title: Named Entity Recognition for Nepali Text Using Support Vector Machines

abstract: Named Entity Recognition aims to identify and ::: to classify rigid designators in text such as proper names, biological species, ::: and temporal expressions into some predefined categories. There has been ::: growing interest in this field of research since the early 1990s. Named Entity ::: Recognition has a vital role in different fields of natural language processing such as ::: Machine Translation, Information Extraction, Question Answering System and ::: various other fields. In this paper, Named Entity Recognition for Nepali text, ::: based on the Support Vector Machine (SVM) is presented which is one of machine ::: learning approaches for the classification task. A set of features are extracted from training data set. Accuracy ::: and efficiency of SVM classifier are analyzed in three different sizes of training data set. Recognition systems are ::: tested with ten datasets for Nepali text. The strength of this work is the ::: efficient feature extraction and the comprehensive recognition techniques. The ::: Support Vector Machine based Named Entity Recognition is limited to use a ::: certain set of features and it uses a small dictionary which affects its ::: performance. The learning performance of recognition system is observed. It is found that system can learn well from the small set of training ::: data and increase the rate of learning on the increment of ::: training size.


title: Human Body Model Acquisition and Tracking Using Voxel Data

abstract: We present an integrated system for automatic acquisition of the human body model and motion tracking using input from multiple synchronized video streams. The video frames are segmented and the 3D voxel reconstructions of the human body shape in each frame are computed from the foreground silhouettes. These reconstructions are then used as input to the model acquisition and tracking algorithms. ::: ::: The human body model consists of ellipsoids and cylinders and is described using the twists framework resulting in a non-redundant set of model parameters. Model acquisition starts with a simple body part localization procedure based on template fitting and growing, which uses prior knowledge of average body part shapes and dimensions. The initial model is then refined using a Bayesian network that imposes human body proportions onto the body part size estimates. The tracker is an extended Kalman filter that estimates model parameters based on the measurements made on the labeled voxel data. A voxel labeling procedure that handles large frame-to-frame displacements was designed resulting in very robust tracking performance. ::: ::: Extensive evaluation shows that the system performs very reliably on sequences that include different types of motion such as walking, sitting, dancing, running and jumping and people of very different body sizes, from a nine year old girl to a tall adult male.


title: EvilCoder: automated bug insertion

abstract: The art of finding software vulnerabilities has been covered extensively in the literature and there is a huge body of work on this topic. In contrast, the intentional insertion of exploitable, security-critical bugs has received little (public) attention yet. Wanting more bugs seems to be counterproductive at first sight, but the comprehensive evaluation of bug-finding techniques suffers from a lack of ground truth and the scarcity of bugs. In this paper, we propose EvilCoder, a system to automatically find potentially vulnerable source code locations and modify the source code to be actually vulnerable. More specifically, we leverage automated program analysis techniques to find sensitive sinks which match typical bug patterns (e.g., a sensitive API function with a preceding sanity check), and try to find data-flow connections to user-controlled sources. We then transform the source code such that exploitation becomes possible, for example by removing or modifying input sanitization or other types of security checks. Our tool is designed to randomly pick vulnerable locations and possible modifications, such that it can generate numerous different vulnerabilities on the same software corpus. We evaluated our tool on several open-source projects such as for example libpng and vsftpd, where we found between 22 and 158 unique connected source-sink pairs per project. This translates to hundreds of potentially vulnerable data-flow paths and hundreds of bugs we can insert. We hope to support future bug-finding techniques by supplying freshly generated, bug-ridden test corpora so that such techniques can (finally) be evaluated and compared in a comprehensive and statistically meaningful way.


title: Applying LTE-D2D to Support V2V Communication Using Local Geographic Knowledge

abstract: This paper applies device-to-device technique to vehicular-to-vehicular communications (D2D-V) in an underlay fashion. Different from conventional D2D systems focusing on smart phones, whose positions are usually assumed to be statical, the D2D-V system suffers more mobility and uncertainties. These dynamic features indeed complicate the design of D2D-V system, but also, give more potential to accomplish some geographic based algorithms. Armed with the geographic knowledge of highway and the dynamic GPS information in each vehicle, we propose a geographic based reuse cellular user selection scheme and two different distributed power control schemes to serve various applications in vehicular networks. We model the problem firstly, then formulate two metrics: the sum rate and the minimum-achievable rate to evaluate the performance of the proposed schemes. Simulations are proposed to validate our schemes and analysis. Influences of different system settings are also discussed here.


title: A survey of energy-efficient caching in information-centric networking

abstract: To better cope with the Internet usage shift from host-centric end-to-end communication to receiver-driven content retrieval, innovative information-centric networking (ICN) architectures have been proposed. A notable advantage of these novel networking architectures is to provide transparent and ubiquitous in-network caching to speed up content distribution and improve network resource utilization. With the explosive increase of global network traffic, the energy efficiency issue in ICN is a growing concern. In this article, we provide a brief survey of energy-efficient caching techniques in ICN from the placement, content placement, and requestto- cache routing perspectives. We also outline some challenges and future research directions about caching policies for green ICN.


title: Distributed Method of Multiplier for Coupled Lagrangian Problems: A Control Approach

abstract: In this paper, we propose a method for solving the distributed optimization problem in which the objective function is the sum of separable convex functions with linear constraints. In our approach, the primal variable is partially updated to make the Method of Multiplier algorithm distributed which is based on the suitable scaling of constraint matrix. The algorithm is then viewed as a dynamical system the convergence analysis of which is done using the passivity concepts of nonlinear control theory. The convexity of the function is related to the passivity of the non-linear functions which is in feedback with the positive real linear system.


title: Downlink Channel Estimation in Multiuser Massive MIMO With Hidden Markovian Sparsity

abstract: Recently, compressive sensing based massive multiple input multiple output (MIMO) channel estimation (CE) has attracted intensive research interest. By exploiting the structured sparsity of massive MIMO channels, it is possible to design structured sparse channel estimation (SSCE) algorithms to significantly reduce the pilot/feedback overheads in massive MIMO downlink CE. However, most existing SSCE algorithms are designed based on oversimplified channel models with restrictive assumptions, and thus perform poorly under realistic channels. In this paper, we propose a Hidden-Markov-Model (HMM) to capture the structured sparsity of multiuser massive MIMO channels. The HMM has the flexibility to model different propagation environments that occur in practice. We derive an efficient approximate message passing algorithm called Turbo-orthogonal approximate message passing (OAMP) to solve the resulting sparse CE problem with an HMM prior . The proposed Turbo-OAMP does not require knowledge of the HMM channel parameters, which is automatically learned based on the expectation maximization framework. As a result, the proposed Turbo-OAMP algorithm derived from the HMM works well for realistic channels with complicated propagation environments and unknown channel parameters. Extensive simulations verify that the proposed Turbo-OAMP can achieve significant gains over the existing SSCE algorithms under realistic channels.


title: An 85-W Multi-Octave Push–Pull GaN HEMT Power Amplifier for High-Efficiency Communication Applications at Microwave Frequencies

abstract: This paper proposes a design methodology for multi-octave planar push–pull power amplifiers (PPPAs) to tackle the challenges associated with the integration of planar balanced to unbalanced transformers (baluns) with packaged transistors characterized by significant parasitics. This methodology relies on carefully selecting the balun’s placement and adding external matching networks to separately control the odd- and even-mode impedances presented to the power amplifier (PA) to ensure high-efficiency operation over a broad range of frequencies. Proper second harmonic impedances are obtained by placing the balun in close proximity to the transistors’ terminals and repurposing the packaged transistors’ leads into a coupled-line with a high coupling coefficient. Furthermore, adequate odd-mode terminations are realized using broadband matching networks at the unbalanced side of the baluns. Following this methodology, an 85-W PPPA was designed using off-the-shelf packaged gallium–nitride high electron-mobility transistors. The fabricated PA demonstrated drain efficiency and output power above 45% and 46.5 dBm, respectively, over the frequency band spanning from 0.45 to 1.95 GHz. Furthermore, the fabricated PA was successfully linearized using digital predistortion when driven with single- and multi-band modulated signals. Overall, the fabricated PA had the best reported power, efficiency, bandwidth, and second harmonic rejection combination for a multi-octave PPPA designed with input and output baluns using packaged off-the-shelf transistors.


title: Interpretable Deep Models for ICU Outcome Prediction.

abstract: Exponential surge in health care data, such as longitudinal data from electronic health records (EHR), sensor data from intensive care unit (ICU), etc., is providing new opportunities to discover meaningful data-driven characteristics and patterns ofdiseases. Recently, deep learning models have been employedfor many computational phenotyping and healthcare prediction tasks to achieve state-of-the-art performance. However, deep models lack interpretability which is crucial for wide adoption in medical research and clinical decision-making. In this paper, we introduce a simple yet powerful knowledge-distillation approach called interpretable mimic learning, which uses gradient boosting trees to learn interpretable models and at the same time achieves strong prediction performance as deep learning models. Experiment results on Pediatric ICU dataset for acute lung injury (ALI) show that our proposed method not only outperforms state-of-the-art approaches for morality and ventilator free days prediction tasks but can also provide interpretable models to clinicians.


