title: Prediction and Portfolio Optimization in Quantitative Trading Using Machine Learning Techniques

abstract: Quantitative trading is an automated trading system in which the trading strategies and decisions are conducted by a set of mathematical models. Quantitative trading applies a wide range of computational approaches such as statistics, physics, or machine learning to analyze, predict, and take advantage of big data in finance for investment. This work studies core components of a quantitative trading system. Machine learning offers a number of important advantages over traditional algorithmic trading. With machine learning, multiple trading strategies are implemented consistently and able to adapt to real-time market. To demonstrate how machine learning techniques can meet quantitative trading, linear regression and support vector regression models are used to predict stock movement. In addition, multiple optimization techniques are used to optimize the return and control risk in trading. One common characteristic for both prediction models is they effectively performed in short-term prediction with high accuracy and return. However, in short-term prediction, the linear regression model is outperform compared to the support vector regression model. The prediction accuracy is considerably improved by adding technical indicators to dataset rather than adjusted price and volume. Despite the gap between prediction modeling and actual trading, the proposed trading strategy achieved a higher return than the S&P 500 ETF-SPY.


title: Better Document-level Sentiment Analysis from RST Discourse Parsing

abstract: Discourse structure is the hidden link between surface features and document-level properties, such as sentiment polarity. We show that the discourse analyses produced by Rhetorical Structure Theory (RST) parsers can improve document-level sentiment analysis, via composition of local information up the discourse tree. First, we show that reweighting discourse units according to their position in a dependency representation of the rhetorical structure can yield substantial improvements on lexicon-based sentiment analysis. Next, we present a recursive neural network over the RST structure, which offers significant improvements over classificationbased methods.


title: Preprocessing Role in Analyzing Tweets Towards Requirement Engineering

abstract: Twitter is an important source for crowdsourcing software requirements and can be used as a communication channel between technical stakeholders and end users of software products. Previous studies have shown that software users share their opinions about the software through short messages called tweets. These tweets might contain worthwhile information (such as bug reports and user requests) which relates to the software requirements. Machine learning and Natural language processing approaches have been applied to elicit these technical tweets. Particularly, when learning techniques are considered, the success rate of the preprocessing process to overcome shortcomings in analyzing tweets becomes crucial. Therefore, in this paper, we propose a method which involves more than 6000 experiments in order to systematically analyze the effects of eleven preprocessing techniques on the classification performance of requirements-related tweets. We train and test three popular supervised learning algorithms on a dataset of 4000 tweets which is a sample of twitter feeds. Finally, we analyze our evaluation results from three different viewpoints to answer the research question. Our findings have demonstrated the crucial role of preprocessing, and also are feasible to provide more insights in designing approaches which can deal more accurately in case of elicitation such tweets.


System Dynamics: Modeling, Simulation, and Control of Mechatronic Systems


title: A User-to-User Relationship-Based Access Control Model for Online Social Networks

abstract: Users and resources in online social networks (OSNs) are interconnected via various types of relationships. In particular, user-to-user relationships form the basis of the OSN structure, and play a significant role in specifying and enforcing access control. Individual users and the OSN provider should be allowed to specify which access can be granted in terms of existing relationships. We propose a novel user-to-user relationship-based access control (UURAC) model for OSN systems that utilizes regular expression notation for such policy specification. We develop a path checking algorithm to determine whether the required relationship path between users for a given access request exists, and provide proofs of correctness and complexity analysis for this algorithm.


Bridging high-level slqvihesis to RTL technology libraries


title: On a Luschny question

abstract: Let $E_n(x)$ be Euler polynomial, $\nu_2(n)$ be $2-$adic order of $n,$ $\{g(n)\}$ be the characteristic sequence for $\{2^{n}-1\}_{n\geq1}.$ Recently Peter Luschny asked (cf. \cite{5}, sequence A290646): is for $n\geq1$ A290646=A135517? According to A135517, A091090 and a formula there, this question is equivalent to the following one: is, for $n\geq1,$ the denominator of $E_n(x) - E_n(1)$ equal to $2^{\nu_2(n+1)-g(n)}$? In this note we answer in the affirmative on this question.


title: The effect of facial asymmetry on nasal deviation.

abstract: The impact of facial asymmetry on nasal deviation is an accepted but poorly understood part of plastic surgery and rhinology training. Recently, an increased understanding of the specific structural issues underlying this deformity has led to improved surgical techniques and patient outcomes.


title: Applying machine learning on sensor data for irrigation recommendations: revealing the agronomist’s tacit knowledge

abstract: Jojoba Israel is a world-leading producer of Jojoba products, whose orchards are covered with sensors that collect soil moisture data for monitoring plant needs at real-time. Based on these data, the company’s agronomist defines a weekly irrigation plan. In addition, data on weather, irrigation, and yield are recorded from other sources (e.g. meteorological station and irrigation-plan records). However, so far, there has been no attempt to use the entire set of collected data to reveal insights and interesting relationships between different variables, such as soil, weather, irrigation characteristics, and resulting yield. By integrating and utilizing data from different sources, our research aims at using the collected data not only for monitoring and controlling the crop, but also for predicting irrigation recommendations. In particular, a dataset was constructed by integrating data collected over almost two years from 22 soil-sensors spread in four major plots (which are divided into 28 subplots and eight irrigation groups), from a meteorological station, and from actual irrigation records. Different regression and classification algorithms were applied on this dataset to develop models that were able to predict the weekly irrigation plan as recommended by the agronomist. The models were developed using eight different subsets of variables to determine which variables consistently contributed to prediction accuracy. By comparing the resulting models, it was shown that the best regression model was Gradient Boosted Regression Trees, with 93% accuracy, and the best classification model was the Boosted Tree Classifier, with 95% accuracy (on the test-set). Data that were not contributing to the model prediction success rate were identified as well. The resulting model can significantly facilitate the agronomist’s irrigation planning process. In addition, the potential of applying machine learning on the company data for yield and disease prediction is discussed.


title: Understanding and overcoming cyber security anti-patterns

abstract: This article presents an empirical and practice-based analysis of the question, why despite substantial investments, there are still major security weaknesses in today's information systems. Acknowledging that cyber security is not a purely technical discipline, the article takes a holistic approach and identifies four anti-patterns that are frequent in practice and detrimental to the goal of achieving strong cyber security. The first anti-pattern is that decisions about security are frequently based on intuition rather than data and rigor; this introduces cognitive biases and undermines decision quality. Second, many organizations fail to implement foundational security controls and consequently, are easy targets for opportunistic and novice attackers. Third, there is an overreliance on the relatively static threat knowledge in products such as virus scanners, while an inability to learn and adapt dynamically opens the door for advanced threats. Fourth, weaknesses in security governance create systemic control gaps and vulnerabilities. The article describes each anti-pattern and presents specific steps that organizations can take to overcome them.


title: Game Theory and Decision Theory in Multi-Agent Systems

abstract: In the last few years, there has been increasing interest from the agent community in the use of techniques from decision theory and game theory. Our aims in this article are firstly to briefly summarize the key concepts of decision theory and game theory, secondly to discuss how these tools are being applied in agent systems research, and finally to introduce this special issue of Autonomous Agents and Multi-Agent Systems by reviewing the papers that appear.


title: Analog Implementation of a Novel Resistive-Type Sigmoidal Neuron

abstract: An important part of any hardware implementation of artificial neural networks (ANNs) is realization of the activation function which serves as the output stage of each layer. In this work, a new NMOS/PMOS design is proposed for realizing the sigmoid function as the activation function. Transistors in the proposed neuron are biased using only one biasing voltage. By operating in both triode and saturation regions, the proposed neuron can provide an accurate approximation of the sigmoid function. The neuron circuit is designed and laid out in 90-nm CMOS technology. The proposed neuron can be potentially used in implementation of both analog and hybrid ANNs.


title: Asynchronous Task-Based Parallelization of Algebraic Multigrid

abstract: As processor clock rates become more dynamic and workloads become more adaptive, the vulnerability to global synchronization that already complicates programming for performance in today's petascale environment will be exacerbated. Algebraic multigrid (AMG), the solver of choice in many large-scale PDE-based simulations, scales well in the weak sense, with fixed problem size per node, on tightly coupled systems when loads are well balanced and core performance is reliable. However, its strong scaling to many cores within a node is challenging. Reducing synchronization and increasing concurrency are vital adaptations of AMG to hybrid architectures. Recent communication-reducing improvements to classical additive AMG by Vassilevski and Yang improve concurrency and increase communication-computation overlap, while retaining convergence properties close to those of standard multiplicative AMG, but remain bulk synchronous. We extend the Vassilevski and Yang additive AMG to asynchronous task-based parallelism using a hybrid MPI+OmpSs (from the Barcelona Supercomputer Center) within a node, along with MPI for internode communications. We implement a tiling approach to decompose the grid hierarchy into parallel units within task containers. We compare against the MPI-only BoomerAMG and the Auxiliary-space Maxwell Solver (AMS) in the hypre library for the 3D Laplacian operator and the electromagnetic diffusion, respectively. In time to solution for a full solve an MPI-OmpSs hybrid improves over an all-MPI approach in strong scaling at full core count (32 threads per single Haswell node of the Cray XC40) and maintains this per node advantage as both weak scale to thousands of cores, with MPI between nodes.


