title: Examining desensitization using facial electromyography: Violent videogames, gender, and affective responding

abstract: The results of research investigating the effects of violent videogames have been inconsistent. Some evidence suggests that exposure to violent videogames decreases physiological arousal and results in desensitization to subsequent exposure to violent stimuli. Other research does not support these findings. This study examined physiological and affective desensitization to violent videogame play. Participants played a violent or non-violent videogame and then viewed aggressive, pleasant, and neutral images. Participants' negative affect in response to the images was assessed by examining activity in the brow muscle region (i.e., corrugator supercilii), heart rate (HR), self-reported affect, and self-reported arousal. Based on previous research regarding the General Aggression Model (GAM), we expected that participants who played the violent videogame would demonstrate desensitization as evidenced by less reactivity in the corrugator supercilii region and less HR reactivity in response to violent images than participants who played the nonviolent videogame. We also expected that participants who played the violent videogame would rate the aggressive images as less aversive than those who played the nonviolent game. Contrary to our expectations, the findings were not consistent with the hypotheses based on the GAM. Examined physiological and affective desensitization to one violent videogame.Participants played a (non)violent videogame then looked at aggressive images.Examined physiological and affective desensitization to one violent video game.Participants did not differ in self-reported affect or arousal.The violent, compared to the nonviolent, videogame did not produce desensitization.


title: Changes in Resting State Effective Connectivity in the Motor Network Following Rehabilitation of Upper Extremity Poststroke Paresis

abstract: Background: A promising paradigm in human neuroimaging is the study of slow (<0.1 Hz) spontaneous fl uctuations in the hemodynamic response measured by functional magnetic resonance imaging (fMRI). Spontaneous activity (i.e., resting state) refers to activity that cannot be attributed to specifi c inputs or outputs, that is, activity intrinsically generated by the brain. Method: This article presents pilot data examining neural connectivity in patients with poststroke hemiparesis before and after 3 weeks of upper extremity rehabilitation in the Accelerated Skill Acquisition Program (ASAP). Resting-state fMRI data acquired pre and post therapy were analyzed using an exploratory adaptation of structural equation modeling (SEM) to evaluate therapy-related changes in motor network effective connectivity. Results: Each ASAP patient showed behavioral improvement. ASAP patients also showed increased infl uence of the affected hemisphere premotor cortex (a-PM) upon the unaffected hemisphere premotor cortex (u-PM) following therapy. The infl uence of a-PM on affected hemisphere primary motor cortex (a-M1) also increased with therapy for 3 of 5 patients, including those with greatest behavioral improvement. Conclusions: Our fi ndings suggest that network analyses of resting-state fMRI constitute promising tools for functional characterization of functional brain disorders, for intergroup comparisons, and potentially for assessing effective


title: Evaluation of communication technologies for IEC 61850 based distribution automation system with distributed energy resources

abstract: This paper presents the study of different communication systems between IEC 61850 based distribution substation and distributed energy resources (DERs). Communication networks have been simulated for a typical distribution automation system (DAS) with DERs using OPNET software. The simulation study shows the performance of wired and wireless communication systems for different messages, such as GOOSE and measured (metered) values between DAS and DERs. A laboratory set-up has been implemented using commercial relay and communication devices for evaluating the performance of GOOSE messages, using wired and wireless physical medium. Finally, simulation and laboratory results are discussed in detail.


title: Text Categorization for Multi-label Documents and Many Categories

abstract: In this paper, we propose a new classification method that addresses classification in multiple categories of textual documents. We call it Matrix Regression (MR) due to its resemblance to regression in a high dimensional space. Experiences on a medical corpus of hospital records to be classified by ICD (International Classification of Diseases) code demonstrate the validity of the MR approach. We compared MR with three frequently used algorithms in text categorization that are k-Nearest Neighbors, Centroide and Support Vector Machine. The experimental results show that our method outperforms them in both precision and time of classification.


title: An improved ptychographical phase retrieval algorithm for diffractive imaging.

abstract: The ptychographical iterative engine (or PIE) is a recently developed phase retrieval algorithm that employs a series of diffraction patterns recorded as a known illumination function is translated to a set of overlapping positions relative to a target sample. The technique has been demonstrated successfully at optical and X-ray wavelengths and has been shown to be robust to detector noise and to converge considerably faster than support-based phase retrieval methods. In this paper, the PIE is extended so that the requirement for an accurate model of the illumination function is removed.


title: Integrating Containers into Workflows: A Case Study Using Makeflow, Work Queue, and Docker

abstract: Workflows are a widely used abstraction for representing large scientific applications and executing them on distributed systems such as clusters, clouds, and grids. However, workflow systems have been largely silent on the question of precisely what environment each task in the workflow is expected to run in. As a result, a workflow may run correctly in the environment in which it was designed, but when moved to another machine, is highly likely to fail due to differences in the operating system, installed applications, available data, and so forth. Lightweight container technology has recently arisen as a potential solution to this problem, by providing a well-defined execution environments at the operating system level. In this paper, we consider how to best integrate container technology into an existing workflow system, using Makeflow, Work Queue, and Docker as examples of current technology. A brief performance study of Docker shows very little overhead in CPU and I/O performance, but significant costs in creating and deleting containers. Taking this into account, we describe four different methods of connecting containers to different points of the infrastructure, and explain several methods of managing the container images that must be distributed to executing tasks. We explore the performance of a large bioinformatics workload on a Docker-enabled cluster, and observe the best configuration to be locally-managed containers that are shared between multiple tasks.


title: Artificial intelligence in GPS navigation systems

abstract: GPS navigation systems use stored map information for determining optimal route selection based on a shortest path algorithm. This technique is quite successful in getting you to where you want to go in a reasonable time and is fault tolerant in the sense that it can automatically reroute in case of error. One disadvantage of this approach is that it does not have any memory. It does not automatically remember the actual time it took you to get there nor does it learn from that experience and use the actual measurements to improve future route selection. A simple method for modifying a GPS navigational system to incorporate a simple learning paradigm using velocity profiles is described. In addition to learning, these velocity profiles can also be used to extract features from the environment which can then be used to further improve the accuracy of optimal route selection. It is assumed to be completely autonomous which means that it requires no user input or intervention. All of the required information is derived from recording GPS location, date and time.


title: Understanding Types of Cyberbullying in an Anonymous Messaging Application

abstract: The possibility of anonymity and lack of effective ways to identify inappropriate messages have resulted in a significant amount of online interaction data that attempt to harass, bully, or offend the recipient. In this work, we perform a preliminary linguistic study on messages exchanged using one such popular web/smartphone application---Sarahah, that allows friends to exchange messages anonymously. Since messages exchanged via Sarahah are private, we collect them when the recipient shares it on Twitter. We then perform an analysis of the different kinds of messages exchanged through this application. Our linguistic analysis reveals that a significant number of these messages (~20%) include inappropriate, hurtful, or profane language intended to embarrass, offend, or bully the recipient. Our analysis helps in understanding the different ways in which anonymous message exchange platforms are used and the different types of bullying present in such exchanges.


title: SNIF: a simple nude image finder

abstract: The lack of control of the content published is broadly regarded as a positive aspect of the Web, assuring freedom of speech to its users. On the other hand, there is also a lack of control of the content accessed by users when browsing Web pages. In some situations this lack of control may be undesired. For instance, parents may not desire their children to have access to offensive content available on the Web. In particular, accessing Web pages with nude images is among the most common problem of this sort. One way to tackle this problem is by using automated offensive image detection algorithms which can filter undesired images. Recent approaches on nude image detection use a combination of features based on color, texture, shape and other low level features in order to describe the image content. These features are then used by a classifier which is able to detect offensive images accordingly. In this paper we propose SNIF - simple nude image finder - which uses a color based feature only, extracted by an effective and efficient algorithm for image description, the border/interior pixel classification (BIC), combined with a machine learning technique, namely support vector machines (SVM). SNIF uses a simpler feature model when compared to previously proposed methods, which makes it a fast image classifier. The experiments carried out depict that the proposed method, despite its simplicity, is capable to identify up to 98% of nude images from the test set. This indicates that SNIF is as effective as previously proposed methods for detecting nude images.


title: What Happened to My Dog in That Network: Unraveling Top-down Generators in Convolutional Neural Networks

abstract: Top-down information plays a central role in human perception, but plays relatively little role in many current state-of-the-art deep networks, such as Convolutional Neural Networks (CNNs). This work seeks to explore a path by which top-down information can have a direct impact within current deep networks. We explore this path by learning and using "generators" corresponding to the network internal effects of three types of transformation (each a restriction of a general affine transformation): rotation, scaling, and translation. We demonstrate how these learned generators can be used to transfer top-down information to novel settings, as mediated by the "feature flows" that the transformations (and the associated generators) correspond to inside the network. Specifically, we explore three aspects: 1) using generators as part of a method for synthesizing transformed images --- given a previously unseen image, produce versions of that image corresponding to one or more specified transformations, 2) "zero-shot learning" --- when provided with a feature flow corresponding to the effect of a transformation of unknown amount, leverage learned generators as part of a method by which to perform an accurate categorization of the amount of transformation, even for amounts never observed during training, and 3) (inside-CNN) "data augmentation" --- improve the classification performance of an existing network by using the learned generators to directly provide additional training "inside the CNN".


