title: A Review on Image Texture Analysis Methods

abstract: Texture classification is an active topic in image processing which plays an important role in many applications such as image retrieval, inspection systems, face recognition, medical image processing, etc. There are many approaches extracting texture features in gray-level images such as local binary patterns, gray level co-occurrence matrices, statistical features, skeleton, scale invariant feature transform, etc. The texture analysis methods can be categorized in 4 groups titles: statistical methods, structural methods, filter-based and model based approaches. In many related researches, authors have tried to extract color and texture features jointly. In this respect, combined methods are considered as efficient image analysis descriptors. Mostly important challenges in image texture analysis are rotation sensitivity, gray scale variations, noise sensitivity, illumination and brightness conditions, etc. In this paper, we review most efficient and state-of-the-art image texture analysis methods. Also, some texture classification approaches are survived.


title: Learning to Run challenge solutions: Adapting reinforcement learning methods for neuromusculoskeletal environments

abstract: In the NIPS 2017 Learning to Run challenge, participants were tasked with building a controller for a musculoskeletal model to make it run as fast as possible through an obstacle course. Top participants were invited to describe their algorithms. In this work, we present eight solutions that used deep reinforcement learning approaches, based on algorithms such as Deep Deterministic Policy Gradient, Proximal Policy Optimization, and Trust Region Policy Optimization. Many solutions use similar relaxations and heuristics, such as reward shaping, frame skipping, discretization of the action space, symmetry, and policy blending. However, each of the eight teams implemented different modifications of the known algorithms.


title: CompNet: Complementary Segmentation Network for Brain MRI Extraction

abstract: Brain extraction is a fundamental step for most brain imaging studies. In this paper, we investigate the problem of skull stripping and propose complementary segmentation networks (CompNets) to accurately extract the brain from T1-weighted MRI scans, for both normal and pathological brain images. The proposed networks are designed in the framework of encoder-decoder networks and have two pathways to learn features from both the brain tissue and its complementary part located outside of the brain. The complementary pathway extracts the features in the non-brain region and leads to a robust solution to brain extraction from MRIs with pathologies, which do not exist in our training dataset. We demonstrate the effectiveness of our networks by evaluating them on the OASIS dataset, resulting in the state of the art performance under the two-fold cross-validation setting. Moreover, the robustness of our networks is verified by testing on images with introduced pathologies and by showing its invariance to unseen brain pathologies. In addition, our complementary network design is general and can be extended to address other image segmentation problems with better generalization.


title: An Automated Approach for Digital Forensic Analysis of Heterogeneous Big Data

abstract: The major challenges with big data examination and analysis are volume, complex interdependence across content, and heterogeneity. The examination and analysis phases are considered essential to a digital forensics process. However, traditional techniques for the forensic investigation use one or more forensic tools to examine and analyse each resource. In addition, when multiple resources are included in one case, there is an inability to cross-correlate findings which often leads to inefficiencies in processing and identifying evidence. Furthermore, most current forensics tools cannot cope with large volumes of data. This paper develops a novel framework for digital forensic analysis of heterogeneous big data. The framework mainly focuses upon the investigations of three core issues: data volume, heterogeneous data and the investigators cognitive load in understanding the relationships between artefacts. The proposed approach focuses upon the use of metadata to solve the data volume problem, semantic web ontologies to solve the heterogeneous data sources and artificial intelligence models to support the automated identification and correlation of artefacts to reduce the burden placed upon the investigator to understand the nature and relationship of the artefacts.


title: Contexts and Data Quality Assessment

abstract: The quality of data is context dependent. Starting from this intuition and experience, we propose and develop a conceptual framework that captures in formal terms the notion of "context-dependent data quality". We start by proposing a generic and abstract notion of context, and also of its uses, in general and in data management in particular. On this basis, we investigate "data quality assessment" and "quality query answering" as context-dependent activities. A context for the assessment of a database D at hand is modeled as an external database schema, with possibly materialized or virtual data, and connections to external data sources. The database D is put in context via mappings to the contextual schema, which produces a collection C of alternative clean versions of D. The quality of D is measured in terms of its distance to C. The class C} is also used to define and do "quality query answering". The proposed model allows for natural extensions, like the use of data quality predicates, the optimization of the access by the context to external data sources, and also the representation of contexts by means of more expressive ontologies.


title: A comparison of linear genetic programming and neural networks in medical data mining

abstract: We introduce a new form of linear genetic programming (GP). Two methods of acceleration of our GP approach are discussed: 1) an efficient algorithm that eliminates intron code and 2) a demetic approach to virtually parallelize the system on a single processor. Acceleration of runtime is especially important when operating with complex data sets, because they are occurring in real-world applications. We compare GP performance on medical classification problems from a benchmark database with results obtained by neural networks. Our results show that GP performs comparably in classification and generalization.


title: Scrum in a Multiproject Environment: An Ethnographically-Inspired Case Study on the Adoption Challenges

abstract: Agile methods continue to gain popularity. In particular, the Scrum method appears to be on the verge of becoming a de-facto standard in the industry, leading the so called Agile movement. While there are success stories and recommendations, there is little scientifically valid evidence of the challenges in the adoption of Agile methods in general, and Scrum in particular. Little, if anything, is empirically known about the application and adoption of Scrum in a multi-team and multi-project situation. The authors carried out an ethnographically informed longitudinal case study in industrial settings and closely followed how the Scrum method was adopted in a 20-person department, working in a simultaneous multi-project R&D environment. Altogether 10 challenges pertinent to the case of multi-team multi-project Scrum adoption were identified in the study. The authors contend that these results carry great relevance for other industrial teams. Future research avenues arising from the study are indicated.


title: STT-RAM designs supporting dual-port accesses

abstract: The spin-transfer torque random access memory (STT-RAM) has been widely investigated as a promising candidate to replace the static random access memory (SRAM) as on-chip cache memories. However, the existing STT-RAM cell designs can be used for only single-port accesses, which limits the memory access bandwidth and constraints the system performance. In this work, we propose the design solutions to provide dual-port accesses for STT-RAM. The area increment by introducing an additional port is reduced by leveraging the shared source-line structure. Detailed analysis on the performance/reliability degradation caused by dual-port accesses and the corresponding design optimization are performed. We propose two types of dual-port STT-RAM cell structures having 2 read/write ports (2RW) or 1-read/1-write port (1R/1W), respectively. Comparison shows that a 2RW STT-RAM cell consumes only 42% of area of a dual-port SRAM. The 1R/1W design further reduces 7.7% of cell area under same performance target.


title: Entropy rate superpixel segmentation

abstract: We propose a new objective function for superpixel segmentation. This objective function consists of two components: entropy rate of a random walk on a graph and a balancing term. The entropy rate favors formation of compact and homogeneous clusters, while the balancing function encourages clusters with similar sizes. We present a novel graph construction for images and show that this construction induces a matroid — a combinatorial structure that generalizes the concept of linear independence in vector spaces. The segmentation is then given by the graph topology that maximizes the objective function under the matroid constraint. By exploiting submodular and mono-tonic properties of the objective function, we develop an efficient greedy algorithm. Furthermore, we prove an approximation bound of ½ for the optimality of the solution. Extensive experiments on the Berkeley segmentation benchmark show that the proposed algorithm outperforms the state of the art in all the standard evaluation metrics.


title: Combining document representations for known-item search

abstract: This paper investigates the pre-conditions for successful combination of document representations formed from structural markup for the task of known-item search. As this task is very similar to work in meta-search and data fusion, we adapt several hypotheses from those research areas and investigate them in this context. To investigate these hypotheses, we present a mixture-based language model and also examine many of the current meta-search algorithms. We find that compatible output from systems is important for successful combination of document representations. We also demonstrate that combining low performing document representations can improve performance, but not consistently. We find that the techniques best suited for this task are robust to the inclusion of poorly performing document representations. We also explore the role of variance of results across systems and its impact on the performance of fusion, with the surprising result that the correct documents have higher variance across document representations than highly ranking incorrect documents.


title: Cancer Classification Based on Microarray Gene Expression Data Using Deep Learning

abstract: The classification of cancer is a major research topic in Medicine. Cancer microarray data normally contains a small number of samples, which have a large number of gene expression levels as features, however, makes the classification quite challenging. Using a deep learning algorithm based on multilayer perceptron, we show that classification performance at least as good as published results can be obtained for cancer classification.


title: High gain Substrate Integrated Waveguide based slot antenna using Circular Cavity Array

abstract: The paper deals with design and analysis of a Substrate Integrated Waveguide (SIW) based antenna by providing an Array of Circular Cavity. The cavity is allowed to radiate by providing Half-Ring Shaped Slots on the surface. Based on the analysis, a Peak Gain of 10.5dBi is obtained at a frequency of 19.6GHz for a 4 element array.


title: Sign Language Recognition using Sequential Pattern Trees

abstract: This paper presents a novel, discriminative, multi-class classifier based on Sequential Pattern Trees. It is efficient to learn, compared to other Sequential Pattern methods, and scalable for use with large classifier banks. For these reasons it is well suited to Sign Language Recognition. Using deterministic robust features based on hand trajectories, sign level classifiers are built from sub-units. Results are presented both on a large lexicon single signer data set and a multi-signer Kinect™ data set. In both cases it is shown to out perform the non-discriminative Markov model approach and be equivalent to previous, more costly, Sequential Pattern (SP) techniques.


title: Understanding DevOps & bridging the gap from continuous integration to continuous delivery

abstract: As part of Agile transformation in past few years we have seen IT organizations adopting continuous integration principles in their software delivery lifecycle, which has improved the efficiency of development teams. With the time it has been realized that this optimization as part of continuous integration - alone - is just not helping to make the entire delivery lifecycle efficient or is not driving the organization efficiency. Unless all the pieces of a software delivery lifecycle work like a well oiled machine – efficiency of organization to optimize the delivery lifecycle can not be met. This is the problem which DevOps tries to address. This paper tries to cover all aspects of Devops applicable to various phases of SDLC and specifically talks about business need, ways to move from continuous integration to continuous delivery and its benefits. Continuous delivery transformation in this paper is explained with a real life case study that how infrastructure can be maintained just in form of code (IAAC). Finally this paper touches upon various considerations one must evaluate before adopting DevOps and what kind of benefits one can expect.


