title: Stacked convolutional auto-encoders for steganalysis of digital images

abstract: In this paper, we point out that SRM (Spatial-domain Rich Model), the most successful steganalysis framework of digital images possesses a similar architecture to CNN (convolutional neural network). The reasonable expectation is that the steganalysis performance of a well-trained CNN should be comparable to or even better than that of the hand-coded SRM. However, a CNN without pre-training always get stuck at local plateaus or even diverge which result in rather poor solutions. In order to circumvent this obstacle, we use convolutional auto-encoder in the pre-training procedure. A stack of convolutional auto-encoders forms a CNN. The experimental results show that initializing a CNN with the mixture of the filters from a trained stack of convolutional auto-encoders and feature pooling layers, although still can not compete with SRM, yields superior performance compared to traditional CNN for the detection of HUGO generated stego images in BOSSBase image database.


title: Use of data mining in crop yield prediction

abstract: Agriculture is the most important sector that influences the economy of India. It contributes to 18% of India's Gross Domestic Product (GDP) and gives employment to 50% of the population of India. People of India are practicing Agriculture for years but the results are never satisfying due to various factors that affect the crop yield. To fulfill the needs of around 1.2 billion people, it is very important to have a good yield of crops. Due to factors like soil type, precipitation, seed quality, lack of technical facilities etc the crop yield is directly influenced. Hence, new technologies are necessary for satisfying the growing need and farmers must work smartly by opting new technologies rather than going for trivial methods. This paper focuses on implementing crop yield prediction system by using Data Mining techniques by doing analysis on agriculture dataset. Different classifiers are used namely J48, LWL, LAD Tree and IBK for prediction and then the performance of each is compared using WEKA tool. For evaluating performance Accuracy is used as one of the factors. The classifiers are further compared with the values of Root Mean Squared Error (RMSE), Mean Absolute Error (MAE) and Relative Absolute Error (RAE). Lesser the value of error, more accurate the algorithm will work. The result is based on comparison among the classifiers.


title: Wireless machine-to-machine communication for intelligent transportation systems: Internet of vehicles and vehicle to grid

abstract: Machine to machine communication in intelligent transportation is a technology that aims to interconnect various components such as sensors, vehicles, road infrastructures and wireless networks. The significance thereof is to solve problems such as road congestion, road accidents and high vehicle fuel consumption. This paper gives an overview of how Machine-to-machine (M2M) communication can be used in intelligent transportation systems (ITS) to improve road safety and efficiency, where Vehicular ad-hoc networks (VANETs) play a major role. These applications include traffic light control, fleet management and smart grid systems. Some of M2M architectures that have been devised are also discussed.


title: Comparing the performance of web server architectures

abstract: In this paper, we extensively tune and then compare the performance of web servers based on three different server architectures. The μserver utilizes an event-driven architecture, Knot uses the highly-efficient Capriccio thread library to implement a thread-per-connection model, and WatPipe uses a hybrid of events and threads to implement a pipeline-based server that is similar in spirit to a staged event-driven architecture (SEDA) server like Haboob. We describe modifications made to the Capriccio thread library to use Linux's zero-copy sendfile interface. We then introduce the SY mmetric Multi-Processor Event Driven (SYMPED) architecture in which relatively minor modifications are made to a single process event-driven (SPED) server (the μserver) to allow it to continue processing requests in the presence of blocking due to disk accesses. Finally, we describe our C++ implementation of WatPipe, which although utilizing a pipeline-based architecture, excludes the dynamic controls over event queues and thread pools used in SEDA. When comparing the performance of these three server architectures on the workload used in our study, we arrive at different conclusions than previous studies. In spite of recent improvements to threading libraries and our further improvements to Capriccio and Knot, both the event-based μserver and pipeline-based Wat-Pipe server provide better throughput (by about 18%). We also observe that when using blocking sockets to send data to clients, the performance obtained with some architectures is quite good and in one case is noticeably better than when using non-blocking sockets.


title: Planning human-aware motions using a sampling-based costmap planner

abstract: This paper addresses the motion planning problem while considering Human-Robot Interaction (HRI) constraints. The proposed planner generates collision-free paths that are acceptable and legible to the human. The method extends our previous work on human-aware path planning to cluttered environments. A randomized cost-based exploration method provides an initial path that is relevant with respect to HRI and workspace constraints. The quality of the path is further improved with a local path-optimization method. Simulation results on mobile manipulators in the presence of humans demonstrate the overall efficacy of the approach.


title: Mobile microrobot characterization through performance-based competitions

abstract: Recent advances in the design and fabrication of microelectromechanical systems (MEMS) have enabled the development of mobile microrobots that can autonomously navigate and manipulate in controlled environments. It is expected that this technology will be critical in applications as varied as intelligent sensor networks, in vivo medical diagnosis and treatment, and adaptive microelectronics. However, many challenges remain, particularly with respect to locomotion, power storage, embedded intelligence, and motion measurement. As a result, the National Institute of Standards and Technology has organized performance-based competitions for mobile microrobots that are designed to: 1) accelerate microrobot development by providing researchers a venue to demonstrate and observe novel technologies, 2) reveal the most pressing technical challenges, and 3) evaluate the most successful methods for locomotion and manipulation at the microscale (e.g., actuation techniques for crawling). This paper will discuss the goals and structure of the competition, results from past competitions, and plans to make performance characterization methods an integral component of future competitions.


title: Topic Based Sentiment Analysis Using Deep Learning

abstract: In this paper , we tackle Sentiment Analysis conditioned on a Topic in Twitter data using Deep Learning . We propose a 2-tier approach : In the first phase we create our own Word Embeddings and see that they do perform better than state-of-the-art embeddings when used with standard classifiers. We then perform inference on these embeddings to learn more about a word with respect to all the topics being considered, and also the top n-influencing words for each topic. In the second phase we use these embeddings to predict the sentiment of the tweet with respect to a given topic, and all other topics under discussion.


title: Original article Electronic Bullying Among Middle School Students

abstract: PURPOSE ::: Electronic communications technologies are affording children and adolescents new means of bullying one another. Referred to as electronic bullying, cyberbullying, or online social cruelty, this phenomenon includes bullying through e-mail, instant messaging, in a chat room, on a website, or through digital messages or images sent to a cell phone. The present study examined the prevalence of electronic bullying among middle school students. ::: ::: ::: METHODS ::: A total of 3,767 middle school students in grades 6, 7, and 8 who attend six elementary and middle schools in the southeastern and northwestern United States completed a questionnaire, consisting of the Olweus Bully/Victim Questionnaire and 23 questions developed for this study that examined participants' experiences with electronic bullying, as both victims and perpetrators. ::: ::: ::: RESULTS ::: Of the students, 11% that they had been electronically bullied at least once in the last couple of months (victims only); 7% indicated that they were bully/victims; and 4% had electronically bullied someone else at least once in the previous couple of months (bullies only). The most common methods for electronic bullying (as reported by both victims and perpetrators) involved the use of instant messaging, chat rooms, and e-mail. Importantly, close to half of the electronic bully victims reported not knowing the perpetrator's identity. ::: ::: ::: CONCLUSIONS ::: Electronic bullying represents a problem of significant magnitude. As children's use of electronic communications technologies is unlikely to wane in coming years, continued attention to electronic bullying is critical. Implications of these findings for youth, parents, and educators are discussed.


title: Tetracycline HCl solution as a root canal irrigant.

abstract: The effect of tetracycline hydrochloride as an endodontic irrigant on smear layer removal was examined by scanning electron microscopy and compared with bidistilled water, 2.5% sodium hypochlorite (NaOCl), and 50% citric acid in 20 extracted teeth. Five teeth, as a control, were irrigated with bidistilled water only in group 1. All the other groups were irrigated with NaOCI during instrumentation. The final rinses were NaOCl in group 2, citric acid in group 3, and 1% tetracycline hydrochloride in group 4. Scanning electron microscopic results and statistical analysis revealed that bidistilled water and NaOCl were ineffective in removing the smear layer, whereas citric acid and tetracycline hydrochloride were significantly more effective (p < 0.001). No statistically significant difference was detected between tetracycline hydrochloride and citric acid (p > 0.05). However tetracycline hydrochloride demineralized less peritubular dentin.


title: Similarity Modeling on Heterogeneous Networks via Automatic Path Discovery

abstract: Heterogeneous networks are widely used to model real-world semi-structured data. The key challenge of learning over such networks is the modeling of node similarity under both network structures and contents. To deal with network structures, most existing works assume a given or enumerable set of meta-paths and then leverage them for the computation of meta-path-based proximities or network embeddings. However, expert knowledge for given meta-paths is not always available, and as the length of considered meta-paths increases, the number of possible paths grows exponentially, which makes the path searching process very costly. On the other hand, while there are often rich contents around network nodes, they have hardly been leveraged to further improve similarity modeling. In this work, to properly model node similarity in content-rich heterogeneous networks, we propose to automatically discover useful paths for pairs of nodes under both structural and content information. To this end, we combine continuous reinforcement learning and deep content embedding into a novel semi-supervised joint learning framework. Specifically, the supervised reinforcement learning component explores useful paths between a small set of example similar pairs of nodes, while the unsupervised deep embedding component captures node contents and enables inductive learning on the whole network. The two components are jointly trained in a closed loop to mutually enhance each other. Extensive experiments on three real-world heterogeneous networks demonstrate the supreme advantages of our algorithm.


title: MapMarker: Extraction of Postal Addresses and Associated Information for General Web Pages

abstract: Address information is essential for people’s daily life. People often need to query addresses of unfamiliar location through Web and then use map services to mark down the location for direction purpose. Although both address information and map services are available online, they are not well combined. Users usually need to copy individual address from a Web site and paste it to another Web site with map services to locate its direction. Such copy and paste operations have to be repeated if multiple addresses are listed on a single page such as public school list or apartment list. Furthermore, associated information with individual address has to be copied and included on each marker for better comprehension. Our research is devoted to automate the above process and make the combination an easier task for users. The main techniques applied here include postal address extraction and associated information extraction. We apply sequence labeling algorithm based on Conditional Random Fields (CRFs) to train models for address extraction. Meanwhile, using the extracted addresses as landmarks, we apply pattern mining to identify the boundaries of address blocks and extract associated information with each individual address. The experimental result shows high F-score at 91% for postal address extraction and 87% accuracy for associated information extraction.