title: Task-driven Visual Saliency and Attention-based Visual Question Answering

abstract: Visual question answering (VQA) has witnessed great progress since May, 2015 as a classic problem unifying visual and textual data into a system. Many enlightening VQA works explore deep into the image and question encodings and fusing methods, of which attention is the most effective and infusive mechanism. Current attention based methods focus on adequate fusion of visual and textual features, but lack the attention to where people focus to ask questions about the image. Traditional attention based methods attach a single value to the feature at each spatial location, which losses many useful information. To remedy these problems, we propose a general method to perform saliency-like pre-selection on overlapped region features by the interrelation of bidirectional LSTM (BiLSTM), and use a novel element-wise multiplication based attention method to capture more competent correlation information between visual and textual features. We conduct experiments on the large-scale COCO-VQA dataset and analyze the effectiveness of our model demonstrated by strong empirical results.


title: Design & comparison of a conventional and permanent magnet based claw-pole machine for automotive application

abstract: This paper presents the design and performance comparison of a conventional claw-pole machine with a permanent magnet based claw-pole machine for automotive application. The magnets are placed in the inter-claw region to decrease the leakage flux and to supply increased magnetic flux in the machine. It has been observed that with the addition of permanent magnets weighing only a few grams, there is a significant increment of more than 22% in output power of the machine. The geometric dimensions of magnets were also varied to verify their effects on performance and it has been observed that with the increase in magnet weight there is a non-linear increment in torque of the machine.


title: Sequential User-based Recurrent Neural Network Recommendations

abstract: Recurrent Neural Networks are powerful tools for modeling sequences. They are flexibly extensible and can incorporate various kinds of information including temporal order. These properties make them well suited for generating sequential recommendations. In this paper, we extend Recurrent Neural Networks by considering unique characteristics of the Recommender Systems domain. One of these characteristics is the explicit notion of the user recommendations are specifically generated for. We show how individual users can be represented in addition to sequences of consumed items in a new type of Gated Recurrent Unit to effectively produce personalized next item recommendations. Offline experiments on two real-world datasets indicate that our extensions clearly improve objective performance when compared to state-of-the-art recommender algorithms and to a conventional Recurrent Neural Network.


title: Hierarchical LSTMs with Adaptive Attention for Visual Captioning

abstract: Recent progress has been made in using attention based encoder-decoder framework for image and video captioning. Most existing decoders apply the attention mechanism to every generated word including both visual words (e.g., “gun” and “shooting”) and non-visual words (e.g., “the”, “a”). However, these non-visual words can be easily predicted using natural language model without considering visual signals or attention. Imposing attention mechanism on non-visual words could mislead and decrease the overall performance of visual captioning. Furthermore, the hierarchy of LSTMs enables more complex representation of visual data, capturing information at different scales. Considering these issues, we propose a hierarchical LSTM with adaptive attention (hLSTMat) approach for image and video captioning. Specifically, the proposed framework utilizes the spatial or temporal attention for selecting specific regions or frames to predict the related words, while the adaptive attention is for deciding whether to depend on the visual information or the language context information. Also, a hierarchical LSTMs is designed to simultaneously consider both low-level visual information and high-level language context information to support the caption generation. We design the hLSTMat model as a general framework, and we first instantiate it for the task of video captioning. Then, we further instantiate our hLSTMarefine it and apply it to the imioning task. To demonstrate the effectiveness of our proposed framework, we test our method on both video and image captioning tasks. Experimental results show that our approach achieves the state-of-the-art performance for most of the evaluation metrics on both tasks. The effect of important components is also well exploited in the ablation study.


title: The impact on ERP implementation by leadership and organisational culture: a case analysis

abstract: In this paper, we seek to investigate Ke and Wei's (2008) propositions that ERP implementation success is positively related with top management leadership and organisational culture. This is accomplished by analysing the implementation of the ERP systems at a company, HealthSouth Corporation, longitudinally during two periods (1997 to 2003 and 2004 to 2007). During 1997 to 2003, leadership and culture in this organisation enabled accounting fraud to occur via the criminal misuse of a heavily-customised, unsupported ERP system. During 2004 to 2007, the leadership and culture enabled the company to avoid bankruptcy and a plain-vanilla (i.e., uncustomised) version of the ERP system was installed successfully. This paper provides valuable lessons to ERP practitioners on how ERP implementations are influenced by leadership, from the top management team, and organisational culture.


title: Deep Background Modeling Using Fully Convolutional Network

abstract: Background modeling plays an important role for video surveillance, object tracking, and object counting. In this paper, we propose a novel deep background modeling approach utilizing fully convolutional network. In the network block constructing the deep background model, three atrous convolution branches with different dilate are used to extract spatial information from different neighborhoods of pixels, which breaks the limitation that extracting spatial information of the pixel from fixed pixel neighborhood. Furthermore, we sample multiple frames from original sequential images with increasing interval, in order to capture more temporal information and reduce the computation. Compared with classical background modeling approaches, our approach outperforms the state-of-art approaches both in indoor and outdoor scenes.


title: Performance Evaluation of SVM and K-Nearest Neighbor Algorithm over Medical Data set

abstract: this age of computer science each and every thing becomes intelligent and perform task as human. For that purpose there are various tools, techniques and methods are proposed. Support vector machine is a model for statistics and computer science, to perform supervised learning, methods that are used to make analysis of data and recognize patterns. SVM is mostly used for classification and regression analysis. And in the same way k-nearest neighbor algorithm is a classification algorithm used to classify data using training examples. In this paper we use SVM and KNN algorithm to classify data and get prediction (find hidden patterns) for target. Here we use medical patients nominal data to classify and discover the data pattern to predict future disease, Uses data mining which is use to classify text analysis in future. KeywordsKNN, Patterns, Analysis, Classification.


title: A broadband 200W GaN push-pull power amplifier enhanced second harmonic suppression with point-symmetric 2-stage baluns

abstract: A broadband 200W Gallium Nitride (GaN) push-pull amplifier enhanced a second harmonic (2fo) suppression is presented. A point-symmetric 2-stage baluns are applied to enhance the 2fo suppression in input and output circuits. With the presented configuration, the 2fo suppression of 10dB is improved. The broadband amplifier is fabricated with the presented configuration. Between 0.2GHz and 0.8GHz, the 2fo distortion bellow −40dBc and power added efficiency (PAE) of over 45% can be achieved at the saturated power of over 52dBm.


Task ontology : Ontology for building conceptual problem solving models


title: Approximating Free-form Geometry with Height Fields for Manufacturing

abstract: We consider the problem of manufacturing free-form geometry with classical manufacturing techniques, such as mold casting or 3-axis milling. We determine a set of constraints that are necessary for manufacturability and then decompose and, if necessary, deform the shape to satisfy the constraints per segment. We show that many objects can be generated from a small number of mold-pieces if slight deformations are acceptable. We provide examples of actual molds and the resulting manufactured objects.


title: Cultural difference in motivations for using social network sites: A comparative study of American and Korean college students

abstract: While the explosive growth of social network sites is a common phenomenon across many countries, the ways people use them and their reasons for doing so may differ depending on their social and cultural milieu, for fundamental values are divergent from culture to culture. This study is an attempt to examine how cultural contexts shape the use of communication technology by examining the motives for and patterns of using social network sites among college students in the US and Korea. The findings of this study suggest that the major motives for using social network sites - seeking friends, social support, entertainment, information, and convenience - are similar between the two countries, though the weights placed on these motives are different. Reflecting the unique social nature of the medium, Korean college students put more weight on obtaining social support from existing social relationships, while American students place relatively greater emphasis on seeking entertainment. Additionally, American college students' networks in an online social venue are far larger than their Korean counterparts, which may reflect the cultural difference between the two countries regarding developing and managing social relationships.


title: Interactive Evolution of Particle Systems for Computer Graphics and Animation

abstract: Interactive Evolutionary Computation (IEC) creates the intriguing possibility that a large variety of useful content can be produced quickly and easily for practical computer graphics and gaming applications. To show that IEC can produce such content, this paper applies IEC to particle system effects, which are the de facto method in computer graphics for generating fire, smoke, explosions, electricity, water, and many other special effects. While particle systems are capable of producing a broad array of effects, they require substantial mathematical and programming knowledge to produce. Therefore, efficient particle system generation tools are required for content developers to produce special effects in a timely manner. This paper details the design, representation, and animation of particle systems via two IEC tools called NEAT Particles and NEAT Projectiles. Both tools evolve artificial neural networks (ANN) with the NeuroEvolution of Augmenting Topologies (NEAT) method to control the behavior of particles. NEAT Particles evolves general-purpose particle effects, whereas NEAT Projectiles specializes in evolving particle weapon effects for video games. The primary advantage of this NEAT-based IEC approach is to decouple the creation of new effects from mathematics and programming, enabling content developers without programming knowledge to produce complex effects. Furthermore, it allows content designers to produce a broader range of effects than typical development tools. Finally, it acts as a concept generator, allowing content creators to interactively and efficiently explore the space of possible effects. Both NEAT Particles and NEAT Projectiles demonstrate how IEC can evolve useful content for graphical media and games, and are together a step toward the larger goal of automated content generation.


