title: Dual-Matrix Composite Wideband Antenna Structures for CubeSats

abstract: A concept for a deployable high-performance antenna for CubeSats is presented. The detailed design of a conical log-spiral antenna with wideband operation between 250 500 MHz, a gain of above 5 dB, and circular polarization is performed using electromagnetic simulations. A structural concept using dual-matrix composites with soft hinge regions, allowing the antenna to be packaged into small volumes, is developed. An antenna prototype is fabricated using Astroquartz fibers in an epoxy matrix; a high-strain silicone matrix is used in the hinge regions. Structural and electromagnetic analyses are conducted and show good agreement with predicted performance thus demonstrating the validity of the proposed antenna concept.


title: The BET Protein BRD2 Cooperates with CTCF to Enforce Transcriptional and Architectural Boundaries.

abstract: Bromodomain and extraterminal motif (BET) proteins are pharmacologic targets for the treatment of diverse diseases, yet the roles of individual BET family members remain unclear. We find that BRD2, but not BRD4, co-localizes with the architectural/insulator protein CCCTC-binding factor (CTCF) genome-wide. CTCF recruits BRD2 to co-bound sites whereas BRD2 is dispensable for CTCF occupancy. Disruption of a CTCF/BRD2-occupied element positioned between two unrelated genes enables regulatory influence to spread from one gene to another, suggesting that CTCF and BRD2 form a transcriptional boundary. Accordingly, single-molecule mRNA fluorescence in situ hybridization (FISH) reveals that, upon site-specific CTCF disruption or BRD2 depletion, expression of the two genes becomes increasingly correlated. HiC shows that BRD2 depletion weakens boundaries co-occupied by CTCF and BRD2, but not those that lack BRD2. These findings indicate that BRD2 supports boundary activity, and they raise the possibility that pharmacologic BET inhibitors can influence gene expression in part by perturbing domain boundary function.


title: Historical Perspective on the Ideologies of Motherhood and its Impact on Social Work

abstract: Throughout history, many attempts have been made to construct femininity. Women were chained to their homes by various rituals, customs and traditions that acted as unwritten laws which must be complied with. The construction of femininity was the most intense in times of industrialisation when migration from the countryside to the city was at its height. Social relations became more complex, with private life in rural communities no longer being subject to an effective social control. The modernization and emergence of new roles and power centres during the late 19th century deprived women of even those rights that they had enjoyed until then. There were several ways in which they were restricted and their roles and social positions determined. The most important were those that were a result of discourses on femininity. In this article, we will concentrate on the ideologies of motherhood and their impact on early social services for women and single mothers. We will restrict ourselves to early 20th century Europe, when contemporary divisions into the east and west, the north and the south had not yet been in place. Although in many ways the constructions of reality have specific local features, feminist theory has shown that the ideologies of motherhood place women in similar positions in the most part of the world. This article will therefore adhere to the general overview that does not comprise specific dimensions of the construction of motherhood within different and specific cultural, social and political contexts.


title: Weighted Association Rule Mining using weighted support and significance framework

abstract: We address the issues of discovering significant binary relationships in transaction datasets in a weighted setting. Traditional model of association rule mining is adapted to handle weighted association rule mining problems where each item is allowed to have a weight. The goal is to steer the mining focus to those significant relationships involving items with significant weights rather than being flooded in the combinatornal explosion of insignificant relationships. We identify the challenge of using weights in the iterative process of generating large itemsets. The problem of invalidation of the "downward closure property" in the weighted setting is solved by using an improved model of weighted support measurements and exploiting a "weighted downward closure property". A new algorithm called WARM (Weighted Association Rule Mining) is developed based on the improved model. The algorithm is both scalable and efficient in discovering significant relationships in weighted settings as illustrated by experiments performed on simulated datasets.


title: Optimization based full body control for the atlas robot

abstract: One popular approach to controlling humanoid robots is through inverse kinematics (IK) with stiff joint position tracking. On the other hand, inverse dynamics (ID) based approaches have gained increasing acceptance by providing compliant motions and robustness to external perturbations. However, the performance of such methods is heavily dependent on high quality dynamic models, which are often very difficult to produce for a physical robot. IK approaches only require kinematic models, which are much easier to generate in practice. In this paper, we supplement our previous work with ID-based controllers by adding IK, which helps compensate for modeling errors. The proposed full body controller is applied to three tasks in the DARPA Robotics Challenge (DRC) Trials in Dec. 2013.


title: A learning theory approach to noninteractive database privacy

abstract: In this article, we demonstrate that, ignoring computational constraints, it is possible to release synthetic databases that are useful for accurately answering large classes of queries while preserving differential privacy. Specifically, we give a mechanism that privately releases synthetic data useful for answering a class of queries over a discrete domain with error that grows as a function of the size of the smallest net approximately representing the answers to that class of queries. We show that this in particular implies a mechanism for counting queries that gives error guarantees that grow only with the VC-dimension of the class of queries, which itself grows at most logarithmically with the size of the query class. We also show that it is not possible to release even simple classes of queries (such as intervals and their generalizations) over continuous domains with worst-case utility guarantees while preserving differential privacy. In response to this, we consider a relaxation of the utility guarantee and give a privacy preserving polynomial time algorithm that for any halfspace query will provide an answer that is accurate for some small perturbation of the query. This algorithm does not release synthetic data, but instead another data structure capable of representing an answer for each query. We also give an efficient algorithm for releasing synthetic data for the class of interval queries and axis-aligned rectangles of constant dimension over discrete domains.