Hepatocellular carcinoma with intravascular and right atrial extension.


title: B-cell differentiation pattern of cutaneous lymphomas in infancy and childhood

abstract: This article documents four children with cutaneous lymphomas. Two of them had regional disease and the other two already had disseminated disease when they were diagnosed. Immunophenotyping of the lymphomatous infiltration disclosed a cell antigen profile with pre-pre-B (HLA-DR+, CALLA+, VIB-C5+), pre-B (HLADR+, CALLA+, VIB-C5+, cytoplasmatic immunoglobulin+), and transitional pre-B characteristics (HLA-DR+, CALLA+, VIB-C5+, surface immunoglobulin+). The morphology of the blast cells in all patients showed features of lymphoblastic lymphoma. Two patients had the convoluted cell type in common, but they were immunologically different, one displaying pre-pre-B and the other pre-B immunophenotypes. The remaining two cases were diagnosed as lymphoblastic lymphomas "type others" according to the Kiel Classification, one with pre-B and the other with transitional pre-B characteristics. The morphologic and immunohistochemical parameters as well as the clinical data from the four cases clearly indicate the heterogeneity of cutaneous non-Hodgkin's lymphomas in children.


title: Early Detection of Configuration Errors to Reduce Failure Damage

abstract: Early detection is the key to minimizing failure damage induced by configuration errors, especially those errors in configurations that control failure handling and fault tolerance. Since such configurations are not needed for initialization, many systems do not check their settings early (e.g., at startup time). Consequently, the errors become latent until their manifestations cause severe damage, such as breaking the failure handling. Such latent errors are likely to escape from sysadmins' observation and testing, and be deployed to production at scale. ::: ::: Our study shows that many of today's mature, widely-used software systems are subject to latent configuration errors (referred to as LC errors) in their critically important configurations--those related to the system's reliability, availability, and serviceability. One root cause is that many (14.0%-93.2%) of these configurations do not have any special code for checking the correctness of their settings at the system's initialization time. ::: ::: To help software systems detect LC errors early, we present a tool named PCHECK that analyzes the source code and automatically generates configuration checking code (called checkers). The checkers emulate the late execution that uses configuration values, and detect LC errors if the error manifestations are captured during the emulated execution. Our results show that PCHECK can help systems detect 75+% of real-world LC errors at the initialization phase, including 37 new LC errors that have not been exposed before. Compared with existing detection tools, it can detect 31% more LC errors.


title: Divide-and-Conquer Reinforcement Learning

abstract: Standard model-free deep reinforcement learning (RL) algorithms sample a new initial state for each trial, allowing them to optimize policies that can perform well even in highly stochastic environments. However, problems that exhibit considerable initial state variation typically produce high-variance gradient estimates for model-free RL, making direct policy or value function optimization challenging. In this paper, we develop a novel algorithm that instead optimizes an ensemble of policies, each on a different"slice"of the initial state space, and gradually unifies them into a single policy that can succeed on the whole state space. This approach, which we term divide-and-conquer RL, is able to solve complex tasks where conventional deep RL methods are ineffective. Our results show that divide-and-conquer RL greatly outperforms conventional policy gradient methods on challenging grasping, manipulation, and locomotion tasks, and exceeds the performance of a variety of prior methods.


title: Facial Action Unit Detection Using Attention and Relation Learning

abstract: Attention mechanism has recently attracted increasing attentions in the area of facial action unit (AU) detection. By finding the region of interest (ROI) of each AU with the attention mechanism, AU related local features can be captured. Most existing attention based AU detection works use prior knowledge to generate fixed attentions or refine the predefined attentions within a small range, which limits their capacity to model various AUs. In this paper, we propose a novel end-to-end weakly-supervised attention and relation learning framework for AU detection with only AU labels, which has not been explored before. In particular, multi-scale features shared by each AU are learned firstly, and then both channel-wise attentions and spatial attentions are learned to select and extract AU related local features. Moreover, pixel-level relations for AUs are further captured to refine spatial attentions so as to extract more relevant local features. Extensive experiments on BP4D and DISFA benchmarks demonstrate that our framework (i) outperforms the state-of-the-art methods for AU detection, and (ii) can find the ROI of each AU and capture the relations among AUs adaptively.


title: A gesture-free geometric approach for mid-air expression of design intent in 3D virtual pottery

abstract: The advent of depth cameras has enabled mid-air interactions for shape modeling with bare hands. Typically, these interactions employ a finite set of pre-defined hand gestures to allow users to specify modeling operations in virtual space. However, human interactions in real world shaping processes (such as pottery or sculpting) are complex, iterative, and continuous. In this paper, we show that the expression of user intent in shaping processes can be derived from the geometry of contact between the hand and the manipulated object. Specifically, we describe the design and evaluation of a geometric interaction technique for bare-hand mid-air virtual pottery. We model the shaping of a pot as a gradual and progressive convergence of the pot's profile to the shape of the user's hand represented as a point-cloud (PCL). Thus, a user does not need to learn, know, or remember any gestures to interact with our system. Our choice of pottery simplifies the geometric representation, allowing us to systematically study how users use their hands and fingers to express the intent of deformation during a shaping process. Our evaluations demonstrate that it is possible to enable users to express their intent for shape deformation without the need for a fixed set of gestures for clutching and deforming a shape. Display Omitted We introduce a geometric approach for mid-air virtual pottery design.A user can design virtual pots without the need to remember gestures.The shape of a pot gradually converges to the point-cloud of the user's hands.Applications are shown with two depth sensors, Leap Motion and SoftKinetic DepthSense.User evaluation demonstrates strengths and weaknesses of our approach.


title: Victim Derogation and Victim Enhancement as Alternate Routes to System Justification

abstract: Numerous studies have documented the potential for victim-blaming attributions to justify the status quo. Recent work suggests that complementary, victim-enhancing stereotypes may also increase support for existing social arrangements. We seek to reconcile these seemingly contradictory findings by proposing that victim derogation and victim enhancement are alternate routes to system justification, with the preferred route depending on the perception of a causal link between trait and outcome. Derogating “losers” (and lionizing “winners”) on traits (e.g., intelligence) that are causally related to outcomes (e.g., wealth vs. poverty) serves to increase system justification, as does compensating “losers” (and down-grading “winners”) on traits (e.g., physical attractiveness) that are causally unrelated to those outcomes. We provide converging evidence using system-threat and stereotype-activation paradigms.


title: Fractal antenna with electromagnetic band gap (EBG) structure for wireless application

abstract: This work proposes a design of Sierpinski Carpet fractal antenna with an Electromagnetic Band Gap (SCFAEBG) structure on the ground plane for wireless application operates at 5.8 GHz frequency. This antenna is designed and fabricated on Rogers RT Duroid 5880 substrate, with dielectric constant of 2.2 and thickness of 0.38 mm. The sierpinski carpet fractal antenna outperforms the conventional antenna in bandwidth performance and the reduction of the antenna size. This antenna is designed and simulated using Computer Simulation Technology (CST) Microwave Studio Suite 2014. The fabricated design is measured using a Vector Network Analyzer (VNA). The simulation result of the return loss of the conventional rectangular patch antenna is -17.65 dB whereas for the Sierpinski Carpet fractal antenna with an EBG structure on ground plane is -44.27 dB. Based on the results, the SCFA shows a reduction of size as compared to the conventional antenna with 25.05%. After the measurement process, the return loss for the SCFA is -19.22 dB. The bandwidth of the measured SCFA-EBG was also increased by 61.29% from 80.6 MHz. It was observed that, both simulation and measurement results show good agreement and acceptance.


title: An Ultra-High-Speed, 500000 rpm, 1 kW Electrical Drive System

abstract: New emerging micro gas turbine generator sets and turbocompressor systems push the speed limits of rotating machinery. To directly connect to these applications, ultra-high-speed electrical drive systems are needed. Therefore a 1 kW, 500000 rpm machine and the according power and control electronics are designed and built. This paper includes design considerations for the mechanical and electromagnetic machine design. Furthermore, a voltage source inverter with an additional dc-dc converter is described, and sensorless rotor position detection and digital control is used to drive the machine. Finally, the hardware and experimental results are presented.


title: Generalized BackPropagation, \'{E}tude De Cas: Orthogonality

abstract: This paper introduces an extension of the backpropagation algorithm that enables us to have layers with constrained weights in a deep network. In particular, we make use of the Riemannian geometry and optimization techniques on matrix manifolds to step outside of normal practice in training deep networks, equipping the network with structures such as orthogonality or positive definiteness. Based on our development, we make another contribution by introducing the Stiefel layer, a layer with orthogonal weights. Among various applications, Stiefel layers can be used to design orthogonal filter banks, perform dimensionality reduction and feature extraction. We demonstrate the benefits of having orthogonality in deep networks through a broad set of experiments, ranging from unsupervised feature learning to fine-grained image classification.