title: Tools for the body (schema

abstract: What happens in our brain when we use a tool to reach for a distant object? Recent neurophysiological, psychological and neuropsychological research suggests that this extended motor capability is followed by changes in specific neural networks that hold an updated map of body shape and posture (the putative ‘Body Schema’ of classical neurology). These changes are compatible with the notion of the inclusion of tools in the ‘Body Schema’, as if our own effector (e.g. the hand) were elongated to the tip of the tool. In this review we present empirical support for this intriguing idea from both single-neuron recordings in the monkey brain and behavioural performance of normal and brain-damaged humans. These relatively simple neural and behavioural aspects of tool-use shed light on more complex evolutionary and cognitive aspects of body representation and multisensory space coding for action. To act efficiently in space, our brain must not only localize any objects of interest in extrapersonal space but also hold a constantly updated status of the body shape and posture. The ‘Body Schema’ of classical neurology [1,2] originally


title: Deep Face Recognition: A Survey

abstract: Face recognition made tremendous leaps in the last five years with a myriad of systems proposing novel techniques substantially backed by deep convolutional neural networks (DCNN). Although face recognition performance sky-rocketed using deep-learning in classic datasets like LFW, leading to the belief that this technique reached human performance, it still remains an open problem in unconstrained environments as demonstrated by the newly released IJB datasets. This survey aims to summarize the main advances in deep face recognition and, more in general, in learning face representations for verification and identification. The survey provides a clear, structured presentation of the principal, state-of-the-art (SOTA) face recognition techniques appearing within the past five years in top computer vision venues. The survey is broken down into multiple parts that follow a standard face recognition pipeline: (a) how SOTA systems are trained and which public data sets have they used; (b) face preprocessing part (detection, alignment, etc.); (c) architecture and loss functions used for transfer learning (d) face recognition for verification and identification. The survey concludes with an overview of the SOTA results at a glance along with some open issues currently overlooked by the community.


title: PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation

abstract: Point cloud is an important type of geometric data structure. Due to its irregular format, most researchers transform such data to regular 3D voxel grids or collections of images. This, however, renders data unnecessarily voluminous and causes issues. In this paper, we design a novel type of neural network that directly consumes point clouds, which well respects the permutation invariance of points in the input. Our network, named PointNet, provides a unified architecture for applications ranging from object classification, part segmentation, to scene semantic parsing. Though simple, PointNet is highly efficient and effective. Empirically, it shows strong performance on par or even better than state of the art. Theoretically, we provide analysis towards understanding of what the network has learnt and why the network is robust with respect to input perturbation and corruption.


title: High speed implementation of authenticated encryption for the MSP430X microcontroller

abstract: Authenticated encryption is a symmetric cryptography scheme that provides both confidentiality and authentication. In this work we describe an optimized implementation of authenticated encryption for the MSP430X family of microcontrollers. The CCM, GCM, SGCM, OCB3, Hummingbird-2 and MASHA authenticated encryption schemes were implemented at the 128-bit level of security and their performance was compared. The AES accelerator included in some models of the MSP430X family is also studied and we explore its characteristics to improve the performance of the implemented modes, achieving up to 10 times of speedup. The CCM and OCB3 schemes were the fastest when using the AES accelerator while MASHA and Hummingbird-2 were the fastest when using only software.


title: Business models for Internet-based e-procurement systems and B2B electronic markets: an exploratory assessment

abstract: Information technology (IT) has long been applied to support the exchange of goods, services and information between organizations. It is with the advent of Internet-based e-procurement systems and business-to-business (B2B) electronic markets that the real opportunities for online transactions have opened up across space and over time. The authors draw on IS and economics theory to investigate the motivation for the various online business models, and the adoption requirements of purchasing firms, through the examination of a set of mini-cases. Our exploratory study finds that private aggregating and negotiating mechanisms are being adopted for large quantity business supply purchases, while public market mechanisms are more often adopted when firms face uncertain and high variance demand. Moreover, market facilitation, expertise sharing and collaboration are gradually attracting more attention, and call for future investigation.


title: Speed control of brushless DC motor with PI and fuzzy logic controller using resonantpole inverter

abstract: Brushless DC motors have a permanent-magnet rotor, and the stator windings are wound such that the back electromotive force (EMF) is trapezoidal. It therefore requires rectangular-shaped stator phase currents to produce constant torque. The Motors possess high torque/weight ratio, operate at very high speed, are very compact and are electronically controlled. The advantage of these motors is the removal of brushes, leading to eliminate many problems associated with brushes. BLDCM drives have been focused on the motor control strategies. Nevertheless, most of these converter topologies employ the hard-switching technique which causes high switching losses and severe electromagnetic interference. Recently, a number of soft-switching techniques, providing zero-voltage switching (ZVS) or zero-current switching (ZCS) condition, have been successfully developed. This paper proposed a fuzzy logic based soft-switching resonant pole inverter using transformer, which can generate dc link voltage notches during chopping which minimize the drawbacks of soft switching. Hence all switches work in zero voltage switching condition. This paper proposed a fuzzy logic based soft-switching resonant pole inverter using transformer, which can generate dc link voltage notches during chopping which minimize the drawbacks of soft switching. Hence all switches work in zero voltage switching condition. Hardware implementation of BLDC motor speed control has been achieved by PIC micro controller 16F877A


title: Are You with Me?” – using accelerometers to determine if two devices are carried by the same person

abstract: As the proliferation of pervasive and ubiquitous computing devices continues, users will carry more devices. Without the ability for these devices to unobtrusively interact with one another, the user’s attention will be spent on coordinating, rather than using, these devices. We present a method to determine if two devices are carried by the same person, by analyzing walking data recorded by low-cost MEMS accelerometers using the coherence function, a measure of linear correlation in the frequency domain. We also show that these low-cost sensors perform similarly to more expensive accelerometers for the frequency range of human motion, 0 to 10Hz. We also present results from a large test group illustrating the algorithm’s robustness and its ability to withstand real world time delays, crucial for wireless technologies like Bluetooth and 802.11. We present results that show that our technique is 100% accurate using a sliding window of 8 seconds of data when the devices are carried in the same location on the body, is tolerant to inter-device communication latencies, and requires little communication bandwidth. In addition we present results for when devices are carried on different parts of the body.


title: Detecting Image Splicing using Geometry Invariants and Camera Characteristics Consistency

abstract: Recent advances in computer technology have made digital image tampering more and more common. In this paper, we propose an authentic vs. spliced image classification method making use of geometry invariants in a semi-automatic manner. For a given image, we identify suspicious splicing areas, compute the geometry invariants from the pixels within each region, and then estimate the camera response function (CRF) from these geometry invariants. The cross-fitting errors are fed into a statistical classifier. Experiments show a very promising accuracy, 87%, over a large data set of 363 natural and spliced images. To the best of our knowledge, this is the first work detecting image splicing by verifying camera characteristic consistency from a single-channel image.


title: Encoding Sentences with Graph Convolutional Networks for Semantic Role Labeling

abstract: Semantic role labeling (SRL) is the task of identifying the predicate-argument structure of a sentence. It is typically regarded as an important step in the standard NLP pipeline. As the semantic representations are closely related to syntactic ones, we exploit syntactic information in our model. We propose a version of graph convolutional networks (GCNs), a recent class of neural networks operating on graphs, suited to model syntactic dependency graphs. GCNs over syntactic dependency trees are used as sentence encoders, producing latent feature representations of words in a sentence. We observe that GCN layers are complementary to LSTM ones: when we stack both GCN and LSTM layers, we obtain a substantial improvement over an already state-of-the-art LSTM SRL model, resulting in the best reported scores on the standard benchmark (CoNLL-2009) both for Chinese and English.


title: Characterizing Videos and Users in YouTube: A Survey

abstract: Web 2.0 has reshaped the way people interact with Web sites. People are now able to view content created by other users as well as publish their own content on Web 2.0 sites, instead of downloading content created by a small number of publishing professionals. Understanding the characteristics of these sites has become a subject of immense interest to the Internet service providers, content makers and on-line advertisers. This understanding is also important for the sustainable development of the content distribution systems themselves. As an approach to comprehend the characteristics of media content delivery systems in Web 2.0, a significant amount of research has been done in investigating the characteristics of YouTube. In this paper, a survey of findings of the characteristics of YouTube and related sites is presented from both video and user perspectives along with some open research issues. This kind of study is instrumental to understand the usage of YouTube and other similar user generated content (UGC) sites.


title: Novel Multi-way Broadband Differential Phase Shifter With Uniform Reference Line Using Coupled Line Structure

abstract: Conventional passive differential phase shifters (DPSs) usually require different reference lines to realize varied differential phase shifts, this results in both complex structure and bulky size for multi-way poly-phase applications. To resolve the problem, a DPS configuration with uniform reference line is proposed. It is constructed using basic coupled lines with simplicity in both design and fabrication procedures. The delay lines of the phase shifter have a similar structure and maintain desired constant phase shift compared with the uniform reference line over a wide bandwidth. To verify the proposed configuration, a four-way DPS which is comprised of 3 delay lines and a reference line, is designed, fabricated and measured. The delay lines provide the corresponding constant phase shifts (45 $^{\circ}$ , 90 $^{\circ}$ , 135 $^{\circ}$ compared to the reference line. Over a bandwidth of 45%, excellent performance is achieved with small insertion loss and phase deviation.


