title: Online anomaly detection with concept drift adaptation using recurrent neural networks

abstract: Anomaly detection in time series is an important task with several practical applications. The common approach of training one model in an offline manner using historical data is likely to fail under dynamically changing and non-stationary environments where the definition of normal behavior changes over time making the model irrelevant and ineffective. In this paper, we describe a temporal model based on Recurrent Neural Networks (RNNs) for time series anomaly detection to address challenges posed by sudden or regular changes in normal behavior. The model is trained incrementally as new data becomes available, and is capable of adapting to the changes in the data distribution. RNN is used to make multi-step predictions of the time series, and the prediction errors are used to update the RNN model as well as detect anomalies and change points. Large prediction error is used to indicate anomalous behavior or a change (drift) in normal behavior. Further, the prediction errors are also used to update the RNN model in such a way that short term anomalies or outliers do not lead to a drastic change in the model parameters whereas high prediction errors over a period of time lead to significant updates in the model parameters such that the model rapidly adapts to the new norm. We demonstrate the efficacy of the proposed approach on a diverse set of synthetic, publicly available and proprietary real-world datasets.


title: Circular Buffer Rate-Matched Polar Codes

abstract: A practical rate-matching system for constructing rate-compatible polar codes is proposed. The proposed polar code circular buffer rate-matching is suitable for transmissions on communication channels that support hybrid automatic repeat request communications, as well as for flexible resource-element rate-matching on single transmission channels. Our proposed circular buffer rate matching scheme also incorporates a bit-mapping scheme for transmission on bit-interleaved coded modulation (BICM) channels using higher order modulations. An interleaver is derived from a puncturing order obtained with a low complexity progressive puncturing search algorithm on a base code of short length, and has the flexibility to achieve any desired rate at the desired code length, through puncturing or repetition. The rate-matching scheme is implied by a two-stage polarization, for transmission at any desired code length, code rate, and modulation order, and is shown to achieve the symmetric capacity of BICM channels. Numerical results on AWGN and fast fading channels show that the rate-matched polar codes have a competitive performance when compared with the spatially-coupled quasi-cyclic LDPC codes or LTE turbo codes, while having similar rate-dematching storage and computational complexities.


title: The application of a three-column internal fixation system with anatomical locking plates on comminuted fractures of the tibial plateau

abstract: AIM ::: The purpose of this study was to describe the operative procedures and clinical outcomes of a new three-column internal fixation system with anatomical locking plates on the tibial plateau to treat complex three-column fractures of the tibial plateau. ::: ::: ::: METHODS ::: From June 2011 to May 2015, 14 patients with complex three-column fractures of the tibial plateau were treated with reduction and internal fixation through an anterolateral approach combined with a posteromedial approach. The patients were randomly divided into two groups: a control group which included seven cases using common locking plates, and an experimental group which included seven cases with a new three-column internal fixation system with anatomical locking plates. ::: ::: ::: RESULTS ::: The mean operation time of the control group was 280.7 ± 53.7 minutes, which was 215.0 ± 49.1 minutes in the experimental group. The mean intra-operative blood loss of the control group was 692.8 ± 183.5 ml, which was 471.4 ± 138.0 ml in the experimental group. The difference was statistically significant between the two groups above. The differences were not statistically significant between the following mean numbers of the two groups: Rasmussen score immediately after operation; active extension-flexion degrees of knee joint at three and 12 months post-operatively; tibial plateau varus angle (TPA) and posterior slope angle (PA) immediately after operation, at three and at 12 months post-operatively; HSS (The Hospital for Special Surgery) knee-rating score at 12 months post-operatively. All fractures healed. ::: ::: ::: CONCLUSION ::: A three-column internal fixation system with anatomical locking plates on tibial plateau is an effective and safe tool to treat complex three-column fractures of the tibial plateau and it is more convenient than the common plate.


title: Meta-Reinforcement Learning of Structured Exploration Strategies

abstract: Exploration is a fundamental challenge in reinforcement learning (RL). Many of the current exploration methods for deep RL use task-agnostic objectives, such as information gain or bonuses based on state visitation. However, many practical applications of RL involve learning more than a single task, and prior tasks can be used to inform how exploration should be performed in new tasks. In this work, we explore how prior tasks can inform an agent about how to explore effectively in new situations. We introduce a novel gradient-based fast adaptation algorithm -- model agnostic exploration with structured noise (MAESN) -- to learn exploration strategies from prior experience. The prior experience is used both to initialize a policy and to acquire a latent exploration space that can inject structured stochasticity into a policy, producing exploration strategies that are informed by prior knowledge and are more effective than random action-space noise. We show that MAESN is more effective at learning exploration strategies when compared to prior meta-RL methods, RL without learned exploration strategies, and task-agnostic exploration methods. We evaluate our method on a variety of simulated tasks: locomotion with a wheeled robot, locomotion with a quadrupedal walker, and object manipulation.


title: Power factor correction in brushless DC motor drive using a boost-forward SSIPP

abstract: This paper deals with the power factor correction (PFC) in a brushless DC (BLDC) motor drive using a boost-forward single-stage isolated power-factor-corrected power supply (SSIPP). The DC link voltage of the voltage source inverter (VSI) feeding BLDC motor is varied for controlling the speed of a BLDC motor. Moreover, the BLDC motor is commutated electronically such that the six solid state switches of VSI operates in low switching frequency and offers low switching losses. A triple-discontinuous inductor current mode (DICM) of the boost-forward SSIPP is used for DC bus voltage control and PFC is achieved inherently. Performance evaluation of proposed drive is carried out at varying speeds and supply voltages with PFC operation at AC mains. The power quality indices achieved are within the limits of international power quality standard.


title: Margined Winner-Take-All: New learning rule for pattern recognition

abstract: The neocognitron is a deep (multi-layered) convolutional neural network that can be trained to recognize visual patterns robustly. In the intermediate layers of the neocognitron, local features are extracted from input patterns. In the deepest layer, based on the features extracted in the intermediate layers, input patterns are classified into classes. A method called IntVec (interpolating-vector) is used for this purpose. This paper proposes a new learning rule called margined Winner-Take-All (mWTA) for training the deepest layer. Every time when a training pattern is presented during the learning, if the result of recognition by WTA (Winner-Take-All) is an error, a new cell is generated in the deepest layer. Here we put a certain amount of margin to the WTA. In other words, only during the learning, a certain amount of handicap is given to cells of classes other than that of the training vector, and the winner is chosen under this handicap. By introducing the margin to the WTA, we can generate a compact set of cells, with which a high recognition rate can be obtained with a small computational cost. The ability of this mWTA is demonstrated by computer simulation.


title: Studying Probabilistic Faults in Evolved Non-Uniform Cellular Automata

abstract: We study the effects of random faults on the behavior of one-dimensional, non-uniform cellular automata (CA), where the local update rule need not be identical for all grid sites. The CA systems examined were obtained via an approach known as cellular programming, which involves the evolution of non-uniform CAs to perform non-trivial computational tasks. Using the "system replicas" methodology, involving a comparison between a perfect, non-perturbed version of the CA and a faulty one, we find that our evolved systems exhibit graceful degradation in performance, able to tolerate a certain level of faults. We then "zoom" into the fault-tolerant zone, where "good" computational behavior is exhibited, introducing measures to fine-tune our understanding of the faulty CAs' operation. We study the error level as a function of time and space, as well as the recuperation time needed to recover from faults. Our investigation reveals an intricate interplay between temporal and spatial factors, with the presence of different rules in the grid giving rise to complex dynamics. Studies along this line may have applications to future computing systems that will contain thousands or even millions of computing elements, rendering crucial the issue of resilience.


title: High-Bandwidth Explicit Model Predictive Control of Electrical Drives

abstract: Field-oriented control (FOC) has proven effective for controlling ac drives with good dynamic performance. However, operation at low-switching frequencies and the sensitivity of traditional feedforward loops to system parameters pose severe limitations on the achievable performance and require a tedious tuning procedure. In this paper, we present a systematic cascade explicit model predictive control framework for the FOC of electrical drives, resolving the aforementioned issues while being sufficiently simple to be widely implemented on various ac drive systems. The resulting closed-loop system exhibits high dynamic performance for all operating points, even at low-switching frequencies. We present experiments with a permanent-magnet machine and an induction motor, demonstrating the practical feasibility and the merits of the proposed framework over traditional controller designs for electrical drives.


title: Incidence of hip fracture in the Republic of Ireland and future projections: a population-based study

abstract: SUMMARY ::: There are no previously published data for hip fracture incidence rates in the Republic of Ireland. The rates of fracture for the total population aged 50 years and over were 407 and 140 per 100,000 for females and males, respectively. Assuming a stable incidence rate, the absolute number of hip fractures occurring on an annual basis is expected to increase by 100% by the year 2026. Health care providers should accommodate these increases into future strategies. ::: ::: ::: INTRODUCTION ::: Hip fractures are an important cause of morbidity in the elderly and represent an increasing burden on health service providers. Significant regional and international variation in incidence rates of hip fracture exists. In order for health service providers to be able to commit adequate resources to the care of hip fracture patients, accurate estimates of hip fracture numbers are required now and for the future. To date, there have been no reported incidence rates for hip fracture in the Republic of Ireland. ::: ::: ::: METHODS ::: We performed a search of the national hospital in-patient enquiry database for all hip fractures occurring in the Republic of Ireland between 2000 and 2004. Population data was obtained from the 2002 national census data. Population projections were made for the years 2016 and 2026 to allow determination of expected hip fracture numbers for those years. ::: ::: ::: RESULTS ::: The rates of hip fracture in the Irish population aged 50 years and over were 407 and 140 per 100,000 for females and males, respectively. Annual hip fracture numbers are expected to increase by 100% by the year 2026, assuming a stable incidence rate. ::: ::: ::: CONCLUSION ::: Health service providers should take into account the expected increase in hip fracture numbers when allocating resources for the care of these patients in the mid-term future.