The Semantics and Pragmatics of . . .


title: A Feature-Integration Theory of Attention

abstract: A new hypothesis about the role of focused attention is proposed. The feature-integration theory of attention suggests that attention must be directed serially to each stimulus in a display whenever conjunctions of more than one separable feature are needed to characterize or distinguish the possible objects presented. A number of predictions were tested in a variety of paradigms including visual search, texture segregation, identification and localization, and using both separable dimensions (shape and color) and local elements or parts of figures (lines, curves, etc. in letters) as the features to be integrated into complex wholes. The results were in general consistent with the hypothesis. They offer a new set of criteria for distinguishing separable from integral features and a new rationale for predicting which tasks will show attention limits and which will not.


Active and Reactive Power Controller for Single-Phase Grid-Connected Photovoltaic Systems


title: Scatteract: Automated extraction of data from scatter plots

abstract: Charts are an excellent way to convey patterns and trends in data, but they do not facilitate further modeling of the data or close inspection of individual data points. We present a fully automated system for extracting the numerical values of data points from images of scatter plots. We use deep learning techniques to identify the key components of the chart, and optical character recognition together with robust regression to map from pixels to the coordinate system of the chart. We focus on scatter plots with linear scales, which already have several interesting challenges. Previous work has done fully automatic extraction for other types of charts, but to our knowledge this is the first approach that is fully automatic for scatter plots. Our method performs well, achieving successful data extraction on 89% of the plots in our test set.


title: Adaptive Apriori Algorithm for frequent itemset mining

abstract: Obtaining frequent itemsets from the dataset is one of the most promising area of data mining. The Apriori algorithm is one of the most important algorithm for obtaining frequent itemsets from the dataset. But the algorithm fails in terms of time required as well as number of database scans. Hence a new improved version of Apriori is proposed in this paper which is efficient in terms of time required as well as number of database scans than the Apriori algorithm. It is well known that the size of the database for defining candidates has great effect on running time and memory need. We presented experimental results, showing that the proposed algorithm always outperform Apriori. To evaluate the performance of the proposed algorithm, we have tested it on Turkey student's database as well as a real time dataset.


title: India development update : fiscal policy for equitable growth

abstract: India's economy expanded by 7.3 percent in FY14-15 and 7.0 percent in Q1 FY15-16 (y/y). Industrial growth increased and despite government services slowing down, services expanded. Domestic drivers increased, while exports declined. Private consumption growth stayed strong and investments gained momentum. Gross domestic product is expected to increase gradually to 7.5 percent in FY15-16. The positive outlook is dependent upon the implementation of important domestic reforms which include: boosting the balance sheets of the banking sector through a sustainable solution of the debt overhang of primarily power and road infrastructure firms, continuing to improve the ease of doing business and enacting the crucial Goods and Services Tax, and enhancing capacity of state and local governments to deliver public services as more resources are devolved from the centre.


title: A Systematic Review and Taxonomy of Published Quality Criteria Related to the Evaluation of User-Facing eHealth Programs

abstract: The purpose of this review was to identify and classify key criteria concepts related to the evaluation of user-facing eHealth programs. In line with the PRISMA statement methodology, computer searches of relevant databases were conducted for studies published between January 1, 2000 and March 1, 2016 that contained explicit quality criteria related to mHealth and eHealth products. Reference lists of included articles, review articles, and grey literature (e.g., books, websites) were searched for additional sources. A team of nine experts led by the first author was gathered to support the classification of these criteria. Identified criteria were extracted, grouped and organized using an inductive thematic analysis. Eighty-four sources – emanating from 26 different courtiers – were included in this review. The team extracted 454 criteria that were grouped into 11 quality domains, 58 criteria concepts and 134 concepts’ sub-groups. Quality domains were: Usability, Visual Design, User Engagement, Content, Behavior Change/Persuasive Design, Influence of Social Presence, Therapeutic Alliance, Classification, Credibility/Accountability, and Privacy/Security. Findings suggest that authors around the globe agree on key criteria concepts when evaluating user-facing eHealth products. The high proportion of new published criteria in the second half of this review time-frame (2008–2016), and more specifically, the high proportion of criteria relating to persuasive design, therapeutic alliance and privacy/security within this time-frame, points to the advancements made in recent years within this field.


title: Optokinetic stimuli: motion sickness, visual acuity, and eye movements.

abstract: BACKGROUND: It is commonly assumed that motion sickness caused by moving visual scenes arises from the illusion of self-motion (i.e., vection). HYPOTHESES: Both studies reported here investigated whether sickness and vection were correlated. The first study compared sickness and vection created by real and virtual visual displays. The second study investigated whether visual fixation to suppress eye movements affected motion sickness or vection. METHOD: In the first experiment subjects viewed an optokinetic drum and a virtual simulation of the optokinetic drum. The second experiment investigated two conditions on a virtual display: a) moving black and white stripes; and b) moving black and white stripes with a stationary cross on which subjects fixated to reduce eye movements. RESULTS: In the first study, ratings of motion sickness were correlated between the conditions (real and the virtual drum), as were ratings of vection. With both conditions, subjects with poor visual acuity experienced greater sickness. There was no correlation between ratings of vection and ratings of sickness in either condition. In the second study, fixation reduced motion sickness but had no affect on vection. Motion sickness was correlated with visual acuity without fixation, but not with fixation. Again, there was no correlation between vection and motion sickness. CONCLUSIONS: Vection is not the primary cause of sickness with optokinetic stimuli. Vection appears to be influenced by peripheral vision whereas motion sickness is influenced by central vision. When the eyes are free to track moving stimuli, there is an association between visual acuity and motion sickness. Virtual displays can create vection and may be used to investigate visually induced motion sickness.


title: Harnessing Mobile Phone Social Network Topology to Infer Users Demographic Attributes

abstract: We study the structure of the social graph of mobile phone users in the country of Mexico, with a focus on demographic attributes of the users (more specifically the users' age). We examine assortativity patterns in the graph, and observe a strong age homophily in the communications preferences. We propose a graph based algorithm for the prediction of the age of mobile phone users. The algorithm exploits the topology of the mobile phone network, together with a subset of known users ages (seeds), to infer the age of remaining users. We provide the details of the methodology, and show experimental results on a network GT with more than 70 million users. By carefully examining the topological relations of the seeds to the rest of the nodes in GT, we find topological metrics which have a direct influence on the performance of the algorithm. In particular we characterize subsets of users for which the accuracy of the algorithm is 62% when predicting between 4 age categories (whereas a pure random guess would yield an accuracy of 25%). We also show that we can use the probabilistic information computed by the algorithm to further increase its inference power to 72% on a significant subset of users.


title: Geography of Twitter networks

abstract: Abstract The paper examines the influence of geographic distance, national boundaries, language, and frequency of air travel on the formation of social ties on Twitter, a popular micro-blogging website. Based on a large sample of publicly available Twitter data, our study shows that a substantial share of ties lies within the same metropolitan region, and that between regional clusters, distance, national borders and language differences all predict Twitter ties. We find that the frequency of airline flights between the two parties is the best predictor of Twitter ties. This highlights the importance of looking at pre-existing ties between places and people.


A Systematic Approach for Attack Analysis and Mitigation in V2V Networks


title: Analysis of factors affecting the performance of partial nitrification in a sequencing batch reactor

abstract: The objective of this study was to analyze the factors affecting the performance of partial nitrification in a sequencing batch reactor. During a 140-day long-term operation, influent pH value, dissolved oxygen (DO), and chemical oxygen demand/nitrogen (COD/N) ratio were selected as operating factors to evaluate the maintenance and recovery of nitrite accumulation. Results showed that high DO concentration (2–4 mg/L) could damage nitrite accumulation immediately. However, nitrite accumulation ratio (NAR) could be increased from 1.68 ± 1.51 to 35.46 ± 7.86 % when increasing the pH values from 7.5 to 8.3 due to the increased free ammonia concentration. Afterwards, stable partial nitrification and high NAR could be recovered when the reactor operated under low DO concentration (0.5–1.0 mg/L). However, it required a long time to recover the partial nitrification of the reactor when the influent COD/N ratios were altered. Fluorescence in situ hybridization analysis implied that ammonium oxidizing bacteria were completely recovered to the dominant nitrifying bacteria in the system. Meanwhile, sludge volumetric index of the reactor gradually decreased from 115.6 to 56.6 mL/g, while the mean diameter of sludge improved from74.57 to 428.8 μm by using the strategy of reducing settling time. The obtained results could provide useful information between the operational conditions and the performance of partial nitrification when treating nitrogen-rich industrial wastewater.