The prize collecting Steiner tree problem: theory and practice


title: A Communication-Optimal Framework for Contracting Distributed Tensors

abstract: Tensor contractions are extremely compute intensive generalized matrix multiplication operations encountered in many computational science fields, such as quantum chemistry and nuclear physics. Unlike distributed matrix multiplication, which has been extensively studied, limited work has been done in understanding distributed tensor contractions. In this paper, we characterize distributed tensor contraction algorithms on torus networks. We develop a framework with three fundamental communication operators to generate communication-efficient contraction algorithms for arbitrary tensor contractions. We show that for a given amount of memory per processor, the framework is communication optimal for all tensor contractions. We demonstrate performance and scalability of the framework on up to 262,144 cores on a Blue Gene/Q supercomputer.


title: Eyewitness testimony.

abstract: The criminal justice system relies heavily on eyewitness identification for investigating and prosecuting crimes. Psychology has built the only scientific literature on eyewitness identification and has warned the justice system of problems with eyewitness identification evidence. Recent DNA exoneration cases have corroborated the warnings of eyewitness identification researchers by showing that mistaken eyewitness identification was the largest single factor contributing to the conviction of these innocent people. We review major developments in the experimental literature concerning the way that various factors relate to the accuracy of eyewitness identification. These factors include characteristics of the witness, characteristics of the witnessed event, characteristics of testimony, lineup content, lineup instructions, and methods of testing. Problems with the literature are noted with respect to both the relative paucity of theory and the scarcity of base-rate information from actual cases.


title: Bayesian Nonparametric Sparse Seemingly Unrelated Regression Model (SUR)

abstract: Seemingly unrelated regression (SUR) models are used in studying the interactions among economic variables of interest. In a high dimensional setting and when applied to large panel of time series, these models have a large number of parameters to be estimated and suffer from inferential problems. We propose a Bayesian nonparametric hierarchical model for multivariate time series in order to avoid the overparametrization and overfitting issues and to allow for shrinkage toward multiple prior means with unknown location, scale and shape parameters. We propose a two-stage hierarchical prior distribution. The first stage of the hierarchy consists in a lasso conditionally independent prior distribution of the Normal-Gamma family for the SUR coefficients. The second stage is given by a random mixture distribution for the Normal-Gamma hyperparameters, which allows for parameter parsimony through two components. The first one is a random Dirac point-mass distribution, which induces sparsity in the SUR coefficients; the second is a Dirichlet process prior, which allows for clustering of the SUR coefficients. We provide a Gibbs sampler for posterior approximations based on introduction of auxiliary variables. Some simulated examples show the efficiency of the proposed methods. We study the effectiveness of our model and inference approach with an application to macroeconomics.


title: A system for high-volume acquisition and matching of fresco fragments: reassembling Theran wall paintings

abstract: Although mature technologies exist for acquiring images, geometry, and normals of small objects, they remain cumbersome and time-consuming for non-experts to employ on a large scale. In an archaeological setting, a practical acquisition system for routine use on every artifact and fragment would open new possibilities for archiving, analysis, and dissemination. We present an inexpensive system for acquiring all three types of information, and associated metadata, for small objects such as fragments of wall paintings. The acquisition system requires minimal supervision, so that a single, non-expert user can scan at least 10 fragments per hour. To achieve this performance, we introduce new algorithms to robustly and automatically align range scans, register 2-D scans to 3-D geometry, and compute normals from 2-D scans. As an illustrative application, we present a novel 3-D matching algorithm that efficiently searches for matching fragments using the scanned geometry.


title: An in-depth study of the promises and perils of mining GitHub

abstract: With over 10 million git repositories, GitHub is becoming one of the most important sources of software artifacts on the Internet. Researchers mine the information stored in GitHub's event logs to understand how its users employ the site to collaborate on software, but so far there have been no studies describing the quality and properties of the available GitHub data. We document the results of an empirical study aimed at understanding the characteristics of the repositories and users in GitHub; we see how users take advantage of GitHub's main features and how their activity is tracked on GitHub and related datasets to point out misalignment between the real and mined data. Our results indicate that while GitHub is a rich source of data on software development, mining GitHub for research purposes should take various potential perils into consideration. For example, we show that the majority of the projects are personal and inactive, and that almost 40 % of all pull requests do not appear as merged even though they were. Also, approximately half of GitHub's registered users do not have public activity, while the activity of GitHub users in repositories is not always easy to pinpoint. We use our identified perils to see if they can pose validity threats; we review selected papers from the MSR 2014 Mining Challenge and see if there are potential impacts to consider. We provide a set of recommendations for software engineering researchers on how to approach the data in GitHub.


title: Additive Manufacturing of Ka-Band Dual-Polarization Waveguide Components

abstract: Additive manufacturing (AM) is emerging as a key technology for the minimization and integration of microwave antenna systems. Among the several AM processes, selective laser melting (SLM) is rather convenient for waveguide components, since it allows for all-metal parts with a mechanical accuracy within 30–60 $\mu \text{m}$ and an equivalent surface electrical resistivity in the range of 10–20 $\mu \Omega \text {cm}$ . This paper reports on the assessment of the SLM applicability to the manufacturing of dual-polarization waveguide components operating in Ka-band, namely, a septum polarizer, a smooth-wall feed horn integrated with the septum polarizer, and an orthomode transducer (OMT). In order to achieve high electromagnetic performance in the Ka-band, the architectures have been optimized for the manufacturing process, above all in terms of orientation on the building platform and minimization of supporting structures. As a consequence, the corresponding prototypes exhibit measured performances in significant agreement with the predicted values. As an example, the return loss of all the components has been measured to be as high as 25 dB, while the measured isolation between the rectangular waveguide ports of the OMT is better than 47 dB.


title: Deep Feature Fusion Network for Answer Quality Prediction in Community Question Answering

abstract: Community Question Answering (cQA) forums have become a popular medium for soliciting direct answers to specific questions of users from experts or other experienced users on a given topic. However, for a given question, users sometimes have to sift through a large number of low-quality or irrelevant answers to find out the answer which satisfies their information need. To alleviate this, the problem of Answer Quality Prediction (AQP) aims to predict the quality of an answer posted in response to a forum question. Current AQP systems either learn models using - a) various hand-crafted features (HCF) or b) use deep learning (DL) techniques which automatically learn the required feature representations. ::: In this paper, we propose a novel approach for AQP known as - "Deep Feature Fusion Network (DFFN)" which leverages the advantages of both hand-crafted features and deep learning based systems. Given a question-answer pair along with its metadata, DFFN independently - a) learns deep features using a Convolutional Neural Network (CNN) and b) computes hand-crafted features using various external resources and then combines them using a deep neural network trained to predict the final answer quality. DFFN achieves state-of-the-art performance on the standard SemEval-2015 and SemEval-2016 benchmark datasets and outperforms baseline approaches which individually employ either HCF or DL based techniques alone.


title: The problem of superintelligence: political, not technological

abstract: The thinkers who have reflected on the problem of a coming superintelligence have generally seen the issue as a technological problem, a problem of how to control what the superintelligence will do. I argue that this approach is probably mistaken because it is based on questionable assumptions about the behavior of intelligent agents and, moreover, potentially counterproductive because it might, in the end, bring about the existential catastrophe that it is meant to prevent. I contend that the problem posed by a future superintelligence will likely be a political problem, that is, one of establishing a peaceful form of coexistence with other intelligent agents in a situation of mutual vulnerability, and not a technological problem of control.


title: Religious coping and psychological adjustment to stress: A meta‐analysis

abstract: A growing body of literature suggests that people often turn to religion when coping with stressful events. However, studies on the efficacy of religious coping for people dealing with stressful situations have yielded mixed results. No published studies to date have attempted to quantitatively synthesize the research on religious coping and psychological adjustment to stress. The purpose of the current study was to synthesize the research on situation-specific religious coping methods and quantitatively determine their efficacy for people dealing with stressful situations. A meta-analysis of 49 relevant studies with a total of 105 effect sizes was conducted in order to quantitatively examine the relationship between religious coping and psychological adjustment to stress. Four types of relationships were investigated: positive religious coping with positive psychological adjustment, positive religious coping with negative psychological adjustment, negative religious coping with positive psychological adjustment, and negative religious coping with negative psychological adjustment. The results of the study generally supported the hypotheses that positive and negative forms of religious coping are related to positive and negative psychological adjustment to stress, respectively. Implications of the findings and their limitations are discussed. © 2004 Wiley Periodicals, Inc. J Clin Psychol.


title: AN EFFICIENT METHOD FOR VESSEL WIDTH MEASUREMENT ON COLOR RETINAL IMAGES

abstract: Vessel diameter is an important factor for indicating retinal microvascular signs. In automated retinal image analysis, the measurement of vascular width is a complicated process as most of the vessels are few pixels wide. In this paper, we propose a new technique to measure the retinal blood vessel diameter which can be used to detect arteriolar narrowing, arteriovenous (AV) nicking, branching coefficients, etc. to diagnose related diseases. First, we apply the Adaptive Region Growing (ARG) segmentation technique to obtain the edges of the blood vessels. Following that we apply the unsupervised texture classification method to segment the blood vessels from where we obtain the vessel centreline. Then we utilize the edge image and vessel centreline image to obtain the potential pixels pairs which pass through a centreline pixel. We apply a rotational invariant mask to search the pixel pairs from the edge image. From those pixels we calculate the shortest distance pair which will be the vessel width for that cross-section. We evaluate our technique with manually measured width for different vessels’ cross-sectional area and achieve an average accuracy of 95.8%.