title: Analysis and modeling of air-gap and zigzag leakage fluxes in a surface-mounted permanent-magnet Machine

abstract: In this paper the magnetic characteristics of surface-mounted permanent-magnet machines are analyzed and modeled. The air-gap and zigzag leakage fluxes are analytically expressed in terms of the magnetic material properties and the motor dimensions. Both factors are essential quantities for the accurate prediction of the flux distribution within the machine and of the machine torque. Therefore, they are desired for the purpose of machine design and optimization. In order to evaluate the validity of the proposed models, the finite-element method (FEM) analysis is used. The results show that the errors between the FEM results and analytical predictions are less than 7% for the nonsaturated tooth flux and less than 17% for the saturated case. Finally, the models are applied to a novel permanent-magnet machine design.


title: How to verify computation with a rational network

abstract: The present paper introduces a practical protocol for provably secure, outsourced computation. Our protocol minimizes overhead for verification by requiring solutions to withstand an interactive game between a prover and challenger. For optimization problems, the best or nearly best of all submitted solutions is expected to be accepted by this approach. Financial incentives and deposits are used in order to overcome the problem of fake participants.


title: Robust and fast moving object detection in a non-stationary camera via foreground probability based sampling

abstract: This paper proposes a robust and fast scheme to detect moving objects in a non-stationary camera. The state-of-the art methods still do not give a satisfactory performance due to drastic frame changes in a non-stationary camera. To improve the robustness in performance, we additionally use the spatio-temporal properties of moving objects. We build the foreground probability map which reflects the spatiotemporal properties, then we selectively apply the detection procedure and update the background model only to the selected pixels using the foreground probability. The foreground probability is also used to refine the initial detection results to obtain a clear foreground region. We compare our scheme quantitatively and qualitatively to the state-of-the-art methods in the detection quality and speed. The experimental results show that our scheme outperforms all other compared methods.


title: Object recognition from local scale-invariant features

abstract: An object recognition system has been developed that uses a new class of local image features. The features are invariant to image scaling, translation, and rotation, and partially invariant to illumination changes and affine or 3D projection. These features share similar properties with neurons in inferior temporal cortex that are used for object recognition in primate vision. Features are efficiently detected through a staged filtering approach that identifies stable points in scale space. Image keys are created that allow for local geometric deformations by representing blurred image gradients in multiple orientation planes and at multiple scales. The keys are used as input to a nearest neighbor indexing method that identifies candidate object matches. Final verification of each match is achieved by finding a low residual least squares solution for the unknown model parameters. Experimental results show that robust object recognition can be achieved in cluttered partially occluded images with a computation time of under 2 seconds.


title: StaDynA: Addressing the Problem of Dynamic Code Updates in the Security Analysis of Android Applications

abstract: Static analysis of Android applications can be hindered by the presence of the popular dynamic code update techniques: dynamic class loading and reflection. Recent Android malware samples do actually use these mechanisms to conceal their malicious behavior from static analyzers. These techniques defuse even the most recent static analyzers that usually operate under the "closed world" assumption (the targets of reflective calls can be resolved at analysis time; only classes reachable from the class path at analysis time are used at runtime). Our proposed solution allows existing static analyzers to remove this assumption. This is achieved by combining static and dynamic analysis of applications in order to reveal the hidden/updated behavior and extend static analysis results with this information. This paper presents design, implementation and preliminary evaluation results of our solution called StaDynA.


title: DCT-based processing of dynamic features for robust speech recognition

abstract: In this paper, we explore the various properties of cepstral time coefficients (CTC) in speech recognition, and then propose several methods to refine the CTC construction process. It is found that CTC are the filtered version of mel-frequency cepstral coefficients (MFCC), and the used filters are from the discrete cosine transform (DCT) matrix. We modify these DCT-based filters by windowing, removing DC gain, and varying the filter length. The speech recognition task using Aurora-2 digit database show that the proposed methods can enhance the original CTC in improving the recognition accuracy. The resulting relative error reduction is around 20%.


title: Exploiting location based social networks in business predictions

abstract: The growing use of Location Based Social Networks especially in recent years provides large amount of data transactions. These data transactions attract many data mining researchers to infer various information from them. In this paper, a geographic business prediction technique is proposed, which infers business usage by exploiting data published about venues in Location Based Social Networks. The proposed technique is beneficial for investors and business decision makers. The proposed geo-business prediction technique considers spatial and categorical factors in the prediction process. Both factors affect the prediction accuracy rather than using traditional spatial prediction techniques, which are usually used where only the location feature is involved in the prediction process. Additionally, an outlier filter is proposed and applied to the data to avoid extreme values involvement in the prediction process in order to achieve better prediction accuracy. To test the proposed technique, an experimental case study is implemented. It uses data extracted from Foursquare about business venues in Texas State in the United States of America. The proposed geo business prediction technique has shown to provide better prediction accuracy than k nearest neighbor spatial prediction. The Application of the outlier filter, results in even higher prediction accuracy for the proposed technique.


title: Stock Market Efficiency and Economic Efficiency: Is There a Connection?

abstract: In a capitalist economy prices serve to equilibrate supply and demand for goods and services, continually changing to reallocate resources to their most efficient uses. However, secondary stock market prices, often viewed as the most 'informationally efficient' prices in the economy, have no direct role in the allocation of equity capital since managers have discretion in determining the level of investment. What is the link between stock price informational efficiency and economic efficiency? We present a model of the stock market in which: (i) managers have discretion in making investments and must be given the right incentives; and (ii) stock market traders may have important information that managers do not have about the value of prospective investment opportunities. In equilibrium, information in stock prices will guide investment decisions because managers will be compensated based on informative stock prices in the future. The stock market indirectly guides investment by transferring two kinds of information: information about investment opportunities and information about managers' past decisions. The fact that stock prices only have an indirect role suggests that the stock market may not be a necessary institution for the efficient allocation of equity. We emphasize this by providing an example of a banking system that performs as well.


Ernst Maier-Reimer: The discovery of silence


title: CALIBRATION OF THE LOCAL VOLATILITY IN A GENERALIZED BLACK–SCHOLES MODEL USING TIKHONOV REGULARIZATION

abstract: Following an approach introduced by Lagnado and Osher [J. Comput. Finance, 1 (1) (1997), pp. 13--25], we study Tikhonov regularization applied to an inverse problem important in mathematical finance, that of calibrating, in a generalized Black--Scholes model, a local volatility function from observed vanilla option prices. We first establish $W^{1,2}_{p}$ estimates for the Black--Scholes and Dupire equations with measurable ingredients. Applying general results available in the theory of Tikhonov regularization for ill-posed nonlinear inverse problems, we then prove the stability of this approach, its convergence towards a minimum norm solution of the calibration problem (which we assume to exist), and discuss convergence rates issues.


title: The Cramer Distance as a Solution to Biased Wasserstein Gradients

abstract: The Wasserstein probability metric has received much attention from the machine learning community. Unlike the Kullback-Leibler divergence, which strictly measures change in probability, the Wasserstein metric reflects the underlying geometry between outcomes. The value of being sensitive to this geometry has been demonstrated, among others, in ordinal regression and generative modelling, and most recently in reinforcement learning. In this paper we describe three natural properties of probability divergences that we believe reflect requirements from machine learning: sum invariance, scale sensitivity, and unbiased sample gradients. The Wasserstein metric possesses the first two properties but, unlike the Kullback-Leibler divergence, does not possess the third. We provide empirical evidence suggesting this is a serious issue in practice. Leveraging insights from probabilistic forecasting we propose an alternative to the Wasserstein metric, the Cramer distance. We show that the Cramer distance possesses all three desired properties, combining the best of the Wasserstein and Kullback-Leibler divergences. We give empirical results on a number of domains comparing these three divergences. To illustrate the practical relevance of the Cramer distance we design a new algorithm, the Cramer Generative Adversarial Network (GAN), and show that it has a number of desirable properties over the related Wasserstein GAN.


title: Algebraic gossip: a network coding approach to optimal multiple rumor mongering

abstract: The problem of simultaneously disseminating k messages in a large network of n nodes, in a decentralized and distributed manner, where nodes only have knowledge about their own contents, is studied. In every discrete time-step, each node selects a communication partner randomly, uniformly among all nodes and only one message can be transmitted. The goal is to disseminate rapidly, with high probability, all messages to all nodes. It is shown that a random linear coding (RLC) based protocol disseminates all messages to all nodes in time ck+/spl Oscr/(/spl radic/kln(k)ln(n)), where c<3.46 using pull-based dissemination and c<5.96 using push-based dissemination. Simulations suggest that c<2 might be a tighter bound. Thus, if k/spl Gt/(ln(n))/sup 3/, the time for simultaneous dissemination RLC is asymptotically at most ck, versus the /spl Omega/(klog/sub 2/(n)) time of sequential dissemination. Furthermore, when k/spl Gt/(ln(n))/sup 3/, the dissemination time is order optimal. When k/spl Lt/(ln(n))/sup 2/, RLC reduces dissemination time by a factor of /spl Omega/(/spl radic/k/lnk) over sequential dissemination. The overhead of the RLC protocol is negligible for messages of reasonable size. A store-and-forward mechanism without coding is also considered. It is shown that this approach performs no better than a sequential approach when k=/spl prop/n. Owing to the distributed nature of the system, the proof requires analysis of an appropriate time-varying Bernoulli process.


