title: Virtual reality and its uses: a systematic literature review

abstract: Virtual reality (VR) has challenged the way we perceive the world and user experience is being explored to achieve an immersive and effective experience. However, the commercial effects and impact of this technology lacks sufficient research. It is hence uncertain which role virtual reality has in information systems nowadays. This systematic literature review (SLR) focuses on the commercial impact of virtual reality and which field of study this technology is most used. To answer this question, a reference manual was used from literature review protocol standards and carried out. Results show that VR systems have a wide specter of applications and a significant potential for revolutionizing our everyday life in the digital world.


title: A Study of Security Vulnerabilities on Docker Hub

abstract: Docker containers have recently become a popular approach to provision multiple applications over shared physical hosts in a more lightweight fashion than traditional virtual machines. This popularity has led to the creation of the Docker Hub registry, which distributes a large number of official and community images. In this paper, we study the state of security vulnerabilities in Docker Hub images. We create a scalable Docker image vulnerability analysis (DIVA) framework that automatically discovers, downloads, and analyzes both official and community images on Docker Hub. Using our framework, we have studied 356,218 images and made the following findings: (1) both official and community images contain more than 180 vulnerabilities on average when considering all versions; (2) many images have not been updated for hundreds of days; and (3) vulnerabilities commonly propagate from parent images to child images. These findings demonstrate a strong need for more automated and systematic methods of applying security updates to Docker images and our current Docker image analysis framework provides a good foundation for such automatic security update.


title: A survey on platforms for big data analytics

abstract: The primary purpose of this paper is to provide an in-depth analysis of different platforms available for performing big data analytics. This paper surveys different hardware platforms available for big data analytics and assesses the advantages and drawbacks of each of these platforms based on various metrics such as scalability, data I/O rate, fault tolerance, real-time processing, data size supported and iterative task support. In addition to the hardware, a detailed description of the software frameworks used within each of these platforms is also discussed along with their strengths and drawbacks. Some of the critical characteristics described here can potentially aid the readers in making an informed decision about the right choice of platforms depending on their computational needs. Using a star ratings table, a rigorous qualitative comparison between different platforms is also discussed for each of the six characteristics that are critical for the algorithms of big data analytics. In order to provide more insights into the effectiveness of each of the platform in the context of big data analytics, specific implementation level details of the widely used k-means clustering algorithm on various platforms are also described in the form pseudocode.


Towards Full-fledged XML Fragmentation for Transactional Distributed Databases


title: Power and bipower variation with stochastic volatility and jumps

abstract: This article shows that realized power variation and its extension, realized bipower variation, which we introduce here, are somewhat robust to rare jumps. We demonstrate that in special cases, realized bipower variation estimates integrated variance in stochastic volatility models, thus providing a model-free and consistent alternative to realized variance. Its robustness property means that if we have a stochastic volatility plus infrequent jumps process, then the difference between realized variance and realized bipower variation estimates the quadratic variation of the jump component. This seems to be the first method that can separate quadratic variation into its continuous and jump components. Various extensions are given, together with proofs of special cases of these results. Detailed mathematical results are reported in Barndorff-Nielsen and Shephard (2003a).


title: Advances in deep neural network approaches to speaker recognition

abstract: The recent application of deep neural networks (DNN) to speaker identification (SID) has resulted in significant improvements over current state-of-the-art on telephone speech. In this work, we report a similar achievement in DNN-based SID performance on microphone speech. We consider two approaches to DNN-based SID: one that uses the DNN to extract features, and another that uses the DNN during feature modeling. Modeling is conducted using the DNN/i-vector framework, in which the traditional universal background model is replaced with a DNN. The recently proposed use of bottleneck features extracted from a DNN is also evaluated. Systems are first compared with a conventional universal background model (UBM) Gaussian mixture model (GMM) i-vector system on the clean conditions of the NIST 2012 speaker recognition evaluation corpus, where a lack of robustness to microphone speech is found. Several methods of DNN feature processing are then applied to bring significantly greater robustness to microphone speech. To direct future research, the DNN-based systems are also evaluated in the context of audio degradations including noise and reverberation.


title: Characteristics comparison of a conventional and novel stator structure of axial flux permanent magnet motor for cooling fan drive system

abstract: This paper compares to investigate output characteristics according to a conventional and novel stator structure of axial flux permanent magnet (AFPM) motor for cooling fan drive system. Segmented core of stator has advantages such as easy winding and fast manufacture speed. However, a unit cost increase due to cutting off tooth tip to constant slot width. To solve the problem, this paper proposes a novel stator structure with three-step segmented core. The characteristics of AFPM were analyzed by time-stepping three dimensional finite element analysis (3D FEA) in two stator models, when stator cores are cutting off tooth tips from rectangular core and three step segmented core. Prototype motors were manufactured based on analysis results, and were tested as a motor.


title: Reward Learning from Narrated Demonstrations

abstract: Humans effortlessly"program"one another by communicating goals and desires in natural language. In contrast, humans program robotic behaviours by indicating desired object locations and poses to be achieved, by providing RGB images of goal configurations, or supplying a demonstration to be imitated. None of these methods generalize across environment variations, and they convey the goal in awkward technical terms. This work proposes joint learning of natural language grounding and instructable behavioural policies reinforced by perceptual detectors of natural language expressions, grounded to the sensory inputs of the robotic agent. Our supervision is narrated visual demonstrations(NVD), which are visual demonstrations paired with verbal narration (as opposed to being silent). We introduce a dataset of NVD where teachers perform activities while describing them in detail. We map the teachers' descriptions to perceptual reward detectors, and use them to train corresponding behavioural policies in simulation.We empirically show that our instructable agents (i) learn visual reward detectors using a small number of examples by exploiting hard negative mined configurations from demonstration dynamics, (ii) develop pick-and place policies using learned visual reward detectors, (iii) benefit from object-factorized state representations that mimic the syntactic structure of natural language goal expressions, and (iv) can execute behaviours that involve novel objects in novel locations at test time, instructed by natural language.


title: Depth estimation from a single camera image using power fit

abstract: The research is focused on depth evaluation using a single camera. 2D images can be used for finding the depth of a particular object in the image. Algorithm uses marker(s) to update its orientation and to compute the distance. Initially the shape of the marker is fixed and is pre-defined but as the research expands improvements will make the algorithm to work autonomously using everyday objects as markers. Secondly, the approach is not dependent upon expensive equipment and overall computation cost is also low. Ultimately the algorithm is capable of producing the output in just a few seconds with almost 91.84% accuracy.


title: Verifying Linearisability: A Comparative Survey

abstract: Linearisability is a key correctness criterion for concurrent data structures, ensuring that each history of the concurrent object under consideration is consistent with respect to a history of the corresponding abstract data structure. Linearisability allows concurrent (i.e., overlapping) operation calls to take effect in any order, but requires the real-time order of nonoverlapping to be preserved. The sophisticated nature of concurrent objects means that linearisability is difficult to judge, and hence, over the years, numerous techniques for verifying lineasizability have been developed using a variety of formal foundations such as data refinement, shape analysis, reduction, etc. However, because the underlying framework, nomenclature, and terminology for each method is different, it has become difficult for practitioners to evaluate the differences between each approach, and hence, evaluate the methodology most appropriate for verifying the data structure at hand. In this article, we compare the major of methods for verifying linearisability, describe the main contribution of each method, and compare their advantages and limitations.


title: Detecting Faulty Nodes with Data Errors for Wireless Sensor Networks

abstract: Wireless Sensor Networks (WSN) promise researchers a powerful instrument for observing sizable phenomena with fine granularity over long periods. Since the accuracy of data is important to the whole system's performance, detecting nodes with faulty readings is an essential issue in network management. As a complementary solution to detecting nodes with functional faults, this article, proposes FIND, a novel method to detect nodes with data faults that neither assumes a particular sensing model nor requires costly event injections. After the nodes in a network detect a natural event, FIND ranks the nodes based on their sensing readings as well as their physical distances from the event. FIND works for systems where the measured signal attenuates with distance. A node is considered faulty if there is a significant mismatch between the sensor data rank and the distance rank. Theoretically, we show that average ranking difference is a provable indicator of possible data faults. FIND is extensively evaluated in simulations and two test bed experiments with up to 25 MicaZ nodes. Evaluation shows that FIND has a less than 5p miss detection rate and false alarm rate in most noisy environments.


title: Gut Health of Pigs: Challenge Models and Response Criteria with a Critical Analysis of the Effectiveness of Selected Feed Additives — A Review

abstract: The gut is the largest organ that helps with the immune function. Gut health, especially in young pigs has a significant benefit to health and performance. In an attempt to maintain and enhance intestinal health in pigs and improve productivity in the absence of in-feed antibiotics, researchers have evaluated a wide range of feed additives. Some of these additives such as zinc oxide, copper sulphate, egg yolk antibodies, mannan-oligosaccharides and spray dried porcine plasma and their effectiveness are discussed in this review. One approach to evaluate the effectiveness of these additives in vivo is to use an appropriate disease challenge model. Over the years, researchers have used a number of challenge models which include the use of specific strains of enterotoxigenic Escherichia coli, bacteria lipopolysaccharide challenge, oral challenge with Salmonella enteric serotype Typhimurium, sanitation challenge, and Lawsonia intercellularis challenge. These challenge models together with the criteria used to evaluate the responses of the animals to them are also discussed in this review.


