title: SLURM Support for Remote GPU Virtualization: Implementation and Performance Study

abstract: SLURM is a resource manager that can be leveraged to share a collection of heterogeneous resources among the jobs in execution in a cluster. However, SLURM is not designed to handle resources such as graphics processing units (GPUs). Concretely, although SLURM can use a generic resource plugin (GRes) to manage GPUs, with this solution the hardware accelerators can only be accessed by the job that is in execution on the node to which the GPU is attached. This is a serious constraint for remote GPU virtualization technologies, which aim at providing a user-transparent access to all GPUs in cluster, independently of the specific location of the node where the application is running with respect to the GPU node. In this work we introduce a new type of device in SLURM, "rgpu", in order to gain access from any application node to any GPU node in the cluster using rCUDA as the remote GPU virtualization solution. With this new scheduling mechanism, a user can access any number of GPUs, as SLURM schedules the tasks taking into account all the graphics accelerators available in the complete cluster. We present experimental results that show the benefits of this new approach in terms of increased flexibility for the job scheduler.


title: Extracting Semantic Information from Visual Data: A Survey

abstract: The traditional environment maps built by mobile robots include both metric ones and topological ones. These maps are navigation-oriented and not adequate for service robots to interact with or serve human users who normally rely on the conceptual knowledge or semantic contents of the environment. Therefore, the construction of semantic maps becomes necessary for building an effective human-robot interface for service robots. This paper reviews recent research and development in the field of visual-based semantic mapping. The main focus is placed on how to extract semantic information from visual data in terms of feature extraction, object/place recognition and semantic representation methods.


title: Fuzzy-Pareto-Dominance and its Application in Evolutionary Multi-Objective Optimization

abstract: This paper studies the fuzzification of the Pareto dominance relation and its application to the design of Evolutionary Multi-Objective Optimization algorithms. A generic ranking scheme is presented that assigns dominance degrees to any set of vectors in a scale-independent, non-symmetric and set-dependent manner. Based on such a ranking scheme, the vector fitness values of a population can be replaced by the computed ranking values (representing the ”dominating strength” of an individual against all other individuals in the population) and used to perform standard single-objective genetic operators. The corresponding extension of the Standard Genetic Algorithm, so-called Fuzzy-Dominance-Driven GA (FDD-GA), will be presented as well. To verify the usefulness of such an approach, an analytic study of the Pareto-Box problem is provided, showing the characteristical parameters of a random search for the Pareto front in a unit hypercube in arbitrary dimension. The basic problem here is the loss of dominated points with increasing problem dimension, which can be successfully resolved by basing the search procedure on the fuzzy dominance degrees.


title: LAN security: problems and solutions for Ethernet networks

abstract: Abstract Despite many research and development efforts in the area of data communications security, the importance of internal local area network (LAN) security is still underestimated. This paper discusses why many traditional approaches to network security (e.g. firewalls, the modern IPSec or various application level protocols) are not so effective in local networks and proposes a prospective solution for building of secure encrypted Ethernet LANs. The architecture presented allows for employment of the existing office network infrastructure, does not require changes to workstations' software, and provides high level of protection. The core idea is to apply security measures in the network interface devices connecting individual computers to the network (i.e. network interface cards (NICs)). This eliminates the physical possibility of sending unprotected information through the network. Implementation details are given for data and key exchange, network management, and interoperability issues. An in-depth security analysis of the proposed architecture is presented and some conclusions are drawn.


title: Research Intrusion Detection Techniques from the Perspective of Machine Learning

abstract: With the rapid development of the Internet services and the fast increasing of intrusion problems, the traditional intrusion detection methods cannot work well with the more and more complicated intrusions. So introducing machine learning into intrusion detection systems to improve the performance has become one of the major concerns in the research of intrusion detection. Intrusion detection systems were proposed to complement prevention-based security measures. In this paper, we first introduces the basic structure of the intrusion detection system, then analysis intrusion Detection Techniques Based on Machine Learning Method, including the Bayesian based method, the neural network based method, the data mining based method and the SVM based method.


title: Automated Bone Age Assessment: Motivation, Taxonomies, and Challenges

abstract: Bone age assessment (BAA) of unknown people is one of the most important topics in clinical procedure for evaluation of biological maturity of children. BAA is performed usually by comparing an X-ray of left hand wrist with an atlas of known sample bones. Recently, BAA has gained remarkable ground from academia and medicine. Manual methods of BAA are time-consuming and prone to observer variability. This is a motivation for developing automated methods of BAA. However, there is considerable research on the automated assessment, much of which are still in the experimental stage. This survey provides taxonomy of automated BAA approaches and discusses the challenges. Finally, we present suggestions for future research.


title: Image-guidance for surgical procedures.

abstract: Contemporary imaging modalities can now provide the surgeon with high quality three- and four-dimensional images depicting not only normal anatomy and pathology, but also vascularity and function. A key component of image-guided surgery (IGS) is the ability to register multi-modal pre-operative images to each other and to the patient. The other important component of IGS is the ability to track instruments in real time during the procedure and to display them as part of a realistic model of the operative volume. Stereoscopic, virtual- and augmented-reality techniques have been implemented to enhance the visualization and guidance process. For the most part, IGS relies on the assumption that the pre-operatively acquired images used to guide the surgery accurately represent the morphology of the tissue during the procedure. This assumption may not necessarily be valid, and so intra-operative real-time imaging using interventional MRI, ultrasound, video and electrophysiological recordings are often employed to ameliorate this situation. Although IGS is now in extensive routine clinical use in neurosurgery and is gaining ground in other surgical disciplines, there remain many drawbacks that must be overcome before it can be employed in more general minimally-invasive procedures. This review overviews the roots of IGS in neurosurgery, provides examples of its use outside the brain, discusses the infrastructure required for successful implementation of IGS approaches and outlines the challenges that must be overcome for IGS to advance further.


title: Unique Case of Imperforate Hymen

abstract: Abstract Background Imperforate hymen typically presents in adolescence with pain, hematocolpometra and primary amenorrhea. This case documents a previously unreported etiology for an atypical presentation with a history of recent menstruation. Case A female adolescent presented with symptoms of urinary retention and leg pain. She reported a history of irregular, painful menses. Clinical examination revealed a pelvic mass and imperforate hymen. Sonography was consistent with hematocolpometra. Before a planned hymenectomy, the patient began to pass dark blood through a fistulous opening in her vulva. Hymenectomy resulted in complete resolution of the pain and hematocolpometra. Summary and Conclusion Identification of the fistulous tract explained the patient's history of menstrual bleeding despite an imperforate hymen. Spontaneous rupture of hematocolpometra through a fistulous tract to the vulva is a previously unreported atypical presentation of imperforate hymen in a “menstruating” adolescent with pain and a pelvic mass.


title: Weakly Supervised Slot Tagging with Partially Labeled Sequences from Web Search Click Logs

abstract: In this paper, we apply a weakly-supervised learning approach for slot tagging using conditional random fields by exploiting web search click logs. We extend the constrained lattice training of T¨¨ om et al. (2013) to non-linear conditional random fields in which latent variables mediate between observations and labels. When combined with a novel initialization scheme that leverages unlabeled data, we show that our method gives significant improvement over strong supervised and weakly-supervised baselines.


title: Why Are Men Satisfied or Dissatisfied with Penile Implants? A Mixed Method Study on Satisfaction with Penile Prosthesis Implantation.

abstract: INTRODUCTION ::: Studies have demonstrated high levels of satisfaction with penile prosthesis implantation (PPI). However, qualitative research exploring the experience of PPI through men's narratives is scarce. ::: ::: ::: AIM ::: The main goals were to analyze (i) the level of sexual satisfaction (quantitatively), and (ii) the reasons for satisfaction and/or dissatisfaction with PPI (qualitatively). ::: ::: ::: METHOD ::: Participants were 47 men with erectile dysfunction who underwent surgery between 2003 and 2012, placed by a single surgeon. Structured telephone interviews were carried out. ::: ::: ::: MAIN OUTCOME MEASURES ::: Satisfaction with PPI was a qualitative and quantitative measure assessed through the following four items: (i) "Would you repeat the PPI surgery?"; (ii) "Would you recommend the PPI surgery?"; (iii) "How satisfied are you with the PP?"; and (iv) "Could you explain the motives of your satisfaction/dissatisfaction?". ::: ::: ::: RESULTS ::: The majority of men (79%) reported to be satisfied with PPI. Content analysis revealed four main themes for men's satisfaction with the PPI: (i) psychological factors were reported 54 times (n = 54) and included positive emotions, self-esteem, confidence, enhancement of male identity, major live change, and self-image; (ii) improvement of sexual function was reported 54 times (n = 54) and referred to achievement of vaginal penetration, increase of sexual desire, sexual satisfaction, penis size, and improvement of erectile function; (iii) relationship factors were reported 11 times (n = 11) and referred to relationship improvement and the possibility of giving pleasure to the partner; and (iv) improvement in urinary function (n = 3). ::: ::: ::: CONCLUSIONS ::: The level of satisfaction with the implementation of penile prostheses is very high, therefore constituting a treatment for erectile dysfunction with a positive impact on the experience of men at sexual, psychological and relational level.


