title: Efficient Retrieval Over Documents Encrypted by Attributes in Cloud Computing

abstract: Secure document storage and retrieval is one of the hottest research directions in cloud computing. Though many searchable encryption schemes have been proposed, few of them support efficient retrieval over the documents which are encrypted based on their attributes. In this paper, a hierarchical attribute-based encryption scheme is first designed for a document collection. A set of documents can be encrypted together if they share an integrated access structure. Compared with the ciphertext-policy attribute-based encryption schemes, both the ciphertext storage space and time costs of encryption/decryption are saved. Then, an index structure named attribute-based retrieval features (ARF) tree is constructed for the document collection based on the TF-IDF model and the documents' attributes. A depth-first search algorithm for the ARF tree is designed to improve the search efficiency which can be further improved by parallel computing. Except for the document collections, our scheme can be also applied to other datasets by modifying the ARF tree slightly. A thorough analysis and a series of experiments are performed to illustrate the security and efficiency of the proposed scheme.


title: Evanescent-mode cavity-based frequency synthesizer

abstract: In this paper, an evanescent-mode (EVA) cavity-based frequency synthesizer is developed, implemented, and experimentally validated. The oscillator is integrated on the same substrate of a high-Q (Q u ≥ 400) EVA resonator board to reduce parasitics in the circuit. A piezoelectric actuator is used as a tuner. A phase-locked loop (PLL) is used to control the output frequency. The frequency tuning range is 610 MHz to 950 MHz (1:1.55). The circuit also achieves a phase noise as low as −104 dBc/Hz at 100 kHz offset, −144 dBc/Hz at 1 MHz offset, and − 150 dBc/Hz as a noise floor. The RMS jitter of the system is 3 ps, and the peak-to-peak jitter is 13 ps at 700 MHz. The frequency synthesizer consumes 340 mW.


title: A comprehensive study of cuckoo-inspired algorithms

abstract: Nature-inspired metaheuristic algorithms are considered as the most effective techniques for solving various optimization problems. This paper provides a briefly review of the key features of the cuckoo-inspired metaheuristics: cuckoo search (CS) and cuckoo optimization algorithm (COA). In addition, it discusses some of their important and emerging studies, investigates their applications in several fields, and finally clarifies the differences between both algorithms so as to remove confusion between them.


title: Evaluating Entity Linking with Wikipedia

abstract: Named Entity Linking (nel) grounds entity mentions to their corresponding node in a Knowledge Base (kb). Recently, a number of systems have been proposed for linking entity mentions in text to Wikipedia pages. Such systems typically search for candidate entities and then disambiguate them, returning either the best candidate or nil. However, comparison has focused on disambiguation accuracy, making it difficult to determine how search impacts performance. Furthermore, important approaches from the literature have not been systematically compared on standard data sets. We reimplement three seminal nel systems and present a detailed evaluation of search strategies. Our experiments find that coreference and acronym handling lead to substantial improvement, and search strategies account for much of the variation between systems. This is an interesting finding, because these aspects of the problem have often been neglected in the literature, which has focused largely on complex candidate ranking algorithms.


title: Special invited paper. additive logistic regression: A statistical view of boosting

abstract: Boosting is one of the most important recent developments in classification methodology. Boosting works by sequentially applying a classification algorithm to reweighted versions of the training data and then taking a weighted majority vote of the sequence of classifiers thus produced. For many classification algorithms, this simple strategy results in dramatic improvements in performance. We show that this seemingly mysterious phenomenon can be understood in terms of well-known statistical principles, namely additive modeling and maximum likelihood. For the two-class problem, boosting can be viewed as an approximation to additive modeling on the logistic scale using maximum Bernoulli likelihood as a criterion. We develop more direct approximations and show that they exhibit nearly identical results to boosting. Direct multiclass generalizations based on multinomial likelihood are derived that exhibit performance comparable to other recently proposed multiclass generalizations of boosting in most situations, and far superior in some. We suggest a minor modification to boosting that can reduce computation, often by factors of 10 to 50. Finally, we apply these insights to produce an alternative formulation of boosting decision trees. This approach, based on best-first truncated tree induction, often leads to better performance, and can provide interpretable descriptions of the aggregate decision rule. It is also much faster computationally, making it more suitable to large-scale data mining applications.


title: Broadband omnidirectional antenna for GSM900, GSM1800, 3G, 4G and Wi-Fi applications

abstract: In this paper, a broadband omnidirectional antenna, consisting of three tapered monopoles is presented. The antenna has |S 11 | < −10dB from 880MHz to 2.52GHz. The gain of the antenna is greater than 2dBi over the entire bandwidth. The measured H-plane radiation pattern is omnidirectional with less than 8dB variation over the entire bandwidth. The antenna covers GSM900, GSM1800, 3G, 4G and Wi-Fi bands.


title: Photonic information processing beyond Turing: an optoelectronic implementation of reservoir computing

abstract: Many information processing challenges are difficult to solve with traditional Turing or von Neumann approaches. Implementing unconventional computational methods is therefore essential and optics provides promising opportunities. Here we experimentally demonstrate optical information processing using a nonlinear optoelectronic oscillator subject to delayed feedback. We implement a neuro-inspired concept, called Reservoir Computing, proven to possess universal computational capabilities. We particularly exploit the transient response of a complex dynamical system to an input data stream. We employ spoken digit recognition and time series prediction tasks as benchmarks, achieving competitive processing figures of merit.


title: Association rules mining analysis of app usage based on mobile traffic flow data

abstract: With the rapid development of mobile Internet, more and more Apps emerge in people's daily life. It is important to analyze the relations among Apps, which is helpful for network management and control. In this paper, we utilize network footprint data which consists of DPI data from ISPs and Crawler data from Web for App usage analysis. Focusing on the most popular Apps in China, we propose a distributed NFP data collection and processing framework. We do association rules mining on NFP data by using Apriori and MS-Apriori algorithm. Experimental results validate our proposed method and present some interesting association rules of Apps.


title: Example-based facial rigging

abstract: We introduce a method for generating facial blendshape rigs from a set of example poses of a CG character. Our system transfers controller semantics and expression dynamics from a generic template to the target blendshape model, while solving for an optimal reproduction of the training poses. This enables a scalable design process, where the user can iteratively add more training poses to refine the blendshape expression space. However, plausible animations can be obtained even with a single training pose. We show how formulating the optimization in gradient space yields superior results as compared to a direct optimization on blendshape vertices. We provide examples for both hand-crafted characters and 3D scans of a real actor and demonstrate the performance of our system in the context of markerless art-directable facial tracking.


title: A Structure Tensor Approach for 3D Image Skeletonization: Applications in Protein Secondary Structure Analysis

abstract: We present an approach for three-dimensional (3D) image skeletonization, based on the local structure tensor idea. Two major types of features are addressed: helical and planar. Accordingly, we also discussed how the skeletonization approach is successfully applied to the protein secondary structure analysis, including the alpha-helix (helical type) and beta-sheet (planar type) identifications.


ACOUSTIC SCENE CLASSIFICATION USING DEEP CNN ON RAW-WAVEFORM Technical Report


title: A hybrid model for state estimation prediction composed of neural network and PSO algorithm for Iraqi national super grid system

abstract: This paper demonstrates the importance of the use of power system State Estimation (SE) to determine the real value of bus voltages and their angles in one line operation states. This is done to create a robust power system network that is able to monitor and analyse operational conditions and predict any emergency that may occur on the Power System Network due to human error, strong system component failure and other factors. A new hybrid (particle swarm optimization-Neural Network) algorithm is proposed to estimate the angles and voltages of buses in electrical system networks. All the results obtained from this study were compared with Phase Measurement Units (PMUs), Weighted Least Square (WLS) and Neural Network. A graph theoretic procedure algorithm (GTPA) was used to make the PMU's placements. The WLS, NN, and PMU and the new algorithm were tested on the 24 bus Iraqi Grid system. A MATLAB program was used to develop WLS codes and a hybrid (particle swarm optimization-Neural Network) algorithm. The MATLAB toolbox (PSAT and NN) were used for PMUs and Neural Network respectively. The simulation results showed that the algorithms were a good visualization and powerful system state estimation tools. The new algorithm was faster analyser than the conventional algorithm (WLS, NN, and PMU), it requires less parameter tuning. The results obtained proved the validation, acceptance and accuracy of the hybrid particle swarm optimization-Neural Network algorithm's state estimation (SE).


title: Block Me If You Can: A Large-Scale Study of Tracker-Blocking Tools

abstract: In this paper, we quantify the effectiveness of third-party tracker blockers on a large scale. First, we analyze the architecture of various state-of-the-art blocking solutions and discuss the advantages and disadvantages of each method. Second, we perform a two-part measurement study on the effectiveness of popular tracker-blocking tools. Our analysis quantifies the protection offered against trackers present on more than 100,000 popular websites and 10,000 popular Android applications. We provide novel insights into the ongoing arms race between trackers and developers of blocking tools as well as which tools achieve the best results under what circumstances. Among others, we discover that rule-based browser extensions outperform learning-based ones, trackers with smaller footprints are more successful at avoiding being blocked, and CDNs pose a major threat towards the future of tracker-blocking tools. Overall, the contributions of this paper advance the field of web privacy by providing not only the largest study to date on the effectiveness of tracker-blocking tools, but also by highlighting the most pressing challenges and privacy issues of third-party tracking.