title: Bayesian Ascent: A Data-Driven Optimization Scheme for Real-Time Control With Application to Wind Farm Power Maximization

abstract: This paper describes a data-driven approach for real-time control of a physical system. Specifically, this paper focuses on the cooperative wind farm control where the objective is to maximize the total wind farm power production by using control actions as an input and measured power as an output. For real time, data-driven wind farm control, it is imperative that the optimization algorithm is able to improve a target wind farm power production by executing as small number of trial actions as possible using the wind farm power monitoring data. To achieve this goal, we develop a Bayesian ascent (BA) algorithm by incorporating into the Bayesian optimization framework a strategy that regulates the search domain, as used in the trust region method. The BA algorithm is composed of two iterative phases, namely, learning and optimization phases. In the learning phase, the BA algorithm approximates the target function using Gaussian process regression to fit the measured input and output of the target system. In the optimization phase, the BA algorithm determines the next sampling point to learn more about the target function (exploration) as well as to improve the target value (exploitation). Specifically, the sampling strategy is designed to ensure that the input is selected within a trust region to improve the target value monotonically by gradually changing the input for a target system. The results from simulation studies using an analytical wind farm power function and experimental studies using scaled wind turbines show that the BA algorithm can achieve an almost monotonic increase in the target value.


title: ConSeq: detecting concurrency bugs through sequential errors

abstract: Concurrency bugs are caused by non-deterministic interleavings between shared memory accesses. Their effects propagate through data and control dependences until they cause software to crash, hang, produce incorrect output, etc. The lifecycle of a bug thus consists of three phases: (1) triggering, (2) propagation, and (3) failure. Traditional techniques for detecting concurrency bugs mostly focus on phase (1)--i.e., on finding certain structural patterns of interleavings that are common triggers of concurrency bugs, such as data races. This paper explores a consequence-oriented approach to improving the accuracy and coverage of state-space search and bug detection. The proposed approach first statically identifies potential failure sites in a program binary (i.e., it first considers a phase (3) issue). It then uses static slicing to identify critical read instructions that are highly likely to affect potential failure sites through control and data dependences (phase (2)). Finally, it monitors a single (correct) execution of a concurrent program and identifies suspicious interleavings that could cause an incorrect state to arise at a critical read and then lead to a software failure (phase (1)). ConSeq's backwards approach, (3)!(2)!(1), provides advantages in bug-detection coverage and accuracy but is challenging to carry out. ConSeq makes it feasible by exploiting the empirical observationthat phases (2) and (3) usually are short and occur within one thread. Our evaluation on large, real-world C/C++ applications shows that ConSeq detects more bugs than traditional approaches and has a much lower false-positive rate.


title: Similarity by composition

abstract: We propose a new approach for measuring similarity between two signals, which is applicable to many machine learning tasks, and to many signal types. We say that a signal S1 is "similar" to a signal S2 if it is "easy" to compose S1 from few large contiguous chunks of S2. Obviously, if we use small enough pieces, then any signal can be composed of any other. Therefore, the larger those pieces are, the more similar S1 is to S2. This induces a local similarity score at every point in the signal, based on the size of its supported surrounding region. These local scores can in turn be accumulated in a principled information-theoretic way into a global similarity score of the entire S1 to S2. "Similarity by Composition" can be applied between pairs of signals, between groups of signals, and also between different portions of the same signal. It can therefore be employed in a wide variety of machine learning problems (clustering, classification, retrieval, segmentation, attention, saliency, labelling, etc.), and can be applied to a wide range of signal types (images, video, audio, biological data, etc.) We show a few such examples.


title: Feature Representation for ICU Mortality

abstract: Good predictors of ICU Mortality have the potential to identify high-risk patients earlier, improve ICU resource allocation, or create more accurate population-level risk models. Machine learning practitioners typically make choices about how to represent features in a particular model, but these choices are seldom evaluated quantitatively. This study compares the performance of different representations of clinical event data from MIMIC II in a logistic regression model to predict 36-hour ICU mortality. The most common representations are linear (normalized counts) and binary (yes/no). These, along with a new representation termed "hill", are compared using both L1 and L2 regularization. Results indicate that the introduced "hill" representation outperforms both the binary and linear representations, the hill representation thus has the potential to improve existing models of ICU mortality.


title: A survey on Mesh Segmentation Techniques

abstract: We present a review of the state of the art of segmentation and partitioning techniques of boundary meshes. Recently, these have become a part of many mesh and object manipulation algorithms in computer graphics, geometric modelling and computer aided design. We formulate the segmentation problem as an optimization problem and identify two primarily distinct types of mesh segmentation, namely part segmentation and surface-patch segmentation. We classify previous segmentation solutions according to the different segmentation goals, the optimization criteria and features used, and the various algorithmic techniques employed. We also present some generic algorithms for the major segmentation techniques.


title: DriveAHead — A Large-Scale Driver Head Pose Dataset

abstract: Head pose monitoring is an important task for driver assistance systems, since it is a key indicator for human attention and behavior. However, current head pose datasets either lack complexity or do not adequately represent the conditions that occur while driving. Therefore, we introduce DriveAHead, a novel dataset designed to develop and evaluate head pose monitoring algorithms in real driving conditions. We provide frame-by-frame head pose labels obtained from a motion-capture system, as well as annotations about occlusions of the driver's face. To the best of our knowledge, DriveAHead is the largest publicly available driver head pose dataset, and also the only one that provides 2D and 3D data aligned at the pixel level using the Kinect v2. Existing performance metrics are based on the mean error without any consideration of the bias towards one position or another. Here, we suggest a new performance metric, named Balanced Mean Angular Error, that addresses the bias towards the forward looking position existing in driving datasets. Finally, we present the Head Pose Network, a deep learning model that achieves better performance than current state-of-the-art algorithms, and we analyze its performance when using our dataset.


title: A Rational Model of Eye Movement Control in Reading

abstract: A number of results in the study of realtime sentence comprehension have been explained by computational models as resulting from the rational use of probabilistic linguistic information. Many times, these hypotheses have been tested in reading by linking predictions about relative word difficulty to word-aggregated eye tracking measures such as go-past time. In this paper, we extend these results by asking to what extent reading is well-modeled as rational behavior at a finer level of analysis, predicting not aggregate measures, but the duration and location of each fixation. We present a new rational model of eye movement control in reading, the central assumption of which is that eye movement decisions are made to obtain noisy visual information as the reader performs Bayesian inference on the identities of the words in the sentence. As a case study, we present two simulations demonstrating that the model gives a rational explanation for between-word regressions.


Machine learning in the Internet of Things: A semantic-enhanced approach


title: Identifying Medicare Provider Fraud with Unsupervised Machine Learning

abstract: With the increasing number of people ages 65 and older, healthcare programs are being relied on more for quality and affordable care. Given these and other factors, healthcare spending continues to increase, particularly for the elderly. Medicare is one such program affected by the aging population. Fraud in the United States (U.S.) Medicare program is an ongoing issue resulting in higher healthcare costs for beneficiaries. In this paper, we present an empirical study of several unsupervised machine learning methods to detect outliers, indicating fraudulent medical providers, using the Medicare Part B big dataset. We employ two methods, Isolation Forest and Unsupervised Random Forest, which have not previously been used for the detection of Medicare fraud, along with more commonly used methods to include Local Outlier Factor, autoencoders, and k-Nearest Neighbors. In order to validate the fraud detection performance of each method, we use the List of Excluded Individuals/Entities (LEIE) database which contains information on excluded providers. Moreover, we present details on processing the Part B data and incorporating the LEIE fraud labels. Our results indicate that Local Outlier Factor is the best outlier detection method and k-Nearest Neighbors, with 5 neighbors, and autoencoders are the worst at detecting Medicare Part B fraud.


title: Artificial Intelligence and Virtual Worlds – Toward Human-Level AI Agents

abstract: Artificial Intelligence (AI) has a long tradition as a scientific field, with tremendous achievements accomplished in the decades behind us. At the same time, in the last few decades, we have witnessed a rising popularity of interactive computer games and multi-user virtual environments, resulting with millions of users inhabiting these virtual worlds. This paper deals with the intersection of AI and virtual worlds, focusing on AI agents and exploring the potential implications toward the human-level AI. It offers a unique multidisciplinary approach to the subject, in order to give a comprehensive view on the elaborated problems and the way they are interrelated. Benefits coming from this kind of broad study are twofold: on one hand, research on advanced AI agents in the virtual worlds is the necessary ingredient of their further evolution; and on the other hand, the virtual worlds represent an excellent platform for research on numerous problems related to the challenging field of AI.


title: code2seq: Generating Sequences from Structured Representations of Code

abstract: The ability to generate natural language sequences from source code snippets has a variety of applications such as code summarization, documentation, and retrieval. Sequence-to-sequence (seq2seq) models, adopted from neural machine translation (NMT), have achieved state-of-the-art performance on these tasks by treating source code as a sequence of tokens. We present ${\rm {\scriptsize CODE2SEQ}}$: an alternative approach that leverages the syntactic structure of programming languages to better encode source code. Our model represents a code snippet as the set of compositional paths in its abstract syntax tree (AST) and uses attention to select the relevant paths while decoding. We demonstrate the effectiveness of our approach for two tasks, two programming languages, and four datasets of up to $16$M examples. Our model significantly outperforms previous models that were specifically designed for programming languages, as well as state-of-the-art NMT models.