title: IFM3IRS: Information fusion retrieval system with knowledge-assisted text and visual features based on medical conceptual model

abstract: The technology of medical data production has been rapidly changed over the past few years. Modern computer technology has created the possibility of creating multi-modal medical images. Medical data often contain multi-modal information such as visual information (image) as well as textual information. Both types of information are important for medical retrieval system (MRS). Due to the information limitation at different levels of sources, the application of information fusion becomes a real need in medical application. In this research, an information fusion framework was built to develop the multi-modality medical image retrieval system (IFM3IRS). The framework utilizes two sources of information involving text and visual-based retrieval process. The application is based on sequential order where the result from text-based process will automatically be the input in visual-based process. The main contributions of this paper are the development of a new ranking model called MedHieCon ranking model which applies semantic concepts of modality, anatomy and pathology in text-based process and also the learning approach of medical images using medical concept model in visual-based process. ImageCLEFmed 2010 data collection was used to evaluate IFM3IRS and it shows that our information fusion framework is in top list among other researchers. Although text-based retrieval system has proven to be a better performance in MRS; it is significant to determine the overall performance improvements which include the fusion of text and image.


Racial and Ethnic Differences in Ocular Anatomy


title: Visualizing Project Evolution through Abstract Syntax Tree Analysis

abstract: What is a developer's contribution to a repository? By only counting commits and number of lines changed, existing tools that visualize source code repositories (such as GitHub's graphs) fall short on showing the effective contributions made by each developer. When many commits are viewed as a group, the details are lost. Commit information can be misleading since lines of code give no indication of what was actually being worked on without careful examination of the changed code. Providing a semantic view of this information could provide deeper insights into how software projects evolve since changes to design and features are not clearly visible from line changes alone. We present TypeV: a method for visualizing Java source code repositories. Instead of counting line changes in a commit we extract detailed type information over time by using the differences between abstract syntax trees (ASTs). We are then able to track the additions and deletions of declarations and invocations for each type. Furthermore, we can track each author's type usage over time. Using TypeV, we examine specific cases in well-known repositories where our tool reveals interesting and useful information. We then compare type coverage information from the AST compared to file coverage to determine if unique information is provided by type information.


title: The Structure of Collaborative Tagging Systems

abstract: Collaborative tagging describes the process by which many users add metadata in the form of keywords to shared content. Recently, collaborative tagging has grown in popularity on the web, on sites that allow users to tag bookmarks, photographs and other content. In this paper we analyze the structure of collaborative tagging systems as well as their dynamical aspects. Specifically, we discovered regularities in user activity, tag frequencies, kinds of tags used, bursts of popularity in bookmarking and a remarkable stability in the relative proportions of tags within a given url. We also present a dynamical model of collaborative tagging that predicts these stable patterns and relates them to imitation and shared knowledge.


title: Dimension reduction techniques for the integrative analysis of multi-omics data

abstract: State-of-the-art next-generation sequencing, transcriptomics, proteomics and other high-throughput ‘omics' technologies enable the efficient generation of large experimental data sets. These data may yield unprecedented knowledge about molecular pathways in cells and their role in disease. Dimension reduction approaches have been widely used in exploratory analysis of single omics data sets. This review will focus on dimension reduction approaches for simultaneous exploratory analyses of multiple data sets. These methods extract the linear relationships that best explain the correlated structure across data sets, the variability both within and between variables (or observations) and may highlight data issues such as batch effects or outliers. We explore dimension reduction techniques as one of the emerging approaches for data integration, and how these can be applied to increase our understanding of biological systems in normal physiological function and disease.


title: A Multi-Modality Deep Network for Cold-Start Recommendation

abstract: Collaborative filtering (CF) approaches, which provide recommendations based on ratings or purchase history, perform well for users and items with sufficient interactions. However, CF approaches suffer from the cold-start problem for users and items with few ratings. Hybrid recommender systems that combine collaborative filtering and content-based approaches have been proved as an effective way to alleviate the cold-start issue. Integrating contents from multiple heterogeneous data sources such as reviews and product images is challenging for two reasons. Firstly, mapping contents in different modalities from the original feature space to a joint lower-dimensional space is difficult since they have intrinsically different characteristics and statistical properties, such as sparse texts and dense images. Secondly, most algorithms only use content features as the prior knowledge to improve the estimation of user and item profiles but the ratings do not directly provide feedback to guide feature extraction. To tackle these challenges, we propose a tightly-coupled deep network model for fusing heterogeneous modalities, to avoid tedious feature extraction in specific domains, and to enable two-way information propagation from both content and rating information. Experiments on large-scale Amazon product data in book and movie domains demonstrate the effectiveness of the proposed model for cold-start recommendation.


title: Thank You, Come Again: Examining the Role of Quality and Trust on eCommerce Repurchase Intentions

abstract: As the pertinence of online consumer shopping continues to grow, more and more e-retailers are erecting websites. In this increasingly competitive environment, building customer loyalty and retaining customers is integral to achieving sustained profitability. While one stream of literature has suggested that e-retailers should concentrate on improving quality, another stream has recommended that the focus should be on building trust with customers. This paper represents an early, working attempt to synthesize these parallel streams, investigating how the interplay between three forms of quality (information, system, and service) and trust help to retain customers. Integrating information systems and marketing research, the results of this paper suggest that trust mediates the relationship between each type of quality and both satisfaction and repurchase intentions. Furthermore, of the three types of quality that are examined, service quality engenders the greatest impact on trust, followed by information quality then system quality. The paper concludes with a discussion of this preliminary model as well as directions for the future development of this project.


title: Security Issues in Networks with Internet Access

abstract: This paper describes the basic principles of designing and administering a relatively secure network. The principles are illustrated by describing the security issues a hypothetical company faces as the networks that support its operations evolve from strictly private, through a mix of Internet and private nets, to a final state in which the Internet is finally integrated into its operations and the company participates in international electronic commerce. At each stage, the vulnerabilities and threats that the company faces, the countermeasures that it considers, and the residual risk the company accepts are noted. Network security policy and services are discussed, and a description of Internet architecture and vulnerabilities provides additional technical detail underlying the scenario. Lastly, a number of building blocks for secure networks are presented that can mitigate some of the vulnerabilities.


title: Facial Recognition Enabled Smart Door Using Microsoft Face API

abstract: Privacy and Security are two universal rights and, to ensure that in our daily life we are secure, a lot of research is going on in the field of home security, and IoT is the turning point for the industry, where we connect everyday objects to share data for our betterment. Facial recognition is a well-established process in which the face is detected and identified out of the image. We aim to create a smart door, which secures the gateway on the basis of who we are. In our proof of concept of a smart door we have used a live HD camera on the front side of setup attached to a display monitor connected to the camera to show who is standing in front of the door, also the whole system will be able to give voice outputs by processing text them on the Raspberry Pi ARM processor used and show the answers as output on the screen. We are using a set of electromagnets controlled by the microcontroller, which will act as a lock. So a person can open the smart door with the help of facial recognition and at the same time also be able to interact with it. The facial recognition is done by Microsoft face API but our state of the art desktop application operating over Microsoft Visual Studio IDE reduces the computational time by detecting the face out of the photo and giving that as the output to Microsoft Face API, which is hosted over Microsoft Azure cloud support.


title: Mulsemedia: State of the Art, Perspectives, and Challenges

abstract: Mulsemedia—multiple sensorial media—captures a wide variety of research efforts and applications. This article presents a historic perspective on mulsemedia work and reviews current developments in the area. These take place across the traditional multimedia spectrum—from virtual reality applications to computer games—as well as efforts in the arts, gastronomy, and therapy, to mention a few. We also describe standardization efforts, via the MPEG-V standard, and identify future developments and exciting challenges the community needs to overcome.


title: Digital creativity: Research themes and framework

abstract: We analysis and review of 3591 digital creativity related publications.Twenty leading research themes and a digital-creativity research framework are identified.Central, popular and peripheral studies in digital creativity research are also indicated.A discussion of the future of digital creativity research is also provided. Digital creativity is defined as the creativity manifested in all forms that are driven by digital technologies. Due to its novelty and interdisciplinary nature, the scope, perspective, and main research themes of digital creativity study are still unclear to date. Therefore, we utilized the intellectual structure technique developed by the information scientist to help clarify the scope and themes of this research domain. We analyzed 3591 relevant literatures and tentatively identified major research themes to facilitate the comprehension and study of digital creativity. The resulting framework summarizes the progress in digital creativity research and provides future research directions.


title: Using Text Mining and Clustering to Group Research Proposals for Research Project Selection

abstract: Great efforts have been taken to guarantee the efficiency of research project selection. Before research proposals are assigned to appropriate experts for evaluation, research proposals needed to be grouped according to their similarities in research topics. This paper applies an ensemble method to cluster research proposals to support research project selection. Several cluster algorithms are applied to group the proposals and the merits of each algorithm in text clustering are made full use of in the form of ensemble method. Based on the real data from Beijing Natural Science Foundation (BNSF), empirical experiments are conducted to validate the effectiveness of the proposed method. Meanwhile, the proposed method promotes the efficiency in the proposal grouping process.


