title: Cyber Security & Ethical Hacking For SMEs

abstract: Literature posits that 30,000 SME websites are hacked daily. Thus it is acknowledged that web-based tools are essential to prevent cyber criminals hacking into online networks to comprise their services and gain access to confidential data for improper purposes. We consider the concept of Ethical Hacking where authorized hackers attempt to infiltrate a business's systems/networks on behalf of the owners with the intention of finding security weaknesses. However such security measures are costly and many SMEs lack the knowledge and resources to accomplish this. Utilizing an SME case study we provide insights into how Ethical Hacking, in the form of Penetration Testing using free open source tools, can be used by SMEs to protect their network's services/operations. Using Nmap, Google Hacking, Nessus and Brutus we uncovered 232 network vulnerabilities. Thus measures were put in to place to resolve these vulnerabilities and prevent the case study's sensitive data from future cyber threats.


title: Capturing the Complexity in Advanced Technology Use: Adaptive Structuration Theory

abstract: The past decade has brought advanced information technologies, which include electronic messaging systems, executive information systems, collaborative systems, group decision support systems, and other technologies that use sophisticated information management to enable multiparty participation in organization activities. Developers and users of these systems hold high hopes for their potential to change organizations for the better, but actual changes often do not occur, or occur inconsistently. We propose adaptive structuration theory (AST) as a viable approach for studying the role of advanced information technologies in organization change. AST examines the change process from two vantage points: (1) the types of structures that are provided by advanced technologies, and (2) the structures that actually emerge in human action as people interact with these technologies. To illustrate the principles of AST, we consider the small group meeting and the use of a group decision support system (GDSS). A GDSS is an interesting technology for study because it can be structured in a myriad of ways, and social interaction unfolds as the GDSS is used. Both the structure of the technology and the emergent structure of social action can be studied.We begin by positioning AST among competing theoretical perspectives of technology and change. Next, we describe the theoretical roots and scope of the theory as it is applied to GDSS use and state the essential assumptions, concepts, and propositions of AST. We outline an analytic strategy for applying AST principles and provide an illustration of how our analytic approach can shed light on the impacts of advanced technologies on organizations. A major strength of AST is that it expounds the nature of social structures within advanced information technologies and the key interaction processes that figure in their use. By capturing these processes and tracing their impacts, we can reveal the complexity of technology-organization relationships. We can attain a better understanding of how to implement technologies, and we may also be able to develop improved designs or educational programs that promote productive adaptations.


title: Why is Posterior Sampling Better than Optimism for Reinforcement Learning

abstract: Computational results demonstrate that posterior sampling for reinforcement learning (PSRL) dramatically outperforms algorithms driven by optimism, such as UCRL2. We provide insight into the extent of this performance boost and the phenomenon that drives it. We leverage this insight to establish an $\tilde{O}(H\sqrt{SAT})$ Bayesian expected regret bound for PSRL in finite-horizon episodic Markov decision processes, where $H$ is the horizon, $S$ is the number of states, $A$ is the number of actions and $T$ is the time elapsed. This improves upon the best previous bound of $\tilde{O}(H S \sqrt{AT})$ for any reinforcement learning algorithm.


title: A second order accurate level set method on non-graded adaptive cartesian grids

abstract: We present a level set method on non-graded adaptive Cartesian grids, i.e. grids for which the ratio between adjacent cells is not constrained. We use quadtree and octree data structures to represent the grid and a simple algorithm to generate a mesh with the finest resolution at the interface. In particular, we present (1) a locally third order accurate reinitialization scheme that transforms an arbitrary level set function into a signed distance function, (2) a second order accurate semi-Lagrangian methods to evolve the linear level set advection equation under an externally generated velocity field, (3) a second order accurate upwind method to evolve the non-linear level set equation under a normal velocity as well as to extrapolate scalar quantities across an interface in the normal direction, and (4) a semi-implicit scheme to evolve the interface under mean curvature. Combined, we obtain a level set method on adaptive Cartesian grids with a negligible amount of mass loss. We propose numerical examples in two and three spatial dimensions to demonstrate the accuracy of the method.


title: A 13.56 MHz CMOS Active Rectifier With Switched-Offset and Compensated Biasing for Biomedical Wireless Power Transfer Systems

abstract: A full-wave active rectifier switching at 13.56 MHz with compensated bias current for a wide input range for wirelessly powered high-current biomedical implants is presented. The four diodes of a conventional passive rectifier are replaced by two cross-coupled PMOS transistors and two comparator- controlled NMOS switches to eliminate diode voltage drops such that high voltage conversion ratio and power conversion efficiency could be achieved even at low AC input amplitude $\vert V_{\rm AC}\vert$ . The comparators are implemented with switched-offset biasing to compensate for the delays of active diodes and to eliminate multiple pulsing and reverse current. The proposed rectifier uses a modified CMOS peaking current source with bias current that is quasi-inversely proportional to the supply voltage to better control the reverse current over a wide AC input range (1.5 to 4 V). The rectifier was fabricated in a standard 0.35 $\mu{\rm m}$ CMOS N-well process with active area of 0.0651 ${\rm mm}^{2}$ . For the proposed rectifier measured at $\vert V_{\rm AC}\vert=3.0\ {\rm V}$ , the voltage conversion ratios are 0.89 and 0.93 for $R_{\rm L}=500\ \Omega$ and 5 ${\rm k}\Omega$ , respectively, and the measured power conversion efficiencies are 82.2% to 90.1% with $\vert V_{\rm AC}\vert$ ranges from 1.5 to 4 V for $R_{\rm L}=500\ \Omega$ .


title: A Self-Resonant Two-Coil Wireless Power Transfer System Using Open Bifilar Coils

abstract: Usually, self-resonant coils are restricted to use on the intermediary circuits of multiple-coil wireless power transfer (WPT) systems. This is because a source sees a parallel resonance when connected to a typical coil at self-resonance, and parallel associations in the primary and secondary sides of a two-coil system are not as practical as the respective series associations. However, many applications require two-coil systems due to their reduced size and simplicity. In this work, a self-resonant two-coil WPT system is proposed. The presented system uses open bifilar coils that lead to a series resonance at the resonant frequency. In this way, no capacitor is used in the whole system and it behaves as a series association of coil self-capacitance and inductance at the resonant frequency. Thus, the proposed system has a reduced size and also avoids capacitor damage due to high individual voltages caused by resonance effects. Practical measurements and theoretical results of the system efficiency and relative power transfer show good agreement and validate the proposed scheme as a promising solution for a two-coil self-resonant WPT system.


title: Pruning Filter via Geometric Median for Deep Convolutional Neural Networks Acceleration.

abstract: Previous works utilized "smaller-norm-less-important" criterion to prune filters with smaller norm values in a convolutional neural network. In this paper, we analyze this norm-based criterion and point out that its effectiveness depends on two requirements that not always met: (1) the norm deviation of the filters should be large; (2) the minimum norm of the filters should be small. To solve this problem, we propose a novel filter pruning method, namely Filter Pruning via Geometric Median (FPGM), to compress the model regardless of those two requirements. Unlike previous methods, PFGM compresses CNN models by determining and pruning those filters with redundant information via Geometric Median (GM), rather than those with "relatively less" importance. When applied to two image classification benchmarks, our method validates its usefulness and strengths. Notably, on Cifar-10, PFGM reduces more than 52% FLOPs on ResNet-110 with even 2.69% relative accuracy improvement. Besides, on ILSCRC-2012, PFGM reduces more than 42% FLOPs on ResNet-101 without top-5 accuracy drop, which has advanced the state-of-the-art.


title: DARTS: Differentiable Architecture Search

abstract: This paper addresses the scalability challenge of architecture search by formulating the task in a differentiable manner. Unlike conventional approaches of applying evolution or reinforcement learning over a discrete and non-differentiable search space, our method is based on the continuous relaxation of the architecture representation, allowing efficient search of the architecture using gradient descent. Extensive experiments on CIFAR-10, ImageNet, Penn Treebank and WikiText-2 show that our algorithm excels in discovering high-performance convolutional architectures for image classification and recurrent architectures for language modeling, while being orders of magnitude faster than state-of-the-art non-differentiable techniques.


title: A novel level shifter for practical IoT applications

abstract: This paper proposed a 0.5 V–1.32 V input to 3.3 V output voltage level shifter (LS) that meets both requirements for current IoT applications, namely a minimal 0.7 V input voltage and 500 MHz speed at nominal 1.2 V. Simulations of this LS using a commercial 55-nm process design kit demonstrated a minimal input voltage in the near-threshold region (500 mV) and speeds in the GHz range when running at 1.2 V nominally. This was achieved using an isolation control signal to time the half-latch that switches input and output voltage, and a NAND gate as an inverting buffer for data transferring in active state or a pull high logic in isolation state. Besides, the proposed LS exhibits average static power of less than 70 pW and a small area consumption which is suitable for IoT applications.