Benchmarking Lexical Simplification Systems.


title: Learning Globally-Consistent Local Distance Functions for Shape-Based Image Retrieval and Classification

abstract: We address the problem of visual category recognition by learning an image-to-image distance function that attempts to satisfy the following property: the distance between images from the same category should be less than the distance between images from different categories. We use patch-based feature vectors common in object recognition work as a basis for our image-to-image distance functions. Our large-margin formulation for learning the distance functions is similar to formulations used in the machine learning literature on distance metric learning, however we differ in that we learn local distance functions?a different parameterized function for every image of our training set?whereas typically a single global distance function is learned. This was a novel approach first introduced in Frome, Singer, & Malik, NIPS 2006. In that work we learned the local distance functions independently, and the outputs of these functions could not be compared at test time without the use of additional heuristics or training. Here we introduce a different approach that has the advantage that it learns distance functions that are globally consistent in that they can be directly compared for purposes of retrieval and classification. The output of the learning algorithm are weights assigned to the image features, which is intuitively appealing in the computer vision setting: some features are more salient than others, and which are more salient depends on the category, or image, being considered. We train and test using the Caltech 101 object recognition benchmark.


title: Clinical trials of resveratrol

abstract: An expanding body of preclinical evidence suggests resveratrol has the potential to impact a variety of human diseases. To translate encouraging experimental findings into human benefits, information is first needed on the safety, pharmacokinetics, pharmacodynamics, and, ultimately, clinical efficacy of resveratrol. Published clinical trials have largely focused on characterizing the pharmacokinetics and metabolism of resveratrol. Recent studies have also evaluated safety and potential mechanisms of activity following multiple dosing, and have found resveratrol to be safe and reasonably well-tolerated at doses of up to 5 g/day. However, the occurrence of mild to moderate side effects is likely to limit the doses employed in future trials to significantly less than this amount. This review describes the available clinical data, outlines how it supports the continuing development of resveratrol, and suggests what additional information is needed to increase the chances of success in future clinical trials.


title: Road model prediction based unstructured road detection

abstract: Vision-based road detection is an important research topic in different areas of computer vision such as the autonomous navigation of mobile robots. In outdoor unstructured environments such as villages and deserts, the roads are usually not well-paved and have variant colors or texture distributions. Traditional region- or edge-based approaches, however, are effective only in specific environments, and most of them have weak adaptability to varying road types and appearances. In this paper we describe a novel top-down based hybrid algorithm which properly combines both region and edge cues from the images. The main difference between our proposed algorithm and previous ones is that, before road detection, an off-line scene classifier is efficiently learned by both low- and high-level image cues to predict the unstructured road model. This scene classification can be considered a decision process which guides the selection of the optimal solution from region- or edge-based approaches to detect the road. Moreover, a temporal smoothing mechanism is incorporated, which further makes both model prediction and region classification more stable. Experimental results demonstrate that compared with traditional region- and edge-based algorithms, our algorithm is more robust in detecting the road areas with diverse road types and varying appearances in unstructured conditions.


title: Active Shape Models for a fully automated 3 D segmentation of the liver – an evaluation on clinical data ?

abstract: This paper presents an evaluation of the performance of a three-dimensional Active Shape Model (ASM) to segment the liver in 48 clinical CT scans. The employed shape model is built from 32 samples using an optimization approach based on the minimum description length (MDL). Three different gray-value appearance models (plain intensity, gradient and normalized gradient profiles) are created to guide the search. The employed segmentation techniques are ASM search with 10 and 30 modes of variation and a deformable model coupled to a shape model with 10 modes of variation. To assess the segmentation performance, the obtained results are compared to manual segmentations with four different measures (overlap, average distance, RMS distance and ratio of deviations larger 5mm). The only appearance model delivering usable results is the normalized gradient profile. The deformable model search achieves the best results, followed by the ASM search with 30 modes. Overall, statistical shape modeling delivers very promising results for a fully automated segmentation of the liver.


title: Enhanced Symbol-Level Interference Cancellation for PDCCH of 3GPP LTE/LTE-A

abstract: In cellular communication systems, such as 3GPP LTE (3rd Generation Partnership Project Long Term Evolution)/LTE-Advanced, mutual co-channel interference from neighboring cells may significantly jeopardize the performance of all downlink physical channels, especially for receivers located at the cell-edge. A conventional approach to cope with those interference issues relies on coordination of neighboring cell transmissions. However, the design of Physical Downlink Control Channel (PDCCH) of LTE/LTE-A is not flexible enough to fully support transmitter-based inter-cell interference coordination (ICIC). Therefore, a more general receiver-based approach to suppress co-channel interference on PDCCH is being considered for LTE/LTE A. This paper proposes an enhanced iterative symbol-level inter-cell interference cancellation (SLIC) receiver designed for PDCCH. Compared to conventional SLIC, the proposed method provides more robust PDCCH link-level performance in scenarios with amplitude ambiguity of the serving and interfering signals which can occur in LTE/LTE-A systems due to the transparent, i.e., without any network signaling assistance to the user, power control on PDCCH. From simulation of PDCCH in LTE/LTE-A systems, we show that the proposed SLIC receiver provides substantial performance gains over the conventional SLIC receiver under typical PDCCH interference conditions.


title: WebQual: An Exploration of Web-Site Quality

abstract: The issue of web-site quality is tackled from the perspective of the ‘voice of the customer’. Quality function deployment (QFD) is adopted as a framework for identifying web-site qualities demanded by users, which are gathered through a quality workshop. From the workshop an instrument for assessing web-site quality is developed (WebQual) and tested in the domain of UK business schools. The results of the WebQual survey are presented and analyzed, leading to the generation of a WebQual Index of web-site quality. Work under way to extend and refine the WebQual instrument includes electronic commerce evaluation, where web-site service quality is proposed as a key issue.


title: Social factors that contribute to attrition in MOOCs

abstract: In this paper, we explore student dropout behavior in a Massively Open Online Course (MOOC). We use a survival model to measure the impact of three social factors that make predictions about attrition along the way for students who have participated in the course discussion forum.


title: A Blockchain-Based Architecture for Collaborative DDoS Mitigation with Smart Contracts

abstract: The rapid growth in the number of insecure portable and stationary devices and the exponential increase of traffic volume makes Distributed Denial-of-Service (DDoS) attacks a top security threat to services provisioning. Existing defense mechanisms lack resources and flexibility to cope with attacks by themselves, and by utilizing other’s companies resources, the burden of the mitigation can be shared. Emerging technologies such as blockchain and smart contracts allows for the sharing of attack information in a fully distributed and automated fashion. In this paper, the design of a novel architecture is proposed by combining these technologies introducing new opportunities for flexible and efficient DDoS mitigation solutions across multiple domains. Main advantages are the deployment of an already existing public and distributed infrastructure to advertise white or blacklisted IP addresses, and the usage of such infrastructure as an additional security mechanism to existing DDoS defense systems, without the need to build specialized registries or other distribution mechanisms, which enables the enforcement of rules across multiple domains.


A Comparison of Frequent Pattern Techniques and a Deep Learning Method for Session-Based Recommendation


title: UCHIME improves sensitivity and speed of chimera detection

abstract: Motivation: Chimeric DNA sequences often form during polymerase chain reaction amplification, especially when sequencing single regions (e.g. 16S rRNA or fungal Internal Transcribed Spacer) to assess diversity or compare populations. Undetected chimeras may be misinterpreted as novel species, causing inflated estimates of diversity and spurious inferences of differences between populations. Detection and removal of chimeras is therefore of critical importance in such experiments. ::: ::: Results: We describe UCHIME, a new program that detects chimeric sequences with two or more segments. UCHIME either uses a database of chimera-free sequences or detects chimeras de novo by exploiting abundance data. UCHIME has better sensitivity than ChimeraSlayer (previously the most sensitive database method), especially with short, noisy sequences. In testing on artificial bacterial communities with known composition, UCHIME de novo sensitivity is shown to be comparable to Perseus. UCHIME is >100× faster than Perseus and >1000× faster than ChimeraSlayer. ::: ::: Contact: [email protected] ::: ::: Availability: Source, binaries and data: http://drive5.com/uchime. ::: ::: Supplementary information:Supplementary data are available at Bioinformatics online.


title: Avatar anthropomorphism and illusion of body ownership in VR

abstract: In this paper we present a novel experiment to explore the impact of avatar realism on the illusion of virtual body ownership (IVBO) in immersive virtual environments, with full-body avatar embodiment and freedom of movement. We evaluated four distinct avatars (a humanoid robot, a block-man, and both male and female human adult) presenting an increasing level of anthropomorphism in their detailed compositions Our results revealed that each avatar elicited a relatively high level of illusion. However both machine-like and cartoon-like avatars elicited an equivalent IVBO, slightly superior to the human-ones. A realistic human appearance is therefore not a critical top-down factor of IVBO, and could lead to an Uncanney Valley effect.