title: Influence of end friction confinement on impact tests of concrete material at high strain rate

abstract: The compressive strength of concrete material increases with the strain rate. The dynamic compressive strength of concrete material is usually obtained by conducting laboratory tests such as split Hopkinson pressure bar (SHPB) test or drop-weight test. It is commonly agreed now that the dynamic increase factor (DIF) obtained from impact test is affected by lateral inertia confinement. In addition, friction at specimen-bar interfaces also constrains the lateral deformation of the specimen under high-speed impact and thus might influence the testing results as well. However, no systematic study that devotes to investigating the influence of end friction on the dynamic compression test of concrete specimen can be found in the literature. Moreover, owing to the complication of including aggregates in concrete specimens in high-speed impact tests, and complexity of including aggregates in numerical simulation of high-speed impact tests of concrete materials, coarse aggregates are usually neglected in both the laboratory tests and numerical simulations, which may not give accurate concrete material dynamic properties. In the present study, a mesoscale concrete model with distinctive consideration of different components in a concrete specimen is developed to simulate SHPB tests and to study the influence of the confinement due to end friction between specimen and pressure bars on impact tests of dynamic concrete material properties. The commercial software AUTODYN is used to carry out the numerical simulations of SHPB tests. The friction coefficient between steel bar and concrete specimen is varied from 0.0 to 0.5 in the simulation. The results confirm that the end friction confinement does affect the testing results, and its influence depends on the L/D ratio (specimen length to diameter). This observation is also verified by experimental tests. The influences with L/D ratio and friction coefficient on stress and strain distributions and failure process of mesoscale concrete specimen under different strain rates are discussed. Based on the results from numerical simulations, an empirical formula is proposed to remove the influence of end friction confinement on dynamic strength increment of concrete material obtained in SHPB tests.


title: Generalized EEG-Based Drowsiness Prediction System by Using a Self-Organizing Neural Fuzzy System

abstract: A generalized EEG-based Neural Fuzzy system to predict driver's drowsiness was proposed in this study. Driver's drowsy state monitoring system has been implicated as a causal factor for the safety driving issue, especially when the driver fell asleep or distracted in driving. However, the difficulties in developing such a system are lack of significant index for detecting the driver's drowsy state in real-time and the interference of the complicated noise in a realistic and dynamic driving environment. In our past studies, we found that the electroencephalogram (EEG) power spectrum changes were highly correlated with the driver's behavior performance especially the occipital component. Different from presented subject-dependent drowsy state monitor systems, whose system performance may decrease rapidly when different subject applies with the drowsiness detection model constructed by others, in this study, we proposed a generalized EEG-based Self-organizing Neural Fuzzy system to monitor and predict the driver's drowsy state with the occipital area. Two drowsiness prediction models, subject-dependent and generalized cross-subject predictors, were investigated in this study for system performance analysis. Correlation coefficients and root mean square errors are showed as the experimental results and interpreted the performances of the proposed system significantly better than using other traditional Neural Networks ( p-value <;0.038). Besides, the proposed EEG-based Self-organizing Neural Fuzzy system can be generalized and applied in the subjects' independent sessions. This unique advantage can be widely used in the real-life applications.


title: Ubi-learning integrates indoor and outdoor experiences

abstract: Digital augmentation dissolves many of the physical barriers to learning by offering tools to integrate data and discoveries that travel with students as they explore new terrain.


title: Transient Stability Analysis of the IEEE 9-Bus Electric Power System

abstract: It is widely accepted that transient stability is an important aspect in designing and upgrading electric power system. This paper covers the modelling and the transient stability analysis of the IEEE 9 bus test system using ETAP. In this, for various faults on the test system fast fault clearing and load shed are analysed to bring back the system to the stability. Frequency is a reliable indicator if deficiency condition in the power system exists or not. Change in power demand or in production causes a fluctuation of the speed of the turbine-generator condition exists on the power system, resulting in fluctuation of the frequency of the power system. So rate of change of frequency is used as indicator of the transient stability of the system and to calculate the amount of load to be shed by adaptive load shedding and measures taken to maintain stability and frequency of the system.


title: Interlinking educational resources and the web of data: A survey of challenges and approaches

abstract: Purpose - Research in the area of technology-enhanced learning (TEL) throughout the last decade has largely focused on sharing and reusing educational resources and data. This effort has led to a fragmented landscape of competing metadata schemas, or interface mechanisms. More recently, semantic technologies were taken into account to improve interoperability. The linked data approach has emerged as the de facto standard for sharing data on the web. To this end, it is obvious that the application of linked data principles offers a large potential to solve interoperability issues in the field of TEL. This paper aims to address this issue. Design/methodology/approach - In this paper, approaches are surveyed that are aimed towards a vision of linked education, i.e. education which exploits educational web data. It particularly considers the exploitation of the wealth of already existing TEL data on the web by allowing its exposure as linked data and by taking into account automated enrichment and interlinking techniques to provide rich and well-interlinked data for the educational domain. Findings - So far web-scale integration of educational resources is not facilitated, mainly due to the lack of take-up of shared principles, datasets and schemas. However, linked data principles increasingly are recognized by the TEL community. The paper provides a structured assessment and classification of existing challenges and approaches, serving as potential guideline for researchers and practitioners in the field. Originality/value - Being one of the first comprehensive surveys on the topic of linked data for education, the paper has the potential to become a widely recognized reference publication in the area.


title: Robust dictionary learning with capped l 1 -norm

abstract: Expressing data vectors as sparse linear combinations of basis elements (dictionary) is widely used in machine learning, signal processing, and statistics. It has been found that dictionaries learned from data are more effective than off-the-shelf ones. Dictionary learning has become an important tool for computer vision. Traditional dictionary learning methods use quadratic loss function which is known sensitive to outliers. Hence they could not learn the good dictionaries when outliers exist. In this paper, aiming at learning dictionaries resistant to outliers, we proposed capped l1-norm based dictionary learning and an efficient iterative re-weighted algorithm to solve the problem. We provided theoretical analysis and carried out extensive experiments on real word datasets and synthetic datasets to show the effectiveness of our method.


title: An Approach to Subjectivity Detection on Twitter Using the Structured Information

abstract: In this paper, we propose an approach to the subjectivity detection on Twitter micro texts that explores the uses of the structured information of the social network framework. The sentiment analysis on Twitter has been usually performed through the automatic processing of the texts. However, the established limit of 140 characters and the particular characteristics of the texts reduce drastically the accuracy of Natural Language Processing (NLP) techniques. Under these circumstances, it becomes necessary to study new data sources that allow us to extract new useful knowledge to represent and classify the texts. The structured information, also called meta-information or meta-data, provide us with alternative features of the texts that can improve the classification tasks. In this study we have analysed the use of features extracted from the structured information in the subjectivity detection task, as a first step of the polarity detection task, and their integration with classical features.


Improving Text Classification by Shrinkage in a Hierarchy of Classes


title: Time lag characterization of two continuous glucose monitoring systems.

abstract: AIM ::: To evaluate the system time lag associated with subcutaneous interstitial glucose (IG) sensing and venous blood glucose (BG) of two continuous glucose monitoring (CGM) systems, the SEVEN((R)) (DexCom, San Diego, CA) and the Navigator((R)) (Abbott Diabetes Care, Alameda, CA), in adults with type 1 diabetes. ::: ::: ::: METHODS ::: Fourteen subjects wore both CGM systems concurrently during the 15-day study. Reference YSI (Yellow Springs, OH) and CGM data from the in-clinic sessions, conducted on day 5, 10 and 15 of the study were evaluated. The system time lag of CGM system was estimated using various regression method related statistical estimators. ::: ::: ::: RESULTS ::: The estimated time lags based on different statistical measures are similar within each CGM system. The time lag based on correlation coefficient criteria is estimated as 4.5+/-5min (median+/-IQR) for the SEVEN((R)), and 15+/-7min for the Navigator((R)). The ranges of these estimators of two CGM systems were different (2-6min for SEVEN((R)) and 14-15min for Navigator((R))). ::: ::: ::: CONCLUSIONS ::: The study findings suggest that commonly accessible statistics, such as correlation statistics, offer estimates that are comparable to complicated approaches. Different time lags were observed with two CGM systems.


Use of a personalized phenytoin dosing approach to manage difficult to control seizures in an infant with a SCN2A mutation


title: A Wideband Low-Power CMOS LNA With Positive–Negative Feedback for Noise, Gain, and Linearity Optimization

abstract: A wideband common-gate (CG) low-noise amplifier (LNA) utilizing positive-negative-feedback technique is presented. The positive-negative-feedback technique boosts effective transconductance (Gm) and output impedance, which leads to an LNA with higher gain and lower noise figure (NF) over the previously reported amplifiers. In addition, this approach provides high linearity with an aid of third harmonic cancellation, and it breaks transconductance (gm) constriction for input matching in CG amplifiers. In this paper, linearity and output impedance improvement through the proposed technique are fully analyzed. An LNA prototype is implemented in 0.18- μm CMOS technology occupying a total area of 0.33 mm2. The implemented LNA delivers a maximum voltage gain of 21 dB, a minimum NF of 2 dB, an third-order intermodulation intercept point of -3.2 dBm, and 3.6 mW of power consumption in 300-920 MHz of 3-dB bandwidth with input matching (S11 <; -10 dB).


title: We Can"See"You via Wi-Fi - WiFi Action Recognition via Vision-based Methods

abstract: Recently, Wi-Fi has caught tremendous attention for its ubiquity, and, motivated by Wi-Fi's low cost and privacy preservation, researchers have been putting lots of investigation into its potential on action recognition and even person identification. In this paper, we offer an comprehensive overview on these two topics in Wi-Fi. Also, through looking at these two topics from an unprecedented perspective, we could achieve generality instead of designing specific ad-hoc features for each scenario. Observing the great resemblance of Channel State Information (CSI, a fine-grained information captured from the received Wi-Fi signal) to texture, we proposed a brand-new framework based on computer vision methods. To minimize the effect of location dependency embedded in CSI, we propose a novel de-noising method based on Singular Value Decomposition (SVD) to eliminate the background energy and effectively extract the channel information of signals reflected by human bodies. From the experiments conducted, we demonstrate the feasibility and efficacy of the proposed methods. Also, we conclude factors that would affect the performance and highlight a few promising issues that require further deliberation.