Recent studies on betulinic acid and its biological and pharmacological activity


The Aesculapian snake Elaphe l.longissima [Laurenti, 1768] population in Bieszczady [Poland] between 1990-1998


title: Forward and inverse kinematic based full body gait generation of biped robot

abstract: Two legged robots have better mobility when compared with wheeled robots, but they tend to tip over easily. This will become a serious problem when the degrees of freedom (DOF) of the two legged walking machine increases. To overcome this problem the gaits of the biped robot need to be planned properly. In the present paper, a closed form solution for whole body motion of a 18-DOF biped robot, based on inverse kinematics has been proposed. The gaits for lower limbs (that is, legs) and upper limbs (that is, hands) are generated based on swing foot and hand trajectories, respectively. Further, the swing foot and hand trajectories are assumed to follow cubic polynomial. The dynamic balance of the generated full body motion of the biped robot is determined by using the concept of zero moment point (ZMP).


title: Large-scale Isolated Gesture Recognition using pyramidal 3D convolutional networks

abstract: Human gesture recognition is one of the central research fields of computer vision, and effective gesture recognition is still challenging up to now. In this paper, we present a pyramidal 3D convolutional network framework for large-scale isolated human gesture recognition. 3D convolutional networks are utilized to learn the spatiotemporal features from gesture video files. Pyramid input is proposed to preserve the multi-scale contextual information of gestures, and each pyramid segment is uniformly sampled with temporal jitter. Pyramid fusion layers are inserted into the 3D convolutional networks to fuse the features of pyramid input. This strategy makes the networks recognize human gestures from the entire video files, not just from segmented clips independently. We present the experiment results on the 2016 ChaLearn LAP Large-scale Isolated Gesture Recognition Challenge, in which we placed third.


title: Context-independent claim detection for argument mining

abstract: Argumentation mining aims to automatically identify structured argument data from unstructured natural language text. This challenging, multi-faceted task is recently gaining a growing attention, especially due to its many potential applications. One particularly important aspect of argumentation mining is claim identification. Most of the current approaches are engineered to address specific domains. However, argumentative sentences are often characterized by common rhetorical structures, independently of the domain. We thus propose a method that exploits structured parsing information to detect claims without resorting to contextual information, and yet achieve a performance comparable to that of state-of-the-art methods that heavily rely on the context.


title: Passive Spine Gripper for Free-Climbing Robot in Extreme Terrain

abstract: In this letter, we present a passive spine gripper that can hold rough rocky surfaces of boulders on cliff walls, and we discuss its application to a four-limbed robot for free-climbing in extreme terrain. The limbed robot has four degrees of freedom in each limb, where three are to drive joints of the limb and one for releasing the gripper. The fine spine of the proposed gripper also enables it to passively and adaptively latch on to microscopic asperities of the rough surface, and it is thus an efficient mechanism. In this letter, we present the fundamental design and mechanism of the proposed gripper, after which we introduce its static gripping model. We verify the gripping model by the experimental gripping performance of the prototype gripper. We show a lightweight four-limbed robot that is equipped with the grippers mounted on each limb. To evaluate the climbing capabilities of the robot, we use it to perform climbing experiments on a rugged and steep slope. The results show that the prototype can safely climb over such challenging terrain that is similar to a gravity offload system.


title: Temporal disease trajectories condensed from population-wide registry data covering 6.2 million patients

abstract: A key prerequisite for precision medicine is the estimation of disease progression from the current patient state. Disease correlations and temporal disease progression (trajectories) have mainly been analysed with focus on a small number of diseases or using large-scale approaches without time consideration, exceeding a few years. So far, no large-scale studies have focused on defining a comprehensive set of disease trajectories. Here we present a discovery-driven analysis of temporal disease progression patterns using data from an electronic health registry covering the whole population of Denmark. We use the entire spectrum of diseases and convert 14.9 years of registry data on 6.2 million patients into 1,171 significant trajectories. We group these into patterns centred on a small number of key diagnoses such as chronic obstructive pulmonary disease (COPD) and gout, which are central to disease progression and hence important to diagnose early to mitigate the risk of adverse outcomes. We suggest such trajectory analyses may be useful for predicting and preventing future diseases of individual patients.


title: English Speech to Sanskrit Speech (ESSS) using Rule based Translation

abstract: development of such system that automatically recognizes the input speech and translates in another language like Sanskrit is a challenging task. Sanskrit language is much more conjured language. The purpose of this paper is to explain a system which convert the English Speech into English text and then translate that English text into Sanskrit text and again convert that into speech. This system falls into the category of Speech-to-Speech translation. It unifies the isolated words class under the Speech Recognition type, traditional dictionary rule based machine translation approach and text to speech synthesizer. So basically it is classifies into three areas: Speech Recognition, Machine Translation and Speech Synthesis. This system matches tokens (1) from database to differentiate Subject, Object, Verb, Preposition, Adjective, and Adverb. This paper presents approach for translating well-structured English sentences into Sanskrit sentences. Since Sanskrit is free ordering language (or syntax free language) or we can say its meaning won't be change even if the order of words changes. Keywordsbased dictionary approach, Synthesizer, Translator, Speech Recognition, Rule based machine translation


Review on Privacy Preserving Deep Computation Model on Cloud for Big Data Feature Learning


title: Region-based shape control for a swarm of robots

abstract: This paper presents a region-based shape controller for a swarm of robots. In this control method, the robots move as a group inside a desired region while maintaining a minimum distance among themselves. Various shapes of the desired region can be formed by choosing the appropriate objective functions. The robots in the group only need to communicate with their neighbors and not the entire community. The robots do not have specific identities or roles within the group. Therefore, the proposed method does not require specific orders or positions of the robots inside the region and yet different formations can be formed for a swarm of robots. A Lyapunov-like function is presented for convergence analysis of the multi-robot systems. Simulation results illustrate the performance of the proposed controller.


title: Horse Racing Prediction at the Champ De Mars using a Weighted Probabilistic Approach

abstract: Horse racing is a popular sport in Mauritius which attracts huge crowds to Champ de Mars. Nevertheless, bettors face many difficulties in predicting winning horses to make profit. The principal factors affecting a race were determined. Each factor, namely jockeys, new horses, favourite horses, previous performance, draw, type of horses, weight, rating and stable have been examined and appropriate weights have been assigned to each of them depending on their importance. Furthermore, data for the whole racing season of 2010 was considered. The results of 240 races of 2010 have been used to determine the degree to which each factor affect the chance of each horse. The weights were then summed up to predict winners. The system can predict winners with an accuracy of 58% which is 4.7 out of 8 winners on average. The software outperformed the predictions made by the best professional tipsters in Mauritius who could forecast only 3.6 winners out of 8 races.


title: Can machine-learning improve cardiovascular risk prediction using routine clinical data?

abstract: BACKGROUND ::: Current approaches to predict cardiovascular risk fail to identify many people who would benefit from preventive treatment, while others receive unnecessary intervention. Machine-learning offers opportunity to improve accuracy by exploiting complex interactions between risk factors. We assessed whether machine-learning can improve cardiovascular risk prediction. ::: ::: ::: METHODS ::: Prospective cohort study using routine clinical data of 378,256 patients from UK family practices, free from cardiovascular disease at outset. Four machine-learning algorithms (random forest, logistic regression, gradient boosting machines, neural networks) were compared to an established algorithm (American College of Cardiology guidelines) to predict first cardiovascular event over 10-years. Predictive accuracy was assessed by area under the 'receiver operating curve' (AUC); and sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) to predict 7.5% cardiovascular risk (threshold for initiating statins). ::: ::: ::: FINDINGS ::: 24,970 incident cardiovascular events (6.6%) occurred. Compared to the established risk prediction algorithm (AUC 0.728, 95% CI 0.723-0.735), machine-learning algorithms improved prediction: random forest +1.7% (AUC 0.745, 95% CI 0.739-0.750), logistic regression +3.2% (AUC 0.760, 95% CI 0.755-0.766), gradient boosting +3.3% (AUC 0.761, 95% CI 0.755-0.766), neural networks +3.6% (AUC 0.764, 95% CI 0.759-0.769). The highest achieving (neural networks) algorithm predicted 4,998/7,404 cases (sensitivity 67.5%, PPV 18.4%) and 53,458/75,585 non-cases (specificity 70.7%, NPV 95.7%), correctly predicting 355 (+7.6%) more patients who developed cardiovascular disease compared to the established algorithm. ::: ::: ::: CONCLUSIONS ::: Machine-learning significantly improves accuracy of cardiovascular risk prediction, increasing the number of patients identified who could benefit from preventive treatment, while avoiding unnecessary treatment of others.


title: Normalized cuts in 3-D for spinal MRI segmentation

abstract: Segmentation of medical images has become an indispensable process to perform quantitative analysis of images of human organs and their functions. Normalized Cuts (NCut) is a spectral graph theoretic method that readily admits combinations of different features for image segmentation. The computational demand imposed by NCut has been successfully alleviated with the Nyström approximation method for applications different than medical imaging. In this paper we discuss the application of NCut with the Nyström approximation method to segment vertebral bodies from sagittal T1-weighted magnetic resonance images of the spine. The magnetic resonance images were preprocessed by the anisotropic diffusion algorithm, and three-dimensional local histograms of brightness was chosen as the segmentation feature. Results of the segmentation as well as limitations and challenges in this area are presented.