Punishment and ethics deterrents: A study of insider security contravention


title: Hybrid genetic algorithm — Artificial neural network classifier for skin cancer detection

abstract: Skin cancer is a deadly form of cancer, affecting skin. It is one of the very deadly form of cancer. It occurs on the melanocytic cells of skin. So skin cancer is also known as Melanoma. Due to the malignancy feature it is also known as malignant melanoma. Melanoma causes the abnormal growth of melanocytic cells which produces the sun protective pigment melanin. Due to that reason melanoma appears as black or brown in color. Main factors that cause melanoma are over exposure to sunlight and genetic factors. Ultraviolet (UV) radiation from the sun is important for maintaining vitamin D levels in the body, but too much UV can cause sunburn, premature ageing, skin and damage and ultimately skin cancer. Early detection can cure skin cancer completely. The probability of cure decreases if it is not treated at early stages. Diagnosing methods like Biopsy, SIAscopy are painful and time consuming. An economical, less time consuming, painless Computer Aided Skin Cancer detection is proposed here. This methodology uses Digital Image processing and Artificial intelligence for skin cancer detection. This methodology involves no direct contact with skin. Only the dermoscopic image is used here. The image after certain image processing techniques is subjected to segmentation. After segmentation, the unique features are extracted from the image using feature extraction techniques. The feature extraction technique used here is GLCM(Gray Level Co-occurrence Matrix) and RGB color feature. These features are used for classification. Artificial neural network classifier is used for classification. In order to improve the accuracy of classification, the ANN is optimized by Genetic Algorithm.


title: CUR Algorithm for Partially Observed Matrices

abstract: CUR matrix decomposition computes the low rank approximation of a given matrix by using the actual rows and columns of the matrix. It has been a very useful tool for handling large matrices. One limitation with the existing algorithms for CUR matrix decomposition is that they need an access to the {\it full} matrix, a requirement that can be difficult to fulfill in many real world applications. In this work, we alleviate this limitation by developing a CUR decomposition algorithm for partially observed matrices. In particular, the proposed algorithm computes the low rank approximation of the target matrix based on (i) the randomly sampled rows and columns, and (ii) a subset of observed entries that are randomly sampled from the matrix. Our analysis shows the relative error bound, measured by spectral norm, for the proposed algorithm when the target matrix is of full rank. We also show that only $O(n r\ln r)$ observed entries are needed by the proposed algorithm to perfectly recover a rank $r$ matrix of size $n\times n$, which improves the sample complexity of the existing algorithms for matrix completion. Empirical studies on both synthetic and real-world datasets verify our theoretical claims and demonstrate the effectiveness of the proposed algorithm.


title: Statistical learning of syntax: The role of transitional probability. Language Learning and Development

abstract: Previous research has shown that, for learners to fully acquire a miniature phrase structure language, the language must contain cues to the phrases—for example, prosodic grouping or morphological agreement of the words within a phrase (Morgan, Meier, & Newport, 1987, 1989). Research on word segmentation has shown that learners can use transitional probabilities between syllables to segment speech into word-like units (Saffran, Aslin, & Newport, 1996). In the present research, we combine and extend these two sets of findings, asking whether learners can use transitional probabilities between words (or word classes) to segment sentences into phrases, and use this phrasal information to fully acquire the syntax of a miniature language. Adult subjects were exposed to sentences from a miniature language. A pattern in the transitional probabilities between words—high within phrases, low at phrase boundaries—was created by adding syntactic properties that are widespread in natural languages: optional phrases, repeated phrases, moved phrases, different-sized form classes, or all four properties combined. All conditions outperformed controls in learning the language. The best learning occurred with all properties combined, despite the fact that this language was the most complex. These data address the important question of how language learning is successful in the face of the massive complexity of natural languages. In our experiments, learning got better, not worse, when properly structured complexity was added to a language. The results also show that the same type of statistical computation useful in word segmentation might be used as well in learning syntax, suggesting that the range of statistics needed for acquiring various types of structure in natural languages might be suitably small. LANGUAGE LEARNING AND DEVELOPMENT, 3(1), 1–42 Copyright © 2007, Lawrence Erlbaum Associates, Inc.


title: Modeling impression discounting in large-scale recommender systems

abstract: Recommender systems have become very important for many online activities, such as watching movies, shopping for products, and connecting with friends on social networks. User behavioral analysis and user feedback (both explicit and implicit) modeling are crucial for the improvement of any online recommender system. Widely adopted recommender systems at LinkedIn such as "People You May Know" and "Endorsements" are evolving by analyzing user behaviors on impressed recommendation items. In this paper, we address modeling impression discounting of recommended items, that is, how to model user's no-action feedback on impressed recommended items. The main contributions of this paper include (1) large-scale analysis of impression data from LinkedIn and KDD Cup; (2) novel anti-noise regression techniques, and its application to learn four different impression discounting functions including linear decay, inverse decay, exponential decay, and quadratic decay; (3) applying these impression discounting functions to LinkedIn's "People You May Know" and "Endorsements" recommender systems.


title: CyCADA: Cycle-Consistent Adversarial Domain Adaptation

abstract: Domain adaptation is critical for success in new, unseen environments. Adversarial adaptation models applied in feature spaces discover domain invariant representations, but are difficult to visualize and sometimes fail to capture pixel-level and low-level domain shifts. Recent work has shown that generative adversarial networks combined with cycle-consistency constraints are surprisingly effective at mapping images between domains, even without the use of aligned image pairs. We propose a novel discriminatively-trained Cycle-Consistent Adversarial Domain Adaptation model. CyCADA adapts representations at both the pixel-level and feature-level, enforces cycle-consistency while leveraging a task loss, and does not require aligned pairs. Our model can be applied in a variety of visual recognition and prediction settings. We show new state-of-the-art results across multiple adaptation tasks, including digit classification and semantic segmentation of road scenes demonstrating transfer from synthetic to real world domains.


title: Semantic Role Labeling Via Integer Linear Programming Inference

abstract: We present a system for the semantic role labeling task. The system combines a machine learning technique with an inference procedure based on integer linear programming that supports the incorporation of linguistic and structural constraints into the decision process. The system is tested on the data provided in CoNLL-2004 shared task on semantic role labeling and achieves very competitive results.


title: THE CHINESE WALL SECURITY POLICY 1

abstract: The authors explore a commercial security policy (the Chinese Wall) which represents the behavior required of those persons who perform corporate analysis for financial institutions. It can be distinguished from Bell-LaPadula-like policies by the way that a user's permitted accesses are constrained by the history of his previous accesses. It is shown that the formal representation of the policy correctly permits a market analyst to talk to any corporation which does not create a conflict of interest with previous assignments. The Chinese Wall policy combines commercial discretion with legally enforceable mandatory controls. It is required in the operation of many financial services organizations; the authors conclude that it is, therefore, perhaps as significant to the financial world as Bell-LaPadula's policies are to the military. >


Climate change , sea-level rise , and conservation : keeping island biodiversity afloat


title: Effects of task complexity on individual creativity through knowledge interaction: A comparison of temporary and permanent teams

abstract: Individual creativity improvement becomes different with team characteristics.Individual creativity is affected by task complexity through knowledge interaction.Knowledge sharing is essential in supporting creativity in temporary teams.Team member exchange is crucial in boosting creativity in permanent teams. Using the interactionist's perspective of creativity, this paper proposes a new research model of creativity manifestation to explore how factors affecting individual creativity depend on team characteristics. We investigated the antecedents of creativity in the literature-task complexity, team member exchange, and knowledge sharing-and then examined the relationships and differences between temporary and permanent teams. To maximize practical implications, we studied two team types like project task force (PTF) and research and development (R&D) teams in the Information and Communication Technology (ICT) industry in Korea, where strong creativity is required for team performance. PTF teams operate with a clear mission to be completed on a deadline, while R&D teams create scientific enhancements for existing products. The proposed structural model was tested empirically with cross-sectional data from 289 professionals from the two team types. Results indicated that, in the case of PTF teams, task complexity had an indirect relationship with individual complexity through knowledge interaction among team members, while for R&D teams, task complexity was directly associated with individual creativity, and indirectly associated with the creativity through team member exchange. Thus, team characteristics must be considered together with task complexity and knowledge interactions in order to achieve team goals more effectively by maximizing each member's creativity.


title: Learning Deep Features for One-Class Classification

abstract: We present a novel deep-learning-based approach for one-class transfer learning in which labeled data from an unrelated task is used for feature learning in one-class classification. The proposed method operates on top of a convolutional neural network (CNN) of choice and produces descriptive features while maintaining a low intra-class variance in the feature space for the given class. For this purpose two loss functions, compactness loss and descriptiveness loss, are proposed along with a parallel CNN architecture. A template matching-based framework is introduced to facilitate the testing process. Extensive experiments on publicly available anomaly detection, novelty detection, and mobile active authentication datasets show that the proposed deep one-class (DOC) classification method achieves significant improvements over the state-of-the-art.