Linked Data City - Visualization of Linked Enterprise Data


title: Comparison of different CBIR techniques

abstract: Image retrieval is a poor stepchild to other forms of information retrieval (IR). Image retrieval has been one of the most interesting and vivid research areas in the field of computer vision over the last decades. Content-Based Image Retrieval (CBIR) systems are used in order to automatically index, search, retrieve, and browse image databases. Color and texture features are important properties in content-based image retrieval systems. In this paper we have mentioned detailed classification of CBIR system. Also we have discussed about the efficiency of different techniques used in CBIR. We have compared different techniques as well as the combinations of them to improve the performance. We have also compared the effect of different matching techniques on the retrieval process.


title: Standard Cell Layout Regularity and Pin Access Optimization Considering Middle-of-Line

abstract: As minimum feature size and pitch spacing further decrease in advanced technology nodes, many new design constraints and challenges are introduced, such as regularity, middle of line (MOL) structures, and pin-access challenges. In this work, we propose a comprehensive study on standard cell layout regularity and pin access optimization. Given irregular cell layout from old technology nodes, our cell optimization tool can search unidirectional migrated result where the self-aligned double patterning (SADP) and MOL based design constraints are satisfied, and the pin-accessibility is optimized. This problem is formulated as a general integer linear programming (ILP), which may suffer from long runtime for some large standard cell cases. Therefore, we also develop a set of hybrid techniques to quickly search for high-quality solutions. The experimental results demonstrate the effectiveness of our approaches.


title: Machine learning methods in chemoinformatics

abstract: Machine learning algorithms are generally developed in computer science or adjacent disciplines and find their way into chemical modeling by a process of diffusion. Though particular machine learning methods are popular in chemoinformatics and quantitative structure–activity relationships (QSAR), many others exist in the technical literature. This discussion is methods-based and focused on some algorithms that chemoinformatics researchers frequently use. It makes no claim to be exhaustive. We concentrate on methods for supervised learning, predicting the unknown property values of a test set of instances, usually molecules, based on the known values for a training set. Particularly relevant approaches include Artificial Neural Networks, Random Forest, Support Vector Machine, k-Nearest Neighbors and naive Bayes classifiers. WIREs Comput Mol Sci 2014, 4:468–481. ::: ::: How to cite this article: WIREs Comput Mol Sci 2014, 4:468–481. doi:10.1002/wcms.1183


title: Answering Opinion Questions on Products by Exploiting Hierarchical Organization of Consumer Reviews

abstract: This paper proposes to generate appropriate answers for opinion questions about products by exploiting the hierarchical organization of consumer reviews. The hierarchy organizes product aspects as nodes following their parent-child relations. For each aspect, the reviews and corresponding opinions on this aspect are stored. We develop a new framework for opinion Questions Answering, which enables accurate question analysis and effective answer generation by making use the hierarchy. In particular, we first identify the (explicit/implicit) product aspects asked in the questions and their sub-aspects by referring to the hierarchy. We then retrieve the corresponding review fragments relevant to the aspects from the hierarchy. In order to generate appropriate answers from the review fragments, we develop a multi-criteria optimization approach for answer generation by simultaneously taking into account review salience, coherence, diversity, and parent-child relations among the aspects. We conduct evaluations on 11 popular products in four domains. The evaluated corpus contains 70,359 consumer reviews and 220 questions on these products. Experimental results demonstrate the effectiveness of our approach.


title: Student use and perception of technology enhanced learning in a mass lecture knowledge-rich domain first year undergraduate module

abstract: The purpose of this study was to examine student use and perceptions of technology enhanced learning tools (TELTs), including their value for learning. Students enrolled onto a 12-week undergraduate science module had access to four TELTs each week, (i) a recording of the lecture (Panopto™), (ii) an animated mini review of the lecture (ShowMe), (iii) a multiple choice quiz hosted on Blackboard® (MCQ), and (iv) a module Twitter feed. Ninety-five students completed a survey at the end of the module, which included quantitative and qualitative questions, to examine whether they perceived the TELT to be useful for their learning. Analysis of the quantitative data suggest that Twitter was used significantly less than the other three TELTs (p < 0.001) with less people agreeing that it helped their learning (p < 0.001), whilst ShowMe and MCQ had a greater occurrence of an ‘agree’ rating compared to Twitter and Panopto (p ≤ 0.002). A thematic analysis of the qualitative responses identified assessment as a common theme across all four TELTs, being a positive factor for Panopto, ShowMe and MCQ, but negative for Twitter. Students highlighted ShowMe as being particularly useful for simplifying information. Based on this study TELTs similar to ShowMe (i.e. animations) are most recommended as this was one of the two highly rated TELTs (alongside MCQ), but may have more potential for crossover with other subjects, and students found it useful for more than just assessment.


title: Multiple Nested Schema of HBase for Migration from SQL

abstract: With the development of web application, the demand of query performance and expansion becomes increasingly necessary. Relational database seems hardly handling with this, more and more applications choose migrating to NoSQL database such as HBase. The features of high reliability, high performance, column-oriented storage, high scalability, and real-time read and write, won favor from public. In this paper, we use two concepts Nested and Multiple Nested to describe join keys reference relationship between tables in relational database. Because HBase only support the column family nest the column qualifier, it is hard to deal with the situation of Multiple Nested after data migration from relational database meanwhile ensure high query performance in transformed HBase. We propose a novel HBase schema for migrating from relational database to HBase, which supports multiple nested. With this schema in HBase, query only once on one table for any query is needed to get the data, and query efficiency has been greatly improved, especially for join query. Additionally, index migration is available in this scheme, which supports faster query. Finally, this paper proposes a mapping algorithm to find column name corresponding to attribute in relational database.


title: Fast Methods for Eikonal Equations: An Experimental Survey

abstract: The Fast Marching Method is a very popular algorithm to compute times-of-arrival maps (distances map measured in time units). Since their proposal in 1995, it has been applied to many different applications such as robotics, medical computer vision, fluid simulation, etc. Many alternatives have been proposed with two main objectives: to reduce its computational time and to improve its accuracy. In this paper, we collect the main approaches which improve the computational time of the standard Fast Marching Method, focusing on single-threaded methods and isotropic environments. 9 different methods are studied under a common mathematical framework and experimentally in representative environments: Fast Marching Method with binary heap, Fast Marching Method with Fibonacci Heap, Simplified Fast Marching Method, Untidy Fast Marching Method, Fast Iterative Method, Group Marching Method, Fast Sweeping Method, Lock Sweeping Method and Double Dynamic Queue Method.


Real-Time Barcode Detection and Classification using Deep Learning


title: Fingerprint liveness detection based on histograms of invariant gradients

abstract: Security of fingerprint authentication systems remains threatened by the presentation of spoof artifacts. Most current mitigation approaches rely upon the fingerprint liveness detection as the main anti-spoofing mechanisms. However, liveness detection algorithms are not robust to sensor variations. In other words, typical liveness detection algorithms need to be retrained and adapted to each and every sensor used for fingerprint capture. In this paper, inspired by popular invariant feature descriptors such as histograms of oriented gradients (HOG) and the scale invariant feature transform (SIFT), we propose a new invariant descriptor of fingerprint ridge texture called histograms of invariant gradients (HIG). The proposed descriptor is designed to preserve robustness to variations in gradient positions. Spoofed fingerprints are detected using multiple histograms of invariant gradients computed from spatial neighborhoods within the fingerprint. Results show that proposed method achieves an average accuracy comparable to the best algorithms of the Fingerprint Liveness Detection Competition 2013, while being applicable with no change to multiple acquisition sensors.


title: Frequency-Domain Compressive Channel Estimation for Frequency-Selective Hybrid Millimeter Wave MIMO Systems

abstract: Channel estimation is useful in millimeter wave (mm-wave) MIMO communication systems. Channel state information allows optimized designs of precoders and combiners under different metrics, such as mutual information or signal-to-interference noise ratio. At mm-wave, MIMO precoders and combiners are usually hybrid, since this architecture provides a means to trade-off power consumption and achievable rate. Channel estimation is challenging when using these architectures, however, since there is no direct access to the outputs of the different antenna elements in the array. The MIMO channel can only be observed through the analog combining network, which acts as a compression stage of the received signal. Most of the prior work on channel estimation for hybrid architectures assumes a frequency-flat mm-wave channel model. In this paper, we consider a frequency-selective mm-wave channel and propose compressed sensing-based strategies to estimate the channel in the frequency domain. We evaluate different algorithms and compute their complexity to expose tradeoffs in complexity overhead performance as compared with those of previous approaches.


Nanoparticles – An Overview of Classification and Applications


title: Deep Sequential Recommendation for Personalized Adaptive User Interfaces

abstract: Adaptive user-interfaces (AUIs) can enhance the usability of complex software by providing real-time contextual adaptation and assistance. Ideally, AUIs should be personalized and versatile, i.e., able to adapt to each user who may perform a variety of complex tasks. But this is difficult to achieve with many interaction elements when data-per-user is sparse. In this paper, we propose an architecture for personalized AUIs that leverages upon developments in (1) deep learning, particularly gated recurrent units, to efficiently learn user interaction patterns, (2) collaborative filtering techniques that enable sharing of data among users, and (3) fast approximate nearest-neighbor methods in Euclidean spaces for quick UI control and/or content recommendations. Specifically, interaction histories are embedded in a learned space along with users and interaction elements; this allows the AUI to query and recommend likely next actions based on similar usage patterns across the user base. In a comparative evaluation on user-interface, web-browsing and e-learning datasets, the deep recurrent neural-network (DRNN) outperforms state-of-the-art tensor-factorization and metric embedding methods.


title: Picasso: A Modular Framework for Visualizing the Learning Process of Neural Network Image Classifiers

abstract: Picasso is a free open-source (Eclipse Public License) web application written in Python for rendering standard visualizations useful for analyzing convolutional neural networks. Picasso ships with occlusion maps and saliency maps, two visualizations which help reveal issues that evaluation metrics like loss and accuracy might hide: for example, learning a proxy classification task. Picasso works with the Tensorflow deep learning framework, and Keras (when the model can be loaded into the Tensorflow backend). Picasso can be used with minimal configuration by deep learning researchers and engineers alike across various neural network architectures. Adding new visualizations is simple: the user can specify their visualization code and HTML template separately from the application code.