title: Texture-Aware Emotional Color Transfer Between Images

abstract: Different colors can evoke different emotions for images. For example, artists usually use different color combinations to express different emotions when creating posters or pictures. Existing color transfer methods can successfully transfer visual appearance between images by changing colors. However, they often fail to change an image to meet an accurate emotion. Nowadays, there are few emotional color transfer methods between images, and those methods are imperfect in transferring the emotion from one image to another image due to the inaccuracy in the emotion calculation of the reference image. Therefore, we propose a texture-aware emotional color transfer method between images, which can adjust an image with an emotion word or a reference image. Each emotion word represents a type of emotion, such as alluring, fresh, and antique. The user simply input an emotion word (e.g., lovely), the system can automatically adjust the image to the target emotion (e.g., fresh). First, we propose a new emotion calculation method to compute the target emotion from a reference image. Unlike previous methods, we take both texture features and main colors in our emotion calculation model. Then, in order to find the proper color combinations to reflect the target emotion, three color-emotion model databases are built by color numbers of the models. Those models are obtained by exploiting the famous art theories, and we design a novel strategy to select the most suitable color-emotion model from the databases. Finally, we propose a new color transfer algorithm by utilizing color adjustment and color blending to guarantee the color gradient and naturalness. Experiments show that our method’s results are more consistent with the emotion of the input image than the state-of-the-art algorithms.


title: Livelihoods perspectives and rural development

abstract: Livelihoods perspectives have been central to rural development thinking and practice in the past decade. But where do such perspectives come from, what are their conceptual roots, and what influences have shaped the way they have emerged? This paper offers an historical review of key moments in debates about rural livelihoods, identifying the tensions, ambiguities and challenges of such approaches. A number of core challenges are identified, centred on the need to inject a more thorough-going political analysis into the centre of livelihoods perspectives. This will enhance the capacity of livelihoods perspectives to address key lacunae in recent discussions, including questions of knowledge, politics, scale and dynamics.


title: Anomaly Detection Using an Ensemble of Feature Models

abstract: We present a new approach to semi-supervised anomaly detection. Given a set of training examples believed to come from the same distribution or class, the task is to learn a model that will be able to distinguish examples in the future that do not belong to the same class. Traditional approaches typically compare the position of a new data point to the set of ``normal'' training data points in a chosen representation of the feature space. For some data sets, the normal data may not have discernible positions in feature space, but do have consistent relationships among some features that fail to appear in the anomalous examples. Our approach learns to predict the values of training set features from the values of other features. After we have formed an ensemble of predictors, we apply this ensemble to new data points. To combine the contribution of each predictor in our ensemble, we have developed a novel, information-theoretic anomaly measure that our experimental results show selects against noisy and irrelevant features. Our results on 47 data sets show that for most data sets, this approach significantly improves performance over current state-of-the-art feature space distance and density-based approaches.


title: Extracting general-purpose features from LIDAR data

abstract: The detection of features from Light Detection and Ranging (LIDAR) data is a fundamental component of feature-based mapping and SLAM systems. Existing detectors tend to exploit characteristics of specific environments: corners and lines from indoor (rectilinear) environments, and trees from outdoor environments. While these detectors work well in their intended environments, their performance in different environments can be very poor. We describe a general purpose feature detector for LIDAR data that is applicable to virtually any environment. Our methods adapt classic feature detection methods from the image processing literature, specifically the multi-scale Kanade-Tomasi corner detector. Our resulting method is capable of identifying stable features at a variety of spatial scales and produces uncertainty estimates for use in a state estimation algorithm. We present results on standard datasets, including Victoria Park and Intel Research Center (both 2D), and the MIT DARPA Urban Challenge dataset (3D).


title: Advanced control engineering methods in modern technological applications

abstract: The paper deals with new directions in research, development and applications of advanced control methods and structures based on the principles of optimality, robustness and intelligence. Present trends in the complex process control design demand an increasing degree of integration of numerical mathematics, control engineering methods, new control structures based of distribution, embedded network control structure and new information and communication technologies. Furthermore, increasing problems with interactions, process non-linearity's, operating constraints, time delays, uncertainties, and significant dead-times consequently lead to the necessity to develop more sophisticated control strategies. Advanced control methods and new distributed embedded control structures represent the most effective tools for realizing high performance of many technological processes. Main ideas covered in this paper are motivated namely by the development of new advanced control engineering methods (predictive, hybrid predictive, optimal, adaptive, robust, fuzzy logic, neural network) and new possibilities of their SW and HW realizations and successful implementation in industry.


title: Interference avoidance communications using wavelet domain transformation techniques

abstract: A wavelet domain communication system architecture is shown, capable of providing acceptable communication performance while 'avoiding' spectral regions containing interference. Demonstrated bit error rate improvement averaged 5.7 and 12.4 dB using orthogonal and antipodal data modulation, respectively, over a range of interference energy-to-signal energy (I/E) ratios of 0 to 16 dB.


title: New variants in COL5A1 gene among Polish patients with Ehlers-Danlos syndrome: analysis of nine cases

abstract: Introduction ::: The Ehlers-Danlos syndrome (EDS) is a non-inflammatory, heritable connective tissue disorder divided into 13 types according to the 2017 International Classification of the Ehlers-Danlos syndromes. One of the subtypes of EDS, classical (cEDS), is characterized by joint hypermobility, skin hyperextensibility and atrophic scars, which are major criteria of cEDS. ::: ::: ::: Aim ::: In this study, the first in Central Eastern Europe, 44 patients were investigated. All of them were tested for COL5A1 mutations with direct DNA sequencing. ::: ::: ::: Material and methods ::: The study group included 44 patients of Polish origin, all of whom fulfilled criteria for the classical type of Ehlers-Danlos syndrome. Direct sequencing of the COL5A1, COL5A2 and COL1A1 c.934C>T genes was performed for all of them. Evaluation of potential pathogenicity of detected missense mutation was conducted using SIFT (Sorting Intolerant from Tolerant), PolyPhen-2, AlignGVGD (Align Grantham Variance/Grantham Difference). The effect of the splice site mutations was predicted by Human Splicing Finder and NetGene2 tools. ::: ::: ::: Results ::: Among all tested patients, nine mutations of COL5A1 gene were detected (8 missense mutations and 1 splice site). The alterations identified by us are new, hitherto not described in other reports. Evaluation of the mutations by in silico tools indicate their pathogenicity. ::: ::: ::: Conclusions ::: Our study is the first COL5A1 gene molecular investigation conducted among cEDS patients from Central Eastern Europe. Besides new COL5A1 variant findings, we gained molecular confirmation of clinical diagnosis of cEDS. In some cases, specific and adequate evaluation and classification of EDS patients based only on clinical features, may be difficult.


title: Sampling in Qualitative Research: Insights from an Overview of the Methods Literature

abstract: Merriam-Webster Dictionary defines sampling as "the act, process, or technique of selecting a representative part of a population for the purpose of determining parameters or characteristics of the whole population." This popular understanding, however, differs from some of the understandings held by researchers and scholars in the qualitative research domain. Influential qualitative methods authorities from diverse backgrounds have contributed to these latter understandings, and there is much natural variation in the sampling-related ideas they present. The existence of even subtle inconsistencies, ambiguities, or incomplete descriptions in the methods literature regarding certain sampling-related issues can be problematic for students and researchers seeking to develop a coherent understanding of sampling that is applicable to their research situation. This problem can be exacerbated by the fact that these individuals often lack the time to search, retrieve, and review the qualitative methods literature systematically and exhaustively to develop comprehensive and balanced knowledge of the available methods guidance. Even seasoned qualitative researchers, who are usually expertly versed in the methods of their chosen research approach or tradition, may come to prefer and become most intimately familiar with the ideas of a subset of methods authors within that tradition. Thus, they may not be comprehensively familiar with the full range of opinions across authors (including any inconsistencies among them) within their tradition regarding a specific methods issue --something that can only be revealed through systematic comparison. Systematic comparison in turn depends on systematic selection of the literature to be compared. Systematically searching and selecting the methods literature, however, is generally more burdensome than it is for the empirical findings literature. This is because a greater proportion of the methods literature is found in books and edited book chapters, which take substantially more time and effort to identify, retrieve, and scan for relevant content compared to journal articles. To fill the need for rigorous synthesis of the guidance on sampling in qualitative research, we conducted a systematic methods overview--our term for a defined approach to reviewing the methods literature from diverse sources, described here. This review method involved a rigorous and transparent, yet purposeful, approach to searching the methods literature aimed at selecting and reviewing the most influential publications--ones that students and researchers from multiple jurisdictions are most likely to encounter among the available writings that address sampling. We chose the literature of grounded theory, phenomenology, and case study because these are popular approaches or traditions used in many health-related disciplines, and are also sufficiently different to allow instructive comparisons to be made within each of the sampling topics addressed below. Our findings are organized under eight distinct topic sections corresponding to the major domains of sampling identified in the review process. In each section, we summarize how the topic is characterized in the literature reviewed, present a comparative analysis of differences among the three research traditions, and finally offer comments representing our analysis of the clarity, consistency and comprehensiveness of the available guidance from the authors reviewed on that topic and potential areas in which more clarity could be provided. Importantly, it is neither our aim nor our intention to convey personal opinions or recommendations about how to do sampling in this review. By unifying the findings and discussion within topic sections, we aim to make it convenient for readers to locate content for any single sampling topic in one place. The Three Research Traditions Reviewed Each of the three traditions whose methods literatures were reviewed is characterized by its unique approach to data collection and analysis, which in turn underlies important variation in researchers' approaches and attention to sampling. …


title: Database Security in Private Database Clouds

abstract: Cloud computing and cloud systems are very popular in today's Information Technology (IT) and systems and have grown very quickly. Cloud computing helps organizations solve increasing IT costs such as their licenses, power consumption, physical protection, by providing better standardization, higher benefit, greater performance, and quicker responses of information services. Carrying vital importance for our private and secret information, cloud systems must provide extra security solutions for database systems with organizations. While using firewalls, and intrusion detection systems on the network for external attacks, some improved access controls must be definitely decided for internal attacks as well. Using database transaction logs and audit can be very helpful on developing a database protection system. However, storing these logs may cause some serious storage and performance problems. Another problem is to manage multiple databases at the same time. In this study we introduce a new lightweight cloud-based database security solution by enhancing our previously developed database security solution, InfoFence, while managing many databases at the same time from a central point. The performance of our proposed database security system in a private cloud has been evaluated against one of the most commonly used products on the market, Oracle Audit Vault Server, and we show that our proposed solution has better performance results in our test environment.