title: The comprehension problems for second-language learners with poor reading comprehension despite adequate decoding: A meta-analysis

abstract: We conducted a meta-analysis of 16 existing studies to examine the nature of the comprehension problems for children who were second-language learners with poor reading comprehension despite adequate decoding. Results indicated that these children had deficits in oral language (d = –0.80), but these deficits were not as severe as their reading comprehension deficit (d = –2.47). Second-language learners also had weaker oral language skills compared with native-speaking children regardless of comprehension status (d = –0.84). We discuss theoretical and practical implications of the finding that second-language learners who are poor at reading comprehension despite adequate decoding have deficits in oral language but the deficit is not sufficient to explain their deficit in reading comprehension.


title: Parallel Multi-Dimensional LSTM, With Application to Fast Biomedical Volumetric Image Segmentation

abstract: Convolutional Neural Networks (CNNs) can be shifted across 2D images or 3D videos to segment them. They have a fixed input size and typically perceive only small local contexts of the pixels to be classified as foreground or background. In contrast, Multi-Dimensional Recurrent NNs (MD-RNNs) can perceive the entire spatio-temporal context of each pixel in a few sweeps through all pixels, especially when the RNN is a Long Short-Term Memory (LSTM). Despite these theoretical advantages, however, unlike CNNs, previous MD-LSTM variants were hard to parallelise on GPUs. Here we re-arrange the traditional cuboid order of computations in MD-LSTM in pyramidal fashion. The resulting PyraMiD-LSTM is easy to parallelise, especially for 3D data such as stacks of brain slice images. PyraMiD-LSTM achieved best known pixel-wise brain image segmentation results on MRBrainS13 (and competitive results on EM-ISBI12).


title: Double pulse test based switching characterization of SiC MOSFET

abstract: Switching characteristics of a 1200 V, 90 A SiC MOSFET in an asymmetric H-bridge power converter are measured. Double pulse tests are carried out at different current and voltage levels. The experimental set-up and procedure are explained in detail. The switching waveforms are used to obtain the voltage and current transition times of the MOSFET. The turn-on and turn-off energy losses are also calculated. Since these are influenced by layout of the power circuit, the power converter layout and its effect on the switching waveform are reported. The turn-off energy loss is lower than the data-sheet specified value, while the turn-on energy loss is higher; the total loss is close to the data-sheet value.


title: Social interaction detection using a multi-sensor approach

abstract: In the context of a social gathering, such as a cocktail party, the memorable moments are often captured by professional photographers or the participants. The latter case is generally undesirable because many participants would rather enjoy the event instead of being occupied by the tedious photo capturing task. Motivated by this scenario, we propose an automated social event photo-capture framework for which, given the multiple sensor data streams and the information from the Web as input, will output the visually appealing photos of the social event. Our proposal consists of three components: (1) social attribute extraction from both the physical space and the cyberspace; (2) social attribute fusion; and (3) active camera control. Current work is presented and we conclude with expected contributions as well as future direction.


title: A Dynamic Deep Neural Network Design for Efficient Workload Allocation in Edge Computing

abstract: Unreliable communication channels and limited computing resources at the edge end are two primary constraints of battery-powered movable devices, such as autonomous robots and unmanned aerial vehicles (UAVs). The impact is especially severe for those performing deep neural network (DNN) computations. With increasing demand for accuracy, the trend in modern DNN designs is the use of cascaded modularized layers. Implementing a deep network at the edge increases computational workloads and resource occupancy, leading to an increase in battery drain. Using a shallow network and offloading workloads to backbone servers, however, incur significant latency overheads caused by unstable communication channels. Hence, dynamic DNN design techniques for efficient workload allocation are urgently required to manage the amount of workload transmissions while achieving the required accuracy. In this paper, we explore the use of authentic operation (AO) unit and dynamic network structure to enhance DNNs. The AO unit defines a set of stochastic threshold values for different DNN output classes and determines at runtime if an input has to be transferred to backbone servers for further analysis. The dynamic network structure adjusts its depth according to channel availability. Experiments have been comprehensively performed on several well-known DNN models and datasets. Our results show that, on an average, the proposed techniques are able to reduce the amount of transmissions by up to 17% compared to previous methods under the same accuracy requirement.


title: On the Distinction of Subjectivity and Objectivity of Emotions in Texts

abstract: Emotion classification in texts is an instance of the text classification problem. It therefore could apply some existing text classifiers by considering each emotion as a label of the text. However, most of recent works does not differentiate the subjectivity and objectivity of the same emotion in the text. This paper firstly builds some datasets whose labels are emotion, in which the subject and object of the same emotion are considered as two separated labels. Secondly, this paper evaluates some existing classifiers via some scenarios on the built datasets. The results are then discussed on some difficulties of these kinds of problem.


title: Addressing Data-Centric Security Requirements for IoT-Based Systems

abstract: Allowing users to control access to their data is paramount for the success of the Internet of Things, therefore, it is imperative to ensure it, even when data has left the users' control, e.g. shared with cloud infrastructure. Consequently, we propose several state of the art mechanisms from the security and privacy research fields to cope with this requirement. To illustrate how each mechanism can be applied, we derive a data-centric architecture providing access control and privacy guaranties for the users of IoT-based applications. Moreover, we discuss the limitations and challenges related to applying the selected mechanisms to ensure access control remotely. Also, we validate our architecture by showing how it empowers users to control access to their health data in a quantified self use case.


title: Tracking the Yak: An Empirical Study of Yik Yak

abstract: To investigate the effects of anonymity on user behavior, we conduct an empirical study of the new and controversial social app, Yik Yak. First, we examine how users use the platform, analyzing patterns in posting, popularity of yaks, and vocabulary. As a comparison, we look at posting patterns on Twitter, which has similar limitations on lengths of posts, but is public and global rather than anonymous and local. Upon a sample of 2.9M posts (1.9M yaks and 1M geotagged tweets) from 20 locations across the USA, we find that interactions on Yik Yak are specific to its location limitations and reflect the schedules of its targeted demographic, college students. Second, we test two hypotheses related to anonymity and communication: (i) whether vulgarity usage is more likely to be acceptable, and (ii) whether unique topics emerge in conversations on Yik Yak. We find that posts on Yik Yak are only slightly more likely to contain vulgarities, and we do not find any significant bias in topic distributions on Yik Yak versus on Twitter; however, differences in vocabulary and most discriminative words used suggest the need for further analysis.


title: Cluster-based Kriging Approximation Algorithms for Complexity Reduction

abstract: Kriging or Gaussian Process Regression is applied in many fields as a non-linear regression model as well as a surrogate model in the field of evolutionary computation. However, the computational and space complexity of Kriging, that is cubic and quadratic in the number of data points respectively, becomes a major bottleneck with more and more data available nowadays. In this paper, we propose a general methodology for the complexity reduction, called cluster Kriging, where the whole data set is partitioned into smaller clusters and multiple Kriging models are built on top of them. In addition, four Kriging approximation algorithms are proposed as candidate algorithms within the new framework. Each of these algorithms can be applied to much larger data sets while maintaining the advantages and power of Kriging. The proposed algorithms are explained in detail and compared empirically against a broad set of existing state-of-the-art Kriging approximation methods on a well-defined testing framework. According to the empirical study, the proposed algorithms consistently outperform the existing algorithms. Moreover, some practical suggestions are provided for using the proposed algorithms.


title: A Multi-Theoretical Literature Review on Information Security Investments using the Resource-Based View and the Organizational Learning Theory

abstract: The protection of information technology (IT) has become and is predicted to remain a key economic challenge for organizations. While research on IT security investment is fast growing, it lacks a theoretical basis for structuring research, explaining economic-technological phenomena and guide future research. We address this shortcoming by suggesting a new theoretical model emerging from a multi-theoretical perspective adopt-ing the Resource-Based View and the Organizational Learning Theory. The joint appli-cation of these theories allows to conceptualize in one theoretical model the organiza-tional learning effects that occur when the protection of organizational resources through IT security countermeasures develops over time. We use this model of IT security invest-ments to synthesize findings of a large body of literature and to derive research gaps. We also discuss managerial implications of (closing) these gaps by providing practical ex-amples.


title: Pure and declarative syntax definition: paradise lost and regained

abstract: Syntax definitions are pervasive in modern software systems, and serve as the basis for language processing tools like parsers and compilers. Mainstream parser generators pose restrictions on syntax definitions that follow from their implementation algorithm. They hamper evolution, maintainability, and compositionality of syntax definitions. The pureness and declarativity of syntax definitions is lost. We analyze how these problems arise for different aspects of syntax definitions, discuss their consequences for language engineers, and show how the pure and declarative nature of syntax definitions can be regained.


title: Improvement of the performances of line start permanent magnet synchronous motor with flux barrier in the rotor

abstract: Design of permanent magnet rotor for LSPMSM with inherent starting torque is the prime consideration for the designer. Presence of short circuiting flux for any kind of the PMSM reduces the starting as well as the steady state performances by reducing the air gap flux density. A new Permanent Magnet rotor with high reluctance flux barrier has been proposed in this paper. The model has been developed and simulated using Maxwell 2D. Two dimensional Finite Element analysis was done for magnetostatics and the transient analysis. The experimental prototype has developed, tested and the results thus obtained have been validated with the simulated results.