title: Recommender Systems Handbook

abstract: The explosive growth of e-commerce and online environments has made the issue of information search and selection increasingly serious; users are overloaded by options to consider and they may not have the time or knowledge to personally evaluate these options. Recommender systems have proven to be a valuable way for online users to cope with the information overload and have become one of the most powerful and popular tools in electronic commerce. Correspondingly, various techniques for recommendation generation have been proposed. During the last decade, many of them have also been successfully deployed in commercial environments. Recommender Systems Handbook, an edited volume, is a multi-disciplinary effort that involves world-wide experts from diverse fields, such as artificial intelligence, human computer interaction, information technology, data mining, statistics, adaptive user interfaces, decision support systems, marketing, and consumer behavior. Theoreticians and practitioners from these fields continually seek techniques for more efficient, cost-effective and accurate recommender systems. This handbook aims to impose a degree of order on this diversity, by presenting a coherent and unified repository of recommender systems major concepts, theories, methodologies, trends, challenges and applications. Extensive artificial applications, a variety of real-world applications, and detailed case studies are included. Recommender Systems Handbook illustrates how this technology can support the user in decision-making, planning and purchasing processes. It works for well known corporations such as Amazon, Google, Microsoft and AT&T. This handbook is suitable for researchers and advanced-level students in computer science as a reference.


title: Improved Interrupted Sampling Repeater Jamming based on DRFM

abstract: Interrupted Sampling Repeater Jamming (ISRJ) can be implemented easily based on Digital Radio Frequency Memory (DRFM) technique. An improved method is presented in this paper which has all advantages of traditional ISRJ. Furthermore, the improved ISRJ obtains a train of false targets that precede the true one. The radar radio signal is downshifted and sampled by receive-transmit time-sharing antenna and then stored to digital memory. Its retransmitting signal reserves the coherent property so that a large number of false targets will be achieved after matched filter of the pulse compression radar. The principal and quantitative relation between the false targets and the true one is deduced and analyzed. Simulation illustrates that the improved ISRJ method obtains several false targets ahead of the true one and the amplitude of the main false target decrease compared with the traditional method. The amplitude decrease can be remedied by enhancing the power of transmitting antenna. Imaging results with improved ISRJ method to real SAR raw are shown at last.


Unchaining Social Businesses : Blockchain as the Basic Technology of a Crowdlending Platform


title: Durability of Hepatitis B Surface Antigen Loss With Nucleotide Analogue and Peginterferon Therapy in Patients With Chronic Hepatitis B

abstract: In patients with chronic hepatitis B (CHB), loss of hepatitis B surface antigen (HBsAg) is considered a functional cure. However, HBsAg loss is uncommon with existing therapies, and predictive factors associated with HBsAg seroreversion are unknown. Using pooled data from three phase 3 clinical trials of patients with CHB treated with nucleos(t)ide analogue (NUC) monotherapy or peginterferon (Peg-IFN) ± NUC combination therapy, we conducted a retrospective analysis to characterize patients who achieved sustained HBsAg loss, the predictors of HBsAg seroreversion, and the impact of hepatitis B surface antibody (anti-HBs) seroconversion on durability of HBsAg loss. In these three international trials, 1,381 adults with CHB received either NUC monotherapy for up to 10 years or Peg-IFN-containing regimens for up to 1 year. A total of 55 patients had confirmed HBsAg loss, defined as two or more consecutive negative-qualitative HBsAg results, with a minimum of one repeat result after the end of treatment. Throughout a median of 96 (quartile [Q]1, Q3, 46, 135) weeks follow-up after HBsAg loss, HBsAg loss was durable in 82% (n = 45) of patients, with 10 patients experiencing HBsAg seroreversion. Anti-HBs seroconversion was observed during follow-up in 78% of patients who lost HBsAg and in 60% of those who subsequently seroreverted. In analyzing predictors of HBsAg seroreversion, study treatment was significant, yet anti-HBs seroconversion and treatment duration after initial HBsAg loss were not. Risk of HBsAg seroreversion was observed to be lower if HBsAg loss was sustained through the off-treatment week 24 visit (8/10 seroreversions occurred by posttreatment week 24). Conclusion: HBsAg loss after NUC or Peg-IFN-containing regimens was durable in 82% of patients with CHB. Anti-HBs seroconversion and treatment duration after initial HBsAg loss were not significantly associated with durability of HBsAg loss.


title: Assessment of degree of risk from sources of microbial contamination in cleanrooms; 3:Overall application

abstract: A method of calculating the degree of risk of sources of microbial contamination to products ::: manufactured in cleanrooms has been described in two previous articles. The degree of risk was ::: ascertained by calculating the number of microbes deposited (NMD) onto, or into, a product from ::: each source of contamination. The first article considered airborne sources, the second article ::: considered surface and liquid sources, and this final article considers all three sources. The NMD ::: method can be applied to various manufacturing methods and designs of cleanrooms but was ::: illustrated by a vial-filling process in a unidirectional airflow (UDAF) workstation located in a non- ::: UDAF cleanroom. The same example was used in this article to demonstrate how to control the ::: microbial risk, and included the use of a restricted access barrier system. ::: The risk to a patient is not only dependent on microbial contamination of pharmaceutical ::: products during manufacture in cleanrooms and controlled zones but the chance that any microbes ::: deposited in the product will survive and multiply during its shelf life, and this aspect of patient risk ::: is considered.


title: End-to-end reliability analysis of an IoT based smart agriculture

abstract: End-to-end reliability of a computer network has been widely researched to improve the performance of the system. This paper evaluates end-to-end reliability characteristics of two main IoT communication network architectures through main reliability network parameters by using OPNET. The reliability effects when increasing the number of sensor nodes are simulated and analyzed. The simulation results show exciting reliability features of the intended IoT communication architecture.


title: Adaptive digital beamforming for interference suppression in automotive FMCW radars

abstract: This paper addresses the problem of mutual interference between automotive radars. This problem is getting more attention with an increase in the number of radar systems used in traffic. An adaptive digital beamforming technique is presented here which suppresses the interference without the exact knowledge of the interfering signal's Direction of Arrival (DoA). The proposed technique is robust and does not rely on any calibration for the interference cancellation. The adaptive interference suppression method is evaluated using a simulated scenario. Up to about 20–23 dB improvement in the target Signal to Interference and Noise Ratio (SINR) is measured in the simulation and a better detection performance is achieved using the proposed interference suppression technique.


title: Cross domain recommendation based on multi-type media fusion

abstract: Due to the scarcity of user interest information in the target domain, recommender systems generally suffer from the sparsity problem. To alleviate this limitation, one natural way is to transfer user interests in other domains to the target domain. However, objects in different domains may be in different media types, which make it very difficult to find the correlations between them. In this paper, we propose a Bayesian hierarchical approach based on Latent Dirichlet Allocation (LDA) to transfer user interests cross domains or media. We model documents (corresponding to media objects) from different domains and user interests in a common topic space, and learn topic distributions for documents and user interests together. Specifically, to learn the model, we combine multi-type media information: media descriptions, user-generated text data and ratings. With this model, recommendation can be done in multiple ways, via predicting ratings, comparing topic distributions of documents and user interests directly and so on. Experiments on two real world datasets demonstrate that our proposed method is effective in addressing the sparsity problem by transferring user interests cross domains.


title: Intrusions detection based on Support Vector Machine optimized with swarm intelligence

abstract: Intrusion Detection Systems(IDS) have become a necessary component of almost every security infrastructure. Recently, Support Vector Machines (SVM) has been employed to provide potential solutions for IDS. With its many variants for classification SVM is a state-of-the-art machine learning algorithm. However, the performance of SVM depends on selection of the appropriate parameters. In this paper we propose an IDS model based on Information Gain for feature selection combined with the SVM classifier. The parameters for SVM will be selected by a swarm intelligence algorithm (Particle Swarm Optimization or Artificial Bee Colony). We use the NSL-KDD data set and show that our model can achieve higher detection rate and lower false alarm rate than regular SVM.


title: Force modeling for needle insertion into soft tissue

abstract: The modeling of forces during needle insertion into soft tissue is important for accurate surgical simulation, preoperative planning, and intelligent robotic assistance for percutaneous therapies. We present a force model for needle insertion and experimental procedures for acquiring data from ex vivo tissue to populate that model. Data were collected from bovine livers using a one-degree-of-freedom robot equipped with a load cell and needle attachment. computed tomography imaging was used to segment the needle insertion process into phases identifying different relative velocities between the needle and tissue. The data were measured and modeled in three parts: 1) capsule stiffness, a nonlinear spring model; 2) friction, a modified Karnopp model; and 3) cutting, a constant for a given tissue. In addition, we characterized the effects of needle diameter and tip type on insertion force using a silicone rubber phantom. In comparison to triangular and diamond tips, a bevel tip causes more needle bending and is more easily affected by tissue density variations. Forces for larger diameter needles are higher due to increased cutting and friction forces.


title: A Dataset of Scratch Programs: Scraped, Shaped and Scored

abstract: Scratch is increasingly popular, both as an introductory programming language and as a research target in the computing education research field. In this paper, we present a dataset of 250K recent Scratch projects from 100K different authors scraped from the Scratch project repository. We processed the projects' source code and metadata to encode them into a database that facilitates querying and further analysis. We further evaluated the projects in terms of programming skills and mastery, and included the project scoring results. The dataset enables the analysis of the source code of Scratch projects, of their quality characteristics, and of the programming skills that their authors exhibit. The dataset can be used for empirical research in software engineering and computing education.


title: Enabling the implementation of evidence based practice: a conceptual framework

abstract: The argument put forward in this paper is that successful implementation of research into practice is a function of the interplay of three core elements--the level and nature of the evidence, the context or environment into which the research is to be placed, and the method or way in which the process is facilitated. It also proposes that because current research is inconclusive as to which of these elements is most important in successful implementation they all should have equal standing. This is contrary to the often implicit assumptions currently being generated within the clinical effectiveness agenda where the level and rigour of the evidence seems to be the most important factor for consideration. The paper offers a conceptual framework that considers this imbalance, showing how it might work in clarifying some of the theoretical positions and as a checklist for staff to assess what they need to do to successfully implement research into practice.