title: Secure communication for the robot operating system

abstract: The boom for robotics technologies in recent years has also empowered a new generation of robotics software. The Robot Operating System (ROS) is one of the most popular frameworks for robotics researchers and makers which is moving towards commercial and industrial use. Security-wise however, ROS is vulnerable to attacks. It is rather easy to inject or eavesdrop data in a ROS application. This opens many different ways to attack a ROS application resulting in data loss, monetary damage or even physical injury. In this paper we present a secure communication channel enabling ROS-nodes to communicate with authenticity and confidentiality. We secure ROS on a peer-to-peer basis in the direct interaction between publishers and subscribers. We describe the implementation changes we have made to the ROS core and assess the overhead introduced by the new security functions.


title: Coccygodynia: evaluation and management.

abstract: Coccygodynia is pain in the region of the coccyx. In most cases, abnormal mobility is seen on dynamic standing and seated radiographs, although the cause of pain is unknown in other patients. Bone scans and magnetic resonance imaging may show inflammation and edema, but neither technique is as accurate as dynamic radiography. Treatment for patients with severe pain should begin with injection of local anesthetic and corticosteroid into the painful segment. Coccygeal massage and stretching of the levator ani muscle can help. Coccygectomy is done only when nonsurgical treatment fails, which is infrequent. Coccygectomy usually is successful in carefully selected patients, with the best results in those with radiographically demonstrated abnormalities of coccygeal mobility.


title: A distributed vertex-centric approach for pattern matching in massive graphs

abstract: Graph pattern matching is fundamentally important to many applications such as analyzing hyper-links in the World Wide Web, mining associations in online social networks, and substructure search in biochemistry. Most existing graph pattern matching algorithms are highly computation intensive, and do not scale to extremely large graphs that characterize many emerging applications. In recent years, graph processing frameworks such as Pregel have sought to harness shared nothing clusters for processing massive graphs through a vertex-centric, Bulk Synchronous Parallel (BSP) programming model. However, developing scalable and efficient BSP-based algorithms for pattern matching is very challenging because this problem does not naturally align with a vertex-centric programming paradigm. This paper presents novel distributed algorithms based on the vertex-centric programming paradigm for a set of pattern matching models, namely, graph simulation, dual simulation and strong simulation. Our algorithms are fine-tuned to consider the challenges of pattern matching on massive data graphs. Furthermore, we introduce a new pattern matching model, called strict simulation, which outperforms strong simulation in terms of scalability while preserving its important properties. We investigate potential performance bottlenecks and propose several techniques to mitigate them. This paper also presents an extensive set of experiments involving massive graphs (millions of vertices and billions of edges) to study the effects of various parameters on the scalability and performance of the proposed algorithms. The results demonstrate that our techniques are highly effective in alleviating performance bottlenecks and yield significant scalability benefits.


Content of phenolic compounds and vitamin C and antioxidant activity in wasted parts of Sudanese citrus fruits


title: Copyright compliance and infringement in ResearchGate full-text journal articles

abstract: ResearchGate is increasingly used by scholars to upload the full-text of their articles and make them freely available for everyone. This study aims to investigate the extent to which ResearchGate members as authors of journal articles comply with publishers’ copyright policies when they self-archive full-text of their articles on ResearchGate. A random sample of 500 English journal articles available as full-text on ResearchGate were investigated. 108 articles (21.6%) were open access (OA) published in OA journals or hybrid journals. Of the remaining 392 articles, 61 (15.6%) were preprint, 24 (6.1%) were post-print and 307 (78.3%) were published (publisher) PDF. The key finding was that 201 (51.3%) out of 392 non-OA articles infringed the copyright and were non-compliant with publishers’ policy. While 88.3% of journals allowed some form of self-archiving (SHERPA/RoMEO green, blue or yellow journals), the majority of non-compliant cases (97.5%) occurred when authors self-archived publishers’ PDF files (final published version). This indicates that authors infringe copyright most of the time not because they are not allowed to self-archive, but because they use the wrong version, which might imply their lack of understanding of copyright policies and/or complexity and diversity of policies.


title: An Investigation of Language Impairment in Autism: Implications for Genetic Subgroups.

abstract: Autism involves primary impairments in both language and communication, yet in recent years the main focus of research has been on the communicative deficits that define the population. The study reported in this paper investigated language functioning in a group of 89 children diagnosed with autism using the ADI-R, and meeting DSM-IV criteria. The children, who were between 4- and 14- years-old were administered a battery of standardized language tests tapping phonological, lexical, and higher-order language abilities. The main findings were that among the children with autism there was significant heterogeneity in their language skills, although across all the children, articulation skills were spared. Different subgroups of children with autism were identified on the basis on their performance on the language measures. Some children with autism have normal language skills; for other children, their language skills are significantly below age expectations. The profile of performance across the standardized measures for the language-impaired children with autism was similar to the profile that defines the disorder specific language impairment (or SLI). The implications of this language impaired subgroup in autism for understanding the genetics and definition of both autism and SLI are discussed.


title: Macro Grammars and Holistic Triggering for Efficient Semantic Parsing

abstract: To learn a semantic parser from denotations, a learning algorithm must search over a combinatorially large space of logical forms for ones consistent with the annotated denotations. We propose a new online learning algorithm that searches faster as training progresses. The two key ideas are using macro grammars to cache the abstract patterns of useful logical forms found thus far, and holistic triggering to efficiently retrieve the most relevant patterns based on sentence similarity. On the WikiTableQuestions dataset, we first expand the search space of an existing model to improve the state-of-the-art accuracy from 38.7% to 42.7%, and then use macro grammars and holistic triggering to achieve an 11x speedup and an accuracy of 43.7%.