title: The structural virality of online diffusion

abstract: Viral products and ideas are intuitively understood to grow through a person-to-person diffusion process analogous to the spread of an infectious disease; however, until recently it has been prohibitively difficult to directly observe purportedly viral events, and thus to rigorously quantify or characterize their structural properties. Here we propose a formal measure of what we label “structural virality” that interpolates between two conceptual extremes: content that gains its popularity through a single, large broadcast and that which grows through multiple generations with any one individual directly responsible for only a fraction of the total adoption. We use this notion of structural virality to analyze a unique data set of a billion diffusion events on Twitter, including the propagation of news stories, videos, images, and petitions. We find that across all domains and all sizes of events, online diffusion is characterized by surprising structural diversity; that is, popular events regularly grow via both broadcast and viral mechanisms, as well as essentially all conceivable combinations of the two. Nevertheless, we find that structural virality is typically low, and remains so independent of size, suggesting that popularity is largely driven by the size of the largest broadcast. Finally, we attempt to replicate these findings with a model of contagion characterized by a low infection rate spreading on a scale-free network. We find that although several of our empirical findings are consistent with such a model, it fails to replicate the observed diversity of structural virality, thereby suggesting new directions for future modeling efforts. This paper was accepted by Lorin Hitt, information systems.


title: Total Data Quality Management and Total Information Quality Management Applied to Costumer Relationship Management

abstract: Data quality (DQ) is an important issue for modern organizations, mainly for decision-making based on information, using solutions such as CRM, Business Analytics, and Big Data. In order to obtain quality data, it is necessary to implement methods, processes, and specific techniques that handle information as a product, with well established, controlled, and managed production processes. The literature provides several types of quality data management methodologies that treat structured data, and few treating semi- and non-structured data. Choosing the methodology to be adopted is one the major issues faced by organizations, when challenged to treat the data quality in a systematic manner. This paper makes a comparative analysis between TDQM -- Total Data Quality Management and TIQM -- Total Information Quality Management approaches, focusing on data quality problems in the context of a CRM -- Costumer Relationship Management application. Such analysis identifies the strengths and weaknesses of each methodology and suggests the most suitable for the CRM scenario.


title: Comparing Phrase-based and Syntax-based Paraphrase Generation

abstract: Paraphrase generation can be regarded as machine translation where source and target language are the same. We use the Moses statistical machine translation toolkit for paraphrasing, comparing phrase-based to syntax-based approaches. Data is derived from a recently released, large scale (2.1M tokens) paraphrase corpus for Dutch. Preliminary results indicate that the phrase-based approach performs better in terms of NIST scores and produces paraphrases at a greater distance from the source.


title: All-dielectric metamaterials

abstract: This Review discusses recent research efforts to confine and guide light at the nanoscale without using metals.


title: Adhesion to tooth structure: A critical review of "micro" bond strength test methods

abstract: The objective of this paper is to critically review the literature regarding the mechanics, geometry, load application and other testing parameters of “micro” shear and tensile adhesion tests, and to outline their advantages and limitations. The testing of multiple specimens from a single tooth conserves teeth and allows research designs not possible using conventional ‘macro’ methods. Specimen fabrication, gripping and load application methods, in addition to material properties of the various components comprising the resin–tooth adhesive bond, will influence the stress distribution and consequently, the nominal bond strength and failure mode. These issues must be understood; as should the limitations inherent to strength-based testing of a complicated adhesive bond joining dissimilar substrates, for proper test selection, conduct and interpretation. Finite element analysis and comprehensive reporting of test conduct and results will further our efforts towards a standardization of test procedures. For the foreseeable future, both “micro” and “macro” bond strength tests will, as well as various morphological and spectroscopic investigative techniques, continue to be important tools for improving resin–tooth adhesion to increase the service life of dental resin-based composite restorations.


title: Digital Array Radar panel development

abstract: The Army Digital Array Radar (DAR) project's goal is to demonstrate how wide-bandgap semiconductor technology, highly-integrated transceivers, and the ever-increasing capabilities of commercial digital components can be leveraged to provide new capabilities and enhanced performance in future low-cost phased array systems. A 16-element, S-band subarray has been developed with panel-integrated, plastic-packaged gallium-nitride (GaN) amplifiers, multi-channel transceiver ICs, and digitization at the element level. In addition to full digital beamforming on transmit and receive, the DAR subarray has demonstrated efficient RF power generation exceeding 25 Watts per element, in-situ, element-level calibration monitoring and self-correction capabilities, simultaneous transmit and receive operation through subarray partitioning for an indoor target tracker, and more. An overview is given of these results and capabilities.


title: An application of reinforcement learning algorithms to industrial multi-robot stations for cooperative handling operation

abstract: This paper presents a novel approach to operate industrial robots as used for manufacturing lines within a cooperative robot station. The proposed framework consists of the application of especially to the cooperative robot handling problem adjusted Reinforcement Learning (RL) algorithms. Such RL-algorithms deal with sequential decision making processes in a trial-and-error learning interaction with the environment, to finally gain an optimal team-working behavior among the robots. In particular application results to a real team-working robot station underline the effectiveness of the novel RL approach.


title: Unsupervised Extraction of Video Highlights via Robust Recurrent Auto-Encoders

abstract: With the growing popularity of short-form video sharing platforms such as Instagram and Vine, there has been an increasing need for techniques that automatically extract highlights from video. Whereas prior works have approached this problem with heuristic rules or supervised learning, we present an unsupervised learning approach that takes advantage of the abundance of user-edited videos on social media websites such as YouTube. Based on the idea that the most significant sub-events within a video class are commonly present among edited videos while less interesting ones appear less frequently, we identify the significant sub-events via a robust recurrent auto-encoder trained on a collection of user-edited videos queried for each particular class of interest. The auto-encoder is trained using a proposed shrinking exponential loss function that makes it robust to noise in the web-crawled training data, and is configured with bidirectional long short term memory (LSTM) [5] cells to better model the temporal structure of highlight segments. Different from supervised techniques, our method can infer highlights using only a set of downloaded edited videos, without also needing their pre-edited counterparts which are rarely available online. Extensive experiments indicate the promise of our proposed solution in this challenging unsupervised setting.


title: An approach to software architecture evaluation with the 4+1 view model of architecture

abstract: Architectural evaluation to determine a software architecture's fitness with respect to its desired quality attributes is one of the most important issues in architecture-based software development. However, existing techniques have too many limitations for widespread application, such as the inappropriate representation of architecture and ambiguities in the evaluation process. Therefore, this paper presents an approach to evaluate software architecture with a 4+1 view model in UML. Our approach is divided into three main areas of activity: the work involved in preparation, execution, and completion of the evaluation. By performing these activities, architecture evaluation can be explicitly described and its result can be systematically organized based on the 4+1 view model. In addition, we show the practical applicability and features of our approach via an ATM example.


title: AppScope: application energy metering framework for android smartphones using kernel activity monitoring

abstract: Understanding the energy consumption of a smartphone application is a key area of interest for end users, as well as application and system software developers. Previous work has only been able to provide limited information concerning the energy consumption of individual applications because of limited access to underlying hardware and system software. The energy consumption of a smartphone application is, therefore, often estimated with low accuracy and granularity. In this paper, we propose AppScope, an Android-based energy metering system. This system monitors application's hardware usage at the kernel level and accurately estimates energy consumption. AppScope is implemented as a kernel module and uses an event-driven monitoring method that generates low overhead and provides high accuracy. The evaluation results indicate that AppScope accurately estimates the energy consumption of Android applications expending approximately 35mW and 2.1% in power consumption and CPU utilization overhead, respectively.


title: A Tale of One City: Using Cellular Network Data for Urban Planning

abstract: Cellular data from call detail records can help urban planners better understand city dynamics. The authors use CDR data to analyze people flow in and out of a suburban city near New York City.


title: Exploiting hierarchical context on a large database of object categories

abstract: There has been a growing interest in exploiting contextual information in addition to local features to detect and localize multiple object categories in an image. Context models can efficiently rule out some unlikely combinations or locations of objects and guide detectors to produce a semantically coherent interpretation of a scene. However, the performance benefit from using context models has been limited because most of these methods were tested on datasets with only a few object categories, in which most images contain only one or two object categories. In this paper, we introduce a new dataset with images that contain many instances of different object categories and propose an efficient model that captures the contextual information among more than a hundred of object categories. We show that our context model can be applied to scene understanding tasks that local detectors alone cannot solve.


title: Skin diseases diagnosis using artificial neural networks

abstract: Medical informatics is an interdisciplinary area (combining more academic fields), which benefits of technology's progress that reflects on any domain. This paper is an example about how artificial neural networks prove their capacities in medical field. They are successfully used in decision-making, having a strong impact on physicians, who can benefit of a faster diagnosis process for some diseases with multiple and confusing symptoms.


title: A 56 Gb/s PAM4 receiver with low-overhead threshold and edge-based DFE FIR and IIR-tap adaptation in 65nm CMOS

abstract: A PAM4 quarter-rate receiver employs a singlestage CTLE and a DFE with 1 FIR and 1 IIR-taps to efficiently compensate for channel loss. In addition to the per-slice main 3 data samplers, an error sampler is utilized for background threshold control and an edge-based sampler performs both PLL-based CDR phase detection and generates information for background DFE tap adaptation. Fabricated in GP 65nm CMOS, the 56Gb/s receiver achieves 4.63mW/Gb/s and compensates for up to 20.8dB loss when operated with a 2-tap FFE transmitter.