title: An efficient k-means clustering algorithm: Analysis and implementation

abstract: In k-means clustering, we are given a set of n data points in d-dimensional space R/sup d/ and an integer k and the problem is to determine a set of k points in Rd, called centers, so as to minimize the mean squared distance from each data point to its nearest center. A popular heuristic for k-means clustering is Lloyd's (1982) algorithm. We present a simple and efficient implementation of Lloyd's k-means clustering algorithm, which we call the filtering algorithm. This algorithm is easy to implement, requiring a kd-tree as the only major data structure. We establish the practical efficiency of the filtering algorithm in two ways. First, we present a data-sensitive analysis of the algorithm's running time, which shows that the algorithm runs faster as the separation between clusters increases. Second, we present a number of empirical studies both on synthetically generated data and on real data sets from applications in color quantization, data compression, and image segmentation.


title: Goal Translation for a Hammer for Coq (Extended Abstract)

abstract: Hammers are tools that provide general purpose automation for formal proof assistants. Despite the gaining popularity of the more advanced versions of type theory, there are no hammers for such systems. We present an extension of the various hammer components to type theory: (i) a translation of a significant part of the Coq logic into the format of automated proof systems; (ii) a proof reconstruction mechanism based on a Ben-Yelles-type algorithm combined with limited rewriting, congruence closure and a first-order generalization of the left rules of Dyckhoff's system LJT.


title: Rotor Angle Instability Prediction Using Post-Disturbance Voltage Trajectories

abstract: A new method for predicting the rotor angle stability status of a power system immediately after a large disturbance is presented. The proposed two stage method involves estimation of the similarity of post-fault voltage trajectories of the generator buses after the disturbance to some pre-identified templates and then prediction of the stability status using a classifier which takes the similarity values calculated at the different generator buses as inputs. The typical bus voltage variation patterns after a disturbance for both stable and unstable situations are identified from a database of simulations using fuzzy C-means clustering algorithm. The same database is used to train a support vector machine classifier which takes proximity of the actual voltage variations to the identified templates as features. Development of the system and its performance were demonstrated using a case study carried out on the IEEE-39 bus system. Investigations showed that the proposed method can accurately predict the stability status six cycles after the clearance of a fault. Further, the robustness of the proposed method was examined by analyzing its performance in predicting the instability when the network configuration is altered.


The Impacts of Public Service Advertising on Materialism among Young Children in Urban China


title: OBDA with the Ontop Framework.

abstract: Ontology-based data access (OBDA) has become a popular paradigm for accessing data stored in legacy sources using Semantic Web technologies. In the OBDA setting, users access the data through a conceptual layer, which provides a convenient query vocabulary abstracting from specific aspects related to the data sources. This conceptual layer is typically expressed as an RDF(S) or OWL ontology, and it is connected to the underlying relational databases using R2RML mappings. When the ontology is queried in SPARQL, the OBDA system exploits the mappings to retrieve elements from the data sources and construct the answers expected by the user. Different approaches for query processing in OBDA have been proposed. We focus here on the virtual approach, which avoids materializing triples retrieved through mappings and answers the SPARQL queries by translating them into SQL queries over the data sources. In this paper we present our mature open-source OBDA framework Ontop, which supports all W3C standards related to OBDA, and which produces efficiently executable SQL queries.


title: Variational Description of Mott Insulators

abstract: The Gutzwiller wave function for a strongly correlated model can, if supplemented with a long-range Jastrow factor, provide a proper variational description of Mott insulators, so far unavailable. We demonstrate this concept in the prototypical one-dimensional $t-t^\prime$ Hubbard model, where at half filling we reproduce all known phases, namely the ordinary Mott undimerized insulator with power-law spin correlations at small $t^\prime/t$, the spin-gapped metal above a critical $t^\prime/t$ and small $U$, and the dimerized Mott insulator at large repulsion.


title: Linguistically Regularized LSTMs for Sentiment Classification

abstract: Sentiment understanding has been a long-term goal of AI in the past decades. This paper deals with sentence-level sentiment classification. Though a variety of neural network models have been proposed very recently, however, previous models either depend on expensive phrase-level annotation, whose performance drops substantially when trained with only sentence-level annotation; or do not fully employ linguistic resources (e.g., sentiment lexicons, negation words, intensity words), thus not being able to produce linguistically coherent representations. In this paper, we propose simple models trained with sentence-level annotation, but also attempt to generating linguistically coherent representations by employing regularizers that model the linguistic role of sentiment lexicons, negation words, and intensity words. Results show that our models are effective to capture the sentiment shifting effect of sentiment, negation, and intensity words, while still obtain competitive results without sacrificing the models' simplicity.


title: Detecting Facial Retouching Using Supervised Deep Learning

abstract: Digitally altering, or retouching, face images is a common practice for images on social media, photo sharing websites, and even identification cards when the standards are not strictly enforced. This research demonstrates the effect of digital alterations on the performance of automatic face recognition, and also introduces an algorithm to classify face images as original or retouched with high accuracy. We first introduce two face image databases with unaltered and retouched images. Face recognition experiments performed on these databases show that when a retouched image is matched with its original image or an unaltered gallery image, the identification performance is considerably degraded, with a drop in matching accuracy of up to 25%. However, when images are retouched with the same style, the matching accuracy can be misleadingly high in comparison with matching original images. To detect retouching in face images, a novel supervised deep Boltzmann machine algorithm is proposed. It uses facial parts to learn discriminative features to classify face images as original or retouched. The proposed approach for classifying images as original or retouched yields an accuracy of over 87% on the data sets introduced in this paper and over 99% on three other makeup data sets used by previous researchers. This is a substantial increase in accuracy over the previous state-of-the-art algorithm, which has shown <50% accuracy in classifying original and retouched images from the ND-IIITD retouched faces database.


title: Analysis and Experimental Verification of Digital Self-Interference Cancelation for Co-time Co-frequency Full-Duplex LTE

abstract: In the communication mechanism of co-time co-frequency full-duplex (CCFD), digital selfinterference cancellation (SIC) is used for suppressing residual self-interference (SI) after antenna and radio frequency (RF) SICs, as well as low-power multipath SI. In this study, the CCFD LTE verification experiment is presented, which adopts digital SIC with the SI signal reconstructed in frequency domain. For the multipath Rayleigh SI channel, the expression of digital SIC capability is derived and the relationship among the channel estimation error, the received SI power, and digital SIC capability is analyzed. Experimental results show that the digital SIC ability is 31.2 dB for a 20 MHz 16QAM modulated LTE SI signal with frequency centered at 2.6 GHz and the interference-to-noise ratio of 40 dB.


title: Distributed deep reinforcement learning on the cloud for autonomous driving

abstract: This paper proposes an architecture for leveraging cloud computing technology to reduce training time for deep reinforcement learning models for autonomous driving by distributing the training process across a pool of virtual machines. By parallelizing the training process, careful design of the reward function and use of techniques like transfer learning, we demonstrate a decrease in training time for our example autonomous driving problem from 140 hours to less than 1 hour. We go over our network architecture, job distribution paradigm, reward function design and report results from experiments on small sized cluster (1--6 training nodes) of machines. We also discuss the limitations of our approach when trying to scale up to massive clusters.


title: Wideband dual-circular polarized spiral antenna phased array

abstract: A wideband dual-polarization, low-profile cavity backed four-arm Archimedean spiral antenna is first presented. This is further extended to a linear array of spiral antennas. Beamforming capability of the linear array for both polarizations is measured and presented for a 4∶1 bandwidth.


title: InformationTheory, Inference, and Learning Algorithms

abstract: Fun and exciting textbook on the mathematics underpinning the most dynamic areas of modern science and engineering.


title: An Offensive and Defensive Exposition of Wearable Computing

abstract: Wearable computing is rapidly getting deployed in many—commercial, medical, and personal—domains of day-to-day life. Wearable devices appear in various forms, shapes, and sizes and facilitate a wide variety of applications in many domains of life. However, wearables raise unique security and privacy concerns. Wearables also hold the promise to help enhance the existing security, privacy, and safety paradigms in unique ways while preserving the system’s usability. The contribution of this research literature survey is threefold. First, as a background, we identify a wide range of existing as well as upcoming wearable devices and investigate their broad applications. Second, we provide an exposition of the security and privacy of wearable computing, studying dual aspects, that is, both attacks and defenses. Third, we provide a comprehensive study of the potential security, privacy, and safety enhancements to existing systems based on the emergence of wearable technology. Although several research works have emerged exploring different offensive and defensive uses of wearables, there is a lack of a broad and precise literature review systematizing all those security and privacy aspects and the underlying threat models. This research survey also analyzes current and emerging research trends and provides directions for future research.


title: Dependency-Based Word Embeddings

abstract: While continuous word embeddings are gaining popularity, current models are based solely on linear contexts. In this work, we generalize the skip-gram model with negative sampling introduced by Mikolov et al. to include arbitrary contexts. In particular, we perform experiments with dependency-based contexts, and show that they produce markedly different embeddings. The dependencybased embeddings are less topical and exhibit more functional similarity than the original skip-gram embeddings.


title: SPADE: Support for provenance auditing in distributed environments

abstract: SPADE is an open source software infrastructure for data provenance collection and management. The underlying data model used throughout the system is graph-based, consisting of vertices and directed edges that are modeled after the node and relationship types described in the Open Provenance Model. The system has been designed to decouple the collection, storage, and querying of provenance metadata. At its core is a novel provenance kernel that mediates between the producers and consumers of provenance information, and handles the persistent storage of records. It operates as a service, peering with remote instances to enable distributed provenance queries. The provenance kernel on each host handles the buffering, filtering, and multiplexing of incoming metadata from multiple sources, including the operating system, applications, and manual curation. Provenance elements can be located locally with queries that use wildcard, fuzzy, proximity, range, and Boolean operators. Ancestor and descendant queries are transparently propagated across hosts until a terminating expression is satisfied, while distributed path queries are accelerated with provenance sketches.