The Dark Side of Trusts: Challenges to Chinese Inheritance Law


title: A Blind-Zone Detection Method Using a Rear-Mounted Fisheye Camera With Combination of Vehicle Detection Methods

abstract: This paper proposes a novel approach for detecting and tracking vehicles to the rear and in the blind zone of a vehicle, using a single rear-mounted fisheye camera and multiple detection algorithms. A maneuver that is a significant cause of accidents involves a target vehicle approaching the host vehicle from the rear and overtaking into the adjacent lane. As the overtaking vehicle moves toward the edge of the image and into the blind zone, the view of the vehicle gradually changes from a front view to a side view. Furthermore, the effects of fisheye distortion are at their most pronounced toward the extremities of the image, rendering detection of a target vehicle entering the blind zone even more difficult. The proposed system employs an AdaBoost classifier at distances of 10-40 m between the host and target vehicles. For detection at short distances where the view of a target vehicle has changed to a side view and the AdaBoost classifier is less effective, identification of vehicle wheels is proposed. Two methods of wheel detection are employed: at distances between 5 and 15 m, a novel algorithm entitled wheel arch contour detection (WACD) is presented, and for distances less than 5 m, Hough circle detection provides reliable wheel detection. A testing framework is also presented, which categorizes detection performance as a function of distance between host and target vehicles. Experimental results indicate that the proposed method results in a detection rate of greater than 93% in the critical range (blind zone) of the host.


title: Adaptive Artificial Intelligence in Games: Issues, Requirements, and a Solution through Behavlets-based General Player Modelling

abstract: We present the last of a series of three academic essays which deal with the question of how and why to build a generalized player model. We propose that a general player model needs parameters for subjective experience of play, including: player psychology, game structure, and actions of play. Based on this proposition, we pose three linked research questions: RQ1 what is a necessary and sufficient foundation to a general player model?; RQ2 can such a foundation improve performance of a computational intelligence- based player model?; and RQ3 can such a player model improve efficacy of adaptive artificial intelligence in games? ::: We set out the arguments behind these research questions in each of the three essays, presented as three preprints. The third essay, in this preprint, presents the argument that adaptive game artificial intelligence will be enhanced by a generalised player model. This is because games are inherently human artefacts which therefore, require some encoding of the human perspective in order to effectively autonomously respond to the individual player. The player model informs the necessary constraints on the adaptive artificial intelligence. A generalised player model is not only more efficient than a per-game solution, but also allows comparison between games which makes it a useful tool for studying play in general. We describe the concept and meaning of an adaptive game. We propose requirements for functional adaptive AI, arguing from first principles drawn from the games research literature. We propose solutions to these requirements, based on a formal model approach to our existing 'Behavlets' method for psychologically-derived player modelling: ::: Cowley, B., & Charles, D. (2016). Behavlets: a Method for Practical Player Modelling using Psychology-Based Player Traits and Domain Specific Features. User Modeling and User-Adapted Interaction, 26(2), 257-306.


title: Non-Deterministic Policies in Markovian Decision Processes

abstract: Markovian processes have long been used to model stochastic environments. Reinforcement learning has emerged as a framework to solve sequential planning and decision-making problems in such environments. In recent years, attempts were made to apply methods from reinforcement learning to construct decision support systems for action selection in Markovian environments. Although conventional methods in reinforcement learning have proved to be useful in problems concerning sequential decision-making, they cannot be applied in their current form to decision support systems, such as those in medical domains, as they suggest policies that are often highly prescriptive and leave little room for the user's input. Without the ability to provide flexible guidelines, it is unlikely that these methods can gain ground with users of such systems. ::: ::: This paper introduces the new concept of non-deterministic policies to allow more flexibility in the user's decision-making process, while constraining decisions to remain near optimal solutions. We provide two algorithms to compute non-deterministic policies in discrete domains. We study the output and running time of these method on a set of synthetic and real-world problems. In an experiment with human subjects, we show that humans assisted by hints based on non-deterministic policies outperform both human-only and computer-only agents in a web navigation task.


title: Magnetic Resonance Imaging Parameters for Assessing Risk of Recurrent Hamstring Injuries in Elite Athletes

abstract: BACKGROUND ::: Magnetic resonance (MR) imaging has established its usefulness in diagnosing hamstring muscle strain and identifying features correlating with the duration of rehabilitation in athletes; however, data are currently lacking that may predict which imaging parameters may be predictive of a repeat strain. ::: ::: ::: PURPOSE ::: This study was conducted to identify whether any MR imaging-identifiable parameters are predictive of athletes at risk of sustaining a recurrent hamstring strain in the same playing season. ::: ::: ::: STUDY DESIGN ::: Cohort study; Level of evidence, 3. ::: ::: ::: METHODS ::: Forty-one players of the Australian Football League who sustained a hamstring injury underwent MR examination within 3 days of injury between February and August 2002. The imaging parameters measured were the length of injury, cross-sectional area, the specific muscle involved, and the location of the injury within the muscle-tendon unit. Players who suffered a repeat injury during the same season were reimaged, and baseline and repeat injury measurements were compared. Comparison was also made between this group and those who sustained a single strain. ::: ::: ::: RESULTS ::: Forty-one players sustained hamstring strains that were positive on MR imaging, with 31 injured once and 10 suffering a second injury. The mean length of hamstring muscle injury for the isolated group was 83.4 mm, compared with 98.7 mm for the reinjury group (P = .35). In the reinjury group, the second strain was also of greater length than the original (mean, 107.5 mm; P = .07). Ninety percent of players sustaining a repeat injury demonstrated an injury length greater than 60 mm, compared with only 58% in the single strain group (P = .01). Only 7% of players (1 of 14) with a strain <60 mm suffered a repeat injury. Of the 27 players sustaining a hamstring strain >60 mm, 33% (9 of 27) suffered a repeat injury. Of all the parameters assessed, only a history of anterior cruciate ligament sprain was a statistically significant predictor for suffering a second strain during the same season of competition. ::: ::: ::: CONCLUSION ::: A history of anterior cruciate ligament injury was the only statistically significant risk factor for a recurrent hamstring strain in our study. Of the imaging parameters, the MR length of a strain had the strongest correlation association with a repeat hamstring strain and therefore may assist in identifying which athletes are more likely to suffer further reinjury.


title: A framework for modeling human-like driving behaviors for autonomous vehicles in driving simulators

abstract: A framework for modeling driver behavior within driving simulators is described in this paper. This framework serves as a basis for building human- like driving behavior models for autonomous vehicles operating within the virtual environment of a driving simulator. The framework consists of four units, the Perception Unit, the Emotions Unit, the Decision- making Unit (DMU), and the Decision- implementation Unit (DIU). The Perception Unit defines how the model perceives its environment in local and global terms. The Emotions Unit defines how the model responds emotionally to its environment. The DMU investigates the environment for possible actions that might potentially serve the model's emotional demands. And finally the DIU tries to implement these decisions when a traffic condition, perceived as safe enough for such an implementation, emerges. Each of these units has its own set of fuzzy variables and fuzzy ifthen rules. Any driving model, that is based on this framework, should provide membership function parameters for these fuzzy variables in accordance with the category of human driving behavior this model is targeting. Our framework addresses decision making and implementation at the maneuvering and operational levels of the driving task. Decisions at the planning level are addressed through a script- based traffic controller. The present model is limited to simulating human behaviors when driving in a two- lane rural environment.


title: Deep convolution network for surveillance records super-resolution

abstract: The aim of image super resolution (SR) is to recover low resolution (LR) input image or video to a visually desirable high-resolution (HR) one. The task of identifying an object in surveillance records is interesting, yet challenging due to the low resolution of the video. This paper, proposed a deep learning method for resolution recovery, the low-resolution objects and points in the surveillance records are up-sampled using a deep Convolutional Neural Network (CNN) to avoid problems of image boundary the data padded with zeros. The network is trained and tested on two surveillance datasets. Dissimilar to the outdated methods which operate components individually, our model performs combined optimization for all the layers. The proposed CNN model has a lightweight structure and minimal data pre-processing and computation cost. Testing our model and comparing with advanced techniques, we observed promising results. The code is accessible at https://github.com/Mzareapoor/Super-resolution


title: Sensor-based Linked Open Rules (S-LOR): An Automated Rule Discovery Approach for IoT Applications and its use in Smart Cities

abstract: This paper introduces an automated rule discovery approach for IoT device data (S-LOR: Sensor-based Linked Open Rules) and its use in smart cities. S-LOR is built following Linked Open Data (LOD) standards and provides support for semantics-based mechanisms to share, reuse and execute logical rules for interpreting data produced by IoT systems. S-LOR follows LOD principles for data re-usability, semantics-based reasoning and interoperability. In this paper, S-LOR main capability is demonstrated in the context of enabling semantics-based reasoning mechanisms and tools according to application-demand and user requirements. S-LOR (i) supports an automated interpretation of IoT data by executing rules, and (ii) allows an automated rule discovery interface. The implemented S-LOR mechanism can automatically process and interpret data from IoT devices by using rule-based discovery paradigm. Its extension called Linked Open Reasoning (LOR) enables and encourages re-usability of reasoning mechanisms and tools for different IoT smart city applications. The use cases described in this paper fits in the domain of smart city applications within Internet of Things deployed systems.


