title: Stereoscopic Highlighting: 2D Graph Visualization on Stereo Displays

abstract: In this paper we present a new technique and prototype graph visualization system, stereoscopic highlighting, to help answer accessibility and adjacency queries when interacting with a node-link diagram. Our technique utilizes stereoscopic depth to highlight regions of interest in a 2D graph by projecting these parts onto a plane closer to the viewpoint of the user. This technique aims to isolate and magnify specific portions of the graph that need to be explored in detail without resorting to other highlighting techniques like color or motion, which can then be reserved to encode other data attributes. This mechanism of stereoscopic highlighting also enables focus+context views by juxtaposing a detailed image of a region of interest with the overall graph, which is visualized at a further depth with correspondingly less detail. In order to validate our technique, we ran a controlled experiment with 16 subjects comparing static visual highlighting to stereoscopic highlighting on 2D and 3D graph layouts for a range of tasks. Our results show that while for most tasks the difference in performance between stereoscopic highlighting alone and static visual highlighting is not statistically significant, users performed better when both highlighting methods were used concurrently. In more complicated tasks, 3D layout with static visual highlighting outperformed 2D layouts with a single highlighting method. However, it did not outperform the 2D layout utilizing both highlighting techniques simultaneously. Based on these results, we conclude that stereoscopic highlighting is a promising technique that can significantly enhance graph visualizations for certain use cases.


title: Gait Deviations in Children with Autism Spectrum Disorders: A Review

abstract: In recent years, it has become clear that children with autism spectrum disorders (ASDs) have difficulty with gross motor function and coordination, factors which influence gait. Knowledge of gait abnormalities may be useful for assessment and treatment planning. This paper reviews the literature assessing gait deviations in children with ASD. Five online databases were searched using keywords “gait” and “autism,” and 11 studies were found which examined gait in childhood ASD. Children with ASD tend to augment their walking stability with a reduced stride length, increased step width and therefore wider base of support, and increased time in the stance phase. Children with ASD have reduced range of motion at the ankle and knee during gait, with increased hip flexion. Decreased peak hip flexor and ankle plantar flexor moments in children with ASD may imply weakness around these joints, which is further exhibited by a reduction in ground reaction forces at toe-off in children with ASD. Children with ASD have altered gait patterns to healthy controls, widened base of support, and reduced range of motion. Several studies refer to cerebellar and basal ganglia involvement as the patterns described suggest alterations in those areas of the brain. Further research should compare children with ASD to other clinical groups to improve assessment and treatment planning.


title: Performance Analysis of Different Feeding Techniques

abstract: This paper describes the performance analysis of different feeding technique for wireless microstrip patch antenna i.e for wi-max applications. In this paper four types of feeding techniques (Microstrip line feed, coaxial probe feed, proximity coupled feed and aperture coupled feed) are used. From the four feeding techniques, microstrip line and coaxial probe feeds are contacting scheme, in which RF power is fed directly to the radiating patch using a connecting element such as a microstrip line whereas proximity and aperture coupled feeds are non-contacting schemes, in which electromagnetic field coupling is done to transfer power between the microstrip line and the radiating patch. Paper describing four feeding technique and gives a better understanding of design parameters of an antenna and their effect on return losses, bandwidth, VSWR and resonant frequency. Finally simulation is done using design software HFSS.


title: Automatic Detection of Fake News

abstract: The proliferation of misleading information in everyday access media outlets such as social media feeds, news blogs, and online newspapers have made it challenging to identify trustworthy news sources, thus increasing the need for computational tools able to provide insights into the reliability of online content. In this paper, we focus on the automatic identification of fake content in online news. Our contribution is twofold. First, we introduce two novel datasets for the task of fake news detection, covering seven different news domains. We describe the collection, annotation, and validation process in detail and present several exploratory analysis on the identification of linguistic differences in fake and legitimate news content. Second, we conduct a set of learning experiments to build accurate fake news detectors. In addition, we provide comparative analyses of the automatic and manual identification of fake news.


title: Error Correction, Sensory Prediction, and Adaptation in Motor Control

abstract: Motor control is the study of how organisms make accurate goal-directed movements. Here we consider two problems that the motor system must solve in order to achieve such control. The first problem is that sensory feedback is noisy and delayed, which can make movements inaccurate and unstable. The second problem is that the relationship between a motor command and the movement it produces is variable, as the body and the environment can both change. A solution is to build adaptive internal models of the body and the world. The predictions of these internal models, called forward models because they transform motor commands into sensory consequences, can be used to both produce a lifetime of calibrated movements, and to improve the ability of the sensory system to estimate the state of the body and the world around it. Forward models are only useful if they produce unbiased predictions. Evidence shows that forward models remain calibrated through motor adaptation: learning driven by sensory prediction errors.


title: Event Identification and Tracking in Social Media Streaming Data

abstract: In recent years, the growing popularity and active use of social media services on the web have resulted in massive amounts of user-generated data. With these data available, there is also an increasing interest in analyzing it and to extract information from it. Since social media analysis is concerned with investigating current events around the world, there is a strong emphasis on identifying these evens as quickly as possible, ideally in real-time. In order to scale with the rapidly increasing volume of social media data, we propose to explore very simple event identification mechanisms, rather than applying the more complex approaches that have been proposed in the literature. In this paper, we present a first investigation along this motivation. We discuss a simple sliding window model, which uses shifts in the inverse document frequency (IDF) to capture trending terms as well as to track the evolution and the context around events. Further, we present an initial experimental evaluation of the results that we obtained by analyzing real-world data streams from Twitter.


title: Coding Together at Scale: GitHub as a Collaborative Social Network

abstract: GitHub is the most popular repository for open source code. It has more than 3.5 million users, as the company declared in April 2013, and more than 10 million repositories, as of December 2013. It has a publicly accessible API and, since March 2012, it also publishes a stream of all the events occurring on public projects. Interactions among GitHub users are of a complex nature and take place in different forms. Developers create and fork repositories, push code, approve code pushed by others, bookmark their favorite projects and follow other developers to keep track of their activities. ::: In this paper we present a characterization of GitHub, as both a social network and a collaborative platform. To the best of our knowledge, this is the first quantitative study about the interactions happening on GitHub. We analyze the logs from the service over 18 months (between March 11, 2012 and September 11, 2013), describing 183.54 million events and we obtain information about 2.19 million users and 5.68 million repositories, both growing linearly in time. We show that the distributions of the number of contributors per project, watchers per project and followers per user show a power-law-like shape. We analyze social ties and repository-mediated collaboration patterns, and we observe a remarkably low level of reciprocity of the social connections. We also measure the activity of each user in terms of authored events and we observe that very active users do not necessarily have a large number of followers. Finally, we provide a geographic characterization of the centers of activity and we investigate how distance influences collaboration.


title: Design of Parallel-Coupled Dual-Mode Resonator Bandpass Filters

abstract: A circuit model for dual-mode resonators is proposed to develop a design procedure that facilitates the design of parallel-coupled dual-mode resonator filters. Based on the design procedure, a parallel-coupled dual-mode resonator filter can be designed in a way similar to the design of parallel-coupled line filters. Compared to the parallel-coupled line filter that uses half-wavelength line resonators, the use of dual-mode resonators not only achieves smaller size and better performance, but does not complicate the design and fabrication processes, as demonstrated in this paper. Accordingly, the parallel-coupled dual-mode resonator filter can be a good candidate for many practical applications that accept the Chebyshev or Butterworth response.


title: Hand sign language recognition for Bangla alphabet based on Freeman Chain Code and ANN

abstract: Hand sign language recognition is one of the fundamental steps to overcome the barrier of communication between a deaf-mute and a normal person in the field of computer vision. In this paper, a hand sign language recognition framework is proposed for various Bangla alphabets using Artificial Neural Network (ANN). For that, initially the input image is normalized and the skin area is extracted on the basis of the YCbCr values corresponding to human skin color. The extracted area i.e., hand sign area is converted into a binary image and the gaps in the binary hand sign area are filled through the morphological operations. After that, the boundary edge of the hand sign area is extracted through the canny edge detector and extracts the hand sign region of interest (ROI). Finally, features are extracted from the hand sign ROI using Freeman Chain Code (FCC). The ANN is used for training and classifies the hand sign images. The proposed method is tested using various hand sign images and results are presented to demonstrate the efficiency and effectiveness.


title: MONTE CARLO SIMULATION OF CHEMICAL GELATION USING A PERCOLATION KINETIC GELATION MODEL

abstract: kinetics of non-linear free radical copolymerization of vinyl-divinyl monomer mixtures or chemical gelation, and to characterize kinetic effects on polymerization statistics and microstructures. New algorithm for random selection of the next neighbour site in a self-avoiding random walk and efficient mechanisms of mobility of components are introduced to improve the universality of the predictions by removing commonly occurring simulation deficiencies due to early trapping of radicals. The model has the capability of predicting the onset of the sol-gel transition and the effect of chemical composition on the transition point. It is shown that there is attained a better understanding of microstructure evolution and appearance of gel phase during polymerization and chemical gelation. Finally, one important benefit of the simulation method is the ability of characterizing system in which, the dominant combination reaction leads to highly branched structure. Majid Ghiass1*, Bahram Dabir2, Manouchehr Nikazar2 and Alejandro D. Rey1


title: Tracing cyber attacks from the practical perspective

abstract: The integrity of the Internet is severely impaired by rampant denial of service and distributed DoS attacks. It is by no means trivial to devise a countermeasure to address these attacks because of their anonymous and distributed nature. This article presents a brief survey of the most promising recently proposed schemes for tracing cyber attacks: IP traceback. Since IP traceback technology is evolving rapidly, for the community to better comprehend and capture the properties of disparate traceback approaches, we first classify these schemes from multiple aspects. From the perspective of practicality and feasibility, we then analyze and explore the advantages and disadvantages of these schemes in depth so that shortcomings and possible enhancements of each scheme are highlighted. Finally, open problems and future work are discussed, and concluding remarks are drawn.