title: Constrained Consensus and Optimization in Multi-Agent Networks∗†

abstract: We present distributed algorithms that can be used by multiple agents to align their estimates with a particular value over a network with time-varying connectivity. Our framework is general in that this value can represent a consensus value among multiple agents or an optimal solution of an optimization problem, where the global objective function is a combination of local agent objective functions. Our main focus is on constrained problems where the estimates of each agent are restricted to lie in different convex sets. To highlight the effects of constraints, we first consider a constrained consensus problem and present a distributed "projected consensus algorithm" in which agents combine their local averaging operation with projection on their individual constraint sets. This algorithm can be viewed as a version of an alternating projection method with weights that are varying over time and across agents. We establish convergence and convergence rate results for the projected consensus algorithm. We next study a constrained optimization problem for optimizing the sum of local objective functions of the agents subject to the intersection of their local constraint sets. We present a distributed "projected subgradient algorithm" which involves each agent performing a local averaging operation, taking a subgradient step to minimize its own objective function, and projecting on its constraint set. We show that, with an appropriately selected stepsize rule, the agent estimates generated by this algorithm converge to the same optimal solution for the cases when the weights are constant and equal, and when the weights are time-varying but all agents have the same constraint set.


title: A 60-GHz CMOS Transmit/Receive Switch

abstract: A single-pole double-throw (SPDT) transmit/receive switch (T/R switch) operating in the 57-66 GHz band is implemented on a 130-nm CMOS process. The switch exhibits an insertion loss from 4.5 dB to 5.8 dB, an isolation from 24.1 dB to 26 dB, a return loss at antenna port from -9.2 dB to -10.5 dB, and a return loss at Tx/Rx port below -15 dB for the frequency band. With a control voltage of 1.2 V the IP1dB of the switch is 4.1 dBm. The switch features fast switching speed with rise-time and fall-time of 400 ps and 360 ps, respectively. This is the first CMOS T/R switch designed for very short range radio in 60-GHz band.


title: An alternative approach to the limits of predictability in human mobility

abstract: Next place prediction algorithms are invaluable tools, capable of increasing the efficiency of a wide variety of tasks, ranging from reducing the spreading of diseases to better resource management in areas such as urban planning. In this work we estimate upper and lower limits on the predictability of human mobility to help assess the performance of competing algorithms. We do this using GPS traces from 604 individuals participating in a multi year long experiment, The Copenhagen Networks study. Earlier works, focusing on the prediction of a participant’s whereabouts in the next time bin, have found very high upper limits (\({>}90\%\)). We show that these upper limits are highly dependent on the choice of a spatiotemporal scales and mostly reflect stationarity, i.e. the fact that people tend to not move during small changes in time. This leads us to propose an alternative approach, which aims to predict the next location, rather than the location in the next bin. Our approach is independent of the temporal scale and introduces a natural length scale. By removing the effects of stationarity we show that the predictability of the next location is significantly lower (71%) than the predictability of the location in the next bin.


title: Automatic removal of eye movement and blink artifacts from EEG data using blind component separation

abstract: Signals from eye movements and blinks can be orders of magnitude larger than brain-generated electrical potentials and are one of the main sources of artifacts in electroencephalographic (EEG) data. Rejecting contaminated trials causes substantial data loss, and restricting eye movements/blinks limits the experimental designs possible and may impact the cognitive processes under investigation. This article presents a method based on blind source separation (BSS) for automatic removal of electroocular artifacts from EEG data. BBS is a signal-processing methodology that includes independent component analysis (ICA). In contrast to previously explored ICA-based methods for artifact removal, this method is automated. Moreover, the BSS algorithm described herein can isolate correlated electroocular components with a high degree of accuracy. Although the focus is on eliminating ocular artifacts in EEG data, the approach can be extended to other sources of EEG contamination such as cardiac signals, environmental noise, and electrode drift, and adapted for use with magnetoencephalographic (MEG) data, a magnetic correlate of EEG.


title: The Effects of a Robot Game Environment on Computer Programming Education for Elementary School Students

abstract: In the past, computer programming was perceived as a task only carried out by computer scientists; in the 21st century, however, computer programming is viewed as a critical and necessary skill that everyone should learn. In order to improve teaching of problem-solving abilities in a computing environment, extensive research is being done on teaching–learning methods, types of teaching software, the educational environment, and related tools. This paper, based on diverse experimental results, proposes an environment where elementary students can easily learn and practice computer programming. The proposed robot game environment used a tangible programming tool with which students can easily create robot programs, without learning syntax, and then validate their programming results; it can also provide various game activities to incite students’ interest. Observation of elementary school students placed in the robot game environment confirmed the tool’s usability and entertainment aspects, and students’ attitudes toward programming and their understanding of programming concepts improved.


title: Hybrid indoor pedestrian navigation combining an INS and a spatial non-uniform UWB-network

abstract: The importance of location based services (LBS) is steadily increasing with progressive automation and interconnectedness of systems and processes. However, a comprehensive localization and navigation solution is still part of research. Especially for dynamic and harsh indoor environments, accurate and affordable localization and navigation remains a challenge. In this paper, we present a hybrid localization system providing position information and navigation aid to pedestrian in dynamic indoor environments, like construction sites, by combining an IMU and a spatial non-uniform UWB-network. The key contribution of this paper is a hybrid localization concept and experimental results, demonstrating in an application near scenario the enhancements introduced by the combination of an inertial navigation system (INS) and a spatial non-uniform UWB-network.


title: LSP-Constrained Supply Chains: A Discrete Event Simulation Model

abstract: In this paper, we present a logistics service provider (LSP) constrained supply chain problem, particularly; we propose a novel supply chain model that consists of three layers of non-cooperative manufacturers, distribution centers and retailers. Products flow from the manufacturers across different warehouses to retailers via LSP. Inventories at warehouses follow smooth and continuous replenishment policy, i.e. , perpetual review. The supply chain is represented as an optimization model that maximizes the revenue of manufacturers meets the retailers’ demand and at the same time identifies the necessary warehouses, particularly for supply chains that are affected by leadtime (LT) variation such as fast response industry and short life products. The model solution is adaptive; it determines the best manufacturing rates and identifies the logistic bottlenecks in dynamic supply chain networks. Numerical solutions along with simulation experiments of different supply chain topologies are presented. The simulation results demonstrate the model capability to maximize the revenues by tuning the manufacturing rates and monitoring the workinprocess, products in transit as well as products in inventories


title: Replication and Generalization of PRECISE

abstract: This report describes an initial replication study of the PRECISE system and develops a clearer, more formal description of the approach. Based on our evaluation, we conclude that the PRECISE results do not fully replicate. However the formalization developed here suggests a road map to further enhance and extend the approach pioneered by PRECISE. ::: After a long, productive discussion with Ana-Maria Popescu (one of the authors of PRECISE) we got more clarity on the PRECISE approach and how the lexicon was authored for the GEO evaluation. Based on this we built a more direct implementation over a repaired formalism. Although our new evaluation is not yet complete, it is clear that the system is performing much better now. We will continue developing our ideas and implementation and generate a future report/publication that more accurately evaluates PRECISE like approaches.


title: Newsworthiness and Network Gatekeeping on Twitter: The Role of Social Deviance

abstract: Publishers of news information are keen to amplify the reach of their content by making it as re-sharable as possible on social media. In this work we study the relationship between the concept of social deviance and the re-sharing of news headlines by network gatekeepers on Twitter. Do network gatekeepers have the same predilection for selecting socially deviant news items as professionals? Through a study of 8,000 news items across 8 major news outlets in the U.S. we predominately find that network gatekeepers re-share news items more often when they reference socially deviant events. At the same time we find and discuss exceptions for two outlets, suggesting a more complex picture where newsworthiness for networked gatekeepers may be moderated by other effects such as topicality or varying motivations and relationships with their audience.


title: Formalizing Classes of Information Fusion Systems

abstract: Abstract This paper provides an outline of a formalization of classes of information fusion systems in terms of category theory and formal languages. The formalization captures both the inputs/outputs of a fusion system and the fusion processing algorithms. The paper also introduces a notion of subclass, which is used to compare classes of fusion systems, whether they are different or one is a special case of another. Two examples of classes of fusion systems formalized in the paper are data fusion and decision fusion; decision fusion is shown to be a subclass of data fusion. A number of other classes of fusion systems are defined. The formalization is extended by adding the notion of measure of effectiveness, which is then used to prove that one of the classes (so called overlapping system) is at least as efficient as a single-source system. And finally it is shown how data association can be formalized in this framework. While at first the formalization could be used by information fusion scientists to formally define various types of fusion systems and then to prove theorems about properties of such systems, it is expected that it should lead to the development of tools that could be used by software engineers to formally derive designs of fusion systems.


title: Ransomware: taking businesses hostage

abstract: Cybercrime has its fashions. As technologies evolve and defences improve, so hackers and cyber-criminals modify their methods of attack. We're currently seeing a burgeoning in the use of ransomware, the digital form of blackmail in which your computer is effectively taken hostage. And both the nature of the chief targets and the ways in which they are being attacked are changing quickly as criminals spot new opportunities for extorting money. Europol recently declared ransomware to be the biggest cyber-threat facing European businesses and citizens. Both the nature of the chief targets and the ways in which they are being attacked are changing quickly as criminals spot new opportunities for extorting money. A large proportion of organisations have been affected at some time, with cyber-criminals apparently turning their attentions to those that are most vulnerable, such as hospitals. The ransomware itself is evolving too, and while some of it is poorly executed, the most advanced strains show great sophistication. Steve Mansfield-Devine explores the nature of the threat and how businesses should respond.