OpenMM: An Open-Source Multimodal Feature Extraction Tool.


title: 0.18 µm BCD technology platform with best-in-class 6 V to 70 V power MOSFETs

abstract: This paper presents a single BCD technology platform with high performance power devices at a wide range of operating voltages. The platform offers 6 V to 70 V LDMOS devices. All devices offer best-in-class specific on-resistance of 20 to 40 % lower than that of the state-of-the-art IC-based LDMOS devices and robustness better than the square SOA (safe-operating-area). Fully isolated LDMOS devices, in which independent bias is capable for circuit flexibility, demonstrate superior specific on-resistance (e.g. 11.9 mΩ-mm2 for breakdown voltage of 39 V). Moreover, the unusual sudden current enhancement appeared in the I D -V D saturation region of most of the high voltage LDMOS devices is significantly suppressed.


title: Modified Perturb and Observe MPPT Algorithm for Drift Avoidance in Photovoltaic Systems

abstract: The perturb and observe (P&O) maximum power point tracking (MPPT) algorithm is a simple and efficient tracking technique. However, the P&O tracking method suffers from drift in case of an increase in insolation $(G) $ , and this drift effect is severe in case of a rapid increase in insolation. Drift occurs due to the incorrect decision taken by the conventional P&O algorithm at the first step change in duty cycle during increase in insolation. A modified P&O technique is proposed to avoid the drift problem by incorporating the information of change in current $(\Delta I) $ in the decision process in addition to change in power $(\Delta P)$ and change in voltage $(\Delta V) $ . The drift phenomena and its effects are clearly demonstrated in this paper for conventional P&O algorithm with both fixed and adaptive step size technique. A single-ended primary inductance converter is considered to validate the proposed drift-free P&O MPPT using direct duty ratio control technique. MATLAB/Simulink is used for simulation studies, and for experimental validation, a microcontroller is used as a digital platform to implement the proposed algorithm. The simulation and experimental results showed that the proposed algorithm accurately tracks the maximum power and avoids the drift in fast changing weather conditions.


title: HEADY: News headline abstraction through event pattern clustering

abstract: This paper presents HEADY: a novel, abstractive approach for headline generation from news collections. From a web-scale corpus of English news, we mine syntactic patterns that a Noisy-OR model generalizes into event descriptions. At inference time, we query the model with the patterns observed in an unseen news collection, identify the event that better captures the gist of the collection and retrieve the most appropriate pattern to generate a headline. HEADY improves over a state-of-theart open-domain title abstraction method, bridging half of the gap that separates it from extractive methods using humangenerated titles in manual evaluations, and performs comparably to human-generated headlines as evaluated with ROUGE.


title: Principle of a Radial-Foree-Based Electromagnetic Swirling Actuator for Low-Speed Applications

abstract: This paper proposes an electromagnetic actuator that generates the radial electromagnetic force, as an extension of bearingless motors, and transforms the radial force into the rotating torque through a swirler and an integrated mechanical gear system. The structure and the operating principle of the actuator are introduced. The mathematical derivation of the airgap flux density, the radial force and the gear ratio are presented. FEM analysis is conducted to verify the feasibility of the proposed actuator. It has the potential to realize flat-shape actuator with short axial length, which is suited for low-speed applications such as robotic-joint and wearable-rehabilitation devices.


FAULT DETECTION AND ISOLATION USING PARITY RELATIONS


Psoriasis in Children and Adolescents: Diagnosis, Management and Comorbidities


title: A lightweight semantic web-based approach for data annotation on IoT gateways

abstract: Abstract Internet of Things (IoT) applications rely on networks composed of set of heterogeneous sensors and smart devices, which have the capability to constantly, observe the surroundings and gather data. This heterogeneity is reflected on raw data gathered by such type of systems. Consequently, the task of high-level IoT applications to interpret such data and detect events in the real world is more complex. Moreover, data heterogeneity leads to the lack of interoperability between IoT applications. Semantic Web (SW) technologies have been widely adopted to model and integrate data from different sources on the web; extending them to the IoT domain can be used to mitigate the aforementioned challenges. Semantically annotating IoT data is a fundamental step toward developing smarter and interoperable IoT applications. However, this type of process requires a large amount of computing resources, especially in scenarios where a large number of sensors is expected to be involved such as smart city. To address these challenges, we propose a lightweight semantic annotation approach that can be implemented on resource-constrained IoT gateways connected to a limited number of sensors. To evaluate the feasibility of the proposed approach, we have carried out a set of experimentations using a middleware prototype implementation. Several benchmarks are considered such as: Data size, response time, and resource utilization.


title: Skew Estimation and Correction of Text Using Bounding Box

abstract: This paper, in addition to reporting some existing techniques, proposes a new technique for skew correction. It includes a novel document skew detection algorithm based on bounding box technique. The algorithm works quite efficiently for detecting the skew and then correcting it. The method has been experimented on various text documents and very promising results have been achieved given more than 97% accuracy. A comparative study has been reported to provide a detailed analysis of the proposed method together with some other existing methods in the literature.


title: ReHAR: Robust and Efficient Human Activity Recognition

abstract: Designing a scheme that can achieve a good performance in predicting single person activities and group activities is a challenging task. In this paper, we propose a novel robust and efficient human activity recognition scheme called ReHAR, which can be used to handle single person activities and group activities prediction. First, we generate an optical flow image for each video frame. Then, both video frames and their corresponding optical flow images are fed into a Single Frame Representation Model to generate representations. Finally, an LSTM is used to pre- dict the final activities based on the generated representations. The whole model is trained end-to-end to allow meaningful representations to be generated for the final activity recognition. We evaluate ReHAR using two well-known datasets: the NCAA Basketball Dataset and the UCFSports Action Dataset. The experimental results show that the pro- posed ReHAR achieves a higher activity recognition accuracy with an order of magnitude shorter computation time compared to the state-of-the-art methods.


title: Bilingual Effects on Cognitive and Linguistic Development: Role of Language, Cultural Background, and Education

abstract: A total of 104 six-year-old children belonging to 4 groups (English monolinguals, Chinese-English bilinguals, French-English bilinguals, Spanish-English bilinguals) were compared on 3 verbal tasks and 1 nonverbal executive control task to examine the generality of the bilingual effects on development. Bilingual groups differed in degree of similarity between languages, cultural background, and language of schooling. On the executive control task, all bilingual groups performed similarly and exceeded monolinguals; on the language tasks the best performance was achieved by bilingual children whose language of instruction was the same as the language of testing and whose languages had more overlap. Thus, executive control outcomes for bilingual children are general but performance on verbal tasks is specific to factors in the bilingual experience.


title: Variable-length compression allowing errors

abstract: This paper studies the fundamental limits of the minimum average length of variable-length compression when a nonzero error probability ε is tolerated. We give non-asymptotic bounds on the minimum average length in terms of Erokhin's rate-distortion function and we use those bounds to obtain a Gaussian approximation on the speed of approach to the limit which is quite accurate for all but small blocklengths: equation where Q-1 (·) is the functional inverse of the Q-function and V (S) is the source dispersion. A nonzero error probability thus not only reduces the asymptotically achievable rate by a factor of 1-ε, but also this asymptotic limit is approached from below, i.e. a larger source dispersion and shorter blocklengths are beneficial. Further, we show that variable-length lossy compression under excess distortion constraint also exhibits similar properties.


title: Exploiting pallidal plasticity for stimulation in Parkinson’s disease

abstract: Objective. Continuous application of high-frequency deep brain stimulation (DBS) often effectively reduces motor symptoms of Parkinson’s disease patients. While there is a growing need for more effective and less traumatic stimulation, the exact mechanism of DBS is still unknown. Here, we present a methodology to exploit the plasticity of GABAergic synapses inside the external globus pallidus (GPe) for the optimization of DBS. Approach. Assuming the existence of spike-timing-dependent plasticity (STDP) at GABAergic GPe–GPe synapses, we simulate neural activity in a network model of the subthalamic nucleus and GPe. In particular, we test different DBS protocols in our model and quantify their influence on neural synchrony. Main results. In an exemplary set of biologically plausible model parameters, we show that STDP in the GPe has a direct influence on neural activity and especially the stability of firing patterns. STDP stabilizes both uncorrelated firing in the healthy state and correlated firing in the parkinsonian state. Alternative stimulation protocols such as coordinated reset stimulation can clearly profit from the stabilizing effect of STDP. These results are widely independent of the STDP learning rule. Significance. Once the model settings, e.g., connection architectures, have been described experimentally, our model can be adjusted and directly applied in the development of novel stimulation protocols. More efficient stimulation leads to both minimization of side effects and savings in battery power.