Recruitment and Retention of Teachers in Tennessee ’ s Achievement School District and iZone Schools A Policy Brief on Driving Improvement in Low Performing Schools


title: Counter-Based Cache Replacement and Bypassing Algorithms

abstract: Recent studies have shown that, in highly associative caches, the performance gap between the least recently used (LRU) and the theoretical optimal replacement algorithms is large, motivating the design of alternative replacement algorithms to improve cache performance. In LRU replacement, a line, after its last use, remains in the cache for a long time until it becomes the LRU line. Such deadlines unnecessarily reduce the cache capacity available for other lines. In addition, in multilevel caches, temporal reuse patterns are often inverted, showing in the L1 cache but, due to the filtering effect of the L1 cache, not showing in the L2 cache. At the L2, these lines appear to be brought in the cache but are never reaccessed until they are replaced. These lines unnecessarily pollute the L2 cache. This paper proposes a new counter-based approach to deal with the above problems. For the former problem, we predict lines that have become dead and replace them early from the L2 cache. For the latter problem, we identify never-reaccessed lines, bypass the L2 cache, and place them directly in the L1 cache. Both techniques are achieved through a single counter-based mechanism. In our approach, each line in the L2 cache is augmented with an event counter that is incremented when an event of interest such as certain cache accesses occurs. When the counter reaches a threshold, the line ";expires"; and becomes replaceable. Each line's threshold is unique and is dynamically learned. We propose and evaluate two new replacement algorithms: Access interval predictor (AIP) and live-time predictor (LvP). AIP and LvP speed up 10 capacity-constrained SPEC2000 benchmarks by up to 48 percent and 15 percent on average (7 percent on average for the whole 21 Spec2000 benchmarks). Cache bypassing further reduces L2 cache pollution and improves the average speedups to 17 percent (8 percent for the whole 21 Spec2000 benchmarks).


title: Exploratory analysis of time-lapse imagery with fast subset PCA

abstract: In surveillance and environmental monitoring applications, it is common to have millions of images of a particular scene. While there exist tools to find particular events, anomalies, human actions and behaviors, there has been little investigation of tools which allow more exploratory searches in the data. This paper proposes modifications to PCA that enable users to quickly recompute low-rank decompositions for select spatial and temporal subsets of the data. This process returns decompositions orders of magnitude faster than general PCA and are close to optimal in terms of reconstruction error. We show examples of real exploratory data analysis across several applications, including an interactive web application.


title: Intelligent traffic monitoring and guidance system for smart city

abstract: IoT stands for Internet of things which is defined as an intercommunication of heterogeneous smart devices acting as a single network. In IoT, every device is connected via internet with different devices having different capabilities within a network. IoT is implemented almost in every field of life which is an extension of Wireless Sensor Networks (WSNs). Smart city is implemented through smart nodes in real scenarios to make digital environment in all field of life including smart parking, banking, sewerage system, waste management, environmental monitoring, smart transport and healthcare centers. However, there is a very little practical use of IoT and smart cities infrastructure in real life. In this paper, we have focused on intelligent traffic monitoring system using graph theory and formal methods. Our proposed model has various nodes that are assumed within a city including roads, objects and traffic signals to make a collective intelligent traffic guidance and monitoring system. Many operations including finding shortest path in terms of time and distance, finding specific area within city and finding safest and low rush ways towards the destination. The graph based model is transformed into a formal model using Vienna Development Method-Specification Language (VDM-SL). The proof of correctness is provided by various facilities available in the VDM-SL toolbox.


title: A containerized analytics framework for data and compute-intensive pipeline applications

abstract: The joint effort of scientific collaborations and the expanding data market creates demand for high-performance and data-intensive analytics infrastructures that can exploit the potential of heterogeneous multi-core architectures with dynamic and scalable execution environments. Contemporary approaches focus on developing efficient parallel application models, but lack the flexibility of efficiently integrating and utilizing native or accelerator-based code. In this work, we illustrate a novel approach on mending this shortcoming and offering seamless application integration into a highly versatile execution infrastructure. The centerpiece is a framework of containerized execution units and management thereof for satisfying the diverse requirements of data analytics pipelines and its stages. Containers not only ease distribution and deployment of applications, but, more importantly enable an efficient synthesis of different stage implementation variants aimed towards exploiting heterogeneous computing resources. Consequently, this approach allows the infrastructure to utilize mainstream data and compute-intensive techniques and paradigms to achieve the goal of efficient pipeline execution. We present our approach in form of a requirement analysis, a multi-tier architecture description, and deployment scenarios based on our current prototype implementation.


title: Efficient and Constructive Algorithms for the Pathwidth and Treewidth of Graphs

abstract: In this paper we give, for all constants k, l , explicit algorithms, that given a graph ::: G = (V, E) with a tree-decomposition of G with treewidth at most l, decide whether ::: the treewidth (or pathwidth) of G is at most k, and if so, find a tree-decomposition or ::: (path-decomposition) of G of width at most k, and that use O(|V|) time. In contrast ::: with previous solutions, our algorithms do not rely on non-constructive reasoning, ::: and are single exponential in k and l. This result can be combined with a result of ::: Reed [37], yielding explicit O(n log n) algorithms for the problem, given a graph G, to determine whether the treewidth (or pathwidth) of G is at most k, and if so, to find a tree- (or path-)decomposition of width at most k (k constant). Also, Bodlaender [13] has used the result of this paper to obtain linear time algorithms for these problems. ::: We also show that for all constants k, there exists a polynomial time algorithm, ::: that, when given a graph G = (V, E) with treewidth ≤ k, computes the pathwidth of G and a path-decomposition of G of minimum width.


Learning English Is Fun via Kahoot : Students ’ Attitude , Motivation and Perceptions


title: A Computer Movie Simulating Urban Growth in the Detroit Region

abstract: considered a demographic model whose primary objectives are instructional.' The model developed here may be used for forecasting, but was not constructed for this specific purpose, and it is a demographic model since it describes only population growth, with particular emphasis on the geographical distribution of this growth. As a premise, I make the assumption that everything is related to everything else. Superficially considered this would suggest a model of infinite complexity; a corollary inference often made is that social systems are difficult because they contain many variables; numerous people confuse the number of variables with the


title: A Double Dwell High Sensitivity GPS Acquisition Scheme Using Binarized Convolution Neural Network

abstract: Conventional GPS acquisition methods, such as Max selection and threshold crossing (MAX/TC), estimate GPS code/Doppler by its correlation peak. Different from MAX/TC, a multi-layer binarized convolution neural network (BCNN) is proposed to recognize the GPS acquisition correlation envelope in this article. The proposed method is a double dwell acquisition in which a short integration is adopted in the first dwell and a long integration is applied in the second one. To reduce the search space for parameters, BCNN detects the possible envelope which contains the auto-correlation peak in the first dwell to compress the initial search space to 1/1023. Although there is a long integration in the second dwell, the acquisition computation overhead is still low due to the compressed search space. Comprehensively, the total computation overhead of the proposed method is only 1/5 of conventional ones. Experiments show that the proposed double dwell/correlation envelope identification (DD/CEI) neural network achieves 2 dB improvement when compared with the MAX/TC under the same specification.


title: Low-Cost Wake-Up Receiver for RF Energy Harvesting Wireless Sensor Networks

abstract: The existing passive wake-up receivers (WuRxs) are radio frequency identification (RFID) tag based, which incur high cost and complexity. In this paper, we study cost-effective and long-range WuRx solutions for range-based wake-up (RW) as well as directed wake-up (DW). In particular, we consider the case of an RF energy harvesting wireless sensor node and investigate how a low-cost WuRx can be built using an RF energy harvester available at the node. Experimental results show that our developed prototype can achieve a wake-up range of 1.16 m with +13 dBm transmit power. Furthermore, our empirical study shows that at +30 dBm transmit power the wake-up distance of our developed RW module is >9 m. High accuracy of DW is demonstrated by sending a 5-bit ID from a transmitter at a bit rate up to 33.33 kbps. Finally, we present optimized WuRx designs for RW and DW using Agilent advanced design system, which offer up to 5.69 times higher wake-up range for RW and energy savings per bit of about 0.41 mJ and 21.40 nJ, respectively, at the transmitter and the sensor node in DW.


title: Haze Detection and Removal in Remotely Sensed Multispectral Imagery

abstract: Haze degrades optical data and reduces the accuracy of data interpretation. Haze detection and removal is a challenging and important task for optical multispectral data correction. This paper presents an empirical and automatic method for inhomogeneous haze detection and removal in medium- and high-resolution satellite optical multispectral images. The dark-object subtraction method is further developed to calculate a haze thickness map, allowing a spectrally consistent haze removal on calibrated and uncalibrated satellite multispectral data. Rare scenes with a uniform and highly reflecting landcover result in limitations of the method. Evaluation on hazy multispectral data (Landsat 8 OLI and WorldView-2) and a comparison to haze-free reference data illustrate the spectral consistency after haze removal.