title: Learning to Track for Spatio-Temporal Action Localization

abstract: We propose an effective approach for spatio-temporal action localization in realistic videos. The approach first detects proposals at the frame-level and scores them with a combination of static and motion CNN features. It then tracks high-scoring proposals throughout the video using a tracking-by-detection approach. Our tracker relies simultaneously on instance-level and class-level detectors. The tracks are scored using a spatio-temporal motion histogram, a descriptor at the track level, in combination with the CNN features. Finally, we perform temporal localization of the action using a sliding-window approach at the track level. We present experimental results for spatio-temporal localization on the UCF-Sports, J-HMDB and UCF-101 action localization datasets, where our approach outperforms the state of the art with a margin of 15%, 7% and 12% respectively in mAP.


title: Design and experiments on a new wheel-based cable climbing robot

abstract: This paper proposes an ameliorated wheel-based cable inspection robot, which is able to climb up a vertical cylindrical cable on the cable-stayed bridge. The newly-designed robot in this paper is composed of two equally spaced modules, which are joined by connecting bars to form a closed hexagonal body to clasp on the cable. Another amelioration is the newly-designed electric circuit, which is employed to limit the descending speed of the robot during its sliding down along the cable. For the safe landing in case of electricity broken-down, a gas damper with a slider-crank mechanism is introduced to exhaust the energy generated by the gravity when the robot is slipping down. For the present design, with payloads below 3.5 kg, the robot can climb up a cable with diameters varying from 65 mm to 205 mm. The landing system is tested experimentally and a simplified mathematical model is analyzed. Several climbing experiments performed on real cables show the capability of the proposed robot.


title: U-V-disparity: an efficient algorithm for stereovision based scene analysis

abstract: Reliable understanding of the 3D driving environment is vital for obstacle detection and adaptive cruise control (ACC) applications. Laser or millimeter wave radars have shown good performance in measuring relative speed and distance in a highway driving environment. However the accuracy of these systems decreases in an urban traffic environment as more confusion occurs due to factors such as parked vehicles, guardrails, poles and motorcycles. A stereovision based sensing system provides an effective supplement to radar-based road scene analysis with its much wider field of view and more accurate lateral information. This paper presents an efficient solution using a stereovision based road scene analysis algorithm, which employs the "U-V-disparity" concept. This concept is used to classify a 3D road scene into relative surface planes and characterize the features of road pavement surfaces, roadside structures and obstacles. Real-time implementation of the disparity map calculation and the "U-V-disparity" classification is also presented.


title: The Impact of Scaffolding and Student Achievement Levels in a Problem-based Learning Environment

abstract: This study examined how scaffolds and student achievement levels influence inquiry and performance in a problem-based learning environment. The scaffolds were embedded within a hypermedia program that placed students at the center of a problem in which they were trying to become the youngest person to fly around the world in a balloon. One-hundred and eleven seventh grade students enrolled in a science and technology course worked in collaborative groups for a duration of 3 weeks to complete a project that included designing a balloon and a travel plan. Student groups used one of three problem-based, hypermedia programs: (1) a no scaffolding condition that did not provide access to scaffolds, (2) a scaffolding optional condition that provided access to scaffolds, but gave students the choice of whether or not to use them, and (3) a scaffolding required condition required students to complete all available scaffolds. Results revealed that students in the scaffolding optional and scaffolding required conditions performed significantly better than students in the no scaffolding condition on one of the two components of the group project. Results also showed that student achievement levels were significantly related to individual posttest scores; higher- achieving students scored better on the posttest than lower-achieving students. In addition, analyses of group notebooks confirmed qualitative differences between stu- dents in the various conditions. Specifically, those in the scaffolding required condition produced more highly organized project notebooks containing a higher percentage of entries directly relevant to the problem. These findings suggest that scaffolds may enhance inquiry and performance, especially when students are required to access and use them.


title: 2.4 GHz DOA finder using modified butler matrix for 2×2 array antennas

abstract: Technique to find direction of arrival (DOA) has been promisingly constructed by smart antenna technology. It consists of array antennas and signal processing unit. Because of the limitation of space in commercial products, the size of antenna array should be designed with the smallest dimension. Also the processing unit is expected for low complexity and expense. Most of DOA finders were recently proposed employing linear array in which the size is linearly increased by a number of antenna elements. Also the processing units were fully comprised with high level of computation. In this paper, the low profile of 2.4 GHz DOA finder is proposed. The DOA finder is designed in the compact size of 2times2 array antennas. Using modified Butler matrix, the processing unit is very simple in which it can be handled by any economic microprocessors. The experimental results confirm the success of proposed DOA finder.


title: Stock market prediction model using TPWS and association rules mining

abstract: The objective of this research is to classify or forecast the stock market from the general investor's point of view. There are three parts in this research. In the first part we performed a survey on most of the well known data mining indicators, implemented the algorithms and calculated the accuracy by applying them on historical data. Then we presented an indicator algorithm which has higher accuracy compare to existing algorithms and it also provides a decision point that helps the investor to understand the significance of the result of the indicator. Finally we applied association rules mining to group the selected (based on precision) indicator algorithms to come up with a model to increase the overall accuracy. However motivating fact is we achieved far better results from our suggested model than other comparable indicator algorithms or strategy. For our research we used the data of Dhaka Stock Exchange (DSE), capital market of Bangladesh.


title: Cross-language compiler benchmarking: are we fast yet?

abstract: Comparing the performance of programming languages is difficult because they differ in many aspects including preferred programming abstractions, available frameworks, and their runtime systems. Nonetheless, the question about relative performance comes up repeatedly in the research community, industry, and wider audience of enthusiasts. This paper presents 14 benchmarks and a novel methodology to assess the compiler effectiveness across language implementations. Using a set of common language abstractions, the benchmarks are implemented in Java, JavaScript, Ruby, Crystal, Newspeak, and Smalltalk. We show that the benchmarks exhibit a wide range of characteristics using language-agnostic metrics. Using four different languages on top of the same compiler, we show that the benchmarks perform similarly and therefore allow for a comparison of compiler effectiveness across languages. Based on anecdotes, we argue that these benchmarks help language implementers to identify performance bugs and optimization potential by comparing to other language implementations.