title: Domain Adaptation Using Adversarial Learning for Autonomous Navigation

abstract: Autonomous navigation has become an increasingly popular machine learning application. Recent advances in deep learning have also brought huge improvements to autonomous navigation. However, prior outdoor autonomous navigation methods depended on various expensive sensors or expensive and sometimes erroneously labeled real data. In this paper, we propose an autonomous navigation method that does not require expensive labeled real images and uses only a relatively inexpensive monocular camera. Our proposed method is based on (1) domain adaptation with an adversarial learning framework and (2) exploiting synthetic data from a simulator. To the best of the authors' knowledge, this is the first work to apply domain adaptation with adversarial networks to autonomous navigation. We present empirical results on navigation in outdoor courses using an unmanned aerial vehicle. The performance of our method is comparable to that of a supervised model with labeled real data, although our method does not require any label information for the real data. Our proposal includes a theoretical analysis that supports the applicability of our approach.


title: Holistic recognition of low quality license plates by CNN using track annotated data

abstract: This work is focused on recognition of license plates in low resolution and low quality images. We present a methodology for collection of real world (non-synthetic) dataset of low quality license plate images with ground truth transcriptions. Our approach to the license plate recognition is based on a Convolutional Neural Network which holistically processes the whole image, avoiding segmentation of the license plate characters. Evaluation results on multiple datasets show that our method significantly outperforms other free and commercial solutions to license plate recognition on the low quality data. To enable further research of low quality license plate recognition, we make the datasets publicly available.


title: Design of 5G Full Dimension Massive MIMO Systems

abstract: Massive multiple-input-multiple-output (MIMO) transmission is a promising technology to improve the capacity and reliability of wireless systems. However, the number of antennas that can be equipped at a base station (BS) is limited by the BS form factor, posing a challenge to the deployment of massive linear arrays. To cope with this limitation, this work discusses Full Dimension MIMO (FD-MIMO), which is currently an active area of research and standardization in the 3rd Generation Partnership Project (3GPP) for evolution towards fifth generation (5G) cellular systems. FD-MIMO utilizes an active antenna system (AAS) with a 2D planar array structure, which provides the ability of adaptive electronic beamforming in the 3D space. This paper presents the design of the AAS and the ongoing efforts in the 3GPP to develop the corresponding 3D channel model. Compact structure of large-scale antenna arrays drastically increases the spatial correlation in FD-MIMO systems. In order to account for its effects, the generalized spatial correlation functions for channels constituted by individual antenna elements and overall antenna ports in the AAS are derived. Exploiting the quasi-static channel covariance matrices of the users, the problem of determining the optimal downtilt weight vector for antenna ports, which maximizes the minimum signal-to-interference ratio of a multi-user multiple-input-single-output system, is formulated as a fractional optimization problem. A quasi-optimal solution is obtained through the application of semi-definite relaxation and Dinkelbach's method. Finally, the user-group specific elevation beamforming scenario is devised, which offers significant performance gains as confirmed through simulations. These results have direct application in the analysis of 5G FD-MIMO systems.


title: INDUSTRIES An analysis of the implications of the "creative industries" approach to arts and media policy making in the United Kingdom

abstract: This article analyses the cultural policy implications in the United Kingdom of a shift in terminology from cultural to creative industries. It argues that the use of the term “creative industries” can only be understood in the context of information society policy. It draws its political and ideological power from the prestige and economic importance attached to concepts of innovation, information, information workers and the impact of information and communication technologies drawn from information society theory. This sustains the unjustified claim of the cultural sector as a key economic growth sector within the global economy and creates a coalition of disparate interests around the extension of intellectual property rights. In the final analysis, it legitimates a return to an artistcentred, supply side defence of state cultural subsidies that is in contradiction to the other major aim of cultural policy ‐ wider access.


title: DNA Sequence Classification by Convolutional Neural Network

abstract: In recent years, a deep learning model called convolutional neural network with an ability of extracting features of high-level abstraction from minimum preprocessing data has been widely used. In this research, we proposed a new approach in classifying DNA sequences using the convolutional neural network while considering these sequences as text data. We used one-hot vectors to represent sequences as input to the model; therefore, it conserves the essential position information of each nucleotide in sequences. Using 12 DNA sequence datasets, we evaluated our proposed model and achieved significant improvements in all of these datasets. This result has shown a potential of using convolutional neural network for DNA sequence to solve other sequence problems in bioinformatics.


title: Unified Invocation of Scripts and Services for Provisioning, Deployment, and Management of Cloud Applications Based on TOSCA

abstract: There are several script-centric approaches, APIs, and tools available to implement automated provisioning, deployment, and management of applications in the Cloud. The automation of all these aspects is key for reducing costs. However, most of these approaches are script-centric and provide proprietary solutions employing different invocation mechanisms, interfaces, and state models. Moreover, most Cloud providers offer proprietary Web services or APIs to be used for provisioning and management purposes. Consequently, it is hard to create deployment and management plans integrating several of these approaches. The goal of our work is to come up with an approach for unified invocation of scripts and services without handling each proprietary interface separately. A prototype realizes the presented approach in a standards-based manner using the Topology and Orchestration Specification for Cloud Applications (TOSCA).


title: Automatic Speech Emotion Recognition: A survey

abstract: The area of Automatic Speech Emotion Recognition (ASER) has garnered a lot of interest among researchers. The framework of ASER typically includes three steps viz. speech feature extraction, dimensionality reduction and feature classification. At the base of this framework lies the design and recording of the database of emotional states through which the most popular set of emotions-happiness, sadness, anger, fear, disgust, boredom (which are typically called as ‘archetypal emotions’) and neutral among others have been obtained. This paper surveys the extent of work done in this field especially highlighting the three steps of the ASER framework. Starting with the different languages that have been explored till date for creating the databases, this paper attempts to categorize the features that have been typically extracted, enlist the dimensionality reduction techniques that have been chosen and discuss the pros and cons, if any, of the feature classifiers that have been modelled.


title: Solving a Real-World Glass Cutting Problem

abstract: We consider the problem of finding two-dimensional cutting patterns for glass sheets in order to produce rectangular elements requested by customers. The number of needed sheets, and therefore the waste, is to be minimized. The cutting facility requires three-staged guillotineable cutting patterns, and in addition, a plan for loading produced elements on transportation wagons. The availability of only three loading docks for wagons imposes additional constraints on the feasibility of cutting patterns. We describe a greedy first fit heuristic, two branch-and-bound based heuristics, and different variants of an evolutionary algorithm for the problem, and compare them on a set of real-world instances. The evolutionary algorithm is based on an order representation, specific recombination and mutation operators, and a decoding heuristic. In one variant, branch-and-bound is occasionally applied to locally optimize parts of a solution during decoding.


title: Cooperation and deception in primates

abstract: Abstract Though competition and cooperation are often considered opposing forces in an arms race driving natural selection, many animals, including humans, cooperate in order to mitigate competition with others. Understanding others’ psychological states, such as seeing and knowing, others’ goals and intentions, and coordinating actions are all important for complex cooperation—as well as for predicting behavior in order to take advantage of others through tactical deception, a form of competition. We outline evidence of primates’ understanding of how others perceive the world, and then consider how the evidence from both deception and cooperation fits this framework to give us a more complete understanding of the evolution of complex social cognition in primates. In experimental food competitions, primates flexibly manipulate group-mates’ behavior to tactically deceive them. Deception can infiltrate cooperative interactions, such as when one takes an unfair share of meat after a coordinated hunt. In order to counter competition of this sort, primates maintain cooperation through partner choice, partner control, and third party punishment. Yet humans appear to stand alone in their ability to understand others’ beliefs, which allows us not only to deceive others with the explicit intent to create a false belief, but it also allows us to put ourselves in others’ shoes to determine when cheaters need to be punished, even if we are not directly disadvantaged by the cheater.