title: The DCB-Tree: A Space-Efficient Delta Coded Cache Conscious B-Tree

abstract: Main-memory index structures have become mainstream for a large number of problem domains. However, in the case of web-based datasets, which feature exponential growth, it is an ongoing challenge to fit those data entirely in main-memory. In this paper, we present the DCB-Tree, an extremely space efficient main-memory index structure for the storage of short fixed-size keys. It features a two-stage cache-line aligned node layout. In comparison to other main-memory index structures it reduces the amount of memory required by 80 % in the best and by 30 % in the worst case. Although it is tailored towards space consumption, it features good overall performance characteristics. In particular, in the case of very large real world datasets it provides performance equal or superior to state of the art main-memory index structures.


title: Characteristics Analysis of Data From News and Social Network Services

abstract: With the rapid proliferation of the Internet and mobile devices, vast amounts of user-generated content has been accumulated through social network services, and massive amounts of news continues to be created and posted online. In this paper, we extract the characteristics of online news and data from social network services, including the differences and similarities between them. We found the following differences: First, the news responds to official events but content on social network services is related to personal interests. Second, the news is continually related to a specific issue or set of issues, whereas topics of conversation change daily in social network services. Third, items from the news can be identified with a single keyword in searches, whereas more keywords are needed to extract the desired information from social network services. At the same time, we found that the words mentioned in both the news and on social network services were similar, and both were used for commercial purposes. Our analysis revealed that the news is related to the keyword generally, uses same words repeatedly, and its range of topics is narrow and public. On the contrary, social network services are not related to the keyword often, and their range of topics is wide and personal. Furthermore our analysis showed that the ranking algorithm improves the topic detection rate and catches the topic quickly. This paper provides useful information to better understand the characteristics of online news and data from social network services.


bioprinting for engineering complex tissues


title: Measuring Surface Water Quality Using a Low-Cost Sensor Kit within the Context of Rural Africa

abstract: Monitoring water quality is done for a variety of reasons, including to determine whether water is suitable for drinking or agricultural purposes. In rural areas of Africa the traditional way of measuring water quality can be costly and time consuming. In this research, we have developed a low-cost water quality measuring device that designed to operate in the context of rural Africa. Firstly we select appropriate water quality sensors. Secondly we developed a water quality monitoring device that takes the contextual requirements and constraints of rural Africa into account. Lastly the device is evaluated and tested using water samples that were collected in rural Africa.


title: Dissociable Connectivity within Human Angular Gyrus and Intraparietal Sulcus: Evidence from Functional and Structural Connectivity

abstract: The inferior parietal lobule (IPL) of the human brain is a heterogeneous region involved in visuospatial attention, memory, and mathematical cognition. Detailed description of connectivity profiles of subdivisions within the IPL is critical for accurate interpretation of functional neuroimaging studies involving this region. We separately examined functional and structural connectivity of the angular gyrus (AG) and the intraparietal sulcus (IPS) using probabilistic cytoarchitectonic maps. Regions-of-interest (ROIs) included anterior and posterior AG subregions (PGa, PGp) and 3 IPS subregions (hIP2, hIP1, and hIP3). Resting-state functional connectivity analyses showed that PGa was more strongly linked to basal ganglia, ventral premotor areas, and ventrolateral prefrontal cortex, while PGp was more strongly connected with ventromedial prefrontal cortex, posterior cingulate, and hippocampus—regions comprising the default mode network. The anterior-most IPS ROIs, hIP2 and hIP1, were linked with ventral premotor and middle frontal gyrus, while the posterior-most IPS ROI, hIP3, showed connectivity with extrastriate visual areas. In addition, hIP1 was connected with the insula. Tractography using diffusion tensor imaging revealed structural connectivity between most of these functionally connected regions. Our findings provide evidence for functional heterogeneity of cytoarchitectonically defined subdivisions within IPL and offer a novel framework for synthesis and interpretation of the task-related activations and deactivations involving the IPL during cognition.


title: Bit-interleaved low density spread (BI-LDS) transmission

abstract: Achieving higher aggregate data rates for many simultaneous users is considered as a fundamental challenge for the next generation of wireless systems. With low-density spreading (LDS), one could transmit information by overloading, i.e., using more spreading sequences than chips with reasonable implementation complexity. In this paper, we propose an improvement to the conventional LDS technique that consists in introducing a bit interleaver in the transmission scheme. Its role is to render the error bursts that appear at the output of the LDS detector into correctable patterns for the channel decoder. Numerical evaluations of the spectral efficiency show SNR improvements of more than 2dB when using convolutional codes in BI-LDS schemes. Slight improvements are also observed when turbo codes are employed.


title: Cognitive engagement in the problem-based learning classroom

abstract: The objective of the present study was to examine to what extent autonomy in problem-based learning (PBL) results in cognitive engagement with the topic at hand. To that end, a short self-report instrument was devised and validated. Moreover, it was examined how cognitive engagement develops as a function of the learning process and the extent to which cognitive engagement determines subsequent levels of cognitive engagement during a one-day PBL event. Data were analyzed by means of confirmatory factor analysis, repeated measures ANOVA, and path analysis. The results showed that the new measure of situational cognitive engagement is valid and reliable. Furthermore, the results revealed that students’ cognitive engagement significantly increased as a function of the learning event. Implications of these findings for PBL are discussed.


title: Longitudinal study of fingerprint recognition.

abstract: Human identification by fingerprints is based on the fundamental premise that ridge patterns from distinct fingers are different (uniqueness) and a fingerprint pattern does not change over time (persistence). Although the uniqueness of fingerprints has been investigated by developing statistical models to estimate the probability of error in comparing two random samples of fingerprints, the persistence of fingerprints has remained a general belief based on only a few case studies. In this study, fingerprint match (similarity) scores are analyzed by multilevel statistical models with covariates such as time interval between two fingerprints in comparison, subject's age, and fingerprint image quality. Longitudinal fingerprint records of 15,597 subjects are sampled from an operational fingerprint database such that each individual has at least five 10-print records over a minimum time span of 5 y. In regard to the persistence of fingerprints, the longitudinal analysis on a single (right index) finger demonstrates that (i) genuine match scores tend to significantly decrease when time interval between two fingerprints in comparison increases, whereas the change in impostor match scores is negligible; and (ii) fingerprint recognition accuracy at operational settings, nevertheless, tends to be stable as the time interval increases up to 12 y, the maximum time span in the dataset. However, the uncertainty of temporal stability of fingerprint recognition accuracy becomes substantially large if either of the two fingerprints being compared is of poor quality. The conclusions drawn from 10-finger fusion analysis coincide with the conclusions from single-finger analysis.


title: Modeling of biomass potential from agricultural land for energy utilization using high resolution spatial data with regard to food security scenarios

abstract: The paper presents results of modeling biomass potential under different scenarios of agricultural land utilization, which represent strategies of national food security. High resolution spatial data (GIS) including valuation of agricultural land, maps of actual utilization of agricultural land, yields of annual food crops and yield curves of perennial energy crops derived from empirical field data were used. The biomass sources used were residual straw from conventional agriculture crops (cereals, rape) and lignocellulose biomass from perennial energy crops (poplar, willow, Miscanthus, reed canary grass, hybrid sorrel, and other grasses). Biomass potential is modeled using original methods and algorithms that enabled the respecting of several its limitations (nature and soil protection, competition of crops for land, and use of straw for animal production). For the actual modeling—calculating the biomass potential for a given territory—a geographic information system is used (software TopoL©). Results of analyses confirmed that residual of biomass has good potential as source of energy in the Czech Republic (about 121PJ/year which equals to 6.8% of primary energy sources used in 2012), though the total number is lower than in previous assessments. The current biomass potential can be significantly increased with allocation of energy crops on less fertile land according to food security scenarios. Modeling also showed that biomass potential is non-linearly dependent on land allocated for energy crops. Soil and climate conditions of agricultural land allocated (available) for biomass production and its suitability for new energy crops play the decisive role in the definition of future biomass potential from agriculture land. Practical outputs of the modeling are yield maps of individual and mixed biomass sources as well as databases of biomass potential under different food security scenarios for regions of the Czech Republic, which can be used for planning of sustainable development of bioenergy.


title: Route packets, not wires: on-chip inteconnection networks

abstract: Using on-chip interconnection networks in place of ad-hoc glo-bal wiring structures the top level wires on a chip and facilitates modular design. With this approach, system modules (processors, memories, peripherals, etc...) communicate by sending packets to one another over the network. The structured network wiring gives well-controlled electrical parameters that eliminate timing iterations and enable the use of high-performance circuits to reduce latency and increase bandwidth. The area overhead required to implement an on-chip network is modest, we estimate 6.6%. This paper introduces the concept of on-chip networks, sketches a simple network, and discusses some challenges in the architecture and design of these networks.


title: Designing Neural Network Architectures using Reinforcement Learning

abstract: At present, designing convolutional neural network (CNN) architectures requires both human expertise and labor. New architectures are handcrafted by careful experimentation or modified from a handful of existing networks. We introduce MetaQNN, a meta-modeling algorithm based on reinforcement learning to automatically generate high-performing CNN architectures for a given learning task. The learning agent is trained to sequentially choose CNN layers using $Q$-learning with an $\epsilon$-greedy exploration strategy and experience replay. The agent explores a large but finite space of possible architectures and iteratively discovers designs with improved performance on the learning task. On image classification benchmarks, the agent-designed networks (consisting of only standard convolution, pooling, and fully-connected layers) beat existing networks designed with the same layer types and are competitive against the state-of-the-art methods that use more complex layer types. We also outperform existing meta-modeling approaches for network design on image classification tasks.