Information Retrieval Based on Word Senses


Feasibility Study on the Use of Biometrics in an Entitlement Scheme


title: The score-distributional threshold optimization for adaptive binary classification tasks

abstract: The thresholding of document scores has proved critical for the effectiveness of classification tasks. We review the most important approaches to thresholding, and introduce the score-distributional (S-D) threshold optimization method. The method is based on score distributions and is capable of optimizing any effectiveness measure defined in terms of the traditional contingency table. As a byproduct, we provide a model for score distributions , and demonstrate its high accuracy in describing empirical data. The estimation method can be performed incrementally, a highly desirable feature for adaptive environments. Our work in modeling score distributions is useful beyond threshold optimization problems. It directly applies to other retrieval environments that make use of score distributions,e.g., distributed retrieval, or topic detection and tracking. The most accurate version of S-D thresholding --- although incremental --- can be computationally heavy. Therefore, we also investigate more practical solutions. We suggest practical approximations and discuss adaptivity, threshold initialization, and incrementality issues. The practical version of S-D thresholding has been tested in the context of the TREC-9 Filtering Track and found to be very effective [2].


title: Direct observation and temperature control of the surface Dirac gap in the topological crystalline insulator (Pb,Sn)Se

abstract: Since the advent of topological insulators hosting Dirac surface states, efforts have been made to gap these states in a controllable way. A new route to accomplish this was opened up by the discovery of topological crystalline insulators where the topological states are protected by crystal symmetries and thus prone to gap formation by structural changes of the lattice. Here we show a temperature-driven gap opening in Dirac surface states within the topological crystalline insulator phase in (Pb,Sn)Se. By using angle-resolved photoelectron spectroscopy, the gap formation and mass acquisition is studied as a function of composition and temperature. The resulting observations lead to the addition of a temperature- and composition-dependent boundary between massless and massive Dirac states in the topological phase diagram for (Pb,Sn)Se (001). Overall, our results experimentally establish the possibility to tune between massless and massive topological states on the surface of a topological system.


title: Dissociation of Brain Activity Related to Syntactic and Semantic Aspects of Language

abstract: In psycholinguistic research, there has been considerable interest in understanding the interactions of difFerent types of linguistic information during language processing. For example, does syntactic information interact with semantic or pragmatic information at an early stage of language processing, or only at later stages in order to resolve ambiguities of language? Developing reliable measures of language processes such as syntax and semantics is important to address many of these theoretical issues in psycholinguistics. In the present study, event-related brain potentials (ERPs) were recorded from healthy young subjects while they read pairs of words presented one word at a time. The ERPs for the second word of each pair were compared as a function of whether the preceding word was or was not (1) semantically related (i.e., synonyms; “semantic condition”) or (2) grammatically correct (“syntactic condition”). In the semantic condition the ERPs obtained to words preceded by nonsemantically related words elicited an N400 component that was maximal over centroparietal scalp regions. In contrast, in the syntactic condition the ERPs obtained to words preceded by grammatically incorrect articles or pronouns yielded a negativity with a later onset, and a frontopolar, left hemisphere scalp maximum. This replicates our previous findings of a syntactic negativity in a word pair design that was performed in the German language. Further, the present data provide scalp distributional information, which suggests that the syntactic negativity represents brain processes that are dissociable from the centroparietal N400 component. Thus, these findings provide strong evidence for a separate negative polarity ERP component that indexes syntactic aspects of language processing.


title: Sparse Overcomplete Representations for Efficient Identification of Power Line Outages

abstract: Fast and accurate unveiling of power-line outages is of paramount importance not only for preventing faults that may lead to blackouts, but also for routine monitoring and control tasks of the smart grid, including state estimation and optimal power flow. Existing approaches are either challenged by the combinatorial complexity issues involved and are thus limited to identifying single and double line-outages or they invoke less pragmatic assumptions such as conditionally independent phasor angle measurements available across the grid. Using only a subset of voltage phasor angle data, the present paper develops a near real-time algorithm for identifying multiple line outages at the affordable complexity of solving a sparse signal reconstruction problem via either greedy steps or coordinate descent iterations. Recognizing that the number of line outages is a small fraction of the total number of lines, the novel approach relies on reformulating the DC linear power flow model as a sparse overcomplete expansion and leveraging contemporary advances in compressive sampling and variable selection. This sparse representation can also be extended to incorporate available information on the internal system and more general line-parameter faults. Analysis and simulated tests on 118-, 300-, and 2383-bus systems confirm the effectiveness of identifying sparse power line outages.