title: Reinforcement learning utilizes proxemics: An avatar learns to manipulate the position of people in immersive virtual reality

abstract: A reinforcement learning (RL) method was used to train a virtual character to move participants to a specified location. The virtual environment depicted an alleyway displayed through a wide field-of-view head-tracked stereo head-mounted display. Based on proxemics theory, we predicted that when the character approached within a personal or intimate distance to the participants, they would be inclined to move backwards out of the way. We carried out a between-groups experiment with 30 female participants, with 10 assigned arbitrarily to each of the following three groups: In the Intimate condition the character could approach within 0.38m and in the Social condition no nearer than 1.2m. In the Random condition the actions of the virtual character were chosen randomly from among the same set as in the RL method, and the virtual character could approach within 0.38m. The experiment continued in each case until the participant either reached the target or 7 minutes had elapsed. The distributions of the times taken to reach the target showed significant differences between the three groups, with 9 out of 10 in the Intimate condition reaching the target significantly faster than the 6 out of 10 who reached the target in the Social condition. Only 1 out of 10 in the Random condition reached the target. The experiment is an example of applied presence theory: we rely on the many findings that people tend to respond realistically in immersive virtual environments, and use this to get people to achieve a task of which they had been unaware. This method opens up the door for many such applications where the virtual environment adapts to the responses of the human participants with the aim of achieving particular goals.


title: Learning to Compose Domain-Specific Transformations for Data Augmentation

abstract: Data augmentation is a ubiquitous technique for increasing the size of labeled training sets by leveraging task-specific data transformations that preserve class labels. While it is often easy for domain experts to specify individual transformations, constructing and tuning the more sophisticated compositions typically needed to achieve state-of-the-art results is a time-consuming manual task in practice. We propose a method for automating this process by learning a generative sequence model over user-specified transformation functions using a generative adversarial approach. Our method can make use of arbitrary, non-deterministic transformation functions, is robust to misspecified user input, and is trained on unlabeled data. The learned transformation model can then be used to perform data augmentation for any end discriminative model. In our experiments, we show the efficacy of our approach on both image and text datasets, achieving improvements of 4.0 accuracy points on CIFAR-10, 1.4 F1 points on the ACE relation extraction task, and 3.4 accuracy points when using domain-specific transformation operations on a medical imaging dataset as compared to standard heuristic augmentation approaches.


title: I get it already! the influence of ChairBot motion gestures on bystander response

abstract: How could a rearranging chair convince you to let it by? This paper explores how robotic chairs might negotiate passage in shared spaces with people, using motion as an expressive cue. The user study evaluates the efficacy of three gestures at convincing a busy participant to let it by. This within-participants study consisted of three subsequent trials, in which a person is completing a puzzle on a standing desk and a robotic chair approaches to squeeze by. The measure was whether participants moved out of the robot's way or not. People deferred to the robot in slightly less than half the trials as they were engaged in the activity. The main finding, however, is that over-communication cues more blocking behaviors, perhaps because it is annoying or because people want chairs to know their place (socially speaking). The Forward-Back gesture that was most effective at negotiating passage in the first trail was least effective in the second and third trial. The more subtle Pause and the slightly loud but less-aggressive Side-to-Side gesture, were much more likely to be deferred to in later trials, but not a single participant deferred to them in the first trial. The results demonstrate that the Forward-Back gesture was the clearest way to communicate the robot's intent, however, they also give evidence that there is a communicative trade-off between clarity and politeness, particularly when direct communication has an association with aggression. The takeaway for robot design is: be informative initially, but avoid over-communicating later.


title: Cyber security risks for minors: A taxonomy and a software architecture

abstract: The explosion of the Internet provides a variety possibilities for communication, finding information and many other activities, turning into an essential tool in our modern everyday life. However, its huge expansion globally has created some serious safety issues, which require a special approach. One of these issues and perhaps the most important one concerns the safety of children on the Internet, as they are more exposed to dangers and threats in comparison with adults. In order to design effective measures against these threats and dangers deep understanding of minors' activities on the Internet, along with their motivation, is a first necessary step. It is shown in this report that minors' Internet activity tends heavily, and in an increasing manner, towards Online Social Networks (OSN). Thus, Internet filtering techniques designed and applied so far for child online protection need to be reconsidered and redesigned in a smarter way such as data analytics, advanced content analysis and data mining techniques are incorporated. OSN fake account identification, sexual content detection and flagging of multiple OSN accounts of the same person are examples that require such sophisticated techniques. This study deals with a literature review concerning the Internet activity and motivation of use by minors and presents in a coherent manner the identified risks and threats that children using the web and online social networks are exposed to. It also presents a systematic process for designing and developing modern and state of the art techniques to prevent minors' exposure to those risks and dangers.


title: A Tutorial on Bernoulli Filters: Theory, Implementation and Applications

abstract: Bernoulli filters are a class of exact Bayesian filters for non-linear/non-Gaussian recursive estimation of dynamic systems, recently emerged from the random set theoretical framework. The common feature of Bernoulli filters is that they are designed for stochastic dynamic systems which randomly switch on and off. The applications are primarily in target tracking, where the switching process models target appearance or disappearance from the surveillance volume. The concept, however, is applicable to a range of dynamic phenomena, such as epidemics, pollution, social trends, etc. Bernoulli filters in general have no analytic solution and are implemented as particle filters or Gaussian sum filters. This tutorial paper reviews the theory of Bernoulli filters as well as their implementation for different measurement models. The theory is backed up by applications in sensor networks, bearings-only tracking, passive radar/sonar surveillance, visual tracking, monitoring/prediction of an epidemic and tracking using natural language statements. More advanced topics of smoothing, multi-target detection/tracking, parameter estimation and sensor control are briefly reviewed with pointers for further reading.


title: Privacy-Preserving Profile Matching for Proximity-Based Mobile Social Networking

abstract: Proximity-based mobile social networking (PMSN) refers to the social interaction among physically proximate mobile users. The first step toward effective PMSN is for mobile users to choose whom to interact with. Profile matching refers to two users comparing their personal profiles and is promising for user selection in PMSN. It, however, conflicts with users' growing privacy concerns about disclosing their personal profiles to complete strangers. This paper tackles this open challenge by designing novel fine-grained private matching protocols. Our protocols enable two users to perform profile matching without disclosing any information about their profiles beyond the comparison result. In contrast to existing coarse-grained private matching schemes for PMSN, our protocols allow finer differentiation between PMSN users and can support a wide range of matching metrics at different privacy levels. The performance of our protocols is thoroughly analyzed and evaluated via real smartphone experiments.


Short title: Transmission of different TSWV isolates by F. occidentalis Transmission of Tomato spotted wilt virus isolates able and unable to overcome tomato or pepper resistance by its vector Frankliniella occidentalis


title: Aerothermodynamic Aspects of Railgun-Assisted Launches of Projectiles With Sub- and Low-Earth-Orbit Payloads

abstract: A Railgun-concept for the launch of small payloads into low-Earth orbits is currently under investigation. The first major project milestone foresees a 32-MJ railgun system able to launch nonpropelled projectiles with experiments for high-atmospheric research to an altitude of 115 km. This will be followed by a system to launch single-stage rocket-propelled projectiles to put in orbit nanosatellites using a 3.4-GJ railgun with a length of 180 m. One of the main issues to be investigated is the extreme thermal conditions the projectile's surfaces are exposed to, namely high temperatures and high thermal loads. These are caused by friction and turbulent flow properties, when the projectile rushes through the dense Earth's atmosphere in less than 30 s. The important questions to be answered for the design of a hypersonic projectile are the determination of the duration, magnitude, and location of the maximum temperatures with respect to the projectile's surface and its corresponding internal structure. For the determination of the complicated physical conditions, a two-step approach is used. In a so-called predesign phase, the HF3T code of the DLR is applied to estimate time-dependent temperature changes on the projectile's surface and/or within the material's structure as a function of the trajectory parameters. Finally, the three-degree-of-freedom trajectory program TRAJECTORY 3D of DLR is used to generate a detailed time-dependent data set, which includes the values for projectile velocity, Mach and Reynolds numbers, and atmospheric properties as a function of altitude. This data set is exchangeable with the TAU code of DLR for the solution of the Navier-Stokes equations, enabling pseudo-unsteady flow solutions along the trajectory. The solutions resulting from both approaches are then compared with emphasis on the surface temperature distributions. This strategy allows the verification of the feasibility of the proposed design solution for the railgun launched hypersonic projectiles


title: Software Development for the Analysis of Heartbeat Sounds with LabVIEW in Diagnosis of Cardiovascular Disease

abstract: In this paper, a time-frequency spectral analysis software (Heart Sound Analyzer) for the computer-aided analysis of cardiac sounds has been developed with LabVIEW. Software modules reveal important information for cardiovascular disorders, it can also assist to general physicians to come up with more accurate and reliable diagnosis at early stages. Heart sound analyzer (HSA) software can overcome the deficiency of expert doctors and help them in rural as well as urban clinics and hospitals. HSA has two main blocks: data acquisition and pre-processing, time---frequency spectral analyses. The heart sounds are first acquired using a modified stethoscope which has an electret microphone in it. Then, the signals are analysed using the time---frequency/scale spectral analysis techniques such as STFT, Wigner---Ville distribution and wavelet transforms. HSA modules have been tested with real heart sounds from 35 volunteers and proved to be quite efficient and robust while dealing with a large variety of pathological conditions.