title: NFC Loop Antenna in Conjunction With the Lower Section of a Metal Cover

abstract: This letter proposes a near-field communication (NFC) antenna for smartphones with a metal cover that includes an embedded narrow slot, therein splitting the metal cover into upper and lower sections. The proposed NFC antenna is a simple loop structure that is formed by incorporating a three-dimensional coupling strip with the upper edge of the metal cover in the lower section. Because this antenna structure can substantially reduce the eddy current, excellent inductive coupling can be achieved through the clearance zone of the narrow slot. The proposed NFC antenna is relatively thin and achieves excellent performance in the read/write mode. Furthermore, in the card emulation mode verified by Europay, MasterCard, and Visa test, it even surpasses the performance of certain other NFC antennas in smartphones with non-metal covers.


title: CryptMDB: A practical encrypted MongoDB over big data

abstract: In big data era, data are usually stored in databases for easy access and utilization, which are now woven into every aspect of our lives. However, traditional relational databases cannot address users' demands for quick data access and calculating, since they cannot process data in a distributed way. To tackle this problem, non-relational databases such as MongoDB have emerged up and been applied in various Scenarios. Nevertheless, it should be noted that most MongoDB products fail to consider user's data privacy. In this paper, we propose a practical encrypted MongoDB (i.e., CryptMDB). Specifically, we utilize an additive homomorphic asymmetric cryptosystem to encrypt user's data and achieve strong privacy protection. Security analysis indicates that the CryptMDB can achieve confidentiality of user's data and prevent adversaries from illegally gaining access to the database. Furthermore, extensive experiments demonstrate that the CryptMDB achieves better efficiency than existing relational database in terms of data access and calculating.


title: Multiplying matrices faster than coppersmith-winograd

abstract: We develop an automated approach for designing matrix multiplication algorithms based on constructions similar to the Coppersmith-Winograd construction. Using this approach we obtain a new improved bound on the matrix multiplication exponent ω


Alpha-Beta-Conspiracy Search


title: A Decision Support System for Global Team Management: Expert Evaluation

abstract: Context: The literature is rich in examples of both successful and failed global software development projects. However, practitioners do not have the time to wade through the many recommendations to work out which ones apply to them. To this end, we developed a prototype Decision Support System (DSS) for Global Teaming (GT), with the goal of making research results available to practitioners. Aims: We want the system we build to be based on the real needs of practitioners: the end users of our system. Therefore the aim of this study is to assess the usefulness and usability of our proof-of-concept in order to create a tool that is actually used by practitioners. Method: Twelve experts in GSD evaluated our system. Each individual participant tested the system and completed a short usability questionnaire. Results: Feedback on the prototype DSS was positive. All experts supported the concept, although many suggested areas that could be improved. Both expert practitioners and researchers participated, providing different perspectives on what we need to do to improve the system. Conclusion: Involving both practitioners (users) and researchers in the evaluation elicited a range of useful feedback, providing useful insights that might not have emerged had we focused on one or the other group. However, even when we implement recommended changes, we still need to persuade practitioner to adopt the new tool.


title: Multiple nuptial flights, sperm transfer and the evolution of extreme polyandry in honeybee queens

abstract: The honeybee, Apis mellifera, has an extremely polyandrous mating system, which often involves multiple nuptial flights by its queens. To understand the evolution of extreme polyandry, we investigated the cost of multiple nuptial flights in relation to potential benefits. We analysed, with eight DNA microsatellite loci, the paternity of worker offspring of naturally mated queens. Queens that were restricted to one nuptial flight, but may have taken additional nuptial flights if allowed to do so, had significantly fewer matings than queens that started oviposition after a single nuptial flight. Furthermore, the number of sperm stored in a spermatheca increased significantly with the number of matings. We suggest that queens adjust their nuptial flight frequencies according to their mating success in their previous nuptial flights. The number of copulations seems to serve as a signal for the initiation of oviposition. In the light of these findings, we reconsider and discuss the significance of the sperm limitation hypothesis for the evolution of extreme polyandry in A. mellifera.


title: Should I use TensorFlow? An evaluation of TensorFlow and its potential to replace pure Python implementations in Machine Learning

abstract: Google's Machine Learning framework TensorFlow was open-sourced in November 2015 [1] and has since built a growing community around it. TensorFlow is supposed to be flexible for research purposes while also allowing its models to be deployed productively. This work is aimed towards people with experience in Machine Learning considering whether they should use TensorFlow in their environment. Several aspects of the framework important for such a decision are examined, such as the heterogenity, extensibility and its computation graph. A pure Python implementation of linear classification is compared with an implementation utilizing TensorFlow. I also contrast TensorFlow to other popular frameworks with respect to modeling capability, deployment and performance and give a brief description of the current adaption of the framework.


title: Flying insect inspired vision for autonomous aerial robot maneuvers in near-earth environments

abstract: Near-earth environments are time consuming, labor intensive and possibly dangerous to safe guard. Accomplishing tasks like bomb detection, search-and-rescue and reconnaissance with aerial robots could save resources. This work describes the adoption of insect behavior and flight patterns to develop a MAV sensor suite. A prototype called CQAR: closed quarter aerial robot, which is capable of flying in and around buildings, through tunnels and in and out of caves is used to validate the efficiency of such a method when equipped with optic flow microsensors.


title: Research and Implementation of Hybrid Recommendation Algorithm Based on Collaborative Filtering and Word2Vec

abstract: According to the traditional recommendation technology which is applied in large-scale mobile data set is difficult to balance the accuracy and efficiency of recommendation, this paper puts forward a hybrid recommendation algorithm based on collaborative filtering and Word2Vec. The traditional collaborative filtering algorithm is realized with MapReduce framework and Hive database on the Hadoop platform. Use the Word2Vec model to train the tag information of the mobile data to get the similarity between the tags, and recommend applications to the user according to the similarity. Finally, according to the feedback behavior of the user, the recommendation results are mixed with weight. The experimental results show that the hybrid recommendation algorithm improves the efficiency and accuracy of recommendation greatly, and makes it more advantageous in the large-scale data set.


Entropy-Driven Adaptive Representation


Machine Learning and Statistical Approaches for Big Data : Issues , Challenges and Research Directions


title: Motivation for online impulse buying: A two-factor theory perspective

abstract: Drawing on two-factor theory, this study successfully identified hygiene and motivation factors that activated online impulse buying.The hygiene factors affecting online impulse buying distributed across the six stages of consumer decision-making processes.The motivation factors affecting online impulse buying distributed across the three stages.Motivation factors that induced online impulse buying were sales promotion stimuli, which can create consumers utilitarian or hedonic benefits.The design factors of online stores were the hygiene factors rather than the motivators. Impulse buying accounts for a large proportion of consumer shopping behavior in the bricks-and-mortar retail market. Online retailers also expect to profit from impulse buying. It is therefore interesting and beneficial to investigate the design elements of online stores and the sales promotion stimuli that e-retailers can use to either arouse consumers' desire or decrease their self-control to evoke their purchase impulses. This study seeks to explicitly identify the factors associated with online store design and sales promotion stimuli that most affect online impulse buying behavior throughout the consumer decision-making process. Drawing on the two-factor theory, it successfully identifies the hygiene and motivation factors that trigger online impulse buying. The questionnaire responses of 239 valid respondents revealed that most of the hygiene factors are associated with the design of online stores, and all of the motivation factors are forms of sales promotion stimuli that effectively facilitate online impulse buying and present utilitarian or hedonic benefits to consumers. This study also identifies the most effective sales promotion stimuli and offers a comprehensive checklist for Web designers. Moreover, the distribution of motivation and hygiene factors for each stage of the EKB model is uneven, and some stages include only hygiene factors. The findings of this study demonstrate that the triggers of consumers' online shopping behavior do not always apply to online impulse buying, and have important implications for impulse buying research and practice.


title: Data Curation with Deep Learning [Vision]

abstract: Past. Data curation - the process of discovering, integrating, and cleaning data - is one of the oldest data management problems. Unfortunately, it is still the most time consuming and least enjoyable work of data scientists. So far, successful data curation stories are mainly ad-hoc solutions that are either domain-specific (for example, ETL rules) or task-specific (for example, entity resolution). ::: Present. The power of current data curation solutions are not keeping up with the ever changing data ecosystem in terms of volume, velocity, variety and veracity, mainly due to the high human cost, instead of machine cost, needed for providing the ad-hoc solutions mentioned above. Meanwhile, deep learning is making strides in achieving remarkable successes in areas such as image recognition, natural language processing, and speech recognition. This is largely due to its ability to understanding features that are neither domain-specific nor task-specific. ::: Future. Data curation solutions need to keep the pace with the fast-changing data ecosystem, where the main hope is to devise domain-agnostic and task-agnostic solutions. To this end, we start a new research project, called AutoDC, to unleash the potential of deep learning towards self-driving data curation. We will discuss how different deep learning concepts can be adapted and extended to solve various data curation problems. We showcase some low-hanging fruits about the early encounters between deep learning and data curation happening in AutoDC. We believe that the directions pointed out by this work will not only drive AutoDC towards democratizing data curation, but also serve as a cornerstone for researchers and practitioners to move to a new realm of data curation solutions.