Skeletal strokes


title: Associative Memory for Online Learning in Noisy Environments Using Self-Organizing Incremental Neural Network

abstract: Associative memory operating in a real environment must perform well in online incremental learning and be robust to noisy data because noisy associative patterns are presented sequentially in a real environment. We propose a novel associative memory that satisfies these requirements. Using the proposed method, new associative pairs that are presented sequentially can be learned accurately without forgetting previously learned patterns. The memory size of the proposed method increases adaptively with learning patterns. Therefore, it suffers neither redundancy nor insufficiency of memory size, even in an environment in which the maximum number of associative pairs to be presented is unknown before learning. Noisy inputs in real environments are classifiable into two types: noise-added original patterns and faultily presented random patterns. The proposed method deals with two types of noise. To our knowledge, no conventional associative memory addresses noise of both types. The proposed associative memory performs as a bidirectional one-to-many or many-to-one associative memory and deals not only with bipolar data, but also with real-valued data. Results demonstrate that the proposed method's features are important for application to an intelligent robot operating in a real environment. The originality of our work consists of two points: employing a growing self-organizing network for an associative memory, and discussing what features are necessary for an associative memory for an intelligent robot and proposing an associative memory that satisfies those requirements.


title: A Report on the First Native Language Identification Shared Task

abstract: Native Language Identification, or NLI, is the task of automatically classifying the L1 of a writer based solely on his or her essay written in another language. This problem area has seen a spike in interest in recent years as it can have an impact on educational applications tailored towards non-native speakers of a language, as well as authorship profiling. While there has been a growing body of work in NLI, it has been difficult to compare methodologies because of the different approaches to pre-processing the data, different sets of languages identified, and different splits of the data used. In this shared task, the first ever for Native Language Identification, we sought to address the above issues by providing a large corpus designed specifically for NLI, in addition to providing an environment for systems to be directly compared. In this paper, we report the results of the shared task. A total of 29 teams from around the world competed across three different sub-tasks.


title: Towards a Visual Turing Challenge

abstract: As language and visual understanding by machines progresses rapidly, we are observing an increasing interest in holistic architectures that tightly interlink both modalities in a joint learning and inference process. This trend has allowed the community to progress towards more challenging and open tasks and refueled the hope at achieving the old AI dream of building machines that could pass a turing test in open domains. In order to steadily make progress towards this goal, we realize that quantifying performance becomes increasingly difficult. Therefore we ask how we can precisely define such challenges and how we can evaluate different algorithms on this open tasks? In this paper, we summarize and discuss such challenges as well as try to give answers where appropriate options are available in the literature. We exemplify some of the solutions on a recently presented dataset of question-answering task based on real-world indoor images that establishes a visual turing challenge. Finally, we argue despite the success of unique ground-truth annotation, we likely have to step away from carefully curated dataset and rather rely on 'social consensus' as the main driving force to create suitable benchmarks. Providing coverage in this inherently ambiguous output space is an emerging challenge that we face in order to make quantifiable progress in this area.


title: The Association Between School-Based Physical Activity, Including Physical Education, and Academic Performance

abstract: Abstract Objective The purpose of this review is to synthesize the scientific literature that has examined the association between school-based physical activity (including physical education) and academic performance (including indicators of cognitive skills and attitudes, academic behaviors, and academic achievement). Method Relevant research was identified through a search of nine electronic databases using both physical activity and academic-related search terms. Forty-three articles (reporting a total of 50 unique studies) met the inclusion criteria and were read, abstracted, and coded for this synthesis. Findings of the 50 studies were then summarized. Results Across all the studies, there were a total of 251 associations between physical activity and academic performance, representing measures of academic achievement, academic behavior, and cognitive skills and attitudes. Slightly more than half (50.5%) of all associations examined were positive, 48% were not significant, and 1.5% were negative. Examination of the findings by each physical activity context provides insights regarding specific relationships. Conclusion Results suggest physical activity is either positively related to academic performance or that there is not a demonstrated relationship between physical activity and academic performance. Results have important implications for both policy and schools.


title: An enhanced Gauss-Markov mobility model for simulations of unmanned aerial ad hoc networks

abstract: Routing protocols are designed assuming certain application-specific network characteristics. In order for a routing protocol to be effective and reliable it needs to be evaluated with a realistic mobility model. The Random Waypoint mobility model, widely used, allows node to stop suddenly and turn sharply, and therefore fails to capture the movement pattern of actual airborne vehicles. In this paper we propose the Enhanced Gauss-Markov (EGM) mobility model, a realistic model for networks of UAVs (UAANETs) based on the Gauss-Markov (GM) mobility model. EGM features mechanisms to eliminate/limit sudden stops and sharp turns within the simulation region. The model, unlike others, also deals explicitly with ensuring smooth trajectories at the boundaries. Simulations in OPNET show that EGM, compared to RWP, results in many more network partitions. This then suggests that network partitioning is a significant issue that ought to be dealt with in the protocol design for UAANETs.


title: Automated test code generation from UML protocol state machines

abstract: This paper presents a framework for automated generation of executable test code from UML 2.0 protocol state machines. It supports several coverage criteria for state models, including state coverage, transition coverage, and basic and extended round-trip coverage. It transforms the state invariants and transition postconditions of a state model into executable assertions to be verified against the actual object states by runtime code instrumentation. Handcrafted test data are reused from one development version to the next due to the change of requirements. This reduces the working load for test regeneration of modified models. Our framework also reports the complexity of generated test suites, which can facilitate empirical evaluation of different coverage criteria for state models.


title: A survey Of procedural Noise functions

abstract: Procedural noise functions are widely used in computer graphics, from off-line rendering in movie production to interactive video games. The ability to add complex and intricate details at low memory and authoring cost is one of its main attractions. This survey is motivated by the inherent importance of noise in graphics, the widespread use of noise in industry and the fact that many recent research developments justify the need for an up-to-date survey. Our goal is to provide both a valuable entry point into the field of procedural noise functions, as well as a comprehensive view of the field to the informed reader. In this report, we cover procedural noise functions in all their aspects. We outline recent advances in research on this topic, discussing and comparing recent and well-established methods. We first formally define procedural noise functions based on stochastic processes and then classify and review existing procedural noise functions. We discuss how procedural noise functions are used for modelling and how they are applied to surfaces. We then introduce analysis tools and apply them to evaluate and compare the major approaches to noise generation. We finally identify several directions for future work.


title: Steering Force Feedback for Human–Machine-Interface Automotive Experiments

abstract: Driving-simulator fidelity is usually defined by the quality of its visual and motion cueing system. However, the quality of its haptic cues is also very important and is determined by both hardware and control properties. Most experiments with haptic steering systems employ commercially available systems and do not address the system's fidelity. The goal of this paper is to offer guidelines for the development of hardware, performance evaluation, and system control in order to engineer realistic haptic cues on the steering wheel. A relatively low-cost solution for hardware is deployed, consisting of a velocity-controlled three-phase brushless servomotor, of which its high-bandwidth control allows for a realistic representation of forces. A method is presented to overcome electromagnetic interference produced by the industrial servomotor and the controller through careful amplification and filtering. To test the system, different inertia-spring-damper systems were simulated and evaluated in time and frequency domain. In conclusion, the designed system allowed reproduction of a large range of steering-wheel dynamics and forces. As a result, the developed system constitutes an efficient haptic device for human-machine-interface automotive experiments.


title: Real-time object detection for "smart" vehicles

abstract: This paper presents an efficient shape-based object detection method based on Distance Transforms and describes its use for real-time vision on-board vehicles. The method uses a template hierarchy to capture the variety of object shapes; efficient hierarchies can be generated offline for given shape distributions using stochastic optimization techniques (i.e. simulated annealing). Online, matching involves a simultaneous coarse-to-fine approach over the shape hierarchy and over the transformation parameters. Very large speed-up factors are typically obtained when comparing this approach with the equivalent brute-force formulation; we have measured gains of several orders of magnitudes. We present experimental results on the real-time detection of traffic signs and pedestrians from a moving vehicle. Because of the highly time sensitive nature of these vision tasks, we also discuss some hardware-specific implementations of the proposed method as far as SIMD parallelism is concerned.


title: Estimating the Customer-Level Demand for Electricity Under Real-Time Market Prices

abstract: This paper presents estimates of the customer-level demand for electricity by industrial and commercial customers purchasing electricity according to the half-hourly energy prices from the England and Wales (E&W) electricity market. These customers also face the possibility of a demand charge on their electricity consumption during the three half-hour periods that are coincident with E&W system peaks. Although energy charges are largely known by 4 PM the day prior to consumption, a fraction of the energy charge and the identity of the half-hour periods when demand charges occur are only known with certainty ex post of consumption. Four years of data from a Regional Electricity Company (REC) in the United Kingdom is used to quantify the half-hourly customer-level demands under this real-time pricing program. The econometric model developed and estimated here quantifies the extent of intertemporal substitution in electricity consumption across pricing periods within the day due to changes in all components of day-ahead E&W electricity prices, the level of the demand charge and the probability that a demand charge will be imposed. The results of this modeling framework can be used by distribution companies supplying consumers purchasing electricity according to real-time market prices to construct demand-side bids into a competitive electricity market. The paper closes with several examples of how this might be done.


title: Joint channel and phase noise estimation for mmWave full-duplex communication systems

abstract: Full-duplex (FD) communication at millimeter-wave (mmWave) frequencies suffers from a strong self-interference (SI) signal, which can only be partially canceled using conventional RF cancelation techniques. This is because current digital SI cancellation techniques, designed for microwave frequencies, ignore the rapid phase noise (PN) variation at mmWave frequencies, which can lead to large estimation errors. In this work, we consider a multiple-input multiple-output mmWave FD communication system. We propose an extended Kalman filter-based estimation algorithm to track the rapid variation of PN at mmWave frequencies. We derive a lower bound for the estimation error of PN at mmWave and numerically show that the mean square error performance of the proposed estimator approaches the lower bound. We also simulate the bit error rate performance of the proposed system and show the effectiveness of a digital canceler, which uses the proposed estimator to estimate the SI channel. The results show that for a 2×2 FD system with 64−QAM modulation and PN variance of 10−4, the residual SI power can be reduced to − 25 dB and − 40 dB, respectively, for signal-to-interference ratio of 0 and 15 dB.