title: Simulation of multi-agent manufacturing systems using Agent-Based Modelling platforms

abstract: Multi-agent systems (MAS) are driving the way to design and engineer control solutions that exhibit flexibility, adaptation and reconfigurability, which are important advantages over traditional centralized systems. The understanding, design and testing of such distributed agent-based approaches, and particularly those exhibiting self-∗properties, are usually a hard task. Simulation assumes a crucial role to analyse the behaviour of MAS solutions during the design phase and before its deployment into the real operation. Particularly, Agent-Based Modelling (ABM) tools are well suited to simulate MAS systems that exhibit complex phenomena, like emergent behaviour and self-organization. This paper discusses the simulation of agent-based manufacturing systems and introduces the advantages of using ABM tools. The NetLogo platform is used to illustrate the benefits of such tools in the manufacturing world on the specification of a MAS system for a washing machine production line.


title: Zorua: Enhancing Programming Ease, Portability, and Performance in GPUs by Decoupling Programming Models from Resource Management

abstract: The application resource specification--a static specification of several parameters such as the number of threads and the scratchpad memory usage per thread block--forms a critical component of the existing GPU programming models. This specification determines the performance of the application during execution because the corresponding on-chip hardware resources are allocated and managed purely based on this specification. This tight coupling between the software-provided resource specification and resource management in hardware leads to significant challenges in programming ease, portability, and performance, as we demonstrate in this work. ::: Our goal in this work is to reduce the dependence of performance on the software-provided resource specification to simultaneously alleviate the above challenges. To this end, we introduce Zorua, a new resource virtualization framework, that decouples the programmer-specified resource usage of a GPU application from the actual allocation in the on-chip hardware resources. Zorua enables this decoupling by virtualizing each resource transparently to the programmer. ::: We demonstrate that by providing the illusion of more resources than physically available, Zorua offers several important benefits: (i) Programming Ease: Zorua eases the burden on the programmer to provide code that is tuned to efficiently utilize the physically available on-chip resources. (ii) Portability: Zorua alleviates the necessity of re-tuning an application's resource usage when porting the application across GPU generations. (iii) Performance: By dynamically allocating resources and carefully oversubscribing them when necessary, Zorua improves or retains the performance of applications that are already highly tuned to best utilize the resources. The holistic virtualization provided by Zorua has many other potential uses which we describe in this paper.


title: BabelView: Evaluating the Impact of Code Injection Attacks in Mobile Webviews

abstract: A Webview embeds a fully-fledged browser in a mobile application and allows that application to expose a custom interface to JavaScript code. This is a popular technique to build so-called hybrid applications, but it circumvents the usual security model of the browser: any malicious JavaScript code injected into the Webview gains access to the custom interface and can use it to manipulate the device or exfiltrate sensitive data. In this paper, we present an approach to systematically evaluate the possible impact of code injection attacks against Webviews using static information flow analysis. Our key idea is that we can make reasoning about JavaScript semantics unnecessary by instrumenting the application with a model of possible attacker behavior—the BabelView. We evaluate our approach on 25,000 apps from various Android marketplaces, finding 10,808 potential vulnerabilities in 4,997 apps. Taken together, the apps reported as problematic have over 3 billion installations worldwide. We manually validate a random sample of 50 apps and estimate that our fully automated analysis achieves a precision of 81% at a recall of 89%.


title: Leveraging mid-level deep representations for predicting face attributes in the wild

abstract: Predicting facial attributes from faces in the wild is very challenging due to pose and lighting variations in the real world. The key to this problem is to build proper feature representations to cope with these unfavourable conditions. Given the success of Convolutional Neural Network (CNN) in image classification, the high-level CNN feature, as an intuitive and reasonable choice, has been widely utilized for this problem. In this paper, however, we consider the mid-level CNN features as an alternative to the high-level ones for attribute prediction. This is based on the observation that face attributes are different: some of them are locally oriented while others are globally defined. Our investigations reveal that the mid-level deep representations outperform the prediction accuracy achieved by the (fine-tuned) high-level abstractions. We empirically demonstrate that the midlevel representations achieve state-of-the-art prediction performance on CelebA and LFWA datasets. Our investigations also show that by utilizing the mid-level representations one can employ a single deep network to achieve both face recognition and attribute prediction.


title: An adaptive and fully automatic method for estimating the 3D position of bendable instruments using endoscopic images

abstract: Background. Flexible bendable instruments are key tools for performing surgical endoscopy. Being able to measure the 3D position of such instruments can be useful for various tasks, such as controlling automatically robotized instruments and analyzing motions. Methods. We propose an automatic method to infer the 3D pose of a single bending section instrument, using only the images provided by a monocular camera embedded at the tip of the endoscope. The proposed method relies on colored markers attached onto the bending section. The image of the instrument is segmented using a graph-based method and the corners of the markers are extracted by detecting the color transition along B{\'e}zier curves fitted on edge points. These features are accurately located and then used to estimate the 3D pose of the instrument using an adaptive model that allows to take into account the mechanical play between the instrument and its housing channel. Results. The feature extraction method provides good localization of markers corners with images of in vivo environment despite sensor saturation due to strong lighting. The RMS error on the estimation of the tip position of the instrument for laboratory experiments was 2.1, 1.96, 3.18 mm in the x, y and z directions respectively. Qualitative analysis in the case of in vivo images shows the ability to correctly estimate the 3D position of the instrument tip during real motions. Conclusions. The proposed method provides an automatic and accurate estimation of the 3D position of the tip of a bendable instrument in realistic conditions, where standard approaches fail.


title: Exploiting distributed version concurrency in a transactional memory cluster

abstract: We investigate a transactional memory runtime system providing scaling and strong consistency, i.e., 1-copy serializability on commodity clusters for both distributed scientific applications and database applications. We introduce a novel page-level distributed concurrency control algorithm, called Distributed Multiversioning (DMV). DMV automatically detects and resolves conflicts caused by data races for distributed transactions accessing shared in-memory data structures. DMV's key novelty is in exploiting the distributed data versions that naturally come about in a replicated cluster in order to avoid read-write conflicts, hence provide scaling. DMV runs conflicting read-only and update transactions in parallel on different replicas instead of using different physical data copies within a single node as in classic multiversioning. In its most general form, DMV can be used to implement a software transactional memory system on a cluster for scaling C++ applications. DMV supports highly multithreaded database applications as well by centralizing updates on a master replica and creating the required page versions for read-only transactions lazily, on a set of slave replicas. We also show that a version-aware scheduling technique can distribute the read-only transactions across the slaves in such a way to minimize version conflicts.In our evaluation, we use DMV as a lightweight approach to scaling a hash table microbenchmark workload and the industry-standard e-commerce workload of the TPC-W benchmark on a commodity cluster. Our measurements show scaling for both benchmarks. In particular, we show near-linear scaling up to 8 transactional nodes for the most common e-commerce workload, the TPC-W shopping mix. We further show that our scaling for the TPC-W e-commerce benchmark compares favorably with that of an existing coarse-grained asynchronous replication technique.


title: Detecting trails in lidar point cloud data

abstract: The goal of this work is to determine methods for detecting trails using statistics of LiDAR point cloud data, ::: while avoiding reliance on a Digital Elevation Model (DEM). Creation of a DEM is a subjective process that ::: requires assumptions be made about the density of the data points, the curvature of the ground, and other factors ::: which can lead to very dierent results in the nal DEM product, with no single correct" result. Exploitation of ::: point cloud data also lends itself well to automation. A LiDAR point cloud based trail detection scheme has been ::: designed in which statistical measures of local neighborhoods of LiDAR points are calculated, image processing ::: techniques employed to mask non-trail areas, and a constrained region growing scheme used to determine a nal ::: trails map. Results of the LiDAR point cloud based trail detection scheme are presented and compared to a ::: DEM-based trail detection scheme. Large trails are detected fairly reliably with some missing gaps, while smaller ::: trails are detected less reliably. Overall results of the LiDAR point cloud based methods are comparable to the ::: DEM-based results, with fewer false alarms.


title: CoGENT: Verifying High-Assurance File System Implementations

abstract: We present an approach to writing and formally verifying high-assurance file-system code in a restricted language called Cogent, supported by a certifying compiler that produces C code, high-level specification of Cogent, and translation correctness proofs. The language is strongly typed and guarantees absence of a number of common file system implementation errors. We show how verification effort is drastically reduced for proving higher-level properties of the file system implementation by reasoning about the generated formal specification rather than its low-level C code. We use the framework to write two Linux file systems, and compare their performance with their native C implementations.


title: Attention-Based Models for Speech Recognition

abstract: Recurrent sequence generators conditioned on input data through an attention mechanism have recently shown very good performance on a range of tasks including machine translation, handwriting synthesis [1,2] and image caption generation [3]. We extend the attention-mechanism with features needed for speech recognition. We show that while an adaptation of the model used for machine translation in [2] reaches a competitive 18.7% phoneme error rate (PER) on the TIMET phoneme recognition task, it can only be applied to utterances which are roughly as long as the ones it was trained on. We offer a qualitative explanation of this failure and propose a novel and generic method of adding location-awareness to the attention mechanism to alleviate this issue. The new method yields a model that is robust to long inputs and achieves 18% PER in single utterances and 20% in 10-times longer (repeated) utterances. Finally, we propose a change to the attention mechanism that prevents it from concentrating too much on single frames, which further reduces PER to 17.6% level.