title: Differentially Private Bayesian Learning on Distributed Data

abstract: Many applications of machine learning, for example in health care, would benefit from methods that can guarantee privacy of data subjects. Differential privacy (DP) has become established as a standard for protecting learning results, but the proposed algorithms require a single trusted party to have access to the entire data, which is a clear weakness. We consider DP Bayesian learning in a distributed setting, where each party only holds a single sample or a few samples of the data. We propose a novel method for DP learning in this distributed setting, based on a secure multi-party sum function for aggregating summaries from the data holders. Each data holder adds their share of Gaussian noise to make the total computation differentially private using the Gaussian mechanism. We prove that the system can be made secure against a desired number of colluding data owners and robust against faulting data owners. The method builds on an asymptotically optimal and practically efficient DP Bayesian inference with rapidly diminishing extra cost.


title: Asymptotically Stable Walking of a Five-Link Underactuated 3D Bipedal Robot

abstract: This paper presents three feedback controllers that achieve an asymptotically stable, periodic, and fast walking gait for a 3-D bipedal robot consisting of a torso, revolute knees, and passive (unactuated) point feet. The walking surface is assumed to be rigid and flat; the contact between the robot and the walking surface is assumed to inhibit yaw rotation. The studied robot has 8 DOF in the single support phase and six actuators. In addition to the reduced number of actuators, the interest of studying robots with point feet is that the feedback control solution must explicitly account for the robot's natural dynamics in order to achieve balance while walking. We use an extension of the method of virtual constraints and hybrid zero dynamics (HZD), a very successful method for planar bipeds, in order to simultaneously compute a periodic orbit and an autonomous feedback controller that realizes the orbit, for a 3-D (spatial) bipedal walking robot. This method allows the computations for the controller design and the periodic orbit to be carried out on a 2-DOF subsystem of the 8-DOF robot model. The stability of the walking gait under closed-loop control is evaluated with the linearization of the restricted Poincare map of the HZD. Most periodic walking gaits for this robot are unstable when the controlled outputs are selected to be the actuated coordinates. Three strategies are explored to produce stable walking. The first strategy consists of imposing a stability condition during the search of a periodic gait by optimization. The second strategy uses an event-based controller to modify the eigenvalues of the (linearized) Poincare map. In the third approach, the effect of output selection on the zero dynamics is discussed and a pertinent choice of outputs is proposed, leading to stabilization without the use of a supplemental event-based controller.


title: A “semi-lazy” approach to probabilistic path prediction in dynamic environments

abstract: Path prediction is useful in a wide range of applications. Most of the existing solutions, however, are based on eager learning methods where models and patterns are extracted from historical trajectories and then used for future prediction. Since such approaches are committed to a set of statistically significant models or patterns, problems can arise in dynamic environments where the underlying models change quickly or where the regions are not covered with statistically significant models or patterns. We propose a "semi-lazy" approach to path prediction that builds prediction models on the fly using dynamically selected reference trajectories. Such an approach has several advantages. First, the target trajectories to be predicted are known before the models are built, which allows us to construct models that are deemed relevant to the target trajectories. Second, unlike the lazy learning approaches, we use sophisticated learning algorithms to derive accurate prediction models with acceptable delay based on a small number of selected reference trajectories. Finally, our approach can be continuously self-correcting since we can dynamically re-construct new models if the predicted movements do not match the actual ones. Our prediction model can construct a probabilistic path whose probability of occurrence is larger than a threshold and which is furthest ahead in term of time. Users can control the confidence of the path prediction by setting a probability threshold. We conducted a comprehensive experimental study on real-world and synthetic datasets to show the effectiveness and efficiency of our approach.


title: Outsource Photo Sharing and Searching for Mobile Devices With Privacy Protection

abstract: With the proliferation of mobile devices, cloud-based photo sharing and searching services are becoming common due to the mobile devices' resource constrains. Meanwhile, there is also increasing concern about privacy in photos. In this work, we present a framework \ourprotocolNSP, which enables cloud servers to provide privacy-preserving photo sharing and search as a service to mobile device users. Privacy-seeking users can share their photos via our framework to allow only their authorized friends to browse and search their photos using resource-bounded mobile devices. This is achieved by our carefully designed architecture and novel outsourced privacy-preserving computation protocols, through which no information about the outsourced photos or even the search contents (including the results) would be revealed to the cloud servers. Our framework is compatible with most of the existing image search technologies, and it requires few changes to the existing cloud systems. The evaluation of our prototype system with 31,772 real-life images shows the communication and computation efficiency of our system.


title: Miniaturization of Square Patch Antenna using Complementary Split Ring Resonators

abstract: In this paper, a novel compact micro strip antenna, designed on FR4 substrate, is proposed. Here, a compact square patch antenna is designed in such a way that the radiating patch is loaded by a layer of metal between the patch and the ground which makes the antenna operate at a lower frequency. The antenna design is optimized to obtain a resonant frequency of 2.45GHz. Miniaturization of the antenna is obtained by using meta-material based structures inscribed on the metal layer. The meta-material structure used here is a square-shaped complementary split ring resonator (CSRR). On varying the number and size of inclusions, high levels of miniaturization are observed.


title: A distributed approach for graph mining in massive networks

abstract: We propose a novel distributed algorithm for mining frequent subgraphs from a single, very large, labeled network. Our approach is the first distributed method to mine a massive input graph that is too large to fit in the memory of any individual compute node. The input graph thus has to be partitioned among the nodes, which can lead to potential false negatives. Furthermore, for scalable performance it is crucial to minimize the communication among the compute nodes. Our algorithm, DistGraph, ensures that there are no false negatives, and uses a set of optimizations and efficient collective communication operations to minimize information exchange. To our knowledge DistGraph is the first approach demonstrated to scale to graphs with over a billion vertices and edges. Scalability results on up to 2048 IBM Blue Gene/Q compute nodes, with 16 cores each, show very good speedup.


title: Low-Rate Denial-of-Service Attacks against HTTP/2 Services

abstract: HTTP/2 is the second major version of the HTTP protocol approved by the Internet Engineering Steering Group (IESG). Although the semantics of how messages are exchanged between clients and servers remains the same, the protocol demands more computing power than its predecessor, HTTP/1.1. Hence HTTP/2 is more vulnerable to Denial-of-Service (DoS) attacks. A variant of the DoS type of attack is to send low-rate traffic that contains resource-hungry instructions, to a victim node. This low-rate DoS attacks can succeed only if the victim hosts an application that consumes large-scale computing resources once activated. With the introduction of HTTP/2, we showed that the attack can be launched at the protocol level by sending low-rate HTTP/2 packets to a web server. To the best of our knowledge, no study has been done on how DoS attacks can be launched against HTTP/2 services. Results obtained prove the effect of a low-rate DoS attack against HTTP/2 services.


title: Highly scalable verifiable encrypted search

abstract: In encrypted search, a server holds an encrypted database of documents but not the keys under which the documents are encrypted. The server answer keyword queries from a client with the list of documents matching the query.


Private Set Relations with Bloom Filters for Outsourced SLA Validation.


title: New materials and advances in making electronic skin for interactive robots

abstract: Flexible electronics has huge potential to bring revolution in robotics and prosthetics as well as to bring about the next big evolution in electronics industry. In robotics and related applications, it is expected to revolutionise the way with which machines interact with humans, real-world objects and the environment. For example, the conformable electronic or tactile skin on robot’s body, enabled by advances in flexible electronics, will allow safe robotic interaction during physical contact of robot with various objects. Developing a conformable, bendable and stretchable electronic system requires distributing electronics over large non-planar surfaces and movable components. The current research focus in this direction is marked by the use of novel materials or by the smart engineering of the traditional materials to develop new sensors, electronics on substrates that can be wrapped around curved surfaces. Attempts are being made to achieve flexibility/stretchability in e-skin while retaining a relia...


title: Automatic electricity meter reading based on image processing

abstract: This paper introduces a system based on image processing to obtain efficiently and accurately reading of the electricity digital meter. In this system the back camera of the mobile phones is used to acquire the image of the electricity meter. The system then applies a sequence of image processing functions to automatically extract and recognize the digits of the meter reading image. This image goes through three main stages: preprocessing which ends up with cropping the numeric reading area, segmentation of individual digits using horizontal and vertical scanning of the cropped numeric area, and recognition of the reading by comparing each segmented digit with the digits templates. The proposed system is implemented using Android Studio software with openCV library and has been tested on 21 images of electric meters captured by Smartphone camera in Saudi Arabia, and results shows a recognition with the accuracy rate of 96,49% (per number digit) and 85.71% accuracy rate for the electricity meter readings. The proposed system will be used in the future to develop a mobile application that could be used by the electricity company employees to facilitate the reading process.


title: A Simple Design and Simulation of Full Bridge LLC Resonant DC-DC Converter for Pv Applications

abstract: This paper deals with the design and simulation of full bridge LLC resonant converter suitable for photovoltaic applications. LLC converter has several desired features such as high efficiency, low electromagnetic interference (EMI) and high power density. This paper provides a detailed practical design aspect of full bridge LLC resonant converter. The LLC converter is implemented with a full-bridge on the primary side and a full-bridge rectifier on the secondary side. It includes designing the transformer turns ratio and selecting the components such as resonant inductor, resonant capacitor and magnetizing inductor. Also performance parameters such as voltage gain and output voltage ripple are calculated. Simulation of LLC resonant converter is carried out using MATLAB / SIMULINK and the results are verified.


title: Wake-up transceiver protocol for power line communications

abstract: This paper presents a wake-up transceiver system based on narrowband power line communications (NB-PLC). An energy-pattern based sequence detection algorithm is proposed for the recognition of a wake-up sequence by the receiver. The method is characterized by low computational complexity and does not require channel parameter estimation and adaption. Furthermore, a blind packet repetition technique is introduced that accomplishes periodic impulsive noise avoidance. A realistic and straightforward narrowband power line channel model is presented and the proposed wake-up protocol is verified against it in terms of Monte-Carlo simulations. It is demonstrated, that the method leads to significant improvements compared to state of the art low-complexity PLC systems in typical NB-PLC channels.