title: Pteromys: interactive design and optimization of free-formed free-flight model airplanes

abstract: This paper introduces novel interactive techniques for designing original hand-launched free-flight glider airplanes which can actually fly. The aerodynamic properties of a glider aircraft depend on their shape, imposing significant design constraints. We present a compact and efficient representation of glider aerodynamics that can be fit to real-world conditions using a data-driven method. To do so, we acquire a sample set of glider flight trajectories using a video camera and the system learns a nonlinear relationship between forces on the wing and wing shape. Our acquisition system is much simpler to construct than a wind tunnel, but using it we can efficiently discover a wing model for simple gliding aircraft. Our resulting model can handle general free-form wing shapes and yet agrees sufficiently well with the acquired airplane flight trajectories. Based on this compact aerodynamics model, we present a design tool in which the wing configuration created by a user is interactively optimized to maximize flight-ability. To demonstrate the effectiveness of our tool for glider design by novice users, we compare it with a traditional design workflow.


title: A Frequency-Reconfigurable Monopole Antenna Using Switchable Slotted Ground Structure

abstract: In this letter, a novel compact frequency-reconfigurable monopole antenna with five switchable states including an ultrawideband (UWB) state, three narrowband states, and a dual-band state is presented. The frequency-reconfigurable capability of the antenna is achieved by using a switchable slotted structure on the ground plane. The designed antenna has a simple structure and compact size of 40×40 mm . The antenna, which supports most applicable frequency bands above 2 GHz, can be used in multiradio wireless systems. Moreover, the proposed antenna, which has a combined UWB and narrowband functionality, has a good potential for use in cognitive radio.


title: Ultra-wideband tightly coupled fractal octagonal phased array antenna

abstract: This paper presents a low profile ultrawideband tightly coupled phased array antenna with integrated feedlines. The aperture array consists of planar element pairs with fractal geometry. In each element these pairs are set orthogonal to each other for dual polarisation. The design is an array of closely capacitively coupled pairs of fractal octagonal rings. The adjustment of the capacitive load at the tip end of the elements and the strong mutual coupling between the elements, enables a wideband conformal performance. Adding a ground plane below the array partly compensates for the frequency variation of the array impedance, providing further enhancement in the array bandwidth. Additional improvement is achieved by placing another layer of conductive elements at a defined distance above the radiating elements. A Genetic Algorithm was scripted in MATLAB and combined with the HFSS simulator, providing an easy optimisation tool across the operational bandwidth for the array unit cell design parameters. The proposed antenna shows a wide-scanning ability with a low cross-polarisation level over a wide bandwidth.


A software defined OFDM modulator


title: Object Detection via End-to-End Integration of Aspect Ratio and Context Aware Part-based Models and Fully Convolutional Networks

abstract: This paper presents a framework of integrating a mixture of part-based models and region-based convolutional networks for accurate and efficient object detection. Each mixture component consists of a small number of parts accounting for both object aspect ratio and contextual information explicitly. The mixture is category-agnostic for the simplicity of scaling up in applications. Both object aspect ratio and context have been extensively studied in traditional object detection systems such as the mixture of deformable part-based models [13]. They are, however, largely ignored in deep neural network based detection systems [17, 16, 39, 8]. The proposed method addresses this issue in two-fold: (i) It remedies the wrapping artifact due to the generic RoI (region-of-interest) pooling (e.g., a 3 x 3 grid) by taking into account object aspect ratios. (ii) It models both global (from the whole image) and local (from the surrounding of a bounding box) context for improving performance. The integrated framework is fully convolutional and enjoys end-to-end training, which we call the aspect ratio and context aware fully convolutional network (ARC-FCN). In experiments, ARC-FCN shows very competitive results on the PASCAL VOC datasets, especially, it outperforms both Faster R-CNN [39] and R-FCN [8] with significantly better mean average precision (mAP) using larger value for the intersection-over-union (IoU) threshold (i.e., 0.7 in the experiments). ARC-FCN is still sufficiently efficient with a test-time speed of 380ms per image, faster than the Faster R-CNN but slower than the R-FCN.


title: Intuitionistic Fuzzy Information Aggregation Using Einstein Operations

abstract: Aggregation of fuzzy information is a new branch of Atanassov's intuitionistic fuzzy set (AIFS) theory, which has attracted significant interest from researchers in recent years. In this paper, we treat the intuitionistic fuzzy aggregation operators with the help of Einstein operations. We first introduce some new operations of AIFSs, such as Einstein sum, Einstein product, and Einstein scalar multiplication. Then, we develop some intuitionistic fuzzy aggregation operators, such as the intuitionistic fuzzy Einstein weighted averaging operator and the intuitionistic fuzzy Einstein ordered weighted averaging operator, which extend the weighted averaging operator and the ordered weighted averaging operator to aggregate Atanassov's intuitionistic fuzzy values, respectively. We further establish various properties of these operators and analyze the relations between these operators and the existing intuitionistic fuzzy aggregation operators. Moreover, we give some numerical examples to illustrate the developed aggregation operators. Finally, we apply the intuitionistic fuzzy Einstein weighted averaging operator to multiple attribute decision making with intuitionistic fuzzy information.


title: Detection of moving objects with a moving camera using non-panoramic background model

abstract: This paper presents a fast and reliable method for moving object detection with moving cameras (including pan---tilt---zoom and hand-held cameras). Instead of building large panoramic background model as conventional approaches, we construct a small-size background model, whose size is the same as input frame, to decrease computation time and memory storage without loss of detection performance. The small-size background model is built by the proposed single spatio-temporal distributed Gaussian model and this can solve false detection results arising from registration error and background adaptation problem in moving background. More than the proposed background model based on spatial and temporal information, several pre- and post-processing methods are adopted and organized systematically to enhance the detection performances. We evaluate the proposed method with several video sequences under difficult conditions, such as illumination change, large zoom variation, and fast camera movement, and present outperforming detection results of our algorithm with fast computation time.


title: Does Test-Driven Development Improve the Program Code? Alarming Results from a Comparative Case Study

abstract: It is suggested that test-driven development (TDD) is one of the most fundamental practices in agile software development, which produces loosely coupled and highly cohesive code. However, how the TDD impacts on the structure of the program code have not been widely studied. This paper presents the results from a comparative case study of five small scale software development projects where the effect of TDD on program design was studied using both traditional and package level metrics. The empirical results reveal that an unwanted side effect can be that some parts of the code may deteriorate. In addition, the differences in the program code, between TDD and the iterative test-last development, were not as clear as expected. This raises the question as to whether the possible benefits of TDD are greater than the possible downsides. Moreover, it additionally questions whether the same benefits could be achieved just by emphasizing unit-level testing activities.


title: A carbon nanotube based resettable sensor for measuring free chlorine in drinking water

abstract: Free chlorine from dissolved chlorine gas is widely used as a disinfectant for drinking water. The residual chlorine concentration has to be continuously monitored and accurately controlled in a certain range around 0.5–2 mg/l to ensure drinking water safety and quality. However, simple, reliable, and reagent free monitoring devices are currently not available. Here, we present a free chlorine sensor that uses oxidation of a phenyl-capped aniline tetramer (PCAT) to dope single wall carbon nanotubes (SWCNTs) and to change their resistance. The oxidation of PCAT by chlorine switches the PCAT-SWCNT system into a low resistance (p-doped) state which can be detected by probing it with a small voltage. The change in resistance is found to be proportional to the log-scale concentration of the free chlorine in the sample. The p-doping of the PCAT-SWCNT film then can be electrochemically reversed by polarizing it cathodically. This sensor not only shows good sensing response in the whole concentration range of free chlorine in drinking water but is also able to be electrochemically reset back many times without the use of any reagents. This simple sensor is ideally suited for measuring free chlorine in drinking water continuously.


title: Online communication among adolescents: an integrated model of its attraction, opportunities, and risks

abstract: Adolescents far outnumber adults in their use of e-communication technologies, such as instant messaging and social network sites. In this article, we present an integrative model that helps us to understand both the appeal of these technologies and their risks and opportunities for the psychosocial development of adolescents. We first outline how the three features (anonymity, asynchronicity, and accessibility) of online communication stimulate controllability of online self-presentation and self-disclosure among adolescents. We then review research on the risks and opportunities of online self-presentation and self-disclosure for the three components of adolescents' psychosocial development, including identity (self-unity, self-esteem), intimacy (relationship formation, friendship quality, cyberbullying), and sexuality (sexual self-exploration, unwanted sexual solicitation). Existing research suggests several opportunities of online communication, such as enhanced self-esteem, relationship formation, friendship quality, and sexual self-exploration. It also yields evidence of several risks, including cyberbullying and unwanted sexual solicitation. We discuss the shortcomings of existing research, the possibilities for future research, and the implications for educators and health care professionals.


title: Fast Human Detection Combining Range Image Segmentation and Local Feature Based Detection

abstract: This paper proposes a human detection method that combines range image segmentation and human detection based on image local features. The method uses a stereo vision system called Subtraction Stereo, which extracts a range image of foreground regions. An extracted range image is segmented for each object by Mean Shift Clustering. Human detection based on local features is applied to each segment of foreground regions to detect humans. In this process, regions to scan a detection window for extracting local features are restricted. In addition, the size of the detection window is obtained using the distance information of a range image and camera parameters. Therefore, processing time and false detection can be reduced. Joint HOG features are used as the image local features. When applying the Joint HOG based human detection, occlusion of multiple humans is considered in construction of a classifier and in integration of detection windows, which improves the detection performance for the occluded humans. The proposed method is evaluated by experiments comparing with the method using Joint HOG features only. 11fps fast human detection is achieved.


