title: AdAttester: Secure Online Mobile Advertisement Attestation Using TrustZone

abstract: Mobile advertisement (ad for short) is a major financial pillar for developers to provide free mobile apps. However, it is frequently thwarted by ad fraud, where rogue code tricks ad providers by forging ad display or user clicks, or both. With the mobile ad market growing drastically (e.g., from $8.76 billion in 2012 to $17.96 billion in 2013), it is vitally important to provide a verifiable mobile ad framework to detect and prevent ad frauds. Unfortunately, this is notoriously hard as mobile ads usually run in an execution environment with a huge TCB. This paper proposes a verifiable mobile ad framework called AdAttester, based on ARM?s TrustZone technology. AdAttester provides two novel security primitives, namely unforgeable clicks and verifiable display. The two primitives attest that ad-related operations (e.g., user clicks) are initiated by the end user (instead of a bot) and that the ad is displayed intact and timely. AdAttester leverages the secure world of TrustZone to implement these two primitives to collect proofs, which are piggybacked on ad requests to ad providers for attestation. AdAttester is non-intrusive to mobile users and can be incrementally deployed in existing ad ecosystem. A prototype of AdAttester is implemented for Android running on a Samsung Exynos 4412 board. Evaluation using 182 typical mobile apps with ad frauds shows that AdAttester can accurately distinguish ad fraud from legitimate ad operations, yet incurs small performance overhead and little impact on user experience.


title: Design and Development of Small Electric Vehicle using MATLAB/Simulink

abstract: The issue of the depletion of oil reserves in the world, and the problem of air pollution produced by motor vehicles, motivate many researchers to seek alternative energy sources to propel the vehicle. One promising way is to replace combustion motor with an electric motor, which is known as an electric vehicle. First stages of this research is to model the flow of power in the electric vehicle energy system to obtain its characteristics. Power flow efficiency in electric vehicle is very important because this type of vehicle is highly dependent on the limited electrical energy supplied by a battery. Therefore it should be managed properly. This study is to look into the power flow calculation so that the amount of electrical energy is in accordance with the needs of electric vehicle. The design of small electric vehicle model using MATLAB/Simulink software is to get the best power flow response to the electric vehicle energy system.


title: Comparison of synchronous PM machine types for wide constant-power speed range operation

abstract: This paper presents a detailed comparison of the high-speed operating characteristics of four synchronous PM machines for applications that require wide speed ranges of constant-power operation. These machines include surface PM machines with both distributed and fractional-slot concentrated windings, and two interior PM machine with distributed windings. These two versions of the interior PM machine include one with and a tight constraint on the machine's back-emf voltage at top speed and one without this constraint The target application is an automotive direct-drive starter/alternator requiring a very wide 10:1 constant power speed ratio (CPSR). Detailed comparisons of the performance characteristics of the machines are presented that include important issues such as the back-emf voltage at top speed, machine mass and cost, and eddy current losses in the magnets. Analytical results are verified using finite element analysis (FEA). Guidelines are developed to help drive designers decide which type of machine is most suitable for high-CPSR applications. Tradeoffs associated with choosing each of these machines are presented.


title: An Integrated 20-kW Motor Drive and Isolated Battery Charger for Plug-In Vehicles

abstract: For vehicles using grid power to charge the battery, traction circuit components are not normally engaged during the charging time, so there is a possibility to use them in the charger circuit to have an on-board integrated motor drive and battery charger. An isolated high-power three-phase integrated motor drive and charger based on a split-phase permanent magnet motor is presented in this paper. The motor winding connections are reversible by a relay-based switching device for traction and battery charging. In traction mode, the motor is a classical three-phase motor, but in charging mode it is a rotating isolating transformer providing a three-phase voltage source for the inverter to charge the battery. A mathematical model of the motor with six stator windings is presented for an arbitrary phase shift in windings. For the charging mode, the split-phase motor grid synchronization process and charge control are explained including the developed controller. A 20 -kW system is designed and implemented to verify the proper operation of the proposed system. Simulation and practical results are provided to show the system performance in terms of functionality, dynamic response, and efficiency. Moreover, some discussions, recommendations, and limitations are provided to give more practical insights.


title: The Facebook Experiment: Quitting Facebook Leads to Higher Levels of Well-Being

abstract: Abstract Most people use Facebook on a daily basis; few are aware of the consequences. Based on a 1-week experiment with 1,095 participants in late 2015 in Denmark, this study provides causal evidence that Facebook use affects our well-being negatively. By comparing the treatment group (participants who took a break from Facebook) with the control group (participants who kept using Facebook), it was demonstrated that taking a break from Facebook has positive effects on the two dimensions of well-being: our life satisfaction increases and our emotions become more positive. Furthermore, it was demonstrated that these effects were significantly greater for heavy Facebook users, passive Facebook users, and users who tend to envy others on Facebook.


title: A Geographically Aware Poll-Based Distributed File Consistency Maintenance Method for P2P Systems

abstract: File consistency maintenance in P2P systems is a technique for maintaining consistency between files and their replicas. Most previous consistency maintenance methods depend on either message spreading or structure-based pushing. Message spreading generates high overhead due to a large amount of messages; structure-based pushing methods reduce this overhead. However, both approaches cannot guarantee that every replica node receives an update in churn, because replica nodes passively wait for updates. As opposed to push-based methods that are not effective in high-churn and low-resource P2P systems, polling is churn resilient and generates low overhead. However, it is faced with a number of challenges: 1) ensuring a limited inconsistency; 2) realizing polling in a distributed manner; 3) considering physical proximity in polling; and 4) leveraging polling to further reduce polling overhead. To handle these challenges, this paper introduces a poll-based distributed file consistency maintenance method called geographically aware wave (GeWave). GeWave further reduces update overhead, enhances the fidelity of file consistency, and takes proximity into account. Using adaptive polling in a dynamic structure, GeWave avoids redundant file updates and ensures that every node receives an update in a limited time period even in churn. Furthermore, it propagates updates between geographically close nodes in a distributed manner. Extensive experimental results from the PlanetLab real-world testbed demonstrate the efficiency and effectiveness of GeWave in comparison with other representative consistency maintenance schemes. It dramatically reduces the overhead and yields significant improvements on effectiveness, scalability, and churn resilience of previous file consistency maintenance methods.


title: Model-Based Recursive Partitioning for Subgroup Analyses

abstract: The identification of patient subgroups with differential treatment effects is the first step towards individualised treatments. A current draft guideline by the EMA discusses potentials and problems in subgroup analyses and formulated challenges to the development of appropriate statistical procedures for the data-driven identification of patient subgroups. We introduce model-based recursive partitioning as a procedure for the automated detection of patient subgroups that are identifiable by predictive factors. The method starts with a model for the overall treatment effect as defined for the primary analysis in the study protocol and uses measures for detecting parameter instabilities in this treatment effect. The procedure produces a segmented model with differential treatment parameters corresponding to each patient subgroup. The subgroups are linked to predictive factors by means of a decision tree. The method is applied to the search for subgroups of patients suffering from amyotrophic lateral sclerosis that differ with respect to their Riluzole treatment effect, the only currently approved drug for this disease.


title: Waveguide Antenna Feeders With Integrated Reconfigurable Dual Circular Polarization

abstract: Waveguide antenna feeders with reconfigurable circular polarization sense are proposed in this paper. They can be switched between right and left hand circular polarization, without any mechanical rotation between the feeder and the antenna. Two types of feeders are presented. The polarizer works with a single switchable polarization for each state. The dual feeder can work with both polarizations at the same frequency band simultaneously, each one associated with a different rectangular waveguide port. The polarization sense is controlled by metallic posts that block or open the signal paths connecting the rectangular ports with a septum-orthomode polarizer. These posts are automated with piezo-motors integrated in the structure, leading to a very compact layout, reduced size, and practical for applications with diverse switching speed. The proof of concept and the automation are shown with experimental Ku-band prototypes.


Traveling in 3D Virtual Environments with Foot Gestures and a Multi-Touch enabled WIM


title: Internet of things (IoT) security: Current status, challenges and prospective measures

abstract: The paper presents a survey and analysis on the current status and concerns of Internet of things (IoT) security. The IoT framework aspires to connect anyone with anything at anywhere. IoT typically has a three layers architecture consisting of Perception, Network, and Application layers. A number of security principles should be enforced at each layer to achieve a secure IoT realization. The future of IoT framework can only be ensured if the security issues associated with it are addressed and resolved. Many researchers have attempted to address the security concerns specific to IoT layers and devices by implementing corresponding countermeasures. This paper presents an overview of security principles, technological and security challenges, proposed countermeasures, and the future directions for securing the IoT.


title: Using physiological signals for emotion recognition

abstract: Recognizing user's emotions is the promising area of research in a field of human-computer interaction. It is possible to recognize emotions using facial expression, audio signals, body poses, gestures etc. but physiological signals are very useful in this field because they are spontaneous and not controllable. In this paper a problem of using physiological signals for emotion recognition is presented. The kinds of physiological signals and sensors are described. The models of emotions, the methods of emotions' elicitation are presented. There is also a brief review of research progress in using physiological signals for emotion recognition presented in literature. It leads to conclusions about challenges and possible future research.