title: Multilinear Hyperplane Hashing

abstract: Hashing has become an increasingly popular technique for fast nearest neighbor search. Despite its successful progress in classic pointto-point search, there are few studies regarding point-to-hyperplane search, which has strong practical capabilities of scaling up applications like active learning with SVMs. Existing hyperplane hashing methods enable the fast search based on randomly generated hash codes, but still suffer from a low collision probability and thus usually require long codes for a satisfying performance. To overcome this problem, this paper proposes a multilinear hyperplane hashing that generates a hash bit using multiple linear projections. Our theoretical analysis shows that with an even number of random linear projections, the multilinear hash function possesses strong locality sensitivity to hyperplane queries. To leverage its sensitivity to the angle distance, we further introduce an angular quantization based learning framework for compact multilinear hashing, which considerably boosts the search performance with less hash bits. Experiments with applications to large-scale (up to one million) active learning on two datasets demonstrate the overall superiority of the proposed approach.


title: CompareSVM: supervised, Support Vector Machine (SVM) inference of gene regularity networks

abstract: BackgroundPredication of gene regularity network (GRN) from expression data is a challenging task. There are many methods that have been developed to address this challenge ranging from supervised to unsupervised methods. Most promising methods are based on support vector machine (SVM). There is a need for comprehensive analysis on prediction accuracy of supervised method SVM using different kernels on different biological experimental conditions and network size.ResultsWe developed a tool (CompareSVM) based on SVM to compare different kernel methods for inference of GRN. Using CompareSVM, we investigated and evaluated different SVM kernel methods on simulated datasets of microarray of different sizes in detail. The results obtained from CompareSVM showed that accuracy of inference method depends upon the nature of experimental condition and size of the network.ConclusionsFor network with nodes (<200) and average (over all sizes of networks), SVM Gaussian kernel outperform on knockout, knockdown, and multifactorial datasets compared to all the other inference methods. For network with large number of nodes (~500), choice of inference method depend upon nature of experimental condition. CompareSVM is available at http://bis.zju.edu.cn/CompareSVM/.


title: Analog Models Manipulation for Effective Integration in Smart System Virtual Platforms

abstract: Analog components are fundamental blocks of smart systems, as they allow a tight interaction with the environment, in terms of both sensing/actuation and communication. This impacts on the design of the overall system, and mainly on the validation phase, that thus requires the joint simulation of digital and analog aspects. In this scenario, this paper proposes the automatic conversion of analog models to C++-based languages, to remove the overhead of co-simulation with traditional virtual platform tools. The proposed methodology allows to convert a given analog description to either: 1) a fully equivalent description or 2) an abstract representation for faster simulation which models only the aspects of interest. Effectiveness and correctness have been proved on a number of case studies that highlight the effectiveness and potentiality of the proposed methodology.


title: CO2 sorption on surface-modified carbonaceous support: Probing the influence of the carbon black microporosity and surface polarity

abstract: Abstract The use of solid sorbents is a convenient option in post-combustion CO 2 capture strategies. Sorbents selection is a key point because the materials are required to be both low-cost and versatile in typical post-combustion conditions in order to guarantee an economically advantageous overall process. This work compares strategies to tailor the chemico-physical features of carbon black (CB) by surface-modification and/or coating with a CO 2 -sorbent phase. The influence of the CB microporosity, enhanced by chemical/thermal treatments, is also taken into account. Three CB surface modifications are performed and compared: (i) oxidation and functionalization with amino-groups, (ii) coating with iron oxides and (iii) impregnation with an ionic liquid (IL). The CO 2 capture performance is evaluated on the basis of the breakthrough curves measured at atmospheric pressure and room temperature in a lab-scale fixed bed micro-reactor. Most of tested solids adsorb a CO 2 amount significantly higher than a 13X zeolite and DARCO FGD (Norit) activated carbon (up to 4 times more in the best case). The sorbents bearing basic functionalities (amino-groups and IL) exhibit the highest CO 2 sorption capacity. The use of a microporous carbonaceous support limits the accessibility of CO 2 toward the adsorbing phase (IL or FM) lowering the number of accessible binding sites for CO 2 .


title: Differentially Private Feature Selection via Stability Arguments

abstract: We design differentially private algorithms for statistical model selection. Given a data set and a large, discrete collection of “models”, each of which is a family of probability distributions, the goal is to determine the model that best “fits” the data. This is a basic problem in many areas of statistics and machine learning. We consider settings in which there is a well-defined answer, in the following sense: Suppose that there is a nonprivate model selection proceduref which is the reference to which we compare our performance. Our differentially private algorithms output the correct valuef(D) wheneverf is stable on the input data setD. We work with two notions, perturbation stability and subsampling stability. We give two classes of results: generic ones, that apply to any function with discrete output set; and specific algorithms for the problem of sparse linear regression. The algorithms we describe are efficient and in some cases match the optimal nonprivate asymptotic sample complexity. Our algorithms for sparse linear regression require analyzing the stability properties of the popular LASSO estimator. We give sufficient conditions for the LASSO estimator to be robust to small changes in the data set, and show that these conditions hold with high probability under essentially the same stochastic assumptions that are used in the literature to analyze convergence of the LASSO.


title: Long-term goals in the management of acute and chronic anxiety disorders.

abstract: Many anxiety disorders are not treated to remission (symptom-free state); however, this should be the minimum goal of therapy. Antidepressant therapies have shown significant beneficial effects in the management of anxiety disorders, with some variability in results in specific disorders. In social anxiety disorder, selective serotonin reuptake inhibitors and venlafaxine extended release (XR) have demonstrated efficacy, with response rates varying between 40% and 68%. Monoamine oxidase inhibitors and cognitive-behavioural therapies are also effective. In patients with generalized anxiety disorder, benzodiazepines, paroxetine, and venlafaxine XR have demonstrated remission rates that are 15% to 25% higher than placebo. In patients with posttraumatic stress disorder, about 60% to 70% of patients experienced a response with antidepressant therapy, compared with about 40% on placebo, while remission rates in one study were 30% with venlafaxine, 24% with sertraline, and 20% with placebo. In patients with obsessive–compulsive disorder, a 25% to 35% improvement in symptom scores was reported in 20% to 65% of patients. In the management of panic disorder, paroxetine and venlafaxine XR doubled the percentage of patients who were panic-free, compared with placebo. Ongoing antidepressant therapy further improved remission rates, and many patients with anxiety disorders required extended treatment trials before experiencing benefit. In most clinical trials, some benefits were seen within 3 to 4 weeks but continued to accrue throughout the 3- to 6-month duration of the trial. In the acute phase, patients with anxiety disorders should be treated aggressively with antidepressants for extended periods and may require long-term therapy to maintain benefits. Cognitive-behavioural therapy is another mainstay in the treatment of all anxiety disorders; exposure to feared situations is necessary to move beyond phobic avoidance and functional impairment to full recovery, the ultimate goal of therapy.


Semantic Web


title: Forensic analysis of wireless networking evidence of Android smartphones

abstract: This paper introduces a method for acquiring forensic-grade evidence from Android smartphones using open source tools. We investigate in particular cases where the suspect has made use of the smartphone's Wi-Fi or Bluetooth interfaces. We discuss the forensic analysis of four case studies, which revealed traces that were left in the inner structure of three mobile Android devices and also indicated security vulnerabilities. Subsequently, we propose a detailed plan for forensic examiners to follow when dealing with investigations of potential crimes committed using the wireless facilities of a suspect Android smartphone. This method can be followed to perform physical acquisition of data without using commercial tools and then to examine them safely in order to discover any activity associated with wireless communications. We evaluate our method using the Association of Chief Police Officers' (ACPO) guidelines of good practice for computer-based, electronic evidence and demonstrate that it is made up of an acceptable host of procedures for mobile forensic analysis, focused specifically on device Bluetooth and Wi-Fi facilities.


title: A fast MoM calculation technique using sinusoidal basis and testing functions for a wire on a dielectric substrate and its application to meander loop and grid array antennas

abstract: The input impedance matrix element of the method of moments (MoM) for an arbitrarily shaped wire antenna printed on a dielectric material Z/sub m,n/ is formulated to be composed of three terms Z/sup /spl psi/s//sub m,n/, Z/sup /spl psi///sub m,n/, and /spl Delta/Z/sub m,n/ involving single-, double-, and triple-integral calculations, respectively. The MoM based on the Z/sub m,n/ formulated in this paper (new MoM) is applied to two antennas-a meander loop antenna and a grid array antenna-as well as a simple loop used as a reference antenna. The computation time to obtain the current distribution of each antenna by the new MoM technique is compared with the time required for the conventional MoM, which has an impedance matrix element composed of four terms, all involving triple-integral calculations. It is revealed that the new MoM drastically reduces the computation time: for example, by a factor of 937 for the grid array antenna. In addition, the radiation characteristics of these two antennas are discussed. It is found that a reduced-size meander loop (62% smaller than the simple loop reference) has a radiation pattern similar to the simple loop reference. It is also found that the grid array has an axial beam radiation pattern without side lobes in the principal planes.