title: Graphene and graphene oxide in calcium silicate hydrates: Chemical reactions, mechanical behavior and interfacial sliding

abstract: Abstract We investigate chemical reactions, mechanical behaviors and interfacial sliding of calcium silicate hydrates (C S H) incorporating graphene nanosheet (GNS) and graphene oxide (GO) containing epoxides (GO-Oo) and hydroxyls (GO-OH) through molecular dynamics simulation. Structural analysis reveals that chemical reactions occur at the interface between GO and C S H matrices because the alkaline environment in C S H provides Ca2+ coordination and hydrogen bonds with GO. The Young's modulus and strength of C S H are enhanced by 52.6% and 23.3% with the incorporation of GO-OH while an increase of 31.6% and 17.5% in Young's modulus and strength is achieved by incorporating GO-Oo, attributed to high interfacial interaction energy and mechanical interlocking. However, GNS can hardly enhance the mechanical properties of C S H because its 2D surface has no interlocking with the matrix. The pull-out test reveals that mechanical interlocking plays a decisive role in the enhancement of the interfacial shear strength. During the pull-out process, functional groups are exfoliated from GO, which is harmful to its reinforcing ability. This study provides, for the first time, valuable insight into interfacial reaction mechanisms and working mechanisms of GNS/GO in realistic C S H structure.


title: Interaction potential of Trigonella foenum graceum through cytochrome P450 mediated inhibition.

abstract: Objective: The seeds of Trigonella foenum-graecum (TFG) (family: Leguminosae) are widely consumed both as a spice in food and Traditional Medicine in India. The present study was undertaken to evaluate the inhibitory effect of standardized extract of TFG and its major constituent trigonelline (TG) on rat liver microsome (RLM) and cytochrome P450 (CYP450) drug metabolizing isozymes (CYP3A4 and CYP2D6), which may indicate the possibility of a probable unwanted interaction. Materials and Methods: Reverse phase-high performance liquid chromatography method was developed to standardize the hydroalcoholic seed extract with standard TG. The inhibitory potential of the extract and TG was evaluated on RLM and CYP isozymes using CYP450-carbon monoxide (CYP450-CO) complex assay and fluorescence assay, respectively. Results: The content of TG in TFG was found to be 3.38% (w/w). The CYP-CO complex assay showed 23.32% inhibition on RLM. Fluorescence study revealed that the extract and the biomarker had some inhibition on CYP450 isozymes e.g. CYP3A4 and CYP2D6 (IC 50 values of the extract: 102.65 ± 2.63–142.23 ± 2.61 µg/ml and TG: 168.73 ± 4.03–180.90 ± 2.49 µg/ml) which was very less compared to positive controls ketoconazole and quinidine. Inhibition potential of TFG was little higher than TG but very less compared to positive controls. Conclusions: From the present study, we may conclude that the TFG or TG has very less potential to inhibit the CYP isozymes (CYP3A4, CYP2D6), so administration of this plant extract or its biomarker TG may be safe.


title: Temporal Abstraction in Temporal-difference Networks

abstract: We present a generalization of temporal-difference networks to include temporally abstract options on the links of the question network. Temporal-difference (TD) networks have been proposed as a way of representing and learning a wide variety of predictions about the interaction between an agent and its environment. These predictions are compositional in that their targets are defined in terms of other predictions, and subjunctive in that that they are about what would happen if an action or sequence of actions were taken. In conventional TD networks, the inter-related predictions are at successive time steps and contingent on a single action; here we generalize them to accommodate extended time intervals and contingency on whole ways of behaving. Our generalization is based on the options framework for temporal abstraction. The primary contribution of this paper is to introduce a new algorithm for intra-option learning in TD networks with function approximation and eligibility traces. We present empirical examples of our algorithm's effectiveness and of the greater representational expressiveness of temporally-abstract TD networks.


Design of Folded Cascode Operational Amplifier ( Op-Amp ) with Common-Mode Feedback ( CMFB ) for Pipeline ADC


title: Analysis of graphene based triangular nano patch antenna using photonic crystal as substrate for wireless applications

abstract: The goal of this paper is to explore the effects of two dimensional photonic band gap (PBG) crystals substrate on the performance of a graphene based triangular nano patch antenna in terahertz region (1–3 THz). Graphene as patch conductor on photonic crystal has been used as substrate material for high bandwidth, radiation efficiency and highly directional resonant antenna. Good impedance matching is achieved at the resonating frequencies with 5.6% bandwidth. The radiation characteristics including radiation efficiency and absorption cross section show significant improve on account of using PBG crystals substrate for graphene based patch antenna. The simulation has been performed using High Frequency Simulator Software (HFSS), which is a commercially available electromagnetic simulator based on finite element method technique.


title: A comprehensive survey for sentiment analysis tasks using machine learning techniques

abstract: Sentiment analysis is one of the most popular research topics in last years. There are lots of data on web which require analysis in order for them to become useful. Many researchers have focused on making sense of these data. Therefore, sentiment analysis concept is proposed. Sentiment analysis methods try to emerge any opinions, feelings, and subjectivity behind the text. Machine learning algorithms and vocabulary based methods are used to perform sentiment analysis. In this research, (i) recently studied researches on machine learning based sentiment analysis are investigated to give background; (ii) they are classified according to their tasks on extracting information; (iii) the encountered and potential challenges on this research topic are revisited and discussed.


title: Comparison of serial and parallel approaches using artificial neural networks for Algerian short-term load forecasting

abstract: Knowing that electrical load is a non storable resource; short term electric load forecasting becomes an important tool to optimise dispatching of electrical load in regular system planning. Several techniques have been used to accomplish this task, from traditional linear regression and Box- Jenkins to artificial intelligence approaches such as Artificial Neural Networks (ANN). This work presents a comparative study of serial and parallel ANN approaches for forecasting 168 hours ahead using a multiple linear regression model as a benchmark for comparison. The results obtained by the latter method, are compared with the ANN serial and parallel developed approaches. These models were trained solemnly on past load consumption data, given by the Algerian national electricity company. This results in Nonlinear Autoregressive Models (NAR), however once the approach validity is proven, the addition of exogenous inputs can only improve model results.


title: Supervised machine learning for grouping sketch diagram strokes

abstract: Grouping of strokes into semantically meaningful diagram elements is a difficult problem. Yet such grouping is needed if truly natural sketching is to be supported in intelligent sketch tools. Using a machine learning approach, we propose a number of new paired-stroke features for grouping and evaluate the suitability of a range of algorithms. Our evaluation shows the new features and algorithms produce promising results that are statistically better than the existing machine learning grouper.


title: Personalized extended (α, k)-anonymity model for privacy-preserving data publishing

abstract: Summary ::: General (α, k)-anonymity model is a widely used method in privacy-preserving data publishing, but it cannot provide personalized anonymity. At present, two main schemes for personalized anonymity are the individual-oriented anonymity and the sensitive value-oriented anonymity. Unfortunately, the existing personalized anonymity models, designed for any of the aforementioned schemes for privacy-preserving data publishing, are not effective enough to meet the personalized privacy preservation requirement. In this paper, we propose a novel personalized extended scheme to provide the personalized services in general (α, k)-anonymity model. The sensitive value-oriented anonymity is combined with the individual-oriented anonymity in the new personalized extended (α, k)-anonymity model by the following two steps: (1) The sensitive attribute values are divided into several groups according to their sensitivities, and each group is assigned with its own frequency constraint threshold. (2) A guarding node is set for each individual to replace his/her sensitive value if necessary. We implement the personalized extended (α, k)-anonymity model with a clustering algorithm. The performance evaluation finally shows that our model can provide stronger privacy preservation efficiently as well as achieving the personalized service. Copyright © 2016 John Wiley & Sons, Ltd.


title: Cannabidiol reduces ethanol consumption, motivation and relapse in mice

abstract: This study evaluated the effects of cannabidiol (CBD) on ethanol reinforcement, motivation and relapse in C57BL/6 J mice. The effects of CBD (60 mg/kg, i.p.) on blood ethanol concentration, hypothermia and handling-induced convulsions associated to acute ethanol administration were evaluated. The two-bottle choice paradigm was performed to assess the effects of CBD (30, 60 and 120 mg/kg/day, i.p.) on ethanol intake and preference. In addition, an oral ethanol self-administration experiment was carried out to evaluate the effects of CBD [a single s.c. administration of a microparticle formulation providing CBD continuous controlled release (30 mg/kg/day)] on the reinforcement and motivation for ethanol. The effects of CBD (60 and 120 mg/kg/day, i.p.) on ethanol-induced relapse were also evaluated. Gene expression analyses of tyrosine hydroxylase in ventral tegmental area and μ-opioid (Oprm1), cannabinoid (CB1r and CB2r) and GPR55 receptors in nucleus accumbens (NAcc) were carried out by real-time polymerase chain reaction. ::: ::: Cannabidiol reduced the ethanol-induced hypothermia and handling-induced convulsion but failed to modify blood ethanol concentration. CBD reduced ethanol consumption and preference in the two-bottle choice, significantly decreased ethanol intake and the number of effective responses in the oral ethanol self-administration, and reduced ethanol-induced relapse. Furthermore, the administration of CBD significantly reduced relative gene expression of tyrosine hydroxylase in the ventral tegmental area, Oprm1, CB1r and GPR55 in the NAcc and significantly increased CB2r in the NAcc. ::: ::: Taken together, these results reveal that the administration of CBD reduced the reinforcing properties, motivation and relapse for ethanol. These findings strongly suggest that CBD may result useful for the treatment of alcohol use disorders.