title: The Heart Trumps the Head: Desirability Bias in Political Belief Revision

abstract: Understanding how individuals revise their political beliefs has important implications for society. In a preregistered study (N = 900), we experimentally separated the predictions of 2 leading theories of human belief revision—desirability bias and confirmation bias—in the context of the 2016 U.S. presidential election. Participants indicated who they desired to win, and who they believed would win, the election. Following confrontation with evidence that was either consistent or inconsistent with their desires or beliefs, they again indicated who they believed would win. We observed a robust desirability bias—individuals updated their beliefs more if the evidence was consistent (vs. inconsistent) with their desired outcome. This bias was independent of whether the evidence was consistent or inconsistent with their prior beliefs. In contrast, we found limited evidence of an independent confirmation bias in belief updating. These results have implications for the relevant psychological theories and for political belief revision in practice.


title: Alterdroid: Differential Fault Analysis of Obfuscated Smartphone Malware

abstract: Malware for smartphones has rocketed over the last years. Market operators face the challenge of keeping their stores free from malicious apps, a task that has become increasingly complex as malware developers are progressively using advanced techniques to defeat malware detection tools. One such technique commonly observed in recent malware samples consists of hiding and obfuscating modules containing malicious functionality in places that static analysis tools overlook (e.g., within data objects). In this paper, we describe Alterdroid , a dynamic analysis approach for detecting such hidden or obfuscated malware components distributed as parts of an app package. The key idea in Alterdroid consists of analyzing the behavioral differences between the original app and a number of automatically generated versions of it, where a number of modifications ( faults ) have been carefully injected. Observable differences in terms of activities that appear or vanish in the modified app are recorded, and the resulting differential signature is analyzed through a pattern-matching process driven by rules that relate different types of hidden functionalities with patterns found in the signature. A thorough justification and a description of the proposed model are provided. The extensive experimental results obtained by testing Alterdroid over relevant apps and malware samples support the quality and viability of our proposal.


title: Integration of text and audio features for genre classification in music information retrieval (accepted for publication

abstract: Multimedia content can be described in versatile ways as its essence is not limited to one view. For music data these multiple views could be a song's audio features as well as its lyrics. Both of these modalities have their advantages as text may be easier to search in and could cover more of the 'content semantics' of a song, while omitting other types of semantic categorisation. (Psycho) acoustic feature sets, on the other hand, provide the means to identify tracks that 'sound similar' while less supporting other kinds of semantic categorisation. Those discerning characteristics of different feature sets meet users' differing information needs. We will explain the nature of text and audio feature sets which describe the same audio tracks. Moreover, we will propose the use of textual data on top of low level audio features for music genre classification. Further, we will show the impact of different combinations of audio features and textual features based on content words.


THE EMERGENCY FUNCTION OF THE ADRENAL MEDULLA IN PAIN AND THE MAJOR EMOTIONS


title: BitStalker: Accurately and efficiently monitoring bittorrent traffic

abstract: BitTorrent is currently the most popular peer-to-peer network for file sharing. However, experience has shown that BitTorrent is often used to distribute copyright protected movie and music files illegally. Consequently, copyright enforcement agencies currently monitor BitTorrent swarms to identify users participating in the illegal distribution of copyright-protected files. These investigations rely on passive methods that are prone to a variety of errors, particularly false positive identification. To mitigate the potential for false positive peer identification, we investigate the feasibility of using active methods to monitor extremely large BitTorrent swarms. We develop an active probing framework called BitStalker that identifies active peers and collects concrete forensic evidence that they were involved in sharing a particular file. We evaluate the effectiveness of this approach through a measurement study with real, large torrents consisting of over 186,000 peers. We find that the current investigative methods produce at least 11% false positives, while we show that false positives are rare with our active approach.


title: Colour and lighting in hospital design

abstract: Abstract Little information or guidance has been available to assist the development of a hospital's visual environment. A report on lighting and colour design schemes, accessible to non professionals with responsibility for refurbishment strategies, was required by NHS Estates. Firstly, 20 hospitals were audited to establish a picture of current practice and to identify key issues where colour design could broadly enhance the environment for patients, staff and visitors. Critical areas were outlined in this report, where colour design can be utilised and applied, for the benefit of all users, from ambience to essential legal requirements such as colour contrast for the visually impaired. Provision of staff relaxation rooms that are different in terms of colour and lux levels from immediate work spaces, or thoughtfully designed areas for patients awaiting intensive treatment, have been shown to have some beneficial effects on a sense of well being. Colour and design have not been established as a definite cure for sickness and ill health, but certainly monotony and poor conditions in premises that have not been refurbished with any care, have had a detrimental affect on recovery rates and staff morale. The realisation that a well balanced and attractive environment is of major importance to patients’ health is, in no way new; Florence Nightingale observed that ‘a variety of form and brilliance of colour in the objects presented to patients are an actual means of recovery’.


title: Continuous Authentication of Smartphones Based on Application Usage

abstract: An empirical investigation of active/continuous authentication for smartphones is presented by exploiting users’ unique application usage data, i.e., distinct patterns of use, modeled by a Markovian process. Specifically, variations of hidden Markov models (HMMs) are evaluated for continuous user verification, and challenges due to the sparsity of session-wise data, an explosion of states, and handling unforeseen events in the test data are tackled. Unlike traditional approaches, the proposed formulation utilizes the complete app-usage information to achieve low latency. Through experimentation, empirical assessment of the impact of unforeseen events, i.e., unknown applications and unforeseen observations, on user verification is done via a modified edit-distance algorithm for sequence matching. It is found that for enhanced verification performance, unforeseen events should be considered. For validation, extensive experiments on two distinct datasets, namely, UMDAA-02 and Securacy, are performed. Using the marginally smoothed HMM a low equal error rate (EER) of 16.16% is reached for the Securacy dataset and the same method is found to be able to detect an intrusion within ~2.5 min of application use.


title: IEEE 802.11ad/WiGig based millimeter-wave small cell systems with adjacent channel interference suppression

abstract: This paper presents a study on adjacent channel interference in millimeter-wave small cell systems based on IEEE 802.11ad/WiGig. It includes hardware prototype development, interference measurements, and performance evaluation of an interference suppression technique. The access point prototype employs three RF modules with 120° beam steering capability, thus enabling 360° coverage. Using the developed prototype, interference measurements were performed and the packet error degradation due to adjacent channel interference was observed. To mitigate the performance degradation, an interference suppression technique using a two stream receiver architecture was applied. The subsequent measurements showed improvement in EVM and also expansion of the cell's coverage area, demonstrating the effectiveness of the applied technique for small cell systems using IEEE 802.11ad/WiGig.


title: A Collaborative Filtering Recommendation Algorithm Based on User Clustering and Item Clustering

abstract: Personalized recommendation systems can help people to find interesting things and they are widely used with the development of electronic commerce. Many recommendation systems employ the collaborative filtering technology, which has been proved to be one of the most successful techniques in recommender systems in recent years. With the gradual increase of customers and products in electronic commerce systems, the time consuming nearest neighbor collaborative filtering search of the target customer in the total customer space resulted in the failure of ensuring the real time requirement of recommender system. At the same time, it suffers from its poor quality when the number of the records in the user database increases. Sparsity of source data set is the major reason causing the poor quality. To solve the problems of scalability and sparsity in the collaborative filtering, this paper proposed a personalized recommendation approach joins the user clustering technology and item clustering technology. Users are clustered based on users’ ratings on items, and each users cluster has a cluster center. Based on the similarity between target user and cluster centers, the nearest neighbors of target user can be found and smooth the prediction where necessary. Then, the proposed approach utilizes the item clustering collaborative filtering to produce the recommendations. The recommendation joining user clustering and item clustering collaborative filtering is more scalable and more accurate than the traditional one.


title: Graphical Models and Belief Propagation-hierarchy for Optimal Physics-Constrained Network Flows

abstract: In this manuscript we review new ideas and first results on application of the Graphical Models approach, originated from Statistical Physics, Information Theory, Computer Science and Machine Learning, to optimization problems of network flow type with additional constraints related to the physics of the flow. We illustrate the general concepts on a number of enabling examples from power system and natural gas transmission (continental scale) and distribution (district scale) systems.


title: Construct validity of the Self-Compassion Scale-Short Form among psychotherapy clients

abstract: Interest has been growing in the mental health benefits of self-compassion. Whereas, most research on this topic has been conducted with the 26-item Self-Compassion Scale (SCS), a briefer 12-item version of the instrument, the Self-Compassion Scale-Short Form (SCS-SF), also exists. The SCS-SF has demonstrated good validity and reliability in non-clinical samples, but it has not been used often in research with psychotherapy clients. This study was designed to examine the factor structure and construct validity of the SCS-SF in a clinical population. Data for this study were collected from 1609 college students receiving services at 10 campus counseling centers. The previously proposed factor structure of the SCS-SF was not supported. Instead, analyses revealed two factors, Self Care and Self Disparagement. Evidence for the construct validity of these factors was found via expected relationships with indices of depression, anxiety, social anxiety, hostility, academic distress, eating concerns, family distr...