title: Networked Pixels: Strategies for Building Visual and Auditory Images with Distributed Independent Devices

abstract: This paper describes the development of the hardware and software for Bloom, a light installation installed at Kew Gardens, London in December of 2016. The system is made up of a set of nearly 1000 distributed pixel devices each with LEDs, GPS sensor, and sound hardware, networked together with WiFi to form a display system. Media design for this system required consideration of the distributed nature of the devices. We outline the software and hardware designed for this system, and describe two approaches to the software and media design, one whereby we employ the distributed devices themselves for computation purposes (the approach we ultimately selected), and another whereby the devices are controlled from a central server that is performing most of the computation necessary. We then review these approaches and outline possibilities for future research.


title: FaceChange: Attaining Neighbor Node Anonymity in Mobile Opportunistic Social Networks With Fine-Grained Control

abstract: In mobile opportunistic social networks (MOSNs), mobile devices carried by people communicate with each other directly when they meet for proximity-based MOSN services (e.g., file sharing) without the support of infrastructures. In current methods, when nodes meet, they simply communicate with their real IDs, which leads to privacy and security concerns. Anonymizing real IDs among neighbor nodes solves such concerns. However, this prevents nodes from collecting real ID-based encountering information, which is needed to support MOSN services. Therefore, in this paper, we propose FaceChange that can support both anonymizing real IDs among neighbor nodes and collecting real ID-based encountering information. For node anonymity, two encountering nodes communicate anonymously. Only when the two nodes disconnect with each other, each node forwards an encrypted encountering evidence to the encountered node to enable encountering information collection. A set of novel schemes are designed to ensure the confidentiality and uniqueness of encountering evidences. FaceChange also supports fine-grained control over what information is shared with the encountered node based on attribute similarity (i.e., trust), which is calculated without disclosing attributes. Advanced extensions for sharing real IDs between mutually trusted nodes and more efficient encountering evidence collection are also proposed. Extensive analysis and experiments show the effectiveness of FaceChange on protecting node privacy and meanwhile supporting the encountering information collection in MOSNs. Implementation on smartphones also demonstrates its energy efficiency.


title: TGF-beta in allogeneic stem cell transplantation: friend or foe?

abstract: Donor treatment with granulocyte-colony-stimulating factor (G-CSF) attenuates the ability of donor T cells to induce acute graft-versus-host disease (aGVHD) but increases the severity of chronic GVHD (cGVHD). We investigated the role of the regulatory cytokine transforming growth factor beta (TGF-beta) in this paradox in well-established murine models of aGVHD and cGVHD wherein recipients undergo transplantation with splenocytes from donors treated with G-CSF. Neutralization of TGF-beta after stem-cell transplantation (SCT) significantly increased the severity of aGVHD, and the concurrent prevention of interleukin-10 (IL-10) production further exaggerated this effect. Early after SCT, donor T cells were the predominant source of TGF-beta and were able to attenuate aGVHD in a TGF-beta-dependent fashion. Although the neutralization of TGF-beta augmented the proliferation and expansion of donor T cells after SCT, it paradoxically impaired cellular cytotoxicity to host antigens and associated graft-versus-leukemia (GVL) effects. In cGVHD, neutralization of TGF-beta from day 14 after SCT attenuated histologic abnormalities, and CD11b+ mononuclear cells infiltrating sclerodermatous skin produced 50-fold more TGF-beta than corresponding T cells. Thus, though the production of TGF-beta by donor T cells early after transplantation attenuates aGVHD and is required for optimal GVL, the production of TGF-beta late after SCT is preferentially from mononuclear cells and mediates cGVHD. These data have important implications for the timing of therapeutic TGF-beta neutralization to prevent cGVHD after allogeneic SCT.


title: A Novel Support Vector Machine Approach to High Entropy Data Fragment Classification.

abstract: A major challenge in digital forensics is the efficient and accurate file type classification of a fragment of evidence data, in the absence of header and file system information. A typical approach to this problem is to classify the fragment based on simple statistics, such as the entropy and the statistical distance of byte histograms. This approach is ineffective when dealing with high entropy data, such as multimedia and compressed files, all of which often appear to be random. We propose a method incorporating a support vector machine (SVM). In particular, we extract feature vectors from the byte frequencies of a given fragment, and use an SVM to predict the type of the fragment under supervised learning. Our method is efficient and achieves high accuracy for high entropy data fragments.


First Case Report of Fecal Microbiota Transplantation in a Cat in


title: Design for Existential Crisis in the Anthropocene Age

abstract: What should be our orientation to the socio-technical as climate predictions worsen; ecological crises and wars escalate mass migration and refugee numbers; right-wing populism sweeps through politics; automation threatens workers' jobs and austerity policies destabilize society? What is to be done when it is not "business as usual" and even broken concepts of progress seem no longer to be progressing? We ask how to design for the common good, focusing on human needs for meaning, fulfillment, dignity and decency, qualities which technology struggles to support but can easily undermine. We juxtapose the design of computing that offers hope with that which offers only distraction, propose four modes to design for (being attentive, critical, different and in it together) and conclude with a plea to avoid tools that encourage a blinkered existence at a time of great uncertainty and change.


title: Pseudo test collections for training and tuning microblog rankers

abstract: Recent years have witnessed a persistent interest in generating pseudo test collections, both for training and evaluation purposes. We describe a method for generating queries and relevance judgments for microblog search in an unsupervised way. Our starting point is this intuition: tweets with a hashtag are relevant to the topic covered by the hashtag and hence to a suitable query derived from the hashtag. Our baseline method selects all commonly used hashtags, and all associated tweets as relevance judgments; we then generate a query from these tweets. Next, we generate a timestamp for each query, allowing us to use temporal information in the training process. We then enrich the generation process with knowledge derived from an editorial test collection for microblog search. We use our pseudo test collections in two ways. First, we tune parameters of a variety of well known retrieval methods on them. Correlations with parameter sweeps on an editorial test collection are high on average, with a large variance over retrieval algorithms. Second, we use the pseudo test collections as training sets in a learning to rank scenario. Performance close to training on an editorial test collection is achieved in many cases. Our results demonstrate the utility of tuning and training microblog search algorithms on automatically generated training material.


EvoloPy: An open-source nature-inspired optimization framework in python


Autonomous Vehicle Navigation by Building 3 D Map and by Detecting Human Trajectory using LIDAR


title: Speech Processing for Early Alzheimer Disease Diagnosis: Machine Learning Based Approach

abstract: Alzheimer's disease (AD) is a neurodegenerative disease characterized by the insidious onset of cognitive, emotional and language disorders. These attacks are sufficiently intense to affect the daily social and professional lives of patients. Today, in the absence of a reliable diagnosis and effective curative treatments, fighting this disease is becoming a real public health issue, prompting research to consider non-drug techniques. Among these techniques, speech processing is proving to be a relevant and innovative field of investigation. Several Machine Learning algorithms achieved promising results in distinguishing AD from healthy control subjects. Alternatively, many other factors such as feature extraction, the number of attributes for feature selection, used classifiers, may affect the prediction accuracy evaluation. To surmount these weaknesses, a model is suggested which include a feature extraction step followed by imperative attribute selection and classification is achieved using a machine learning classifiers. The current findings show that the proposed model can be strongly recommended for classifying Alzheimer's patient from healthy individuals with an accuracy of 79%.


title: Authentication and Authorization for Mobile IoT Devices using Bio-features: Recent Advances and Future Trends

abstract: Biofeatures are fast becoming a key tool to authenticate the IoT devices; in this sense, the purpose of this investigation is to summarise the factors that hinder biometrics models’ development and deployment on a large scale, including human physiological (e.g., face, eyes, fingerprints-palm, or electrocardiogram) and behavioral features (e.g., signature, voice, gait, or keystroke). The different machine learning and data mining methods used by authentication and authorization schemes for mobile IoT devices are provided. Threat models and countermeasures used by biometrics-based authentication schemes for mobile IoT devices are also presented. More specifically, we analyze the state of the art of the existing biometric-based authentication schemes for IoT devices. Based on the current taxonomy, we conclude our paper with different types of challenges for future research efforts in biometrics-based authentication schemes for IoT devices.


title: Requirements interaction management

abstract: Requirements interaction management (RIM) is the set of activities directed toward the discovery, management, and disposition of critical relationships among sets of requirements, which has become a critical area of requirements engineering. This survey looks at the evolution of supporting concepts and their related literature, presents an issues-based framework for reviewing processes and products, and applies the framework in a review of RIM state-of-the-art. Finally, it presents seven research projects that exemplify this emerging discipline.


title: Skene’s gland cyst: a simple marsupialization technique

abstract: INTRODUCTION AND HYPOTHESIS ::: We present a simple approach to the marsupialization of a Skene's gland cyst. ::: ::: ::: METHODS ::: Our technique facilitates suture placement to exteriorize the cyst wall to efficiently treat a distal Skene's gland cyst and reduce the risk of recurrence. ::: ::: ::: CONCLUSION ::: Marsupialization is an accepted option for the surgical management of Skene's gland cyst. This technique is an effective and streamlined approach to Skene's gland marsupialization.