title: What hides behind the MASC: clinical response and acquired resistance to entrectinib after ETV6-NTRK3 identification in a mammary analogue secretory carcinoma (MASC)

abstract: Background ::: Mammary analogue secretory carcinoma (MASC) is a recently described pathologic entity. We report the case of a patient with an initial diagnosis of salivary acinic cell carcinoma later reclassified as MASC after next-generation sequencing revealed an ETV6-NTRK3 fusion.


title: Feature Selection and Dimension Reduction Techniques in SAS

abstract: In the field of predictive modeling, variable selection methods can significantly drive the final outcome. While the focus of the analysis may generally be to get the most accurate predictions, it is incomplete without key driver analysis. These drivers could be demographics, geography, credit worthiness, payments history, usage, pricing, and potentially a host of many other key characteristics. Due to a large number of dimensions, many features of these broad categories are bound to remain untested. A million dollar question is how to get to a subset of effects that must definitely be tested. In this paper, we highlight what we have found to be the most effective ways of feature selection along with illustrative applications and best practices on implementation in SAS®. These methods range from simple correlation procedure (PROC CORR) to more complex techniques involving variable clustering (PROC VARCLUS), decision tree importance list (PROC SPLIT) and EXL‟s proprietary process of random feature selection from models developed on bootstrapped samples. By applying these techniques, we have been able to deliver robust and high quality statistical models with the right mix of dimensions.


title: A Polarization-Reconfigurable Dipole Antenna Using Polarization Rotation AMC Structure

abstract: A novel polarization-reconfigurable mechanism based on a polarization-rotation artificial magnetic conductor (PRAMC) structure is proposed. A new polarization-reconfigurable antenna is designed by combining a PRAMC-based multipolarized dipole antenna with a RFIC switch-based network, in which three polarization states of right-handed, left-handed circular polarization (RHCP, LHCP) and $+45^{\circ}$ linear polarization (LP) can be realized by controlling the dc bias of the switches accordingly. The multipolarization antenna is well designed for offering three polarization states. A large 3-dB axial ratio (AR) bandwidth of 15.5%, a wide 3-dB AR beamwidth for CP states and a large cross-polarization isolation of more than 50 dB for LP state together with a broad impedance bandwidth of 20.5% are obtained. A switch network is used to generate three feeding modes, and the performance of the RF-integrated circuit (RFIC) switches is experimentally verified. A prototype of the proposed polarization-reconfigurable antenna is fabricated and tested. Good agreements between the measured and simulated results are observed, which indicates good performances of the proposed antenna.


title: The eight-item modified Medical Outcomes Study Social Support Survey: psychometric evaluation showed excellent performance.

abstract: OBJECTIVE ::: Evaluation and validation of the psychometric properties of the eight-item modified Medical Outcomes Study Social Support Survey (mMOS-SS). ::: ::: ::: STUDY DESIGN AND SETTING ::: Secondary analyses of data from three populations: Boston breast cancer study (N=660), Los Angeles breast cancer study (N=864), and Medical Outcomes Study (N=1,717). The psychometric evaluation of the eight-item mMOS-SS compared performance across populations and with the original 19-item Medical Outcomes Study Social Support Survey (MOS-SS). Internal reliability, factor structure, construct validity, and discriminant validity were evaluated using Cronbach's alpha, principal factor analysis (PFA), and confirmatory factor analysis (CFA), Spearman and Pearson correlation, t-test and Wilcoxon rank sum tests. ::: ::: ::: RESULTS ::: mMOS-SS internal reliability was excellent in all three populations. PFA factor loadings were similar across populations; one factor >0.6, well-discriminated two factor (instrumental/emotional social support four items each) >0.5. CFA with a priori two-factor structure yielded consistently adequate model fit (root mean squared errors of approximation 0.054-0.074). mMOS-SS construct and discriminant validity were similar across populations and comparable to MOS-SS. Psychometric properties held when restricted to women aged ≥ 65 years. ::: ::: ::: CONCLUSION ::: The psychometric properties of the eight-item mMOS-SS were excellent and similar to those of the original 19-item instrument. Results support the use of briefer mMOS-SS instrument; better suited to multidimensional geriatric assessments and specifically in older women with breast cancer.


title: Biochemical conversions and biomass morphology in a long-term operated SBR with aerobic granular sludge

abstract: Abstract The experiment was carried on for about one year (995 cycles) in a constantly aerated, column sequencing batch reactor with granular sludge fed with a mixture of anaerobic sludge digester supernatant and synthetic wastewater. The research proved the usefulness of granular sludge technology for the removal of ammonium from highly concentrated ammonium streams with an unfavorable chemical oxygen demand (COD)/nitrogen (N) ratio. In the period of stable reactor performance, all ammonium in the influent was oxidized and nitrification–denitrification via nitrite occurred with an efficiency of 26%. The calculated carbon (COD) and N balances showed how the substances were distributed in the reactor under the conditions of low organic accessibility and high N load. Despite the low organic availability, intracellular storage occurred and the yield of polyhybroksybutyrate (PHB) was YPHB = 0.206 g COD/g COD. Detailed characteristics of the biomass operating under conditions of low COD/N was performed and sho...


title: A practical solution for scripting language compilers

abstract: Although scripting languages are becoming increasingly popular, even mature scripting language implementations remain interpreted. Several compilers and reimplementations have been attempted, generally focusing on performance. Based on our survey of these reimplementations, we determine that there are three important features of scripting languages that are difficult to compile or reimplement. Since scripting languages are defined primarily through the semantics of their original implementations, they often change semantics between releases. They provide large standard libraries, which are difficult to re-use, and costly to reimplement. They provide C APIs, used both for foreign-function-interfaces and to write third-party extensions. These APIs typically have tight integration with the original implementation. Finally, they support run-time code generation. These features make the important goal of correctness difficult to achieve. We present a technique to support these features in an ahead-of-time compiler for PHP. Our technique uses the original PHP implementation through the provided C API, both in our compiler, and an our generated code. We support all of these important scripting language features, particularly focusing on the correctness of compiled programs. Additionally, our approach allows us to automatically support limited future language changes. We present a discussion and performance evaluation of this technique, which has not previously been published.


title: Ensemble Strategy for Hard Classifying Samples in Class-Imbalanced Data Set

abstract: Imbalanced data is ubiquitous and brings much difficulty for data classification. In this paper, we propose an ensemble strategy to address binary classification imbalanced problem by treatment of difficult samples or hard classifying samples. The ensemble strategy is within a two layers framework, which combines the advantages of resampling method and classifier. In the first layer, a novel resampling strategy is proposed to increase the weight of hard classifying samples in the dataset. Then, an efficient ensemble strategy is utilized in the second layer, which applies extreme gradient boosting classifier combined with the proposed resampling strategy to train the classifier model. The experiment uses 44 datasets from KEEL dataset repository and the results reveal that the proposed ensemble strategy outperforms four other state-of-the-art ensemble strategies. For the imbalanced big data, three metrics of the proposed ensemble strategy are better than various ensemble strategies.


title: Human grasping database for activities of daily living with depth, color and kinematic data streams

abstract: This paper presents a grasping database collected from multiple human subjects for activities of daily living in unstructured environments. The main strength of this database is the use of three different sensing modalities: color images from a head-mounted action camera, distance data from a depth sensor on the dominant arm and upper body kinematic data acquired from an inertial motion capture suit. 3826 grasps were identified in the data collected during 9-hours of experiments. The grasps were grouped according to a hierarchical taxonomy into 35 different grasp types. The database contains information related to each grasp and associated sensor data acquired from the three sensor modalities. We also provide our data annotation software written in Matlab as an open-source tool. The size of the database is 172 GB. We believe this database can be used as a stepping stone to develop big data and machine learning techniques for grasping and manipulation with potential applications in rehabilitation robotics and intelligent automation.


title: Vehicle license plate recognition system based on digital image processing

abstract: This paper analyzes the basic method of digital video image processing, studies the vehicle license plate recognition system based on image processing in intelligent transport system, presents a character recognition approach based on neural network perceptron to solve the vehicle license plate recognition in real-time traffic flow. Experimental results show that the approach can achieve better positioning effect, has a certain robustness and timeliness.


title: General relativity in electrical engineering

abstract: In electrical engineering metamaterials have been developed that offer unprecedented control over electromagnetic fields. Here, we show that general relativity provides the theoretical tools for designing devices made of such versatile materials. Given a desired device function, the theory describes the electromagnetic properties that turn this function into fact. We consider media that facilitate space-time transformations and include negative refraction. Our theory unifies the concepts operating behind the scenes of perfect invisibility devices, perfect lenses, the optical Aharonov–Bohm effect and electromagnetic analogues of the event horizon, and may lead to further applications.


