarks to real urban scenes demonstrate the effectiveness of the proposed framework.


title: Retinal vessel segmentation using multiwavelet kernels and multiscale hierarchical decomposition

abstract: We propose a comprehensive method for segmenting the retinal vasculature in fundus camera images. Our method does not require preprocessing and training and can therefore be used directly on different images sets. We enhance the vessels using matched filtering with multiwavelet kernels (MFMK), separating vessels from clutter and bright, localized features. Noise removal and vessel localization are achieved by a multiscale hierarchical decomposition of the normalized enhanced image. We show a necessary condition to achieve the optimal decomposition and derive the associated value of the scale parameter controlling the amount of details captured. Finally, we obtain a binary map of the vasculature by locally adaptive thresholding, generating a threshold surface based on the vessel edge information extracted by the previous processes. We report experimental results on two public retinal data sets, DRIVE and STARE, demonstrating an excellent performance in comparison with retinal vessel segmentation methods reported recently.


Maturation of Binocular Pattern Visual Evoked Potentials in Normal Full-Term and Preterm Infants from 1 to 6 Months of Age


title: Influence of Emotional Engagement and Habituation on Exposure Therapy for PTSD

abstract: This study examined 2 process variables, emotional engagement and habituation, and outcome of exposure therapy for posttraumatic stress disorder. Thirty-seven female assault victims received treatment that involved repeated imaginal reliving of their trauma, and rated their distress at 10-min intervals. The average distress levels during each of 6 exposure sessions were submitted to a cluster analysis. Three distinct groups of clients with different patterns of distress were found: high initial engagement and gradual habituation between sessions, high initial engagement without habituation, and moderate initial engagement without habituation. Clients with the 1st distress pattern improved more in treatment than the other clients. The results are discussed within the framework of emotional processing theory, emphasizing the crucial role of emotional engagement and habituation in exposure therapy.


title: A study of user behavior in an immersive virtual environment for digital libraries

abstract: In this paper we present a 2x3 factorial design study evaluating the limits and differences on the behavior of 10 users when searching in a virtual reality representation that mimics the arrangement of a traditional library. The focus of this study was the effect of clustering techniques and query highlighting on search strategy users develop in the virtual environment, and whether position or spatial arrangement influenced user behavior. We found several particularities that can be attributed to the differences in the VR environment.This study's results identify: 1) the need of co-designing both spatial arrangement and interaction method; 2) adifficulty novice users faced when using clusters to identify commontopics; 3) the influence of position and distance on users' selection of collection items to inspect; and 4) that users did not search until found the best match, but only until they found a satisfactory match.


Sentiment Analysis in Turkish : Towards a Complete Framework


title: Modular and High-Resolution Channel State Information and Beam Management for 5G New Radio

abstract: This article provides an overview of key features pertaining to CSI reporting and beam management for the 5G New Radio (NR) currently being standardized in 3GPP. For CSI reporting, the modular design framework and high-resolution spatial information feedback offer not only flexibility in a host of use cases and deployment scenarios, but also improved average user throughput over state-of-the-art 4G LTE. To accommodate cellular communications in the milimeter-wave regime where a combination of analog and digital beamforming is typically used at both a base station and user equipment, beam management procedures such as measurement, reporting, and recovery are introduced. The utility and joint usage of these two features are demonstrated along with some potential upgrades for the next phase of 5G NR. Introduction


Theta variation and spatiotemporal scaling along the septotemporal axis of the hippocampus