title: Divide and Grow: Capturing Huge Diversity in Crowd Images with Incrementally Growing CNN

abstract: Automated counting of people in crowd images is a challenging task. The major difficulty stems from the large diversity in the way people appear in crowds. In fact, features available for crowd discrimination largely depend on the crowd density to the extent that people are only seen as blobs in a highly dense scene. We tackle this problem with a growing CNN which can progressively increase its capacity to account for the wide variability seen in crowd scenes. Our model starts from a base CNN density regressor, which is trained in equivalence on all types of crowd images. In order to adapt with the huge diversity, we create two child regressors which are exact copies of the base CNN. A differential training procedure divides the dataset into two clusters and fine-tunes the child networks on their respective specialties. Consequently, without any hand-crafted criteria for forming specialties, the child regressors become experts on certain types of crowds. The child networks are again split recursively, creating two experts at every division. This hierarchical training leads to a CNN tree, where the child regressors are more fine experts than any of their parents. The leaf nodes are taken as the final experts and a classifier network is then trained to predict the correct specialty for a given test image patch. The proposed model achieves higher count accuracy on major crowd datasets. Further, we analyse the characteristics of specialties mined automatically by our method.


title: Image Generation from Sketch Constraint Using Contextual GAN

abstract: In this paper we investigate image generation guided by hand sketch. When the input sketch is badly drawn, the output of common image-to-image translation follows the input edges due to the hard condition imposed by the translation process. Instead, we propose to use sketch as weak constraint, where the output edges do not necessarily follow the input edges. We address this problem using a novel joint image completion approach, where the sketch provides the image context for completing, or generating the output image. We train a generated adversarial network, i.e, contextual GAN to learn the joint distribution of sketch and the corresponding image by using joint images. Our contextual GAN has several advantages. First, the simple joint image representation allows for simple and effective learning of joint distribution in the same image-sketch space, which avoids complicated issues in cross-domain learning. Second, while the output is related to its input overall, the generated features exhibit more freedom in appearance and do not strictly align with the input features as previous conditional GANs do. Third, from the joint image's point of view, image and sketch are of no difference, thus exactly the same deep joint image completion network can be used for image-to-sketch generation. Experiments evaluated on three different datasets show that our contextual GAN can generate more realistic images than state-of-the-art conditional GANs on challenging inputs and generalize well on common categories.


title: Children’s Moral Motivation, Sympathy, and Prosocial Behavior

abstract: Two studies investigated the role of children’s moral motivation and sympathy in prosocial behavior. Study 1 measured other-reported prosocial behavior and self- and other-reported sympathy. Moral motivation was assessed by emotion attributions and moral reasoning following hypothetical transgressions in a representative longitudinal sample of Swiss 6-year-old children (N = 1,273). Prosocial behavior increased with increasing sympathy, especially if children displayed low moral motivation. Moral motivation and sympathy were also independently related to prosocial behavior. Study 2 extended the findings of Study 1 with a second longitudinal sample of Swiss 6-year-old children (N = 175) using supplementary measures of prosocial behavior, sympathy, and moral motivation. The results are discussed in regard to the precursors of the moral self in childhood.


title: The Role of E-Commerce in Electronic Customer Relationship Management (ECRM) and the Factors Affecting It

abstract: The emerging of new technologies have been primary and tremendous effects on different facets of commercial organizations in today's world. One of the facets of this matter is customer satisfaction and trust through this way.The senior organization and company's managers evidently know that the success and also their survival depended upon their customer's satisfaction. Customer satisfaction is also a key for them which are also so sensitive. ECRM or customer relationship management electronically has managed to help organizations in this field. In article, the contents, aims and the benefits of CRM and ECRM have been considered. Their considerations as recognizing the behavioral types of customers to customize and increase customer's loyalties to organizations have also been emphasized.


title: Adaptive wavelet rendering

abstract: Effects such as depth of field, area lighting, antialiasing and global illumination require evaluating a complex high-dimensional integral at each pixel of an image. We develop a new adaptive rendering algorithm that greatly reduces the number of samples needed for Monte Carlo integration. Our method renders directly into an image-space wavelet basis. First, we adaptively distribute Monte Carlo samples to reduce the variance of the wavelet basis' scale coefficients, while using the wavelet coefficients to find edges. Working in wavelets, rather than pixels, allows us to sample not only image-space edges but also other features that are smooth in the image plane but have high variance in other integral dimensions. In the second stage, we reconstruct the image from these samples by using a suitable wavelet approximation. We achieve this by subtracting an estimate of the error in each wavelet coefficient from its magnitude, effectively producing the smoothest image consistent with the rendering samples. Our algorithm renders scenes with significantly fewer samples than basic Monte Carlo or adaptive techniques. Moreover, the method introduces minimal overhead, and can be efficiently included in an optimized ray-tracing system.


Representations for Parsing with Universal Dependencies


title: Inhibition of neuronal ferroptosis protects hemorrhagic brain.

abstract: Intracerebral hemorrhage (ICH) causes high mortality and morbidity, but our knowledge of post-ICH neuronal death and related mechanisms is limited. In this study, we first demonstrated that ferroptosis, a newly identified form of cell death, occurs in the collagenase-induced ICH model in mice. We found that administration of ferrostatin-1, a specific inhibitor of ferroptosis, prevented neuronal death and reduced iron deposition induced by hemoglobin in organotypic hippocampal slice cultures (OHSCs). Mice treated with ferrostatin-1 after ICH exhibited marked brain protection and improved neurologic function. Additionally, we found that ferrostatin-1 reduced lipid reactive oxygen species production and attenuated the increased expression level of PTGS2 and its gene product cyclooxygenase-2 ex vivo and in vivo. Moreover, ferrostatin-1 in combination with other inhibitors that target different forms of cell death prevented hemoglobin-induced cell death in OHSCs and human induced pluripotent stem cell-derived neurons better than any inhibitor alone. These results indicate that ferroptosis contributes to neuronal death after ICH, that administration of ferrostatin-1 protects hemorrhagic brain, and that cyclooxygenase-2 could be a biomarker of ferroptosis. The insights gained from this study will advance our knowledge of the post-ICH cell death cascade and be essential for future preclinical studies.


Series No . 16 Profiting from Innovation in the Digital Economy : Standards , Complementary Assets , and Business Models In the Wireless World


title: Combinatorial codes in ventral temporal lobe for object recognition: Haxby (2001) revisited: is there a “face” area?

abstract: Abstract Haxby et al. [Science 293 (2001) 2425] recently argued that category-related responses in the ventral temporal (VT) lobe during visual object identification were overlapping and distributed in topography. This observation contrasts with prevailing views that object codes are focal and localized to specific areas such as the fusiform and parahippocampal gyri. We provide a critical test of Haxby's hypothesis using a neural network (NN) classifier that can detect more general topographic representations and achieves 83% correct generalization performance on patterns of voxel responses in out-of-sample tests. Using voxel-wise sensitivity analysis we show that substantially the same VT lobe voxels contribute to the classification of all object categories, suggesting the code is combinatorial. Moreover, we found no evidence for local single category representations. The neural network representations of the voxel codes were sensitive to both category and superordinate level features that were only available implicitly in the object categories.


title: Unsupervised Learning for Physical Interaction through Video Prediction

abstract: A core challenge for an agent learning to interact with the world is to predict how its actions affect objects in its environment. Many existing methods for learning the dynamics of physical interactions require labeled object information. However, to scale real-world interaction learning to a variety of scenes and objects, acquiring labeled data becomes increasingly impractical. To learn about physical object motion without labels, we develop an action-conditioned video prediction model that explicitly models pixel motion, by predicting a distribution over pixel motion from previous frames. Because our model explicitly predicts motion, it is partially invariant to object appearance, enabling it to generalize to previously unseen objects. To explore video prediction for real-world interactive agents, we also introduce a dataset of 59,000 robot interactions involving pushing motions, including a test set with novel objects. In this dataset, accurate prediction of videos conditioned on the robot's future actions amounts to learning a "visual imagination" of different futures based on different courses of action. Our experiments show that our proposed method produces more accurate video predictions both quantitatively and qualitatively, when compared to prior methods.


title: Improved preprocessing, labeling and scaling algorithms for the Weight-Constrained Shortest Path Problem

abstract: Much has been written on shortest path problems with weight, or resource, constraints. However, relatively little of it has provided systematic computational comparisons for a representative selection of algorithms. Furthermore, there has been almost no work showing numerical performance of scaling algorithms, although worst-case complexity guarantees for these are well known, nor has the effectiveness of simple preprocessing techniques been fully demonstrated. Here, we provide a computational comparison of three scaling techniques and a standard label-setting method. We also describe preprocessing techniques which take full advantage of cost and upper-bound information that can be obtained from simple shortest path information. We show that integrating information obtained in preprocessing within the label-setting method can lead to very substantial improvements in both memory required and run time, in some cases, by orders of magnitude. Finally, we show how the performance of the label-setting method can be further improved by making use of all Lagrange multiplier information collected in a Lagrangean relaxation first step. © 2003 Wiley Periodicals, Inc.