title: The Internet of Things Promoting Higher Education Revolution

abstract: Based on "information complex holographic person" hypothesis, any systems involved to person can be considered a giant complexity intelligent system. The Internet of Things (IoT) can bring Interconnection, Human to Human (H2H), Human to Things (H2T), Things to Things(T2T). So higher education and the Internet of Things should be giant complexity intelligent systems. In essence, the Internet of Things is an part of Internet, IoT is an intelligent network tools ,which can make a entitybeing virtual digital, to communicate the real world and digital world, to form their own adaptive network, to achieve social harmony and happiness in life. IoT are promoting changes in higher education, such as;education and teaching changes, learning changes,management changes, experiment and training changes, school change, and so on.Now IoT are causing higher education revolution.


title: A Tunable RF Front-End With Narrowband Antennas for Mobile Devices

abstract: In conventional full-duplex radio communication systems, the transmitter (Tx) is active at the same time as the receiver (Rx). The isolation between the Tx and the Rx is ensured by duplex filters. However, an increasing number of long-term evolution (LTE) bands crave multiband operation. Therefore, a new front-end architecture, addressing the increasing number of LTE bands, as well as multiple standards, is presented. In such an architecture, the Tx and Rx chains are separated throughout the front-end. Addition of bands is solved by making the antennas and filters tunable. Banks of duplex filters are replaced by tunable filters and antennas, providing a duplexer function over the air between the Tx and the Rx. A hardware system has been designed and fabricated to demonstrate the performance of this front-end architecture. Measurements demonstrate how the architecture addresses inter-modulation and Rx desensitization due to the Tx signal. The filters and antennas demonstrate tunability across multiple bands. System validation is detailed for LTE band I. Frequency response, as well as linearity measurements of the complete Tx and Rx front-end chains, show that the system requirements are fulfilled.


title: A Puzzle-Based Defense Strategy Against Flooding Attacks Using Game Theory

abstract: In recent years, a number of puzzle-based defense mechanisms have been proposed against flooding denial-of-service (DoS) attacks in networks. Nonetheless, these mechanisms have not been designed through formal approaches and thereby some important design issues such as effectiveness and optimality have remained unresolved. This paper utilizes game theory to propose a series of optimal puzzle-based strategies for handling increasingly sophisticated flooding attack scenarios. In doing so, the solution concept of Nash equilibrium is used in a prescriptive way, where the defender takes his part in the solution as an optimum defense against rational attackers. This study culminates in a strategy for handling distributed attacks from an unknown number of sources.


title: Planar High-efficiency Antenna Array Using New Printed Ridge Gap Waveguide Technology

abstract: High-efficiency millimeter wave antenna array is proposed. A cooperate feeding network is designed using the new printed ridge gap waveguide technology, which is self-packaged with low loss. The feeding network is designed to couple to narrow slots arranged in a planar array that is coupled to printed broadband magnetoelectric (ME) dipoles. An array of $4 \times 4$ ME dipoles is designed. The simulated results are verified with measurements. The measured reflection coefficient is less than −10 dB over 16.5% bandwidth (28.8–34 GHz). The measured antenna gain is higher than 19 dBi, and the cross polarization measured gain is around −35 dB within the main beam. Furthermore, the measured total radiation efficiency is 70%, and the measured aperture efficiency corresponds to 90% at 30.2 GHz.


title: The 8 requirements of real-time stream processing

abstract: Applications that require real-time processing of high-volume data steams are pushing the limits of traditional data processing infrastructures. These stream-based applications include market feed processing and electronic trading on Wall Street, network and infrastructure monitoring, fraud detection, and command and control in military environments. Furthermore, as the "sea change" caused by cheap micro-sensor technology takes hold, we expect to see everything of material significance on the planet get "sensor-tagged" and report its state or location in real time. This sensorization of the real world will lead to a "green field" of novel monitoring and control applications with high-volume and low-latency processing requirements.Recently, several technologies have emerged---including off-the-shelf stream processing engines---specifically to address the challenges of processing high-volume, real-time data without requiring the use of custom code. At the same time, some existing software technologies, such as main memory DBMSs and rule engines, are also being "repurposed" by marketing departments to address these applications.In this paper, we outline eight requirements that a system software should meet to excel at a variety of real-time stream processing applications. Our goal is to provide high-level guidance to information technologists so that they will know what to look for when evaluation alternative stream processing solutions. As such, this paper serves a purpose comparable to the requirements papers in relational DBMSs and on-line analytical processing. We also briefly review alternative system software technologies in the context of our requirements.The paper attempts to be vendor neutral, so no specific commercial products are mentioned.


title: Optimized Operation of Current-Fed Dual Active Bridge DC–DC Converter for PV Applications

abstract: The current-fed dual active bridge (CF-DAB) dc–dc converter gains growing applications in photovoltaic (PV) and energy storage systems due to its advantages, e.g., a wide input voltage range, a high step-up ratio, a low input current ripple, and a multiport interface capability. In addition, the direct input current controllability and extra control freedom of the CF-DAB converter make it possible to buffer the double-line-frequency energy in grid-interactive PV systems without using electrolytic capacitors in the dc link. Therefore, a PV system achieves high reliability and highly efficient maximum power point tracking. This paper studies the optimized operation of a CF-DAB converter for a PV application in order to improve the system efficiency. The operating principle and soft-switching conditions over the wide operating range are thoroughly analyzed with phase-shift control and duty-cycle control, and an optimized operating mode is proposed to achieve the minimum root-mean-square transformer current. The proposed operating mode can extend the soft-switching region and reduce the power loss, particularly under a heavy load and a high input voltage. Moreover, the efficiency can be further improved with a higher dc-link voltage. A 5-kW hardware prototype was built in the laboratory, and experimental results are provided for verification. This paper provides a design guideline for the CF-DAB converter applied to PV systems, as well as other applications with a wide input voltage variation.


title: Support vector machine learning for image retrieval

abstract: A novel method of relevance feedback is presented based on support vector machine learning in the content-based image retrieval system. A SVM classifier can be learned from training data of relevance images and irrelevance images marked by users. Using the classifier, the system can retrieve more images relevant to the query in the database efficiently. Experiments were carried out on a large-size database of 9918 images. It shows that the interactive learning and retrieval process can find correct images increasingly. It also shows the generalization ability of SVM under the condition of limited training samples.


title: Visual analysis of self-organizing maps

abstract: In the article, an additional visualization of self-organizing maps (SOM) has been investigated. The main objective of self-organizing maps is data clustering and their graphical presentation. Opportunities of SOM visualization in four systems (NeNet, SOM-Toolbox, Data- bionic ESOM and Viscovery SOMine) have been investigated. Each system has its additional tools for visualizing SOM. A comparative analysis has been made for two data sets: Fisher's iris data set and the economic indices of the European Union countries. A new SOM system is also introduced and researched. The system has a specific visualization tool. It is missing in other SOM systems. It helps to see the proportion of neurons, corresponding to the data items, belonging to the different classes, and fallen in the same SOM cell.


title: Fragmented Romanian Sociology: Growth and Structure of the Collaboration Network

abstract: Structural patterns in collaboration networks are essential for understanding how new ideas, research practices, innovation or cooperation circulate and develop within academic communities and between and within university departments. In our research, we explore and investigate the structure of the collaboration network formed by the academics working full-time within all the 17 sociology departments across Romania. We show that the collaboration network is sparse and fragmented, and that it constitutes an environment that does not promote the circulation of new ideas and innovation within the field. Although recent years have witnessed an increase in the productivity of Romanian sociologists, there is still ample room for improvement in terms of the interaction infrastructure that ought to link individuals together so that they could maximize their potentials. We also fail to discern evidence in favor of the Matthew effect governing the growth of the network, which suggests scientific success and productivity are not rewarded. Instead, the structural properties of the collaboration network are partly those of a core-periphery network, where the spread of innovation and change can be explained by structural equivalence rather than by interpersonal influence models. We also provide support for the idea that, within the observed network, collaboration is the product of homophily rather than prestige effects. Further research on the subject based on data from other countries in the region is needed to place our results in a comparative framework, in particular to discern whether the behavior of the Romanian sociologist community is unique or rather common.


title: Building an Ontology-Based Framework for Tourism Recommendation Services

abstract: The tourism product has an intangible nature in that customers cannot physically evaluate the services on offer until practically experienced. This makes having access to credible and authentic information about tourism products before the actual experience very valuable. An Ontology being a formal, explicit specification of concepts of a domain provides a viable platform for the development of credible knowledge-based tourism information services. In this paper, we present an approach aimed at enabling assorted intelligent recommendations services in tourism support systems using ontologies. A suite of tourism ontologies was developed and engaged to enable a prototypical e-tourism system with various knowledge-based recommendation capabilities. A usability evaluation of the system yields encouraging results as a demonstration of the viability of our approach.