title: Tabula rasa: Model transfer for object category detection

abstract: Our objective is transfer training of a discriminatively trained object category detector, in order to reduce the number of training images required. To this end we propose three transfer learning formulations where a template learnt previously for other categories is used to regularize the training of a new category. All the formulations result in convex optimization problems. Experiments (on PASCAL VOC) demonstrate significant performance gains by transfer learning from one class to another (e.g. motorbike to bicycle), including one-shot learning, specialization from class to a subordinate class (e.g. from quadruped to horse) and transfer using multiple components. In the case of multiple training samples it is shown that a detection performance approaching that of the state of the art can be achieved with substantially fewer training samples.


title: Robust free space detection in occupancy grid maps by methods of image analysis and dynamic B-spline contour tracking

abstract: We propose a new method for free space detection and description for Advanced Driver Assistance Systems (ADAS) and autonomous vehicles. The detection is based on successive morphological image processing steps that are applied to an occupancy grid map-based environment representation acquired by an automotive radar sensor. The boundary of the found free space segment is traced and serves as a virtual measurement for a time-variant Kalman Filter in order to estimate and track the control points of a two-dimensional B-spline closed free space contour over time. In contrast to existing free space detection methods, the proposed solution incorporates knowledge about the vehicle's dimensions and does not exclude free space that is not directly in the line of sight, but mapped beforehand, as well as free space behind obstacles. Furthermore, the algorithm shows advantages in terms of an intuitive control over spatial and temporal smoothness of the solution as well as an inherent robustness due to model-based filtering. Moreover, the control points of the B-spline curve are proposed as a new low-dimensional representation of drivable free space of arbitrary shape. The effectiveness of the algorithm is demonstrated in real traffic scenarios.


title: A gentle introduction to bilateral filtering and its applications

abstract: The bilateral filter is a non-linear technique that can blur an image while respecting strong edges. Its ability to decompose an image into different scales without causing haloes after modification has made it ubiquitous in computational photography applications such as tone mapping, style transfer, relighting, and desnoising. This text provides a graphical, intuitive introduction to bilateral filtering, a practical guide for efficient implementation, an overview of its numerous applications, as well as mathematical analysis.


title: Combining ICA with SVR for prediction of finance time series

abstract: Due to the complexity of real task for learning, the learning algorithms based on ERM (empirical risk minimization) principle always have good fit for the training samples, but bad prediction for future samples. SVM(support vector machine) as a new kernel learning algorithm has embodied Vapnik's SRM (structure risk minimization) principle. It overcomes the problem for ERM posing by optimizing the object consisting of the learning error on the training samples and the capacity of hypothesis space. We'll use nonlinear support vector regression (SVR) in the prediction of finance time series. This may be illumined by the success of BP neural network and RBF neural network applied in finance time series. Before applying SVR, we use another new hot tool called ICA(independent component analysis)for feature extraction. Traditional PCA only takes into account the uncorrelated between features, however ICA considers the independence which is a more strict condition than PCA. Experiments have shown that our method based on ICA+SVR are superior to other methods, i.e. PCA+SVR, KPCA+SVR.


title: MPD: multiplex primer design for next-generation targeted sequencing

abstract: BackgroundTargeted resequencing offers a cost-effective alternative to whole-genome and whole-exome sequencing when investigating regions known to be associated with a trait or disease. There are a number of approaches to targeted resequencing, including microfluidic PCR amplification, which may be enhanced by multiplex PCR. Currently, there is no open-source software that can design next-generation multiplex PCR experiments that ensures primers are unique at a genome-level and efficiently pools compatible primers.ResultsWe present MPD, a software package that automates the design of multiplex PCR primers for next-generation sequencing. The core of MPD is implemented in C for speed and uses a hashed genome to ensure primer uniqueness, avoids placing primers over sites of known variation, and efficiently pools compatible primers. A JavaScript web application (http://multiplexprimer.io) utilizing the MPD Perl package provides a convenient platform for users to make designs. Using a realistic set of genes identified by genome-wide association studies (GWAS), we achieve 90% coverage of all exonic regions using stringent design criteria. Using the first 47 primer pools for wet-lab validation, we sequenced ~25Kb at 99.7% completeness with a mean coverage of 300X among 313 samples simultaneously and identified 224 variants. The number and nature of variants we observe are consistent with high quality sequencing.ConclusionsMPD can successfully design multiplex PCR experiments suitable for next-generation sequencing, and simplifies retooling targeted resequencing pipelines to focus on new targets as new genetic evidence emerges.


title: Data compression

abstract: This paper surveys a variety of data compression methods spanning almost 40 years of research, from the work of Shannon, Fano, and Huffman in the late 1940s to a technique developed in 1986. The aim of data compression is to reduce redundancy in stored or communicated data, thus increasing effective data density. Data compression has important application in the areas of file storage and distributed systems. Concepts from information theory as they relate to the goals and evaluation of data compression methods are discussed briefly. A framework for evaluation and comparison of methods is constructed and applied to the algorithms presented. Comparisons of both theoretical and empirical natures are reported, and possibilities for future research are suggested.


title: Temporal Network Analysis of Literary Texts

abstract: We study temporal networks of characters in literature focussing on Alice’s Adventures in Wonderland (1865) by Lewis Carroll and the anonymous La Chanson de Roland (around 1100). The former, one of the most influential pieces of nonsense literature ever written, describes the adventures of Alice in a fantasy world with logic plays interspersed along the narrative. The latter, a song of heroic deeds, depicts the Battle of Roncevaux in 778 A.D. during Charlemagne’s campaign on the Iberian Peninsula. We apply methods recently developed by Taylor et al. [Taylor, D., Myers, S. A., Clauset, A., Porter, M. A. and Mucha, P. J., Eigenvector-based centrality measures for temporal networks, CoRR (2015).] to find time-averaged eigenvector centralities, Freeman indices and vitalities of characters. We show that temporal networks are more appropriate than static ones for studying stories, as they capture features that the time-independent approaches fail to yield.


title: Interference between Concurrent Resistance and Endurance Exercise: Molecular Bases and the Role of Individual Training Variables

abstract: Concurrent training is defined as simultaneously incorporating both resistance and endurance exercise within a periodized training regime. Despite the potential additive benefits of combining these divergent exercise modes with regards to disease prevention and athletic performance, current evidence suggests that this approach may attenuate gains in muscle mass, strength, and power compared with undertaking resistance training alone. This has been variously described as the interference effect or concurrent training effect. In recent years, understanding of the molecular mechanisms mediating training adaptation in skeletal muscle has emerged and provided potential mechanistic insight into the concurrent training effect. Although it appears that various molecular signaling responses induced in skeletal muscle by endurance exercise can inhibit pathways regulating protein synthesis and stimulate protein breakdown, human studies to date have not observed such molecular 'interference' following acute concurrent exercise that might explain compromised muscle hypertrophy following concurrent training. However, given the multitude of potential concurrent training variables and the limitations of existing evidence, the potential roles of individual training variables in acute and chronic interference are not fully elucidated. The present review explores current evidence for the molecular basis of the specificity of training adaptation and the concurrent interference phenomenon. Additionally, insights provided by molecular and performance-based concurrent training studies regarding the role of individual training variables (i.e., within-session exercise order, between-mode recovery, endurance training volume, intensity, and modality) in the concurrent interference effect are discussed, along with the limitations of our current understanding of this complex paradigm.


title: Evaluation of bilirubin concentration in hemolysed samples, is it really impossible? The altitude-curve cartography approach to interfered assays.

abstract: BACKGROUND ::: Neonatal jaundice might lead to severe clinical consequences. Measurement of bilirubin in samples is interfered by hemolysis. Over a method-depending cut-off value of measured hemolysis, bilirubin value is not accepted and a new sample is required for evaluation although this is not always possible, especially with newborns and cachectic oncological patients. When usage of different methods, less prone to interferences, is not feasible an alternative recovery method for analytical significance of rejected data might help clinicians to take appropriate decisions. ::: ::: ::: METHODS ::: We studied the effects of hemolysis over total bilirubin measurement, comparing hemolysis-interfered bilirubin measurement with the non-interfered value. Interference curves were extrapolated over a wide range of bilirubin (0-30 mg/mL) and hemolysis (H Index 0-1100). ::: ::: ::: RESULTS ::: Interference "altitude" curves were calculated and plotted. A bimodal acceptance table was calculated. Non-interfered bilirubin of given samples was calculated, by linear interpolation between the nearest lower and upper interference curves. ::: ::: ::: CONCLUSIONS ::: Rejection of interference-sensitive data from hemolysed samples for every method should be based not upon the interferent concentration but upon a more complex algorithm based upon the concentration-dependent bimodal interaction between the interfered analyte and the measured interferent. The altitude-curve cartography approach to interfered assays may help laboratories to build up their own method-dependent algorithm and to improve the trueness of their data by choosing a cut-off value different from the one (-10% interference) proposed by manufacturers. When re-sampling or an alternative method is not available the altitude-curve cartography approach might also represent an alternative recovery method for analytical significance of rejected data.