title: Interest flooding attack and countermeasures in Named Data Networking

abstract: Distributed Denial of Service (DDoS) attacks are an ongoing problem in today's Internet, where packets from a large number of compromised hosts thwart the paths to the victim site and/or overload the victim machines. In a newly proposed future Internet architecture, Named Data Networking (NDN), end users request desired data by sending Interest packets, and the network delivers Data packets upon request only, effectively eliminating many existing DDoS attacks. However, an NDN network can be subject to a new type of DDoS attack, namely Interest packet flooding. In this paper we investigate effective solutions to mitigate Interest flooding. We show that NDN's inherent properties of storing per packet state on each router and maintaining flow balance (i.e., one Interest packet retrieves at most one Data packet) provides the basis for effective DDoS mitigation algorithms. Our evaluation through simulations shows that the solution can quickly and effectively respond and mitigate Interest flooding.


title: Video summagator: an interface for video summarization and navigation

abstract: This paper presents Video Summagator (VS), a volume-based interface for video summarization and navigation. VS models a video as a space-time cube and visualizes the video cube using real-time volume rendering techniques. VS empowers a user to interactively manipulate the video cube. We show that VS can quickly summarize both the static and dynamic video content by visualizing the space-time information in 3D. We demonstrate that VS enables a user to quickly look into the video cube, understand the content, and navigate to the content of interest.


title: An approach to face shape classification for hairstyle recommendation

abstract: It is important to choose a good hairstyle for women because it can enhance their beauty, personality, and confidence. One of the most important factors to consider for choosing the right hairstyle is the individuals face shape. An effective face shape classification can be used for constructing a hairstyle recommendation system. This paper presents a classification approach that divides face shapes into 5 different shapes: round, oval, oblong, square, and heart. This approach, which is based on an Active Appearance Model (AAM) and a face segmentation technique, produces a set of features that can be evaluated by several popular machine learning methods, namely, Linear Discriminant Analysis (LDA), Artificial Neural Networks (ANN), and Support Vector Machine (SVM). Our results show that the Support Vector Machine with Radial Basis function kernel was the best algorithm that predicted accurately up to 72%.


title: An Analogy Ontology for Integrating Analogical Processing and First-Principles Reasoning

abstract: This paper describes an analogy ontology, a formal representation of some key ideas in analogical processing, that supports the integration of analogical processing with first-principles reasoners. The ontology is based on Gentner's structure-mapping theory, a psychological account of analogy and similarity. The semantics of the ontology are enforced via procedural attachment, using cognitive simulations of structure-mapping to provide analogical processing services. Queries that include analogical operations can be formulated in the same way as standard logical inference, and analogical processing systems in turn can call on the services of first-principles reasoners for creating cases and validating their conjectures. We illustrate the utility of the analogy ontology by demonstrating how it has been used in three systems: A crisis management analogical reasoner that answers questions about international incidents, a course of action analogical critiquer that provides feedback about military plans, and a comparison question-answering system for knowledge capture. These systems rely on large, general-purpose knowledge bases created by other research groups, thus demonstrating the generality and utility of these ideas.


title: Efficient Rendering of Local Subsurface Scattering

abstract: A novel approach is presented to efficiently render local subsurface scattering effects. We introduce an importance sampling scheme for a practical subsurface scattering model. It leads to a simple and efficient rendering algorithm, which operates in image space, and which is even amenable for implementation on graphics hardware. We demonstrate the applicability of our technique to the problem of skin rendering, for which the subsurface transport of light typically remains local. Our implementation shows that plausible images can be rendered interactively using hardware acceleration.


title: Novel Strategies for Cytokine Administration Via Targetting

abstract: Immunocytokines (IC) are fusion proteins that use genetic linkage to fuse cytokines with immunologically reactive monoclonal antibodies (mAb) or components thereof. The goal is to retain the functions of both the cytokine and the antibody components in a single bifunctional molecule. As such, the functions of the antibody (long circulating half life, antigen specific binding, complement activation, interaction with Fc Receptors (FcRs) to induce antibody dependent cytotoxicity [ADCC]) are retained. In addition, the functions of the cytokine that are linked to the antibody (such as IL-2, IL-12, or IL-7), are also retained. The goal is to allow the biologic activities of one component of the IC (the antibody) to be expanded by colocalizing it with the biologic function of the other component of the IC (the cytokine). A few distinct immunocytokines have been created by EMD-Lexigen (1–3), and by others (4–7). These have used antibodies with some degree of selectivity for molecules expressed predominantly by tumors, and have been linked to different cytokines. The first IC to be described resulted from linking IL-2 to the 14.18 mAb that recognizes the GD2 disialoganglioside. Testing of this agent, and its derivatives, has moved forward in parallel with preclinical and clinical testing of other ICs (including the huKS-IL-2 IC that recognizes the epithelial cell adhesion molecule, which is overexpressed on many epithelial carcinomas).The purpose of this chapter is to summarize the preclinical testing of ICs, and a recently completed Phase I study, with an emphasis on recent data with the hul4.18-IL-2 IC. We also present a brief summary of preclinical data utilizing newer ICs being developed for future clinical testing.


title: Tester Experience: Concept, Issues and Definition

abstract: Testing is a very essential activity for software development. It is the act of executing a software product in order to validate whether it behaves as intended and identify possible malfunctions. Studies have shown that testing makes up more than 50% of the development cost. Besides, much effort and emphasis have now been placed on tasks related to automation with the purpose of reducing cost and the participation of the human element in software testing activities. Testing is still a human-based activity. Therefore, efficient ways of testing software products for quality assurance will require a better and more comprehensive understanding of testers' feelings, perceptions and motivations, in this paper referred to as Tester Experience (TX). Thus, the better the tester's experience during the software testing process, the better the result. TX can be defined as a means of capturing how testers think and feel about their activities within the software testing environment, with the assumption that an improvement of the tester's experience has positive impact on quality assurance. This paper motivates the importance of TX, highlights related approaches from other domains, proposes a definition based on similar concepts in other domains, and proposes future research activities.


title: Several cases of undesirable effects caused by methacrylate ultraviolet-curing nail polish for non-professional use.

abstract: BACKGROUND ::: Ultraviolet (UV)-curing nail polishes based on acrylates or methacrylates are currently also available for non-professional use. The Swedish Medical Products Agency recently prohibited one brand of UV-curing polish, because several consumers reported undesirable effects after using it. ::: ::: ::: OBJECTIVES ::: To investigate whether consumers with undesirable effects after using the UV-curing nail polish that was later prohibited were contact allergic to the polish and its individual ingredients. ::: ::: ::: MATERIALS/METHODS ::: Eight patients who had reported severe skin reactions after the use of the UV-curing polish were patch tested with two coatings of the nail polish and its ingredients at five dermatology departments in Sweden. ::: ::: ::: RESULTS ::: All patients tested except one showed contact allergic reactions to one or several of the acrylate-based or methacrylate-based ingredients in the nail polish. ::: ::: ::: CONCLUSIONS ::: The non-professional use of UV-curing nail polishes poses a risk of sensitization from acrylates and methacrylates.


title: Efficient Vector Representation for Documents through Corruption

abstract: We present an efficient document representation learning framework, Document Vector through Corruption (Doc2VecC). Doc2VecC represents each document as a simple average of word embeddings. It ensures a representation generated as such captures the semantic meanings of the document during learning. A corruption model is included, which introduces a data-dependent regularization that favors informative or rare words while forcing the embeddings of common and non-discriminative ones to be close to zero. Doc2VecC produces significantly better word embeddings than Word2Vec. We compare Doc2VecC with several state-of-the-art document representation learning algorithms. The simple model architecture introduced by Doc2VecC matches or out-performs the state-of-the-art in generating high-quality document representations for sentiment analysis, document classification as well as semantic relatedness tasks. The simplicity of the model enables training on billions of words per hour on a single machine. At the same time, the model is very efficient in generating representations of unseen documents at test time.


title: New Approach of Radiation Pattern Control for Leaky-Wave Antennas Based on the Effective Radiation Sections

abstract: In this paper, a new radiation pattern-control approach for leaky-wave antenna designs is proposed based on the theory of effective radiation sections. This approach can be used to generate radiation null regions around desired direction or to reduce the side lobe levels of leaky-wave antennas in a simple and efficient way, i.e., by controlling the radiation from the effective radiation sections. The traveling-wave line-source model is employed for simplifying the analysis at first. Then, the approach is implemented in the sub strate integrated waveguide (SIW) long-straight slot Leaky-wave antennas (LWA) by changing the slot width at the locations corresponding to the effective radiation sections (ERSs). The simulation results show that the radiation null region can be introduced around the desired direction, and the side lobe level can be reduced. Furthermore, the cross-polar level can be controlled to a very low level. Experimental results are also given as verification. In addition, the proposed method still has some limitations, which are discussed in detail and will be improved in the future.


title: Runtime Models Based on Dynamic Decision Networks: Enhancing the Decision-making in the Domain of Ambient Assisted Living Applications

abstract: Dynamic decision-making for self-Adaptive systems (SAS) requires the runtime trade-off of multiple non-functional requirements (NFRs) -Aka quality properties-And the costsbenefits analysis of the alternative solutions. Usually, it requires the specification of utility preferences for NFRs and decisionmaking strategies. Traditionally, these preferences have been defined at design-Time. In this paper we develop further our ideas on re-Assessment of NFRs preferences given new evidence found at runtime and using dynamic decision networks (DDNs) as the runtime abstractions. Our approach use conditional probabilities provided by DDNs, the concepts of Bayesian surprise and Primitive Cognitive Network Process (P-CNP), for the determination of the initial preferences. Specifically, we present a case study in the domain problem of ambient assisted living (AAL). Based on the collection of runtime evidence, our approach allows the identification of unknown situations at the design stage.