title: View-Consistent MeshFlow for Stereoscopic Video Stabilization

abstract: This paper presents a method to stabilize shaky stereoscopic videos captured by hand-held stereo cameras. It is often problematic to apply a traditional monocular video stabilization techniques directly to the stereoscopic views independently. This is mainly because some undesirable vertical disparities and inaccurate horizontal disparities are produced, which violates the original stereoscopic disparity constraint, leading to erroneous depth perceptions. In this paper, we show that the MeshFlow stabilization method for monocular videos can be extended for stereoscopic videos by taking additional disparity constraints during the stabilization. In particular, we first estimate disparities between two views. Then, we compute camera motions by the MeshFlow motion model, in which the camera paths can be extracted from the meshes at each view. Next, we smooth these paths of two views separately. After path optimization, we adjust the meshes of one view by our proposed joint the disparity and stability mesh warp, so that the temporal stability and the correct depth perception can be achieved simultaneously. We evaluate our method on various challenging stereoscopic videos with different camera motions and scene types. In the experiment, we adapt the objective quality assessment of single videos to evaluate our stereoscopic video stabilization. We further propose an objective evaluation method to assess the quality of the disparities in terms of the spatial and temporal coherence after the stabilization. The experimental results demonstrate the effectiveness of our method both quantitatively and qualitatively.


title: Sequential Optimization and Reliability Assessment Method for Efficient Probabilistic Design

abstract: Apparatus is provided for lifting a container and dumping its contents into a receptacle such as a truck body. The apparatus has a horizontal fulcrum mounted on the outside of the receptacle and an elongated frame is swingable about the fulcrum between a lowered and a raised position. Endless chains on the frame have drive sprockets coaxial with the fulcrum and hooks on the chains engage the container to first lift the same while the frame remains in its lowered position. In the lifted position of the container the hooks cooperate with abutments on the frame so that continued movement of the chains swings the frame with the container to a raised position in which contents of the container are dumped into the receptacle.


title: Omnidirectional Steering Interface and Control for a Four-Wheel Independent Steering Vehicle

abstract: This paper presents an omnidirectional steer-by-wire system for a four-wheel independent steering vehicle. The system consists of an extended steering interface and a behavior-based steering controller. The extended steering interface provides a novel manipulation way to vehicle driver. The driver can control the vehicle in traditional way or omnidirectionally without any mode switching operation. The reservation of the traditional driving way makes the driver adapt the novel steering interface easily. The steering interface also has a force-feedback function to synchronize the extended steering interface and the orientations of wheels so as to improve vehicle handling. The behavior-based steering controller is designed for controlling the orientations of wheels. It acts as virtual linkage among each wheel to minimize wheel slip resulted by the misalignment of wheels. As there is no trajectory planning needed in the control algorithm, the proposed controller is especially suitable for a nonautonomous vehicle, the driving path of which cannot be predetermined.


title: Effect of geraniol, a plant-based alcohol monoterpene oil, against Meloidogyne javanica

abstract: Terpenes are secondary metabolites produced in different biological pathways as defense mechanisms against, among others, nematodes, insects and herbivores. Root-knot nematodes (RKN; Meloidogyne spp.) are one of the most serious pests of economically importance for vegetable production in Greece. The aim of this study was to investigate the nematicidal activity of geraniol on different life stages at 35–1000 ppm doses against the root-knot nematode Meloidogyne javanica. To our knowledge, this study is the first to report the effect of geraniol on egg differentiation and also its sub-lethal doses effect. Experiments testing the contact action of geraniol resulted in about 100% mortality of J2 s, whereas vapor showed only about 10%. Particularly, geraniol paralyzed 100% of second-stage juveniles (J2 s) and inhibited egg hatching in about 70%, at a dose of 500 ppm. In pot experiment, the use of geraniol at sub-lethal doses reduced female numbers in tomato roots. To the contrary, no nematostatic effects were observed in paralysis bioassays. The present study strongly demonstrates geraniol’s toxic effect against root-knot nematode Meloidogyne javanica.


title: Sales Promotion and Purchasing Intention: Applying the Technology Acceptance Model in Consumer-to-Consumer Marketplaces

abstract: This paper studies the impact of sales promotion on consumers’ intention to purchase online in the setting of a consumer-to-consumer (C2C) marketplace. The research is grounded in the theory of Technology Acceptance Model (TAM). We test a number of hypotheses empirically about the relationship between online sales promotion, TAM and C2C online purchasing intention. The finding of this study suggests that, by increasing consumers’ perception of ease of use and by decreasing consumer’s perceived risk, online sales promotion has a significant impact on their purchasing intention.


title: A robust human activity recognition system using smartphone sensors and deep learning

abstract: Abstract In last few decades, human activity recognition grabbed considerable research attentions from a wide range of pattern recognition and human–computer interaction researchers due to its prominent applications such as smart home health care. For instance, activity recognition systems can be adopted in a smart home health care system to improve their rehabilitation processes of patients. There are various ways of using different sensors for human activity recognition in a smartly controlled environment. Among which, physical human activity recognition through wearable sensors provides valuable information about an individual’s degree of functional ability and lifestyle. In this paper, we present a smartphone inertial sensors-based approach for human activity recognition. Efficient features are first extracted from raw data. The features include mean, median, autoregressive coefficients, etc. The features are further processed by a kernel principal component analysis (KPCA) and linear discriminant analysis (LDA) to make them more robust. Finally, the features are trained with a Deep Belief Network (DBN) for successful activity recognition. The proposed approach was compared with traditional expression recognition approaches such as typical multiclass Support Vector Machine (SVM) and Artificial Neural Network (ANN) where it outperformed them.


The impact of brand equity on brand loyalty: The mediating role of customer satisfaction


title: Stability of thioester intermediates in ubiquitin-like modifications

abstract: Ubiquitin-like modifications are important mechanisms in cellular regulation, and are carried out through several steps with reaction intermediates being thioester conjugates of ubiquitin-like proteins with E1, E2, and sometimes E3. Despite their importance, a thorough characterization of the intrinsic stability of these thioester intermediates has been lacking. In this study, we investigated the intrinsic stability by using a model compound and the Ubc9∼SUMO-1 thioester conjugate. The Ubc9∼SUMO-1 thioester intermediate has a half life of approximately 3.6 h (hydrolysis rate k = 5.33 ± 2.8 ×10−5 s−1), and the stability decreased slightly under denaturing conditions (k = 12.5 ± 1.8 × 10−5 s−1), indicating a moderate effect of the three-dimensional structural context on the stability of these intermediates. Binding to active and inactive E3, (RanBP2) also has only a moderate effect on the hydrolysis rate (13.8 ± 0.8 × 10−5 s−1 for active E3 versus 7.38 ± 0.7 × 10−5 s−1 for inactive E3). The intrinsically high stability of these intermediates suggests that unwanted thioester intermediates may be eliminated enzymatically, such as by thioesterases, to regulate their intracellular abundance and trafficking in the control of ubiquitin-like modifications.


title: Detection of pneumonia clouds in chest X-ray using image processing approach

abstract: Finding ways to automate diagnostics from medical images, has continuously been one of the most interesting areas of software development. This article presents a novel approach for detecting the presence of pneumonia clouds in chest X-rays (CXR) by using only Image processing techniques. For this, we have worked on 40 analog chest CXRs pertaining to Normal and Pneumonia infected patients. Indigenous algorithms have been developed for cropping and for extraction of the lung region from the images. To detect pneumonia clouds we have used Otsu thresholding which will segregate the healthy part of lung from the pneumonia infected cloudy regions. We are proposing to compute the ratio of area of healthy lung region to total lung region to establish a result. The task has been performed using Python and OpenCV as they are free, opensource tools and can be used by all, without any legality issues or cost implication.


title: Haptic Rendering of 3D Geometry on 2D Touch Surface Based on Mechanical Rotation

abstract: In this paper, we present a robotic surface display that physically imitates the orientation of virtual 3D geometry touched through a 2D flat screen. The proposed approach renders the surface orientation of 3D geometry such that users can tactually obtain relative geometric information, which plays a significant role in the process of real-world haptic object perception. Taking advantage of the planar aspect of touch surfaces, the system constructs a rotation matrix to control the pose of a surface with minimal mechanical movements with given partial geometric information (i.e., normal vector at the point of touch). To evaluate the proposed rendering scheme, we conducted a geometric task (two alternative forced choices) with a set of hand-sized cylindrically curved geometries in which participants were asked to identify which of the two surfaces they perceived as being more curved. Curvatures with the same polarities (i.e., convex-convex and concave-concave) were employed in the study and psychometric curves estimated to obtain the threshold of the curvature difference and to validate the proposed rendering scheme. Possible applications of the proposed system and its limitations are also presented.


title: Internet of Things: Survey on Security and Privacy

abstract: ABSTRACTThe Internet of things (IoT) is intended for ubiquitous connectivity among different entities or “things”. While it provides effective and efficient solutions to many real world challenges,...


title: Intelligent Traffic Light Flow Control System Using Wireless Sensors Networks

abstract: Vehicular traffic is continuously increasing around the world, especially in large urban areas. The resulting congestion has become a major concern to transportation specialists and decision makers. The existing methods for traffic management, surveillance and control are not adequately efficient in terms of performance, cost, maintenance, and support. In this paper, the design of a system that utilizes and efficiently manages traffic light controllers is presented. In particular, we present an adaptive traffic control systembased on a new traffic infrastructure using Wireless Sensor Network (WSN) and using new techniques for controlling the traffic flow sequences. These techniques are dynamically adaptive to traffic conditions on both single and multiple intersections. A WSN is used as a tool to instrument and control traffic signals roadways, while an intelligent traffic controller is developed to control the operation of the traffic infrastructure supported by the WSN. The controller embodies traffic system communication algorithm (TSCA) and the traffic signals time manipulation algorithm (TSTMA). Both algorithms are able to provide the system with adaptive and efficient traffic estimation represented by the dynamic change in the traffic signals' flow sequence and traffic variation. Simulation results show the efficiency of the proposed scheme in solving traffic congestion in terms of the average waiting time and average queue length on the isolated (single) intersection and efficient global traffic flow control on multiple intersections. A test bed was also developed and deployed for real measurements. The paper concludes with some future highlights and useful remarks.