title: Surgical dislocation of the hip for the fixation of acetabular fractures

abstract: Surgical dislocation of the hip in the treatment of acetabular fractures allows the femoral head to be safely displaced from the acetabulum. This permits full intra-articular acetabular and femoral inspection for the evaluation and potential treatment of cartilage lesions of the labrum and femoral head, reduction of the fracture under direct vision and avoidance of intra-articular penetration with hardware. We report 60 patients with selected types of acetabular fracture who were treated using this approach. Six were lost to follow-up and the remaining 54 were available for clinical and radiological review at a mean follow-up of 4.4 years (2 to 9). Substantial damage to the intra-articular cartilage was found in the anteromedial portion of the femoral head and the posterosuperior aspect of the acetabulum. Labral lesions were predominantly seen in the posterior acetabular area. Anatomical reduction was achieved in 50 hips (93%) which was considerably higher than that seen in previous reports. There were no cases of avascular necrosis. Four patients subsequently required total hip replacement. Good or excellent results were achieved in 44 hips (81.5%). The cumulative eight-year survivorship was 89.0% (95% confidence interval 84.5 to 94.1). Significant predictors of poor outcome were involvement of the acetabular dome and lesions of the femoral cartilage greater than grade 2. The functional mid-term results were better than those of previous reports. Surgical dislocation of the hip allows accurate reduction and a predictable mid-term outcome in the management of these difficult injuries without the risk of the development of avascular necrosis.


Finding Steiner forests in planar graphs


title: Branch prediction, instruction-window size, and cache size: Performance tradeoffs and simulation techniques

abstract: Design parameters interact in complex ways in modern processors, especially because out-of-order issue and decoupling buffers allow latencies to be overlapped. Trade-offs among instruction-window size, branch-prediction accuracy, and instruction- and data-cache size can change as these parameters move through different domains. For example, modeling unrealistic caches can under- or overstate the benefits of better prediction or a larger instruction window. Avoiding such pitfalls requires understanding how all these parameters interact. Because such methodological mistakes are common, this paper provides a comprehensive set of SimpleScalar simulation results from SPECint95 programs, showing the interactions among these major structures. In addition to presenting this database of simulation results, major mechanisms driving the observed trade-offs are described. The paper also considers appropriate simulation techniques when sampling full-length runs with the SPEC reference inputs. In particular, the results show that branch mispredictions limit the benefits of larger instruction windows, that better branch prediction and better instruction cache behavior have synergistic effects, and that the benefits of larger instruction windows and larger data caches trade off and have overlapping effects. In addition, simulations of only 50 million instructions can yield representative results if these short windows are carefully selected.


title: The Design with Intent Method: A design tool for influencing user behaviour

abstract: Using product and system design to influence user behaviour offers potential for improving performance and reducing user error, yet little guidance is available at the concept generation stage for design teams briefed with influencing user behaviour. This article presents the Design with Intent Method, an innovation tool for designers working in this area, illustrated via application to an everyday human–technology interaction problem: reducing the likelihood of a customer leaving his or her card in an automatic teller machine. The example application results in a range of feasible design concepts which are comparable to existing developments in ATM design, demonstrating that the method has potential for development and application as part of a user-centred design process.


title: Temporal Order-Preserving Dynamic Quantization for Human Action Recognition from Multimodal Sensor Streams

abstract: Recent commodity depth cameras have been widely used in the applications of video games, business, surveillance and have dramatically changed the way of human-computer interaction. They provide rich multimodal information that can be used to interpret the human-centric environment. However, it is still of great challenge to model the temporal dynamics of the human actions and great potential can be exploited to further enhance the retrieval accuracy by adequately modeling the patterns of these actions. To address this challenge, we propose a temporal order-preserving dynamic quantization method to extract the most discriminative patterns of the action sequence. We further present a multimodal feature fusion method that can be derived in this dynamic quantization framework to exploit different discriminative capability of features from multiple modalities. Experiments based on three public human action datasets show that the proposed technique has achieved state-of-the-art performance.


title: Design of an optimal nearest neighbor classifier using an intelligent genetic algorithm

abstract: The goal of designing an optimal nearest neighbor classifier is to maximize the classification accuracy while minimizing the sizes of both the reference and feature sets. A novel intelligent genetic algorithm (IGA) superior to conventional GAs in solving large parameter optimization problems is used to effectively achieve this goal. It is shown empirically that the IGA-designed classifier outperforms existing GA-based and non-GA-based classifiers in terms of classification accuracy and total number of parameters of the reduced sets.


Abstract Lessons from the Adoption of MusicXML as an Interchange Standard


title: Color Quantization Programming and

abstract: Color quantization is a process of choosing a set of K representative colors to approximate the N colors of an image, K , such that the resulting K -color image looks as much like the original N -color image as possible. This is an optimization problem known to be NP-complete in K . However, this paper shows that by ordering the N colors along their principal axis and partitioning the color space with respect to this ordering, the resulting constrained optimization problem can be solved in O ( N + KM 2 ) time by dynamic programming (where M is the intensity resolution of the device). Traditional color quantization algorithms recursively bipartition the color space. By using the above dynamic-programming algorithm, we can construct a globally optimal K -partition, K >2, of a color space in the principal direction of the input data. This new partitioning strategy leads to smaller quantization error and hence better image quality. Other algorithmic issues in color quantization such as efficient statistical computations and nearest-neighbor searching are also studied. The interplay between luminance and chromaticity in color quantization with and without color dithering is investigated. Our color quantization method allows the user to choose a balance between the image smoothness and hue accuracy for a given K .