Can You Verifi This? Studying Uncertainty and Decision-Making About Misinformation Using Visual Analytics.


title: Microbial and Functional Diversity within the Phyllosphere of Espeletia Species in an Andean High-Mountain Ecosystem.

abstract: Microbial populations residing in close contact with plants can be found in the rhizosphere, in the phyllosphere as epiphytes on the surface, or inside plants as endophytes. Here, we analyzed the microbiota associated with Espeletia plants, endemic to the Páramo environment of the Andes Mountains and a unique model for studying microbial populations and their adaptations to the adverse conditions of high-mountain neotropical ecosystems. Communities were analyzed using samples from the rhizosphere, necromass, and young and mature leaves, the last two analyzed separately as endophytes and epiphytes. The taxonomic composition determined by performing sequencing of the V5-V6 region of the 16S rRNA gene indicated differences among populations of the leaf phyllosphere, the necromass, and the rhizosphere, with predominance of some phyla but only few shared operational taxonomic units (OTUs). Functional profiles predicted on the basis of taxonomic affiliations differed from those obtained by GeoChip microarray analysis, which separated community functional capacities based on plant microenvironment. The identified metabolic pathways provided insight regarding microbial strategies for colonization and survival in these ecosystems. This study of novel plant phyllosphere microbiomes and their putative functional ecology is also the first step for future bioprospecting studies in search of enzymes, compounds, or microorganisms relevant to industry or for remediation efforts.


title: The prevalence of multiple sclerosis in the world: an update

abstract: The systematic study of multiple sclerosis (MS) in populations, started in 1929 by Sydney Allison, now consists of over 400 publications dealing with the prevalence of MS throughout the world. However, any attempt to redefine the pattern of geographical differences in MS frequency remains as difficult as ever. The comparison of prevalence studies carried out in different areas and times is made difficult by the variability in surveyed population sizes, age structures, ethnic origins and composition, and the difficult quantification of numerators, especially regarding the recognition of benign and very early cases. Additionally, complete case ascertainment depends on access to medical care, local medical expertise, number of neurologists, accessibility and availability of new diagnostic procedures, the degree of public awareness about MS, and the investigators' zeal and resources. Critical examination of the more recent data on MS prevalence leads to some revisions of previously held concepts, the most interesting of which is the appreciation of the greater influence of genetic factors on disease acquisition. The rarity of MS among Samis, Turkmen, Uzbeks, Kazakhs, Kyrgyzis, native Siberians, North and South Amerindians, Chinese, Japanese, African blacks and New Zealand Maoris, as well as the high risk among Sardinians, Parsis and Palestinians, clearly indicate that the different susceptibilities of distinct racial and ethnic groups are an important determinant of the uneven geographic distribution of the disease. The updated distribution of MS in Europe, showing many exceptions to the previously described north-south gradient, requires more explanation than simply a prevalence-latitude relationship. Prevalence data imply that racial and ethnic differences are important in influencing the worldwide distribution of MS and that its geography must be interpreted in terms of the probable discontinuous distribution of genetic susceptibility alleles, which can however be modified by environment. Because the environmental and genetic determinants of geographic gradients are by no means mutually exclusive, the race versus place controversy is, to some extent, a useless and sterile debate.


title: Dualband meander monopole antenna

abstract: Due to their simple structure and omnidirectional radiation, monopole antennas have been found widespread applications in mobile communication systems. To achieve dual-band operation in a compact monopole antenna design, we propose a novel design of monopole antenna by extending a conductor line from the end of a rectangular meander monopole. The proposed antenna is fed by a CPW line and is printed in a single metal layer. Therefore, this antenna can be manufactured in a simple procedure. Moreover, due to the presence of an extended conductor line in the proposed design, the antenna is not only capable of providing the GSM/DCS dual-frequency operations, but also can have an antenna-size reduction at a given dual-frequency operation.


title: Multi-valued attribute and multi-labeled data decision tree algorithm

abstract: This paper analyzes the existing decision tree algorithms for dealing with multi-valued and multi-labeled data. These algorithms have the following shortcomings: The choice of which attributes is difficult and the calculation for similarity is not precise enough. Based on these deficiencies, this paper proposes a new decision tree algorithm for multi-valued and multi-labeled data (AMDT). In the algorithm, firstly a new formula sim5 is proposed for calculating the similarity between two label-sets in the child nodes. It comprehensively considers the condition which the elements appear and not appear in both of the two label-sets at the same time and adjusts the proportion of them by the coefficient α, so that the similarity calculations of the label-sets are more comprehensive and accurate. Secondly, we propose the new conditions of the corresponding node to stop splitting. Lastly, we give the prediction method. Results of comparison experiments with the existing algorithms (MMC, SSC and SCC_SP_1) show that AMDT has the higher predictive accuracy.


title: A fingerprint biometric cryptosystem in FPGA

abstract: This paper presents the implementation of a complete fingerprint biometric cryptosystem in a Field Programmable Gate Array (FPGA). This is possible thanks to the use of a novel fingerprint feature, named QFingerMap, which is binary, length-fixed, and ordered. Security of Authentication on FPGA is further improved because information stored is protected due to the design of a cryptosystem based on Fuzzy Commitment. Several samples of fingers as well as passwords can be fused at feature level with codewords of an error correcting code to generate non-sensitive data. System performance is illustrated with experimental results corresponding to 560 fingerprints acquired in live by an optical sensor and processed by the system in a Xilinx Virtex 6 FPGA. Depending on the realization, more or less accuracy is obtained, being possible a perfect authentication (zero Equal Error Rate), with the advantages of real-time operation, low power consumption, and a very small device.


title: A comparison of classification models for natural disaster and critical event detection from news

abstract: We present a contrastive study of document-level event classification of a range of seven different event types, namely floods, storms, fires, armed conflict, terrorism, infrastructure breakdown and labour unavailability from English-language news. Our study compares different supervised classification approaches, namely Support Vector Machine (SVM), Random Forest (RF), Convolutional Neural Network (CNN) and Hierarchical Attention Network (HAN). While past systems for Topic Detection and Tracking (TDT) and event extraction have proposed different machine learning models, to date SVMs, RFs, CNNs and HANs have not been compared on this task. Our classifiers are also informed by word embeddings trained on large amounts of high-quality agency news, which leads to improvements compared to the use of pre-trained embedding vectors. We report a detailed quantitative error analysis.


title: A Broadband Stacked Power Amplifier in 45-nm CMOS SOI Technology

abstract: A fully integrated broadband power amplifier (PA) is implemented in a standard 45-nm CMOS SOI technology. The PA is designed using a dynamically biased stacked SOI transistor approach, which constructively adds drain-source voltage signals of individual transistors while keeping their gate voltages within source and drain voltage limits. The design overcomes both low gate-oxide breakdown and low source-drain reach through voltages of nanoscale CMOS transistors. The number, size, and topology of transistors in the stack are optimized to deliver a relatively high linear output power over a wide range of frequencies. The amplifier under a supply voltage of 4.5 V measures a flat gain of 6 dB with -1-dB bandwidth of 6 to 26.5 GHz ( X-band to K-band). At 18 GHz, the PA under a supply voltage of 7.2 V measures a saturated output power (PSAT) of 26.1 dBm ( ~ 400 mW), a linear output power (P1 dB) of 22.5 dBm, and a peak power-added efficiency (PAE) of 11%. With a lower power supply voltage of 4.5 V, the PAE increases to more than 20% and stays above 17% with relatively constant PSAT and P1 dB for several measured frequencies in the range of 6 to 20 GHz. The PA occupies an active chip area of only 0.16 mm2.


title: Adversarial Examples for Malware Detection

abstract: Machine learning models are known to lack robustness against inputs crafted by an adversary. Such adversarial examples can, for instance, be derived from regular inputs by introducing minor—yet carefully selected—perturbations.


title: Swarm based autonomous landmine detecting robots

abstract: Recently it has been found out that, at least 60 countries in the world are contaminated by landmines. These landmines have become a growing concern, due to their threat to lives of human and cattle. There are more than 50–100 million landmines still not diffused in more than 50 countries like Egypt, Angola, Iran, Afghanistan, Cambodia, etc. Though several countries have started taking some important measures like funding the demining projects, training people with demining techniques, etc., landmines have become a major threat to the mortals. There are many detection techniques, but most of these involve humans, which in turn creates a great threat to their lives and limbs. The autonomous swarm based landmine detecting robots proposed in this paper involves a swarm of robots that could autonomously detect landmines and share the location to the control station. The algorithm and the theoretical concepts are shaped into a working model. This paper gives the reader a clear picture about how these robots help in demining.