title: Efficient Similarity Join and Search on Multi-Attribute Data

abstract: In this paper we study similarity join and search on multi- attribute data. Traditional methods on single-attribute data have pruning power only on single attributes and cannot efficiently support multi-attribute data. To address this problem, we propose a prefix tree index which has holis- tic pruning ability on multiple attributes. We propose a cost model to quantify the prefix tree which can guide the prefix tree construction. Based on the prefix tree, we devise a filter-verification framework to support similarity search and join on multi-attribute data. The filter step prunes a large number of dissimilar results and identifies some candi- dates using the prefix tree and the verification step verifies the candidates to generate the final answer. For similar- ity join, we prove that constructing an optimal prefix tree is NP-complete and develop a greedy algorithm to achieve high performance. For similarity search, since one prefix tree cannot support all possible search queries, we extend the cost model to support similarity search and devise a budget-based algorithm to construct multiple high-quality prefix trees. We also devise a hybrid verification algorithm to improve the verification step. Experimental results show our method significantly outperforms baseline approaches.


title: Multi-dimensional particle swarm optimization for dynamic environments

abstract: Particle swarm optimization (PSO) was proposed as an optimization technique for static environments; however, many real problems are dynamic, meaning that the environment and the characteristics of the global optimum can change in time. In this paper, we adapt recent techniques, which successfully address several major problems of PSO and exhibit a significant performance over multi-modal and non-stationary environments. In order to address the pre-mature convergence problem and improve the rate of PSO's convergence to the global optimum, Fractional Global Best Formation (FGBF) technique is used. FGBF basically collects all the best dimensional components and fractionally creates an artificial Global Best particle (aGB) that has the potential to be a better ''guide'' than the PSO's native gbest particle. To establish follow-up of local optima, we then introduce a novel multi-swarm algorithm, which enables each swarm to converge to a different optimum and use FGBF technique distinctively. Finally for the multi-dimensional dynamic environments where the optimum dimension also changes in time, we utilize a recent PSO technique, the multi-dimensional (MD) PSO, which re-forms the native structure of the swarm particles in such a way that they can make inter-dimensional passes with a dedicated dimensional PSO process. Therefore, in a multi-dimensional search space where the optimum dimension is unknown, swarm particles can seek for both positional and dimensional optima. This eventually pushes the frontier of the optimization problems in dynamic environments towards a global search in a multi-dimensional space, where there exists a multi-modal problem possibly in each dimension. We investigated both standalone and mutual applications of the proposed methods over the moving peaks benchmark (MPB), which originally simulates a dynamic environment in a unique (fixed) dimension. MPB is appropriately extended to accomplish the simulation of a multi-dimensional dynamic system, which contains dynamic environments active in several dimensions. An extensive set of experiments show that in traditional MPB application domain, FGBF technique applied with multi-swarms exhibits an impressive speed gain and tracks the global peak with the minimum error so far achieved with respect to the other competitive PSO-based methods. When applied over the extended MPB, MD PSO with FGBF can find optimum dimension and provide the (near-) optimal solution in this dimension.


title: 5G small cell optimized radio design

abstract: The 5th generation (5G) of mobile radio access technologies is expected to become available for commercial launch around 2020. In this paper, we present our envisioned 5G system design optimized for small cell deployment taking a clean slate approach, i.e. removing most compatibility constraints with the previous generations of mobile radio access technologies. This paper mainly covers the physical layer aspects of the 5G concept design.


title: On matching latent fingerprints

abstract: Latent fingerprint identification is of critical importance to law enforcement agencies in forensics application. While tremendous progress has been made in the field of automatic fingerprint matching, latent fingerprint matching continues to be a difficult problem because the challenges involved in latent print matching are quite different from plain or rolled fingerprint matching. Poor quality of friction ridge impressions, small finger area and large non-linear distortion are some of the main difficulties in latent fingerprint matching. We propose a system for matching latent images to rolled fingerprints that takes into account the specific characteristics of the latent matching problem. In addition to minutiae, additional features like orientation field and quality map are also used in our system. Experimental results on the NIST SD27 latent database indicate that the introduction of orientation field and quality map to minutiae-based matching leads to good recognition performance despite the inherently difficult nature of the problem. We achieve the rank-20 accuracy of 93.4% in retrieving 258 latents from a background database of 2,258 rolled fingerprints.


title: Relativistic Monte Carlo

abstract: Hamiltonian Monte Carlo (HMC) is a popular Markov chain Monte Carlo (MCMC) algorithm that generates proposals for a Metropolis-Hastings algorithm by simulating the dynamics of a Hamiltonian system. However, HMC is sensitive to large time discretizations and performs poorly if there is a mismatch between the spatial geometry of the target distribution and the scales of the momentum distribution. In particular the mass matrix of HMC is hard to tune well. In order to alleviate these problems we propose relativistic Hamiltonian Monte Carlo, a version of HMC based on relativistic dynamics that introduce a maximum velocity on particles. We also derive stochastic gradient versions of the algorithm and show that the resulting algorithms bear interesting relationships to gradient clipping, RMSprop, Adagrad and Adam, popular optimisation methods in deep learning. Based on this, we develop relativistic stochastic gradient descent by taking the zero-temperature limit of relativistic stochastic gradient Hamiltonian Monte Carlo. In experiments we show that the relativistic algorithms perform better than classical Newtonian variants and Adam.


title: Towards a Framework of Process Owner Competencies and Tasks

abstract: Process owners are vital to the establishment and functioning of process oriented organisations. However there is a paucity of understanding regarding the tasks process owners should undertake and what competencies they require. Sets of process owner competencies and process owner tasks emerged from interviews with executives from three financial services organisations in South Africa. The first set highlighted business process management, interpersonal, and holistic thinking competencies while from a tasks perspective process measuring, customer experience, staffing, and process improvement were highly ranked. Nonetheless process orientation was found to be more aspirational than applied which was ascribed in part to a lack of business process education.


title: Optimal Energy Management Policies for Energy Harvesting Sensor Nodes

abstract: We study a sensor node with an energy harvesting source. The generated energy can be stored in a buffer. The sensor node periodically senses a random field and generates a packet. These packets are stored in a queue and transmitted using the energy available at that time. We obtain energy management policies that are throughput optimal, i.e., the data queue stays stable for the largest possible data rate. Next we obtain energy management policies which minimize the mean delay in the queue.We also compare performance of several easily implementable sub-optimal energy management policies. A greedy policy is identified which, in low SNR regime, is throughput optimal and also minimizes mean delay.


title: Methods for evaluating games: how to measure usability and user experience in games?

abstract: This workshop addresses current needs in the games developers' community and games industry to evaluate the overall user experience of games. New forms of interaction techniques, like gestures, eye-tracking or even bio-physiological input and feedback present the limits of current evaluation methods for user experience, and even standard usability evaluation used during game development. This workshop intends to bring together practitioners and researchers sharing their experiences using methods from HCI to explore and measure usability and user experience in games. To this workshop we also invite contributions from other disciplines (especially from the games industry) showing new concepts for user experience evaluation.


title: Augmented Reality in Architectural Construction , Inspection , and Renovation

abstract: We present our preliminary work in developing augmented reality systems to improve methods for the construction, inspection, and renovation of architectural structures. Augmented reality systems add virtual computer-generated material to the surrounding physical world. Our augmented reality systems use see-through headworn displays to overlay graphics and sounds on a person’s naturally occurring vision and hearing. As the person moves about, the position and orientation of his or her head is tracked, allowing the overlaid material to remain tied to the physical world. We describe an experimental augmented reality system that shows the location of columns behind a finished wall, the location of re-bars inside one of the columns, and a structural analysis of the column. We also discuss our preliminary work in developing an augmented reality system for improving the construction of spaceframes. Potential uses of more advanced augmented reality systems are presented.


title: Design of integrated radial and dual axial-flux ferrite magnet synchronous machine

abstract: Recently, significant attention has been paid to the use of ferrite magnets in the main traction machines of electric vehicles/hybrid electric vehicles (EVs/HEVs). However, while ferrite magnets are advantageous in terms of cost, they provide poor magnetic performance in comparison to the neodymium (NdFeB) magnets used in high-performance, high-efficiency machines with high torque densities. To overcome this issue, the application of axial-flux structures (AFSs) to ferrite magnets has been proposed, and it is anticipated that the extended air gap area created when an AFS is used could compensate for their poor magnetic properties. As an additional countermeasure, we propose an integrated radial and dual axial-flux (IRDAF) structure that has one radial-flux gap and two axial-flux gaps. Evaluations performed using finite element analysis show that a machine designed with our proposed IRDAF structure would provide good performance in terms of output torque, efficiency, and irreversible demagnetization.


title: Gut Microbiota: The Link to Your Second Brain

abstract: Serotonin is a highly ubiquitous signaling molecule that plays a role in the regulation of various physiological functions. Several lines of evidence, including the present work from Hsiao and colleagues, demonstrate that, in the gut, microbial-derived metabolites affect the production of serotonin that in turn impacts host physiological functions.


title: Improvements in the implementation of log-homotopy based particle flow filters

abstract: State estimation of a non-linear system perturbed by non Gaussian noise is a challenging task. Typical solutions like EKF/UKF could fail while Monte Carlo methods, even though more accurate, are computationally expensive. Recently proposed log homotopy based particle flow filter, also known as Daum-Huang filter (DHF) provides an alternative way of non-linear state estimation. There have been a number of DHFs derived, based on solutions of the homotopy flow equation. The performance of these new filters depends a lot on the implementation methodology. In this paper, we highlight the key factors affecting the DHF performance and investigate them individually. We then make recommendations based on our results. It is shown that a properly designed DHF can outperform a basic particle filter, with less execution time.