title: Making a Life Worth Living Neural Correlates of Well-Being

abstract: Despite the vast literature that has implicated asymmetric activation of the prefrontal cortex in approach-withdrawal motivation and emotion, no published reports have directly explored the neural correlates of well-being. Eighty-four right-handed adults (ages 57–60) completed self-report measures of eudaimonic well-being, hedonic well-being, and positive affect prior to resting electroencephalography. As hypothesized, greater left than right superior frontal activation was associated with higher levels of both forms of well-being. Hemisphere-specific analyses documented the importance of goal-directed approach tendencies beyond those captured by approach-related positive affect for eudaimonic but not for hedonic well-being. Appropriately engaging sources of appetitive motivation, characteristic of higher left than right baseline levels of prefrontal activation, may encourage the experience of well-being.


title: Multilinear Discriminant Analysis for Face Recognition

abstract: There is a growing interest in subspace learning techniques for face recognition; however, the excessive dimension of the data space often brings the algorithms into the curse of dimensionality dilemma. In this paper, we present a novel approach to solve the supervised dimensionality reduction problem by encoding an image object as a general tensor of second or even higher order. First, we propose a discriminant tensor criterion, whereby multiple interrelated lower dimensional discriminative subspaces are derived for feature extraction. Then, a novel approach, called k-mode optimization, is presented to iteratively learn these subspaces by unfolding the tensor along different tensor directions. We call this algorithm multilinear discriminant analysis (MDA), which has the following characteristics: 1) multiple interrelated subspaces can collaborate to discriminate different classes, 2) for classification problems involving higher order tensors, the MDA algorithm can avoid the curse of dimensionality dilemma and alleviate the small sample size problem, and 3) the computational cost in the learning stage is reduced to a large extent owing to the reduced data dimensions in k-mode optimization. We provide extensive experiments on ORL, CMU PIE, and FERET databases by encoding face images as second- or third-order tensors to demonstrate that the proposed MDA algorithm based on higher order tensors has the potential to outperform the traditional vector-based subspace learning algorithms, especially in the cases with small sample sizes.


title: Trends in IoT security

abstract: The incredible rapid development of internet technologies, primarily thanks to omnipresent access of high speed broadband internet access and supporting technologies like Big Data, Cloud Computing, REST/Web services as well as cheap electronic equipment that use new wireless communications standards, lead to equal rapid growth of number of smart devices - “things” - connected to the internet. Increased number of connected smart devices results with huge daily data traffic on the internet and data volume stored and available on the internet. IoT becomes part of our homes and our companies, and security in these systems is very important. What does that outlook hold for the next 5, 10 or 20 years, will mostly depend on the development of security standards, user behavior and education in a next few years. This paper is trying to summary and analyze trends in IoT standards regarding security.


title: GraVF: A vertex-centric distributed graph processing framework on FPGAs

abstract: FPGAs are promising platforms to efficiently execute distributed graph algorithms. Unfortunately, they are notoriously hard to program, especially when the problem size and system complexity increases. In this paper, we propose GraVF, a high-level design framework for distributed graph processing on FPGAs. It leverages the vertex-centric paradigm, which is naturally distributed and requires the user to define only very small kernels and their associated message semantics for the target application. The user design may subsequently be elaborated and compiled to the target system automatically by the framework. To demonstrate the flexibility and capabilities of the proposed framework, 4 graph algorithms with distinct requirements have been implemented, namely breadth-first search, PageRank, single source shortest path, and connected component. Results show that the proposed framework is capable of producing FPGA designs with performance comparable to similar custom designs while requiring only minimal input from the user.


A combinatorial infinitesimal representation of Lévy processes and an application to incomplete markets


title: Deep Sketch-Photo Face Recognition Assisted by Facial Attributes

abstract: In this paper, we present a deep coupled framework to address the problem of matching sketch image against a gallery of mugshots. Face sketches have the essential in- formation about the spatial topology and geometric details of faces while missing some important facial attributes such as ethnicity, hair, eye, and skin color. We propose a cou- pled deep neural network architecture which utilizes facial attributes in order to improve the sketch-photo recognition performance. The proposed Attribute-Assisted Deep Con- volutional Neural Network (AADCNN) method exploits the facial attributes and leverages the loss functions from the facial attributes identification and face verification tasks in order to learn rich discriminative features in a common em- bedding subspace. The facial attribute identification task increases the inter-personal variations by pushing apart the embedded features extracted from individuals with differ- ent facial attributes, while the verification task reduces the intra-personal variations by pulling together all the fea- tures that are related to one person. The learned discrim- inative features can be well generalized to new identities not seen in the training data. The proposed architecture is able to make full use of the sketch and complementary fa- cial attribute information to train a deep model compared to the conventional sketch-photo recognition methods. Exten- sive experiments are performed on composite (E-PRIP) and semi-forensic (IIIT-D semi-forensic) datasets. The results show the superiority of our method compared to the state- of-the-art models in sketch-photo recognition algorithms


title: Processing: programming for the media arts

abstract: Processing is a programming language and environment built for the media arts communities. It is created to teach fundamentals of computer programming within the media arts context and to serve as a software sketchbook. It is used by students, artists, designers, architects, and researchers for learning, prototyping, and production. This essay discusses the ideas underlying the software and presents its relationship to open source software and the idea of software literacy. Additionally, Processing is discussed in relation to education and online communities.


title: Preferences for pink and blue: the development of color preferences as a distinct gender-typed behavior

abstract: Many gender differences are thought to result from interactions between inborn factors and sociocognitive processes that occur after birth. There is controversy, however, over the causes of gender-typed preferences for the colors pink and blue, with some viewing these preferences as arising solely from sociocognitive processes of gender development. We evaluated preferences for gender-typed colors, and compared them to gender-typed toy and activity preferences in 126 toddlers on two occasions separated by 6–8 months (at Time 1, M = 29 months; range 20–40). Color preferences were assessed using color cards and neutral toys in gender-typed colors. Gender-typed toy and activity preferences were assessed using a parent-report questionnaire, the Preschool Activities Inventory. Color preferences were also assessed for the toddlers’ parents using color cards. A gender difference in color preferences was present between 2 and 3 years of age and strengthened near the third birthday, at which time it was large (d > 1). In contrast to their parents, toddlers’ gender-typed color preferences were stronger and unstable. Gender-typed color preferences also appeared to establish later and were less stable than gender-typed toy and activity preferences. Gender-typed color preferences were largely uncorrelated with gender-typed toy and activity preferences. These results suggest that the factors influencing gender-typed color preferences and gender-typed toy and activity preferences differ in some respects. Our findings suggest that sociocognitive influences and play with gender-typed toys that happen to be made in gender-typed colors contribute to toddlers’ gender-typed color preferences.


title: Gradient Similarity: An Explainable Approach to Detect Adversarial Attacks against Deep Learning

abstract: Deep neural networks are susceptible to small-but-specific adversarial perturbations capable of deceiving the network. This vulnerability can lead to potentially harmful consequences in security-critical applications. To address this vulnerability, we propose a novel metric called \emph{Gradient Similarity} that allows us to capture the influence of training data on test inputs. We show that \emph{Gradient Similarity} behaves differently for normal and adversarial inputs, and enables us to detect a variety of adversarial attacks with a near perfect ROC-AUC of 95-100\%. Even white-box adversaries equipped with perfect knowledge of the system cannot bypass our detector easily. On the MNIST dataset, white-box attacks are either detected with a high ROC-AUC of 87-96\%, or require very high distortion to bypass our detector.


title: Object recognition in images using convolutional neural network

abstract: Object detection from repository of images is challenging task in the area of computer vision and image processing in this work we present object classification and detection using cifar-10 data set with intended classification and detection of airplain images. So we used convolutional neural network on keras with tensorflow support the experimental results shows the time required to train, test and create the model in limited computing system. We train the system with 60,000 images with 25 epochs each epoch is taking 722to760 seconds in training step on tensorflow cpu system. At the end of 25 epochs the training accuracy is 96 percentage and the system can recognition input images based on train model and the output is respective label of images.


title: New Simple-Structured AC Solid-State Circuit Breaker

abstract: Recently, distributed generators and sensitive loads have been widely used. They enable a solid-state circuit breaker (SSCB), which is an imperative device to get acceptable power quality of ac power grid systems. The existing ac SSCB composed of a silicon-controlled rectifier requires some auxiliary mechanical devices to achieve the reclosing operation before fault recovery. However, the new ac SSCB can achieve a quick breaking operation and then be reclosed with no auxiliary mechanical devices or complex control even under sustained short-circuit fault because the commutation capacitors are charged naturally without any complex control of main thyristors and auxiliary ones. The performance features of the proposed ac SSCB are verified through the experiment results of the short-circuit faults.


title: Vaccination with CpG-adjuvanted avian influenza virosomes promotes antiviral immune responses and reduces virus shedding in chickens.

abstract: The use of virosomes as a vaccine platform has proven successful against several viruses. Here we examined the protective efficacy of a virosome-based vaccine consisting of avian influenza virus (AIV) A/Duck/Czech/56/H4N6 in chickens against a homologous AIV challenge. Virosomes adjuvanted with CpG-ODN or recombinant chicken interferon (IFN)-γ significantly reduced virus shedding after virus challenge. Furthermore, immunization with virosomes adjuvanted with CpG-ODN increased hemagglutination inhibition (HI) and virus-specific neutralizing serum antibodies, as well as virus-specific serum IgG and mucosal IgA responses. We also found a significant increase in the expression of type I and II interferon genes in the protected birds following virus challenge. In summary, this study demonstrated the ability of virosomes adjuvanted with CpG-ODN to reduce AIV shedding, and elicit virus-specific protective antibody responses in vaccinated birds.


title: Mapping Out Instruments, Affordances, and Mobiles

abstract: This paper reviews and extends questions of the scope of an interactive musical instrument and mapping strategies for expressive performance. We apply notions of embodiment and affordance to characterize gestural instruments. We note that the democratization of sensor technology in consumer devices has extended the cultural contexts for interaction. We revisit questions of mapping drawing upon the theory of affordances to consider mapping and instrument together. ::: ::: This is applied to recent work by the author and his collaborators in the development of instruments based on mobile devices designed for specific performance situations.