Automatic Construction of Inference-Supporting Knowledge Bases


Turning Distributional Thesauri into Word Vectors for Synonym Extraction and Expansion


title: Enhancement of dispersion and bonding of graphene-polymer through wet transfer of functionalized graphene oxide

abstract: Dispersion of nanomaterials in polymeric matrices plays an important role in determining the final properties of the composites. Dispersion in nano scale, and especially in single layers, provides best opportunity for bonding. In this study, we propose that by proper functionalization and mixing strategy of graphene its dispersion, and bonding to the polymeric matrix can be improved. We then apply this strategy to graphene-epoxy system by amino functionalization of graphene oxide (GO). The process included two phase extraction, and resulted in better dispersion and higher loading of graphene in epoxy matrix. Rheological evaluation of different graphene-epoxy dispersions showed a rheological percolation threshold of 0.2 vol% which is an indication of highly dispersed nanosheets. Observation of the samples by optical microscopy, scanning electron microscopy (SEM), and atomic force microscopy (AFM), showed dispersion homogeneity of the sheets at micro and nano scales. Study of graphene-epoxy composites showed good bonding between graphene and epoxy. Mechanical properties of the samples were consistent with theoretical predictions for ideal composites indicating molecular level dispersion and good bonding between nanosheets and epoxy matrix.


Flexible Path Planning for Nonholonomic Mobile Robots


1 Analysis of a Tightly-coupled Mems Imu Based Gnss / Ins with an External Odometer


title: Growth Characteristics of Infantile Hemangiomas: Implications for Management

abstract: OBJECTIVES.Infantile hemangiomas often are inapparent at birth and have a period of rapid growth during early infancy followed by gradual involution. More precise information on growth could help predict short-term outcomes and make decisions about when referral or intervention, if needed, should be initiated. The objective of this study was to describe growth characteristics of infantile hemangioma and compare growth with infantile hemangioma referral patterns. METHODS.A prospective cohort study involving 7 tertiary care pediatric dermatology practices was conducted. Growth data were available for a subset of 526 infantile hemangiomas in 433 patients from a cohort study of 1096 children. Inclusion criteria were age younger than 18 months at time of enrollment and presence of at least 1 infantile hemangioma. Growth stage and rate were compared with clinical characteristics and timing of referrals. RESULTS.Eighty percent of hemangioma size was reached during the early proliferative stage at a mean age of 3 months. Differences in growth between hemangioma subtypes included that deep hemangiomas tend to grow later and longer than superficial hemangiomas and that segmental hemangiomas tended to exhibit more continued growth after 3 months of age. The mean age of first visit was 5 months. Factors that predicted need for follow-up included ongoing proliferation, larger size, deep component, and segmental and indeterminate morphologic subtypes. CONCLUSIONS.Most infantile hemangioma growth occurs before 5 months, yet 5 months was also the mean age at first visit to a specialist. Recognition of growth characteristics and factors that predict the need for follow-up could help aid in clinical decision-making. The first few weeks to months of life are a critical time in hemangioma growth. Infants with hemangiomas need close observation during this period, and those who need specialty care should be referred and seen as early as possible within this critical growth period. Pediatrics 2008;122:360‐367


title: Identification and Analysis of Bacterial Genomic Metabolic Signatures

abstract: With continued rapid growth in the number and quality of fully sequenced and accurately annotated bacterial genomes, we have unprecedented opportunities to understand metabolic diversity. We selected 101 diverse and representative completely sequenced bacteria and implemented a manual curation effort to identify 846 unique metabolic variants present in these bacteria. The presence or absence of these variants act as a metabolic signature for each of the bacteria, which can then be used to understand similarities and differences between and across bacterial groups. We propose a novel and robust method of summarizing metabolic diversity using metabolic signatures and use this method to generate a metabolic tree, clustering metabolically similar organisms. Resulting analysis of the metabolic tree confirms strong associations with well-established biological results along with direct insight into particular metabolic variants which are most predictive of metabolic diversity. The positive results of this manual curation effort and novel method development suggest that future work is needed to further expand the set of bacteria to which this approach is applied and use the resulting tree to test broad questions about metabolic diversity and complexity across the bacterial tree of life.


title: REV2: Fraudulent User Prediction in Rating Platforms

abstract: Rating platforms enable large-scale collection of user opinion about items(e.g., products or other users). However, untrustworthy users give fraudulent ratings for excessive monetary gains. In this paper, we present REV2, a system to identify such fraudulent users. We propose three interdependent intrinsic quality metrics---fairness of a user, reliability of a rating and goodness of a product. The fairness and reliability quantify the trustworthiness of a user and rating, respectively, and goodness quantifies the quality of a product. Intuitively, a user is fair if it provides reliable scores that are close to the goodness of products. We propose six axioms to establish the interdependency between the scores, and then, formulate a mutually recursive definition that satisfies these axioms. We extend the formulation to address cold start problem and incorporate behavior properties. We develop the REV2 algorithm to calculate these intrinsic quality scores for all users, ratings, and products. We show that this algorithm is guaranteed to converge and has linear time complexity. By conducting extensive experiments on five rating datasets, we show that REV2 outperforms nine existing algorithms in detecting fair and unfair users. We reported the 150 most unfair users in the Flipkart network to their review fraud investigators, and 127 users were identified as being fraudulent(84.6% accuracy). The REV2 algorithm is being deployed at Flipkart.


title: Teacher Feedback, Writing Assignment Quality, and Third-Grade Students' Revision in Lower-And Higher-Achieving Urban Schools

abstract: The relation of the quality of writing assignments and written instructor responses to student writings to the quality of subsequent student work was investigated in 29 urban third-grade classrooms in 8 schools. Writing assignments were generally of a higher quality in the 4 schools that served primarily middle-class, higher-achieving students, most of whom were white or Asian, versus the 4 schools that served primarily low-income and lower-achieving students, the majority of whom were Latino. Across all classrooms, however, teachers focused on standardizing students' written output, which led to marked improvement in the writing mechanics of students' work. Results of regression analyses indicated that the amount and type of feedback students received predicted a significant, although small, proportion of the variance in the quality of the content, organization, and mechanics of students' final drafts. The quality of the writing assignments predicted a small but significant proportion of the variance in ...


title: Conservative treatment of carpal tunnel syndrome: comparison between laser therapy and Fascial Manipulation(®).

abstract: Summary The etiopathogenesis of Carpal Tunnel Syndrome (CTS) is multifactorial and most cases are classified as idiopathic (Thurston 2013). A randomized controlled trial was performed to compare the effectiveness of Fascial Manipulation ® (FM) and Low-Level Laser Therapy (LLLT) for CTS. This prospective trial included 42 patients (70 hands with symptoms) with clinical and electroneuromyographic diagnosis of CTS. The patients were randomly assigned to receive multiple sessions of FM or multiple session of LLLT. The Visual Analogic Scale (VAS) and Boston Carpal Tunnel Questionnaire (BCTQ) were performed at baseline, end of treatment and after three months. The group that received FM showed a significant reduction in subjective pain perception and an increased function assessed by BCTQ at the end of the treatment and follow-up. The group that received LLLT showed an improvement in the BCTQ at the end of the treatment but the improvement level was not sustained at the three month follow-up. FM is a valid alternative treatment for CTS.


title: Carousel: Scalable Traffic Shaping at End Hosts

abstract: Traffic shaping, including pacing and rate limiting, is fundamental to the correct and efficient operation of both datacenter and wide area networks. Sample use cases include policy-based bandwidth allocation to flow aggregates, rate-based congestion control algorithms, and packet pacing to avoid bursty transmissions that can overwhelm router buffers. Driven by the need to scale to millions of flows and to apply complex policies, traffic shaping is moving from network switches into the end hosts, typically implemented in software in the kernel networking stack. In this paper, we show that the performance overhead of end-host traffic shaping is substantial limits overall system scalability as we move to thousands of individual traffic classes per server. Measurements from production servers show that shaping at hosts consumes considerable CPU and memory, unnecessarily drops packets, suffers from head of line blocking and inaccuracy, and does not provide backpressure up the stack. We present Carousel, a framework that scales to tens of thousands of policies and flows per server, built from the synthesis of three key ideas: i) a single queue shaper using time as the basis for releasing packets, ii) fine-grained, just-in-time freeing of resources in higher layers coupled to actual packet departures, and iii) one shaper per CPU core, with lock-free coordination. Our production experience in serving video traffic at a Cloud service provider shows that Carousel shapes traffic accurately while improving overall machine CPU utilization by 8% (an improvement of 20% in the CPU utilization attributed to networking) relative to state-of-art deployments. It also conforms 10 times more accurately to target rates, and consumes two orders of magnitude less memory than existing approaches.


title: Towards a 30 kW/liter, Three-Phase Unity Power Factor Rectifier

abstract: In this paper, a forced air-cooled, 400kHz, 10kW Vienna Rectifier with a power density of 8.5kW/liter is presented. To further improve the power density, the switching frequency is increased to reduce the EMI filter volume. A design for an 18.5kW/liter, 2.5MHz rectifier is proposed. By using today's technology, it is calculated that the maximum power density for a forced air-cooled and water-cooled rectifier is 20.8kW/liter and 24kW/liter respectively. To achieve 30kW/liter, improvements must be made in passive component material properties, reducing semiconductor losses and improving the cooling system.