Graham Little Picardi Lassueur syndrome


title: Text Summarization within the Latent Semantic Analysis Framework: Comparative Study

abstract: It is very difficult for human beings to manually summarize large documents of text. Text summarization solves this problem. Nowadays, Text summarization systems are among the most attractive research areas. Text summarization (TS) is used to provide a shorter version of the original text and keeping the overall meaning. There are various methods that aim to find out well-formed summaries. One of the most commonly used methods is the Latent Semantic Analysis (LSA). In this review, we present a comparative study among almost algorithms based on Latent Semantic Analysis (LSA) approach. General Terms Natural Language Processing (NLP).


title: A document-based neural relevance model for effective clinical decision support

abstract: Clinical Decision Support (CDS) can be regarded as an information retrieval (IR) task, where medical records are used to retrieve the full-text biomedical articles to satisfy the information needs from physicians, aiming at better medical solutions. Recent attempts have introduced the advances of deep learning by employing neural IR methods for CDS, where, however, only the document-query relationship is modeled, resulting in non-optimal results in that a medial record can barely reflect the information included in a relevant biomedical article which is usually much longer. Therefore, in addition to the document-query relationship, we propose a document-based neural relevance model (DNRM), addressing the mismatch by utilizing the content of relevant articles to complement the medical records. Specifically, our DNRM model evaluates a document relative to a query and to several pseudo relevant documents for the query at the same time, capturing the interactions from both parts with a feed forward network. Experimental results on the standard Text REtrieval Conference (TREC) CDS track dataset confirm the superior performance of the proposed DNRM model.


title: The LED Block Cipher

abstract: We present a new block cipher LED. While dedicated to compact hardware implementation, and offering the smallest silicon footprint among comparable block ciphers, the cipher has been designed to simultaneously tackle three additional goals. First, we explore the role of an ultra-light (in fact non-existent) key schedule. Second, we consider the resistance of ciphers, and LED in particular, to related-key attacks: we are able to derive simple yet interesting AES-like security proofs for LED regarding related- or single-key attacks. And third, while we provide a block cipher that is very compact in hardware, we aim to maintain a reasonable performance profile for software implementation.


title: A survey on semantic document clustering

abstract: Clustering is the process of partitioning a set of data objects into subsets. It is commonly used technique in data mining, information retrieval, and knowledge discovery for finding hidden patterns or objects from a data of different category. Text clustering process deals with grouping of an unstructured collection of documents into semantically related groups. A document is considered as a bag of words in traditional document clustering methods; however, semantic meaning of word is not considered. Thus, more informative features like concept weight are important to achieve accurate document clustering and this can be achieved through semantic document clustering because it takes meaningful relationship into account. This paper highlights major challenges in traditional document clustering and semantic document clustering along with brief discussion. This paper identifies five major areas under semantic clustering and presents a survey of 17 papers that has studied, covering major significant works. Moreover, this paper also provides a survey of tools, ontology databases, and algorithms, which help in applying and evaluating document clustering. The presented survey is used in preparing the proposed work in the same direction. This proposed work uses the concept weight for text clustering system which is to be developed based on a Hierarchical Agglomerative Clustering, Bisecting k-means algorithm, and Self Organized Map Neural Network in accordance with the principles of WordNet ontology as a background knowledge.


title: Critical Factors in Agile Software Projects according to People, Process and Technology Perspective

abstract: Agile Software Development have become very important over the years for organizations of the modern world, however, software development itself is a very complex process. There are so many factors that affect project success or failure. This paper is a systematic review on the critical success factors of agile software development projects. We classified fourteen critical factors into three categories based on the pillars of Information Systems: people, process, and technology. In addition, primary studies were selected to identify the profile of agile development organizations, projects' characteristics, and implications of agile studies in these projects.


title: Crowd Analysis Using Computer Vision Techniques

abstract: This article presents a survey on crowd analysis using computer vision techniques, covering different aspects such as people tracking, crowd density estimation, event detection, validation, and simulation. It also reports how related the areas of computer vision and computer graphics should be to deal with current challenges in crowd analysis.


title: Computer Security Incident Response Team Development and Evolution

abstract: When computer security incidents occur, it's critical that organizations be able to handle them in a timely manner. The speed with which an organization can recognize, analyze, and respond to an incident will affect the damage and lower recovery costs. Organized incident management requires defined, repeatable processes and the ability to learn from incidents that threaten the confidentiality, availability, and integrity of critical systems and data. Some organizations assign responsibility for incident management to a defined group of people or a designated unit, such as a computer security incident response team. This article looks at the development, purpose, and evolution of such specialized teams; the evolving nature of attacks they must deal with; and methods to evaluate the performance of such teams as well as the emergence of information sharing as a core service.


title: Spam Deobfuscation using a Hidden Markov Model

abstract: To circumvent spam filters, many spammers attempt to obfuscate their emails by deliberately misspelling words or introducing other errors into the text. For example viagra may be written vigra, or mortgage written m0rt gage. Even though humans have little difficulty reading obfuscated emails, most content-based filters are unable to recognize these obfuscated spam words. In this paper, we present a hidden Markov model for deobfuscating spam emails. We empirically demonstrate that our model is robust to many types of obfuscation including misspellings, incorrect segmentations (adding/removing spaces), and substitutions/insertions of non-alphabetic characters.


title: Emotion in Motion: A Study of Music and Affective Response

abstract: 'Emotion in Motion' is an experiment designed to understand the emotional responses of people to a variety of musical excerpts, via self-report questionnaires and the recording of electrodermal activity EDA and heart rate HR signals. The experiment ran for 3 months as part of a public exhibition in Dublin, having nearly 4000 participants and over 12000 listening samples. This paper presents the methodology used by the authors to approach this research, as well as preliminary results derived from the self-report data and the physiology.


title: Lymphedema treatment decreases pain intensity in lipedema.

abstract: Lipedema is a disproportional obesity featuring light pressure-induced or spontaneous pain. On the basis of our clinical observations, lymphedema therapy, as practiced in our clinic, reduces the perception of pain beyond leg volume reduction. We therefore aimed to measure pain intensity prior and subsequent to treatment. 38 women with lipedema were enrolled in the study with 19 patients undergoing treatment and 19 serving as the control group using exclusively moisturizers. Treatment consisted of once daily manual lymph drainage (MLD), intermittent pneumatic compression (IPC), and multilayered short-stretch bandaging performed throughout a 5-day-course. Pain was evaluated with a 10-item questionnaire, a pain rating scale (PRS), and the Wong-Baker Faces scale. Treatment resulted in a significant reduction of pain with a decrease in mean scores of all three measures. In the control group, only PRS showed significant decrease. Our study results indicate that this treatment regimen not only reduces leg volume and capillary fragility, but also improves pain intensity in patients with lipedema.


title: Neural conditional random fields Trinh-Minh-Tri Do † ‡ Thierry Artières ‡

abstract: We propose a non-linear graphical model for structured prediction. It combines the power of deep neural networks to extract high level features with the graphical framework of Markov networks, yielding a powerful and scalable probabilistic model that we apply to signal labeling tasks.


title: Greedy Algorithms for Joint Sparse Recovery

abstract: Five known greedy algorithms designed for the single measurement vector setting in compressed sensing and sparse approximation are extended to the multiple measurement vector scenario: Iterative Hard Thresholding (IHT), Normalized IHT (NIHT), Hard Thresholding Pursuit (HTP), Normalized HTP (NHTP), and Compressive Sampling Matching Pursuit (CoSaMP). Using the asymmetric restricted isometry property (ARIP), sufficient conditions for all five algorithms establish bounds on the discrepancy between the algorithms' output and the optimal row-sparse representation. When the initial multiple measurement vectors are jointly sparse, ARIP-based guarantees for exact recovery are also established. The algorithms are then compared via the recovery phase transition framework. The strong phase transitions describing the family of Gaussian matrices which satisfy the sufficient conditions are obtained via known bounds on the ARIP constants. The algorithms' empirical weak phase transitions are compared for various numbers of multiple measurement vectors. Finally, the performance of the algorithms is compared against a known rank aware greedy algorithm, Rank Aware Simultaneous Orthogonal Matching Pursuit + MUSIC. Simultaneous recovery variants of NIHT, NHTP, and CoSaMP all outperform the rank-aware algorithm.