BEYOND METADATA: TOWARDS USER-CENTRIC DESCRIPTION OF DATA QUALITY


title: Multi-vehicle detection with identity awareness using cascade Adaboost and Adaptive Kalman filter for driver assistant system

abstract: Vision-based vehicle detection is an important issue for advanced driver assistance systems. In this paper, we presented an improved multi-vehicle detection and tracking method using cascade Adaboost and Adaptive Kalman filter(AKF) with target identity awareness. A cascade Adaboost classifier using Haar-like features was built for vehicle detection, followed by a more comprehensive verification process which could refine the vehicle hypothesis in terms of both location and dimension. In vehicle tracking, each vehicle was tracked with independent identity by an Adaptive Kalman filter in collaboration with a data association approach. The AKF adaptively adjusted the measurement and process noise covariance through on-line stochastic modelling to compensate the dynamics changes. The data association correctly assigned different detections with tracks using global nearest neighbour(GNN) algorithm while considering the local validation. During tracking, a temporal context based track management was proposed to decide whether to initiate, maintain or terminate the tracks of different objects, thus suppressing the sparse false alarms and compensating the temporary detection failures. Finally, the proposed method was tested on various challenging real roads, and the experimental results showed that the vehicle detection performance was greatly improved with higher accuracy and robustness.


title: binpac: a yacc for writing application protocol parsers

abstract: A key step in the semantic analysis of network traffic is to parse the traffic stream according to the high-level protocols it contains. This process transforms raw bytes into structured, typed, and semantically meaningful data fields that provide a high-level representation of the traffic. However, constructing protocol parsers by hand is a tedious and error-prone affair due to the complexity and sheer number of application protocols.This paper presents binpac, a declarative language and compiler designed to simplify the task of constructing robust and efficient semantic analyzers for complex network protocols. We discuss the design of the binpac language and a range of issues in generating efficient parsers from high-level specifications. We have used binpac to build several protocol parsers for the "Bro" network intrusion detection system, replacing some of its existing analyzers (handcrafted in C++), and supplementing its operation with analyzers for new protocols. We can then use Bro's powerful scripting language to express application-level analysis of network traffic in high-level terms that are both concise and expressive. binpac is now part of the open-source Bro distribution.


title: Systematic Polar Coding

abstract: Polar codes were originally introduced as a class of non-systematic linear block codes. This paper gives encoding and decoding methods for systematic polar coding that preserve the low-complexity nature of non-systematic polar coding while guaranteeing the same frame error rate. Simulation results are given to show that systematic polar coding offers significant advantages in terms of bit error rate performance.


title: A Framework for Co-simulation of AI Tools with Power Systems Analysis Software

abstract: As the smart grid initiative is pushing for smarter controls, the need for artificial intelligence-based decision-making tools, such as agent-based programs, is becoming more prevalent. However, these tools alone are not sufficient to study the behavior of the algorithms in simulations. They need to be interfaced with power systems analysis tools such as PowerWorld Simulator. This paper proposes a framework for co-simulation with two types of tools: a) MathWorks Matlab and PowerWorld, and b) the multiagent middleware JADE and PowerWorld. The source code for this framework has been made available in the public domain for the research community.


title: Supply chain management: exploring the intellectual structure

abstract: The purpose of this article is to explore the intellectual structure of supply chain management area by employing a co-citation method. Data was collected (4652 articles and 145,242 references) from the Web of Science online database. Author productivity and 41 frequently cited articles were identified through citation and document co-citation analysis. The result of this study identified four core topics to supply chain management research: sustainable supply chain management, strategic competition, value of information, and development of supply chain management. This study illustrates the shift in the intellectual structure of SCM, indicating research priorities, such as sustainability in supply chain management, relationships among organisations, the bullwhip effect within the supply chain, and performance measurement in supply chain management. This study implies that managers should expend effort formulating effective performance measurements in supply chain management and share information within the supply chain to mitigate the bullwhip effect.


Blockchain-Free Cryptocurrencies : A Framework for Truly Decentralised Fast Transactions


Windows API based Malware Detection and Framework Analysis


Introduction to Inverse Kinematics with Jacobian Transpose , Pseudoinverse and Damped Least Squares methods


title: Evaluating Sustainability of Architectural Designs Using Building Information Modeling

abstract: BIM (Building Information Modeling) has been recently implemented by many Architecture, Engineering, and Construction firms due to its productivity gains and long term benefits. This paper presents the development and imple- mentation of a sustainability assessment framework for an architectural design using BIM technology in extracting data from the digital building model needed for determining the level of sustainability. The sustainability assessment is based on the LEED (Leadership in Energy and Environmental Design) Green Building Rating System, a widely accepted national standards for sustainable building design in the United States. The architectural design of a hotel project is used as a case study to verify the applicability of the framework.


MOVEMENT OF MATERIALS IN THE PORT OF MANAUS MODERN IN BRAZILIAN AMAZON


title: Multi-View Perceptron: a Deep Model for Learning Face Identity and View Representations

abstract: Various factors, such as identity, view, and illumination, are coupled in face images. Disentangling the identity and view representations is a major challenge in face recognition. Existing face recognition systems either use handcrafted features or learn features discriminatively to improve recognition accuracy. This is different from the behavior of primate brain. Recent studies [5, 19] discovered that primate brain has a face-processing network, where view and identity are processed by different neurons. Taking into account this instinct, this paper proposes a novel deep neural net, named multi-view perceptron (MVP), which can untangle the identity and view features, and in the meanwhile infer a full spectrum of multi-view images, given a single 2D face image. The identity features of MVP achieve superior performance on the MultiPIE dataset. MVP is also capable to interpolate and predict images under viewpoints that are unobserved in the training data.


title: CORONET: Fault tolerance for Software Defined Networks

abstract: Software Defined Networking, or SDN, based networks are being deployed not only in testbed networks, but also in production networks. Although fault-tolerance is one of the most desirable properties in production networks, there are not much study in providing fault-tolerance to SDN-based networks. The goal of this work is to develop a fault tolerant SDN architecture that can rapidly recover from faults and scale to large network sizes. This paper presents CORONET, a SDN fault-tolerant system that recovers from multiple link failures in the data plane. We describe a prototype implementation based on NOX that demonstrates fault recovery for emulated topologies using Mininet. We also discuss possible extensions to handle control plane and controller faults.


Standards and Guidelines for the Interpretation of Sequence Variants: A Joint Consensus Recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology


title: A Highly Integrated and Reconfigurable Microgrid Testbed with Hybrid Distributed Energy Sources

abstract: A highly integrated and reconfigurable microgrid testbed is presented in this paper. The microgrid testbed contains various distributed generation units and diverse energy storage systems. Apart from electrical power, it can also provide energy in forms of hydrogen and thermal energy. The topology of this testbed is very flexible with different combinations of buses and feeders. The deployment of hybrid distributed energy sources and the highly reconfigurable structure are available to meet different research requirements. Extensive experiments have been carried out to provide verification for microgrid research and guides for microgrid projects. Some experimental results are shown in this paper in several aspects of microgrid research, including performance evaluation of distributed energy sources, verification of control schemes in ac/dc hybrid microgrid, investigation of fault transients, and feasibility of protection schemes.


title: Communities of practice and organizational performance

abstract: As organizations grow in size, geographical scope, and complexity, it is increasingly apparent that sponsorship and support of communities of practice--groups whose members regularly engage in sharing and learning, based on common interests--can improve organizational performance. Although many authors assert that communities of practice create organizational value, there has been relatively little systematic study of the linkage between community outcomes and the underlying social mechanisms that are at work. To build an understanding of how communities of practice create organizational value, we suggest thinking of a community as an engine for the development of social capital. We argue that the social capital resident in communities of practice leads to behavioral changes, which in turn positively influence business performance. We identify four specific performance outcomes associated with the communities of practice we studied and link these outcomes to the basic dimensions of social capital. These dimensions include connections among practitioners who may or may not be co-located, relationships that build a sense of trust and mutual obligation, and a common language and context that can be shared by community members. Our conclusions are based on a study of seven organizations where communities of practice are acknowledged to be creating value.


title: Mathematical modelling of zika virus in Brazil

abstract: In this paper we study some deterministic mathematical models that seek to explain the expansion of zika virus, as a viral epidemic, using published data for Brazil. SIR type models are proposed and validated using the epidemic data found, considering several aspects in the spread of the disease. Finally, we confirmed that the crucial epidemic parameter such as $R_0$ is consistent with those previously reported in the literature for other areas. We also explored variations of the parameters within Brazil for different federal entities. We concluded that a parsimonious model that includes both human and vector populations best describe the epidemic parameters.