Directions in the modernization of modern seaports


title: Variables That Affect the Clinical Use and Abuse of Methylphenidate in the Treatment of ADHD

abstract: OBJECTIVE: Methylphenidate, the most common treatment for attention deficit hyperactivity disorder (ADHD), increases extracellular dopamine in the brain, which is associated with its reinforcing as well as its therapeutic effects. The authors evaluated variables that distinguish these two properties. METHOD: The brain imaging and clinical literatures were analyzed to identify variables that contribute to the abuse liability as well as to the clinical efficacy of methylphenidate. RESULTS: Four variables were identified. 1) Dose—there is a threshold for methylphenidate-induced dopamine increases to be perceived as reinforcing and to produce therapeutic effects. 2) Pharmacokinetics—the reinforcing effects of methylphenidate are associated with rapid changes in serum concentrations and presumably fast dopamine increases (as achieved with intravenous injection or insufflation), whereas the therapeutic effects are associated with slowly ascending serum concentrations and presumably smoothly rising dopamine leve...


title: Recent advances on active noise control: open issues and innovative applications

abstract: The problem of acoustic noise is becoming increasingly serious with the growing use of industrial and medical equipment, appliances, and consumer electronics. Active noise control (ANC), based on the principle of superposition, was developed in the early 20th century to help reduce noise. However, ANC is still not widely used owing to the effectiveness of control algorithms, and to the physical and economical constraints of practical applications. In this paper, we briefly introduce some fundamental ANC algorithms and theoretical analyses, and focus on recent advances on signal processing algorithms, implementation techniques, challenges for innovative applications, and open issues for further research and development of ANC systems.


title: THE RAPID GROWTH OF CYBERCRIMES AFFECTING INFORMATION SYSTEMS IN THE GLOBAL: IS THIS A MYTH OR REALITY IN TANZANIA?

abstract: Abstract-. The main objective of this study was to determine whether the rapid growth of cybercrimes affecting information systems in the global: is a myth or reality in Tanzania. The study was undertaken using a mixed research (quantitative and qualitative) methods. The research findings reveals that 12.8 % of users are victims of cybercrimes due to visiting unhealthy websites in cyberspace, more than 90.89% of users have been denied access to torrents/p2p applications (with malicious codes); more than 63.29% of e-mails received by users are spam. It has been noted that Internet users has risen to 5.63 million users in 2012 out of 46.9 million of population in Tanzania; and is increasing at the rate of 416.98% per year; with this increase rate, there will be 7.34 million users of internet by December 2015 in Tanzania. The increase in the number of Internet users has direct implication to the increase in cybercrimes affecting information systems. The Government of Tanzania has declared that “about 320 people were apprehended between July and December 2011; there are over 1bn/- , Euro 8,897 and USD 551,777 reported to have been stolen through cyber. The study concluded that there is a need for holistic approach in addressing cybercrimes in developing world countries.


title: Question Relevance in Visual Question Answering

abstract: Free-form and open-ended Visual Question Answering systems solve the problem of providing an accurate natural language answer to a question pertaining to an image. Current VQA systems do not evaluate if the posed question is relevant to the input image and hence provide nonsensical answers when posed with irrelevant questions to an image. In this paper, we solve the problem of identifying the relevance of the posed question to an image. We address the problem as two sub-problems. We first identify if the question is visual or not. If the question is visual, we then determine if it's relevant to the image or not. For the second problem, we generate a large dataset from existing visual question answering datasets in order to enable the training of complex architectures and model the relevance of a visual question to an image. We also compare the results of our Long Short-Term Memory Recurrent Neural Network based models to Logistic Regression, XGBoost and multi-layer perceptron based approaches to the problem.


title: Robust Contextual Outlier Detection: Where Context Meets Sparsity

abstract: Outlier detection is a fundamental data science task with applications ranging from data cleaning to network security. Recently, a new class of outlier detection algorithms has emerged, called contextual outlier detection, and has shown improved performance when studying anomalous behavior in a specific context. However, as we point out in this article, such approaches have limited applicability in situations where the context is sparse (i.e., lacking a suitable frame of reference). Moreover, approaches developed to date do not scale to large datasets. To address these problems, here we propose a novel and robust approach alternative to the state-of-the-art called RObust Contextual Outlier Detection (ROCOD). We utilize a local and global behavioral model based on the relevant contexts, which is then integrated in a natural and robust fashion. We run ROCOD on both synthetic and real-world datasets and demonstrate that it outperforms other competitive baselines on the axes of efficacy and efficiency. We also drill down and perform a fine-grained analysis to shed light on the rationale for the performance gains of ROCOD and reveal its effectiveness when handling objects with sparse contexts.


title: Interval Type-2 Fuzzy Logic Systems Made Simple

abstract: To date, because of the computational complexity of using a general type-2 fuzzy set (T2 FS) in a T2 fuzzy logic system (FLS), most people only use an interval T2 FS, the result being an interval T2 FLS (IT2 FLS). Unfortunately, there is a heavy educational burden even to using an IT2 FLS. This burden has to do with first having to learn general T2 FS mathematics, and then specializing it to an IT2 FSs. In retrospect, we believe that requiring a person to use T2 FS mathematics represents a barrier to the use of an IT2 FLS. In this paper, we demonstrate that it is unnecessary to take the route from general T2 FS to IT2 FS, and that all of the results that are needed to implement an IT2 FLS can be obtained using T1 FS mathematics. As such, this paper is a novel tutorial that makes an IT2 FLS much more accessible to all readers of this journal. We can now develop an IT2 FLS in a much more straightforward way


title: On the Performance of Non-orthogonal Multiple Access Systems With Partial Channel Information

abstract: In this paper, a downlink single-cell non-orthogonal multiple access (NOMA) network with uniformly deployed users is considered and an analytical framework to evaluate its performance is developed. Particularly, the performance of NOMA is studied by assuming two types of partial channel state information (CSI). For the first one, which is based on imperfect CSI , we present a simple closed-form approximation for the outage probability and the average sum rate, as well as their high signal-to-noise ratio (SNR) expressions. For the second type of CSI, which is based on second order statistics (SOS) , we derive a closed-form expression for the outage probability and an approximate expression for the average sum rate for the special case two users. For the addressed scenario with the two types of partial CSI, the results demonstrate that NOMA can achieve superior performance compared to the traditional orthogonal multiple access (OMA). Moreover, SOS-based NOMA always achieves better performance than that with imperfect CSI, while it can achieve similar performance to the NOMA with perfect CSI at the low SNR region. The provided numerical results confirm that the derived expressions for the outage probability and the average sum rate match well with the Monte Carlo simulations.


title: Loose-limbed People: Estimating 3D Human Pose and Motion Using Non-parametric Belief Propagation

abstract: We formulate the problem of 3D human pose estimation and tracking as one of inference in a graphical model. Unlike traditional kinematic tree representations, our model of the body is a collection of loosely-connected body-parts. In particular, we model the body using an undirected graphical model in which nodes correspond to parts and edges to kinematic, penetration, and temporal constraints imposed by the joints and the world. These constraints are encoded using pair-wise statistical distributions, that are learned from motion-capture training data. Human pose and motion estimation is formulated as inference in this graphical model and is solved using Particle Message Passing (PaMPas). PaMPas is a form of non-parametric belief propagation that uses a variation of particle filtering that can be applied over a general graphical model with loops. The loose-limbed model and decentralized graph structure allow us to incorporate information from "bottom-up" visual cues, such as limb and head detectors, into the inference process. These detectors enable automatic initialization and aid recovery from transient tracking failures. We illustrate the method by automatically tracking people in multi-view imagery using a set of calibrated cameras and present quantitative evaluation using the HumanEva dataset.


title: UpStream: motivating water conservation with low-cost water flow sensing and persuasive displays

abstract: Water is our most precious and most rapidly declining natural resource. We explore pervasive technology as an approach for promoting water conservation in public and private spaces. We hope to motivate immediate reduction in water use as well as higher-order behaviors (seeking new information, etc) through unobtrusive low-cost water flow sensing and several persuasive displays. Early prototypes were installed at public faucets and a private (shared) shower, logging water usage first without and then with ambient displays. This pilot study led to design iterations, culminating in long-term deployment of sensors in four private showers over the course of three weeks. Sensors first logged baseline water usage without visualization. Then, two display styles, ambient and numeric, were deployed in random order, each showing individual and average water consumption. Quantitative data along with participants' feedback contrast the effectiveness of numeric displays against abstract visualization in this very important domain of water conservation and public health.


title: A PID Controller Approach for Stochastic Optimization of Deep Networks

abstract: Deep neural networks have demonstrated their power in many computer vision applications. State-of-the-art deep architectures such as VGG, ResNet, and DenseNet are mostly optimized by the SGD-Momentum algorithm, which updates the weights by considering their past and current gradients. Nonetheless, SGD-Momentum suffers from the overshoot problem, which hinders the convergence of network training. Inspired by the prominent success of proportional-integral-derivative (PID) controller in automatic control, we propose a PID approach for accelerating deep network optimization. We first reveal the intrinsic connections between SGD-Momentum and PID based controller, then present the optimization algorithm which exploits the past, current, and change of gradients to update the network parameters. The proposed PID method reduces much the overshoot phenomena of SGD-Momentum, and it achieves up to 50% acceleration on popular deep network architectures with competitive accuracy, as verified by our experiments on the benchmark datasets including CIFAR10, CIFAR100, and Tiny-ImageNet.


title: Design of a highly biomimetic anthropomorphic robotic hand towards artificial limb regeneration

abstract: A wide range of research areas, from telemanipulation in robotics to limb regeneration in tissue engineering, could benefit from an anthropomorphic robotic hand that mimics the salient features of the human hand. The challenges of designing such a robotic hand are mainly resulted from our limited understanding of the human hand from engineering point of view and our ability to replicate the important biomechanical features with conventional mechanical design. We believe that the biomechanics of human hand is an essential component of the hand dexterity and can be replicated with highly biomimetic design. To this end, we reinterpret the important biomechanical advantages of the human hand from roboticist's perspective and design a biomimetic robotic hand that closely mimics its human counterpart with artificial joint capsules, crocheted ligaments and tendons, laser-cut extensor hood, and elastic pulley mechanisms. We experimentally identify the workspaces of the fingertips and successfully demonstrate that our proofof- concept design can be teleoperated to grasp and manipulate daily objects with a variety of natural hand postures based on hand taxonomy.