title: Design and Fabrication of Fine-Pitch Pixelated-Addressed Micro-LED Arrays on Printed Circuit Board for Display and Communication Applications

abstract: In this paper, we report the design and fabrication of fine-pitch and pixelated-addressed micro-light emitting diode (LED) arrays with emission wavelengths of red (R), green (G), blue (B), and infrared (IR). The arrays have a resolution of $8\times 8$ with monochromatic LED chips directly bonded to custom-designed printed circuit boards to form pixelated matrix. R, G, B, and IR micro-LED arrays with a pixel pitch of 1 mm were demonstrated. Sequential row scanning and multiple columns programming is performed by two CMOS-based application specific integrated circuit chips providing constant-sinking current and forward voltage for each pixel of the micro-LED arrays. The uniformity of operating current and forward voltage of the micro-LED pixels was measured with 1.94% coefficient of variation. The micro-LED pixel has much shorter rising time and higher response frequency than those of regular packaged blue and white LEDs. The implemented R–G–B micro-LED arrays are the excellent candidate for high-definition fine-pitch LED displays and demonstrated its versatile functionalities such as visible light communications. The IR micro-LED arrays show great potential in health medical treatment, IR detection imaging and local positioning system.


title: Parallel geometric-algebraic multigrid on unstructured forests of octrees

abstract: We present a parallel multigrid method for solving variable-coefficient elliptic partial differential equations on arbitrary geometries using highly adapted meshes. Our method is designed for meshes that are built from an unstructured hexahedral macro mesh, in which each macro element is adaptively refined as an octree. This forest-of-octrees approach enables us to generate meshes for complex geometries with arbitrary levels of local refinement. We use geometric multigrid (GMG) for each of the octrees and algebraic multigrid (AMG) as the coarse grid solver. We designed our GMG sweeps to entirely avoid collectives, thus minimizing communication cost. We present weak and strong scaling results for the 3D variable-coefficient Poisson problem that demonstrate high parallel scalability. As a highlight, the largest problem we solve is on a non-uniform mesh with 100 billion unknowns on 262,144 cores of NCCS's Cray XK6 "Jaguar"; in this solve we sustain 272 TFlops/s.


title: Line-sweep: Cross-ratio for wide-baseline matching and 3D reconstruction

abstract: We propose a simple and useful idea based on cross-ratio constraint for wide-baseline matching and 3D reconstruction. Most existing methods exploit feature points and planes from images. Lines have always been considered notorious for both matching and reconstruction due to the lack of good line descriptors. We propose a method to generate and match new points using virtual lines constructed using pairs of keypoints, which are obtained using standard feature point detectors. We use cross-ratio constraints to obtain an initial set of new point matches, which are subsequently used to obtain line correspondences. We develop a method that works for both calibrated and uncalibrated camera configurations. We show compelling line-matching and large-scale 3D reconstruction.


Large Scale Multi-label Text Classification with Semantic Word Vectors


title: A Privacy Preserving Communication Protocol for IoT Applications in Smart Homes

abstract: Internet of things is the trending direction when it comes to designing smart living environment. One of the popular applications of Internet of things is the smart home system. A smart home system usually consists of various types of connected sensors, actuators, appliances and a controller. The controller analyzes the data reported by the sensors and sends out messages to electric appliances and other sensors to ask them to behave accordingly. There are a lot of smart home designs proposed in the literature to make the systems smarter and more comfortable. However, little work has considered the security and privacy issues in smart home systems. In this paper, we analyze the differences of security and privacy issues that lie in the smart home systems, smart grid, and wireless sensor networks. Further, we propose our own solutions that achieves privacy preservation during the communications between end sensors and appliances and the controller.


title: Stuttering in relation to anxiety, temperament, and personality: Review and analysis with focus on causality

abstract: Anxiety and emotional reactions have a central role in many theories of stuttering, for example that persons who stutter would tend to have an emotionally sensitive temperament. The possible relati ...


title: Introduction to the special issue on bibliometric-enhanced information retrieval and natural language processing for digital libraries (BIRNDL)

abstract: The large scale of scholarly publications poses a challenge for scholars in information seeking and sensemaking. Bibliometric, information retrieval (IR), text mining, and natural language processing techniques can assist to address this challenge, but have yet to be widely used in digital libraries (DL). This special issue on bibliometric-enhanced information retrieval and natural language processing for digital libraries (BIRNDL) was compiled after the first joint BIRNDL workshop that was held at the joint conference on digital libraries (JCDL 2016) in Newark, New Jersey, USA. It brought together IR and DL researchers and professionals to elaborate on new approaches in natural language processing, information retrieval, scientometric, and recommendation techniques that can advance the state of the art in scholarly document understanding, analysis, and retrieval at scale. This special issue includes 14 papers: four extended papers originating from the first BIRNDL workshop 2016 and the BIR workshop at ECIR 2016, four extended system reports of the CL-SciSumm Shared Task 2016 and six original research papers submitted via the open call for papers.


title: A 1 GHz OTA-based low-pass filter with a high-speed automatic tuning scheme

abstract: A continuous-time 4th-order equiripple linear phase G m -C filter with an automatic tuning circuit is presented. A high speed OTA based on the inverter structure is realized. The combined CMFF and CMFB circuit ensures the input and output common-mode stability. The gain performance could be maintained by combining a negative resistor at the output nodes. Transconductance tuning can be achieved by adjusting the bulk voltage by using the Deep-NWELL technology. Through the use of the OTA as a building block with a modified automatic tuning scheme, the filter -3 dB cutoff frequency is 1 GHz with the group delay less than 4% variation up to 1.5 fc frequency. The -43 dB of IM3 at filter cutoff frequency is obtained with -4 dbm two tone signals. Implemented in 0.18-mum CMOS process, the chip occupies 1mm2 and consumes 175 mW at a 1.5-V supply voltage.


title: Accurate 3D pose estimation from a single depth image

abstract: This paper presents a novel system to estimate body pose configuration from a single depth map. It combines both pose detection and pose refinement. The input depth map is matched with a set of pre-captured motion exemplars to generate a body configuration estimation, as well as semantic labeling of the input point cloud. The initial estimation is then refined by directly fitting the body configuration with the observation (e.g., the input depth). In addition to the new system architecture, our other contributions include modifying a point cloud smoothing technique to deal with very noisy input depth maps, a point cloud alignment and pose search algorithm that is view-independent and efficient. Experiments on a public dataset show that our approach achieves significantly higher accuracy than previous state-of-art methods.


title: A sensor network for non-intrusive and efficient leak detection in long pipelines

abstract: Our society relies on extensive pipeline networks to transfer and deliver water, oil, etc. In many cases the pipeline extends over hundreds of miles and run through inhospitable environments. The pipes are often subject to erosion over time due to the parametric conditions. In addition, pipes carrying valuable commodity may be subject to theft, sabotage, etc. Leakage not only would waste resources, but also can be harm and hazardous. Therefore detecting leakage and containing its negative effect is very important. Current pipeline monitoring systems are inefficient and costly. They lack responsiveness and often report the problem after significant fluid is spilled. Furthermore, current systems involve mobile equipment and significant manpower. This paper describes the design of a wireless sensor networks that continuously monitor a pipeline and provide an early warning when leakage starts. The proposed leak detection system employs non-intrusive methods to track the flow speed and interprets the drop of flow volume as indication of liquid loss through a crack in a pipe. The system is composed of a set of ultrasonic transducers that are mounted around the pipe in multiple locations. The system combines the transit-time and Doppler based flow speed measurements in order to deal with various liquid characteristics. The data collected by the individual components are sent over multi-hop routes to a base-station over radio links for further analysis. The simulation results have confirmed the effectiveness of the proposed system.


title: A Redundancy Study for Feature Selection in Biological Data

abstract: The curse of dimensionality is one of the well known issues in Biological data bases. A possible solution to avoid this issue is to use feature selection approach. Filter feature selection are well know feature selection methods that selects the most significant features and discards the rest according to their significance level. In general The set of eliminated features may hide some useful information that may be valuable in further studies. Hence, this paper present a new approach for filter feature selection that uses redundant features to create new instances and avoid the curse of dimensionality.