title: Dialog state tracking, a machine reading approach using a memory-enhanced neural network

abstract: In an end-to-end dialog system, the aim of dialog state tracking is to accurately estimate a compact representation of the current dialog status from a sequence of noisy observations produced by the speech recognition and the natural language understanding modules. A state tracking module is primarily meant to act as support for a dialog policy but it can also be used as support for dialog corpus summarization and other kinds of information extraction from transcription of dialogs. From a probabilistic view, this is achieved by maintaining a posterior distribution over hidden dialog states composed, in the simplest case, of a set of context dependent variables. Once a dialog policy is defined, deterministic or learnt, it is in charge of selecting an optimal dialog act given the estimated dialog state and a defined reward function. This paper introduces a novel method of dialog state tracking based on the general paradigm of machine reading and proposes to solve it using a memory-enhanced neural network architecture. We evaluate the proposed approach on the second Dialog State Tracking Challenge (DSTC-2) dataset that has been converted for the occasion in order to fit the relaxed assumption of a machine reading formulation where the true state is only provided at the very end of each dialog instead of providing the state updates at the utterance level. We show that the proposed tracker gives encouraging results. Finally, we propose to extend the DSTC-2 dataset with specific reasoning capabilities requirement like counting, list maintenance, yes-no question answering and indefinite knowledge management.


title: On the Universality of Invariant Networks

abstract: Constraining linear layers in neural networks to respect symmetry transformations from a group $G$ is a common design principle for invariant networks that has found many applications in machine learning. ::: In this paper, we consider a fundamental question that has received little attention to date: Can these networks approximate any (continuous) invariant function? ::: We tackle the rather general case where $G\leq S_n$ (an arbitrary subgroup of the symmetric group) that acts on $\mathbb{R}^n$ by permuting coordinates. This setting includes several recent popular invariant networks. We present two main results: First, $G$-invariant networks are universal if high-order tensors are allowed. Second, there are groups $G$ for which higher-order tensors are unavoidable for obtaining universality. ::: $G$-invariant networks consisting of only first-order tensors are of special interest due to their practical value. We conclude the paper by proving a necessary condition for the universality of $G$-invariant networks that incorporate only first-order tensors.


The KDD process for extracting useful knowledge from volumes of data


title: Common Spatial Pattern Method for Channel Selelction in Motor Imagery Based Brain-computer Interface

abstract: A brain-computer interface (BCI) based on motor imagery (MI) translates the subject's motor intention into a control signal through classifying the electroencephalogram (EEG) patterns of different imagination tasks, e.g. hand and foot movements. Characteristic EEG spatial patterns make MI tasks substantially discriminable. Multi-channel EEGs are usually necessary for spatial pattern identification and therefore MI-based BCI is still in the stage of laboratory demonstration, to some extent, due to the need for constantly troublesome recording preparation. This paper presents a method for channel reduction in Mi-based BCI. Common spatial pattern (CSP) method was employed to analyze spatial patterns of imagined hand and foot movements. Significant channels were selected by searching the maximums of spatial pattern vectors in scalp mappings. A classification algorithm was developed by means of combining linear discriminant analysis towards event-related desynchronization (ERD) and readiness potential (RP). The classification accuracies with four optimal channels were 93.45% and 91.88% for two subjects


title: Combining Concurrent Think-Aloud Protocols and Eye-Tracking Observations: An Analysis of Verbalizations and Silences

abstract: Research problem: Concurrent think-aloud (CTA) protocols are one of the dominant approaches of usability testing. However, there is still debate about the validity of the method, partly focusing on the usefulness and exhaustiveness of participants' verbalizations. The rise of eye-tracking technology sheds new light on this discussion, as participants' working processes can now be observed in more detail. Research questions: (1) What kinds of verbalizations do participants produce, and how do they relate to the information that can be directly observed using eye tracking? (2) What do eye movements reveal about cognitive processes at times when participants stop verbalizing? Literature review: Our study replicates an earlier study by Cooke (2010), who used a combination of CTA protocols and eye tracking in a small sample with experienced and highly educated participants to investigate the validity of CTA. Cooke's results suggest that the additional value of participants' verbalizations is limited: at least 77% of the verbalizations referred to things that could be easily observed with eye tracking. Methodology: We conducted a study in which 60 participants with different characteristics performed tasks on informational websites. During their task performance, they verbalized their thoughts, and simultaneously their eye movements were measured. The resulting think-aloud protocols were divided in verbalization units, which were coded into content types. Silences were registered, and eye movements during these silences were analyzed. Results and discussion: We found a different distribution of verbalization types than Cooke (2010) reported, with far more verbalizations where participants formulated doubts, judgments on the website, or expressions of frustration. In our study, verbalizations provided a substantial contribution in addition to the directly observable user problems. We measured a rather high percentage of silences (27%), during which participants most often were scanning pages for information. During these silences, interesting observations could be made about users' processes and obstacles on the website. The implication of our study is that we now have a better understanding of the types of verbalizations that a CTA evaluation might generate. Further, we know that relevant usability observations can be made during silences. A limitation is that we do not know yet the influence of specific characteristics of the evaluation setting on the types of verbalizations and silences. Future research should focus on the influence of evaluation settings on the outcomes of an evaluation, in particular, the influence of characteristics of the participants who are involved in the study.


title: Explosive Impact on Real-time

abstract: In this paper, we describe methods for real-time modelling explosions and their impacts on realistic deformable terrains. Our simulation method elaborates a strength and damage model for terrain material under explosion shock wave loading. The creation of the terrain deformation is tightly coupled with an adaptive mesh algorithm for dynamic level of detail terrain rendering to achieve both visual realism and frame coherences. A novel particle system is developed to model the effects of falling soil and rock masses distributed around the explosion area to create realistic variations. The techniques presented here can be adapted by the most of systems involving dynamic terrain rendering for modelling explosions and topology changes of the terrain for real-time interactive applications. The integrated terrain deformation also offers adaptive mechanisms for further system development.


title: Exploring Network Structure, Dynamics, and Function using NetworkX

abstract: NetworkX is a Python language package for exploration and analysis of networks and network algorithms. The core package provides data structures for representing many types of networks, or graphs, including simple graphs, directed graphs, and graphs with parallel edges and self-loops. The nodes in NetworkX graphs can be any (hashable) Python object and edges can contain arbitrary data; this flexibility makes NetworkX ideal for representing networks found in many dierent scientific fields. In addition to the basic data structures many graph algorithms are implemented for calculating network properties and structure measures: shortest paths, betweenness centrality, clustering, and degree distribution and many more. NetworkX can read and write various graph formats for easy exchange with existing data, and provides generators for many classic graphs and popular graph models, such as the Erdos-Renyi, Small World, and Barabasi-Albert models. The ease-of-use and flexibility of the Python programming language together with connection to the SciPy tools make NetworkX a powerful tool for scientific computations. We discuss some of our recent work studying synchronization of coupled oscillators to demonstrate how NetworkX enables research in the field of computational networks.


title: Envelope-Tracking Two-Stage Power Amplifier With Dual-Mode Supply Modulator for LTE Applications

abstract: This paper presents an envelope tracking power amplifier (ET PA) using a dual-mode supply modulator for handset application. The supply modulator has a combined structure with a linear amplifier and a switching amplifier. The dual-mode supply modulator operates in high-power mode and low-power mode by providing the supply voltage of the linear amplifier of 5 and 2.5 V, respectively. For 1.74-GHz long-term evolution signal with 10-MHz bandwidth, 6.44-dB peak-to-average power ratio, and 16-quadrature amplitude modulation, the ET PA delivers a power-added efficiency (PAE) of 39.8%, an evolved universal terrestrial radio access adjacent channel leakage ratio (E-UTRAACLR) of - 35.7 dBc, and an error vector magnitude (EVM) of 3.81% at an average output power of 27 dBm. The ET PA also delivers a PAE of 22.6% at an average output power of 18 dBm, which is 6.6% higher than that of the conventional ET PA and 13.1% higher than that of the standalone PA. The supply modulator is connected to a drive stage and a power stage of the PA simultaneously for further enhanced efficiency at a power back-off region. The dual-mode two-stage ET PA delivers a PAE of 38.1%, an E-UTRAACLR of - 32.9 dBc, and an EVM of 4.74% at an average output power of 27 dBm. The dual-mode two-stage ET PA also delivers a PAE of 26.3% at an average output power of 18 dBm, which is 7.8% higher than that of the conventional two-stage ET PA and 16.7% higher than that of the standalone PA.


title: Violence exposure in real-life , video games , television , movies , and the internet : is there desensitization ?

abstract: Abstract It is believed that repeated exposure to real-life and to entertainment violence may alter cognitive, affective, and behavioral processes, possibly leading to desensitization. The goal of the present study was to determine if there are relationships between real-life and media violence exposure and desensitization as reflected in related characteristics. One hundred fifty fourth and fifth graders completed measures of real-life violence exposure, media violence exposure, empathy, and attitudes towards violence. Regression analyses indicated that only exposure to video game violence was associated with (lower) empathy. Both video game and movie violence exposure were associated with stronger proviolence attitudes. The active nature of playing video games, intense engagement, and the tendency to be translated into fantasy play may explain negative impact, though causality was not investigated in the present design. The samples’ relatively low exposure to real-life violence may have limited the identification of relationships. Although difficult to quantify, desensitization to violence should be further studied using related characteristics as in the present study. Individual differences and causal relationships should also be examined.