A Joint Sequential and Relational Model for Frame-Semantic Parsing


title: Visual Reinforcement Learning with Imagined Goals

abstract: For an autonomous agent to fulfill a wide range of user-specified goals at test time, it must be able to learn broadly applicable and general-purpose skill repertoires. Furthermore, to provide the requisite level of generality, these skills must handle raw sensory input such as images. In this paper, we propose an algorithm that acquires such general-purpose skills by combining unsupervised representation learning and reinforcement learning of goal-conditioned policies. Since the particular goals that might be required at test-time are not known in advance, the agent performs a self-supervised "practice" phase where it imagines goals and attempts to achieve them. We learn a visual representation with three distinct purposes: sampling goals for self-supervised practice, providing a structured transformation of raw sensory inputs, and computing a reward signal for goal reaching. We also propose a retroactive goal relabeling scheme to further improve the sample-efficiency of our method. Our off-policy algorithm is efficient enough to learn policies that operate on raw image observations and goals for a real-world robotic system, and substantially outperforms prior techniques.


title: Principles for Conducting Critical Realist Case Study Research in Information Systems

abstract: Critical realism is emerging as a viable philosophical paradigm for conducting social science research, and has been proposed as an alternative to the more prevalent paradigms of positivism and interpretivism. Few papers, however, have offered clear guidance for applying this philosophy to actual research methodologies. Under critical realism, a causal explanation for a given phenomenon is inferred by explicitly identifying the means by which structural entities and contextual conditions interact to generate a given set of events. Consistent with this view of causality, we propose a set of methodological principles for conducting and evaluating critical realism-based explanatory case study research within the information systems field. The principles are derived directly from the ontological and epistemological assumptions of critical realism. We demonstrate the utility of each of the principles through examples drawn from existing critical realist case studies. The article concludes by discussing the implications of critical realism based research for IS research and practice.


title: Open-domain Multi-Document Summarization via Information Extraction: Challenges and Prospects

abstract: Information Extraction (IE) and Summarization share the same goal of extracting and presenting the relevant information of a document. While IE was a primary element of early abstractive summarization systems, it’s been left out in more recent extractive systems. However, extracting facts, recognizing entities and events should provide useful information to those systems and help resolve semantic ambiguities that they cannot tackle. This paper explores novel approaches to taking advantage of cross-document IE for multi-document summarization. We propose multiple approaches to IE-based summarization and analyze their strengths and weaknesses. One of them, re-ranking the output of a high performing summarization system with IE-informed metrics, leads to improvements in both manually-evaluated content quality and readability.


KGEval: Accuracy Estimation of Automatically Constructed Knowledge Graphs


title: Low-contrast surface inspection of mura defects in liquid crystal displays using optical flow-based motion analysis

abstract: This paper proposes a machine vision scheme for mura defect detection in LCD manufacturing. Mura is a Japanese word for blemish, which typically shows brightness imperfections from its surroundings in the surface. It appears as a low-contrast region without clear edges. Traditional automatic visual inspection algorithms detect mura defects from individual still images. They neglect that a mura defect may not be visually sensed in the image from a stationary system. In this study, the LCD panel is assumed to move along a track. While the panel passes through a fixed camera, the light reflection from different angles can effectively enhance the mura defect in the low-contrast images. The mura detection problem is therefore treated as a motion analysis in image sequences using optical flow techniques. Since a LCD panel moves along a single direction, both two-dimensional and one-dimensional optical flow methods are developed. Three discriminative features based on flow magnitude, mean flow magnitude and flow density in the optical flow field are presented to extract the defective regions. Both real panel images and synthetic surface images are used to evaluate the efficacy of the proposed methods. Experimental results have shown that the proposed 1D optical flow method works as well as the 2D optical flow method to detect very low-contrast mura defects of small size, and achieves a high processing rate around 20 frames per second for images of size 200 × 200.


title: Pseudo-Constant Switching Frequency in On-Time Controlled Buck Converter with Predicting Correction Techniques

abstract: The predicting correction technique (PCT) is proposed to achieve an adaptive on-time control for ripple-based buck converters. The switching frequency $f_{{\rm SW}}$ variation is suppressed when the PCT technique considers complete parasitic resistances of the components and devices. Even without extra clock-controlled circuits and current sensing circuits, the buck converter operates with a nearly constant $f_{{\rm SW}}$ over a wide load range. Parasitic resistances almost cause no influence and restriction on $f_{{\rm SW}}$ . Only input voltage is used to predict the adaptive on-time. Measurement results show only 0.32% in $\Delta f_{{\rm SW}}/f_{{\rm SW}}$ and 5.7 kHz/A in $\Delta f_{{\rm SW}}/\Delta I_{{\rm LOAD}}$ in case of 1.4 A load current change and $f_{{\rm SW}}$ is 2.5 MHz. Consequently, a pseudo-constant $f_{{\rm SW}}$ with well-defined noise spectrum strongly benefits the solution of electromagnetic interference (EMI) for system-on-a-chip (SoC) applications.


title: Improving Transition-Based Dependency Parsing of Hindi and Urdu by Modeling Syntactically Relevant Phenomena

abstract: In recent years, transition-based parsers have shown promise in terms of efficiency and accuracy. Though these parsers have been extensively explored for multiple Indian languages, there is still considerable scope for improvement by properly incorporating syntactically relevant information. In this article, we enhance transition-based parsing of Hindi and Urdu by redefining the features and feature extraction procedures that have been previously proposed in the parsing literature of Indian languages. We propose and empirically show that properly incorporating syntactically relevant information like case marking, complex predication and grammatical agreement in an arc-eager parsing model can significantly improve parsing accuracy. Our experiments show an absolute improvement of ∼2% LAS for parsing of both Hindi and Urdu over a competitive baseline which uses rich features like part-of-speech (POS) tags, chunk tags, cluster ids and lemmas. We also propose some heuristics to identify ezafe constructions in Urdu texts which show promising results in parsing these constructions.


title: WSN for forest monitoring to prevent illegal logging

abstract: Illegal logging is in these days widespread problem. In this paper we propose the system based on principles of WSN for monitoring the forest. Acoustic signal processing and evaluation system described in this paper is dealing with the detection of chainsaw sound with autocorrelation method. This work is describing first steps in building the integrated system.


title: OAM modulation induced in millimeter waves using impedance matched flat phase plate

abstract: Orbital angular momentum (OAM) based communication has been investigated as it provides an additional degree of freedom to the electromagnetic waves which enables to transmit and receive data in various OAM modes at a given operation frequency, thus increasing the data rate. A method for designing very low reflective flat spiral phase plate that generates OAM modes is proposed in this paper. Such phase plates could provide one or more OAM modes at a signal frequency irrespective of the polarization. The design incorporates variable sizes of perforated unit cells along with matched layers to provide good impedance matching, and the flat disc shape of the phase plate provides the advantage of being compact and easy to install. The OAM phase plate is designed for operation around 30 GHz frequency.


title: Engineering safety in machine learning

abstract: Machine learning algorithms are increasingly influencing our decisions and interacting with us in all parts of our daily lives. Therefore, just like for power plants, highways, and myriad other engineered sociotechnical systems, we must consider the safety of systems involving machine learning. In this paper, we first discuss the definition of safety in terms of risk, epistemic uncertainty, and the harm incurred by unwanted outcomes. Then we examine dimensions, such as the choice of cost function and the appropriateness of minimizing the empirical average training cost, along which certain real-world applications may not be completely amenable to the foundational principle of modern statistical machine learning: empirical risk minimization. In particular, we note an emerging dichotomy of applications: ones in which safety is important and risk minimization is not the complete story (we name these Type A applications), and ones in which safety is not so critical and risk minimization is sufficient (we name these Type B applications). Finally, we discuss how four different strategies for achieving safety in engineering (inherently safe design, safety reserves, safe fail, and procedural safeguards) can be mapped to the machine learning context through interpretability and causality of predictive models, objectives beyond expected prediction accuracy, human involvement for labeling difficult or rare examples, and user experience design of software.


title: Machine Teaching for Inverse Reinforcement Learning: Algorithms and Applications

abstract: Inverse reinforcement learning (IRL) infers a reward function from demonstrations, allowing for policy improvement and generalization. However, despite much recent interest in IRL, little work has been done to understand of the minimum set of demonstrations needed to teach a specific sequential decision-making task. We formalize the problem of finding maximally informative demonstrations for IRL as a machine teaching problem where the goal is to find the minimum number of demonstrations needed to specify the reward equivalence class of the demonstrator. We extend previous work on algorithmic teaching for sequential decision-making tasks by showing a reduction to the set cover problem which enables an efficient approximation algorithm for determining the set of maximally-informative demonstrations. We apply our proposed machine teaching algorithm to two novel applications: providing a lower bound on the number of queries needed to learn a policy using active IRL and developing a novel IRL algorithm that can learn more efficiently from informative demonstrations than a standard IRL approach.