title: FD-GAN: Pose-guided Feature Distilling GAN for Robust Person Re-identification

abstract: Person re-identification (reID) is an important task that requires to retrieve a person's images from an image dataset, given one image of the person of interest. For learning robust person features, the pose variation of person images is one of the key challenges. Existing works targeting the problem either perform human alignment, or learn human-region-based representations. Extra pose information and computational cost is generally required for inference. To solve this issue, a Feature Distilling Generative Adversarial Network (FD-GAN) is proposed for learning identity-related and pose-unrelated representations. It is a novel framework based on a Siamese structure with multiple novel discriminators on human poses and identities. In addition to the discriminators, a novel same-pose loss is also integrated, which requires appearance of a same person's generated images to be similar. After learning pose-unrelated person features with pose guidance, no auxiliary pose information and additional computational cost is required during testing. Our proposed FD-GAN achieves state-of-the-art performance on three person reID datasets, which demonstrates that the effectiveness and robust feature distilling capability of the proposed FD-GAN.


title: Security Challenges in Cloud Computing.

abstract: Cloud Computing is one of the biggest buzzwords in the computer world these days. It allows resource sharing that includes software, platform and infrastructure by means of virtualization. Virtualization is the core technology behind cloud resource sharing. This environment strives to be dynamic, reliable, and customizable with a guaranteed quality of service. Security is as much of an issue in the cloud as it is anywhere else. Different people share different point of view on cloud computing. Some believe it is unsafe to use cloud. Cloud vendors go out of their way to ensure security. This paper investigates few major security issues with cloud computing and the existing counter measures to those security challenges in the world of cloud computing..


title: Searching the Web for conflicting topics: Page and user factors

abstract: Web users tend to search only the pages displayed at the top of the search engine results page (the 'top link' heuristic). Although it might be reasonable to use this heuristic to navigate simple and unambiguous facts, it might be risky when searching for conflicting socio-scientific topics, such as potential measures to reduce greenhouse gas emissions. In the present study, we explored the extent to which students consider other Web page characteristics, such as topic relevance and trustworthiness, when searching and bookmarking pages concerning a conflicting topic. We also examined the extent to which prior background knowledge moderates students' behavior. The results revealed that while the study participants actually used a 'top link' heuristic to navigate the results, they engaged in more systematic processes to bookmark pages for further study. Furthermore, the students' background knowledge was related to the assessment of Web page trustworthiness. We discuss these results from the perspective of a dual-processing model.


title: An Empirical Investigation of Antecedents of Internet Abuse in the Workplace

abstract: This study examined the extent to which employees engage in Internet abuse, and whether any of 15 antecedents predict the amount of that abuse. Data were collected from 571 Usenet users in an on-line survey. Aggregating the time for each of the eleven listed methods of Internet abuse revealed a total of 5.8 hours per week, on average. Most of the antecedents in two of the three Theory of Planned Behavior (TPB) categories (Attitudes and Subjective Norms), were significant, and none of the antecedents in the third TPB category (Perceived Behavioral Control) showed significance. addiction, self-justification, job satisfaction, peer culture, and supervisor culture were significant predictors of Internet abuse. Exploratory demographic factors computer experience, gender, and firm revenue also showed predictive power.


title: A new approach for supply chain risk management: Mapping SCOR into Bayesian network

abstract: Purpose: Increase of costs and complexities in organizations beside the increase of uncertainty and risks have led the managers to use the risk management in order to decrease risk taking and deviation from goals. SCRM has a close relationship with supply chain performance. During the years different methods have been used by researchers in order to manage supply chain risk but most of them are either qualitative or quantitative. Supply chain operation reference (SCOR) is a standard model for SCP evaluation which have uncertainty in its metrics. In This paper by combining qualitative and quantitative metrics of SCOR, supply chain performance will be measured by Bayesian Networks. Design/methodology/approach: First qualitative assessment will be done by recognizing uncertain metrics of SCOR model and then by quantifying them, supply chain performance will be measured by Bayesian Networks (BNs) and supply chain operations reference (SCOR) in which making decision on uncertain variables will be done by predictive and diagnostic capabilities. Findings: After applying the proposed method in one of the biggest automotive companies in Iran, we identified key factors of supply chain performance based on SCOR model through predictive and diagnostic capability of Bayesian Networks. After sensitivity analysis, we find out that ‘Total cost’ and its criteria that include costs of labors, warranty, transportation and inventory have the widest range and most effect on supply chain performance. So, managers should take their importance into account for decision making. We can make decisions simply by running model in different situations. Research limitations/implications: A more precise model consisted of numerous factors but it is difficult and sometimes impossible to solve big models, if we insert all of them in a Bayesian model. We have adopted real world characteristics with our software and method abilities. On the other hand, fewer data exist for some of the performance metrics. Practical implications: Mangers often use simple qualitative metrics for SCRM. However, combining qualitative and quantitative metrics will be more useful. Industries can recognize the important uncertain metrics by predicting supply chain performance and diagnosing possible happenings. Originality/value: This paper proposed a Bayesian method based on SCOR metrics which has the ability to manage supply chain risks and improve supply chain performance. This is the only presented case study for measuring supply chain performance by SCOR metrics.


title: Health related quality of life of university students in Lebanon: Lifestyles behaviors and socio-demographic predictors

abstract: Measuring and monitoring health related ::: quality of life (HRQoL) in youth are important for both researchers and ::: decision makers. During the time that young people are at university, many will ::: encounter a number of academic, as well as social, emotional and psychological ::: difficulties. The aims of this study are to: 1) assess the health related ::: quality of life of youths using SF-36 questionnaire and its factor ::: determinants, and 2) provide an objective basis for a health promotion ::: structure. Methods: This is a ::: cross-sectional study in South Lebanon, in a population of 282 young ::: university students attending a public university which is the largest and most ::: widespread institution of higher education in Lebanon. Socioeconomic and lifestyle behaviors data were collected. Students’ ::: HRQoL was measured using the SF-36 Health Survey. Results: The mean age of the respondents ::: was 18.7 years (SD = 1.16), 80.1% were women, 64.5% were living in rural area. ::: The proportion of current smokers was significantly greater among men than ::: women (26.8% vs. 11.5%, p Functioning (β = -5.16, 95% CI = -7.67 -2.65), and Vitality ( β= -5.85, ::: 95% CI = -10.24 -1.45). The HRQoL of the students studying business and ::: economics was higher than those studying health. Conclusion: Youths’ HRQoL is ::: affected by socio-demographic and behavioral characteristics. Effective ::: health-promoting actions, social support, and counseling services should be implemented ::: in the campus and are a necessary step towards the main goal of improving the ::: overall HRQoL of the university young students in Lebanon.


title: Missing Data Estimation in High-Dimensional Datasets: A Swarm Intelligence-Deep Neural Network Approach

abstract: In this paper, we examine the problem of missing data in high-dimensional datasets by taking into consideration the Missing Completely at Random and Missing at Random mechanisms, as well as theArbitrary missing pattern. Additionally, this paper employs a methodology based on Deep Learning and Swarm Intelligence algorithms in order to provide reliable estimates for missing data. The deep learning technique is used to extract features from the input data via an unsupervised learning approach by modeling the data distribution based on the input. This deep learning technique is then used as part of the objective function for the swarm intelligence technique in order to estimate the missing data after a supervised fine-tuning phase by minimizing an error function based on the interrelationship and correlation between features in the dataset. The investigated methodology in this paper therefore has longer running times, however, the promising potential outcomes justify the trade-off. Also, basic knowledge of statistics is presumed.


title: Robust design of electrostatic separation processes

abstract: The aim of this paper is to analyze the robustness of the electrostatic separation process control. The objective was to reduce variation in the process outcome by finding operating conditions (high-voltage level, roll speed), under which uncontrollable variation in the noise factors (granule size, composition of the material to be separated) has minimal impact on the quantity (and the quality) of the recovered products. The experiments were carried out on a laboratory roll-type electrostatic separator, provided with a corona electrode and a tubular electrode, both connected to a dc high-voltage supply. The samples of processed material were prepared from genuine chopped electric wire wastes (granule size >1 mm and <5 mm) containing various proportions of copper and PVC. The design and noise factors were combined into one single experimental design, based on Taguchi's approach, and a regression model of the process was fitted. The impact of the noise factors could be estimated, as well as the interactions between the design and noise factors. The conditions of industry application of Taguchi's methodology are discussed, as well as the possibility of adapting it to other electrostatic processes.


title: Bridging the gap - Organizational mindfulness and mindful organizing in mobile work environments

abstract: Organizational Mindfulness (OM) can support management and employees working in increasingly dynamic, mobile work environments driven by cloud computing or mobile devices. Specifically, in mindful organizations, reliable outcomes arise from cognitive processes of revealing and redirecting events and their potential negative consequences in the face of complex Information System (IS). However, despite OM's relevance across hierarchical levels, extant IS research has not yet clearly distinguished its results depending on the different levels on which mindfulness emerges. Thus, this paper analyses OM from a multi hierarchy level perspective and thereby augments the concept by Mindful Organizing (MO). In contrast to OM as managerial process, MO evolves as a bottom-up process from the employee level of an organization. Thus, based on data from 256 users of an organization wide cloud-based Desktop as a Service system, we found significant positive influence of a differentiated effect of both, OM and MO, as well as a highly significant positive effect of the combination of both on job performance. Therefore, we suggest (1) a nuanced view on mindfulness in future IS research incorporating the influence of hierarchical levels regarding this organizational phenomenon and (2) mindfulness as concept to cognitively handle the challenges of mobile work.


title: Epigenetic inheritance of acquired traits through sperm RNAs and sperm RNA modifications

abstract: Studies have demonstrated that paternal traits acquired in response to environmental conditions can be inherited by the offspring, sometimes persisting for multiple generations. In this Review, the authors discuss the accumulating evidence of a major role for sperm RNAs and RNA modifications in the inheritance of acquired traits and the mechanisms that may underlie this.