title: Cerebellar stroke without motor deficit: clinical evidence for motor and non-motor domains within the human cerebellum

abstract: OBJECTIVE ::: To determine whether there are non-motor regions of cerebellum in which sizeable infarcts have little or no impact on motor control. ::: ::: ::: EXPERIMENTAL PROCEDURES ::: We evaluated motor deficits in patients following cerebellar stroke using a modified version of the International Cooperative Ataxia Rating Scale (MICARS). Lesion location was determined using magnetic resonance imaging (MRI) and computerized axial tomography (CT). Patients were grouped by stroke location-Group I, stroke within the anterior lobe (lobules I-V); Group 2, anterior lobe and lobule VI; Group 3, posterior lobe (lobules VI-IX; including flocculonodular lobe, lobule X); Group 4, posterior lobe but excluding lobule VI (i.e. lobules VII-X); Group 5, stroke within anterior lobe plus posterior lobe. ::: ::: ::: RESULTS ::: Thirty-nine patients were examined 8.0+/-6.0 days following stroke. There were no Group 1 patients. As mean MICARS scores for Groups 2 through 5 differed significantly (one-way analysis of variance, F(3,35)=10.9, P=0.000 03), post hoc Tukey's least significant difference tests were used to compare individual groups. Group 2 MICARS scores (n=6; mean+/-SD, 20.2+/-6.9) differed from Group 3 (n=6; 7.2+/-3.8; P=0.01) and Group 4 (n=13; 2.5+/-2.0; P=0.000 02); Group 5 (n=14; 18.6+/-12.8) also differed from Group 3 (P=0.009) and Group 4 (P=0.000 02). There were no differences between Groups 2 and 5 (P=0.71), or between Group 3 and Group 4 (P=0.273). However, Group 3 differed from Group 4 when analyzed with a two-sample t-test unadjusted for multiple comparisons (P=0.03). Thus, the cerebellar motor syndrome resulted from stroke in the anterior lobe, but not from stroke in lobules VII-X (Groups 2 plus 5, n=20, MICARS 19.1+/-11.2, vs. Group 4; P=0.000 002). Strokes involving lobule VI produced minimal motor impairment. ::: ::: ::: CONCLUSION ::: These findings demonstrate that cerebellar stroke does not always result in motor impairment, and they provide clinical evidence for topographic organization of motor versus nonmotor functions in the human cerebellum.


title: Surgical Structured Light for 3D minimally invasive surgical imaging

abstract: Surgeons perform minimally invasive surgery using an image delivered by a laparoscope and a camera system that provides a high definition 2D image, but this leaves the surgeon without 3D depth perception. The lack of depth perception can slow the surgeon, increase the risk of misidentifying structures, and/or inadvertently cause unwanted injury to tissues surrounding the surgical site. To address the lack of depth perception, we propose a Surgical Structured Light (SSL) system that includes a 3D sensor capable of measuring and modeling the surgical site during a procedure. The 3D information provided by this system can enable the surgeon to: 1) improve the navigation of tools based on precise localization of instruments in relation to structures in the surgical site, 2) allow 3D visualizations side-by-side with a standard 2D color image, and 3) precisely measure sizes of structures (e.g., tumors) and distances between structures with simple mouse clicks. We demonstrate the accuracy of our SSL system using ex-vivo data on both a cylinder calibration object as well as various plastic organs.


title: Optimizing Performance of Recurrent Neural Networks on GPUs

abstract: As recurrent neural networks become larger and deeper, training times for single networks are rising into weeks or even months. As such there is a significant incentive to improve the performance and scalability of these networks. While GPUs have become the hardware of choice for training and deploying recurrent models, the implementations employed often make use of only basic optimizations for these architectures. In this article we demonstrate that by exposing parallelism between operations within the network, an order of magnitude speedup across a range of network sizes can be achieved over a naive implementation. We describe three stages of optimization that have been incorporated into the fifth release of NVIDIA's cuDNN: firstly optimizing a single cell, secondly a single layer, and thirdly the entire network.


title: Analysis of Technology Management Functions in Finnish High Tech Companies

abstract: Technology management (TM) for companies is about sustaining and improving a company's competitiveness in the long-term. The aim of this study is to identify the common perception of TM functions in Finnish high tech compa- nies, which elements are the most critical for them, and where the biggest d evelopment needs are in practice. Th e study was realised qualitatively in 15 Finnish high tech companies. Interviews were held with persons responsible for company management ac tivities i n t he ar ea of technology. The r esults show m any s imilar characteristics i n t he ca se companies, such as the nature of the technology strategy, the mode of co-operation in technology development, or the ways of acquir- ing technologies. However, differences were also identified mainly in the companies' business models and company size. None of the functions of TM, were evaluated as the most important by the case companies. However, certain functions of TM were highly appreciated and are mostly related to the engineering activities, such as product development, technology development, information and knowledge management, life cycle management, and production process management.


title: Functional Frank-Wolfe Boosting for General Loss Functions

abstract: Boosting is a generic learning method for classification and regression. Yet, as the number of base hypotheses becomes larger, boosting can lead to a deterioration of test performance. Overfitting is an important and ubiquitous phenomenon, especially in regression settings. To avoid overfitting, we consider using $l_1$ regularization. We propose a novel Frank-Wolfe type boosting algorithm (FWBoost) applied to general loss functions. By using exponential loss, the FWBoost algorithm can be rewritten as a variant of AdaBoost for binary classification. FWBoost algorithms have exactly the same form as existing boosting methods, in terms of making calls to a base learning algorithm with different weights update. This direct connection between boosting and Frank-Wolfe yields a new algorithm that is as practical as existing boosting methods but with new guarantees and rates of convergence. Experimental results show that the test performance of FWBoost is not degraded with larger rounds in boosting, which is consistent with the theoretical analysis.


title: Challenges for Database Management in the Internet of Things

abstract: This article discusses the challenges for Database Management in the Internet of Things. We provide scenarios to illustrate the new world that will be produced by the Internet of Things, where physical objects are fully integrated into the information highway. We discuss the different types of data that will be part of the Internet of Things. These include identification, positional, environmental, historical, and descriptive data. We consider the challenges brought by the need to manage vast quantities of data across heterogeneous systems. In particular, we consider the areas of querying, indexing, process modeling, transaction handling, and integration of heterogeneous systems. We refer to the earlier work that might provide solutions for these challenges. Finally we discuss a road map for the Internet of Things and respective technical priorities.


ASSESSMENT AND COMPARISON OF METADATA SCHEMAS FOR ARCHITECTURAL HERITAGE


title: Learning Ordinal Relationships for Mid-Level Vision

abstract: We propose a framework that infers mid-level visual properties of an image by learning about ordinal relationships. Instead of estimating metric quantities directly, the system proposes pairwise relationship estimates for points in the input image. These sparse probabilistic ordinal measurements are globalized to create a dense output map of continuous metric measurements. Estimating order relationships between pairs of points has several advantages over metric estimation: it solves a simpler problem than metric regression, humans are better at relative judgements, so data collection is easier, ordinal relationships are invariant to monotonic transformations of the data, thereby increasing the robustness of the system and providing qualitatively different information. We demonstrate that this frame-work works well on two important mid-level vision tasks: intrinsic image decomposition and depth from an RGB image. We train two systems with the same architecture on data from these two modalities. We provide an analysis of the resulting models, showing that they learn a number of simple rules to make ordinal decisions. We apply our algorithm to depth estimation, with good results, and intrinsic image decomposition, with state-of-the-art results.


title: Learning Words Using Augmented Reality

abstract: This paper presents an Augmented Reality (AR) game for learning words. Thirty-two children played the AR game and the equivalent real game. We have compared the results of the two games. The results indicate that children did not found significant differences between the two games except for one question, but 81% of the children liked most the AR game.


title: Finite State Automata and Simple Recurrent Networks

abstract: We explore a network architecture introduced by Elman (1988) for predicting successive elements of a sequence. The network uses the pattern of activation over a set of hidden units from time-step t1, together with element t, to predict element t 1. When the network is trained with strings from a particular finite-state grammar, it can learn to be a perfect finite-state recognizer for the grammar. When the network has a minimal number of hidden units, patterns on the hidden units come to correspond to the nodes of the grammar, although this correspondence is not necessary for the network to act as a perfect finite-state recognizer. We explore the conditions under which the network can carry information about distant sequential contingencies across intervening elements. Such information is maintained with relative ease if it is relevant at each intermediate step; it tends to be lost when intervening elements do not depend on it. At first glance this may suggest that such networks are not relevant to natural language, in which dependencies may span indefinite distances. However, embeddings in natural language are not completely independent of earlier information. The final simulation shows that long distance sequential contingencies can be encoded by the network even if only subtle statistical properties of embedded strings depend on the early information.


title: Comparison of Safety and Efficacy of Buflomedil and Naftidrofuryl in the Treatment of Intermittent Claudication

abstract: In a group of 58 patients with peripheral arteriopathies, we have studied efficacy and safety of buflomedil hydrochloride and naftidrofuryl. Both drugs have been shown to have vasoactive properties evaluated through walking capacity and time of hyperemia. According to our results, buflomedil is more effective and safe than naftidrofuryl in the treatment of patients with intermittent claudication.