title: Cloud data governance maturity model

abstract: To ensure data governance in cloud computing, it is important to build-in data governance in the planning, strategy and the design phases of cloud computing and adapt a data governance program architecture which makes sure that regular and governance related tasks, are deployed correctly. Cloud data governance requirements must be linked to the business goals in organisations, when they adopt cloud computing. Cloud data governance is an executive level concern in many organisations today, but a method for credible, reliable, and cost-efficient cloud data governance maturity assessment has been lacking. In order to identify and explore the strength and weaknesses of a particular organization's data governance in cloud computing, this paper proposed as a cloud data governance maturity model and is intended as a tool to evaluate the ability of organizations to meet the objectives of data governance for cloud computing. The maturity model helps organisations understand their current level of data governance before and after adopting cloud computing based on key dimension for cloud data governance. More importantly, the cloud data governance maturity model can identify a path for future growth in, an organisation toward the ultimate goal which invariably follows an understood and established path. This paper will describe the cloud data governance maturity model and explains how organizations can leverage it to help them govern the use of data more effectively in the cloud environment.


title: Autoerotic asphyxia by hanging

abstract: A case of sexual asphyxial death by hanging is presented. A 41-year-old man was found dead hanging by a towrope. A mirror was placed in front of a table, reflecting him in full height. A photograph of a celebrity mannequin was found against him. The towrope was arranged for the compression of the neck, and controlled voluntarily by a shower apparatus. Autopsy findings revealed an ascending ligature mark, 2 cm in width on the neck. Although this is a very ancient behaviour, we are presenting a very rare case from Turkey. We aim to discuss sexual asphyxiation phenomenon together with the features of the previous Turkish cases on the aspect of the forensic viewpoint, as well as the similarities and differences between different nations and religious. Language: en


title: Accurate and Efficient Stochastic Computing Hardware for Convolutional Neural Networks

abstract: This paper presents an efficient unipolar stochastic computing hardware for convolutional neural networks (CNNs). It includes stochastic ReLU and optimized max function, which are key components in a CNN. To avoid the range limitation problem of stochastic numbers and increase the signal-to-noise ratio, we perform weight normalization and upscaling. In addition, to reduce the overhead of binary-to-stochastic conversion, we propose a scheme for sharing stochastic number generators among the neurons in a CNN. Experimental results show that our approach outperforms the previous ones based on stochastic computing in terms of accuracy, area, and energy consumption.


title: The evolution of functional hand replacement: From iron prostheses to hand transplantation.

abstract: The hand is an integral component of the human body, with an incredible spectrum of functionality. In addition to possessing gross and fine motor capabilities essential for physical survival, the hand is fundamental to social conventions, enabling greeting, grooming, artistic expression and syntactical communication. The loss of one or both hands is, thus, a devastating experience, requiring significant psychological support and physical rehabilitation. The majority of hand amputations occur in working-age males, most commonly as a result of work-related trauma or as casualties sustained during combat. For millennia, humans have used state-of-the-art technology to design clever devices to facilitate the reintegration of hand amputees into society. The present article provides a historical overview of the progress in replacing a missing hand, from early iron hands intended primarily for use in battle, to today's standard body-powered and myoelectric prostheses, to revolutionary advancements in the restoration of sensorimotor control with targeted reinnervation and hand transplantation.


title: Ploidy analysis in water yam, Dioscorea alata L. germplasm

abstract: Poor reproductive development in yams (Dioscorea spp.) has often been attributed to the polyploid nature of the crop. In this study, flow cytometry was used to determine the ploidy level of 53 accessions of Dioscorea alata, mostly from West African countries, Chad and Puerto Rico. Nuclei were isolated from young leaf material and stained with DAPI(4,6-diamidino-2-phenylindole). The nuclear genome size (2C) was measured as an indicator of the ploidy level. Dioscorea rotundata genotypes with known ploidy levels were used as standards. The results showed that the majority of plants were hexaploid (84.9%) with a smaller percentage of tetraploids (15.1%). A higher number of male plants were hexaploid than tetraploids. This is at variance with earlier findings, which reported that hexaploid male plants are rare. Higher ploidy levels were not directly related to sparse or erratic flowering as previously reported as profuse flowering occurred in some male hexaploid accessions. These findings have important implications for yam breeding in relation to yam genetic resources.


title: Social Network De-anonymization with Overlapping Communities: Analysis, Algorithm and Experiments

abstract: The advent of social networks poses severe threats on user privacy as adversaries can de-anonymize users' identities by mapping them to correlated cross-domain networks. Without ground-truth mapping, prior literature proposes various cost functions in hope of measuring the quality of mappings. However, there is generally a lacking of rationale behind the cost functions, whose minimizer also remains algorithmically unknown. We jointly tackle above concerns under a more practical social network model parameterized by overlapping communities, which, neglected by prior art, can serve as side information for de-anonymization. Regarding the unavailability of ground-truth mapping to adversaries, by virtue of the Minimum Mean Square Error (MMSE), our first contribution is a well-justified cost function minimizing the expected number of mismatched users over all possible true mappings. While proving the NP-hardness of minimizing MMSE, we validly transform it into the weighted-edge matching problem (WEMP), which, as disclosed theoretically, resolves the tension between optimality and complexity: (i) WEMP asymptotically returns a negligible mapping error in large network size under mild conditions facilitated by higher overlapping strength; (ii) WEMP can be algorithmically characterized via the convex-concave based de-anonymization algorithm (CBDA), finding the optimum of WEMP. Extensive experiments further confirm the effectiveness of CBDA under overlapping communities, in terms of averagely 90% re-identified users in the rare true cross-domain co-author networks when communities overlap densely, and roughly 70% enhanced reidentification ratio compared to non-overlapping cases.


title: Probabilistic Natural Language Generation with Wasserstein Autoencoders

abstract: Probabilistic generation of natural language sentences is an important task in NLP. Existing models such as variational autoencoders (VAE) for sequence generation are extremely difficult to train due to the issues associated with the Kullback-Leibler (KL) loss collapsing to zero. One has to implement various heuristics such as KL weight annealing and word dropout in a carefully engineered manner to successfully train a text VAE. In this paper, we propose the use of Wasserstein autoencoders (WAE) for probabilistic natural language sentence generation. We show that sequence-to-sequence WAEs are more robust towards hyperparameters and can be trained in a straightforward manner without the need for any weight annealing. Empirical evidence shows that the latent space learned by WAEs exhibits properties of continuity and smoothness as in VAEs, while simultaneously achieving much higher BLEU scores for sentence reconstruction.


title: Deep Drone Racing: Learning Agile Flight in Dynamic Environments

abstract: Autonomous agile flight brings up fundamental challenges in robotics, such as coping with unreliable state estimation, reacting optimally to dynamically changing environments, and coupling perception and action in real time under severe resource constraints. In this paper, we consider these challenges in the context of autonomous, vision-based drone racing in dynamic environments. Our approach combines a convolutional neural network (CNN) with a state-of-the-art path-planning and control system. The CNN directly maps raw images into a robust representation in the form of a waypoint and desired speed. This information is then used by the planner to generate a short, minimum-jerk trajectory segment and corresponding motor commands to reach the desired goal. We demonstrate our method in autonomous agile flight scenarios, in which a vision-based quadrotor traverses drone-racing tracks with possibly moving gates. Our method does not require any explicit map of the environment and runs fully onboard. We extensively test the precision and robustness of the approach in simulation and in the physical world. We also evaluate our method against state-of-the-art navigation approaches and professional human drone pilots.


title: Hand Gesture Recognition for Real Time Human Machine Interaction System

abstract: Real Time Human-machine Interaction system using hand gesture Recognition to handle the mouse event , media player , image viewer .Users have to repeat same mouse and keyboard actions, inducing waste of time. Gestures have long been considered as an interaction technique that can potentially deliver more natural. A fast gesture recognition scheme is proposed to be an interface for the human-machine interaction (HMI) of systems. The system presents some low- complexity algorithms and gestures to reduce the gesture recognition complexity and be more suitable for controlling real-time computer systems. In this paper we use the webcam for capturing the image. After capturing the image it converts into the binary image. A gesture is a specific combination of hand position.


A Japanese-English Patent Parallel Corpus


title: The Uncanny in the Wild. Analysis of Unscripted Human–Android Interaction in the Field

abstract: Against the background of the uncanny valley hypothesis we investigated how people react towards an android robot in a natural environment dependent on the behavior displayed by the robot (still vs. moving) in a quasi-experimental observational field study. We present data on unscripted interactions between humans and the android robot “Geminoid HI-1” in an Austrian public cafe and subsequent interviews. Data were analyzed with regard to the participants’ nonverbal behavior (e.g. attention paid to the robot, proximity). We found that participants’ behavior towards the android robot as well as their interview answers were influenced by the behavior the robot displayed. In addition, we found huge inter-individual differences in the participants’ behavior. Implications for the uncanny valley and research on social human–robot interactions are discussed.