title: Inferior Pedicle Breast Reduction for the Management of Gestational Gigantomastia: Literature Review and a Case Presentation

abstract: Background: The actual cause of Gestational gigantomastia (Gg) remains a dilemma. Its treatment has ideally been surgical, employing most commonly the different pedicle techniques in the last decade. Aim: This paper reviews the literature on the management of Gg using the inferior pedicle technique (IPT) and supports it with a successful case presentation using the IPT. Method: Using the PubMed search engine and Google scholar, literature search was done for Gg treated with the inferior pedicle reduction method. Published literature from other sources was also included. Data obtained was cleaned and analysed. The inferior pedicle breast reduction technique was employed without any modification in the normal procedure for the case presented. Results: Thirty-one literature addressing Gg and IPT were identified. Most surgeons use this technique with very good outcomes. Our patient confirmed reduced breast mould, firm and adolescent looking breast with no back, shoulder, neck and rib pains. She also experienced with time increased nipple areolar sensation, ability to undertake all tasks with ease and a new sense of confidence. Conclusion: The inferior pedicle breast reduction technique can be the standard treatment for gestational gigantomastia with the length from suprasternal notch to the nipple ≤ 50 cm that present without any anatomical complications.


title: Speculative Memory Checkpointing

abstract: High-frequency memory checkpointing is an important technique in several application domains, such as automatic error recovery (where frequent checkpoints allow the system to transparently mask failures) and application debugging (where frequent checkpoints enable fast and accurate time-traveling support). Unfortunately, existing (typically incremental) checkpointing frameworks incur substantial performance overhead in high-frequency memory checkpointing applications, thus discouraging their adoption in practice. This paper presents Speculative Memory Checkpointing (SMC), a new low-overhead technique for high-frequency memory checkpointing. Our motivating analysis identifies key bottlenecks in existing frameworks and demonstrates that the performance of traditional incremental checkpointing strategies in high-frequency checkpointing scenarios is not optimal. To fill the gap, SMC relies on working set estimation algorithms to eagerly checkpoint the memory pages that belong to the writable working set of the running program and only lazily checkpoint the memory pages that do not. Our experimental results demonstrate that SMC is effective in reducing the performance overhead of prior solutions, is robust to variations in the workload, and incurs modest memory overhead compared to traditional incremental checkpointing.


title: Localization in highly dynamic environments using dual-timescale NDT-MCL

abstract: Industrial environments are rarely static and oftentheir configuration is continuously changing due to the materialtransfer flow. This is a major challenge for infrastructure freelocalization syste ...


title: A Highly Integrated 60 GHz 6-Channel Transceiver With Antenna in Package for Smart Sensing and Short-Range Communications

abstract: This work presents a highly integrated 57-64 GHz 4-channel receiver 2-channel transmitter chip targeting short range sensing and large bandwidth communications. The chip is housed in an embedded wafer level ball grid array package. The package includes 6 integrated patch antennas realized with a metal redistribution layer. The receiver patch antennas have a combined antenna gain of ≈10 dBi while each transmitter antenna has a gain of ≈6 dBi. The chip features a wide tuning range integrated VCO with a measured phase noise lower than -80 dBc/Hz at 100 kHz offset. Each of the differential transmitter channels shows a measured output power of 2-5 dBm over the complete frequency range. In addition, one transmitter channel features a modulator that can be digitally programmed to operate in either radar or communication mode. Each of the receiver channels has a measured conversion gain of 19 dB, a single-side-band noise figure of less than 10 dB and an input referred 1 dB compression point of less than 10 dBm. With all channels turned on the chip consumes a current of 300 mA from a 3.3 V supply. The functionality of the chip is demonstrated for both sensing and short range wireless communications.


title: Low Power FinFET based 10T SRAM cell

abstract: Memory occupies more than 70 percent of area in today's system on chip and the trends continue to be increases in coming years. As the technology is scaling the bulk MOSFET faces various challenges which lead to increased leakage. Below 32nm technology, FinFET is the most promising substitute to bulk CMOS technology because of reduced short channel effect. The proposed 10T SRAM cell is designed using MOSFET, FinFET at 16nm technology node and its performance parameters such as power, delay, Power Delay Product (PDP), leakage current and Static Noise Margin (SNM) are compared with conventional 6T SRAM cell.


title: (In)Visibility in Disability and Assistive Technology

abstract: In this article, we present a meta-analysis of research examining visibility of disability. In interrogating the issue of visibility and invisibility in the design of assistive technologies, we open a discussion about how perceptions surrounding disability can be probed through an examination of visibility and how these tensions do, and perhaps should, influence assistive technology design and research.


title: A scalable multi-core architecture with heterogeneous memory structures for Dynamic Neuromorphic Asynchronous Processors (DYNAPs)

abstract: Neuromorphic computing systems comprise networks of neurons that use asynchronous events for both computation and communication. This type of representation offers several advantages in terms of bandwidth and power consumption in neuromorphic electronic systems. However, managing the traffic of asynchronous events in large scale systems is a daunting task, both in terms of circuit complexity and memory requirements. Here, we present a novel routing methodology that employs both hierarchical and mesh routing strategies and combines heterogeneous memory structures for minimizing both memory requirements and latency, while maximizing programming flexibility to support a wide range of event-based neural network architectures, through parameter configuration. We validated the proposed scheme in a prototype multicore neuromorphic processor chip that employs hybrid analog/digital circuits for emulating synapse and neuron dynamics together with asynchronous digital circuits for managing the address-event traffic. We present a theoretical analysis of the proposed connectivity scheme, describe the methods and circuits used to implement such scheme, and characterize the prototype chip. Finally, we demonstrate the use of the neuromorphic processor with a convolutional neural network for the real-time classification of visual symbols being flashed to a dynamic vision sensor (DVS) at high speed.


title: Importance weighted generative networks

abstract: Deep generative networks can simulate from a complex target distribution, by minimizing a loss with respect to samples from that distribution. However, often we do not have direct access to our target distribution - our data may be subject to sample selection bias, or may be from a different but related distribution. We present methods based on importance weighting that can estimate the loss with respect to a target distribution, even if we cannot access that distribution directly, in a variety of settings. These estimators, which differentially weight the contribution of data to the loss function, offer both theoretical guarantees and impressive empirical performance.


title: Caspases and their substrates

abstract: Protease biology is intimately linked to the functional consequences of substrate cleavage events. Human caspases are a family of 12 fate-determining cysteine proteases that are best known for driving cell death, either apoptosis or pyroptosis. More recently, caspases have been shown to be involved in other cellular remodeling events as well including stem cell fate determination, spermatogenesis, and erythroid differentiation. Recent global proteomics methods enable characterization of the substrates that caspases cleave in live cells and cell extracts. The number of substrate targets identified for individual caspases can vary widely ranging from only a (few) dozen targets for caspases-4, -5, -9, and -14 to hundreds of targets for caspases-1, -2, -3, -6, -7, and -8. Proteomic studies characterizing the rates of target cleavage show that each caspase has a preferred substrate cohort that sometimes overlaps between caspases, but whose rates of cleavage vary over 500-fold within each group. Determining the functional consequences of discrete proteolytic events within the global substrate pool is a major challenge for the field. From the handful of individual targets that have been studied in detail, there are only a few so far that whose single cleavage event is capable of sparking apoptosis alone, such as cleavage of caspase-3/-7 and BIMEL, or for pyroptosis, gasdermin D. For the most part, it appears that cleavage events function cooperatively in the cell death process to generate a proteolytic synthetic lethal outcome. In contrast to apoptosis, far less is known about caspase biology in non-apoptotic cellular processes, such as cellular remodeling, including which caspases are activated, the mechanisms of their activation and deactivation, and the key substrate targets. Here we survey the progress made in global identification of caspase substrates using proteomics and the exciting new avenues these studies have opened for understanding the molecular logic of substrate cleavage in apoptotic and non-apoptotic processes.


title: DTN: an architectural retrospective

abstract: We review the rationale behind the current design of the Delay/Disruption Tolerant Networking (DTN) Architecture and highlight some remaining open issues. Its evolution, from a focus on deep space to a broader class of heterogeneous networks that may suffer disruptions, affected design decisions spanning naming and addressing, message formats, data encoding methods, routing, congestion management and security. Having now achieved relative stability with the design, additional experience is required in long-running operational environments in order to fine tune our understanding of DTN concepts and the types of capabilities that are worth the investment in implementation complexity. We expect key management, handling of congestion, multicasting capability, and routing to remain active areas of research and development, and that DTN may continue to be an active research endeavor for at least the next few years.


title: A hierarchical edge cloud architecture for mobile computing

abstract: The performance of mobile computing would be significantly improved by leveraging cloud computing and migrating mobile workloads for remote execution at the cloud. In this paper, to efficiently handle the peak load and satisfy the requirements of remote program execution, we propose to deploy cloud servers at the network edge and design the edge cloud as a tree hierarchy of geo-distributed servers, so as to efficiently utilize the cloud resources to serve the peak loads from mobile users. The hierarchical architecture of edge cloud enables aggregation of the peak loads across different tiers of cloud servers to maximize the amount of mobile workloads being served. To ensure efficient utilization of cloud resources, we further propose a workload placement algorithm that decides which edge cloud servers mobile programs are placed on and how much computational capacity is provisioned to execute each program. The performance of our proposed hierarchical edge cloud architecture on serving mobile workloads is evaluated by formal analysis, small-scale system experimentation, and large-scale trace-based simulations.


title: When do changes induce fixes?