title: A Cortical Mechanism for Triggering Top-Down Facilitation in Visual Object Recognition

abstract: The majority of the research related to visual recognition has so far focused on bottom-up analysis, where the input is processed in a cascade of cortical regions that analyze increasingly complex information. Gradually more studies emphasize the role of top-down facilitation in cortical analysis, but it remains something of a mystery how such processing would be initiated. After all, top-down facilitation implies that high-level information is activated earlier than some relevant lower-level information. Building on previous studies, I propose a specific mechanism for the activation of top-down facilitation during visual object recognition. The gist of this hypothesis is that a partially analyzed version of the input image (i.e., a blurred image) is projected rapidly from early visual areas directly to the prefrontal cortex (PFC). This coarse representation activates in the PFC expectations about the most likely interpretations of the input image, which are then back-projected as an "initial guess" to the temporal cortex to be integrated with the bottom-up analysis. The top-down process facilitates recognition by substantially limiting the number of object representations that need to be considered. Furthermore, such a rapid mechanism may provide critical information when a quick response is necessary.


title: Fast image deconvolution using hyper-laplacian priors

abstract: The heavy-tailed distribution of gradients in natural scenes have proven effective priors for a range of problems such as denoising, deblurring and super-resolution. These distributions are well modeled by a hyper-Laplacian (p(x) ∝ e-k|x|α ), typically with 0.5 ≤ α ≤ 0.8. However, the use of sparse distributions makes the problem non-convex and impractically slow to solve for multi-megapixel images. In this paper we describe a deconvolution approach that is several orders of magnitude faster than existing techniques that use hyper-Laplacian priors. We adopt an alternating minimization scheme where one of the two phases is a non-convex problem that is separable over pixels. This per-pixel sub-problem may be solved with a lookup table (LUT). Alternatively, for two specific values of α, 1/2 and 2/3 an analytic solution can be found, by finding the roots of a cubic and quartic polynomial, respectively. Our approach (using either LUTs or analytic formulae) is able to deconvolve a 1 megapixel image in less than ~3 seconds, achieving comparable quality to existing methods such as iteratively reweighted least squares (IRLS) that take ~20 minutes. Furthermore, our method is quite general and can easily be extended to related image processing problems, beyond the deconvolution application demonstrated.


title: Multi-UAV cooperation and control for load transportation and deployment

abstract: This paper deals with the cooperation and control of multiple UAVs with sensing and actuation capabilities. An architecture to perform cooperative missions with a multi-UAV platform is presented. The interactions between UAVs are not only information exchanges but also physical couplings required to cooperate in the joint transportation of a single load. Then, the paper also presents the control system for the transportation of a slung load by means of one or several helicopters. Experimental results of the load transportation system with one and three helicopters are shown. On the other hand, the UAVs considered in the platform can also deploy small objects, such as sensor nodes, on different locations if it is required. This feature along with the whole platform architecture are illustrated in the paper with a real multi-UAV mission for the deployment of sensor nodes to repair the connectivity of a wireless sensor network.


A state of the art survey of data mining-based fraud detection and credit scoring


title: A Question-Answering framework for plots using Deep learning.

abstract: Deep Learning has managed to push boundaries in a wide variety of tasks. One area of interest is to tackle problems in reasoning and understanding, in an aim to emulate human intelligence. In this work, we describe a deep learning model that addresses the reasoning task of question-answering on bar graphs and pie charts. We introduce a novel architecture that learns to identify various plot elements, quantify the represented values and determine a relative ordering of these statistical values. We test our model on the recently released FigureQA dataset, which provides images and accompanying questions, for bar graphs and pie charts, augmented with rich annotations. Our approach outperforms the state-of-the-art Relation Networks baseline and traditional CNN-LSTM models when evaluated on this dataset. Our model also has a considerably faster training time of approximately 2 days on 1 GPU compared to the Relation Networks baseline


title: No Place to Hide that Bytes won't Reveal: Sniffing Location-Based Encrypted Traffic to Track User Position

abstract: News reports of the last few years indicated that several intelligence agencies are able to monitor large networks or entire portions of the Internet backbone. Such a powerful adversary has only recently been considered by the academic literature.


title: Paraphrase identification with lexico-syntactic graph subsumption

abstract: The paper presents a new approach to the problem of paraphrase identification. The new approach extends a previously proposed method for the task of textual entailment. The relationship between paraphrases and entailment is discussed to theoretically justify the new approach. The proposed approach is useful because it uses relatively few resources compared to similar systems yet it produces results similar or better than other approaches to paraphrase identification. The approach also offers significantly better results than two baselines. We report results on a standard data set as well as on a new, balanced data set.


title: Data mining with big data

abstract: Big Data concern large-volume, complex, growing data sets with multiple, autonomous sources. With the fast development of networking, data storage, and the data collection capacity, Big Data are now rapidly expanding in all science and engineering domains, including physical, biological and biomedical sciences. This paper presents a HACE theorem that characterizes the features of the Big Data revolution, and proposes a Big Data processing model, from the data mining perspective. This data-driven model involves demand-driven aggregation of information sources, mining and analysis, user interest modeling, and security and privacy considerations. We analyze the challenging issues in the data-driven model and also in the Big Data revolution.


title: Measuring Concentration of Distances—An Effective and Efficient Empirical Index

abstract: High dimensional data analysis gives rise to many challenges. One such that has come to gain a lot of attention recently is the concentration of distances (CoD) phenomenon, which is the inability of distance functions to distinguish points well in high dimensions. CoD affects almost every machine learning and data analysis algorithm in high dimensions. In this work, we present a novel efficient and effective empirical index that not only illustrates whether a distance function tends to concentrate for a given data set, but also enables us to measure the rate of concentration and allows us to compare different distance functions vis-a-vis their rate of concentration. As opposed to existing empirical indices, the proposed empirical measure uses only the internal characteristics of a given data set and hence is applicable on real data sets, which was hitherto not possible.