An objective and standardized test of hand function.


title: Zero-Shot Hashing via Transferring Supervised Knowledge

abstract: Hashing has shown its efficiency and effectiveness in facilitating large-scale multimedia applications. Supervised knowledge (\emph{e.g.}, semantic labels or pair-wise relationship) associated to data is capable of significantly improving the quality of hash codes and hash functions. However, confronted with the rapid growth of newly-emerging concepts and multimedia data on the Web, existing supervised hashing approaches may easily suffer from the scarcity and validity of supervised information due to the expensive cost of manual labelling. In this paper, we propose a novel hashing scheme, termed \emph{zero-shot hashing} (ZSH), which compresses images of "unseen" categories to binary codes with hash functions learned from limited training data of "seen" categories. Specifically, we project independent data labels (i.e., 0/1-form label vectors) into semantic embedding space, where semantic relationships among all the labels can be precisely characterized and thus seen supervised knowledge can be transferred to unseen classes. Moreover, in order to cope with the semantic shift problem, we rotate the embedded space to more suitably align the embedded semantics with the low-level visual feature space, thereby alleviating the influence of semantic gap. In the meantime, to exert positive effects on learning high-quality hash functions, we further propose to preserve local structural property and discrete nature in binary codes. Besides, we develop an efficient alternating algorithm to solve the ZSH model. Extensive experiments conducted on various real-life datasets show the superior zero-shot image retrieval performance of ZSH as compared to several state-of-the-art hashing methods.


title: Work Breakdown Structure: A Tool for Software Project Scope Verification

abstract: Software project scope verification is a very iprocess in project scope management and it needs to be performed properly and thoroughly so as to avoid project rework and scope creep. Moreover, software scope verification is crucial in the process of delivering exactly what the customer request ed and minimizing project scope changes. Well defined software scope eases the process of scope verification and contributes to project success. Furthermore, adeliverable-oriented WBS provides a road map to a well defined software scope of work. It is on the basis of this that this paper extends the use of deliverable oriented WBS to that of scope verification process. This paper argues that adeliverable-oriented WBS is a tool for software scope verification


title: A critical review of psychological treatments of posttraumatic stress disorder in refugees.

abstract: Abstract Despite much research evidence that refugees suffer from elevated rates of posttraumatic stress disorder (PTSD), relatively few studies have examined the effectiveness of psychological treatments for PTSD in refugees. The field of refugee mental health intervention is dominated by two contrasting approaches, namely trauma-focused therapy and multimodal interventions. This article firstly defines these two approaches, then provides a critical review of 19 research studies that have been undertaken to investigate the efficacy of these treatments. Preliminary research evidence suggests that trauma-focused approaches may have some efficacy in treating PTSD in refugees, but limitations in the methodologies of studies caution against drawing definitive inferences. It is clear that research assessing the treatment of PTSD in refugees is lagging behind that available for other traumatized populations. The review examines important considerations in the treatment of refugees. A theoretical framework is offered that outlines contextual issues, maintaining factors, change mechanisms and the distinctive challenges to traditional trauma-focused treatments posed by the needs of refugees with PTSD.


Industrial globalisation, segmentation and technology innovation in the New Zealand plastics industry


title: A Bit of Progress in Language Modeling

abstract: In the past several years, a number of different language modeling improvements over simple trigram models have been found, including caching, higher-order n -grams, skipping, interpolated Kneser?Ney smoothing, and clustering. We present explorations of variations on, or of the limits of, each of these techniques, including showing that sentence mixture models may have more potential. While all of these techniques have been studied separately, they have rarely been studied in combination. We compare a combination of all techniques together to a Katz smoothed trigram model with no count cutoffs. We achieve perplexity reductions between 38 and 50% (1 bit of entropy), depending on training data size, as well as a word error rate reduction of 8.9%. Our perplexity reductions are perhaps the highest reported compared to a fair baseline.


title: A reputation-based approach for choosing reliable resources in peer-to-peer networks

abstract: Peer-to-peer (P2P) applications have seen an enormous success, and recently introduced P2P services have reached tens of millions of users. A feature that significantly contributes to the success of many P2P applications is user anonymity. However, anonymity opens the door to possible misuses and abuses, exploiting the P2P network as a way to spread tampered with resources, including Trojan Horses, viruses, and spam. To address this problem we propose a self-regulating system where the P2P network is used to implement a robust reputation mechanism. Reputation sharing is realized through a distributed polling algorithm by which resource requestors can assess the reliability of a resource offered by a participant before initiating the download. This way, spreading of malicious contents will be reduced and eventually blocked. Our approach can be straightforwardly piggybacked on existing P2P protocols and requires modest modifications to current implementations.


title: A soft robotic exomusculature glove with integrated sEMG sensing for hand rehabilitation

abstract: Stroke affects 750,000 people annually, and 80% of stroke survivors are left with weakened limbs and hands. Repetitive hand movement is often used as a rehabilitation technique in order to regain hand movement and strength. In order to facilitate this rehabilitation, a robotic glove was designed to aid in the movement and coordination of gripping exercises. This glove utilizes a cable system to open and close a patients hand. The cables are actuated by servomotors, mounted in a backpack weighing 13.2lbs including battery power sources. The glove can be controlled in terms of finger position and grip force through switch interface, software program, or surface myoelectric (sEMG) signal. The primary control modes of the system provide: active assistance, active resistance and a preprogrammed mode. This project developed a working prototype of the rehabilitative robotic glove which actuates the fingers over a full range of motion across one degree-of-freedom, and is capable of generating a maximum 15N grip force.


title: Miniaturized UWB Antipodal Vivaldi Antenna and Its Application for Detection of Void Inside Concrete Specimens

abstract: A miniaturized antipodal Vivaldi antenna to operate from 1 to 30 GHz is designed for nondestructive testing and evaluation of construction materials, such as concrete, polymers, and dielectric composites. A step-by-step procedure has been employed to design and optimize performance of the proposed antenna. First, a conventional antipodal Vivaldi antenna (CAVA) is designed as a reference. Second, the CAVA is shortened to have a small size of the CAVA. Third, to extend the low end of frequency band, the inner edges of the top and bottom radiators of the shortened CAVA have been bent. To enhance gain at lower frequencies, regular slit edge technique is employed. Finally, a half elliptical-shaped dielectric lens as an extension of the antenna substrate is added to the antenna to feature high gain and front-to-back ratio. A prototype of the antenna is employed as a part of the microwave imaging system to detect voids inside concrete specimen. High-range resolution images of voids are achieved by applying synthetic aperture radar algorithm.


title: Network-wide virtual firewall using SDN/OpenFlow

abstract: Traditional firewalls are used to enforce network security policies at boundaries within a network. However, this can leave hosts vulnerable to attacks that originate from within the network they are part of. We leverage the flexibility of Software Defined Networking to turn the network infrastructure into a virtual firewall thus improving security across an entire network. We present ACLSwitch, a network-wide virtual firewall that utilises the OpenFlow protocol to filter traffic across a network comprised of OpenFlow switches. We also define “policy domains” that allow different filtering configurations to be applied to different switches of the network. The solution allows rules to be distributed across a network without the need for a human operator to send the rules to switches separately, yet it is flexible enough to allow subsets of the switches to enforce different security policies.


title: Evaluating Hive and Spark SQL with BigBench

abstract: The objective of this work was to utilize BigBench [1] as a Big Data benchmark and evaluate and compare two processing engines: MapReduce [2] and Spark [3]. MapReduce is the established engine for processing data on Hadoop. Spark is a popular alternative engine that promises faster processing times than the established MapReduce engine. BigBench was chosen for this comparison because it is the first end-to-end analytics Big Data benchmark and it is currently under public review as TPCx-BB [4]. One of our goals was to evaluate the benchmark by performing various scalability tests and validate that it is able to stress test the processing engines. First, we analyzed the steps necessary to execute the available MapReduce implementation of BigBench [1] on Spark. Then, all the 30 BigBench queries were executed on MapReduce/Hive with different scale factors in order to see how the performance changes with the increase of the data size. Next, the group of HiveQL queries were executed on Spark SQL and compared with their respective Hive runtimes. This report gives a detailed overview on how to setup an experimental Hadoop cluster and execute BigBench on both Hive and Spark SQL. It provides the absolute times for all experiments preformed for different scale factors as well as query results which can be used to validate correct benchmark execution. Additionally, multiple issues and workarounds were encountered and solved during our work. An evaluation of the resource utilization (CPU, memory, disk and network usage) of a subset of representative BigBench queries is presented to illustrate the behavior of the different query groups on both processing engines. Last but not least it is important to mention that larger parts of this report are taken from the master thesis of Max-Georg Beer, entitled "Evaluation of BigBench on Apache Spark Compared to MapReduce" [5].