title: Use of explicit memory in the dynamic traveling salesman problem

abstract: In the dynamic traveling salesman problem (DTSP), the weights and vertices of the graph representing the TSP are allowed to change during the optimization. This work first discusses some issues related to the use of evolutionary algorithms in the DTSP. When efficient algorithms used for the static TSP are applied with restart in the DTSP, we observe that only some edges are generally inserted in and removed from the best solutions after the changes. This result indicates a possible beneficial use of memory approaches, usually employed in cyclic dynamic environments. We propose a memory approach and a hybrid approach that combines our memory approach with the elitism-based immigrants genetic algorithm (EIGA). We compare these two algorithms to four existing algorithms and show that memory approaches can be beneficial for the DTSP with random changes.


title: Matching Real Fabrics with Micro-Appearance Models

abstract: Micro-appearance models explicitly model the interaction of light with microgeometry at the fiber scale to produce realistic appearance. To effectively match them to real fabrics, we introduce a new appearance matching framework to determine their parameters. Given a micro-appearance model and photographs of the fabric under many different lighting conditions, we optimize for parameters that best match the photographs using a method based on calculating derivatives during rendering. This highly applicable framework, we believe, is a useful research tool because it simplifies development and testing of new models. Using the framework, we systematically compare several types of micro-appearance models. We acquired computed microtomography (micro CT) scans of several fabrics, photographed the fabrics under many viewing/illumination conditions, and matched several appearance models to this data. We compare a new fiber-based light scattering model to the previously used microflake model. We also compare representing cloth microgeometry using volumes derived directly from the micro CT data to using explicit fibers reconstructed from the volumes. From our comparisons, we make the following conclusions: (1) given a fiber-based scattering model, volume- and fiber-based microgeometry representations are capable of very similar quality, and (2) using a fiber-specific scattering model is crucial to good results as it achieves considerably higher accuracy than prior work.


title: Multiagent Optimization System for Solving the Traveling Salesman Problem (TSP)

abstract: The multiagent optimization system (MAOS) is a nature-inspired method, which supports cooperative search by the self-organization of a group of compact agents situated in an environment with certain sharing public knowledge. Moreover, each agent in MAOS is an autonomous entity with personal declarative memory and behavioral components. In this paper, MAOS is refined for solving the traveling salesman problem (TSP), which is a classic hard computational problem. Based on a simplified MAOS version, in which each agent manipulates on extremely limited declarative knowledge, some simple and efficient components for solving TSP, including two improving heuristics based on a generalized edge assembly recombination, are implemented. Compared with metaheuristics in adaptive memory programming, MAOS is particularly suitable for supporting cooperative search. The experimental results on two TSP benchmark data sets show that MAOS is competitive as compared with some state-of-the-art algorithms, including the Lin-Kernighan-Helsgaun, IBGLK, PHGA, etc., although MAOS does not use any explicit local search during the runtime. The contributions of MAOS components are investigated. It indicates that certain clues can be positive for making suitable selections before time-consuming computation. More importantly, it shows that the cooperative search of agents can achieve an overall good performance with a macro rule in the switch mode, which deploys certain alternate search rules with the offline performance in negative correlations. Using simple alternate rules may prevent the high difficulty of seeking an omnipotent rule that is efficient for a large data set.


title: A Fully Integrated Reconfigurable Self-Startup RF Energy-Harvesting System With Storage Capability

abstract: This paper introduces a fully integrated RF energy-harvesting system. The system can simultaneously deliver the current demanded by external dc loads and store the extra energy in external capacitors, during periods of extra output power. The design is fabricated in 0.18-μm CMOS technology, and the active chip area is 1.08 mm ::: 2 ::: . The proposed self-startup system is reconfigurable with an integrated LC matching network, an RF rectifier, and a power management/controller unit, which consumes 66-157 nW. The required clock generation and the voltage reference circuit are integrated on the same chip. Duty cycle control is used to operate for the low input power that cannot provide the demanded output power. Moreover, the number of stages of the RF rectifier is reconfigurable to increase the efficiency of the available output power. For high available power, a secondary path is activated to charge an external energy storage element. The measured RF input power sensitivity is -14.8 dBm at a 1-V dc output.


title: Modeling and optimal sizing of hybrid renewable energy system

abstract: This paper presents a new methodology of sizing optimization of a stand-alone hybrid renewable energy system. The developed approach makes use of a deterministic algorithm to minimize the life cycle cost of the system while guaranteeing the availability of the energy. Firstly, the mathematical modeling of the principal elements of the hybrid wind/PV system is exposed showing the main sizing variables. Then, the deterministic algorithm is presented and implemented to minimize the objective function which is equal to the life cycle cost of the hybrid system and finally, the obtained results are exposed and discussed.


title: Direct Power Control of a Three-Phase Inverter for Grid Input Current Shaping of a Single-Phase Diode Rectifier With a Small DC-Link Capacitor

abstract: This paper describes motor drive system fed by a single-phase diode rectifier without power factor correction (PFC) circuit or the input filter. The system considered in this paper consists of a single-phase diode rectifier, a three-phase inverter, and a small dc-link capacitor. Since dc-link capacitance of the proposed system is few microfarads, the shape of the grid input current is directly affected by electrical output power of the inverter. Using this aspect, two goals of the drive system, controlling torque of the motor and suppressing the grid input current harmonics, can be simultaneously achieved by controlling the output power of the inverter. The proposed method includes the motor current reference generation and the direct output power regulation by modifying the output voltage reference. With the proposed method, the harmonic components of the grid input current can be reduced under the limits of the regulation IEC61000-3-2, lower than those in the conventional method. Also, the cost and size of the inverter system can be significantly reduced by removing electrolytic dc-link capacitor and input filter from the system. The performance of the proposed shaping method was validated by the experimental results using the motor drive system with a 5μF film capacitor at dc link.


title: A goal-based modeling approach to develop requirements for adaptive systems with environmental uncertainty

abstract: Dynamically adaptive systems (DASs) are intended to monitor the execution environment and then dynamically adapt their behavior in response to changing environmental conditions. The uncertainty of the execution environment is a major motivation for dynamic adaptation; it is impossible to know at development time all of the possible combinations of environmental conditions that will be encountered. To date, the work performed in requirements engineering for a DAS includes requirements monitoring and reasoning about the correctness of adaptations, where the DAS requirements are assumed to exist. This paper introduces a goal-based modeling approach to develop the requirements for a DAS, while explicitly factoring uncertainty into the process and resulting requirements. We introduce a variation of threat modeling to identify sources of uncertainty and demonstrate how the RELAX specification language can be used to specify more flexible requirements within a goal model to handle the uncertainty.


title: Multitask Learning with Deep Neural Networks for Community Question Answering

abstract: In this paper, we developed a deep neural network (DNN) that learns to solve simultaneously the three tasks of the cQA challenge proposed by the SemEval-2016 Task 3, i.e., question-comment similarity, question-question similarity and new question-comment similarity. The latter is the main task, which can exploit the previous two for achieving better results. Our DNN is trained jointly on all the three cQA tasks and learns to encode questions and comments into a single vector representation shared across the multiple tasks. The results on the official challenge test set show that our approach produces higher accuracy and faster convergence rates than the individual neural networks. Additionally, our method, which does not use any manual feature engineering, approaches the state of the art established with methods that make heavy use of it.


title: Computers and Creativity

abstract: This interdisciplinary volume introduces new theories and ideas on creativity from the perspectives of science and art. Featuring contributions from leading researchers, theorists and artists working in artificial intelligence, generative art, creative computing, music composition, and cybernetics, the book examines the relationship between computation and creativity from both analytic and practical perspectives. Each contributor describes innovative new ways creativity can be understood through, and inspired by, computers. The book tackles critical philosophical questions and discusses the major issues raised by computational creativity, including: whether a computer can exhibit creativity independently of its creator; what kinds of creativity are possible in light of our knowledge from computational simulation, artificial intelligence, evolutionary theory and information theory; and whether we can begin to automate the evaluation of aesthetics and creativity in silico. These important, often controversial questions are contextualised by current thinking in computational creative arts practice. Leading artistic practitioners discuss their approaches to working creatively with computational systems in a diverse array of media, including music, sound art, visual art, and interactivity. The volume also includes a comprehensive review of computational aesthetic evaluation and judgement research, alongside discussion and insights from pioneering artists working with computation as a creative medium over the last fifty years. A distinguishing feature of this volume is that it explains and grounds new theoretical ideas on creativity through practical applications and creative practice. Computers and Creativity will appeal to theorists, researchers in artificial intelligence, generative and evolutionary computing, practicing artists and musicians, students and any reader generally interested in understanding how computers can impact upon creativity. It bridges concepts from computer science, psychology, neuroscience, visual art, music and philosophy in an accessible way, illustrating how computers are fundamentally changing what we can imagine and create, and how we might shape the creativity of the future. Computers and Creativity will appeal to theorists, researchers in artificial intelligence, generative and evolutionary computing, practicing artists and musicians, students and any reader generally interested in understanding how computers can impact upon creativity. It bridges concepts from computer science, psychology, neuroscience, visual art, music and philosophy in an accessible way, illustrating how computers are fundamentally changing what we can imagine and create, and how we might shape the creativity of the future.