title: Two Dimensional (2D) Convolutional Neural Network for Nepali Sign Language Recognition

abstract: Sign language is a basic mode of communication between people who have difficulties in speech and hearing. If computers can detect and distinguish these signs, communication would be easier and dependency on a translator reduces. This paper provides the structure of the system which translates Nepali signs from Nepali Sign Language (NSL) into their respective meaningful words. It captures the static hand gestures and translates the pictures into their corresponding meanings using 2D Convolutional Neural Network. Red glove is used for segmentation purpose. Data set is obtained by manually capturing images using a front camera of a laptop. The system got higher accuracy for the model that recognizes 5 signs than the 7 and the 9 signs model. It also facilitates the users to search for the signs using their corresponding English words. Its core objective is to make easy communication between differently-abled people and who do not understand sign language without involvement of a translator.


title: Text Detection Based on MSER and CNN Features

abstract: Text detection in natural scenes holds great importance in the field of research and still remains a challenge and an important task because of size, various fonts, line orientation, different illumination conditions, weak characters and complex backgrounds in image. The contribution of our proposed method is to filtering out complex backgrounds by combining three strategies. These are enhancing the edge candidate detection in HSV space color using the fractal dimension (FD) to transform the image intensities, then using MSER candidate detection to get different masks applied in HSV space color as well as gray color. After that, we opt for the Stroke Width Transform (SWT) and heuristic filtering. Such strategies are followed so as to maximize the capacity of zones text pixels candidates and distinguish between text boxes and the rest of the image. The components selected non text are filtered by classifying the characters candidates using Support Vector Machines (SVM) exploring Convolutional Neural Networks (CNN) features and Histogram of Oriented Gradients (HOG) vector features. We use the technique of word grouping who the boundary box localization select different words in the image where false positives text blocks are eliminated by geometrical properties. The evaluation of the proposed method demonstrate the effectiveness of our method for complex foreground through the experimental results tested on three benchmarks ICDAR2013, ICDAR2015 and MSRA-TD500.


title: IoT-based personal health care monitoring device for diabetic patients

abstract: This paper presents a non-invasive breath test to monitor the condition of diabetic patients. It is identified as an easier technique and quick diagnoses of diabetic ketoacidosis (DKA). DKA is a preventable acute complication of type 1 diabetes mellitus. Common diabetic test on patients are done on urinary test and blood ketone test to monitor for diabetes condition. However, those methods are considers as invasive, inconvenient and expensive. Recently, breath acetone has been considered as a new ketone biomarker because it is non-invasive, convenient, and accurate reflection of the body's ketone level. This research presents a method of monitoring ketone level by using breath measurement. Main objective of this research is to present an easy handheld health care on monitoring diabetic level with breath. Method consists of development of hardware connection with Internet of Things (IoT) system to facilitate the process of patients' diagnosis and personal monitoring. In this system, Arduino board is used to read the sensor with sense the breath. Breath value level is log to system using wireless communication. Data collection is interfaced to web page. Ketone level is measured as the amount of breath acetone is collected when patients exhale into a mouthpiece that consists of gas sensor. The reading from Arduino is shared to the database via ESP 8266 Wi-Fi Module and can be accessed by the patients or registered doctors. This research is significant where patients can independently monitor their diabetic health and the IoT system can be alerted directly to medial officers in the hospitals.


title: Mitigating Unwanted Biases with Adversarial Learning

abstract: Machine learning is a tool for building models that accurately represent input training data. When undesired biases concerning demographic groups are in the training data, well-trained models will reflect those biases. We present a framework for mitigating such biases by including a variable for the group of interest and simultaneously learning a predictor and an adversary. The input to the network X, here text or census data, produces a prediction Y, such as an analogy completion or income bracket, while the adversary tries to model a protected variable Z, here gender or zip code. ::: The objective is to maximize the predictor's ability to predict Y while minimizing the adversary's ability to predict Z. Applied to analogy completion, this method results in accurate predictions that exhibit less evidence of stereotyping Z. When applied to a classification task using the UCI Adult (Census) Dataset, it results in a predictive model that does not lose much accuracy while achieving very close to equality of odds (Hardt, et al., 2016). The method is flexible and applicable to multiple definitions of fairness as well as a wide range of gradient-based learning models, including both regression and classification tasks.


title: Reliability of Indium Solder for Cold Temperature Packaging

abstract: A failure model has been developed for indium die attach fatigue in extreme cold temperature environment applications, such as distributed control systems for planetary exploration. The model contains both a stress component, which is based on the packaging structure, and a damage component, which is based solely on the material properties. The stress model is developed for a wire bonded, hermetic ceramic module containing a SiGe hetero-junction bipolar transistor intended for use in a lunar base. The model was calibrated using results from −196°C to 125°C thermal shock testing.Copyright © 2007 by ASME