title: A research paper recommender system using a Dynamic Normalized Tree of Concepts model for user modelling

abstract: The enormous growth of information on the Internet makes finding information challenging and time consuming. Recommender systems provide a solution to this problem by automatically capturing user interests and recommending related information the user may also find interesting. In this paper, we present a novel recommender system for the research paper domain using a Dynamic Normalized Tree of Concepts (DNTC) model. Our system improves existing vector and tree of concepts models to be adaptable with a complex ontology and a large number of papers. The proposed system uses the 2012 version of the ACM Computing Classification System (CCS) ontology. This ontology has a much deeper structure than previous versions, which makes it challenging for previous ontology-based approaches to recommender systems. We performed offline evaluations using papers provided by ACM digital library for classifier training, and papers provided by CiteSeerX digital library for measuring the performance of the proposed DNTC model. Our evaluation results show that the novel DNTC model significantly outperforms the other two models: non-normalized tree of concepts and the vector of concepts models. Further, our DNTC model provides high average precision and reliable results when used in a context which the user has multiple interests and reads a large quantity of papers over time.


title: N-BaIoT: Network-based Detection of IoT Botnet Attacks Using Deep Autoencoders

abstract: The proliferation of IoT devices which can be more easily compromised than desktop computers has led to an increase in the occurrence of IoT based botnet attacks. In order to mitigate this new threat there is a need to develop new methods for detecting attacks launched from compromised IoT devices and differentiate between hour and millisecond long IoTbased attacks. In this paper we propose and empirically evaluate a novel network based anomaly detection method which extracts behavior snapshots of the network and uses deep autoencoders to detect anomalous network traffic emanating from compromised IoT devices. To evaluate our method, we infected nine commercial IoT devices in our lab with two of the most widely known IoT based botnets, Mirai and BASHLITE. Our evaluation results demonstrated our proposed method's ability to accurately and instantly detect the attacks as they were being launched from the compromised IoT devices which were part of a botnet.


title: Unsupervised Learning for Trustworthy IoT

abstract: The advancement of Internet-of-Things (IoT) edge devices with various types of sensors enables us to harness diverse information with Mobile Crowd-Sensing applications (MCS). This highly dynamic setting entails the collection of ubiquitous data traces, originating from sensors carried by people, introducing new information security challenges; one of them being the preservation of data trustworthiness. What is needed in these settings is the timely analysis of these large datasets to produce accurate insights on the correctness of user reports. Existing data mining and other artificial intelligence methods are the most popular to gain hidden insights from IoT data, albeit with many challenges. In this paper, we first model the cyber trustworthiness of MCS reports in the presence of intelligent and colluding adversaries. We then rigorously assess, using real IoT datasets, the effectiveness and accuracy of well-known data mining algorithms when employed towards IoT security and privacy. By taking into account the spatio-temporal changes of the underlying phenomena, we demonstrate how concept drifts can masquerade the existence of attackers and their impact on the accuracy of both the clustering and classification processes. Our initial set of results clearly show that these unsupervised learning algorithms are prone to adversarial infection, thus, magnifying the need for further research in the field by leveraging a mix of advanced machine learning models and mathematical optimization techniques.


title: Towards A Model-Based Prognostics Methodology for Electrolytic Capacitors: A Case Study Based on Electrical Overstress Accelerated Aging

abstract: A remaining useful life prediction methodology for electrolytic capacitors is presented. This methodology is based on the Kalman filter framework and an empirical degradation model. Electrolytic capacitors are used in several applications ranging from power supplies on critical avionics equipment to power drivers for electro-mechanical actuators. These devices are known for their comparatively low reliability and given their criticality in electronics subsystems they are a good candidate for component level prognostics and health management. Prognostics provides a way to assess remaining useful life of a capacitor based on its current state of health and its anticipated future usage and operational conditions. We present here also, experimental results of an accelerated aging test under electrical stresses. The data obtained in this test form the basis for a remaining life prediction algorithm where a model of the degradation process is suggested. This preliminary remaining life prediction algorithm serves as a demonstration of how prognostics methodologies could be used for electrolytic capacitors. In addition, the use degradation progression data from accelerated aging, provides an avenue for validation of applications of the Kalman filter based prognostics methods typically used for remaining useful life predictions in other applications.


title: A survey of skin-colormodeling and detectionmethods

abstract: Skin detection plays an important role in a wide range of image processing applications ranging from face detection, face tracking, gesture analysis, content-based image retrieval systems and to various human computer interaction domains. Recently, skin detection methodologies based on skin-color information as a cue has gained much attention as skin-color provides computationally effective yet, robust information against rotations, scaling and partial occlusions. Skin detection using color information can be a challenging task as the skin appearance in images is affected by various factors such as illumination, background, camera characteristics, and ethnicity. Numerous techniques are presented in literature for skin detection using color. In this paper, we provide a critical up-to-date review of the various skin modeling and classification strategies based on color information in the visual spectrum. The review is divided into three different categories: first, we present the various color spaces used for skin modeling and detection. Second, we present different skin modeling and classification approaches. However, many of these works are limited in performance due to real-world conditions such as illumination and viewing conditions. To cope up with the rapidly changing illumination conditions, illumination adaptation techniques are applied along with skin-color detection. Third, we present various approaches that use skin-color constancy and dynamic adaptation techniques to improve the skin detection performance in dynamically changing illumination and environmental conditions. Wherever available, we also indicate the various factors under which the skin detection techniques perform well.


title: A challenge-response authentication for RFID using Enhanced-Trivium

abstract: RFID (Radio frequency identification) technology which uses radio waves to identify objects has been applied widely. The security and privacy of the system is increasingly concerned. In this paper, we design a Trivium-like cipher algorithm without compromising the security and consuming less resource, which is called Enhanced-Trivium. We illustrate a Challenge-Response authentication protocol using Enhanced-Trivium as cryptographic primitive. The proposed protocol satisfies the requirements regarding low power consumption and low die-size and it is also resistant to various attacks.


title: Sentiment topic models for social emotion mining

abstract: The rapid development of social media services has facilitated the communication of opinions through online news, blogs, microblogs/tweets, instant-messages, and so forth. This article concentrates on the mining of readers' emotions evoked by social media materials. Compared to the classical sentiment analysis from writers' perspective, sentiment analysis of readers is sometimes more meaningful in social media. We propose two sentiment topic models to associate latent topics with evoked emotions of readers. The first model which is an extension of the existing Supervised Topic Model, generates a set of topics from words firstly, followed by sampling emotions from each topic. The second model generates topics from social emotions directly. Both models can be applied to social emotion classification and generate social emotion lexicons. Evaluation on social emotion classification verifies the effectiveness of the proposed models. The generated social emotion lexicon samples further show that our models can discover meaningful latent topics exhibiting emotion focus.


title: An Experimental Study on Pedestrian Classification

abstract: Detecting people in images is key for several important application domains in computer vision. This paper presents an in-depth experimental study on pedestrian classification; multiple feature-classifier combinations are examined with respect to their ROC performance and efficiency. We investigate global versus local and adaptive versus nonadaptive features, as exemplified by PCA coefficients, Haar wavelets, and local receptive fields (LRFs). In terms of classifiers, we consider the popular support vector machines (SVMs), feedforward neural networks, and k-nearest neighbor classifier. Experiments are performed on a large data set consisting of 4,000 pedestrian and more than 25,000 nonpedestrian (labeled) images captured in outdoor urban environments. Statistically meaningful results are obtained by analyzing performance variances caused by varying training and test sets. Furthermore, we investigate how classification performance and training sample size are correlated. Sample size is adjusted by increasing the number of manually labeled training data or by employing automatic bootstrapping or cascade techniques. Our experiments show that the novel combination of SVMs with LRF features performs best. A boosted cascade of Haar wavelets can, however, reach quite competitive results, at a fraction of computational cost. The data set used in this paper is made public, establishing a benchmark for this important problem


title: Survey of Estrogenic Activity of Organic Extracts from Water Body from Jilin City Reach of the Second Songhua River

abstract: Objective:To survey estrogenic activity of Environmental estrogens (EEs) pollution in Songhua River water of the second Songhua River.Methods: human breast cancer MCF-7 cell proliferation experiment were used to detect estrogenic activity of organic extracts of organic extracts from water body. Results : MCF7 cell proliferation rate increased at first and later decreased with the organic extracts concentration. Bcl-2 mRNA expression of MCF-7 cell increased and Bax mRNA expression decreased with the concentration of organic matter. Cell proliferation was the fastest in organic extracts group equivalent to the raw water 1000 mLConclusion :The organic extract could promote cell proliferation through the regulation of Bcl-2 and Bax expression. The harm risks on the human body of the organic matter in water still need further research on environmental toxicology and epidemiology. Keywords-water pollution;organic extracts; estrogenic activity; MCF7;Bcl-2;Bax


title: Two-Level Neural Network for Multi-label Document Classification

abstract: This paper deals with multi-label document classification using neural networks. We propose a novel neural network which is composed of two sub-nets: the first one estimates the scores for all classes, while the second one determines the number of classes assigned to the document. The proposed approach is evaluated on Czech and English standard corpora. The experimental results show that the proposed method is competitive with state of the art on both languages.