title: CodeArmor: Virtualizing the Code Space to Counter Disclosure Attacks

abstract: Code diversification is an effective strategy to prevent modern code-reuse exploits. Unfortunately, diversification techniques are inherently vulnerable to information disclosure. Recent diversification-aware ROP exploits have demonstrated that code disclosure attacks are a realistic threat, with an attacker able to read or execute arbitrary code memory and gather enough gadgets to bypass state-of-the-art code diversification defenses. In this paper, we present CodeArmor, a binary-level system to harden code diversification against all the existing read-based and execution-based code disclosure attacks. To counter such attacks, CodeArmor virtualizes the code space to completely decouple code pointer values from the concrete location of their targets in the memory address space. Using a combination of run-time randomization and pervasively deployed honey gadgets, code space virtualization probabilistically ensures that only code references that can legitimately be issued by the program are effectively translated to the concrete code space. This strategy significantly reduces the attack surface, limiting the attacker to only code pointer gadgets that can be leaked from data memory. In addition, unlike existing leakage-resistant code diversification techniques that provide similar security guarantees, CodeArmor requires no access to source code, hypervisors, or special hardware support. Our experimental results show that CodeArmor significantly raises the bar against existing and future attacks, at the cost of relatively low average performance overhead (6.9% on SPEC and 14.5% on popular server programs, and even lower—roughly halving such average overheads—when operating aggressive inlining optimizations at the binary level).


title: Learning Ontology-Based User Profiles: A Semantic Approach to Personalized Web Search

abstract: Every user has a distinct background and a specific goal when searching for information on the Web. The goal of Web search personalization is to tailor search results to a particular user based on that user's interests and preferences. Effective personalization of information access involves two important challenges: accurately identifying the user context, and organizing the information in such a way that matches the particular context. We present an approach to personalized search that involves modeling the user context as ontological profiles by assigning implicitly derived interest scores to existing concepts in a domain ontology. A spreading activation algorithm is used to maintain and incrementally update the interest scores based on the user's ongoing behavior. Our experiments show that re-ranking the search results based on the interest scores and the semantic evidence captured in an ontological user profile enables an adaptive system to present the most relevant results to the user. or localized context of current activity, semantic knowledge about the domain being investigated, and the user's profile that captures long-term interests. Each of these elements are considered to be critical sources of contextual evidence, a piece of knowledge that supports the disambiguation of the user's context for information access. In this paper, we present a novel approach for building ontological user profiles by assigning interest scores to existing concepts in a domain ontology. These profiles are maintained and updated as annotated specializations of a pre-existing reference domain ontology. We propose a spreading activation algorithm for maintaining the interest scores in the user profile based on the user's ongoing behavior. Our experimental results show that re-ranking the search results based on the interest scores and the semantic evidence in an ontological user profile successfully provides the user with a personalized view of the search results by bringing results closer to the top when they are most relevant to the user. We begin by discussing the related work and the moti- vational background behind this work. We then present our approach for building the ontological user profiles. Finally, we discuss the application of our contextual user model to Search Personalization and present the results of an extensive experimental evaluation.


title: Making Neural QA as Simple as Possible but not Simpler

abstract: Recent development of large-scale question answering (QA) datasets triggered a substantial amount of research into end-to-end neural architectures for QA. Increasingly complex systems have been conceived without comparison to simpler neural baseline systems that would justify their complexity. In this work, we propose a simple heuristic that guides the development of neural baseline systems for the extractive QA task. We find that there are two ingredients necessary for building a high-performing neural QA system: first, the awareness of question words while processing the context and second, a composition function that goes beyond simple bag-of-words modeling, such as recurrent neural networks. Our results show that FastQA, a system that meets these two requirements, can achieve very competitive performance compared with existing models. We argue that this surprising finding puts results of previous systems and the complexity of recent QA datasets into perspective.


title: The use of data mining and neural networks for forecasting stock market returns

abstract: It has been widely accepted by many studies that non-linearity exists in the financial markets and that neural networks can be effectively used to uncover this relationship. Unfortunately, many of these studies fail to consider alternative forecasting techniques, the relevance of input variables, or the performance of the models when using different trading strategies. This paper introduces an information gain technique used in machine learning for data mining to evaluate the predictive relationships of numerous financial and economic variables. Neural network models for level estimation and classification are then examined for their ability to provide an effective forecast of future values. A cross-validation technique is also employed to improve the generalization ability of several models. The results show that the trading strategies guided by the classification models generate higher risk-adjusted profits than the buy-and-hold strategy, as well as those guided by the level-estimation based forecasts of the neural network and linear regression models.


title: Gated convolutional networks based hybrid acoustic models for low resource speech recognition

abstract: In acoustic modeling for large vocabulary speech recognition, recurrent neural networks (RNN) have shown great abilities to model temporal dependencies. However, the performance of RNN is not prominent in resource limited tasks, even worse than the traditional feedforward neural networks (FNN). Furthermore, training time for RNN is much more than that for FNN. In recent years, some novel models are provided. They use non-recurrent architectures to model long term dependencies. In these architectures, they show that using gate mechanism is an effective method to construct acoustic models. On the other hand, it has been proved that using convolution operation is a good method to learn acoustic features. We hope to take advantages of both these two methods. In this paper we present a gated convolutional approach to low resource speech recognition tasks. The gated convolutional networks use convolutional architectures to learn input features and a gate to control information. Experiments are conducted on the OpenKWS, a series of low resource keyword search evaluations. From the results, the gated convolutional networks relatively decrease the WER about 6% over the baseline LSTM models, 5% over the DNN models and 3% over the BLSTM models. In addition, the new models accelerate the learning speed by more than 1.8 and 3.2 times compared to that of the baseline LSTM and BLSTM models.


Sentiment Composition


title: Data Vitalization's Perspective Towards Smart City: A Reference Model for Data Service Oriented Architecture

abstract: The data are complexity and heterogeneous in the city-scale. When the tasks are nonlinear, existing systems cannot perform well. This paper proposes a data service oriented architecture that is based on the data vitalization theory. In this perspective, the vitalized cells are the basic units of a system, which are organized through nested and/or layer structure. A smart service platform bases on crowd intelligence network is developed according the theory of data vitalization. It allows the developers upload their own services and data to open for other developers. Then the architecture and implement technologies of the platform are introduced. At last, an application example of this architecture is given, which is used to sense social hot-spots in a period and rebuild the scene of the hot-spots. The platform and example show that the data vitalization theory achieves high scalability and flexibility for both linear and non-linear tasks.


title: Network-based prediction of protein function

abstract: Functional annotation of proteins is a fundamental problem in the post-genomic era. The recent availability of protein interaction networks for many model species has spurred on the development of computational methods for interpreting such data in order to elucidate protein function. In this review, we describe the current computational approaches for the task, including direct methods, which propagate functional information through the network, and module-assisted methods, which infer functional modules within the network and use those for the annotation task. Although a broad variety of interesting approaches has been developed, further progress in the field will depend on systematic evaluation of the methods and their dissemination in the biological community.


title: GPU-based Pedestrian Detection for Autonomous Driving

abstract: We propose a real-time pedestrian detection system for the embedded Nvidia Tegra X1 GPU-CPU hybrid platform. The pipeline is composed by the following state-of-the-art algorithms: Histogram of Local Binary Patterns (LBP) and Histograms of Oriented Gradients (HOG) features extracted from the input image; Pyramidal Sliding Window technique for candidate generation; and Support Vector Machine (SVM) for classification. Results show a 8x speedup in the target Tegra X1 platform and a better performance/watt ratio than desktop CUDA platforms in study.


title: Acoustic Scene Classification

abstract: In this article, we present an account of the state of the art in acoustic scene classification (ASC), the task of classifying environments from the sounds they produce. Starting from a historical review of previous research in this area, we define a general framework for ASC and present different implementations of its components. We then describe a range of different algorithms submitted for a data challenge that was held to provide a general and fair benchmark for ASC techniques. The data set recorded for this purpose is presented along with the performance metrics that are used to evaluate the algorithms and statistical significance tests to compare the submitted methods.


title: Analysis of the Cyber-Security of industry 4.0 technologies based on RAMI 4.0 and identification of requirements

abstract: The exhaustive digitalization of the economy, and to be more specific, of industrial production systems results in a new quality of information transparency. This is the basis for added values in terms of effectiveness, quality, and individuality. However, these added values also result in an increased exposure to Cyber-Security threats, due to the increased digitalization, information transparency and standardization. In this work, the procedural model for a Cyber-Security analysis based on reference architecture model Industry 4.0 (RAMI 4.0) and the VDI/VDE guideline 2182 is exemplary shown for the use case of a Cloud-based monitoring of the production. The derived procedure supports the identification of protection demands and allows a risk-based selection of suitable countermeasures.


title: Automatically Mining Facets for Queries from Their Search Results

abstract: We address the problem of finding query facets which are multiple groups of words or phrases that explain and summarize the content covered by a query. We assume that the important aspects of a query are usually presented and repeated in the query’s top retrieved documents in the style of lists, and query facets can be mined out by aggregating these significant lists. We propose a systematic solution, which we refer to as QDMiner, to automatically mine query facets by extracting and grouping frequent lists from free text, HTML tags, and repeat regions within top search results. Experimental results show that a large number of lists do exist and useful query facets can be mined by QDMiner. We further analyze the problem of list duplication, and find better query facets can be mined by modeling fine-grained similarities between lists and penalizing the duplicated lists.


title: SEMROD: Secure and Efficient MapReduce Over HybriD Clouds

abstract: This paper describes SEMROD, a sensitive data aware MapReduce (MR) framework for hybrid clouds. SEMROD steers data and computation through public and private machines in such a way that no knowledge about sensitive data is leaked to public machines. For this purpose, SEMROD keeps trace of intermediate keys (generated during MR execution) that become sensitive, based on which it makes dynamic task scheduling decisions. SEMROD guarantees that adversaries viz. public machines) cannot gain any ``additional'' information about sensitive data from either the data stored on public machines or the communication between public and private machines during job execution. SEMROD extends naturally from a single MR job to multi-phase MR jobs that result, for instance, from compiling Hive queries into MR jobs. Using SEMROD, computation that may involve sensitive data can exploit public machines, thereby bringing significant performance benefits. Such computation would otherwise be restricted to only private clouds. Our experiments clearly demonstrate performance advantages to using SEMROD as compared with other secure alternatives, even when the percentage of sensitive data is as high as 50%.