title: Joint Learning for Attribute-Consistent Person Re-Identification

abstract: Person re-identification has recently attracted a lot of attention in the computer vision community. This is in part due to the challenging nature of matching people across cameras with different viewpoints and lighting conditions, as well as across human pose variations. The literature has since devised several approaches to tackle these challenges, but the vast majority of the work has been concerned with appearance-based methods. We propose an approach that goes beyond appearance by integrating a semantic aspect into the model. We jointly learn a discriminative projection to a joint appearance-attribute subspace, effectively leveraging the interaction between attributes and appearance for matching. Our experimental results support our model and demonstrate the performance gain yielded by coupling both tasks. Our results outperform several state-of-the-art methods on VIPeR, a standard re-identification dataset. Finally, we report similar results on a new large-scale dataset we collected and labeled for our task.


title: User churn in focused question answering sites: characterizations and prediction

abstract: Given a user on a Q&A site, how can we tell whether s/he is engaged with the site or is rather likely to leave? What are the most evidential factors that relate to users churning? Question and Answer (Q&A) sites form excellent repositories of collective knowledge. To make these sites self- sustainable and long-lasting, it is crucial to ensure that new users as well as the site veterans who provide most of the answers keep engaged with the site. As such, quantifying the engagement of users and preventing churn in Q&A sites are vital to improve the lifespan of these sites. We study a large data collection from stackoverflow.com to identify significant factors that correlate with newcomer user churn in the early stage and those that relate to veterans leaving in the later stage. We consider the problem under two settings: given (i) the first k posts, or (ii) first T days of activity of a user, we aim to identify evidential features to automatically classify users so as to spot those who are about to leave. We find that in both cases, the time gap between subsequent posts is the most significant indicator of diminishing interest of users, besides other indicative factors like answering speed, reputation of those who answer their questions, and number of answers received by the user.


title: All your network are belong to us: a transport framework for mobile network selection

abstract: Mobile devices come with an assortment of networks: WiFi in two different frequency bands, each of which can run in infrastructure-mode, WiFi-Direct mode, or ad hoc mode; cellular radios, which can run in LTE/4G, 3G, or EDGE modes; and Bluetooth. But how should an app choose which network to use? There is no systematic solution to this problem today: in current practice the choice is almost always left to the user, who usually has no idea what's best. In fact, what's best for a user depends on the app's performance objectives (throughput, delay, object load time, etc.) and the user's constraints on cost and battery life. Besides, what's best for a single user or app must be balanced with what's best for the wireless network as a whole (individual optimality vs. social utility). This paper introduces Delphi, a transport-layer module to resolve these issues. Delphi has three noteworthy components: "local learning", in which a mobile device estimates or infers useful properties of different networks efficiently, "property sharing", in which mobile devices share what they learn with other nearby devices, and "selection", in which each node selects a network using what it has observed locally and/or from its neighbors.


title: RevoMaker: Enabling Multi-directional and Functionally-embedded 3D printing using a Rotational Cuboidal Platform

abstract: In recent years, 3D printing has gained significant attention from the maker community, academia, and industry to support low-cost and iterative prototyping of designs. Current unidirectional extrusion systems require printing sacrificial material to support printed features such as overhangs. Furthermore, integrating functions such as sensing and actuation into these parts requires additional steps and processes to create "functional enclosures", since design functionality cannot be easily embedded into prototype printing. All of these factors result in relatively high design iteration times. We present "RevoMaker", a self-contained 3D printer that creates direct out-of-the-printer functional prototypes, using less build material and with substantially less reliance on support structures. By modifying a standard low-cost FDM printer with a revolving cuboidal platform and printing partitioned geometries around cuboidal facets, we achieve a multidirectional additive prototyping process to reduce the print and support material use. Our optimization framework considers various orientations and sizes for the cuboidal base. The mechanical, electronic, and sensory components are preassembled on the flattened laser-cut facets and enclosed inside the cuboid when closed. We demonstrate RevoMaker directly printing a variety of customized and fully-functional product prototypes, such as computer mice and toys, thus illustrating the new affordances of 3D printing for functional product design.


Confidence Intervals For An Effect Size When Variances Are Not Equal


title: CS-Based Secured Big Data Processing on FPGA

abstract: The four V's in Big data sets, Volume, Velocity, Variety, and Veracity, provides challenges in many different aspects of real-time systems. Out of these areas securing big data sets, reduction in processing time and communication bandwidth are of utmost importance. In this paper we adopt Compressive Sensing (CS) based framework to address all three issues. We implement compressive Sensing using Deterministic Random Matrix (DRM) on Artix-7 FPGA, and CS reconstruction using Orthogonal Matching Pursuit (OMP) algorithm on Virtex-7 FPGA. The results show that our implementations for CS sampling and reconstruction are 183x and 2.7x respectively faster when compared to previously published work. We also perform case study of two different applications i.e. multi-channel Seizure Detection and Image processing to demonstrate the efficiency of our proposed CS-based framework. CS-based framework allows us to reduce communication transfers up to 75% while achieving satisfactory range of quality. The results show that our proposed framework is 290x faster and has 7.9x less resource utilization as compared to previously published AES based encryption.


title: Functional organization of thalamocortical relays

abstract: The thalamus has long been seen as responsible for relaying information on the way to the cerebral cortex, but it has not been until the last decade or so that the functional nature of this relay has attracted significant attention. Whereas earlier views tended to relegate thalamic function to a simple, machine-like relay process, recent research, reviewed in this article, demonstrates complicated circuitry and a rich array of membrane properties underlying the thalamic relay. It is now clear that the thalamic relay does not have merely a trivial function. Suggestions that the thalamic circuits and cell properties only come into play during certain phases of sleep to effectively disconnect the relay are correct as far as they go, but they are incomplete, because they fail to take into account interesting and variable properties of the relay that, we argue, occur during normal waking behavior. Although the specific function of the circuits and cellular properties of the thalamic relay for waking behavior is far from clear, we offer two related hypotheses based on recent experimental evidence. One is that the thalamus is not used just to relay peripheral information from, for example, visual, auditory, or cerebellar inputs, but that some thalamic nuclei are arranged instead to relay information from one cortical area to another. The second is that the thalamus is not a simple, passive relay of information to cortex but instead is involved in many dynamic processes that significantly alter the nature of the information relayed to cortex.


title: Predicting distributions with Linearizing Belief Networks

abstract: Conditional belief networks introduce stochastic binary variables in neural networks. Contrary to a classical neural network, a belief network can predict more than the expected value of the output $Y$ given the input $X$. It can predict a distribution of outputs $Y$ which is useful when an input can admit multiple outputs whose average is not necessarily a valid answer. Such networks are particularly relevant to inverse problems such as image prediction for denoising, or text to speech. However, traditional sigmoid belief networks are hard to train and are not suited to continuous problems. This work introduces a new family of networks called linearizing belief nets or LBNs. A LBN decomposes into a deep linear network where each linear unit can be turned on or off by non-deterministic binary latent units. It is a universal approximator of real-valued conditional distributions and can be trained using gradient descent. Moreover, the linear pathways efficiently propagate continuous information and they act as multiplicative skip-connections that help optimization by removing gradient diffusion. This yields a model which trains efficiently and improves the state-of-the-art on image denoising and facial expression generation with the Toronto faces dataset.


title: The Social Dynamics of Pair Programming

abstract: This paper presents data from a four month ethnographic study of professional pair programmers from two software development teams. Contrary to the current conception of pair programmers, the pairs in this study did not hew to the separate roles of "driver" and "navigator". Instead, the observed programmers moved together through different phases of the task, considering and discussing issues at the same strategic "range " or level of abstraction and in largely the same role. This form of interaction was reinforced by frequent switches in keyboard control during pairing and the use of dual keyboards. The distribution of expertise among the members of a pair had a strong influence on the tenor of pair programming interaction. Keyboard control had a consistent secondary effect on decisionmaking within the pair. These findings have implications for software development managers and practitioners as well as for the design of software development tools.


title: Neuronal Coding of Prediction Errors

abstract: Associative learning enables animals to anticipate the occurrence of important outcomes. Learning occurs when the actual outcome differs from the predicted outcome, resulting in a prediction error. Neurons in several brain structures appear to code prediction errors in relation to rewards, punishments, external stimuli, and behavioral reactions. In one form, dopamine neurons, norepinephrine neurons, and nucleus basalis neurons broadcast prediction errors as global reinforcement or teaching signals to large postsynaptic structures. In other cases, error signals are coded by selected neurons in the cerebellum, superior colliculus, frontal eye fields, parietal cortex, striatum, and visual system, where they influence specific subgroups of neurons. Prediction errors can be used in postsynaptic structures for the immediate selection of behavior or for synaptic changes underlying behavioral learning. The coding of prediction errors may represent a basic mode of brain function that may also contribute to the processing of sensory information and the short-term control of behavior.


title: Analysis of five rice 4-coumarate:coenzyme A ligase enzyme activity and stress response for potential roles in lignin and flavonoid biosynthesis in rice.