title: An Orchestrated Empirical Study on Deep Learning Frameworks and Platforms

abstract: Deep learning (DL) has recently achieved tremendous success in a variety of cutting-edge applications, e.g., image recognition, speech and natural language processing, and autonomous driving. Besides the available big data and hardware evolution, DL frameworks and platforms play a key role to catalyze the research, development, and deployment of DL intelligent solutions. However, the difference in computation paradigm, architecture design and implementation of existing DL frameworks and platforms brings challenges for DL software development, deployment, maintenance, and migration. Up to the present, it still lacks a comprehensive study on how current diverse DL frameworks and platforms influence the DL software development process. In this paper, we initiate the first step towards the investigation on how existing state-of-the-art DL frameworks (i.e., TensorFlow, Theano, and Torch) and platforms (i.e., server/desktop, web, and mobile) support the DL software development activities. We perform an in-depth and comparative evaluation on metrics such as learning accuracy, DL model size, robustness, and performance, on state-of-the-art DL frameworks across platforms using two popular datasets MNIST and CIFAR-10. Our study reveals that existing DL frameworks still suffer from compatibility issues, which becomes even more severe when it comes to different platforms. We pinpoint the current challenges and opportunities towards developing high quality and compatible DL systems. To ignite further investigation along this direction to address urgent industrial demands of intelligent solutions, we make all of our assembled feasible toolchain and dataset publicly available.


title: Combining Similarity Features and Deep Representation Learning for Stance Detection in the Context of Checking Fake News

abstract: Fake news is nowadays an issue of pressing concern, given its recent rise as a potential threat to high-quality journalism and well-informed public discourse. The Fake News Challenge (FNC-1) was organized in early 2017 to encourage the development of machine-learning-based classification systems for stance detection (i.e., for identifying whether a particular news article agrees, disagrees, discusses, or is unrelated to a particular news headline), thus helping in the detection and analysis of possible instances of fake news. This article presents a novel approach to tackle this stance detection problem, based on the combination of string similarity features with a deep neural network architecture that leverages ideas previously advanced in the context of learning-efficient text representations, document classification, and natural language inference. Specifically, we use bi-directional Recurrent Neural Networks (RNNs), together with max-pooling over the temporal/sequential dimension and neural attention, for representing (i) the headline, (ii) the first two sentences of the news article, and (iii) the entire news article. These representations are then combined/compared, complemented with similarity features inspired on other FNC-1 approaches, and passed to a final layer that predicts the stance of the article toward the headline. We also explore the use of external sources of information, specifically large datasets of sentence pairs originally proposed for training and evaluating natural language inference methods to pre-train specific components of the neural network architecture (e.g., the RNNs used for encoding sentences). The obtained results attest to the effectiveness of the proposed ideas and show that our model, particularly when considering pre-training and the combination of neural representations together with similarity features, slightly outperforms the previous state of the art.


title: A Survey on Natural Language Processing for Fake News Detection

abstract: Fake news detection is a critical yet challenging problem in Natural Language Processing (NLP). The rapid rise of social networking platforms has not only yielded a vast increase in information accessibility but has also accelerated the spread of fake news. Thus, the effect of fake news has been growing, sometimes extending to the offline world and threatening public safety. Given the massive amount of Web content, automatic fake news detection is a practical NLP problem useful to all online content providers, in order to reduce the human time and effort to detect and prevent the spread of fake news. In this paper, we describe the challenges involved in fake news detection and also describe related tasks. We systematically review and compare the task formulations, datasets and NLP solutions that have been developed for this task, and also discuss the potentials and limitations of them. Based on our insights, we outline promising research directions, including more fine-grained, detailed, fair, and practical detection models. We also highlight the difference between fake news detection and other related tasks, and the importance of NLP solutions for fake news detection.


title: Internet of Things (IoT): A vision, architectural elements, and security issues

abstract: The Internet of Things is an emerging technology across the world, which helps to connect sensors, vehicles, hospitals, industries and consumers through internet connectivity. This type of architecture leads to Smart Cities, Smart home, Smart agriculture and Smart World. Architecture of IoT is very complex because of the large number of devices, link layer technology and services that are involved in this system. However, security in IoT is the most important parameter. In this paper, we give an overview of the architecture of IoT with the help of Smart World. In the second phase of this paper, we discuss the security challenges in IoT followed by the security measures in IoT. Finally, these challenges, which are discussed in the paper, could be research direction for future work in security for IoT.


title: Precocious Puberty—Perspectives on Diagnosis and Management

abstract: The term 'precocious puberty' signifies the onset of secondary sexual characters before the age of 9 y in boys and 8 y in girls. Menarche before 9.5 y is also considered precocious. These definitions are constantly evolving due to the secular trends observed all over the world. It is crucial to decide whether the child has central (gonadotropin-dependent, GDPP) or peripheral (gonadotropin-independent, GIPP) form of precocious puberty. Some benign conditions such as premature thelarche and premature pubarche may mimic precocious puberty. A systematic approach with detailed history and clinical examination helps to arrive at a diagnosis in most cases. An underlying neurologic disorder is more likely in a very young boy. Basal LH level is the best screening test to diagnose GDPP. LH level less than 0.1 IU/L by a very sensitive assay indicates prepubertal stage. Stimulation tests using gonadotropin releasing hormone (GnRH) or its analog (GnRHa), leuprolide help to confirm the diagnosis of GDPP. High resolution MRI of brain helps to detect abnormalities in hypothalamus and pituitary region. GnRH analogs (GnRHa) are the only effective treatment for GDPP at present. In girls, breast size may regress; menses ceases and vaginal mucosa becomes non-estrogenized. In boys testicular volumes remain static or decrease and genital growth regresses. The effects of GnRH analogs are reversible on discontinuation of therapy, with restoration of normal function within 3 mo after stopping treatment. Treatment of GIPP however is far from satisfactory.


The stage-quarrel between Ben Jonson and the so-called poetasters


title: Wearable Health Devices—Vital Sign Monitoring, Systems and Technologies

abstract: Wearable Health Devices (WHDs) are increasingly helping people to better monitor their health status both at an activity/fitness level for self-health tracking and at a medical level providing more data to clinicians with a potential for earlier diagnostic and guidance of treatment. The technology revolution in the miniaturization of electronic devices is enabling to design more reliable and adaptable wearables, contributing for a world-wide change in the health monitoring approach. In this paper we review important aspects in the WHDs area, listing the state-of-the-art of wearable vital signs sensing technologies plus their system architectures and specifications. A focus on vital signs acquired by WHDs is made: first a discussion about the most important vital signs for health assessment using WHDs is presented and then for each vital sign a description is made concerning its origin and effect on heath, monitoring needs, acquisition methods and WHDs and recent scientific developments on the area (electrocardiogram, heart rate, blood pressure, respiration rate, blood oxygen saturation, blood glucose, skin perspiration, capnography, body temperature, motion evaluation, cardiac implantable devices and ambient parameters). A general WHDs system architecture is presented based on the state-of-the-art. After a global review of WHDs, we zoom in into cardiovascular WHDs, analysing commercial devices and their applicability versus quality, extending this subject to smart t-shirts for medical purposes. Furthermore we present a resumed evolution of these devices based on the prototypes developed along the years. Finally we discuss likely market trends and future challenges for the emerging WHDs area.


title: Understanding of abstract nouns in linguistic disciplines

abstract: Despite centuries of studying abstract nouns, linguists have so far failed to come to unanimous understanding of this category but agree on the ambiguity of the term and the concept itself. The main issue every researcher faces is the lack of clearly defined term ‘abstract’. Moreover, there have been suggested numerous and diverse approaches to distinguishing between abstract and concrete nouns ranging from selected formal grammar criteria to semantic and extra linguistic parameters. This paper aims at considering the existing methods of determining abstract words with an attempt to understand which are better applicable. Besides, the article covers the progress in this research covering the period from Ancient Greece to present day researchers in various fields of knowledge, including but not limited to linguistics, clinical psychology, and philosophy, as all these areas propose their own outlook on the issue of the nature of abstractions and their distinction from concrete words.


title: Managing Platform Ecosystems

abstract: We examine how control over a technology platform can increase profits and innovation. By choosing how much to open and when to bundle enhancements, platform sponsors can influence choices of ecosystem partners. Platform openness invites developer participation but sacrifices direct sales. Bundling enhancements early drives developers away but bundling late delays platform growth. Ironically, developers can prefer sponsored platforms to unmanaged open standards despite giving up their applications. Results can inform antitrust law and innovation strategy.


title: Will video be the next generation of e-commerce product reviews? Presentation format and the role of product type

abstract: Researchers have systematically investigated the influence of online reviews on consumer perceptions and decisions to purchase products, but hitherto have not attended to the presentation format. Increasingly, video reviews are making their way into various websites, and their impact on consumer perceptions is not yet known. While prior research has investigated the impact of video presentation of vendor supplied product information, the effects of video presentation formats of online reviews posted by consumers, in relation to other commonly used formats such as text, are not known. The conditions of effectiveness of these presentation formats should also to be investigated so that guidelines for appropriate use can be developed. Hence, in this study, using theoretical foundations anchored on elaboration likelihood model and cognitive fit theory, we conducted a laboratory experiment comparing the impact of online reviews displayed in three presentation formats-text, image, and video-on three products-a digital camera, backpack, and videogame.We find that the presentation format of online reviews has a substantive and nuanced impact on consumer perceptions. Product type significantly moderates the effect of presentation format on consumer perceptions. Highlighting the role of presentation format and product type, our findings indicate that more research on presentation format is warranted, and vendors should make efforts to manage the presentation of online reviews. We discuss these findings, and theoretical and practical implications of the study. New video formats of online reviews are useful and influence consumer perceptions in a very nuanced wayProduct types play an important role, and moderate the effect of presentation formatsVideo formats may even change consumers perception of product typeAn Information Technology (IT) feature of online reviews -presentation format - is critical.Presentation formats should be managed, because they impact consumer's perception and intentions to purchase the product.