title: Learning is planning: near Bayes-optimal reinforcement learning via Monte-Carlo tree search

abstract: Bayes-optimal behavior, while well-defined, is often difficult to achieve. Recent advances in the use of Monte-Carlo tree search (MCTS) have shown that it is possible to act near-optimally in Markov Decision Processes (MDPs) with very large or infinite state spaces. Bayes-optimal behavior in an unknown MDP is equivalent to optimal behavior in the known belief-space MDP, although the size of this belief-space MDP grows exponentially with the amount of history retained, and is potentially infinite. We show how an agent can use one particular MCTS algorithm, Forward Search Sparse Sampling (FSSS), in an efficient way to act nearly Bayes-optimally for all but a polynomial number of steps, assuming that FSSS can be used to act efficiently in any possible underlying MDP.


title: HiBOp: a History Based Routing Protocol for Opportunistic Networks

abstract: In opportunistic networks the existence of a simultaneous path between a sender and a receiver is not assumed. This model (which fits well to pervasive networking environments) completely breaks the main assumptions on which MANET routing protocols are built. Routing in opportunistic networks is usually based on some form of controlled flooding. But often this results in very high resource consumption and network congestion. In this paper we advocate context-based routing for opportunistic networks. We provide a general framework for managing and using context for taking forwarding decisions. We propose a context-based protocol (HiBOp), and compare it with popular solutions, i.e., Epidemic Routing and PROPHET. Results show that HiBOp is able to drastically reduce resource consumption. At the same time, it significantly reduces the message loss rate, and preserves the performance in terms of message delay.


title: Air leak syndrome as one of the manifestations of bronchiolitis obliterans organizing pneumonia.

abstract: A 46-year-old man developed respiratory distress with air leak syndrome (ALS), including pneumothorax, pneumomediastinum, and subcutaneous emphysema. Open lung biopsy was performed and revealed the histopathologic evidence of bronchiolitis obliterans organizing pneumonia (BOOP), which responded well to steroid treatment. As far as we know, this appears to be the first case of BOOP presenting with ALS as one of its major complications.


title: Intrinsically motivated learning of hierarchical collections of skills

abstract: Humans and other animals often engage in activities for their own sakes rather than as steps toward solving practical problems. Psychologists call these intrinsically motivated behaviors. What we learn during intrinsically motivated behavior is essential for our development as competent autonomous entities able to efficiently solve a wide range of practical problems as they arise. In this paper we present initial results from a computational study of intrinsically motivated learning aimed at allowing artificial agents to construct and extend hierarchies of reusable skills that are needed for competent autonomy. At the core of the model are recent theoretical and algorithmic advances in computational reinforcement learning, specifically, new concepts related to skills and new learning algorithms for learning with skill hierarchies.


title: Leading Edge Review Autophagy and Aging

abstract: Genetic inhibition of autophagy induces degenerative changes in mammalian tissues that resemble those associated with aging, and normal and pathological aging are often associated with a reduced autophagic potential. Pharmacological or genetic manipulations that increase life span in model organisms often stimulate autophagy, and its inhibition compromises the longevity-promoting effects of caloric restriction, Sirtuin 1 activation, inhibition of insulin/insulin growth factor signaling, or the administration of rapamycin, resveratrol, or spermidine. Here, we discuss the probable cause and effect relationship between perturbed autophagy and aging, as well as possible molecular mechanisms that may mediate the anti-aging effects of autophagy.


title: Organizing master data management: findings from an expert survey

abstract: Master data management (MDM) is defined as an application-independent process which describes, owns and manages core business data entities. The establishment of the MDM process is a Business Engineering (BE) tasks which requires organizational design. This paper reports on the results of a questionnaire survey among large enterprises aiming at delivering insight into what tasks and master data classes MDM organizations cover ("scope") and how many people they employ ("size"). The nature of the study is descriptive, i.e. it allows for the identification of patterns and trends in organizing the MDM process.


title: HapThimble: A Wearable Haptic Device towards Usable Virtual Touch Screen

abstract: A virtual touch screen concept using an optical see-through head-mounted display has been suggested. With a virtual touch screen, the user's direct-touch interactions are allowed in much the same way as a conventional touch screen, but the absence of haptic feedback and physical constraint leads to poor user performance. To overcome this issue, we developed a wearable haptic device, called HapThimble. It provides various types of haptic feedback (tactile, pseudo-force, and vibrotactile) to the user's fingertip and mimics physical buttons based on force-penetration depth curves. We conducted three experiments with HapThimble. The first experiment confirmed that HapThimble could increase a users' performance when conducting clicking and dragging tasks. The second experiment revealed that users could differentiate between six types of haptic feedback, rendered based on different force-penetration depth curves obtained using HapThimble. Last, we conducted a test to investigate the similarity between the physical buttons and the mimicked haptic buttons and obtained a 90.3% success rate.


title: Dependency Parsing with Transformed Feature

abstract: Dependency parsing is an important subtask of natural language processing. In this paper, we propose an embedding feature transforming method for graph-based parsing, transform-based parsing, which directly utilizes the inner similarity of the features to extract information from all feature strings including the un-indexed strings and alleviate the feature sparse problem. The model transforms the extracted features to transformed features via applying a feature weight matrix, which consists of similarities between the feature strings. Since the matrix is usually rank-deficient because of similar feature strings, it would influence the strength of constraints. However, it is proven that the duplicate transformed features do not degrade the optimization algorithm: the margin infused relaxed algorithm. Moreover, this problem can be alleviated by reducing the number of the nearest transformed features of a feature. In addition, to further improve the parsing accuracy, a fusion parser is introduced to integrate transformed and original features. Our experiments verify that both transform-based and fusion parser improve the parsing accuracy compared to the corresponding feature-based parser.


title: How online social ties and product-related risks influence purchase intentions: A Facebook experiment

abstract: Drawing on information processing theory and the stimulus-organism-response model, we developed research hypotheses about consumers' decision-making processes. Specifically, we examined the effects of online tie strength, perceived diagnosticity, and product-related risks on consumers' purchase intentions. We conducted a field experiment on Facebook to test these hypotheses. We found that the product information and recommendations provided by friends with whom consumers have strong ties are perceived as having a high level of diagnosticity. The latter increases the probability that the consumers will purchase the product in question. Product-related risks moderate the effect of tie strength on perceived diagnosticity. For high-risk products, the information and recommendations provided by strong-tie contacts have a greater effect on purchase intentions than the information and recommendations provided by weak-tie contacts. However, we did not find this effect for low-risk products. We discuss the implications of our findings for both theory and practice.


title: Wireless sensor networks localization algorithms: a comprehensive survey

abstract: Wireless sensor networks (WSNs) have recently gained a lot of attention by scientific community. Small and inexpensive devices with low energy consumption and limited computing resources are increasingly being adopted in different application scenarios including environmental monitoring, target tracking and biomedical health monitoring. In many such applications, node localization is inherently one of the system parameters. Localization process is necessary to report the origin of events, routing and to answer questions on the network coverage,assist group querying of sensors. In general, localization schemes are classified into two broad categories: range-based and range-free. However, it is difficult to classify hybrid solutions as range-based or range-free. In this paper we make this classification easy, where range-based schemes and range-free schemes are divided into two types: fully schemes and hybrid schemes. Moreover, we compare the most relevant localization algorithms and discuss the future research directions for wireless sensor networks localization schemes.


title: Marketing strategies, perceived risks, and consumer trust in online buying behaviour

abstract: Despite the rapid increase in online shopping, the literature is silent in terms of the interrelationship between perceived risk factors, the marketing impacts, and their influence on product and web-vendor consumer trust. This research focuses on holidaymakers' perspectives using Internet bookings for their holidays. The findings reveal the associations between Internet perceived risks and the relatively equal influence of product and e-channel risks in consumers' trust, and that online purchasing intentions are equally influenced by product and e-channel consumer trust. They also illustrate the relationship between marketing strategies and perceived risks, and provide managerial suggestions for further e-purchasing tourism improvement.


title: Deadline Guaranteed Service for Multi-Tenant Cloud Storage

abstract: It is imperative for cloud storage systems to be able to provide deadline guaranteed services according to service level agreements (SLAs) for online services. In spite of many previous works on deadline aware solutions, most of them focus on scheduling work flows or resource reservation in datacenter networks but neglect the server overload problem in cloud storage systems that prevents providing the deadline guaranteed services. In this paper, we introduce a new form of SLAs, which enables each tenant to specify a percentage of its requests it wishes to serve within a specified deadline. We first identify the multiple objectives (i.e., traffic and latency minimization, resource utilization maximization) in developing schemes to satisfy the SLAs. To satisfy the SLAs while achieving the multi-objectives, we propose a Parallel Deadline Guaranteed ( PDG ) scheme, which schedules data reallocation (through load re-assignment and data replication) using a tree-based bottom-up parallel process. The observation from our model also motivates our deadline strictness clustered data allocation algorithm that maps tenants with the similar SLA strictness into the same server to enhance SLA guarantees. We further enhance PDG in supplying SLA guaranteed services through two algorithms: i) a prioritized data reallocation algorithm that deals with request arrival rate variation, and ii) an adaptive request retransmission algorithm that deals with SLA requirement variation. Our trace-driven experiments on a simulator and Amazon EC2 show the effectiveness of our schemes for guaranteeing the SLAs while achieving the multi-objectives.