abstract: 4-Coumarate:coenzyme A ligase (4CL) catalyzes the conversion of hydroxycinnamates into corresponding CoA esters for biosynthesis of flavonoids and lignin. In this study, five members of the 4CL gene family from rice were cloned and analyzed. Recombinant 4CL data revealed that 4-coumaric acid and ferulic acid were the two main substrates of 4CL (Os4CL1/3/4/5) for monolignol biosynthesis in rice. Os4CL2 was specifically expressed in the anther and was strongly activated by UV irradiation, suggesting its potential involvement in flavonoid formation. Moreover, bioinformatics analysis showed that the existence of valine residue at the substrate-binding pocket may mainly affect rice 4CL activities toward sinapic acid.


title: Filter Bank Common Spatial Pattern (FBCSP) algorithm using online adaptive and semi-supervised learning

abstract: The Filter Bank Common Spatial Pattern (FBCSP) algorithm employs multiple spatial filters to automatically select key temporal-spatial discriminative EEG characteristics and the Naive Bayesian Parzen Window (NBPW) classifier using offline learning in EEG-based Brain-Computer Interfaces (BCI). However, it has yet to address the non-stationarity inherent in the EEG between the initial calibration session and subsequent online sessions. This paper presents the FBCSP that employs the NBPW classifier using online adaptive learning that augments the training data with available labeled data during online sessions. However, employing semi-supervised learning that simply augments the training data with available data using predicted labels can be detrimental to the classification accuracy. Hence, this paper presents the FBCSP using online semi-supervised learning that augments the training data with available data that matches the probabilistic model captured by the NBPW classifier using predicted labels. The performances of FBCSP using online adaptive and semi-supervised learning are evaluated on the BCI Competition IV datasets IIa and IIb and compared to the FBCSP using offline learning. The results showed that the FBCSP using online semi-supervised learning yielded relatively better session-to-session classification results compared against the FBCSP using offline learning. The FBCSP using online adaptive learning on true labels yielded the best results in both datasets, but the FBCSP using online semi-supervised learning on predicted labels is more practical in BCI applications where the true labels are not available.


title: Edge Detection Techniques For Image Segmentation

abstract: Interpretation of image contents is one of the objectives in computer vision specifically in image processing. In this era it has received much awareness of researchers. In image interpretation the partition of the image into object and background is a severe step. Segmentation separates an image into its component regions or objects. Image segmentation t needs to segment the object from the background to read the image properly and identify the content of the image carefully. In this context, edge detection is a fundamental tool for image segmentation. In this paper an attempt is made to study the performance of most commonly used edge detection techniques for image segmentation and also the comparison of these techniques is carried out with an experiment by using MATLAB software.


title: Executive involvement and participation in the management of information technology

abstract: Executive support is often prescribed as critical for fully tapping the benefits of information technology (IT). However, few investigations have attempted to determine what type of executive support is likely or organizationally appropriate. This article puts forward alternative models of executive support. The models are tested by examining chief executive officers' behaviors in and perceptions of IT activities. CEOs and information systems executives are surveyed and further data collected from industry handbooks and from chairmen's annual letters to shareholders. The results suggest that executive involvement (a psychological state) is more strongly associated with the firm's progressive use of IT than executive participation (actual behaviors) in IT activities. Executive involvement is influenced by a CEO's participation, prevailing organizational conditions, and the executive's functional background. CEO's perceptions about the importance of IT in their firms were generally positive, although they participated in IT activities rather infrequently.


title: An intelligent intrusion detection system using genetic based feature selection and Modified J48 decision tree classifier

abstract: In this paper, intelligent algorithms for intrusion detection are proposed which detect the network attacks as normal or anomaly based attacks by performing effective preprocessing and classification. This system uses a new genetic algorithm approach for pre-processing and Modified J48 classification algorithm to identify the intended activities. The new genetic based feature selection algorithm proposed in this paper is helpful to identify the important features needed to classify the normal and anomaly records. The proposed intelligent IDS has been empirically tested in a simulated environment and the experimental results show that the proposed method provides higher detection accuracy than the existing methods in terms of detection rate with reduced false rate. The salient contributions of this paper are, the proposed a new processing technique for removing noisy data in the KDD cup'99 dataset, identification of the optimal features selection by applying modified genetic algorithm and finally the proposed of modified J48 decision tree algorithm for efficient classification for providing intelligent network intrusion detection.


title: Development of electronic voting machine with the inclusion of Near Field Communication ID cards and biometric fingerprint identifier

abstract: The basis of this project is to create an electronic voting machine that will help to eradicate defrauding of the manual voting systems by multiple votes cast by the same user. With the inclusion of a Near Field Communication ID card reader and biometric fingerprint device, each voter will be entered into the system through a swift process only after being recognized and checked to the given database of enlisted voters. Once the corresponding fingerprint is matched with the information provided by the identification card, the voter will be allowed to vote for their preferred candidate through a panel of buttons. The respective card will then be marked for further referencing, and the voter will not be allowed to take part in multiple votes. The proposed project also carries the unique feature of being autonomous during the course of operation, which helps to diminish the issue of hacking occurring in previous attempts of electronic voting machines.


title: Common-Specific Multimodal Learning for Deep Belief Network

abstract: Multimodal Deep Belief Network has been widely used to extract representations for multimodal data by fusing the high-level features of each data modality into common representations. Such straightforward fusion strategy can benefit the classification and information retrieval tasks. However, it may introduce noise in case the high-level features are not naturally common hence non-fusable for different modalities. Intuitively, each modality may have its own specific features and corresponding representation capabilities thus should not be simply fused. Therefore, it is more reasonable to fuse only the common features and represent the multimodal data by both the fused features and the modality-specific features. To distinguish common features from modal-specific features is a challenging task for traditional DBN models where all features are crudely mixed. This paper proposes the Common-Specific Multimodal Deep Belief Network (CSDBN) to solve the problem. CS-DBN automatically separates common features from modal-specific features and fuses only the common ones for data representation. Experimental results demonstrate the superiority of CS-DBN for classification tasks compared with the baseline approaches.


title: Compositional Symbolic Execution through Program Specialization

abstract: Scalability is a major challenge in symbolic execution. The large number of paths that need to be explored and the large size of the constraints that must be carried often compromise the eectiveness of symbolic execution for software testing in practice. Compositional symbolic execution aims to alleviate these scalability issues by executing the methods of a program separately, stowing their results in method summaries and using such summaries to incrementally execute the complete program. We present a novel compositional approach that leverages partial evaluation, a well-established technique that aims at automatically specializing a program with respect to some of its input. We report on its design and implementation in Symbolic PathFinder and on preliminary promising evaluation results.


title: The analytic hierarchy process in medical and health care decision making: A literature review

abstract: This paper presents a literature review of the application of the analytic hierarchy process (AHP) to important problems in medical and health care decision making. The literature is classified by year of publication, health care category, journal, method of analyzing alternatives, participants, and application type. Very few articles were published prior to 1988 and the level of activity has increased to about three articles per year since 1997. The 50 articles reviewed were classified in seven categories: diagnosis, patient participation, therapy/treatment, organ transplantation, project and technology evaluation and selection, human resource planning, and health care evaluation and policy. The largest number of articles was found in the project and technology evaluation and selection category (14) with substantial activity in patient participation (9), therapy/treatment (8), and health care evaluation and policy (8). The AHP appears to be a promising support tool for shared decision making between patient and doctor, evaluation and selection of therapies and treatments, and the evaluation of health care technologies and policies. We expect that AHP research will continue to be an important component of health care and medical research.


title: Knowledge graph for TCM health preservation

abstract: We construct a knowledge graph for TCM Health Preservation, which integrates terms, databases and other resources.This knowledge graph facilitates knowledge services such as visualization, knowledge retrieval, and recommendation.This knowledge graph helps the sharing, interpretation, and utilization of TCM knowledge. Traditional Chinese Medicine (TCM) is one of the important non-material cultural heritages of the Chinese nation. It is an important development strategy of Chinese medicine to collect, analyzes, and manages the knowledge assets of TCM health care. As a novel and massive knowledge management technology, knowledge graph provides an ideal technical means to solve the problem of Knowledge Island in the field of traditional Chinese medicine. In this study, we construct a large-scale knowledge graph, which integrates terms, documents, databases and other knowledge resources. This knowledge graph can facilitate various knowledge services such as knowledge visualization, knowledge retrieval, and knowledge recommendation, and helps the sharing, interpretation, and utilization of TCM health care knowledge.


title: 5G Internet of Things: A survey

abstract: The existing 4G networks have been widely used in the Internet of Things (IoT) and is continuously evolving to match the needs of the future Internet of Things (IoT) applications. The 5G networks are expected to massive expand today's IoT that can boost cellular operationgs, IoT security, and network challenges and driving the Internet future to the edge. The existing IoT solutions are facing a number of challenges such as large number of conneciton of nodes, security, and new standards. This paper reviews the current research state-of-the-art of 5G IoT, key enabling technologies, and main research trends and challenges in 5G IoT.