title: On Growing and Pruning Kneser–Ney Smoothed $ N$-Gram Models

abstract: N-gram models are the most widely used language models in large vocabulary continuous speech recognition. Since the size of the model grows rapidly with respect to the model order and available training data, many methods have been proposed for pruning the least relevant -grams from the model. However, correct smoothing of the N-gram probability distributions is important and performance may degrade significantly if pruning conflicts with smoothing. In this paper, we show that some of the commonly used pruning methods do not take into account how removing an -gram should modify the backoff distributions in the state-of-the-art Kneser-Ney smoothing. To solve this problem, we present two new algorithms: one for pruning Kneser-Ney smoothed models, and one for growing them incrementally. Experiments on Finnish and English text corpora show that the proposed pruning algorithm provides considerable improvements over previous pruning algorithms on Kneser-Ney smoothed models and is also better than the baseline entropy pruned Good-Turing smoothed models. The models created by the growing algorithm provide a good starting point for our pruning algorithm, leading to further improvements. The improvements in the Finnish speech recognition over the other Kneser-Ney smoothed models are statistically significant, as well.


title: ERP event log preprocessing: timestamps vs. accounting logic

abstract: Process mining has been gaining significant attention in academia and practice. A promising first step to apply process mining in the audit domain was taken with the mining of process instances from accounting data. However, the resulting process instances constitute graphs. Commonly, timestamp oriented event log formats require a sequential list of activities and do not support graph structures. Thus, event log based process mining techniques cannot readily be applied to accounting data. To close this gap, we present an algorithm that determines an activity sequence from accounting data. With this algorithm, mined process instance graphs can be decomposed in a way they fit into sequential event log formats. Event log based process mining techniques can then be used to construct process models. A case study demonstrates the effectiveness of the presented approach. Results reveal that the preprocessing of the event logs considerably improves the derived process models.


title: An Interval-Based Representation of Temporal Knowledge

abstract: This paper describes a method for maintaining the relationships between temporal intervals in a hierarchical manner using constraint propagation techniques. The representation includes a notion of the present moment (i.e., "now"), and allows one to represent intervals that may extend indefinitely into the past/future. ::: ::: This research was supported in part by the National Science Foundation under Grant Number IST-80-12418, and in part by the Office of Naval Research under Grant Number N00014-80-O0197.


title: Robots, Pancakes, and Computer Games: Designing Serious Games for Robot Imitation Learning

abstract: Autonomous manipulation robots can be valuable aids as interactive agents in the home, yet it has proven extremely difficult to program their behavior. Imitation learning uses data on human demonstrations to build behavioral models for robots. In order to cover a wide range of action strategies, data from many individuals is needed. Acquiring such large amounts of data can be a challenge. Tools for data capturing in this domain must thus implement a good user experience. We propose to use human computation games in order to gather data on human manual behavior. We demonstrate the idea with a strategy game that is operated via a natural user interface. A comparison between using the game for action execution and demonstrating actions in a virtual environment shows that people interact longer and have a better experience when playing the game.


A Neural Approach to Automated Essay Scoring


title: Incomplete circle of Willis is associated with a higher incidence of neurologic events during carotid eversion endarterectomy without shunting

abstract: Abstract Objective A complete circle of Willis (CoW) is considered an important collateral network to maintain blood flow during cross-clamping in carotid endarterectomy (CEA). The aim of this study was to evaluate the impact of an incomplete CoW with isolated middle cerebral artery (iMCA) on immediate neurologic events (INEs) after CEA. Methods We prospectively collected the clinical data and outcomes of 902 patients who underwent CEA under general anesthesia between 2013 and 2015. All patients had preoperative computed tomography angiography of the extracranial and intracranial cerebral circulation. Indications were asymptomatic (52%) and symptomatic (48%) carotid artery disease. Patients who had CEA with shunt (n = 35) and those with inadequate intracranial imaging to assess CoW were excluded (n = 322) only. Computed tomography angiography images were reviewed retrospectively and independently by two vascular radiologists who were blinded for treatment outcomes. Imaging assessment included the vertebral and carotid circulation and each segment of the CoW, which was classified as normal, hypoplastic (diameter Results Of the 545 included patients (331 males; mean age, 69 ± 8 years), 12 (2.2%) had a stroke in the postoperative period. There were 20 INEs (8 strokes and 12 TIAs). A complete CoW was rare; it was only detected in 19 patients (3.5%) and an iMCA was found in 34 patients (6.3%). When at least one collateral circulation was complete (in 330 patients), we observed only four INEs (1.2%). Of the 34 patients with an iMCA, 8 (24%) had INE (6 TIAs and 2 strokes). Overall, iMCA was an independent predictor of INEs (odds ratio, 11.12; 95% confidence interval, 3.57-35.87; P P = .02), whereas all other parameters were not significant. Conclusions An iMCA carries more than a 10-fold higher the risk of INEs after CEA with cross-clamping without shunt protection. In these patients, routine shunting is recommended to prevent INEs.


title: Large-Complex-Surface Defect Detection by Hybrid Gradient Threshold Segmentation and Image Registration

abstract: Machine vision-based object detection techniques have been widely used in product inspection, defect detection, and dimension measurement. These techniques have largely improved the efficiency of industrial production and increased the level of production autonomy. However, demands on advance hardware design and image processing algorithms are needed for the quality inspection of a large-complex-surface. In order to solve this problem, a hybrid surface defect detection method is developed. An image of the product surface is first divided into two areas: background with similar features and special pattern area, such as product trademarks. For the background area, defects have significant differences in gray intensity from the normal area. Fault detection is conducted using a gradient threshold segmentation method that can limit segmentation errors arising from uneven illuminations. For the special pattern area, image registration and image difference are adopted to detect defects, which are adaptive to irregular image contents with discontinuous shapes and appearances. Experimental results indicate that the proposed method achieves about 1.21 times and 2.94 times higher accuracy, in F-measure , for large-complex-surface defect detection than the traditional methods of gradient threshold segmentation and template matching, respectively. The proposed image processing technique can be applied in product quality inspections.


title: The use of Lean and Six Sigma methodologies in surgery: a systematic review.

abstract: BACKGROUND ::: Lean and Six Sigma are improvement methodologies developed in the manufacturing industry and have been applied to healthcare settings since the 1990 s. They use a systematic and reproducible approach to provide Quality Improvement (QI), with a flexible process that can be applied to a range of outcomes across different patient groups. This review assesses the literature with regard to the use and utility of Lean and Six Sigma methodologies in surgery. ::: ::: ::: METHODS ::: MEDLINE, Embase, PsycINFO, Allied and Complementary Medicine Database, British Nursing Index, Cumulative Index to Nursing and Allied Health Literature, Health Business Elite and the Health Management Information Consortium were searched in January 2014. Experimental studies were included if they assessed the use of Lean or Six Sigma on the ability to improve specified outcomes in surgical patients. ::: ::: ::: RESULTS ::: Of the 124 studies returned, 23 were suitable for inclusion with 11 assessing Lean, 6 Six Sigma and 6 Lean Six Sigma. The broad range of outcomes can be collated into six common aims: to optimise outpatient efficiency, to improve operating theatre efficiency, to decrease operative complications, to reduce ward-based harms, to reduce mortality and to limit unnecessary cost and length of stay. The majority of studies (88%) demonstrate improvement; however high levels of systematic bias and imprecision were evident. ::: ::: ::: CONCLUSION ::: Lean and Six Sigma QI methodologies have the potential to produce clinically significant improvement for surgical patients. However there is a need to conduct high-quality studies with low risk of systematic bias in order to further understand their role.


title: Differential mode of antimicrobial actions of arginine-rich and lysine-rich histones against Gram-positive Staphylococcus aureus

abstract: We previously reported the activities and modes of action of arginine (Arg)-rich histones H3 and H4 against Gram-negative bacteria. In the present study, we investigated the properties of the Arg-rich histones against Gram-positive bacteria in comparison with those of lysine (Lys)-rich histone H2B. In a standard microdilution assay, calf thymus histones H2B, H3, and H4 showed growth inhibitory activity against Staphylococcus aureus with minimum effective concentration values of 4.0, 4.0, and 5.6 μM, respectively. Laser confocal microscopic analyses revealed that both the Arg-rich and Lys-rich histones associated with the surface of S. aureus. However, while the morphology of S. aureus treated with histone H2B appeared intact, those treated with the histones H3 and H4 closely resembled each other, and the cells were blurred. Electrophoretic mobility shift assay results revealed these histones have binding affinity to lipoteichoic acid (LTA), one of major cell surface components of Gram-positive bacteria. Scanning electron microscopic analyses demonstrated that while histone H2B elicited no obvious changes in cell morphology, histones H3 and H4 disrupted the cell membrane structure with bleb formation in a manner similar to general antimicrobial peptides. Consequently, our results suggest that bacterial cell surface LTA initially attracts both the Arg- and Lys-rich histones, but the modes of antimicrobial action of these histones are different; the former involves cell membrane disruption and the latter involves the cell integrity disruption.