title: Benchmarking and Comparison of the Task Graph Scheduling Algorithms 1

abstract: The problem of scheduling a parallel program represented by a weighted directed acyclic graph (DAG) to a set of homogeneous processors for minimizing the completion time of the program has been extensively studied. The NP-completeness of the problem has stimulated researchers to propose a myriad of heuristic algorithms. While most of these algorithms are reported to be efficient, it is not clear how they compare against each other. A meaningful performance evaluation and comparison of these algorithms is a complex task and it must take into account a number of issues. First, most scheduling algorithms are based upon diverse assumptions, making the performance comparison rather meaningless. Second, there does not exist a standard set of benchmarks to examine these algorithms. Third, most algorithms are evaluated using small problem sizes, and, therefore, their scalability is unknown. In this paper, we first provide a taxonomy for classifying various algorithms into distinct categories according to their assumptions and functionalities. We then propose a set of benchmarks that are based on diverse structures and are not biased toward a particular scheduling technique. We have implemented 15 scheduling algorithms and compared them on a common platform by using the proposed benchmarks, as well as by varying important problem parameters. We interpret the results based upon the design philosophies and principles behind these algorithms, drawing inferences why some algorithms perform better than others. We also propose a performance measure called scheduling scalability (SS) that captures the collective effectiveness of a scheduling algorithm in terms of its solution quality, the number of processors used, and the running time.


title: Perception of Robot Smiles and Dimensions for Human-Robot Interaction Design

abstract: As robots enter everyday life and start to interact with ordinary people the question of their appearance becomes increasingly important. Our perception of a robot can be strongly influenced by its facial appearance. Synthesizing relevant ideas from narrative art design, the psychology of face recognition, and recent HRI studies into robot faces, we discuss effects of the uncanny valley and the use of iconicity and its relationship to the self-other perceptive divide, as well as abstractness and realism, classifying existing designs along these dimensions. A new expressive HRI research robot called KASPAR is introduced and the results of a preliminary study on human perceptions of robot expressions are discussed.


title: Review of Ship Microgrids: System Architectures, Storage Technologies and Power Quality Aspects

abstract: Ship microgrids have recently received increased attention, mainly due to the extensive use of power electronically interfaced loads and sources. Characteristics of these microgrids are similar to islanded terrestrial microgrids, except the presence of highly dynamic large loads, such as propulsion loads. The presence of such loads and sources with power-electronic converter interfaces lead to severe power quality issues in ship microgrids. Generally, these issues can be classified as voltage variations, frequency variations and waveform distortions which are commonly referred to as harmonic distortions. Amongst the solutions identified, energy storage is considered to be the most promising technology for mitigating voltage and/or frequency deviations. Passive filtering is the commonly used technology for reducing harmonic distortions, which requires bulky capacitors and inductors. Active filtering is emerging as an alternative, which could be realised even within the same interfacing converter of the energy storage system. The aim of this paper is to investigate recent developments in these areas and provide readers with a critical review on power quality issues, energy storage technologies and strategies that could be used to improve the power quality in ship microgrids. Moreover, a brief introduction to ship power system architectures is also presented in the paper.


J.Manjula et.al / International Journal of Engineering and Technology (IJET) Performance Analysis of Active Inductor Based Tunable Band Pass Filter for Multiband RF Front end


title: A low cost W-band multilayer SIW filter

abstract: Using commercially available printed-circuit-board (PCB) technology, a compact W-band substrate integrated waveguide (SIW) filter is developed with multilayer topology in this letter. Full-wave simulations based on coupling matrix theory have been used to synthesize and optimize the proposed filter. A coupling aperture interface structure is developed for experiments. As demonstrations, the proposed multilayer SIW filter is fabricated on a 1.016 mm thick RO5880 substrate with a compact footprint as small as 3.7 mm by 13mm, and measured by vector network analyser (VNA). Good agreement is obtained between simulated and measured results. This work demonstrates that high performance W-band multilayer SIW filters can be massively realized by using low cost PCB process.


Comprehensive Review: Intrusion Detection System and Techniques


title: Improving trace accuracy through data-driven configuration and composition of tracing features

abstract: Software traceability is a sought-after, yet often elusive quality in large software-intensive systems primarily because the cost and effort of tracing can be overwhelming. State-of-the art solutions address this problem through utilizing trace retrieval techniques to automate the process of creating and maintaining trace links. However, there is no simple one- size-fits all solution to trace retrieval. As this paper will show, finding the right combination of tracing techniques can lead to significant improvements in the quality of generated links. We present a novel approach to trace retrieval in which the underlying infrastructure is configured at runtime to optimize trace quality. We utilize a machine-learning approach to search for the best configuration given an initial training set of validated trace links, a set of available tracing techniques specified in a feature model, and an architecture capable of instantiating all valid configurations of features. We evaluate our approach through a series of experiments using project data from the transportation, healthcare, and space exploration domains, and discuss its implementation in an industrial environment. Finally, we show how our approach can create a robust baseline against which new tracing techniques can be evaluated.


title: ProfileRank: finding relevant content and influential users based on information diffusion

abstract: Understanding information diffusion processes that take place on the Web, specially in social media, is a fundamental step towards the design of effective information diffusion mechanisms, recommendation systems, and viral marketing/advertising campaigns. Two key concepts in information diffusion are influence and relevance. Influence is the ability to popularize content in an online community. To this end, influentials introduce and propagate relevant content, in the sense that such content satisfies the information needs of a significant portion of this community. In this paper, we study the problem of identifying influential users and relevant content in information diffusion data. We propose ProfileRank, a new information diffusion model based on random walks over a user-content graph. ProfileRank is a PageRank inspired model that exploits the principle that relevant content is created and propagated by influential users and influential users create relevant content. A convenient property of ProfileRank is that it can be adapted to provide personalized recommendations. Experimental results demonstrate that ProfileRank makes accurate recommendations, outperforming baseline techniques. We also illustrate relevant content and influential users discovered using ProfileRank. Our analysis shows that ProfileRank scores are more correlated with content diffusion than with the network structure. We also show that our new modeling is more efficient than PageRank to perform these calculations.


title: Energy and performance exploration of accelerator coherency port using Xilinx ZYNQ

abstract: Cooperation of CPU and hardware accelerator to accomplish computational intensive tasks, provides significant advantages in run-time speed and energy. Efficient management of data sharing among multiple computational kernels can rapidly turn into a complicated problem. The Accelerator coherency port (ACP) emerges as a possible solution by enabling hardware accelerators to issue coherent accesses to the memory space. In this paper, we quantify the advantages of using ACP over the traditional method of sharing data on the DRAM. We select the Xilinx ZYNQ as target and develop an infrastructure to stress the ACP and high-performance (HP) AXI interfaces of the ZYNQ device. Hardware accelerators on both of HP and ACP AXI interfaces reach full duplex data processing bandwidth of over 1.6 GBytes/s running at 125 MHz on a XC7Z020-1C device. The effect of background DRAM and cache traffic on the performance of accelerators is analyzed. For a sample image filtering task, the cooperative operation of CPU and ACP accelerator (CPU-ACP) gains a speed-up of 1.2X over CPU and HP acceleration (CPU-HP). In terms of energy efficiency, an improvement of 2.5 nJ (> 20%) is shown for each byte of processed data. This is the first work which represents detailed practical comparisons on the speed and energy efficiency of various processor-accelerator memory sharing techniques in a configurable heterogeneous platform.


title: Action recognition technique based on fast HOG3D of integral foreground snippets and random forest

abstract: Action recognition is considered a promising field in computer vision and can be used in many applications such as video indexing and retrieval. In this paper, we present a novel technique for action recognition based on traditional three stages of feature extraction, action learning, and action recognition. The proposed technique builds a foreground snippet from the input video file, then uses integral videos representation of the foreground snippet to extract HOG3D feature vector. After that, random forest is constructed and trained from feature space to classify the Weizmann actions. Several experiments are performed to show the effectiveness, invariance and speed of the proposed technique against state of the art techniques. Experiments are made on ten different human actions on the Weizmann dataset. The best obtained average recall and average specificity values were 95.68 and 93.21, respectively.


title: Design of Ultrawideband Planar Monopole Antennas of Circular and Elliptical Shape

abstract: An efficient approach is described for designing ultrawideband (UWB) antennas in the form of planar monopoles of elliptical and circular shape. To avoid the time consuming trial-and-error approach presented in other works, simple design formulas for this type of radiators are described and their validity is tested via electromagnetic analysis and measurements. Full electromagnetic wave investigations are performed assuming three types of substrates with wide range of dielectric constant and thickness. The presented results show that the proposed method can be applied directly to design planar antennas that cover the ultrawide frequency band from 3.1 GHz to more than 10.6 GHz. Four types of monopole antennas were manufactured using RT6010LM substrate and their operation was tested in terms of return loss, radiation pattern characteristics, gain, and time domain response. The developed antennas feature UWB behavior with near omnidirectional characteristics and good radiation efficiency. The time domain transmission tests between two identical elements show that the manufactured circular monopoles offer better performance in terms of distortionless pulse transmission than their elliptically shaped counter parts. These antennas are also assessed in terms of fidelity factor. The manufactured antennas show a high fidelity factor which is more than 90% for the face-to-face orientation.