Scrotal Hair in the First Year of Life


title: Organizational members' use of social networking sites and job performance: An exploratory study

abstract: Purpose – There is considerable debate among academics and business practitioners on the value of the use of social networking by organizational members. Some, fearing presenteeism (i.e. being at the workplace but working below peak capacity), claim that the use of social networking sites by organizational members is a waste of time, while others believe it leads to improvements in job performance, partly due to employees’ successful efforts to balance work‐life realms. This paper aims to inform this debate by examining the use of social networking sites by organizational members and its effect on job satisfaction, organizational commitment, and job performance.Design/methodology/approach – The exploratory study is based on a survey of 193 employees, focusing on the following constructs: social networking site use intensity, perceived job satisfaction, perceived organizational commitment, and job performance. The authors’ proposed model was evaluated using variance‐based structural equation modeling (SEM)...


title: Picture Recommendation System Built on Instagram

abstract: In this paper, we propose a picture recommendation system built on Instagram, which facilitates users to query correlated pictures by keying in hashtags or clicking images. Users can access the value-added information (or pictures) on Instagram through the recommendation platform. In addition to collecting available hashtags using the Instagram API, the system also uses the Free Dictionary to build the relationships between all the hashtags in a knowledge base. Thus, two kinds of correlations can be provided for a query in the system; i.e., user-defined correlation and system-defined correlation. Finally, the experimental results show that users have good satisfaction degrees with both user-defined correlation and system-defined correlation methods.


title: EEG source imaging

abstract: Abstract Objective: Electroencephalography (EEG) is an important tool for studying the temporal dynamics of the human brain's large-scale neuronal circuits. However, most EEG applications fail to capitalize on all of the data's available information, particularly that concerning the location of active sources in the brain. Localizing the sources of a given scalp measurement is only achieved by solving the so-called inverse problem. By introducing reasonable a priori constraints, the inverse problem can be solved and the most probable sources in the brain at every moment in time can be accurately localized. Methods and Results: Here, we review the different EEG source localization procedures applied during the last two decades. Additionally, we detail the importance of those procedures preceding and following source estimation that are intimately linked to a successful, reliable result. We discuss (1) the number and positioning of electrodes, (2) the varieties of inverse solution models and algorithms, (3) the integration of EEG source estimations with MRI data, (4) the integration of time and frequency in source imaging, and (5) the statistical analysis of inverse solution results. Conclusions and Significance: We show that modern EEG source imaging simultaneously details the temporal and spatial dimensions of brain activity, making it an important and affordable tool to study the properties of cerebral, neural networks in cognitive and clinical neurosciences.


title: Low Bandwidth Offload for Mobile AR

abstract: Environmental fingerprinting has been proposed as a key enabler to immersive, highly contextualized mobile computing applications, especially augmented reality. While fingerprints can be constructed in many domains (e.g., wireless RF, magnetic field, and motion patterns), visual fingerprinting is especially appealing due to the inherent heterogeneity in many indoor spaces. This visual diversity, however, is also its Achilles' heel -- matching a unique visual signature against a database of millions requires either impractical computation for a mobile device, or to upload large quantities of visual data for cloud offload. Further, most visual "features" tend to be low entropy -- e.g., homogeneous repetitions of floor and ceiling tiles. Our system VisualPrint, proposes a means to offload only the most distinctive visual data, that is, only those visual signatures which stand a good chance to yield a unique match. VisualPrint enables cloud-offloaded visual fingerprinting with efficacy comparable to using whole images, but with an order reduction in network transfer.


title: WARPWING: A complete open source control platform for miniature robots

abstract: The electronics packages for many robot control systems have very similar requirements, yet are often redesigned for each custom application. To reduce wasted time and effort, the project presented in this paper (the Wireless Autonomous Robot Platform with Inertial Navigation and Guidance, WARP-WING) is intended to create a complete and easily customizable general purpose control system for miniature robotic systems, in particular micro air vehicles. In its default configuration, hardware designs, firmware, and software are all available to deliver an out-of-the-box robot control solution comprising 6 degree-of-freedom inertial sensors, a microprocessor, and wireless communication, along with general purpose input/output pins, serial ports, and control outputs for interfacing to additional sensors and actuators. The entire project is open source and a process is in place to enable modification of any component, allowing for easy adaptation to any need. WARPWING is already in use in a number of labs, with each research group contributing its expertise to enhance the platform and make such modifications available to others as well.


title: A Concise Introduction to Decentralized POMDPs

abstract: This book introduces multiagent planning under uncertainty as formalized by decentralized partially observable Markov decision processes (Dec-POMDPs). The intended audience is researchers and graduate students working in the fields of artificial intelligence related to sequential decision making: reinforcement learning, decision-theoretic planning for single agents, classical multiagent planning, decentralized control, and operations research.


title: Continuous deployment at Facebook and OANDA

abstract: Continuous deployment is the software engineering practice of deploying many small incremental software updates into production, leading to a continuous stream of 10s, 100s, or even 1,000s of deployments per day. High-profile Internet firms such as Amazon, Etsy, Facebook, Flickr, Google, and Netflix have embraced continuous deployment. However, the practice has not been covered in textbooks and no scientific publication has presented an analysis of continuous deployment. In this paper, we describe the continuous deployment practices at two very different firms: Facebook and OANDA. We show that continuous deployment does not inhibit productivity or quality even in the face of substantial engineering team and code size growth. To the best of our knowledge, this is the first study to show it is possible to scale the size of an engineering team by 20X and the size of the code base by 50X without negatively impacting developer productivity or software quality. Our experience suggests that top-level management support of continuous deployment is necessary, and that given a choice, developers prefer faster deployment. We identify elements we feel make continuous deployment viable and present observations from operating in a continuous deployment environment.


title: Assessing automotive functional safety microprocessor with ISO 26262 hardware requirements

abstract: This paper provides a step-by-step guideline for the assessment of an automotive safety microprocessor with ISO 26262 hardware requirements. ISO 26262 part 5 — Product development at the hardware level — specifies the safety activities during the phase of the automotive hardware development. In this phase, hardware safety design is derived (from the results of ISO 26262 part 3 and 4), implemented, integrated, and tested. To prove the compliance with ISO 26262 hardware development process, quantitative evaluations on the hardware are indispensable. These quantitative evaluations are known as hardware architecture metrics and probabilistic hardware metrics. The assessment results qualify a design with an automotive safety integrity level (ASIL) which ranges from ASIL-A (lowest) to ASIL-D (highest). In this paper, we implemented an exemplary safety microprocessor to demonstrate the ISO 26262 hardware assessment process. The derivation procedures of the ASIL level from the hardware architecture metrics and probabilistic hardware metrics are fully discussed. Based on the evaluation results, we also provide design suggestions for the ISO 26262 safety hardware design.


title: Hierarchical Self-Organization in Genetic programming

abstract: This paper presents an approach to automatic discovery of functions in Genetic Programming. The approach is based on discovery of useful building blocks by analyzing the evolution trace, generalizing blocks to define new functions, and finally adapting the problem representation on-the-fly. Adaptating the representation determines a hierarchical organization of the extended function set which enables a restructuring of the search space so that solutions can be found more easily. Measures of complexity of solution trees are defined for an adaptive representation framework. The minimum description length principle is applied to justify the feasibility of approaches based on a hierarchy of discovered functions and to suggest alternative ways of defining a problem's fitness function. Preliminary empirical results are presented.


title: Toward Machine Emotional Intelligence: Analysis of Affective Physiological State

abstract: The ability to recognize emotion is one of the hallmarks of emotional intelligence, an aspect of human intelligence that has been argued to be even more important than mathematical and verbal intelligences. This paper proposes that machine intelligence needs to include emotional intelligence and demonstrates results toward this goal: developing a machine's ability to recognize the human affective state given four physiological signals. We describe difficult issues unique to obtaining reliable affective data and collect a large set of data from a subject trying to elicit and experience each of eight emotional states, daily, over multiple weeks. This paper presents and compares multiple algorithms for feature-based recognition of emotional state from this data. We analyze four physiological signals that exhibit problematic day-to-day variations: The features of different emotions on the same day tend to cluster more tightly than do the features of the same emotion on different days. To handle the daily variations, we propose new features and algorithms and compare their performance. We find that the technique of seeding a Fisher Projection with the results of sequential floating forward search improves the performance of the Fisher Projection and provides the highest recognition rates reported to date for classification of affect from physiology: 81 percent recognition accuracy on eight classes of emotion, including neutral.


title: Process mining can be applied to software too!

abstract: Modern information systems produce tremendous amounts of event data. The area of process mining deals with extracting knowledge from this data. Real-life processes can be effectively discovered, analyzed and optimized with the help of mature process mining techniques. There is a variety of process mining case studies and experience reports from such business areas as healthcare, public, transportation and education. Although nowadays, these techniques are mostly used for discovering business processes. The goal of this industrial paper is to show that process mining can be applied to software too. Here we present and analyze our experiences on applying process mining in different productive software systems used in the touristic domain. Process models and user interface workflows underlie the functional specifications of the systems we experiment with. When the systems are utilized, user interaction is recorded in event logs. After applying process mining methods to these logs, process and user interface flow models are automatically derived. These resulting models provide insight regarding the real usage of the software, motivate the changes in the functional specifications, enable usability improvements and software redesign. Thus, with the help of our examples we demonstrate that process mining facilitates new forms of software analysis. The user interaction with almost every software system can be mined in order to improve the software and to monitor and measure its real usage.