Reverse Word Order Models


title: High-power AlGaN/GaN MIS-HFETs with field-plates on Si substrates

abstract: In this paper, GaN-based MIS-HFET devices on 4-inch Si substrates were fabricated, and the device characteristics were examined. As a result, the breakdown voltage was improved to be over 2.45 kV using high-resistive carbon doped buffer layers with a larger thickness of over 7.3 µm. The maximum drain current was estimated to be over 115 A using MIS-structures. A trade-off between the specific on-resistance (RonA) and the breakdown voltage (Vb) was improved using carbon doped buffer layers, resulting in obtaining RonA = 5.9 mΩcm2 and Vb = 1730 V simultaneously with the gate-width of a 340 mm. Furthermore, the field-plate structure was introduced into MIS-HFET structures. We have examined the suppression of a current collapse phenomenon owing to the combination of the gate field-plate structure and a conductive Si substrate with MIS-HFET devices.


title: MIR_EVAL: A Transparent Implementation of Common MIR Metrics

abstract: Central to the field of MIR research is the evaluation of algorithms used to extract information from music data. We present mir_eval, an open source software library which provides a transparent and easy-to-use implementation of the most common metrics used to measure the performance of MIR algorithms. In this paper, we enumerate the metrics implemented by mir_eval and quantitatively compare each to existing implementations. When the scores reported by mir_eval differ substantially from the reference, we detail the differences in implementation. We also provide a brief overview of mir_eval’s architecture, design, and intended use. 1. EVALUATING MIR ALGORITHMS Much of the research in Music Information Retrieval (MIR) involves the development of systems that process raw music data to produce semantic information. The goal of these systems is frequently defined as attempting to duplicate the performance of a human listener given the same task [5]. A natural way to determine a system’s effectiveness might be for a human to study the output produced by the system and judge its correctness. However, this would yield only subjective ratings, and would also be extremely timeconsuming when evaluating a system’s output over a large corpus of music. Instead, objective metrics are developed to provide a well-defined way of computing a score which indicates each system’s output’s correctness. These metrics typically involve a heuristically-motivated comparison of the system’s output to a reference which is known to be correct. Over time, certain metrics have become standard for each ∗Please direct correspondence to craffel@gmail.com c © Colin Raffel, Brian McFee, Eric J. Humphrey, Justin Salamon, Oriol Nieto, Dawen Liang, Daniel P. W. Ellis. Licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0). Attribution: Colin Raffel, Brian McFee, Eric J. Humphrey, Justin Salamon, Oriol Nieto, Dawen Liang, Daniel P. W. Ellis.


title: Output-Sensitive 3D Line Integral Convolution

abstract: We propose a largely output-sensitive visualization method for 3D line integral convolution (LIC) whose rendering speed is mainly independent of the data set size and mostly governed by the complexity of the output on the image plane. Our approach of view-dependent visualization tightly links the LIC generation with the volume rendering of the LIC result in order to avoid the computation of unnecessary LIC points: early-ray termination and empty-space leaping techniques are used to skip the computation of the LIC integral in a lazy-evaluation approach; both ray casting and texture slicing can be used as volume-rendering techniques. The input noise is modeled in object space to allow for temporal coherence under object and camera motion. Different noise models are discussed, covering dense representations based on filtered white noise all the way to sparse representations similar to oriented LIC. Aliasing artifacts are avoided by frequency control over the 3D noise and by employing a 3D variant of MlPmapping. A range of illumination models is applied to the LIC streamlines: different codimension-2 lighting models and a novel gradient-based illumination model that relies on precomputed gradients and does not require any direct calculation of gradients after the LIC integral is evaluated. We discuss the issue of proper sampling of the LIC and volume-rendering integrals by employing a frequency-space analysis of the noise model and the precomputed gradients. Finally, we demonstrate that our visualization approach lends itself to a fast graphics processing unit (GPU) implementation that supports both steady and unsteady flow. Therefore, this 3D LIC method allows users to interactively explore 3D flow by means of high-quality, view-dependent, and adaptive LIC volume visualization. Applications to flow visualization in combination with feature extraction and focus-and-context visualization are described, a comparison to previous methods is provided, and a detailed performance analysis is included.


title: Web based water quality monitoring with sensor network: Employing ZigBee and WiMax technologies

abstract: This paper presents the development of a web-based wireless sensor network application for monitoring water pollution using Zigbee and WiMax technologies. The developed system consists of a local Zigbee network, capable of acquiring various water quality parameters, a WiMax network and a host computer for web based monitoring. The system is designed to collect and process information, thus making decisions in realtime via a remote web server. The data is sent from sensor nodes through the Zigbee gateway to the web server via WiMax network, thus allowing users to remotely monitor the water quality from their offices instead of gathering data from the scence. Experimental results demonstrate the ability of the sensor network to monitor water pollution in real time. This system can be easily configured to support other applications, such as e-health and security.


title: Microalgal biomass production and on-site bioremediation of carbon dioxide, nitrogen oxide and sulfur dioxide from flue gas using Chlorella sp. cultures.

abstract: The growth and on-site bioremediation potential of an isolated thermal- and CO2-tolerant mutant strain, Chlorella sp. MTF-7, were investigated. The Chlorella sp. MTF-7 cultures were directly aerated with the flue gas generated from coke oven of a steel plant. The biomass concentration, growth rate and lipid content of Chlorella sp. MTF-7 cultured in an outdoor 50-L photobioreactor for 6 days was 2.87 g L � 1 (with an initial culture biomass concentration of 0.75 g L � 1 ), 0.52 g L � 1 d � 1 and 25.2%, respectively. By the operation with intermittent flue gas aeration in a double-set photobioreactor system, average efficiency of CO2 removal from the flue gas could reach to 60%, and NO and SO2 removal efficiency was maintained at approximately 70% and 50%, respectively. Our results demonstrate that flue gas from coke oven could be directly introduced into Chlorella sp. MTF-7 cultures to potentially produce algal biomass and efficiently capture CO2, NO and SO2 from flue gas.


title: A knowledge-based approach for duplicate elimination in data cleaning

abstract: Abstract Existing duplicate elimination methods for data cleaning work on the basis of computing the degree of similarity between nearby records in a sorted database. High recall can be achieved by accepting records with low degrees of similarity as duplicates, at the cost of lower precision. High precision can be achieved analogously at the cost of lower recall. This is the recall–precision dilemma . We develop a generic knowledge-based framework for effective data cleaning that can implement any existing data cleaning strategies and more. We propose a new method for computing transitive closure under uncertainty for dealing with the merging of groups of inexact duplicate records and explain why small changes to window sizes has little effect on the results of the sorted neighborhood method. Experimental study with two real-world datasets show that this approach can accurately identify duplicates and anomalies with high recall and precision, thus effectively resolving the recall–precision dilemma.


title: An Overview of Noise-Robust Automatic Speech Recognition

abstract: New waves of consumer-centric applications, such as voice search and voice interaction with mobile devices and home entertainment systems, increasingly require automatic speech recognition (ASR) to be robust to the full range of real-world noise and other acoustic distorting conditions. Despite its practical importance, however, the inherent links between and distinctions among the myriad of methods for noise-robust ASR have yet to be carefully studied in order to advance the field further. To this end, it is critical to establish a solid, consistent, and common mathematical foundation for noise-robust ASR, which is lacking at present. This article is intended to fill this gap and to provide a thorough overview of modern noise-robust techniques for ASR developed over the past 30 years. We emphasize methods that are proven to be successful and that are likely to sustain or expand their future applicability. We distill key insights from our comprehensive overview in this field and take a fresh look at a few old problems, which nevertheless are still highly relevant today. Specifically, we have analyzed and categorized a wide range of noise-robust techniques using five different criteria: 1) feature-domain vs. model-domain processing, 2) the use of prior knowledge about the acoustic environment distortion, 3) the use of explicit environment-distortion models, 4) deterministic vs. uncertainty processing, and 5) the use of acoustic models trained jointly with the same feature enhancement or model adaptation process used in the testing stage. With this taxonomy-oriented review, we equip the reader with the insight to choose among techniques and with the awareness of the performance-complexity tradeoffs. The pros and cons of using different noise-robust ASR techniques in practical application scenarios are provided as a guide to interested practitioners. The current challenges and future research directions in this field is also carefully analyzed.


title: Empirical Vulnerability Analysis of Automated Smart Contracts Security Testing on Blockchains

abstract: The emerging blockchain technology supports decentralized computing paradigm shift and is a rapidly approaching phenomenon. While blockchain is thought primarily as the basis of Bitcoin, its application has grown far beyond cryptocurrencies due to the introduction of smart contracts. Smart contracts are self-enforcing pieces of software, which reside and run over a hosting blockchain. Using blockchain-based smart contracts for secure and transparent management to govern interactions (authentication, connection, and transaction) in Internet-enabled environments, mostly IoT, is a niche area of research and practice. However, writing trustworthy and safe smart contracts can be tremendously challenging because of the complicated semantics of underlying domain-specific languages and its testability. There have been high-profile incidents that indicate blockchain smart contracts could contain various code-security vulnerabilities, instigating financial harms. When it involves security of smart contracts, developers embracing the ability to write the contracts should be capable of testing their code, for diagnosing security vulnerabilities, before deploying them to the immutable environments on blockchains. However, there are only a handful of security testing tools for smart contracts. This implies that the existing research on automatic smart contracts security testing is not adequate and remains in a very stage of infancy. With a specific goal to more readily realize the application of blockchain smart contracts in security and privacy, we should first understand their vulnerabilities before widespread implementation. Accordingly, the goal of this paper is to carry out a far-reaching experimental assessment of current static smart contracts security testing tools, for the most widely used blockchain, the Ethereum and its domain-specific programming language, Solidity to provide the first...


title: Action-conditional Sequence Modeling for Recommendation

abstract: In many online applications interactions between a user and a web-service are organized in a sequential way, e.g., user browsing an e-commerce website. In this setting, recommendation system acts throughout user navigation by showing items. Previous works have addressed this recommendation setup through the task of predicting the next item user will interact with. In particular, Recurrent Neural Networks (RNNs) has been shown to achieve substantial improvements over collaborative filtering baselines. In this paper, we consider interactions triggered by the recommendations of deployed recommender system in addition to browsing behavior. Indeed, it is reported that in online services interactions with recommendations represent up to 30\% of total interactions. Moreover, in practice, recommender system can greatly influence user behavior by promoting specific items. In this paper, we extend the RNN modeling framework by taking into account user interaction with recommended items. We propose and evaluate RNN architectures that consist of the recommendation action module and the state-action fusion module. Using real-world large-scale datasets we demonstrate improved performance on the next item prediction task compared to the baselines.