title: Understanding customer knowledge sharing in web-based discussion boards: An exploratory study

abstract: Purpose – The proliferation and advance of web‐based technologies create expanded opportunities for retailers to gain a better understanding of their customers. However, the success of these web‐based discussion boards depends solely on whether customers are willing to share their knowledge and experience with other customers in these discussion boards. Thus, this study aims at identifying the factors that drive knowledge sharing among customers in web‐based discussion boards.Design/methodology/approach – An exploratory study with 104 respondents was conducted to identify and categorize the key factors of customer knowledge sharing in web‐based discussion boards.Findings – The results indicate that the enjoyment of helping others is the most frequently cited reason for customer knowledge sharing in web‐based discussion boards. On the other hand, the lack of knowledge self‐efficacy is the mostly cited reason explaining why customers do not want to share knowledge with others.Research limitations/implicatio...


title: Evaluating user interface systems research

abstract: The development of user interface systems has languished with the stability of desktop computing. Future systems, however, that are off-the-desktop, nomadic or physical in nature will involve new devices and new software systems for creating interactive applications. Simple usability testing is not adequate for evaluating complex systems. The problems with evaluating systems work are explored and a set of criteria for evaluating new UI systems work is presented.


title: Prospective study of talc use and ovarian cancer.

abstract: BACKGROUND ::: Perineal talc use has been associated with an increased risk of ovarian cancer in a number of case-control studies; however, this association remains controversial because of limited supporting biologic evidence and the potential for recall bias or selection bias in case-control studies. In this study, we conducted a prospective analysis of perineal talc use and the risk of ovarian cancer. ::: ::: ::: METHODS ::: The Nurses' Health Study is a prospective study of 121 700 female registered nurses in the United States who were aged 30-55 years at enrollment in 1976. Talc use was ascertained in 1982 by use of a self-administered questionnaire: after exclusions, 78 630 women formed the cohort for analysis. Three hundred seven epithelial ovarian cancers subsequently diagnosed in this cohort through June 1, 1996, were confirmed by medical record review and met inclusion criteria. Proportional hazards models by use of pooled logistic regression were used to derive relative risks (RRs) and 95% confidence intervals (CIs). ::: ::: ::: RESULTS ::: In 1982, 40.4% (n = 31 789) of the cohort reported ever using talc, and 14.5% (n = 11 411) reported ever using talc daily. We observed no overall association with ever talc use and epithelial ovarian cancer (multivariate RR = 1.09; 95% CI = 0.86-1.37) and no increase in risk of ovarian cancer with increasing frequency of use. There was a modest elevation in risk for ever talc use and invasive serous ovarian cancer (multivariate RR = 1.40; 95% CI = 1.02-1.91). The risk of epithelial ovarian cancer for talc users was not greater among women who had never had a tubal ligation (multivariate RR = 0.97; 95% CI = 0.71-1.32). ::: ::: ::: CONCLUSION ::: Our results provide little support for any substantial association between perineal talc use and ovarian cancer risk overall; however, perineal talc use may modestly increase the risk of invasive serous ovarian cancer.


Overview of the ImageCLEF 2016 Medical Task


title: Dual Attention Network for Scene Segmentation

abstract: In this paper, we address the scene segmentation task by capturing rich contextual dependencies based on the self-attention mechanism. Unlike previous works that capture contexts by multi-scale features fusion, we propose a Dual Attention Networks (DANet) to adaptively integrate local features with their global dependencies. Specifically, we append two types of attention modules on top of traditional dilated FCN, which model the semantic interdependencies in spatial and channel dimensions respectively. The position attention module selectively aggregates the features at each position by a weighted sum of the features at all positions. Similar features would be related to each other regardless of their distances. Meanwhile, the channel attention module selectively emphasizes interdependent channel maps by integrating associated features among all channel maps. We sum the outputs of the two attention modules to further improve feature representation which contributes to more precise segmentation results. We achieve new state-of-the-art segmentation performance on three challenging scene segmentation datasets, i.e., Cityscapes, PASCAL Context and COCO Stuff dataset. In particular, a Mean IoU score of 81.5% on Cityscapes test set is achieved without using coarse data.


title: Coherence-Aware Neural Topic Modeling

abstract: Topic models are evaluated based on their ability to describe documents well (i.e. low perplexity) and to produce topics that carry coherent semantic meaning. In topic modeling so far, perplexity is a direct optimization target. However, topic coherence, owing to its challenging computation, is not optimized for and is only evaluated after training. In this work, under a neural variational inference framework, we propose methods to incorporate a topic coherence objective into the training process. We demonstrate that such a coherence-aware topic model exhibits a similar level of perplexity as baseline models but achieves substantially higher topic coherence.


Intent Discovery Through Unsupervised Semantic Text Clustering


A Novel Approach for Effective Recognition of the Code-Switched Data on Monolingual Language Model


title: 3D-TV Content Creation: Automatic 2D-to-3D Video Conversion

abstract: Three-dimensional television (3D-TV) is the next major revolution in television. A successful rollout of 3D-TV will require a backward-compatible transmission/distribution system, inexpensive 3D displays, and an adequate supply of high-quality 3D program material. With respect to the last factor, the conversion of 2D images/videos to 3D will play an important role. This paper provides an overview of automatic 2D-to-3D video conversion with a specific look at a number of approaches for both the extraction of depth information from monoscopic images and the generation of stereoscopic images. Some challenging issues for the success of automatic 2D-to-3D video conversion are pointed out as possible research topics for the future.


title: A learning-based approach to direction of arrival estimation in noisy and reverberant environments

abstract: This paper presents a learning-based approach to the task of direction of arrival estimation (DOA) from microphone array input. Traditional signal processing methods such as the classic least square (LS) method rely on strong assumptions on signal models and accurate estimations of time delay of arrival (TDOA) . They only work well in relatively clean conditions, but suffer from noise and reverberation distortions. In this paper, we propose a learning-based approach that can learn from a large amount of simulated noisy and reverberant microphone array inputs for robust DOA estimation. Specifically, we extract features from the generalised cross correlation (GCC) vectors and use a multilayer perceptron neural network to learn the nonlinear mapping from such features to the DOA. One advantage of the learning based method is that as more and more training data becomes available, the DOA estimation will become more and more accurate. Experimental results on simulated data show that the proposed learning based method produces much better results than the state-of-the-art LS method. The testing results on real data recorded in meeting rooms show improved root-mean-square error (RMSE) compared to the LS method.


title: Optimal Combined Overcurrent and Distance Relays Coordination Incorporating Intelligent Overcurrent Relays Characteristic Selection

abstract: In this paper, a new objective function (OF) within the genetic algorithm (GA) approach is presented to solve the optimization problem of coordination of overcurrent and distance relays. The existing objective function of overcurrent (O/C) relays coordination is improved and extended to include the coordination of O/C and distance relays. Various O/C relays characteristics are considered within the approach to select the best of them by GA to fulfill optimal coordination. The proposed method is applied to two different power system networks. Simulation results demonstrate that the method can obtain feasible and effective solutions for optimal coordination in the practical power system networks.


title: Short Tamil sentence similarity calculation using knowledge-based and corpus-based similarity measures

abstract: Sentence similarity calculation plays an important role in text processing-related research. Many unsupervised techniques such as knowledge-based techniques, corpus-based techniques, string similarity based techniques, and graph alignment techniques are available to measure sentence similarity. However, none of these techniques have been experimented with Tamil. In this paper, we present the first-ever system to measure semantic similarity for Tamil short phrases using a hybrid approach that makes use of knowledge-based and corpus-based techniques. We tested this system with 2000 general sentence pairs and 100 mathematical sentence pairs. For the dataset of 2000 sentence pairs, this approach achieved a Mean Squared Error of 0.195 and a Pearson Correlation factor of 0.815. For the 100 mathematical sentence pairs, this approach achieved an 85% of accuracy.


title: SPHINCS: practical stateless hash-based signatures

abstract: This paper introduces a high-security post-quantum stateless hash-based signature scheme that signs hundreds of messages per second on a modern 4-core 3.5GHz Intel CPU. Signatures are 41 KB, public keys are 1 KB, and private keys are 1 KB. The signature scheme is designed to provide long-term \(2^{128}\) security even against attackers equipped with quantum computers. Unlike most hash-based designs, this signature scheme is stateless, allowing it to be a drop-in replacement for current signature schemes.


title: Robust and Secure Image Hashing via Non-Negative Matrix Factorizations

abstract: In this paper, we propose the use of non-negative matrix factorization (NMF) for image hashing. In particular, we view images as matrices and the goal of hashing as a randomized dimensionality reduction that retains the essence of the original image matrix while preventing intentional attacks of guessing and forgery. Our work is motivated by the fact that standard-rank reduction techniques, such as QR and singular value decomposition, produce low-rank bases which do not respect the structure (i.e., non-negativity for images) of the original data. We observe that NMFs have two very desirable properties for secure image hashing applications: 1) The additivity property resulting from the non-negativity constraints results in bases that capture local components of the image, thereby significantly reducing misclassification and 2) the effect of geometric attacks on images in the spatial domain manifests (approximately) as independent identically distributed noise on NMF vectors, allowing the design of detectors that are both computationally simple and, at the same time, optimal in the sense of minimizing error probabilities. Receiver operating characteristics analysis over a large image database reveals that the proposed algorithms significantly outperform existing approaches for image hashing.