title: Cache Hierarchy-Aware Query Mapping on Emerging Multicore Architectures

abstract: One of the important characteristics of emerging multicores/manycores is the existence of “shared on-chip caches,” through which different threads/processes can share data (help each other) or displace each other’s data (hurt each other). Most of current commercial multicore systems on the market have on-chip cache hierarchies with multiple layers (typically, in the form of L1, L2 and L3, the last two being either fully or partially shared). In the context of database workloads, exploiting full potential of these caches can be critical. Motivated by this observation, our main contribution in this work is to present and experimentally evaluate a cache hierarchy-aware query mapping scheme targeting workloads that consist of batch queries to be executed on emerging multicores. Our proposed scheme distributes a given batch of queries across the cores of a target multicore architecture based on the affinity relations among the queries. The primary goal behind this scheme is to maximize the utilization of the underlying on-chip cache hierarchy while keeping the load nearly balanced across domain affinities. Each domain affinity in this context corresponds to a cache structure bounded by a particular level of the cache hierarchy. A graph partitioning-based method is employed to distribute queries across cores, and an integer linear programming (ILP) formulation is used to address locality and load balancing concerns. We evaluate our scheme using the TPC-H benchmarks on an Intel Xeon based multicore. Our solution achieves up to 25 percent improvement in individual query execution times and 15-19 percent improvement in throughput over the default Linux-based process scheduler.


title: ElastiStore: An elastic buffer architecture for Network-on-Chip routers

abstract: The design of scalable Network-on-Chip (NoC) architectures calls for new implementations that achieve high-throughput and low-latency operation, without exceeding the stringent area-energy constraints of modern Systems-on-Chip (SoC). The router's buffer architecture is a critical design aspect that affects both network-wide performance and implementation characteristics. In this paper, we extend Elastic Buffer (EB) architectures to support multiple Virtual Channels (VC) and we derive ElastiStore, a novel lightweight elastic buffer architecture that minimizes buffering requirements, without sacrificing performance. The integration of the proposed elastic buffering scheme in the NoC router enables the design of new router architectures - both single-cycle and two-stage pipelined - which offer the same performance as baseline VC-based routers, albeit at a significantly lower area/power cost.


title: Deeply learned face representations are sparse, selective, and robust

abstract: This paper designs a high-performance deep convolutional network (DeepID2+) for face recognition. It is learned with the identification-verification supervisory signal. By increasing the dimension of hidden representations and adding supervision to early convolutional layers, DeepID2+ achieves new state-of-the-art on LFW and YouTube Faces benchmarks. Through empirical studies, we have discovered three properties of its deep neural activations critical for the high performance: sparsity, selectiveness and robustness. (1) It is observed that neural activations are moderately sparse. Moderate sparsity maximizes the discriminative power of the deep net as well as the distance between images. It is surprising that DeepID2+ still can achieve high recognition accuracy even after the neural responses are binarized. (2) Its neurons in higher layers are highly selective to identities and identity-related attributes. We can identify different subsets of neurons which are either constantly excited or inhibited when different identities or attributes are present. Although DeepID2+ is not taught to distinguish attributes during training, it has implicitly learned such high-level concepts. (3) It is much more robust to occlusions, although occlusion patterns are not included in the training set.


title: Equation Parsing: Mapping Sentences to Grounded Equations

abstract: Identifying mathematical relations expressed in text is essential to understanding a broad range of natural language text from election reports, to financial news, to sport commentaries to mathematical word problems. This paper focuses on identifying and understanding mathematical relations described within a single sentence. We introduce the problem of Equation Parsing -- given a sentence, identify noun phrases which represent variables, and generate the mathematical equation expressing the relation described in the sentence. We introduce the notion of projective equation parsing and provide an efficient algorithm to parse text to projective equations. Our system makes use of a high precision lexicon of mathematical expressions and a pipeline of structured predictors, and generates correct equations in $70\%$ of the cases. In $60\%$ of the time, it also identifies the correct noun phrase $\rightarrow$ variables mapping, significantly outperforming baselines. We also release a new annotated dataset for task evaluation.


title: App Store Analysis: Mining App Stores for Relationships between Customer, Business and Technical Characteristics

abstract: This paper argues that App Store Analysis can be used to understand the rich interplay between app customers and their developers. We use data mining to extract price and popularity information and natural language processing and data mining to elicit each app�s claimed features from the Blackberry App Store, revealing strong correlations between customer rating and popularity (rank of app downloads). We found evidence for a mild correlation between price and the number of features claimed for an app and also found that higher priced features tended to be lower rated by their users. We also found that free apps have significantly (p-value <0.001) higher rating than non-free apps, with a moderately high effect size (�12 = 0.68). We also provide initial evidence that extracted claimed features are meaningful to developers (precision = 0.71, recall = 0.77). All data from our experiments and analysis are made available on-line to support further analysis.


title: Recommending similar items in large-scale online marketplaces

abstract: We are proposing a new similarity based recommendation system for large-scale dynamic marketplaces. Our solution consists of an offline process, which generates long-term cluster definitions grouping short-lived item listings, and an online system, which utilizes these clusters to first focus on important similarity dimensions and next conducts a trade-off between further similarity and other quality factors such as seller trustworthiness. Our system generates these clusters from several hundred millions of item listings using a large Hadoop map-reduce based system. The clusters are learned using user queries as the main information source and therefore biased towards how users conceptually group items. Our system is deployed on several eBay sites in large-scale and has increased user-engagement and business metrics compared to the previous system. We show that utilizing user queries helps capturing similarity better. We also present experiments demonstrating that adapting the ranking function, which controls the trade-off between similarity and quality, to a specific context improves recommendation performance.


title: Robust Propensity Score Computation Method based on Machine Learning with Label-corrupted Data

abstract: In biostatistics, propensity score is a common approach to analyze the imbalance of covariate and process confounding covariates to eliminate differences between groups. While there are an abundant amount of methods to compute propensity score, a common issue of them is the corrupted labels in the dataset. For example, the data collected from the patients could contain samples that are treated mistakenly, and the computing methods could incorporate them as a misleading information. In this paper, we propose a Machine Learning-based method to handle the problem. Specifically, we utilize the fact that the majority of sample should be labeled with the correct instance and design an approach to first cluster the data with spectral clustering and then sample a new dataset with a distribution processed from the clustering results. The propensity score is computed by Xgboost, and a mathematical justification of our method is provided in this paper. The experimental results illustrate that xgboost propensity scores computing with the data processed by our method could outperform the same method with original data, and the advantages of our method increases as we add some artificial corruptions to the dataset. Meanwhile, the implementation of xgboost to compute propensity score for multiple treatments is also a pioneering work in the area.


Army Physical Fitness Test 2-mile Run Correlates With Peak Oxygen Uptake In Infantry Soldiers


title: Training cost-sensitive neural networks with methods addressing the class imbalance problem

abstract: This paper studies empirically the effect of sampling and threshold-moving in training cost-sensitive neural networks. Both oversampling and undersampling are considered. These techniques modify the distribution of the training data such that the costs of the examples are conveyed explicitly by the appearances of the examples. Threshold-moving tries to move the output threshold toward inexpensive classes such that examples with higher costs become harder to be misclassified. Moreover, hard-ensemble and soft-ensemble, i.e., the combination of above techniques via hard or soft voting schemes, are also tested. Twenty-one UCl data sets with three types of cost matrices and a real-world cost-sensitive data set are used in the empirical study. The results suggest that cost-sensitive learning with multiclass tasks is more difficult than with two-class tasks, and a higher degree of class imbalance may increase the difficulty. It also reveals that almost all the techniques are effective on two-class tasks, while most are ineffective and even may cause negative effect on multiclass tasks. Overall, threshold-moving and soft-ensemble are relatively good choices in training cost-sensitive neural networks. The empirical study also suggests that some methods that have been believed to be effective in addressing the class imbalance problem may, in fact, only be effective on learning with imbalanced two-class data sets.


title: An array-based algorithm for simultaneous multidimensional aggregates

abstract: Computing multiple related group-bys and aggregates is one of the core operations of On-Line Analytical Processing (OLAP) applications. Recently, Gray et al. [GBLP95] proposed the “Cube” operator, which computes group-by aggregations over all possible subsets of the specified dimensions. The rapid acceptance of the importance of this operator has led to a variant of the Cube being proposed for the SQL standard. Several efficient algorithms for Relational OLAP (ROLAP) have been developed to compute the Cube. However, to our knowledge there is nothing in the literature on how to compute the Cube for Multidimensional OLAP (MOLAP) systems, which store their data in sparse arrays rather than in tables. In this paper, we present a MOLAP algorithm to compute the Cube, and compare it to a leading ROLAP algorithm. The comparison between the two is interesting, since although they are computing the same function, one is value-based (the ROLAP algorithm) whereas the other is position-based (the MOLAP algorithm). Our tests show that, given appropriate compression techniques, the MOLAP algorithm is significantly faster than the ROLAP algorithm. In fact, the difference is so pronounced that this MOLAP algorithm may be useful for ROLAP systems as well as MOLAP systems, since in many cases, instead of cubing a table directly, it is faster to first convert the table to an array, cube the array, then convert the result back to a table.