Open Source Integrated Planner for Autonomous Navigation in Highly Dynamic Environments


title: Scientometrics of Deception, Counter-deception, and Deception Detection in Cyber-space

abstract: The concepts of deception, counter-deception, and deception detection in the cyber-space domain have been the subject of little systematic analysis. Our objective was to conduct scientometric analyses of these concepts in the cyber-space domain. We observed the following: Although various deceptive tactics are addressed in the cyber-security literature, it appears they are characterized more from the standpoint of technology than from their social, behavioral, or cognitive elements; these cyber-tactics are not mapped into the classic components of denial and deception tactics; there is no conventional terminology to describe the phenomenon of deception in cyber-space; classic deception domain terminology is rarely used; and classic deception domain researchers are rarely cited. These observations suggest that cyber-deception is an emerging field.


title: A model-driven approach for real-time road recognition

abstract: This article describes a method designed to detect and track road edges starting from images provided by an on-board monocular monochromic camera. Its implementation on specific hardware is also presented in the framework of the VELAC project. The method is based on four modules: (1) detection of the road edges in the image by a model-driven algorithm, which uses a statistical model of the lane sides which manages the occlusions or imperfections of the road marking --- this model is initialized by an off-line training step; (2) localization of the vehicle in the lane in which it is travelling; (3) tracking to define a new search space of road edges for the next image; and (4) management of the lane numbers to determine the lane in which the vehicle is travelling. The algorithm is implemented in order to validate the method in a real-time context. Results obtained on marked and unmarked road images show the robustness and precision of the method.


title: Reevaluating the Impact of Video Games

abstract: The evolution of the video game phenomenon is reviewed and contemporary data are presented. A survey assessing frequency and location of play and game preference was completed by 357 seventh- and eighth-grade students. In this middle-class sample, about two thirds of girls played video games at least one to two hours per week at home, but only 20% played in arcades. About 90% of boys played in the home and about 50% in arcades. Approximately half of preferred games were from one of two categories of violent games, while 2% of preferred games were educational. Parent education about the influence of the media should include recommendations to monitor game playing and influence game selection.


title: Solution to the Tic-Tac-Toe Problem Using Hamming Distance Approach in a Neural Network

abstract: Paper focuses on developing an algorithm using a Hamming Distance Classifier in Neural Networks to find the most optimal move to be made in the Tic-Tac-Toe problem such that the game always ends in a win or a draw. The basic step involves an eight-class Hamming network which has nine inputs corresponding to each cell of the grid and eight outputs respectively. The algorithm computes the Hamming Distance of the current input configuration as compared to the weight matrix and the maximum of the output corresponds to the minimum distance. The iterative step is carried out to anticipate the next move for every possible current move. The hamming distance of all the iterations is added to basic step and the gross maximum gives the most profitable move. The algorithm proceeds such that the neural network prefers to itself win rather than preventing the opponent from winning in least possible moves.


title: Turkish Prospective Teachers' Perceptions Regarding the Use of Computer Games with Educational Features

abstract: Teachers’ perceptions are critical to the success or failure of integrating computer games with educational features into classrooms. Consequently, it is essential to gather information about concerns they might have in regard to using these games in the classroom. This study was conducted to investigate the perceptions and future plans of a group of prospective computer teachers in Turkey. One-hundred sixteen students, from four different universities’ Computer Education and Instructional Technology (CEIT) departments, participated in this study. The results of the study reveal that the prospective computer teachers who participated have positive perceptions regarding the use of computer games with educational features in education. Moreover, most of the participants plan to use such games in their future professions, according to their responses. However, some of the participants have doubts, especially concerning the issues of classroom management and the educational effectiveness of computer games currently on the market.


title: A Survey of Fault Detection, Isolation, and Reconfiguration Methods

abstract: Fault detection, isolation, and reconfiguration (FDIR) is an important and challenging problem in many engineering applications and continues to be an active area of research in the control community. This paper presents a survey of the various model-based FDIR methods developed in the last decade. In the paper, the FDIR problem is divided into the fault detection and isolation (FDI) step, and the controller reconfiguration step. For FDI, we discuss various model-based techniques to generate residuals that are robust to noise, unknown disturbance, and model uncertainties, as well as various statistical techniques of testing the residuals for abrupt changes (or faults). We then discuss various techniques of implementing reconfigurable control strategy in response to faults.


title: CHARADE: Remote Control of Objects using FreeHand Gestures

abstract: This paper presents an application that uses hand gesture input to control a computer while giving a presentation. In order to develop a prototype of this application, we have defined an interaction model, a notation for gestures, and a set of guidelines to design gestural command sets. This works aims to define interaction styles that work in computerized reality environments. In our application, gestures are used for interacting with the computer as well as for communicating with other people or operating other devices.


title: A flexible and efficient algorithm for regularized fisher discriminant analysis

abstract: Fisher linear discriminant analysis (LDA) and its kernel extension--kernel discriminant analysis (KDA)--are well known methods that consider dimensionality reduction and classification jointly. While widely deployed in practical problems, there are still unresolved issues surrounding their efficient implementation and their relationship with least mean squared error procedures. In this paper we address these issues within the framework of regularized estimation. Our approach leads to a flexible and efficient implementation of LDA as well as KDA. We also uncover a general relationship between regularized discriminant analysis and ridge regression. This relationship yields variations on conventional LDA based on the pseudoinverse and a direct equivalence to an ordinary least squares estimator. Experimental results on a collection of benchmark data sets demonstrate the effectiveness of our approach.


title: Application of data mining techniques in sports training

abstract: Data mining techniques have been successfully applied in stock, insurance, medicine, banking and retailing domains. In the sport domain, for transforming sport data into actionable knowledge, coaches can use data mining techniques to plan training sessions more effectively, and to reduce the impact of testing activity on athletes. This paper presents one such model, which uses clustering techniques, such as improved K-Means, Expectation-Maximization (EM), DBSCAN, COBWEB and hierarchical clustering approaches to analyze sport physiological data collected during incremental tests. Through analyzing the progress of a test session, the authors assign the tested athlete to a group of athletes and evaluate these groups to support the planning of training sessions.


title: Deep Learning for target recognition from SAR images

abstract: This paper deals with the problematic of automatic target recognition (ATR) using Synthetic Aperture Radar (SAR) images. In this work, the Deep Learning (DL) architecture is proposed and applied in order to recognize military vehicles from SAR images. We propose mainly in this work the deep learning algorithms based on convolutional neural network architecture. In the second step and in order to optimize the convolution of DL steps, we propose to use a convolutional auto-encoder which may be better suited to image processing. Its use provides several areas of the best results in the presence of noise on shifted and truncated images. To validate our approach, some experimentation results are given and compared. The obtained results show that the proposed approach of DL achieves a height recognition accuracy of 93%.


title: 8T SRAM Cell Design for Dynamic and Leakage Power Reduction

abstract: his paper addresses a, novel eight transistor (8T) CMOS SRAM cell design to enhance the stability and to reduce dynamic and leakage power. For the validation of proposed 8T SRAM cell, compared results with reported data. The parameters used in the proposed cell are comparable to the existing 8T SRAM cell at same technology and design rules. The stability of the proposed cell has been analyzed using N-curve metrics. Write operation is achieved in the proposed 8T SRAM cell by charging / discharging single Bit Line (BL), which results in reduction of dynamic power consumption. The proposed 8T SRAM cell has achieved 38.33% dynamic power reduction and 25.31% reduction in leakage power comparing with the reported data of 8T SRAM cell, which validate the desired design approach.


title: Learning from Maps: Visual Common Sense for Autonomous Driving

abstract: Today's autonomous vehicles rely extensively on high-definition 3D maps to navigate the environment. While this approach works well when these maps are completely up-to-date, safe autonomous vehicles must be able to corroborate the map's information via a real time sensor-based system. Our goal in this work is to develop a model for road layout inference given imagery from on-board cameras, without any reliance on high-definition maps. However, no sufficient dataset for training such a model exists. Here, we leverage the availability of standard navigation maps and corresponding street view images to construct an automatically labeled, large-scale dataset for this complex scene understanding problem. By matching road vectors and metadata from navigation maps with Google Street View images, we can assign ground truth road layout attributes (e.g., distance to an intersection, one-way vs. two-way street) to the images. We then train deep convolutional networks to predict these road layout attributes given a single monocular RGB image. Experimental evaluation demonstrates that our model learns to correctly infer the road attributes using only panoramas captured by car-mounted cameras as input. Additionally, our results indicate that this method may be suitable to the novel application of recommending safety improvements to infrastructure (e.g., suggesting an alternative speed limit for a street).


title: Printed in Great Britain OCULAR DOMINANCE IN LAYER IV OF THE CAT'S VISUAL CORTEX AND THE EFFECTS OF MONOCULAR DEPRIVATION

abstract: 1. The relation between the physiological pattern of ocular dominance and the anatomical distribution of geniculocortical afferents serving each eye was studied in layer IV of the primary visual cortex of normal and monocularly deprived cats. 2. One eye was injected with radioactive label. After allowing sufficient time for transeuronal transport, micro-electrode recordings were made, and the geniculocoritcal afferents serving the injected eye were located autoradiographically. 3. In layer IV of normal cats, cell were clustered according to eye preference, and fewer cells were binocularly driven than in other layers. Points of transition between groups of cells dominated by one eye and those dominated by the other were marked with electrolytic lesions. A good correspondence was found between the location of cells dominated by the injected eye and the patches of radioactively labelled geniculocortical afferents. 4. Following prolonged early monocular deprivation, the patches of geniculocortical afferents in layer IV serving the deprived eye were smaller, and those serving the non-deprived eye larger, than normal. Again there was a coincidence between the patches of radioactively labelled afferents and the location of cells dominated by the injected eye. 5. The deprived eye was found to dominate a substantial fraction (22%) of cortical cells in the fourth layer. In other cortical layers, only 7% of the cells were dominated by the deprived eye. 6. These findings suggest that the thalamocortical projection is physically rearranged as a consequence of monocular deprivation, as has been demonstrated for layer IVc of the monkey's visual cortex (Hubel, Wiesel & Le Vay, 1977).