title: Theano: A CPU and GPU Math Compiler in Python

abstract: Theano is a compiler for mathematical expressions in Python that combines the convenience of NumPy's syntax with the speed of optimized native machine language. The user composes mathematical expressions in a high-level description that mimics NumPy's syntax and semantics, while being statically typed and functional (as opposed to imperative). These expressions allow Theano to provide symbolic differentiation. Before performing computation, Theano optimizes the choice of expressions, translates them into C++ (or CUDA for GPU), compiles them into dynamically loaded Python modules, all automatically. Common machine learn- ing algorithms implemented with Theano are from 1:6 to 7:5 faster than competitive alternatives (including those implemented with C/C++, NumPy/SciPy and MATLAB) when compiled for the CPU and between 6:5 and 44 faster when compiled for the GPU. This paper illustrates how to use Theano, outlines the scope of the compiler, provides benchmarks on both CPU and GPU processors, and explains its overall design.


title: Character Networks and Book Genre Classification

abstract: We compare the social character networks of biographical, legendary and fictional texts, in search for marks of genre differentiation. We examine the degree distribution of character appearance and find a power law that does not depend on the literary genre or historical content. We also analyze local and global complex networks measures, in particular, correlation plots between the recently introduced Lobby (or Hirsh $H(1)$) index and Degree, Betweenness and Closeness centralities. Assortativity plots, which previous literature claims to separate fictional from real social networks, were also studied. We've found no relevant differences in the books for these network measures and we give a plausible explanation why the previous assortativity result is not correct.


title: Surface corona inception on stress grading system in end-turn regions of rotating machines

abstract: The transients generated by drives based on pulse width modulation (PWM) have adverse effects on the insulation system of rotating machines, especially the stress grading (SG) system used in the endturn region. The surface corona inception voltage on SG system under repetitive impulse, 1kHz sinusoidal, and 1kHz square voltages was measured using a photomultiplier connected to optical fiber bundle, and the evolution of corona was observed using a ICCD camera. Four samples were evaluated to assess process-induced variations in performance. The macroscopic repetitive corona inception field was computed for each waveform from the measured surface corona initiation voltage on the SG system and was 25% to 50% of critical field for gas ionization.


title: A survey on IoT communication and computation frameworks: An industrial perspective

abstract: This paper surveys fog computing and embedded systems platforms as the building blocks of Internet of Things (IoT). Many concepts around IoT architectures, with various examples, are also discussed. This paper reviews a high-level conceptual layered architecture for IoT from a computational perspective. The architecture incorporates fog computing to address several issues associated with cloud computing; however, it is never a binary decision between fog and cloud. Many of the world's physical objects are being embedded with sensors and actuators, tied by communication infrastructures, and managed by computational algorithms. IoT sensor networks and embedded systems connecting smart objects are revolutionizing how we approach our daily lives, health care, energy, and transportation. Such computational needs are addressed with an array of various models and frameworks. In an attempt to consolidate the use of these models, this paper reviews the state-of-the-art research in IoT, cloud computing, and fog computing.


title: Statistical approaches to DDoS attack detection and response

abstract: The nature of the threats posed by distributed denial of service (DDoS) attacks on large networks, such as the Internet, demands effective detection and response methods. These methods must be deployed not only at the edge but also at the core of the network This paper presents methods to identify DDoS attacks by computing entropy and frequency-sorted distributions of selected packet attributes. The DDoS attacks show anomalies in the characteristics of the selected packet attributes. The detection accuracy and performance are analyzed using live traffic traces from a variety of network environments ranging from points in the core of the Internet to those inside an edge network The results indicate that these methods can be effective against current attacks and suggest directions for improving detection of more stealthy attacks. We also describe our detection-response prototype and how the detectors can be extended to make effective response decisions.


title: COCOpf: An Algorithm Portfolio Framework

abstract: Algorithm portfolios represent a strategy of composing multiple heuristic algorithms, each suited to a different class of problems, within a single general solver that will choose the best suited algorithm for each input. This approach recently gained popularity especially for solving combinatoric problems, but optimization applications are still emerging. The COCO platform of the BBOB workshop series is the current standard way to measure performance of continuous black-box optimization algorithms. As an extension to the COCO platform, we present the Python-based COCOpf framework that allows composing portfolios of optimization algorithms and running experiments with different selection strategies. In our framework, we focus on black-box algorithm portfolio and online adaptive selection. As a demonstration, we measure the performance of stock SciPy optimization algorithms and the popular CMA algorithm alone and in a portfolio with two simple selection strategies. We confirm that even a naive selection strategy can provide improved performance across problem classes.


title: Clone region descriptors: Representing and tracking duplication in source code

abstract: Source code duplication, commonly known as code cloning, is considered an obstacle to software maintenance because changes to a cloned region often require consistent changes to other regions of the source code. Research has provided evidence that the elimination of clones may not always be practical, feasible, or cost-effective. We present a clone management approach that describes clone regions in a robust way that is independent from the exact text of clone regions or their location in a file, and that provides support for tracking clones in evolving software. Our technique relies on the concept of abstract clone region descriptors (CRDs), which describe clone regions using a combination of their syntactic, structural, and lexical information. We present our definition of CRDs, and describe a clone tracking system capable of producing CRDs from the output of different clone detection tools, notifying developers of modifications to clone regions, and supporting updates to the documented clone relationships. We evaluated the performance and usefulness of our approach across three clone detection tools and five subject systems, and the results indicate that CRDs are a practical and robust representation for tracking code clones in evolving software.


title: Novel Anti-forensics Approaches for Smart Phones

abstract: Anti-forensics is defined as a method undertaken to thwart the digital investigation process conducted by forensic investigators. In this paper we introduce the design and implementation of three novel anti-forensic approaches for data deletion and manipulation on three of the market-leading forensics tools. The evaluation results of our proposed anti-forensic approaches on a typical Android cell phone device demonstrate the severe limitations of current forensic tools and conclude the critical need for modifications and/or redesigns of these contemporary forensic tools to maintain accuracy and legitimacy of forensic data acquisition and analysis.


title: The Nature and Role of Feedback Text Comments in Online Marketplaces: Implications for Trust Building, Price Premiums, and Seller Differentiation

abstract: For online marketplaces to succeed and prevent a market of lemons, their feedback mechanism (reputation system) must differentiate among sellers and create price premiums for trustworthy sellers as returns to their reputation. However, the literature has solely focused on numerical (positive and negative) feedback ratings, alas ignoring the role of feedback text comments. These text comments are proposed to convey useful reputation information about a seller's prior transactions that cannot be fully captured with crude numerical ratings. Building on the economics and trust literatures, this study examines the rich content of feedback text comments and their role in building a buyer's trust in a seller's benevolence and credibility. In turn, benevolence and credibility are proposed to differentiate among sellers by influencing the price premiums that a seller receives from buyers. ::: ::: This paper utilizes content analysis to quantify over 10,000 publicly available feedback text comments of 420 sellers in eBay's online auction marketplace, and to match them with primary data from 420 buyers that recently transacted with these 420 sellers. These dyadic data show that evidence of extraordinary past seller behavior contained in the sellers' feedback text comments creates price premiums for reputable sellers by engendering buyer's trust in the sellers' benevolence and credibility (controlling for the impact of numerical ratings). The addition of text comments and benevolence helps explain a greater variance in price premiums (R2 = 50%) compared to the existing literature (R2 = 20%--30%). By showing the economic value of feedback text comments through trust in a seller's benevolence and credibility, this study helps explain the success of online marketplaces that primarily rely on the text comments (versus crude numerical ratings) to differentiate among sellers and prevent a market of lemon sellers. By integrating the economics and trust literatures, the paper has theoretical and practical implications for better understanding the nature and role of feedback mechanisms, trust building, price premiums, and seller differentiation in online marketplaces.


title: Learning from History and Present: Next-item Recommendation via Discriminatively Exploiting User Behaviors

abstract: In the modern e-commerce, the behaviors of customers contain rich information, e.g., consumption habits, the dynamics of preferences. Recently, session-based recommendationsare becoming popular to explore the temporal characteristics of customers' interactive behaviors. However, existing works mainly exploit the short-term behaviors without fully taking the customers' long-term stable preferences and evolutions into account. In this paper, we propose a novel Behavior-Intensive Neural Network (BINN) for next-item recommendation by incorporating both users' historical stable preferences and present consumption motivations. Specifically, BINN contains two main components, i.e., Neural Item Embedding, and Discriminative Behaviors Learning. Firstly, a novel item embedding method based on user interactions is developed for obtaining an unified representation for each item. Then, with the embedded items and the interactive behaviors over item sequences, BINN discriminatively learns the historical preferences and present motivations of the target users. Thus, BINN could better perform recommendations of the next items for the target users. Finally, for evaluating the performances of BINN, we conduct extensive experiments on two real-world datasets, i.e., Tianchi and JD. The experimental results clearly demonstrate the effectiveness of BINN compared with several state-of-the-art methods.


title: On the applicability of Two-Ray path loss models for vehicular network simulation

abstract: We discuss the applicability of simplified Two-Ray Ground path loss models to simulation-based performance evaluation studies of Inter-Vehicle Communication (IVC) protocols. We contrast this with the applicability of a more exact Two-Ray Interference model. A key result is that, in most cases, the commonly used simplified Two-Ray Ground models add no additional value compared to the most simple Free-space model — in particular in highway and suburban environments. We further argue that replacing a simplified with a fully featured Two-Ray Interference model can not only substantially improve the accuracy of simulation results but also allow capturing one notable artifact that becomes immediately visible in field tests, namely strong signal attenuation at short and medium ranges. We implemented the Two-ray Interference model within the Veins simulation framework and validated it using analytical predictions and field measurements. We show the impact of the more accurate Two-Ray Interference model, which only comes with negligible additional computational cost for simulation experiments.