Enhanced Classification Model for Cervical Cancer Dataset based on Cost Sensitive Classifier Hayder


title: Construct validity testing of a laparoscopic surgery simulator (Lap Mentor®)

abstract: BACKGROUND ::: Before surgical simulators can be implemented for assessment of surgical training, their construct validity should be assessed. ::: ::: ::: METHODS ::: Nine novices (NOV), nine medical students (MS), and nine residents (RES) underwent a laparoscopic skills training on the virtual reality (VR) simulator Lap Mentor. Assessment of laparoscopic skill was based on parameters measured by the computer system before and after training. ::: ::: ::: RESULTS ::: Significant difference existed between RES and NOV at seven of nine tasks before training on the VR simulator. After the training in some tasks significant differences were observed between the experienced group (RES) and the nonexperienced groups (MS and NOV) or between medical groups (RES and MS) and nonmedical group (NOV). ::: ::: ::: CONCLUSIONS ::: Performance parameters of the Lap-Mentor can be used to distinguish between subjects with varying laparoscopic experience.


title: A vision-based method for weeds identification through the Bayesian decision theory

abstract: One of the objectives of precision agriculture is to minimize the volume of herbicides that are applied to the fields through the use of site-specific weed management systems. This paper outlines an automatic computer vision-based approach for the detection and differential spraying of weeds in corn crops. The method is designed for post-emergence herbicide applications where weeds and corn plants display similar spectral signatures and the weeds appear irregularly distributed within the crop's field. The proposed strategy involves two processes: image segmentation and decision making. Image segmentation combines basic suitable image processing techniques in order to extract cells from the image as the low level units. Each cell is described by two area-based measuring relationships between crop and weeds. The decision making determines the cells to be sprayed based on the computation of a posterior probability under a Bayesian framework. The a priori probability in this framework is computed taking into account the dynamic of the physical system (tractor) where the method is embedded. The main contributions of this paper are: (1) the combination of the image segmentation and decision making processes and (2) the decision making itself which exploits a previous knowledge which is mapped as the a priori probability. The performance of the method is illustrated by comparative analysis against some existing strategies.


title: A Database and Evaluation Methodology for Optical Flow

abstract: The quantitative evaluation of optical flow algorithms by Barron et al. led to significant advances in the performance of optical flow methods. The challenges for optical flow today go beyond the datasets and evaluation methods proposed in that paper and center on problems associated with nonrigid motion, real sensor noise, complex natural scenes, and motion discontinuities. Our goal is to establish a new set of benchmarks and evaluation methods for the next generation of optical flow algorithms. To that end, we contribute four types of data to test different aspects of optical flow algorithms: sequences with nonrigid motion where the ground-truth flow is determined by tracking hidden fluorescent texture; realistic synthetic sequences; high frame-rate video used to study interpolation error; and modified stereo sequences of static scenes. In addition to the average angular error used in Barron et al., we compute the absolute flow endpoint error, measures for frame interpolation error, improved statistics, and flow accuracy at motion boundaries and in textureless regions. We evaluate the performance of several well-known methods on this data to establish the current state of the art. Our database is freely available on the Web together with scripts for scoring and publication of the results at http://vision.middlebury.edu/flow/.


title: Detecting hip fractures with radiologist-level performance using deep neural networks

abstract: We developed an automated deep learning system to detect hip fractures from frontal pelvic x-rays, an important and common radiological task. Our system was trained on a decade of clinical x-rays (~53,000 studies) and can be applied to clinical data, automatically excluding inappropriate and technically unsatisfactory studies. We demonstrate diagnostic performance equivalent to a human radiologist and an area under the ROC curve of 0.994. Translated to clinical practice, such a system has the potential to increase the efficiency of diagnosis, reduce the need for expensive additional testing, expand access to expert level medical image interpretation, and improve overall patient outcomes.


title: Learning of Human-like Algebraic Reasoning Using Deep Feedforward Neural Networks

abstract: Abstract Human-like rewriting, which is an algebraic reasoning system imitating human intelligence of problem solving, is proposed in this work. In order to imitate both learning and reasoning aspects of human cognition, a deep feedforward neural network learns from algebraic reasoning examples produced by humans and then uses learnt experiences to guide other reasoning processes. This work shows that the neural network can learn human’s behaviours of solving mathematical problems, and it can indicate suitable directions of reasoning, so that intelligent and heuristic reasoning can be performed. Moreover, human-like rewriting bridges the gap between symbolic reasoning and biologically inspired machine learning. To enable the neural network to recognise patterns of symbolic expressions with non-deterministic sizes, the expressions are reduced to partial tree representations and then vectorised as numeric features. Further, the centralisation method, symbolic association vectors and rule application records are used to improve the vectorised features. With these approaches, human-like rewriting shows satisfactory performance on the tasks of solving linear equations and computing derivations and indefinite integrals.


title: Self-Paced Learning with Diversity

abstract: Self-paced learning (SPL) is a recently proposed learning regime inspired by the learning process of humans and animals that gradually incorporates easy to more complex samples into training. Existing methods are limited in that they ignore an important aspect in learning: diversity. To incorporate this information, we propose an approach called self-paced learning with diversity (SPLD) which formalizes the preference for both easy and diverse samples into a general regularizes This regularization term is independent of the learning objective, and thus can be easily generalized into various learning tasks. Albeit non-convex, the optimization of the variables included in this SPLD regularization term for sample selection can be globally solved in linearithmic time. We demonstrate that our method significantly outperforms the conventional SPL on three real-world datasets. Specifically, SPLD achieves the best MAP so far reported in literature on the Hollywood2 and Olympic Sports datasets.


title: Leadership and the fate of organizations.

abstract: This article concerns the real-world importance of leadership for the success or failure of organizations and social institutions. The authors propose conceptualizing leadership and evaluating leaders in terms of the performance of the team or organization for which they are responsible. The authors next offer a taxonomy of the dependent variables used as criteria in leadership studies. A review of research using this taxonomy suggests that the vast empirical literature on leadership may tell us more about the success of individual managerial careers than the success of these people in leading groups, teams, and organizations. The authors then summarize the evidence showing that leaders do indeed affect the performance of organizations-for better or for worse-and conclude by describing the mechanisms through which they do so.


title: Class-D Audio Amplifier Using 1-Bit Fourth-Order Delta-Sigma Modulation

abstract: We present the design of a single-chip delta-sigma modulation-based class-D amplifier for driving head- phones in portable audio applications. The presented class-D amplifier generates output pulse signals using a single-bit fourth-order high-performance modulator. To achieve a high signal-to-noise ratio and ensure system stability for a large input range, the locations of the modulator loop filter poles and zeros are optimized and thoroughly simulated. The test chip is fabricated using a standard 0.18- m CMOS process. The active area of the chip is 1.6 mm . It operates for the signal bandwidth from 20 Hz to 20 kHz. The measured total harmonic distortion plus noise at the 32- load terminal is 0.022% from a single 3-V power supply. Index Terms—Class-D amplifier, delta-sigma modulator, power switching, pulsewidth modulation (PWM), signal-to-noise ratio (SNR), total harmonic distortion plus noise (THD+N).


title: Frangi-Net: A Neural Network Approach to Vessel Segmentation

abstract: In this paper, we reformulate the conventional 2-D Frangi vesselness measure into a pre-weighted neural network ("Frangi-Net"), and illustrate that the Frangi-Net is equivalent to the original Frangi filter. Furthermore, we show that, as a neural network, Frangi-Net is trainable. We evaluate the proposed method on a set of 45 high resolution fundus images. After fine-tuning, we observe both qualitative and quantitative improvements in the segmentation quality compared to the original Frangi measure, with an increase up to $17\%$ in F1 score.


title: A learning-to-rank based fault localization approach using likely invariants

abstract: Debugging is a costly process that consumes much of developer time and energy. To help reduce debugging effort, many studies have proposed various fault localization approaches. These approaches take as input a set of test cases (some failing, some passing) and produce a ranked list of program elements that are likely to be the root cause of the failures (i.e., failing test cases). In this work, we propose Savant, a new fault localization approach that employs a learning-to-rank strategy, using likely invariant diffs and suspiciousness scores as features, to rank methods based on their likelihood to be a root cause of a failure. Savant has four steps: method clustering & test case selection, invariant mining, feature extraction, and method ranking. At the end of these four steps, Savant produces a short ranked list of potentially buggy methods. We have evaluated Savant on 357 real-life bugs from 5 programs from the Defects4J benchmark. Out of these bugs, averaging over 100 repeated trials with different seeds to randomly break ties, we find that on average Savant can identify correct buggy methods for 63.03, 101.72, and 122 bugs at top 1, 3, and 5 positions in the ranked lists that Savant produces. We have compared Savant against several state-of-the-art fault localization baselines that work on program spectra. We show that Savant can successfully locate 57.73%, 56.69%, and 43.13% more bugs at top 1, top 3, and top 5 positions than the best performing baseline, respectively.


title: Special Section on Mobile Augmented Reality

abstract: Mobile Augmented Reality is one of the fastest growing research areas in Augmented Reality, partially due to the emergence of smart phones that provide powerful and ubiquitous platforms for supporting mobile Augmented Reality. This special section contains seven papers on mobile AR, covering a range of topics from tracking, user studies, visualization, and collaborative applications.


title: Optimization of sensor deployment in WSN for precision irrigation using spatial arrangement of permanent crop

abstract: Precision irrigation is the best practice for efficient water management. Wireless Sensor Networks (WSN) are widely used in environment monitoring especially in the precision irrigation. One of the fundamental issues of WSN is optimum senor deployment. Type of radio communication, number, and location of sensor deployment will have direct impact on the coverage, connectivity, cost and life of the sensors in WSN. This paper explores the problem of optimal WSN deployment by considering the unique way of spatial arrangement of the permanent crop during transplantation. Instead of conventional horizontal or linear row farming we propose the hexagonal pattern farming for the suitable permanent crops. This modified spatial arrangement will increase the coverage of the WSN without losing the specification of growing conditions. There is an improvement in total number of trees covered by 13 percentage in this method. Mathematical model is proposed, which justify the increase in coverage by using this modified spatial arrangement.