title: Efficient multilabel classification algorithms for large-scale problems in the legal domain

abstract: In this paper we apply multilabel classification algorithms to the EUR-Lex database of legal documents of the European Union. For this document collection, we studied three different multilabel classification problems, the largest being the categorization into the EUROVOC concept hierarchy with almost 4000 classes. We evaluated three algorithms: (i) the binary relevance approach which independently trains one classifier per label; (ii) the multiclass multilabel perceptron algorithm, which respects dependencies between the base classifiers; and (iii) the multilabel pairwise perceptron algorithm, which trains one classifier for each pair of labels. All algorithms use the simple but very efficient perceptron algorithm as the underlying classifier, which makes them very suitable for large-scale multilabel classification problems. The main challenge we had to face was that the almost 8,000,000 perceptrons that had to be trained in the pairwise setting could no longer be stored in memory. We solve this problem by resorting to the dual representation of the perceptron, which makes the pairwise approach feasible for problems of this size. The results on the EUR-Lex database confirm the good predictive performance of the pairwise approach and demonstrates the feasibility of this approach for large-scale tasks.


title: One-Shot Learning of Multi-Step Tasks from Observation via Activity Localization in Auxiliary Video

abstract: Due to burdensome data requirements, learning from demonstration often falls short of its promise to allow users to quickly and naturally program robots. Demonstrations are inherently ambiguous and incomplete, making correct generalization to unseen situations difficult without a large number of demonstrations in varying conditions. By contrast, humans are often able to learn complex tasks from a single demonstration (typically observations without action labels) by leveraging context learned over a lifetime. Inspired by this capability, our goal is to enable robots to perform one-shot learning of multi-step tasks from observation by leveraging auxiliary video data as context. Our primary contribution is a novel system that achieves this goal by: (1) using a single user-segmented demonstration to define the primitive actions that comprise a task, (2) localizing additional examples of these actions in unsegmented auxiliary videos via a metalearning-based approach, (3) using these additional examples to learn a reward function for each action, and (4) performing reinforcement learning on top of the inferred reward functions to learn action policies that can be combined to accomplish the task. We empirically demonstrate that a robot can learn multi-step tasks more effectively when provided auxiliary video, and that performance greatly improves when localizing individual actions, compared to learning from unsegmented videos.


title: OBJECT RECOGNITION FROM IMAGE USING GRID BASED COLOR MOMENTS FEATURE EXTRACTION METHOD

abstract: Image processing is a mechanism to convert an image into digital form and perform various operations on it, in order to get an enhanced image or to extract some useful information from it. In Image processing system, it treats images as two dimensional signals while applying number of image processing methods to them. This leads to an increasing number of generated digital images. Therefore it is required automatic systems to recognize the objects from the images. These systems may collect the number of features of a image and specification of image and consequently the different features of an object will identify the object from the image. Image processing is among rapidly growing technologies today, with its applications in various aspects of a business. Image Processing forms core research area within engineering and computer science disciplines too. Its most common and effective method is retrieve the textual features from various methods. But most of the methods do not yield the more accurate features form the image. So there is a requirement of an effective and efficient method for features extraction from the image. Moreover, images are rich in content, so some approaches are proposed based on various features derived directly from the content of the image: these are the grid-based-color-moments (GBCM) approaches. They allow users to search the desired object from image by specifying visual features (e.g., colour, texture and shape). Once the features have been defined and extracted, the retrieval becomes a task of measuring similarity between image features. In this paper, we have proposed a number of existing methods for GBCM applications.


title: Bare bones differential evolution

abstract: The barebones differential evolution (BBDE) is a new, almost parameter-free optimization algorithm that is a hybrid of the barebones particle swarm optimizer and differential evolution. Differential evolution is used to mutate, for each particle, the attractor associated with that particle, defined as a weighted average of its personal and neighborhood best positions. The performance of the proposed approach is investigated and compared with differential evolution, a Von Neumann particle swarm optimizer and a barebones particle swarm optimizer. The experiments conducted show that the BBDE provides excellent results with the added advantage of little, almost no parameter tuning. Moreover, the performance of the barebones differential evolution using the ring and Von Neumann neighborhood topologies is investigated. Finally, the application of the BBDE to the real-world problem of unsupervised image classification is investigated. Experimental results show that the proposed approach performs very well compared to other state-of-the-art clustering algorithms in all measured criteria.


title: Facebook as a Corpus for Emoticons-Based Sentiment Analysis

abstract: With the growing popularity of the social networking sites, usage of informal language, short cuts & emoticons is increasing rapidly. The use of emoticons in text in order to express sentiments is posing a challenge to the automated sentiment analysis tools to correctly account for such graphical cues for sentiment. This paper aims at demonstrating how emoticons typically convey sentiments and how we can exploit emoticons by using a manually created emoticon sentiment lexicon and then using finite state machines to find out the polarity of the sentence or paragraph. We evaluate our approach on 1,250 Facebook status and 2,050 Facebook comments, which all contain emoticons and have been manually annotated for sentiment. We identified the most commonly and frequently used emoticons & classified them on the basis of the sentiment they strengthen which eventually decides the polarity of the sentence. In this paper we want to introduce a method to perform a sentiment analysis on text-based status updates & comments, disregarding all verbal information and using only emoticons to detect both positive and negative sentiments.


title: Surveying Wearable Human Assistive Technology for Life and Safety Critical Applications: Standards, Challenges and Opportunities

abstract: In this survey a new application paradigm life and safety for critical operations and missions using wearable Wireless Body Area Networks (WBANs) technology is introduced. This paradigm has a vast scope of applications, including disaster management, worker safety in harsh environments such as roadside and building workers, mobile health monitoring, ambient assisted living and many more. It is often the case that during the critical operations and the target conditions, the existing infrastructure is either absent, damaged or overcrowded. In this context, it is envisioned that WBANs will enable the quick deployment of ad-hoc/on-the-fly communication networks to help save many lives and ensuring people's safety. However, to understand the applications more deeply and their specific characteristics and requirements, this survey presents a comprehensive study on the applications scenarios, their context and specific requirements. It explores details of the key enabling standards, existing state-of-the-art research studies, and projects to understand their limitations before realizing aforementioned applications. Application-specific challenges and issues are discussed comprehensively from various perspectives and future research and development directions are highlighted as an inspiration for new innovative solutions. To conclude, this survey opens up a good opportunity for companies and research centers to investigate old but still new problems, in the realm of wearable technologies, which are increasingly evolving and getting more and more attention recently.


title: Measuring author research relatedness: A comparison of word-based, topic-based, and author cocitation approaches

abstract: Relationships between authors based on characteristics of published literature have been studied for decades. Author cocitation analysis using mapping techniques has been most frequently used to study how closely two authors are thought to be in intellectual space based on how members of the research community co-cite their works. Other approaches exist to study author relatedness based more directly on the text of their published works. In this study we present static and dynamic word-based approaches using vector space modeling, as well as a topic-based approach based on latent Dirichlet allocation for mapping author research relatedness. Vector space modeling is used to define an author space consisting of works by a given author. Outcomes for the two word-based approaches and a topic-based approach for 50 prolific authors in library and information science are compared with more traditional author cocitation analysis using multidimensional scaling and hierarchical cluster analysis. The two word-based approaches produced similar outcomes except where two authors were frequent co-authors for the majority of their articles. The topic-based approach produced the most distinctive map. © 2012 Wiley Periodicals, Inc.


title: Visual aesthetic quality assessment with a regression model

abstract: Aesthetic image analysis has drawn much attention in recent years. However, assessing the aesthetic quality especially aesthetic score prediction is a challenging problem. In this paper, we interpret aesthetic quality assessment as a regression problem and present a new framework by directly training a regression model using a neural network. Firstly, to extract the aesthetic features which are difficult to design manually, we utilize the convolutional network to learn the features. Then, a regression model is trained based on the aesthetic features. Different from classification models which can only predict aesthetic class (high or low) in most existing works, the regression model can predict continuous aesthetic score. Experimental results on a recently published large-scale dataset show that the proposed method can assess the degree of aesthetic quality similar to human visual system effectively and outperforms the state-of-the-art methods.


title: Requirements, Design Challenges, and Review of Routing and MAC Protocols for CR-Based Smart Grid Systems

abstract: Cognitive radio technology can facilitate communication in smart grid applications through dynamic spectrum access. However, traditional routing and MAC protocols adopted for cognitive radio networks may not be beneficial in CR-based smart grid environments due to large data sizes and variable link quality among different functional blocks of smart grids. The interference and fading in wireless links necessitate efficient routing for reliable low-latency data delivery of smart grid applications. This low-latency data delivery must be achieved while protecting the legitimate primary users. Besides efficient routing, MAC layer protocols should be enhanced to achieve successful data delivery with simultaneous spectrum sensing and duty cycling for energy-efficient operation. In this article, we evaluate the requirements and key design challenges for routing and MAC protocols in the CR-based smart grid. We also provide a review of research carried out to date for routing and MAC protocols for the CR-based smart grid.


An Analysis of Digital Transformation in the History and Future of Modern Ports


title: AIR-EDUTECH: Augmented immersive reality (AIR) technology for high school Chemistry education

abstract: As technology is exponentially regenerated and developed; new educational methods are needed to assure better educational outcomes. Recently schools have started integrating recent technology into the educational process in order to attract students and increase their engagement in the process. This paper will demonstrate how an augmented immersive reality mobile application can work to enrich the learning environment and attract the students to learn and explore subjects such as sciences, more specifically Chemistry. Augmented immersive reality allows students to learn in their own way and pace while encouraging them to explore and be creative as their `trial-and-error' mistakes will not impose any major consequences in the virtual world. A field study was conducted on grade 11 students to verify the benefits of using the developed application in education.