title: Applying test-driven code search to the reuse of auxiliary functionality

abstract: Software developers spend considerable effort implementing auxiliary functionality used by the main features of a system (e.g. compressing/decompressing files, encryption/decription of data, scaling/rotating images). With the increasing amount of open source code available on the Internet, time and effort can be saved by reusing these utilities through informal practices of code search and reuse. However, when this type of reuse is performed in an ad hoc manner, it can be tedious and error-prone: code results have to be manually inspected and extracted into the workspace. In this paper we introduce the use of test cases as an interface for automating code search and reuse and evaluate its applicability and performance in the reuse of auxiliary functionality. We call our approach Test-Driven Code Search (TDCS). Test cases serve two purposes: (1) they define the behavior of the desired functionality to be searched; and (2) they test the matching results for suitability in the local context. We present CodeGenie, an Eclipse plugin that performs TDCS using a code search engine called Sourcerer. Our evaluation presents evidence of the applicability and good performance of TDCS in the reuse of auxiliary functionality.


title: Experimental Evaluation of Autonomous Driving Based on Visual Memory and Image-Based Visual Servoing

abstract: In this paper, the performance of a topological-metric visual-path-following framework is investigated in different environments. The framework relies on a monocular camera as the only sensing modality. The path is represented as a series of reference images such that each neighboring pair contains a number of common landmarks. Local 3-D geometries are reconstructed between the neighboring reference images to achieve fast feature prediction. This condition allows recovery from tracking failures. During navigation, the robot is controlled using image-based visual servoing. The focus of this paper is on the results from a number of experiments that were conducted in different environments, lighting conditions, and seasons. The experiments with a robot car show that the framework is robust to moving objects and moderate illumination changes. It is also shown that the system is capable of online path learning.


title: Music Therapy Modulates Fronto-Temporal Activity in Rest-EEG in Depressed Clients

abstract: Fronto-temporal areas process shared elements of speech and music. Improvisational psychodynamic music therapy (MT) utilizes verbal and musical reflection on emotions and images arising from clinical improvisation. Music listening is shifting frontal alpha asymmetries (FAA) in depression, and increases frontal midline theta (FMT). In a two-armed randomized controlled trial (RCT) with 79 depressed clients (with comorbid anxiety), we compared standard care (SC) versus MT added to SC at intake and after 3 months. We found that MT significantly reduced depression and anxiety symptoms. The purpose of this study is to test whether or not MT has an impact on anterior fronto-temporal resting state alpha and theta oscillations. Correlations between anterior EEG, Montgomery–Asberg Depression Rating Scale (MADRS) and the Hospital Anxiety and Depression Scale—Anxiety Subscale (HADS-A), power spectral analysis (topography, means, asymmetry) and normative EEG database comparisons were explored. After 3 month of MT, lasting changes in resting EEG were observed, i.e., significant absolute power increases at left fronto-temporal alpha, but most distinct for theta (also at left fronto-central and right temporoparietal leads). MT differed to SC at F7–F8 (z scored FAA, p < .03) and T3–T4 (theta, p < .005) asymmetry scores, pointing towards decreased relative left-sided brain activity after MT; pre/post increased FMT and decreased HADS-A scores (r = .42, p < .05) indicate reduced anxiety after MT. Verbal reflection and improvising on emotions in MT may induce neural reorganization in fronto-temporal areas. Alpha and theta changes in fronto-temporal and temporoparietal areas indicate MT action and treatment effects on cortical activity in depression, suggesting an impact of MT on anxiety reduction.


title: Deep Speaker: an End-to-End Neural Speaker Embedding System

abstract: We present Deep Speaker, a neural speaker embedding system that maps utterances to a hypersphere where speaker similarity is measured by cosine similarity. The embeddings generated by Deep Speaker can be used for many tasks, including speaker identification, verification, and clustering. We experiment with ResCNN and GRU architectures to extract the acoustic features, then mean pool to produce utterance-level speaker embeddings, and train using triplet loss based on cosine similarity. Experiments on three distinct datasets suggest that Deep Speaker outperforms a DNN-based i-vector baseline. For example, Deep Speaker reduces the verification equal error rate by 50% (relatively) and improves the identification accuracy by 60% (relatively) on a text-independent dataset. We also present results that suggest adapting from a model trained with Mandarin can improve accuracy for English speaker recognition.


title: Parasitic Inductance and Capacitance-Assisted Active Gate Driving Technique to Minimize Switching Loss of SiC MOSFET

abstract: High di/dt and dv/dt of SiC MOSFET cause a considerable amount of overshoot in device voltage and current during switching transients in the presence of inverter layout parasitic inductance and load parasitic capacitance. The excessive overshoots in device voltage and current cause failure of the device. Moreover, these uncontrolled overshoots increase the switching loss in the inverter. It is difficult to reduce parasitic inductance beyond a certain point. This paper proposes an active gate driving technique, which allows inverter to operate with moderate amount of layout parasitic inductance and load parasitic capacitance. The proposed technique dramatically reduces switching loss of the SiC MOSFET with the help of existing parasitic elements. The proposed switching loss reduction technique is termed as quasi zero switching . The developed active gate driver has been tested in a double pulse test setup and a 10 kW two-level voltage source inverter driving an induction motor.


title: Helical actuation on a soft inflated robot body

abstract: Continuum and soft robots can leverage routed actuation schemes to take on useful shapes with few actuated degrees of freedom. The addition of vine-like growth to soft continuum robots opens up possibilities for creating deployable structures from compact packages and allowing manipulation and grasping of objects in cluttered or difficult-to-navigate environments. Helical shapes, with constant curvature and torsion, provide a starting point for the shapes and actuation strategies required for such applications. Building on the geometric and static solutions for continuum robot kinematics given constant curvature assumptions, we develop a static model of helical actuation and present the implementation and validation of this model. We also discuss the forces applied by the soft robot when wrapped around an object that deforms the static shape, allowing a quantification of grasping capabilities.


title: Authorship classification: a syntactic tree mining approach

abstract: In the past, there have been dozens of studies on automatic authorship classification, and many of these studies concluded that the writing style is one of the best indicators of original authorship. From among the hundreds of features which were developed, syntactic features were best able to reflect an author's writing style. However, due to the high computational complexity of extracting and computing syntactic features, only simple variations of basic syntactic features of function words and part-of-speech tags were considered. In this paper, we propose a novel approach to mining discriminative k-embedded-edge subtree patterns from a given set of syntactic trees that reduces the computational burden of using complex syntactic structures as a feature set. This method is shown to increase the classification accuracy. We also design a new kernel based on these features. Comprehensive experiments on real datasets of news articles and movie reviews demonstrate that our approach is reliable and more accurate than previous studies.


title: Enhancement of Latent Fingerprint Images with Segmentation Perspective

abstract: Latent fingerprints are of decisive influence whenit comes to identifying suspects, as these are usuallyencountered at crime scenes. Also, these serve as acompelling evidence in a court of law. Some of theformidable challenges with latent fingerprints are deficientridge information and poor ridge clarity due to backgroundnoise and non-linear distortions. An effective fingerprintenhancement scheme is suggested to meliorate the clarityand continuity of ridges and valleys in a latent fingerprintimage. The proposed method deals with both local (minutiaor singular points, ridge termination, bifurcation, brokenridges, short ridges, core and delta) and global features(ridge orientation and ridge frequency), while retaining thetrue ridge-valley structures and removing noise at the sametime. The proposed design establishes scalability, accessibility and flexibility.


title: Computer science education in Japan

abstract: Firsthand observations trace the current state, future potential, and obstacles ahead for Japanese academia.


title: Automatic localization of optic disc based on deep learning in fundus images

abstract: The optic disc (OD) contains lots of important information in retinal image analysis. Detecting the region of OD correctly is important for subsequent analysis of retinal images. It is challenging to locate the OD precisely due to the various reasons including low image quality or lesions around the OD. In this paper, we propose a cascading localization method based on deep learning with feedback to improve the accuracy of OD localization. The method employs the model of saliency-based visual attention to find the most salient region and implements deep convolution neural network (CNN) to determine whether it contains OD. If a region is classified as non-OD region, we find the next salient region and input it into the CNN to classify. The algorithm ends when the CNN finds a region with OD. The proposed method is evaluated on the ORIGA and MESSIDOR datasets. Our experimental results show that the proposed method achieved significant improvement in detection of OD compared with previous methods.


title: Optimal placement of Phasor Measurement Units using Particle swarm Optimization

abstract: This paper solves the Phasor Measurement Unit (PMU) Placement problem by Particle swarm Optimization (PSO). The PSO algorithm is implemented on three bus systems namely the 7, 14, 57 IEEE standard Bus systems. In this paper it has been proved that the placing of Phasor measurement units only at buses with the highest number of incident branches surely doesn't yield the optimal placement of the PMUs .This encouraged the idea of random placement of PMUs in the system and selection of the specific arrangement of PMUs which accomplished the task of complete observability of the system with minimum number of PMUs thus optimizing the PMU placement problem. The performance is compared with that of Adaptive GA, CLONALG and Adaptive CLONALG. The proposed method was found better.