title: A Bidirectional DC–DC Converter for an Energy Storage System With Galvanic Isolation

abstract: This paper addresses a bidirectional dc-dc converter suitable for an energy storage system with an additional function of galvanic isolation. An energy storage device such as an electric double layer capacitor is directly connected to a dc side of the dc-dc converter without any chopper circuit. Nevertheless, the dc-dc converter can continue operating when the voltage across the energy storage device drops along with its discharge. Theoretical calculation and experimental measurement reveal that power loss and peak current impose limitations on a permissible dc-voltage range. This information may be useful in design of the dc-dc converter. Experimental results verify proper charging and discharging operation obtained from a 200-V, 2.6-kJ laboratory model of the energy storage system. Moreover, the dc-dc converter can charge the capacitor bank from zero to the rated voltage without any external precharging circuit.


title: A new sub-threshold 7T SRAM cell design with capability of bit-interleaving in 90 nm CMOS

abstract: In this paper we present a new Static Random Access Memory (SRAM) that has 7 transistors in each cell. This idea allows for bit-interleaving that makes the SRAM more reliable against the soft errors. One of the challenges of a conventional 6 transistor (6T) SRAM cell in sub-threshold region is sizing of its access transistors. Here by separating access transistors of reading and writing, we mitigate this challenge. By using a minimum-size access transistor for reading, probability of unsuccessful read reduces. To have a more successful write operation, we make one of the inverters of the cell that is fighting with the write access transistor, weaker during write operation by floating its supply voltage and ground rails. After write operation, this inverter returns back to normal mode. Simulation results in 90 nm CMOS technology show that our design satisfies 4.5-sigma criterion for reading and writing at supply voltage of 300 mV. Compared to conventional 6T SRAM cell, our design improves read-time and write-time significantly. For example at supply voltage of 500 mV, these improvements are 137 and 83 percents, respectively. Comparing power and energy consumption for single write operation of the proposed 7T SRAM cell at supply voltage of 300mV with conventional 6T SRAM cell at 800mV (i.e. minimum achievable voltage for this SRAM cell) shows improvements of 133.4X and 266.78X, respectively.


title: WebSets: extracting sets of entities from the web using unsupervised information extraction

abstract: We describe a open-domain information extraction method for extracting concept-instance pairs from an HTML corpus. Most earlier approaches to this problem rely on combining clusters of distributionally similar terms and concept-instance pairs obtained with Hearst patterns. In contrast, our method relies on a novel approach for clustering terms found in HTML tables, and then assigning concept names to these clusters using Hearst patterns. The method can be efficiently applied to a large corpus, and experimental results on several datasets show that our method can accurately extract large numbers of concept-instance pairs.


title: Polinode: A web application for the collection and analysis of network data

abstract: We introduce Polinode, an online tool for performing network analysis. Polinode is aimed at commercial and non-commercial users alike and supports both research-related use cases as well as teaching network analysis to students. One of its primary advantages is that it is web-based. It therefore doesn't require any software downloads and opens up new avenues for collaboration and the incorporation of online content into network analysis. With Polinode you can upload arbitrary network data for online visualisation and analysis and you can also use the built-in relationship-based survey functionality to collect network data from respondents.


title: Knowledge Acquisition for Visual Question Answering via Iterative Querying

abstract: Humans possess an extraordinary ability to learn new skills and new knowledge for problem solving. Such learning ability is also required by an automatic model to deal with arbitrary, open-ended questions in the visual world. We propose a neural-based approach to acquiring task-driven information for visual question answering (VQA). Our model proposes queries to actively acquire relevant information from external auxiliary data. Supporting evidence from either human-curated or automatic sources is encoded and stored into a memory bank. We show that acquiring task-driven evidence effectively improves model performance on both the Visual7W and VQA datasets, moreover, these queries offer certain level of interpretability in our iterative QA model.


title: A metric for distributions with applications to image databases

abstract: We introduce a new distance between two distributions that we call the Earth Mover's Distance (EMD), which reflects the minimal amount of work that must be performed to transform one distribution into the other by moving "distribution mass" around. This is a special case of the transportation problem from linear optimization, for which efficient algorithms are available. The EMD also allows for partial matching. When used to compare distributions that have the same overall mass, the EMD is a true metric, and has easy-to-compute lower bounds. In this paper we focus on applications to image databases, especially color and texture. We use the EMD to exhibit the structure of color-distribution and texture spaces by means of Multi-Dimensional Scaling displays. We also propose a novel approach to the problem of navigating through a collection of color images, which leads to a new paradigm for image database search.


title: Bi-Directional Block Self-Attention for Fast and Memory-Efficient Sequence Modeling

abstract: Recurrent neural networks (RNN), convolutional neural networks (CNN) and self-attention networks (SAN) are commonly used to produce context-aware representations. RNN can capture long-range dependency but is hard to parallelize and not time-efficient. CNN focuses on local dependency but does not perform well on some tasks. SAN can model both such dependencies via highly parallelizable computation, but memory requirement grows rapidly in line with sequence length. In this paper, we propose a model, called "bi-directional block self-attention network (Bi-BloSAN)", for RNN/CNN-free sequence encoding. It requires as little memory as RNN but with all the merits of SAN. Bi-BloSAN splits the entire sequence into blocks, and applies an intra-block SAN to each block for modeling local context, then applies an inter-block SAN to the outputs for all blocks to capture long-range dependency. Thus, each SAN only needs to process a short sequence, and only a small amount of memory is required. Additionally, we use feature-level attention to handle the variation of contexts around the same word, and use forward/backward masks to encode temporal order information. On nine benchmark datasets for different NLP tasks, Bi-BloSAN achieves or improves upon state-of-the-art accuracy, and shows better efficiency-memory trade-off than existing RNN/CNN/SAN.


title: Modeling of a variable speed wind turbine with a Permanent Magnet Synchronous Generator

abstract: The aim of this work is to analyze a typical configuration of a Wind Turbine Generator System (WTGS) equipped with a Variable Speed Generator. Nowadays, doubly-fed induction generators are being widely used on WTGS, although synchronous generators are being extensively utilized too. There are different types of synchronous generators, but the multi-pole Permanent Magnet Synchronous Generator (PMSG) is chosen in order to obtain its model. It offers better performance due to higher efficiency and less maintenance since it does not have rotor current and can be used without a gearbox, which also implies a reduction of the weight of the nacelle and a reduction of costs. Apart from the generator, the analyzed WTGS consists of another three parts: wind speed, wind turbine and drive train. These elements have been modeled and the equations that explain their behavior have been introduced. What is more, the whole WTGS has been implemented in MATLAB/Simulink interface. Moreover, the concept of the Maximum Power Point Tracking (MPPT) has been presented in terms of the adjustment of the generator rotor speed according to instantaneous wind speed.


title: Proceedings of the Twenty-Sixth AAAI Conference on Artificial Intelligence BabelRelate! A Joint Multilingual Approach to Computing Semantic Relatedness

abstract: We present a knowledge-rich approach to computing semantic relatedness which exploits the joint contribution of different languages. Our approach is based on the lexicon and semantic knowledge of a wide-coverage multilingual knowledge base, which is used to compute semantic graphs in a variety of languages. Complementary information from these graphs is then combined to produce a 'core' graph where disambiguated translations are connected by means of strong semantic relations. We evaluate our approach on standard monolingual and bilingual datasets, and show that: i) we outperform a graph-based approach which does not use multilinguality in a joint way; ii) we achieve uniformly competitive results for both resource-rich and resource-poor languages.


title: Characterizing Google Hacking: A First Large-Scale Quantitative Study

abstract: Google Hacking continues to be abused by attackers to find vulnerable websites on current Internet. Through searching specific terms of vulnerabilities in search engines, attackers can easily and automatically find a lot of vulnerable websites in a large scale. However, less work has been done to study the characteristics of vulnerabilities targeted by Google Hacking (e.g., what kind of vulnerabilities are typically targeted by Google Hacking? What kind of vulnerabilities usually have a large victim population? What is the impact of Google Hacking and how easy to defend against Google Hacking?).


title: Influence of wettability on the properties of thin porous platinum films as gates of metal-oxide-semiconductor devices in electrolytes

abstract: Thin discontinuous (4 and 35 nm thick) platinum films were used as the gate metal of metal-oxide-semiconductor capacitors. It was observed that the metal film acts as a gas permeable structure in electrolyte solutions when the surface was hydrophobic and sufficiently rough. The probable explanation to this is that the electrolyte does not penetrate into the voids of the metal film. The geometry of the surface was investigated by transsion electron microscopy and profilometer measurements. Surfaces with different contact angles were studied in order to determine whether the electrolyte penetrates into the voids in the platinum film when the surface is sufficiently hydrophilic.


title: A novel portable, low-cost kinect-based system for motion analysis in neurological diseases

abstract: Many neurological diseases, such as Parkinson's disease and epilepsy, can significantly impair the motor function of the patients, often leading to a dramatic loss of their quality of life. Human motion analysis is regarded as fundamental towards an early diagnosis and enhanced follow-up in this type of diseases. In this contribution, we present NeuroKinect, a novel system designed for motion analysis in neurological diseases. This system includes an RGB-D camera (Microsoft Kinect) and two integrated software applications, KiT (KinecTracker) and KiMA (Kinect Motion Analyzer). The applications enable the preview, acquisition, review and management of data provided by the sensor, which are then used for motion analysis of relevant events. NeuroKinect is a portable, low-cost and markerless solution that is suitable for use in the clinical environment. Furthermore, it is able to provide quantitative support to the clinical assessment of different neurological diseases with movement impairments, as demonstrated by its usage in two different clinical routine scenarios: gait analysis in Parkinson's disease and seizure semiology analysis in epilepsy.