abstract: As a software system evolves, programmers make changes that sometimes cause problems. We analyze CVS archives for fix-inducing changes---changes that lead to problems, indicated by fixes. We show how to automatically locate fix-inducing changes by linking a version archive (such as CVS) to a bug database (such as BUGZILLA). In a first investigation of the MOZILLA and ECLIPSE history, it turns out that fix-inducing changes show distinct patterns with respect to their size and the day of week they were applied.


title: Movement time prediction in human-computer interfaces

abstract: Abstract The prediction of movement time in human-computer interfaces as undertaken using Fitts' law is reviewed. Techniques for model building are summarized and three refinements to improve the theoretical and empirical accuracy of the law are presented. Refinements include (1) the Shannon formulation for the index of task difficulty, (2) new interpretations of “target width'' for two- and three-dimensional tasks, and (3) a technique for normalizing error rates across experimental factors. Finally, a detailed application example is developed showing the potential of Fitts' law to predict and compare the performance of user interfaces before designs are finalized.


title: Speech and Music Classification and Separation: A Review

abstract: Abstract The classification and separation of speech and music signals have attracted attention by many researchers. The purpose of the classification process is needed to build two different libraries: speech library and music library, from a stream of sounds. However, the separation process is needed in a cocktail-party problem to separate speech from music and remove the undesired one. In this paper, a review of the existing classification and separation algorithms is presented and discussed. The classification algorithms will be divided into three categories: time-domain, frequency-domain, and time-frequency domain approaches. The time-domain approaches used in literature are: the zero-crossing rate (ZCR), the short-time energy (STE), the ZCR and the STE with positive derivative, with some of their modified versions, the variance of the roll-off, and the neural networks. The frequency-domain approaches are mainly based on: spectral centroid, variance of the spectral centroid, spectral flux, variance of the spectral flux, roll-off of the spectrum, cepstral residual, and the delta pitch. The time-frequency domain approaches have not been yet tested thoroughly in literature; so, the spectrogram and the evolutionary spectrum will be introduced. Also, some new algorithms dealing with music and speech separation and segregation processes will be presented.


Bayesian Factorization Machines


title: Recognizing emotion in speech using neural networks

abstract: Emotion recognition is an important factor of affective computing and has potential use in assistive technologies. In this paper we used landmark and other acoustic features to recognize different emotional states in speech. We analyzed 2442 utterances from the Emotional Prosody Speech and Transcripts corpus and extracted 62 features from each utterance. A neural network classifier was built to recognize different emotional states of these utterances. We obtained over 90% accuracy in distinguishing hot anger and neutral states, over 80% accuracy in distinguishing happy and sadness as well as in distinguishing hot anger and cold anger. We also achieved 62% and 49% accuracy for classifying 4 and 6 emotions respectively. We had 20% accuracy in classifying all 15 emotions in the corpus which is a large improvement over other studies. We plan to apply our work to developing a tool to help people who have difficulty in identifying emotion.


title: A Systematic Mapping Review of Software Quality Measurement: Research Trends, Model, and Method

abstract: Software quality is a key for the success in the business of information and technology. Hence, before be marketed, it needs the software quality measurement to fulfill the user requirements. Some methods of the software quality analysis have been tested in a different perspective, and we have presented the software method in the point of view of users and experts. This study aims to map the method of software quality measurement in any models of quality. Using the method of Systematic Mapping Study, we did a searching and filtering of papers using the inclusion and exclusion criteria. 42 relevant papers have been obtained then. The result of the mapping showed that though the model of ISO SQuaRE has been widely used since the last five years and experienced the dynamics, the researchers in Indonesia still used ISO9126 until the end of 2016.The most commonly used method of the software quality measurement Method is the empirical method, and some researchers have done an AHP and Fuzzy approach in measuring the software quality.


title: Human neural systems for face recognition and social communication

abstract: Face perception is mediated by a distributed neural system in humans that consists of multiple, bilateral regions. The functional organization of this system embodies a distinction between the representation of invariant aspects of faces, which is the basis for recognizing individuals, and the representation of changeable aspects, such as eye gaze, expression, and lip movement, which underlies the perception of information that facilitates social communication. The system also has a hierarchical organization. A core system, consisting of occipitotemporal regions in extrastriate visual cortex, mediates the visual analysis of faces. An extended system consists of regions from neural systems for other cognitive functions that can act in concert with the core system to extract meaning from faces. Of regions in the extended system for face perception, the amygdala plays a central role in processing the social relevance of information gleaned from faces, particularly when that information may signal a potential threat.


title: QUWI: An Arabic and English Handwriting Dataset for Offline Writer Identification

abstract: This paper presents a new offline dataset called the Qatar University Writer Identification dataset (QUWI). This dataset contains both Arabic and English handwritings and can be used to evaluate the performance of offline writer identification systems. It consists of handwritten documents of 1017 volunteers of different ages, nationalities, genders and education levels. The writers were asked to copy a specific text and to generate a random text, which allows the dataset to be used for both text-dependent and text-independent writer identification tasks. We describe the gathering and processing steps and define several evaluation tasks regarding the use of this dataset.


title: On the k-Anonymization of Time-varying and Multi-layer Social Graphs

abstract: The popularity of online social media platforms provides an unprecedented opportunity to study real-world complex networks of interactions. However, releasing this data to researchers and the public comes at the cost of potentially exposing private and sensitive user information. It has been shown that a naive anonymization of a network by removing the identity of the nodes is not sufficient to preserve users' privacy. In order to deal with malicious attacks, k-anonymity solutions have been proposed to partially obfuscate topological information that can be used to infer nodes' identity. ::: In this paper, we study the problem of ensuring k-anonymity in time-varying graphs, i.e., graphs with a structure that changes over time, and multi-layer graphs, i.e., graphs with multiple types of links. More specifically, we examine the case in which the attacker has access to the degree of the nodes. The goal is to generate a new graph where, given the degree of a node in each (temporal) layer of the graph, such a node remains indistinguishable from other k-1 nodes in the graph. In order to achieve this, we find the optimal partitioning of the graph nodes such that the cost of anonymizing the degree information within each group is minimum. We show that this reduces to a special case of a Generalized Assignment Problem, and we propose a simple yet effective algorithm to solve it. Finally, we introduce an iterated linear programming approach to enforce the realizability of the anonymized degree sequences. The efficacy of the method is assessed through an extensive set of experiments on synthetic and real-world graphs.


title: Planning for an electricity sector with carbon capture and storage: Case of the Netherlands

abstract: Before energy companies will invest in power plants with CCS, appropriate climate policy should be in place, a need for new power plants must exist, CCS technology should be cost-effective, and CO2 transport infrastructure and CO2 sinks must be available. In order to get more grip on planning, we carried out a quantitative scenario study for the electricity and cogeneration sector in the Netherlands using the energy bottom-up model generated with MARKAL. We analysed strategies to realise a 15% and 50% reduction of CO2 emissions in respectively, 2020 and 2050 compared to the 1990 level. We found that, if nuclear energy is excluded as a mitigation option, CCS can be sufficiently cost-effective in 2020 to avoid 29 Mt per year in 2020 in the Dutch electricity sector (which is half of the CO2 emission abatement necessary in this year). We identified the following important factors for planning. In a postponement strategy in which CO2 is reduced from 2020, CO2 can be abated at less than 30 €/t up to 2020. A gradual reduction of 2.5% annually from 2010, asks for a climate policy that makes expenditures possible of 50 €/t CO2 before 2015. Construction of coal-fired power plants without CCS are preferably not built or, in the postponement strategy, only to a limited extent. Finally, early planning is required to realise the construction of a transport infrastructure with a length of around 450 km before 2020.


title: Monitoring local synaptic activity with astrocytic patch pipettes

abstract: Rapid signal exchange between astroglia and neurons has emerged as a key player in neural communication in the brain. To understand the mechanisms involved, it is often important to have access to individual astrocytes while monitoring the activity of nearby synapses. Achieving this with standard electrophysiological tools is not always feasible. The protocol presented here enables the monitoring of synaptic activity using whole-cell current-clamp recordings from a local astrocyte. This approach takes advantage of the fact that the low input resistance of electrically passive astroglia allows extracellular currents to pass through the astrocytic membrane with relatively little attenuation. Once the slice preparation is ready, it takes ~30 min to several hours to implement this protocol, depending on the experimental design, which is similar to other patch-clamp techniques. The technique presented here can be used to directly access the intracellular medium of individual astrocytes while examining synapses functioning in their immediate proximity.