What Does Pain Hurt?


title: Comparing the sensitivity of information retrieval metrics

abstract: Information retrieval effectiveness is usually evaluated using measures such as Normalized Discounted Cumulative Gain (NDCG), Mean Average Precision (MAP) and Precision at some cutoff (Precision@k) on a set of judged queries. Recent research has suggested an alternative, evaluating information retrieval systems based on user behavior. Particularly promising are experiments that interleave two rankings and track user clicks. According to a recent study, interleaving experiments can identify large differences in retrieval effectiveness with much better reliability than other click-based methods. We study interleaving in more detail, comparing it with traditional measures in terms of reliability, sensitivity and agreement. To detect very small differences in retrieval effectiveness, a reliable outcome with standard metrics requires about 5,000 judged queries, and this is about as reliable as interleaving with 50,000 user impressions. Amongst the traditional measures, NDCG has the strongest correlation with interleaving. Finally, we present some new forms of analysis, including an approach to enhance interleaving sensitivity.


title: Evaluation of LDR, tone mapped and HDR stereo matching using cost-volume filtering approach

abstract: We present stereo matching solutions based on a fast costvolume filtering approach for High Dynamic Range (HDR) scenes. Multi-exposed stereo images are captured and used to generate HDR and Tone Mapped (TM) images of the left and right views. We perform stereo matching on conventional, Low Dynamic Range (LDR) images, original HDR, as well as TM images by customizing the matching algorithm for each of them. An evaluation on the disparity maps computed from the different approaches demonstrates that stereo matching on HDR images outperforms conventional LDR stereo matching and TM stereo matching, with the most discriminative disparity maps achieved by using HDR color information and log-luminance gradient values for matching cost calculation.


title: Forgery Detection Based on Intrinsic Document Contents

abstract: Nowadays, Document forgery detection is becoming increasingly important as forgery techniques are becoming available even to untrained users. Hence, documents that do not contain any extrinsic security features (e.g. invoices) have become easier to forge. We previously presented a method to detect manipulated documents based on distortions introduced during the forgery creation process. In this paper, several approaches are explored to improve accuracy and time taken to detect forgeries based on document distortions. The main idea behind the presented approaches is to automatically identify which parts of a document belong to the template (and hence would remain static across different documents originating from the same source) and then detect distortions in those parts only. An improvement up to 29% in accuracy of forgery detection is observed compared to our previous work. Furthermore, we also present an approximation of the original method that results in a reduction in run time of the method by several orders of magnitude, while having only a marginal reduction in its accuracy.


A Lane Detection Algorithm Using Geometry Information and Modified Hough Transform


title: Are there cultural differences in learning style

abstract: Abstract This study examines the role that culture plays in the way individuals learn. Experiential learning theory is used to describe the learning process and the Kolb Learning Style Inventory is used to assess differences in how individuals learn. Using the framework for categorizing cultural differences from the Global Leadership and Organizational Effectiveness (GLOBE) study, national cultures are examined by cultural clusters and individual cultural dimensions. The first part of the study assesses the relative influence of culture in comparison to gender, age, level of education and area of specialization of 533 respondents born in and currently residing in 7 nations. We found that a significant portion of the variance in the preference for abstract conceptualization was explained by culture, gender, level of education and area of specialization. The variability in preference for active experimentation over reflective observation was accounted for by age and area of specialization. The impact of culture was only marginally significant. In the second part of the study where we examined the influence of individual culture dimensions in shaping the learning style preferences, we discovered that individuals tend to have a more abstract learning style in countries that are high in in-group collectivism, institutional collectivism, uncertainty avoidance, future orientation and gender egalitarianism. Individuals may have a more reflective learning style in countries that are high in in-group collectivism, uncertainty avoidance and assertiveness.


title: Circuit dynamics of adaptive and maladaptive behaviour

abstract: The recent development of technologies for investigating specific components of intact biological systems has allowed elucidation of the neural circuitry underlying adaptive and maladaptive behaviours. Investigators are now able to observe and control, with high spatio-temporal resolution, structurally defined intact pathways along which electrical activity flows during and after the performance of complex behaviours. These investigations have revealed that control of projection-specific dynamics is well suited to modulating behavioural patterns that are relevant to a broad range of psychiatric diseases. Structural dynamics principles have emerged to provide diverse, unexpected and causal insights into the operation of intact and diseased nervous systems, linking form and function in the brain.


title: A Principled Way of Assessing Visualization Literacy

abstract: We describe a method for assessing the visualization literacy (VL) of a user. Assessing how well people understand visualizations has great value for research (e. g., to avoid confounds), for design (e. g., to best determine the capabilities of an audience), for teaching (e. g., to assess the level of new students), and for recruiting (e. g., to assess the level of interviewees). This paper proposes a method for assessing VL based on Item Response Theory. It describes the design and evaluation of two VL tests for line graphs, and presents the extension of the method to bar charts and scatterplots. Finally, it discusses the reimplementation of these tests for fast, effective, and scalable web-based use.


title: Simultaneous tracking and balancing of humanoid robots for imitating human motion capture data

abstract: This paper presents a control framework for humanoid robots that uses all joints simultaneously to track motion capture data and maintain balance. The controller comprises two main components: a balance controller and a tracking controller. The balance controller uses a regulator designed for a simplified humanoid model to obtain the desired input to keep balance based on the current state of the robot. The simplified model is chosen so that a regulator can be designed systematically using, for example, optimal control. An example of such controller is a linear quadratic regulator designed for an inverted pendulum model. The desired inputs are typically the center of pressure and/or torques of some representative joints. The tracking controller then computes the joint torques that minimize the difference from desired inputs as well as the error from desired joint accelerations to track the motion capture data, considering exact full-body dynamics. We demonstrate that the proposed controller effectively reproduces different styles of storytelling motion using dynamics simulation considering limitations in hardware.


title: Recognition of Power-Quality Disturbances Using S-Transform-Based ANN Classifier and Rule-Based Decision Tree

abstract: This paper deals with a modified technique for the recognition of single stage and multiple power quality (PQ) disturbances. An algorithm based on Stockwell's transform and artificial neural network-based classifier and a rule-based decision tree is proposed in this paper. The analysis and classification of single stage PQ disturbances consisting of both events and variations such as sag, swell, interruption, harmonics, transients, notch, spike, and flicker are presented. Moreover, the proposed algorithm is also applied on multiple PQ disturbances such as harmonics with sag, swell, flicker, and interruption. A database of these PQ disturbances based on IEEE-1159 standard is generated in MATLAB for simulation studies. The proposed algorithm extracts significant features of various PQ disturbances using S-transform, which are used as input to this hybrid classifier for the classification of PQ disturbances. Satisfactory results of effective recognition and classification of PQ disturbances are obtained with the proposed algorithm. Finally, the proposed method is also implemented on real-time PQ events acquired in a laboratory to confirm the validity of this algorithm in practical conditions.


title: Tunneling for Transparency: A Large-Scale Analysis of End-to-End Violations in the Internet

abstract: Detecting violations of application-level end-to-end connectivity on the Internet is of significant interest to researchers and end users; recent studies have revealed cases of HTTP ad injection and HTTPS man-in-the-middle attacks. Unfortunately, detecting such end-to-end violations at scale remains difficult, as it generally requires having the cooperation of many nodes spread across the globe. Most successful approaches have relied either on dedicated hardware, user-installed software, or privileged access to a popular web site. In this paper, we present an alternate approach for detecting end-to-end violations based on Luminati, a HTTP/S proxy service that routes traffic through millions of end hosts. We develop measurement techniques that allow Luminati to be used to detect end-to-end violations of DNS, HTTP, and HTTPS, and, in many cases, enable us to identify the culprit. We present results from over 1.2m nodes across 14k ASes in 172 countries, finding that up to 4.8% of nodes are subject to some type of end-to-end connectivity violation. Finally, we are able to use Luminati to identify and measure the incidence of content monitoring, where end-host software or ISP middleboxes record users' HTTP requests and later re-download the content to third-party servers.


The Pocket Guide to Critical Appraisal: A Handbook for Health Care Professionals


title: Extension of an iterative closest point algorithm for simultaneous localization and mapping in corridor environments

abstract: Three-dimensional (3-D) simultaneous localization and mapping (SLAM) is a crucial technique for intelligent robots to navigate autonomously and execute complex tasks. It can also be applied to shape measurement, reverse engineering, and many other scientific or engineering fields. A widespread SLAM algorithm, named KinectFusion, performs well in environments with complex shapes. However, it cannot handle translation uncertainties well in highly structured scenes. This paper improves the KinectFusion algorithm and makes it competent in both structured and unstructured environments. 3-D line features are first extracted according to both color and depth data captured by Kinect sensor. Then the lines in the current data frame are matched with the lines extracted from the entire constructed world model. Finally, we fuse the distance errors of these line-pairs into the standard KinectFusion framework and estimate sensor poses using an iterative closest point-based algorithm. Comparative experiments with the KinectFusion algorithm and one state-of-the-art method in a corridor scene have been done. The experimental results demonstrate that after our improvement, the KinectFusion algorithm can also be applied to structured environments and has higher accuracy. Experiments on two open access datasets further validated our improvements.