Dialogues in Promoting Knowledge Construction


title: An inner-loop free solution to inverse problems using deep neural networks

abstract: We propose a new method that uses deep learning techniques to accelerate the popular alternating direction method of multipliers (ADMM) solution for inverse problems. The ADMM updates consist of a proximity operator, a least squares regression that includes a big matrix inversion, and an explicit solution for updating the dual variables. Typically, inner loops are required to solve the first two sub-minimization problems due to the intractability of the prior and the matrix inversion. To avoid such drawbacks or limitations, we propose an inner-loop free update rule with two pre-trained deep convolutional architectures. More specifically, we learn a conditional denoising auto-encoder which imposes an implicit data-dependent prior/regularization on ground-truth in the first sub-minimization problem. This design follows an empirical Bayesian strategy, leading to so-called amortized inference. For matrix inversion in the second sub-problem, we learn a convolutional neural network to approximate the matrix inversion, i.e., the inverse mapping is learned by feeding the input through the learned forward network. Note that training this neural network does not require ground-truth or measurements, i.e., it is data-independent. Extensive experiments on both synthetic data and real datasets demonstrate the efficiency and accuracy of the proposed method compared with the conventional ADMM solution using inner loops for solving inverse problems.


title: Region-based license plate detection

abstract: Automatic license plate recognition (ALPR) is one of the most important aspects of applying computer techniques towards intelligent transportation systems. In order to recognize a license plate efficiently, however, the location of the license plate, in most cases, must be detected in the first place. Due to this reason, detecting the accurate location of a license plate from a vehicle image is considered to be the most crucial step of an ALPR system, which greatly affects the recognition rate and speed of the whole system. In this paper, a region-based license plate detection method is proposed. In this method, firstly, mean shift is used to filter and segment a color vehicle image in order to get candidate regions. These candidate regions are then analyzed and classified in order to decide whether a candidate region contains a license plate. Unlike other existing license plate detection methods, the proposed method focuses on regions, which demonstrates to be more robust to interference characters and more accurate when compared with other methods.


title: Analysis and minimization of the input current ripple of Interleaved Boost Converter

abstract: This paper proposes a new method to quantify the input current ripple cancellation effect of Interleaved Boost Converter (IBC) and derives an universal formula to quantify the input current ripple waveform of IBC. The practical factors that affect the input current ripple are presented and the comprehensive design of the input current ripple minimization of IBC is discussed based on this universal formula and the other practical factors. Simulation and experimental results verify the theoretical analysis.


title: High temperature superconductive Butler matrix beam former for satellite applications

abstract: This paper presents a novel configuration for a superconducting satellite beamforming network. The beamformer implements planar HTS 8/spl times/8 Butlers matrices with folded structures and multi-layer cross-overs. The matrix is optimized using full-EM simulation. Simulated and measured data are presented for the Butler matrix. The measurements show that the beamformer has an amplitude variation of less than 1 dB and a phase variation of less than 10/spl deg/. The proposed layout of a complete beamformer with sixteen 8/spl times/8 Butler matrices is given with performance comparisons to multi-layer and co-axial technology.


title: Social psychological face perception: Why appearance matters.

abstract: We form first impressions from faces despite warnings not to do so. Moreover, there is considerable agreement in our impressions, which carry significant social outcomes. Appearance matters because some facial qualities are so useful in guiding adaptive behavior that even a trace of those qualities can create an impression. Specifically, the qualities revealed by facial cues that characterize low fitness, babies, emotion, and identity are overgeneralized to people whose facial appearance resembles the unfit (anomalous face overgeneralization), babies (babyface overgeneralization), a particular emotion (emotion face overgeneralization), or a particular identity (familiar face overgeneralization). We review studies that support the overgeneralization hypotheses and recommend research that incorporates additional tenets of the ecological theory from which these hypotheses are derived: the contribution of dynamic and multi-modal stimulus information to face perception; bidirectional relationships between behavior and face perception; perceptual learning mechanisms and social goals that sensitize perceivers to particular information in faces.


title: A Path-constrained Framework for Discriminating Substitutable and Complementary Products in E-commerce

abstract: In personalized recommendation, candidate generation plays an infrastructural role by retrieving candidates out of billions of items. During this process, substitutes and complements constitute two main classes of retrieved candidates: substitutable products are interchangeable, whereas complementary products might be purchased together by users. Discriminating substitutable and complementary products is playing an increasingly important role in e-commerce portals by affecting the performance of candidate generation, e.g., when a user has browsed a t-shirt, it is reasonable to retrieve similar t-shirts, i.e., substitutes; whereas if the user has already purchased one, it would be better to retrieve trousers, hats or shoes, as complements of t-shirts. In this paper, we propose a path-constrained framework (PMSC) for discriminating substitutes and complements. Specifically, for each product, we first learn its embedding representations in a general semantic space. Thereafter, we project the embedding vectors into two separate spaces via a novel mapping function. In the end, we incorporate each embedding with path-constraints to further boost the discriminative ability of the model. Extensive experiments conducted on two e-commerce datasets show the effectiveness of our proposed method.


title: A new balanced-to-single-ended (BTSE) power divider

abstract: A balanced-to-single-ended (BTSE) power divider is proposed in this paper. Its input is a differential pair while both the outputs are single-ended ports. It can replace the cascade of a balun and a conventional power divider. The characteristics requirements of a two-way BTSE power divider with equal power division are first derived, and then a prototype is designed for 2.0 GHz. In order to construct the component compactly, a short-ended four-wire coupled line and T-shaped sections are utilized to realize a -90° transmission line and a phase inverter, respectively. The developed prototype occupies an area of 0.077 λ ::: g 2 ::: , where λ ::: g ::: is the guided wavelength of central frequency. It provides all the desired performances within the operating bandwidth of 25%, with low insertion loss and good balance also achieved.


title: Modeling cyber-physical systems for automatic verification

abstract: In this paper we show how the open standard modeling language Modelica can be effectively used to support model-based design and verification of cyber-physical systems stemming from complex power electronics systems. To this end we present a Modelica model for a Distributed Maximum Power Point Tracking system along with model validation results.


title: CrowdBC: A Blockchain-Based Decentralized Framework for Crowdsourcing

abstract: Crowdsourcing systems which utilize the human intelligence to solve complex tasks have gained considerable interest and adoption in recent years. However, the majority of existing crowdsourcing systems rely on central servers, which are subject to the weaknesses of traditional trust-based model, such as single point of failure. They are also vulnerable to distributed denial of service (DDoS) and Sybil attacks due to malicious users involvement. In addition, high service fees from the crowdsourcing platform may hinder the development of crowdsourcing. How to address these potential issues has both research and substantial value. In this paper, we conceptualize a blockchain-based decentralized framework for crowdsourcing named CrowdBC, in which a requester's task can be solved by a crowd of workers without relying on any third trusted institution, users' privacy can be guaranteed and only low transaction fees are required. In particular, we introduce the architecture of our proposed framework, based on which we give a concrete scheme. We further implement a software prototype on Ethereum public test network with real-world dataset. Experiment results show the feasibility, usability, and scalability of our proposed crowdsourcing system.


Differential Cryptanalysis of Salsa20/8


title: Multifirm models of cybersecurity investment competition vs. cooperation and network vulnerability

abstract: In this paper, we develop and compare three distinct models for cybersecurity investment in competitive and cooperative situations to safeguard against potential and ongoing threats. We introduce a Nash equilibrium model of noncooperation in terms of cybersecurity levels of the firms involved, which is formulated, analyzed, and solved using variational inequality theory. The equilibrium of this model then acts as the disagreement point over which bargaining takes place in the setting of the second model, which yields a cooperative solution in which the firms are guaranteed that their expected utilities are no lower than those achieved under noncooperation. Nash bargaining theory is utilized to argue for information sharing and to quantify its monetary and security benefits in terms of reduction in network vulnerability to cyberattacks. The third model in this paper also focuses on cooperation among the firms in terms of their cybersecurity levels, but from a system-optimization perspective in which the sum of the expected utilities is maximized. Qualitative properties are provided for the models in terms of existence and uniqueness results along with numerical solutions to two cases focusing on retailers and financial service firms, since these have been subject to some of the most damaging cyberattacks. Sensitivity analysis results are also provided. We compare the solutions of the models for the cases and recommend a course of action that has both financial and policy-related implications.