title: iClay: Digitizing Cuneiform

abstract: Advances in digital technology for the graphic and textual representation of manuscripts have not, until recently, been applied to the world's oldest manuscripts, cuneiform tablets. This is due in large part both to the three-dimensional nature of cuneiform tablets and to the complexity of the cuneiform script system. The Digital Hammurabi Project and the Initiative for Cuneiform Encoding announce success in encoding Sumero-Akkadian cuneiform in Unicode while also demonstrating advances in 3D scanning and visualization of cuneiform tablets, showcased by iClay, a cross-platform, Internet-deployable, Java applet that allows for the viewing and manipulation of 2D+ images of cuneiform tablets.


title: Nonlinear dynamics of vibrating MEMS

abstract: Let us consider a MEMS translational gyroscope. When significantly displacing the proof mass, the nonlinear hardening characteristic of the supporting beams becomes visible. Thus, the resonance peak of the structure bends towards the higher frequencies. This property is useful to easily synchronise sense and drive resonances thus increasing the sensibility of the MEMS gyroscope. Through a test structure designed to access the high deformation range of the supporting beams, its nonlinear vibrations were investigated both experimentally and numerically. It is shown that a simple nonlinear lumped parameter model is sufficient to schematise the gyroscope and that a semi-analytical integration method allows to quickly determine both stable and unstable branches of the system's dynamic response.


title: Creative Invention Benchmark

abstract: In this paper we present the Creative Invention Benchmark (CrIB), a 2000-problem benchmark for evaluating a particular facet of computational creativity. Specifically, we address combinational p-creativity, the creativity at play when someone combines existing knowledge to achieve a solution novel to that individual. We present generation strategies for the five problem categories of the benchmark and a set of initial baselines.


Pig Identification Based on MXNet


title: Design Guidelines for Gap Waveguide Technology Based on Glide-Symmetric Holey Structures

abstract: The behavior of a glide-symmetric holey periodic structure as electromagnetic bandgap is studied in this letter. A number of numerical simulations have been carried out in order to define the importance of each constituent parameter of the unit cell. Our proposed structure finds potential application in antennas and circuits based on gap waveguide technology for the millimeter band. The experimental verifications confirm the effects previously analyzed with the numerical studies.


title: Comment on"Biologically inspired protection of deep networks from adversarial attacks"

abstract: A recent paper suggests that Deep Neural Networks can be protected from gradient-based adversarial perturbations by driving the network activations into a highly saturated regime. Here we analyse such saturated networks and show that the attacks fail due to numerical limitations in the gradient computations. A simple stabilisation of the gradient estimates enables successful and efficient attacks. Thus, it has yet to be shown that the robustness observed in highly saturated networks is not simply due to numerical limitations.


Human echolocation: The detection of objects by the blind


title: Pelvic Girdle Pain during or after Pregnancy: a review of recent evidence and a clinical care path proposal

abstract: The tremendous development of knowledge, the accumulation of clinical experience, and the enriching of everyday practice with the results of basic and clinical research created the need for splitting medicine in specialities and sub-specialities. Although this evolution is inevitable in order to meet the complex needs and demands of patients, we have to be aware of the risk of neglecting the holistic approach of a person in distress. Indeed, the organ-focused and pathology-centred approach of a patient may not exempt us of the rudiments of care such as the notion that a human being is more than the sum of its parts. This involves the absolute obligation for intra and inter-disciplinary cooperation. Recently, the European leading societies in Gynaecology, the European Board Obstetricians and Gynaecologists (EBCOG), the European Society of Human reproduction and Embryology (ESHRE), the European Society for Gynaecological Endoscopy (ESGE) and the European Academy for Gynaecological Surgery (+he Academy), have established some common impressive initiatives to improve women’s health care. As a result of an intensive research programme coordinated by +he Academy with a multidisciplinary team of gynaecologists and surgeons a unique structured training and certification programme in endoscopic surgery is established. This project is a fine example making the dream of multidisciplinary approach reality and achieving some most impressive results. It is without any doubt a major step forwards in the improvement of the quality assurance in endoscopic surgery in benefit of the patients care and has been accepted by our leading European and national societies. Recently, ESGE and ESHRE developed a new classification on female genital anomalies. An evidence-based updated new classification published in the official journal of both societies was the ended result. This creative, successful and uneventful collaboration underlines the need for respecting some crucial points: to clearly outline the topic of common action, to find the working platform, to define the method and to respect independence of each society by clarifying the borders and the responsibilities. Keeping the window of the collaboration open is crucial for keeping the window to medicine of the future unlocked.


title: Learning Analytics and Educational Data Mining in Practice: A Systematic Literature Review of Empirical Evidence.

abstract: This paper aims to provide the reader with a comprehensive background for understanding current knowledge on Learning Analytics (LA) and Educational Data Mining (EDM) and its impact on adaptive learning. It constitutes an overview of empirical evidence behind key objectives of the potential adoption of LA/EDM in generic educational strategic planning. We examined the literature on experimental case studies conducted in the domain during the past six years (2008-2013). Search terms identified 209 mature pieces of research work, but inclusion criteria limited the key studies to 40. We analyzed the research questions, methodology and findings of these published papers and categorized them accordingly. We used non-statistical methods to evaluate and interpret findings of the collected studies. The results have highlighted four distinct major directions of the LA/EDM empirical research. We discuss on the emerged added value of LA/EDM research and highlight the significance of further implications. Finally, we set our thoughts on possible uncharted key questions to investigate both from pedagogical and technical considerations.


title: Generation of short electrical pulses based on bipolar transistorsny

abstract: Abstract. A system for the generation of short electrical pulses based on the minority carrier charge storage and the step recovery effect of bipolar transistors is presented. Electrical pulses of about 90 ps up to 800 ps duration are generated with a maximum amplitude of approximately 7V at 50Ω. The bipolar transistor is driven into saturation and the base-collector and base-emitter junctions become forward biased. The resulting fast switch-off edge of the transistor’s output signal is the basis for the pulse generation. The fast switching of the transistor occurs as a result of the minority carriers that have been injected and stored across the base-collector junction under forward bias conditions. If the saturated transistor is suddenly reverse biased the pn-junction will appear as a low impedance until the stored charge is depleted. Then the impedance will suddenly increase to its normal high value and the flow of current through the junction will turn to zero, abruptly. A differentiation of the output signal of the transistor results in two short pulses with opposite polarities. The differentiating circuit is implemented by a transmission line network, which mainly acts as a high pass filter. Both the transistor technology (pnp or npn) and the phase of the transfer function of the differentating circuit influence the polarity of the output pulses. The pulse duration depends on the transistor parameters as well as on the transfer function of the pulse shaping network. This way of generating short electrical pulses is a new alternative for conventional comb generators based on steprecovery diodes (SRD). Due to the three-terminal structure of the transistor the isolation problem between the input and the output signal of the transistor network is drastically simplified. Furthermore the transistor is an active element in contrast to a SRD, so that its current gain can be used to minimize the power of the driving signal.


title: The power of obfuscation techniques in malicious JavaScript code: A measurement study

abstract: JavaScript based attacks have been reported as the top Internet security threats in recent years. Since most of the Internet users rely on anti-virus software to protect themselves from malicious JavaScript code, attackers exploit JavaScript obfuscation techniques to evade the detection of anti-virus software. To better understand the obfuscation techniques adopted by malicious JavaScript code, we conduct a measurement study. We first categorize observed JavaScript obfuscation techniques. Then we conduct a statistic analysis on the usage of different categories of obfuscation techniques in real-world malicious JavaScript samples. We also study the detection effectiveness of 20 most popular anti-virus software against obfuscation techniques. Based on the results, we analyze the cause of the popularity of obfuscation in malicious JavaScript code; the reason behind the choice of obfuscation techniques and the difference between benign obfuscation and malicious obfuscation. Moreover, we also provide suggestions for designing effective obfuscation detection approaches in future.


title: An experimental mechatronic design and control of a 5 DOF Robotic arm for identification and sorting of different sized objects

abstract: The purpose of this paper is to present the construction and programming of a five degrees of freedom robotic arm which interacts with an infrared sensor for the identification and sorting of different sized objects. The main axis of the construction design will be up to the three main branches of science that make up the Mechatronics: Mechanical Engineering, Electronic-Electrical Engineering and Computer Engineering. The methods that have been used for the construction are presented as well as the methods for the programming of the arm in cooperation with the sensor. The aim is to present the manual and automatic control of the arm for the recognition and the installation of the objects through a simple (in operation) and low in cost sensor like the one that was used by this paper. Furthermore, this paper presents the significance of this robotic arm design and its further applications in contemporary industrial forms of production.


title: Investigation of a New Motor Assessment Scale for Stroke Patients

abstract: The purpose of this paper is to present and describe a motor assessment scale (MAS) for stroke patients and to report on the investigation of two aspects of its reliability. The MAS is a brief and easily administered assessment of eight areas of motor function and one item related to muscle tone. Each item is scored on a scale from 0 to 6. To check interrater reliability, we videotaped five stroke patients while they were being assessed with the MAS. These scores were used as the criterion ratings. Twenty raters then assessed these patients, and their results were correlated with the criterion ratings. We determined test-retest reliability by assessing on two occasions, separated by a four-week interval, 14 stroke patients whose recovery was considered to be stable and by correlating these scores. The MAS was found to be highly reliable with an average interrater correlation of .95 and an average test-retest correlation of .98.