Cramer-Rao Lower Bounds for Estimation of Doppler Frequency in Emitter Location Systems


title: The Effects of Nurse Staffing on Adverse Events, Morbidity, Mortality, and Medical Costs

abstract: Results: Three statistically significant relationships were found between nurse staffing and adverse events. An increase of 1 hour worked by registered nurses (RN) per patient day was associated with an 8.9% decrease in the odds of pneumonia. Similarly, a 10% increase in RN Proportion was associated with a 9.5% decrease in the odds of pneumonia. Providing a greater number of nursing hours per patient day was associated with a higher probability of pressure ulcers. The occurrence of each adverse event was associated with a significantly prolonged length of stay and increased medical costs. Patients who had pneumonia, wound infection or sepsis had a greater probability of death during hospitalization. Conclusion: Patients are experiencing adverse events during hospitalization. Care systems to reduce adverse events and their consequences are needed. Having appropriate nurse staffing is a significant consideration in some cases.


title: A review of toxicity from topical salicylic acid preparations

abstract: Topical salicylic acid is often used in dermatologic conditions because of its keratolytic, bacteriostatic, fungicidal, and photoprotective properties. The bioavailability of salicylic acid differs depending on the vehicle used and pH of transcellular fluids. Although rare, salicylic acid toxicity (salicylism) can occur from topical application. Physicians should be mindful of the potential for salicylism or even death from topically applied salicylic acid.


title: Impact of task switching and work interruptions on software development processes

abstract: Software developers often work on multiple projects and tasks throughout a work day, which may affect their productivity and quality of work. Knowing how working on several projects at a time affects productivity can improve cost and schedule estimations. It also can provide additional insights for better work scheduling and the development process. We want to achieve a better productivity without losing the benefits of work interruptions and multitasking for developers involved in the process. To understand how the development process can be improved, first, we identify work interruptions that mostly have a negative effect on productivity, second, we need to quantitatively evaluate impact of multitasking (task switching, work context switching) and work interruptions on productivity. In this research we study cross-project multitasking among the developers working on multiple projects in an educational setting. We propose a way to evaluate the number of cross-project interruptions among software developers using self-reported work logs. This paper describes the research that found: a) software developers involved in two or more projects on average spend 17% of their development effort on cross-project interruptions, b) the amount of effort spent on interruptions is overestimated by the G. Weinberg’s heuristic, c) the correlation between the number of projects and effort spent by developers on cross-project interruptions is relatively weak, and d) there is strong correlation between the number of projects and the number of interruptions developers reported.


title: Business Models and the Internet of Things

abstract: This article provides theoretically and practically grounded assistance to companies that are todayengaged primarily in non?digital industries in the development and implementation of business models that use the Internet of Things. To that end, we investigate the role of the Internet in business models in general in the first section. We conclude that the significance of the Internet in business model innovation has increased steadily since the 1990s, that each new Internet wave has given rise to new digital business model patterns, and that the biggest breakthroughs to date have been made in digital industries. In the second section, we show that digital business model patterns have now become relevant in physical industries as well. The separation between physical and digital industries is now consigned to the past. The key to this transformation is the Internet of Things which makes possible hybrid solutions that merge physical products and digital services. From this, we derive very general business model logic for the Internet of Things and some specific components and patterns for business models. Finally we sketch out the central challenges faced in implementing such hybrid business models and point to possible solutions.


An Interactive Visualisation for RDF Data


title: Motion Planning for Mobile Robots: A Method for the Selection of a Combination of Motion-Planning Algorithms

abstract: A motion planner for mobile robots is commonly built out of a number of algorithms that solve the two steps of motion planning: 1) representing the robot and its environment and 2) searching a path through the represented environment. However, the available literature on motion planning lacks a generic methodology to arrive at a combination of representations and search algorithm classes for a practical application. This article presents a method to select appropriate algorithm classes that solve both the steps of motion planning and to select a suitable approach to combine those algorithm classes. The method is verified by comparing its outcome with three different motion planners that have been successfully applied on robots in practice.


title: Fine-Grained Air Quality Prediction using Attention Based Neural Network

abstract: We present a new two-stage fine-grained air Particulate Matter (PM) prediction system using a variety of deep memory networks. Our model is significantly simpler than traditional weather report systems, which rely heavily on the atmospheric reaction equations and pollutant emissions inventories. Pollutant inventories are notoriously difficult to obtain and are often packed with declination for multiple reasons, while reaction equations are impossible to exhaust. These traditional models also tend to perform poorly when affected by strong convective weather. In contrast, our model does not need precise hand collected inventories of pollution sources. It can utilize the potential of Deep-Neural-Networks (DNN) to reveal the relationship among different locations and even find relations between known social events and air quality. Both potentially provide a valid path to air pollution control. The key to our approach is a well-tuned sophisticated attention based network that uses multiple GPUs, allowing us to transform a traditional sparse prediction problem into a sequence-to-sequence learning problem and train it end-to-end. By evaluating the model on over 1,625 instances of data of Northern China collected by our team, we show that it is not only computationally efficient but also accurately feasible compared with other optional models.


title: The UrtheCast SAR-XL multi-band, multi-aperture spaceborne SAR system

abstract: UrtheCast is currently developing the SAR-XL technology for its OptiSAR™ Constellation that comprises 8 tandem pairs of SAR and Optical Satellites in two orbit planes. This paper briefly outlines the OptiSAR™ mission and then describes the SAR-XL instrument that is carried by the SAR satellite. This novel technology is the world's first dual-frequency, circular/quad polarization, multi-aperture, fully-active phased-array and digitally-beamformed (in both azimuth and elevation) space-based Synthetic Aperture Radar (SAR). This instrument is highly flexible and can be used to generate many different types of beam modes enabling a large number of applications. The baseline beam modes used for the OptiSAR™ mission are described and their performance capabilities are given. Additional advanced beam modes that are possible using the SAR-XL technology are also identified. Finally, the modular nature of the instrument is also described that enables the SAR-XL to be easily configured for different antenna sizes with up to 16 apertures that can enable a totally new class of performance.


title: Learning to rank for top mobile games prediction

abstract: Learning to rank as one of machine learning techniques has been widely used in document categorization, information retrieval, text processing, product rating, and other ranking problem domains. Ranking task has become an interesting research in terms of big data and analytics in data mining. Meanwhile, top mobile games prediction is one of the popular topics in terms of increasing games excitement in mobile devices today. This paper introduces an approach for top mobile games prediction using learning to rank. This paper proposes a prediction system which is called MGPrediction. Experiments have been conducted by performing our prediction system and executing various algorithms to our ranking dataset of mobile games using learning to rank in RankLib. Preliminary results present that our system has been successfully applied and we have discovered that MART gives the highest accuracy among others in training model and ranking prediction samples. Thus, MART is the most considered algorithm towards top mobile games predictions problem.


title: Monotonic models for real-time dynamic malware detection

abstract: In dynamic malware analysis, programs are classified as malware or benign based on their execution logs. We propose a concept of applying monotonic classification models to the analysis process, to make the trained model's predictions consistent over execution time and provably stable to the injection of any noise or `benign-looking' activity into the program's behavior. The predictions of such models change monotonically through the log in the sense that the addition of new lines into the log may only increase the probability of the file being found malicious, which make them suitable for real-time classification on a user's machine. We evaluate monotonic neural network models based on the work by Chistyakovet al. (2017) and demonstrate that they provide stable and interpretable results.


title: QsRank: Query-sensitive hash code ranking for efficient ∊-neighbor search

abstract: Although binary hash code-based image indexing methods have been recently developed for large-scale applications, the problem of ranking such hash codes has been barely studied. In this paper, we propose a query sensitive ranking algorithm (QsRank) to rank PCA-based hash codes for the ∊-neighbor search problem. The QsRank algorithm takes the target neighborhood radius ∊ and the raw feature of a given query as input, and models the statistical properties of the target ∊-neighbors in the space of hash codes. Unlike the Hamming distance, the proposed algorithm does not compress query points to hash codes. Therefore, it suffers less information loss and is more effective than Hamming distance-based approaches. Based on the QsRank method, we developed an efficient indexing structure and retrieval algorithm for large-scale ∊-neighbor search. Evaluations on two datasets of 10 million web images and 10 million SIFT descriptors demonstrate that the proposed retrieval system achieves higher accuracy with less memory cost and faster speed.


title: A Streaming Data Anomaly Detection Analytic Engine for Mobile Network Management

abstract: With emerging Network Functions Virtualization (NFV), Software Defined Networking (SDN) paradigms in Network Management (NM), new network devices, features can immediately become available. Available network resources, services can be altered, optimized in real time to gain the maximum benefit. However, this requires real time analytics information sent to SDN controllers rather than traditional manual offline or batch analytics which deliver outputs in hourly or monthly in NM Systems. As a result, real time analytic is becoming a critical element for NM. In this paper, we describe a configuration free (i.e., non-parametric) streaming data anomaly detection analytic engine for automatic NM system development. We describe the design principles, innovative algorithm design, architecture, implementation of the engine in relation to streaming data, mobile NM. Finally, we present use cases, evaluation results.


title: Improved Redirection with Distractors: A large-scale-real-walking locomotion interface and its effect on navigation in virtual environments

abstract: Users in virtual environments often find navigation more difficult than in the real world. Our new locomotion interface, Improved Redirection with Distractors (IRD), enables users to walk in larger-than-tracked space VEs without predefined waypoints. We compared IRD to the current best interface, really walking, by conducting a user study measuring navigational ability. Our results show that IRD users can really walk through VEs that are larger than the tracked space and can point to targets and complete maps of VEs no worse than when really walking.


title: Single-source multiple-coil homogeneous induction heating

abstract: Due to cleanliness, safety and high efficiency, induction heating has been developed rapidly for industrial and domestic heating applications.