Cross Lingual Syntax Projection for Resource-Poor Languages


Content-Based Image Retrieval: Theory and Applications


title: A Cyber Security Ontology for BPMN-Security Extensions

abstract: Every so often a paper is published presenting a new extension for modelling cyber security requirements in Business Process Model and Notation (BPMN). The frequent production of new extensions by experts belies the need for a richer and more expressive representation of security requirements in BPMN processes. One reason for this is that current extensions focus on only specific areas and so fail to provide adequate coverage of the cyber security domain. In this paper, we present our work considering an analysis of existing extensions and identify the security concepts used within each of them. We discuss how there is as yet no single extension which covers a comprehensive range of cyber security concepts. Consequently there is no adequate solution for accurately specifying cyber security requirements within BPMN. In order to address this, we propose a new comprehensive ontology which includes all concepts potentially modellable in BPMN related to cyber security. We explain how this ontology can be used as the basis for developing future BPMN-security extensions, and explore the challenges that must be overcome in order to develop a representation that is both effective and with adequate coverage of security requirements.


title: The Exact Security of Digital Signatures-How to Sign with RSA and Rabin

abstract: We describe an RSA-based signing scheme which combines essentially optimal efficiency with attractive security properties. Signing takes one RSA decryption plus some hashing, verification takes one RSA encryption plus some hashing, and the size of the signature is the size of the modulus. Assuming the underlying hash functions are ideal, our schemes are not only provably secure, but are so in a tight way-- an ability to forge signatures with a certain amount of computational resources implies the ability to invert RSA (on the same size modulus) with about the same computational effort. Furthermore, we provide a second scheme which maintains all of the above features and in addition provides message recovery. These ideas extend to provide schemes for Rabin signatures with analogous properties; in particular their security can be tightly related to the hardness of factoring.


title: Dynamic Difficulty Adjustment in Computer Games Through Real-Time Anxiety-Based Affective Feedback

abstract: A number of studies in recent years have investigated the dynamic difficulty adjustment (DDA) mechanism in computer games to automatically tailor gaming experience to individual player's characteristics. Although most of these existing works focus on game adaptation based on player's performance, affective state experienced by the players could play a key role in gaming experience and may provide a useful indicator for a DDA mechanism. In this article, an affect-based DDA was designed and implemented for computer games. In this DDA mechanism, a player's physiological signals were analyzed to infer his or her probable anxiety level, which was chosen as the target affective state, and the game difficulty level was automatically adjusted in real time as a function of the player's affective state. Peripheral physiological signals were measured through wearable biofeedback sensors and several physiological indices were explored to determine their correlations with anxiety. An experimental study was conducted t...


title: Visual short-term memory: Activity supporting encoding and maintenance in retinotopic visual cortex

abstract: Abstract Recent studies have demonstrated that retinotopic cortex maintains information about visual stimuli during retention intervals. However, the process by which transient stimulus-evoked sensory responses are transformed into enduring memory representations is unknown. Here, using fMRI and short-term visual memory tasks optimized for univariate and multivariate analysis approaches, we report differential involvement of human retinotopic areas during memory encoding of the low-level visual feature orientation. All visual areas show weaker responses when memory encoding processes are interrupted, possibly due to effects in orientation-sensitive primary visual cortex (V1) propagating across extrastriate areas. Furthermore, intermediate areas in both dorsal (V3a/b) and ventral (LO1/2) streams are significantly more active during memory encoding compared with non-memory (active and passive) processing of the same stimulus material. These effects in intermediate visual cortex are also observed during memory encoding of a different stimulus feature (spatial frequency), suggesting that these areas are involved in encoding processes on a higher level of representation. Using pattern-classification techniques to probe the representational content in visual cortex during delay periods, we further demonstrate that simply initiating memory encoding is not sufficient to produce long-lasting memory traces. Rather, active maintenance appears to underlie the observed memory-specific patterns of information in retinotopic cortex.


title: The Shock Tube: A Facility for Investigations in Fluid Dynamics

abstract: A qualitative description of the theory of the operation of a shock tube is given together with a discussion of its design. The construction of a particular tube 4 in.×18 in.×38 ft. is described as well as certain auxiliary equipment needed in its operation. The technique of operating the device and comparisons of its performance with expectations are discussed. This tube has been used to study certain properties of shock waves and to obtain a flow field of marked uniformity and freedom from turbulence. The Mach number of this flow may be adjusted at will from zero to the supersonic range without difficulty. An interferometer makes it possible to obtain an instantaneous picture of the density field so that the apparatus may be used as a very efficient transonic wind tunnel.


A Tangled Web: The Faint Signals of Deception in Text - Boulder Lies and Truth Corpus (BLT-C).


title: Uniform access to NoSQL systems

abstract: Non-relational databases (often termed as NoSQL) have recently emerged and have generated both interest and criticism. Interest because they address requirements that are very important in large-scale applications, criticism because of the comparison with well known relational achievements. One of the major problems often mentioned is the heterogeneity of the languages and of the interfaces they offer to developers and users. Different platforms and languages have been proposed, and applications developed for one system require significant effort to be migrated to another one. Here we propose a common programming interface to NoSQL systems called SOS (Save Our Systems). Its goal is to support application development by hiding the specific details of the various systems. It is based on a metamodelling approach, in the sense that the specific interfaces of the individual systems are mapped to a common one. The tool provides interoperability as well, since a single application can interact with several systems at the same time. HighlightsA platform that can provide uniform access to multiple NoSQL systems is provided.Key-value stores, extensible record stores and document stores are handled.It supports application development at high level as well as interoperability between diverse systems.


title: An efficient technique for author name disambiguation

abstract: Scholarly digital libraries have become an important source of bibliographic records for scientific communities. Author name search is one of the most common query exercised in digital libraries. The name ambiguity problem in the context of author search in digital libraries, arising from multiple authors sharing the same name, poses many challenges. A number of name disambiguation methods have been proposed in the literature so far. A variety of bibliographic attributes have been considered in these methods. However, hardly any effort has been made to assess the potential contribution of these attributes. We, for the first time, evaluate the potential strength and/or weaknesses of these attributes by a rigorous course of experiments on a large data set. We also explore the potential utility of some attributes from different perspective. A close look reveals that most of the earlier work require one or more attributes which are difficult to obtain in practical applications. Based on this empirical study, we identify three very common and easy to access attributes and propose a two-step hierarchical clustering technique to solve name ambiguity using these attributes only. Experimental results on data set extracted from a popular digital library show that the proposed method achieves significantly high level of accuracy (≫> 90%) for most of the instances.


title: Multi-band meta-material structures based on hexagonal shaped magnetic resonators

abstract: In the present paper, we propose a new multi band metamaterial structure composed of a unit cell that has multiple hexagonal-shaped metal rings printed one inside the other. The introduced metamaterial composition exhibits multi-resonant behavior due to distinct magnetic resonances caused by the self and mutual couplings of each inclusion. The number of distinct magnetic resonances is determined by the number of hexagonal-shaped rings placed in the unit cell. Using three rings in this paper, three distinct resonance frequencies can be shifted to desired values simply by changing the design parameters. Transmission and reflection spectra of the proposed structure are obtained using the CST Microwave Studio to observe its resonance frequencies. Then, the effective permeability and effective permittivity are retrieved from the already computed complex S-parameters to demonstrate the presence of three distinct negative permeability bandwidths.


title: The virtual showcase

abstract: We present the Virtual Showcase, a new multiviewer augmented reality display device that has the same form factor as a real showcase traditionally used for museum exhibits.


Discourse Annotation of Non-native Spontaneous Spoken Responses Using the Rhetorical Structure Theory Framework.


title: CRF Learning with CNN Features for Image Segmentation

abstract: Conditional Random Rields (CRF) have been widely applied in image segmentations. While most studies rely on hand-crafted features, we here propose to exploit a pre-trained large convolutional neural network (CNN) to generate deep features for CRF learning. The deep CNN is trained on the ImageNet dataset and transferred to image segmentations here for constructing potentials of superpixels. Then the CRF parameters are learnt using a structured support vector machine (SSVM). To fully exploit context information in inference, we construct spatially related co-occurrence pairwise potentials and incorporate them into the energy function. This prefers labelling of object pairs that frequently co-occur in a certain spatial layout and at the same time avoids implausible labellings during the inference. Extensive experiments on binary and multi-class segmentation benchmarks demonstrate the promise of the proposed method. We thus provide new baselines for the segmentation performance on the Weizmann horse, Graz-02, MSRC-21, Stanford Background and PASCAL VOC 2011 datasets. HighlightsA deep CNN pretrained on ImageNet generalizes well to various segmentation datasets.Deep features significantly outperform BoW and unsupervisd feature learning.Combining deep CNN features with CRF yields new state-of-the-art results.Incorporating spatial related co-occurrence potentials further improves the accuracy.


title: Don't panic: reverse debugging of kernel drivers

abstract: Debugging of device drivers' failures is a very tough task because of kernel panics, blue screens of death, hardware volatility, long periods of time required to expose the bug, perturbation of the drivers by the debugger, and non-determinism of multi-threaded environment. This paper shows how reverse debugging reduces the influence of these factors to the process of drivers debugging. We present reverse debugger as a practical tool, which was tested for i386, x86-64, and ARM platforms, for Windows and Linux guest operating systems. We show that our tool incurs very low overhead (about 10%), which allows using it for debugging of the time sensitive applications. The paper also presents the case study which demonstrates reverse debugging of the USB kernel drivers for Linux.


title: A 16b 12GS/S single/dual-rate DAC with successive bandpass delta-sigma modulator achieving <-67dBc IM3 within DC-to-6GHz tunable passbands

abstract: The agile allocation of signal bands over RF frequencies and high in-band spectral purity (both SFDR and NSD) can enable higher-order modulation in high-throughput flexible wireless/wireline transmitters, where signals are often channelized at certain center frequencies. Using a Nyquist DAC to cover the entire signal spectrum is thus unnecessary, as this trades the achievable dynamic range with the bandwidth. For such applications, a narrowband Nyquist DAC followed by a mixer, RF mixing DAC [1], or exploiting higher Nyquist zones [2] are typically adopted; these are either limited by deliverable output power or nearby spectral images due to lower input data rates. A dual-rate hybrid DAC [3] uses a delta-sigma modulated LSB path to compensate for the non-idealities of the Nyquist path, allowing high linearity and a low noise floor within the Nyquist band while limiting out-of-band quantization noise. Still, this only synthesizes the baseband signal and is difficult when covering wide RF spectra due to the DSM OSR requirements.