Published as a conference paper at ICLR 2018 S IMULATING A CTION D YNAMICS WITH N EURAL P ROCESS N ETWORKS


title: Assumptions of multiple regression: Correcting two misconceptions

abstract: Permission is granted to distribute this article for nonprofit, educational purposes if it is copied in its entirety and the journal is credited. PARE has the right to authorize third party reproduction of this article in print, electronic and database forms. Volume 18, Number 11, September 2013 ISSN 1531-7714


title: Software Aging Analysis of the Linux Operating System

abstract: Software systems running continuously for a long time tend to show degrading performance and an increasing failure occurrence rate, due to error conditions that accrue over time and eventually lead the system to failure. This phenomenon is usually referred to as \textit{Software Aging}. Several long-running mission and safety critical applications have been reported to experience catastrophic aging-related failures. Software aging sources (i.e., aging-related bugs) may be hidden in several layers of a complex software system, ranging from the Operating System (OS) to the user application level. This paper presents a software aging analysis at the Operating System level, investigating software aging sources inside the Linux kernel. Linux is increasingly being employed in critical scenarios; this analysis intends to shed light on its behaviour from the aging perspective. The study is based on an experimental campaign designed to investigate the kernel internal behaviour over long running executions. By means of a kernel tracing tool specifically developed for this study, we collected relevant parameters of several kernel subsystems. Statistical analysis of collected data allowed us to confirm the presence of aging sources in Linux and to relate the observed aging dynamics to the monitored subsystems behaviour. The analysis output allowed us to infer potential sources of aging in the kernel subsystems.


title: PFC interleaved buck-boost converter for telecom power application

abstract: At present, the field of power factor corrected (PFC) rectifiers are very much matured. Typically a single phase PFC converter requires measurements from three independent sensors i.e. input voltage, input current and output voltage. By considering a need of farther compact and more flexible in terms of input to output voltage gain, a new UPF rectifier with two buck-boost converter operating in interleaved condition with use of only one output voltage sensor is presented. The proposed control technique uses only integrator to compensate the error between measured voltage and reference voltage and it is very simple to implement. The proposed Buck-boost converter operates in Discontinuous Conduction Mode (DCM) to achieve UPF at AC input. The required buck-boost inductance in DCM operation is quite small. In contrast to this, the current ripple of buck-boost inductor is quite high. Interleaving is the viable option to reduce the inductor current ripple, component stress, input filter size and enhances the power density. To verify the feasibility of the proposed scheme, the proposed converter is repeatedly simulated in PSIM and in real time platform OP4510. A scaled down lab prototype is built and experimental results are presented to verify the suitability of the proposed converter for the practical application.


title: QR-code generator

abstract: Quick Response (QR) codes seem to appear everywhere these days. We can see them on posters, magazine ads, websites, product packaging and so on. Using the QR codes is one of the most intriguing ways of digitally connecting consumers to the internet via mobile phones since the mobile phones have become a basic necessity thing of everyone. In this paper, we present a methodology for creating QR codes by which the users enter text into a web browser and get the QR code generated. Drupal module was used in conjunction with the popular libqrencode C library to develop user interface on the web browser and encode data in a QR Code symbol. The experiment was conducted using single and multiple lines of text in both English and Thai languages. The result shows that all QR encoding outputs were successfully and correctly generated.


title: Linear Consistency for Proof-of-Stake Blockchains.

abstract: The blockchain data structure maintained via the longest-chain rule---popularized by Bitcoin---is a powerful algorithmic tool for consensus algorithms. Such algorithms achieve consistency for blocks in the chain as a function of their depth from the end of the chain. While the analysis of Bitcoin guarantees consistency with error $2^{-k}$ for blocks of depth $O(k)$, the state-of-the-art of proof-of-stake (PoS) blockchains suffers from a quadratic dependence on $k$: these protocols, exemplified by Ouroboros (Crypto 2017), Ouroboros Praos (Eurocrypt 2018) and Sleepy Consensus (Asiacrypt 2017), can only establish that depth $\Theta(k^2)$ is sufficient. Whether this quadratic gap is an intrinsic limitation of PoS---due to issues such as the nothing-at-stake problem---has been an urgent open question, as deployed PoS blockchains further rely on consistency for protocol correctness. ::: We give an axiomatic theory of blockchain dynamics that permits rigorous reasoning about the longest-chain rule and achieve, in broad generality, $\Theta(k)$ dependence on depth in order to achieve consistency error $2^{-k}$. In particular, for the first time, we show that PoS protocols can match proof-of-work protocols for linear consistency. We analyze the associated stochastic process, give a recursive relation for the critical functionals of this process, and derive tail bounds in both i.i.d. and martingale settings via associated generating functions.


title: Enabling co-channel coexistence of 802.22 and 802.11af systems in TV White Spaces

abstract: In this paper, we investigate the coexistence problem between the 802.22 and the 802.11af systems in the TV White Spaces (TVWS). We focus on the design of a co-channel coexistence scheme for the 802.22 customer-premises equipments (CPE) and the 802.11af systems. 802.22 and 802.11af are two typical standards envisioned to be widely adopted in the future. However, these two standards are heterogeneous in both power level and PHY/MAC design, making their coexistence challenging. To avoid mutual interference between the two systems, existing solutions have to allocate different channels for the two networks. Due to the city-wide coverage of the 802.22 base station (BS), the spectrum utilization is compromised with existing schemes. In this paper, we first identify the challenges to enable the co-channel coexistence of the 802.22 and the 802.11af systems and then propose a busy-tone based framework. We design a busy-tone for the 802.22 CPEs to exclude the hidden 802.11af terminals. We also show that it is possible for the 802.11af systems to identify the exposed 802.22 CPE transmitters and conduct successful transmissions under interference. We show through extensive simulations that the spectrum utilization can be increased with the proposed co-channel coexistence scheme.


title: Synthetic aperture radar at millimeter wavelength for UAV surveillance applications

abstract: The airborne monitoring of scenes using unmanned aircrafts is becoming increasingly important. Several types of airborne sensors - in the optical, infrared or millimeter wave spectrum - are available for the different platforms. Beside the all-weather suitability of the sensors, the deployment scenarios, often also demand for the ability to look through dust clouds, smoke, and fog. The only sensor, which is capable to cope with such environmental restrictions and is able to deliver high-resolution images, is the synthetic aperture radar (SAR).


Comparison between LSA-LDA-Lexical Chains


title: Immune‐Related Adverse Events Associated with Immune Checkpoint Blockade

abstract: The wide range of immune-related adverse effects associated with immune checkpoint blockade can complicate this effective therapy and limit its use in patients with cancer. This review surveys the adverse effects and their management.


title: Modeling and Investigation of Thermal Characteristics of a Water-Cooled Permanent-Magnet Linear Motor

abstract: For permanent-magnet linear motors (PMLMs), high-temperature rise can deteriorate their electromagnetic performance and limit their thrust output. A water-cooling system can be employed to reduce the temperature rise and to improve the thrust density. In order to study the thermal performance of a water-cooled, 14-pole, and 12-slot PMLM, a finite-element model and thermal network model are developed. Under continuous duty, the temperature distribution of this PMLM is investigated based on the two developed models, which are validated by experimental results. The effect of the water-cooling system and the impact of its parameters on the temperature rise are investigated. Then, the influence of the temperature rise on the motor thrust force and efficiency is studied. Finally, based on the finite-element model, thermal characteristics of this PMLM under short-time duty and intermittent duty are studied, and the relationship is obtained between the operation duration and the maximum allowed short-time current, as well as the impact of load sustained rate.


title: The Efficacy of Violence Prediction: A Meta-Analytic Comparison of Nine Risk Assessment Tools

abstract: Actuarial risk assessment tools are used extensively to predict future violence, but previous studies comparing their predictive accuracies have produced inconsistent findings as a result of various methodological issues. We conducted meta-analyses of the effect sizes of 9 commonly used risk assessment tools and their subscales to compare their predictive efficacies for violence. The effect sizes were extracted from 28 original reports published between 1999 and 2008, which assessed the predictive accuracy of more than one tool. We used a within-subject design to improve statistical power and multilevel regression models to disentangle random effects of variation between studies and tools and to adjust for study features. All 9 tools and their subscales predicted violence at about the same moderate level of predictive efficacy with the exception of Psychopathy Checklist—Revised (PCL-R) Factor 1, which predicted violence only at chance level among men. Approximately 25% of the total variance was due to differences between tools, whereas approximately 85% of heterogeneity between studies was explained by methodological features (age, length of follow-up, different types of violent outcome, sex, and sex-related interactions). Sex-differentiated efficacy was found for a small number of the tools. If the intention is only to predict future violence, then the 9 tools are essentially interchangeable; the selection of which tool to use in practice should depend on what other functions the tool can perform rather than on its efficacy in predicting violence. The moderate level of predictive accuracy of these tools suggests that they should not be used solely for some criminal justice decision making that requires a very high level of accuracy such as preventive detention.


title: Memory engram storage and retrieval

abstract: A great deal of experimental investment is directed towards questions regarding the mechanisms of memory storage. Such studies have traditionally been restricted to investigation of the anatomical structures, physiological processes, and molecular pathways necessary for the capacity of memory storage, and have avoided the question of how individual memories are stored in the brain. Memory engram technology allows the labeling and subsequent manipulation of components of specific memory engrams in particular brain regions, and it has been established that cell ensembles labeled by this method are both sufficient and necessary for memory recall. Recent research has employed this technology to probe fundamental questions of memory consolidation, differentiating between mechanisms of memory retrieval and the true neurobiology of memory storage.


title: Online Disclosure of Personally Identifiable Information with Strangers: Effects of Public and Private Sharing

abstract: Safeguarding personally identifiable information (PII) is crucial because such information is increasingly used to engineer privacy attacks, identity thefts and security breaches. But is it likely that individuals may choose to just share this information with strangers? This study examines how reciprocation can lead to the disclosure of PII between strangers in online social networking. We demonstrate that the widespread use of public, one-to-many, communication channels such as ‘wall posts’ and profile pages in online social networks poses an exception to the assumption that reciprocationhappensonone-to-onechannels.Wefindthatindividualsnotonlyreciprocateandshare PII when the disclosure of such information is private and directed towards them by a stranger, but also when the stranger shares this information through a public channel that is not directed towards anyone in particular. Implications for privacy and design are discussed.