title: Leveraging Text and Knowledge Bases for Triple Scoring: An Ensemble Approach - The BOKCHOY Triple Scorer at WSDM Cup 2017

abstract: We present our winning solution for the WSDM Cup 2017 triple scoring task. We devise an ensemble of four base scorers, so as to leverage the power of both text and knowledge bases for that task. Then we further refine the outputs of the ensemble by trigger word detection, achieving even better predictive accuracy. The code is available at this https URL


title: Design and Comparison of Interior Permanent Magnet Motor Topologies for Traction Applications

abstract: Traction motors play a critical role in electrified vehicles, including electric, hybrid electric, and plug-in hybrid electric vehicles. With high efficiency and power density, interior permanent magnet (IPM) synchronous machines have been employed in many commercialized electrified powertrains. In this paper, three different IPM rotor design configurations, which have been used in electrified powertrains from Toyota, Nissan, and General Motors, are comparatively investigated. Each topology is redesigned and improved to meet new design requirements based on the same constraints. The designed motors are then compared and comprehensively evaluated for motor performance, torque segregation, demagnetization, mechanical stress, and radial forces. The results suggest that the single V-shaped configuration achieves the best overall performance and is thus recommended as the best candidate.


title: The Teacher as Strategic Scientist: A Solution to Our Educational Crisis?

abstract: The solution to the educational dilemma is tied necessarily to the ability of teachers to insure that their students achieve learning objectives that are germane to the survival of the culture and the species. The growing body of scientifically based practices necessary to eliminate the existing educational crisis is briefly outlined. It is argued that these practices can be implemented by teachers who function as strategic scientists. The science used by such teachers include research findings and teaching models incorporating that research (Precision Teaching, Direct Instruction, Programmed Instruction, Personalized System of Instruction). In order to train, support, and motivate the pervasive and sustained application of effective pedagogy by teachers who function as strategic scientists, behavior analysis must be applied to all components of schooling-students, parents, teachers, and supervisors. One model for doing so has developed over the last decade. Research studies and applications in six schools for handicapped children in this country and abroad suggest that this comprehensive application of behavior analysis to schooling (CABAS) is effective, efficient, and viable.


title: In-strip matching and reconstruction of line segments from UHR aerial image triplets

abstract: In this study, we propose a new line matching and reconstruction methodology for aerial image triplets that are acquired within a single strip. The newly developed stereo reconstruction approach gives us better line predictions in the third image which in turn helps to improve the performance of the matching. The redundancy information generated in each stereo match gives us ability to reduce the number of false matches while preserving high levels of matching completeness. The approach is tested over four test patches and produced highly promising line matching and reconstruction results.


title: CLASSIFICATION OF DISEASED PLANT LEAVES USING NEURAL NETWORK ALGORITHMS

abstract: Agriculture is the mother of all cultures. It played a vital role in the development of human civilization. But plant leaf diseases can damage the crops there may be economic losses in crops. Without knowing about the diseases affected in the plant, the farmers are using excessive pesticides for the plant disease treatment. To overcome this, the detected spot diseases in leaves are classified based on the diseased leaf types using various neural network algorithms. By this approach one can detect the diseased leaf variety and thus can take necessary steps in time to minimize the loss of production. The proposed methodology uses to classify the diseased plant leaves using Feed Forward Neural Network (FFNN), Learning Vector Quantization (LVQ) and Radial Basis Function Networks (RBF) by processing the set of shape and texture features from the affected leaf image. The simulation results show the effectiveness of the proposed scheme. With the help of this work, a machine learning based system can be formed for the improvement of the crop quality in the Indian Economy.


title: High-Density and Near-Linear Synaptic Device Based on a Reconfigurable Gated Schottky Diode

abstract: A reconfigurable gated Schottky diode is proposed as new high-density and low-power synaptic device that has near-linear changes in conductance. The device has a reverse current of less than 12 nA/ $\mu \text{m}$ and an effective device area of 6F2. Since the Al/poly-Si Schottky junction is located on the bottom gate, which has a SiO2/Si3N4/SiO2 charge trap layer, the effective Schottky barrier height is modulated by the bottom gate bias or by the amount of charge trapped in the Si3N4 layer. The Schottky reverse current has an exponential relationship with the effective Schottky barrier height associated with the amount of stored charge, and the amount of stored charge is logarithmically proportional to the number of potentiation pulses. Because the exponential and logarithmic relationships cancel each other out, a near-linear conductance response to the number of potentiation pulses is obtained from the proposed device.


title: Deep Learning on Graphs: A Survey

abstract: Deep learning has been shown to be successful in a number of domains, ranging from acoustics, images, to natural language processing. However, applying deep learning to the ubiquitous graph data is non-trivial because of the unique characteristics of graphs. Recently, substantial research efforts have been devoted to applying deep learning methods to graphs, resulting in beneficial advances in graph analysis techniques. In this survey, we comprehensively review the different types of deep learning methods on graphs. We divide the existing methods into five categories based on their model architectures and training strategies: graph recurrent neural networks, graph convolutional networks, graph autoencoders, graph reinforcement learning, and graph adversarial methods. We then provide a comprehensive overview of these methods in a systematic manner mainly by following their development history. We also analyze the differences and compositions of different methods. Finally, we briefly outline the applications in which they have been used and discuss potential future research directions.


title: Centrifugal fan design for permanent magnet synchronous motor in a traction application

abstract: The requirements of high torque density and high efficiency, which are particularly pronounced in electric traction applications, often result in substantial thermal loading of electric machines for driving trams, electric multiple units (EMU) or electric cars. Permanent magnet synchronous machines are suitable candidates for traction applications due to their inherently high torque density and high efficiency. At the same time they are sensitive to temperature rise, especially in permanent magnets, highlighting the need for implementation of efficient cooling system. The performance of the cooling system and its ability to remove heat directly affect the attainable torque and efficiency of the electric machine. In this paper, the selection and sizing of the cooling system for an interior permanent magnet motor designed to drive a low-floor tram is presented. The procedure for selecting the basic dimensions of the centrifugal fan according to the analytical formulas in combination with computational fluid dynamics (CFD) analysis are explained. In addition to the geometry of the centrifugal fan itself, the geometry of the passive system components (e.g. air flow router) which have a significant impact on the performance of the cooling system, are also considered. The results of computer aided CFD analysis, which is taken as a benchmark of system performance in the design stage of the cooling system, have been confirmed with measurements on the machine prototype.


title: OPAESFH: Ontology-based personalized adaptive e-learning system using FPN and HMM

abstract: Nowadays, often it is found that the developed elearning systems for Web 2.0 are based on the one-size-fits-all content delivery approach whereas we require personalized, adaptive, upgraded e-learning content delivery approach for evolving Web 3.0. Therefore, we require studying few aspects example adaptive learning style, background knowledge, student ability and knowledge level, Learning Path Sequence (LPS), and personalization for improving e-learning system. In this paper, we have proposed an Ontology-based Personalized Adaptive E-learning system using FPN and HMM (OPAESFH). Ontologies are machine-readable and used to provide semantics to e-learning course content for sharing and reusing. The course content store in ontology given appropriate LPS using the FPN and HMM algorithm is to tackle the problem of procedural learning by making it adaptive. OPAESFH use Petri Net for modeling e-learning course content and HMM algorithm for adaptability of course content according to the learner's performance in pre-test/post-test. This LPS adjusts itself according to the learner's performance in pre-test/post-test for the e-learning course content. The proposed e-learning system demonstrates that the personalized and adaptive LPS can enhance a learning experience. We evaluate OPAESFH with respect to an experimental and control group and the results that are obtained from the experimental group are better than the results from the control group.


title: The foundations and assumptions of technology-enhanced student-centered learning environments

abstract: Direct instruction approaches, as well as the design processes that support them, have been criticized for failing to reflect contemporary research and theory in teaching, learning, and technology. Learning systems are needed that encourage divergent reasoning, problem solving, and critical thinking. Student-centered learning environments have been touted as a means to support such processes. With the emergence of technology, many barriers to implementing innovative alternatives may be overcome. The purposes of this paper are to review and critically analyze research and theory related to technology-enhanced student-centered learning environments and to identify their foundations and assumptions.


title: An optimal checkpoint/restart model for a large scale high performance computing system

abstract: The increase in the physical size of high performance computing (HPC) platform makes system reliability more challenging. In order to minimize the performance loss (rollback and checkpoint overheads) due to unexpected failures or unnecessary overhead of fault tolerant mechanisms, we present a reliability-aware method for an optimal checkpoint/restart strategy. Our scheme aims at addressing fault tolerance challenge, especially in a large-scale HPC system, by providing optimal checkpoint placement techniques that are derived from the actual system reliability. Unlike existing checkpoint models, which can only handle Poisson failure and a constant checkpoint interval, our model can deal with a varying checkpoint interval and with different failure distributions. In addition, the approach considers optimality for both checkpoint overhead and rollback time. Our validation results suggest a significant improvement over existing techniques.


title: MDMA enhances “mind reading” of positive emotions and impairs “mind reading” of negative emotions

abstract: Rationale ::: 3,4-Methylenedioxymethamphetamine (MDMA, ecstasy) increases sociability. The prosocial effects of MDMA may result from the release of the “social hormone” oxytocin and associated alterations in the processing of socioemotional stimuli.


Forecasting Word Model: Twitter-based Influenza Surveillance and Prediction