title: “Cotranscriptionality”: The Transcription Elongation Complex as a Nexus for Nuclear Transactions

abstract: Much of the complex process of RNP biogenesis takes place at the gene cotranscriptionally. The target for RNA binding and processing factors is, therefore, not a solitary RNA molecule but, rather, a transcription elongation complex (TEC) comprising the growing nascent RNA and RNA polymerase traversing a chromatin template with associated passenger proteins. RNA maturation factors are not the only nuclear machines whose work is organized cotranscriptionally around the TEC scaffold. Additionally, DNA repair, covalent chromatin modification, "gene gating" at the nuclear pore, Ig gene hypermutation, and sister chromosome cohesion have all been demonstrated or suggested to involve a cotranscriptional component. From this perspective, TECs can be viewed as potent "community organizers" within the nucleus.


title: Food, mood, and attitude: reducing risk for eating disorders in college women.

abstract: Food, Mood, and Attitude (FMA) is a CD-ROM prevention program developed to decrease risk for eating disorders in college women. Female 1st-year students (N = 240) were randomly assigned to the intervention (FMA) or control group. Equal numbers of students at risk and of low risk for developing an eating disorder were assigned to each condition. Participants in the FMA condition improved on all measures relative to controls. Significant 3-way interactions (Time X Condition X Risk Status) were found on measures of internalization of sociocultural attitudes about thinness, shape concerns, and weight concerns, indicating that at-risk participants in the intervention group improved to a greater extent than did low-risk participants. At follow-up, significantly fewer women in the FMA group reported overeating and excessive exercise relative to controls.


title: The Faults in Our Pi Stars: Security Issues and Open Challenges in Deep Reinforcement Learning

abstract: Since the inception of Deep Reinforcement Learning (DRL) algorithms, there has been a growing interest in both research and industrial communities in the promising potentials of this paradigm. The list of current and envisioned applications of deep RL ranges from autonomous navigation and robotics to control applications in the critical infrastructure, air traffic control, defense technologies, and cybersecurity. While the landscape of opportunities and the advantages of deep RL algorithms are justifiably vast, the security risks and issues in such algorithms remain largely unexplored. To facilitate and motivate further research on these critical challenges, this paper presents a foundational treatment of the security problem in DRL. We formulate the security requirements of DRL, and provide a high-level threat model through the classification and identification of vulnerabilities, attack vectors, and adversarial capabilities. Furthermore, we present a review of current literature on security of deep RL from both offensive and defensive perspectives. Lastly, we enumerate critical research venues and open problems in mitigation and prevention of intentional attacks against deep RL as a roadmap for further research in this area.


title: Multi-User Proportional Fair Scheduling for Uplink Non-Orthogonal Multiple Access (NOMA)

abstract: Non-orthogonal multiple access (NOMA) is a promising radio access technique for LTE release 13 and beyond. In this paper, we focus on the investigation of the system performance for uplink NOMA with an advanced successive interference cancellation (SIC) receiver applied in the base station side. In NOMA, more than one user can be multiplexed simultaneously in the same frequency bandwidth, which demands multi-user scheduling. In our study, we propose an enhanced proportional fair (PF) based scheduling scheme for non-orthogonal multiplexed users with contiguous resource allocation to retain the SC-FDM property. In order to reduce the scheduling complexity for non-orthogonal multiple access, a greedy consecutive resource allocation method is adopted. Moreover, since the interference condition becomes more complicated in NOMA, we consider the application of fractional frequency reuse (FFR) to NOMA to further enhance the performance of cell-edge users. In simulation results, the system performance of NOMA by using the proposed PF-based scheduling algorithm is evaluated. The results show that NOMA significantly enhances the uplink system performance compared to the conventional orthogonal multiple access.


Research on Cultural Ecology of Modern Historical Architecture based on Digital Simulation Technology


title: Impact of Personality on Task Selection in Crowdsourcing Software Development: A Sorting Approach

abstract: Growing demand for software has attracted the attention of the software development industry. Crowdsourced software development has provided a new method for the software industry to produce quality software based on an open-call format. Selecting an appropriate task to develop (developer-end) or evaluate (platform-end) is one of the primary problems in this type of open-call format. Receiving or assigning an improper task to an improper crowdsource (CS) developer does not only decrease the quality of the software deliverables, but also causes overburden on both the platform and the developers. To solve this problem, sorting the tasks based on the developers’ human characteristics may increase task relevancy for developers, which can accelerate efficiency and lessen complexity. Thus, this paper has conducted an empirical experiment to measure the influence of personality on task selection based on the important characteristics of a task: money, time, and type. A total of 83 students from the University of Sindh voluntarily participated in four different short-duration rounds of task development using the developed CS platform. The personality types of the participants were measured based on the Myers-Briggs type indicator. In addition, a complex network technique called weighted degree centrality was applied to identify the most suitable personality for task sorting based on money or complexity attractions (i.e., time or type). Based on the results, it can be observed that personality has a significant relationship with task selection. For instance, developers with intuitive (N) and feeling (F) personality traits are primarily focused on the time duration of a project.


title: Hardware-security technologies for industrial IoT: TrustZone and security controller

abstract: The transition from product-centric to service-centric business models presents a major challenge to industrial automation and manufacturing systems. This transition increases Machine-to-Machine connectivity among industrial devices, industrial controls systems, and factory floor devices. While initiatives like Industry 4.0 or the Industrial Internet Consortium motivate this transition, the emergence of the Internet of Things and Cyber Physical Systems are key enablers. However, automated and autonomous processes require trust in the communication entities and transferred data. Therefore, we study how to secure a smart service use case for industrial maintenance scenarios. In this use case, equipment needs to securely transmit its status information to local and remote recipients. We investigate and compare two security technologies that provide isolation and a secured execution environment: ARM TrustZone and a Security Controller. To compare these technologies we design and implement a device snapshot authentication system. Our results indicate that the TrustZone based approach promises greater flexibility and performance, but only the Security Controller strongly protects against physical attacks. We argue that the best technology actually depends on the use case and propose a hybrid approach that maximizes security for high-security industrial applications. We believe that the insights we gained will help introducing advanced security mechanisms into the future Industrial Internet of Things.


title: Barriers and Negative Nudges: Exploring Challenges in Food Journaling

abstract: Although food journaling is understood to be both important and difficult, little work has empirically documented the specific challenges people experience with food journals. We identify key challenges in a qualitative study combining a survey of 141 current and lapsed food journalers with analysis of 5,526 posts in community forums for three mobile food journals. Analyzing themes in this data, we find and discuss barriers to reliable food entry, negative nudges caused by current techniques, and challenges with social features. Our results motivate research exploring a wider range of approaches to food journal design and technology.


title: Job resources boost work engagement, particularly when job demands are high.

abstract: This study of 805 Finnish teachers working in elementary, secondary, and vocational schools tested 2 interaction hypotheses. On the basis of the job demands-resources model, the authors predicted that job resources act as buffers and diminish the negative relationship between pupil misbehavior and work engagement. In addition, using conservation of resources theory, the authors hypothesized that job resources particularly influence work engagement when teachers are confronted with high levels of pupil misconduct. In line with these hypotheses, moderated structural equation modeling analyses resulted in 14 out of 18 possible 2-way interaction effects. In particular, supervisor support, innovativeness, appreciation, and organizational climate were important job resources that helped teachers cope with demanding interactions with students.


title: Development of the Highly Precise Magnetic Current Sensor Module of +/−300 A Utilizing AMR Element With Bias-Magnet

abstract: More and more sensitivity improvement is required for current sensors that are used in new area of applications, such as electric vehicle, smart meter, and electricity usage monitoring system. To correspond with the technical needs, a high precision magnetic current sensor module has been developed. The sensor module features an excellent linearity and a small magnetic hysteresis. In addition, it offers 2.5–4.5 V voltage output for 0–300 A positive input current and 0.5–2.5 V voltage output for 0–300 A negative input current under −40 °C–125 °C, VCC = 5 V condition.


title: FSX: fine-grained incremental unit test generation for C/C++ programs

abstract: Automated unit test generation bears the promise of significantly reducing test cost and hence improving software quality. However, the maintenance cost of the automatically generated tests presents a significant barrier to adoption of this technology. To address this challenge, we propose a novel technique for automated and fine-grained incremental generation of unit tests through minimal augmentation of an existing test suite. The technique uses iterative, incremental refinement of test-drivers and symbolic execution, guided by a diagnostics engine. The diagnostics engine works off a novel precise and efficient byte-level dynamic dependence analysis built using Reduced Ordered Binary Decision Diagrams (ROBDDs). We present a tool FSX implementing this technique and evaluate it under two practical use-cases of incremental unit test generation, on five revisions of the open-source software iPerf, as well as on 3 large subjects, comprising more than 60 thousand lines of code, from in-house commercial network products. The evaluation shows that FSX can generate high-quality unit tests on large industrial software while minimizing the maintenance cost of the overall test-suite.


title: CESSE'07 (2007)" Advanced Metamorphic Techniques in Computer Viruses

abstract: Nowadays viruses use polymorphic techniques to mutate their code on each replication, thus evading detection by antiviruses. However detection by emulation can defeat simple polymorphism: thus metamorphic techniques are used which thoroughly change the viral code, even after decryption. We briefly detail this evolution of virus protection techniques against detection and then study the MetaPHOR virus, today's most advanced metamorphic virus.


Entity-Relationship Extraction from Wikipedia Unstructured Text