title: The Continuous Interaction Space: Interaction Techniques Unifying Touch and Gesture On and Above a Digital Surface

abstract: The rising popularity of digital table surfaces has spawned considerable interest in new interaction techniques. Most interactions fall into one of two modalities: 1) direct touch and multi-touch (by hand and by tangibles) directly on the surface, and 2) hand gestures above the surface. The limitation is that these two modalities ignore the rich interaction space between them. To move beyond this limitation, we first contribute a unification of these discrete interaction modalities called the continuous interaction space. The idea is that many interaction techniques can be developed that go beyond these two modalities, where they can leverage the space between them. That is, we believe that the underlying system should treat the space on and above the surface as a continuum, where a person can use touch, gestures, and tangibles anywhere in the space and naturally move between them. Our second contribution illustrates this, where we introduce a variety of interaction categories that exploit the space between these modalities. For example, with our Extended Continuous Gestures category, a person can start an interaction with a direct touch and drag, then naturally lift off the surface and continue their drag with a hand gesture over the surface. For each interaction category, we implement an example (or use prior work) that illustrates how that technique can be applied. In summary, our primary contribution is to broaden the design space of interaction techniques for digital surfaces, where we populate the continuous interaction space both with concepts and examples that emerge from considering this space as a continuum.


title: Structured Prediction of Sequences and Trees using Infinite Contexts

abstract: Linguistic structures exhibit a rich array of global phenomena, however commonly used Markov models are unable to adequately describe these phenomena due to their strong locality assumptions. We propose a novel hierarchical model for structured prediction over sequences and trees which exploits global context by conditioning each generation decision on an unbounded context of prior decisions. This builds on the success of Markov models but without imposing a fixed bound in order to better represent global phenomena. To facilitate learning of this large and unbounded model, we use a hierarchical Pitman-Yor process prior which provides a recursive form of smoothing. We propose prediction algorithms based on A* and Markov Chain Monte Carlo sampling. Empirical results demonstrate the potential of our model compared to baseline finite-context Markov models on three tasks: morphological parsing, syntactic parsing and part-of-speech tagging.


title: Intelligent Control for Unmanned Aerial Systems with System Uncertainties and Disturbances Using Artificial Neural Network

abstract: Stabilizing the Unmanned Aircraft Systems (UAS) under complex environment including system uncertainties, unknown noise and/or disturbance is so challenging. Therefore, this paper proposes an adaptive neural network based intelligent control method to overcome these challenges. Based on a class of artificial neural network, named Radial Basis Function (RBF) networks an adaptive neural network controller is designed. To handle the unknown dynamics and uncertainties in the system, firstly, we develop a neural network based identifier. Then, a neural network based controller is generated based on both the identified model of the system and the linear or nonlinear controller. To ensure the stability of the system during its online training phase, the linear or nonlinear controller is utilized. The learning capability of the proposed intelligent controller makes it a promising approach to take system uncertainties, noises and/or disturbances into account. The satisfactory performance of the proposed intelligent controller is validated based on the computer based simulation results of a benchmark UAS with system uncertainties and disturbances, such as wind gusts disturbance.


title: Genetic causes of microcephaly and lessons for neuronal development

abstract: The study of human developmental microcephaly is providing important insights intobraindevelopment.Ithasbecomeclearthatdevelopmentalmicrocephaliesare associated with abnormalities in cellular production, and that the pathophysiology of microcephaly provides remarkable insights into how the brain generates the proper number of neurons that determine brain size. Most of the genetic causes of ‘primary’ developmental microcephaly (i.e., not associated with other syndromic features) are associated with centrosomal abnormalities. In addition to other functions, centrosomal proteins control the mitotic spindle, which is essential for normal cell proliferation during mitosis. However, the brain is often uniquely affected when microcephaly genes are mutated implying special centrosomalrelated functions in neuronal production. Although models explaining how this could occur have some compelling data, they are not without controversy. Interestingly, some of the microcephaly genes show evidence that they were targets of evolutionary selection in primates and human ancestors, suggesting potential evolutionary roles in controlling neuronal number and brain volume across species. Mutations in DNA repair pathway genes also lead to microcephaly. Double-stranded DNA breaks appear to be a prominent type of damage that needs to be repaired during brain development, yet why defects in DNA repair affect the brain preferentially and if DNA repair relates to centrosome function, are not clearly understood. © 2012 Wiley Periodicals, Inc.


title: Tacit knowledge: review and possible research directions

abstract: – Tacit knowledge is often difficult to define, given its inexpressible characteristics. Literature review highlights the impact of tacit knowledge on certain knowledge management topics and these include organizational learning, intellectual capital, knowledge management strategy and so forth, but some research gaps remain. The paper aims to propose directions for future research in this domain of discourse., – A review of existing studies highlights some gaps in the literature on the role of tacit knowledge, which is followed by questions for future research., – Given the richness of tacit knowledge discourse, the authors believe that the proposed questions offer avenues for scholars to explore and develop greater understanding of the role of tacit know‐how in certain knowledge management topics., – The authors acknowledge that there are certain limitations to this paper, namely, focusing on the review of tacit knowledge and not on other forms of knowledge. The review presents the role of tacit knowledge and its use in the context of knowledge management related topics. Finally this study proposes only future research directions that are far from being exhaustive, rather than presenting field study results., – This paper reviews the existing literature on how tacit knowledge is perceived and used in certain knowledge management areas. Reviewing the current literature uncovers a number of gaps regarding the role of tacit knowledge.


Qubes OS Architecture


Physically-based Simulation of Cuts in Deformable Bodies: A Survey


title: High voltage charge pump with triple well diodes in a 0.13 µm bulk CMOS process

abstract: This paper presents charge pump circuits with modified triple well diodes as charge transfer switches for charging on-chip pulse generation circuits in a low-voltage bulk CMOS process. Guard-rings and isolation deep n-wells are used to improve the breakdown voltages and reduce leakage currents of the diodes. Model parameters of the triple well diode are extracted based on measured diode characteristics. These parameters are then used to analyze our charge pump. The proposed charge pump circuits are implemented in a commercial 0.13 μm bulk CMOS process. The output voltage of the four-stage charge pump circuit can be up to 18.1 V, which is much higher than the n-well/p-substrate breakdown voltage (~10 V) of the given process.


title: A view on latest audio steganography techniques

abstract: Steganography has been proposed as a new alternative technique to enforce data security. Lately, novel and versatile audio steganographic methods have been proposed. A perfect audio Steganographic technique aim at embedding data in an imperceptible, robust and secure way and then extracting it by authorized people. Hence, up to date the main challenge in digital audio steganography is to obtain robust high capacity steganographic systems. Leaning towards designing a system that ensures high capacity or robustness and security of embedded data has led to great diversity in the existing steganographic techniques. In this paper, we present a current state of art literature in digital audio steganographic techniques. We explore their potentials and limitations to ensure secure communication. A comparison and an evaluation for the reviewed techniques is also presented in this paper.


title: Modular design and modeling of an upper limb exoskeleton

abstract: In this paper, we use modular design method to construct a 4-DoF (Degrees Of Freedom) upper limb exoskeleton. The structure is very simple, and is easy to be modified. Articulation of the exoskeleton is achieved four revolute joints: three for the shoulder and one for the elbow. Static and dynamic models of this exoskeleton are proposed. Experiments and analysis of the exoskeleton robot are carried out to evaluate the effectiveness of the design.


title: Identifying patent infringement using SAO based semantic technological similarities

abstract: Companies should investigate possible patent infringement and cope with potential risks because patent litigation may have a tremendous financial impact. An important factor to identify the possibility of patent infringement is the technological similarity among patents, so this paper considered technological similarity as a criterion for judging the possibility of infringement. Technological similarities can be measured by transforming patent documents into abstracted forms which contain specific technological key-findings and structural relationships among technological components in the invention. Although keyword-based technological similarity has been widely adopted for patent analysis related research, it is inadequate for identifying patent infringement because a keyword vector cannot reflect specific technological key-findings and structural relationships among technological components. As a remedy, this paper exploited a subject---action---object (SAO) based semantic technological similarity. An SAO structure explicitly describes the structural relationships among technological components in the patent, and the set of SAO structures is considered to be a detailed picture of the inventor's expertise, which is the specific key-findings in the patent. Therefore, an SAO based semantic technological similarity can identify patent infringement. Semantic similarity between SAO structures is automatically measured using SAO based semantic similarity measurement method using WordNet, and the technological relationships among patents were mapped onto a 2-dimensional space using multidimensional scaling (MDS). Furthermore, a clustering algorithm is used to automatically suggest possible patent infringement cases, allowing large sets of patents to be handled with minimal effort by human experts. The proposed method will be verified by detecting real patent infringement in prostate cancer treatment technology, and we expect this method to relieve human experts' work in identifying patent infringement.


title: Outnumbered but Well-Spoken: Female Commenters in the New York Times

abstract: Using eight months of online comments on New York Times articles, we find that only 28% of commenters of identifiable gender are female, but that their comments receive more recommendations from other readers. Comments from women are more common on forums about parenting, fashion, and health, and on articles written by women. The number of recommendations comments from women receive is positively correlated with the percentage of men on a forum, and the number of recommendations men receive is negatively correlated with the percentage of men on a forum. Female commenters are more likely to remain anonymous and anonymous commenters receive fewer recommendations. Male and female commenters differ in their choice of topics to emphasize, backgrounds, and language; we find three specific examples in responses to articles about sexual assault, contraception, and farm subsidies. We discuss the implications of these gender differences for democratic discourse and suggest ways to increase gender parity.