title: AES CCMP Algorithm with N-Way Interleaved Cipher Block Chaining

abstract: Zadia Codabux-Rossan * Faculty of Engineering, University of Mauritius Email: z.codabux@uom.ac.mu M. Razvi Doomun Faculty of Engineering, University of Mauritius Email: r.doomun@uom.ac.mu Paper Accepted on 02 October 2009 Abstract Nowadays, the increased use of battery-powered mobile appliances and the urge to access time-sensitive data anytime anywhere has fuelled a high demand for wireless networks. However, wireless networks are susceptible to intrusion and security problems. There is an inherent need to secure the wireless data communication to ensure the confidentiality, authenticity, integrity and non repudiation of the data being exchanged. On the other hand, the computation and the resultant energy consumption to achieve sufficient security can be high. Encryption algorithms are generally computationally intensive, and consume a significant amount of computing resources (such as CPU time, memory, and battery power). Considering the limited resources on wireless devices, it is crucial that security protocols be implemented efficiently. This manuscript focuses on how energy consumption is impacted by the use of unoptimised AES-CCMP algorithms and proposes an optimized AES CCMP algorithm using 2-way interleaving that does not compromise the security of wireless communication sessions. There is also analysis of the performance of AES (a.k.a. Rijndael) in its AES–CCMP implementation. The 2-way interleaving technique is an optimization of the CBC-MAC that is investigated using two performance metrics (namely encryption time and throughput). Keywords: IEEE 802.11i security, AES-CCMP, Optimization, Interleaved Cipher Block Chaining


title: SuperedgeRank algorithm and its application in identifying opinion leader of online public opinion supernetwork

abstract: Opinion leaders on the internet are very important figures in online communities, which play an important role in promoting the formation of public opinions. Many theories have been introduced to identify opinion leaders by social network analysis, text mining and PageRank-based algorithm in different fields, but few has addressed the issue of opinion leader identification by combining the methods above, and there is no research using supernetwork analysis to identify opinion leaders. This paper proposed an SuperedgeRank algorithm for opinion leader identification based on supernetwork theory, which combined the network topology analysis and text mining. First, the study established a supernetwork model with multidimensional subnetworks, which are social, psychological, environmental and viewpoint subnetworks. Then, the study proposed four supernetwork indexes: node superdegree, superedge degree, superedge-superedge distance and superedge overlap. The later two indexes are developed by us to help evaluate the identified opinion leaders. Based on them, our study applied SuperedgeRank algorithm to rank superedges, and used the ranking result to identify opinion leaders in opinion supernetwork model. Finally, the feasibility and innovativeness of this method were verified by a case study.


title: Multi-MOSFET-Based Series Resonant Inverter for Improved Efficiency and Power Density Induction Heating Applications

abstract: Resonant converters featuring soft switching are commonly used in domestic induction heating applications due to their high efficiency and high power density. In this paper, the design and implementation procedure of an improved efficiency and low-profile resonant inverter for induction heating applications is presented. The proposed converter is based on a multi-MOSFET cell implementation, reducing the equivalent on-state resistance per chip-area. By using automotive-grade MOSFET devices, a converter capable of delivering up to 4 kW has been built, reducing conduction losses against the classical IGBT-based converter. In addition to this, the reduced switching times of MOSFET devices decrease switching losses, further increasing the conversion efficiency. The main design challenges including the device selection, gate drive circuit, and cooling have been addressed. As a conclusion, a low-profile implementation without fan and heat sink is obtained, which significantly improves state-of-the-art technology in terms of efficiency and power density.


title: Employing Qualitative Methods in the Private Sector

abstract: Private Sector Research Settings Renaming Your Skills Business Activities That Require Research Marketing Yourself to the Private Sector Project Management Business Management Conclusion


title: A review of adaptive population sizing schemes in genetic algorithms

abstract: This paper reviews the topic of population sizing in genetic algorithms. It starts by revisiting theoretical models which rely on a facetwise decomposition of genetic algorithms, and then moves on to various self-adjusting population sizing schemes that have been proposed in the literature. The paper ends with recommendations for those who design and compare adaptive population sizing schemes for genetic algorithms.


title: A Video Representation Using Temporal Superpixels

abstract: We develop a generative probabilistic model for temporally consistent super pixels in video sequences. In contrast to supermodel methods, object parts in different frames are tracked by the same temporal super pixel. We explicitly model flow between frames with a bilateral Gaussian process and use this information to propagate super pixels in an online fashion. We consider four novel metrics to quantify performance of a temporal super pixel representation and demonstrate superior performance when compared to supermodel methods.


Developing multi-agent systems with a FIPA-compliant agent framework


title: Kalman-Filter-Based Orientation Determination Using Inertial/Magnetic Sensors: Observability Analysis and Performance Evaluation

abstract: In this paper we present a quaternion-based Extended Kalman Filter (EKF) for estimating the three-dimensional orientation of a rigid body. The EKF exploits the measurements from an Inertial Measurement Unit (IMU) that is integrated with a tri-axial magnetic sensor. Magnetic disturbances and gyro bias errors are modeled and compensated by including them in the filter state vector. We employ the observability rank criterion based on Lie derivatives to verify the conditions under which the nonlinear system that describes the process of motion tracking by the IMU is observable, namely it may provide sufficient information for performing the estimation task with bounded estimation errors. The observability conditions are that the magnetic field, perturbed by first-order Gauss-Markov magnetic variations, and the gravity vector are not collinear and that the IMU is subject to some angular motions. Computer simulations and experimental testing are presented to evaluate the algorithm performance, including when the observability conditions are critical.


title: Scalable, graph-based network vulnerability analysis

abstract: Even well administered networks are vulnerable to attack. Recent work in network security has focused on the fact that combinations of exploits are the typical means by which an attacker breaks into a network. Researchers have proposed a variety of graph-based algorithms to generate attack trees (or graphs). Either structure represents all possible sequences of exploits, where any given exploit can take advantage of the penetration achieved by prior exploits in its chain, and the final exploit in the chain achieves the attacker's goal. The most recent approach in this line of work uses a modified version of the model checker NuSMV as a powerful inference engine for chaining together network exploits, compactly representing attack graphs, and identifying minimal sets of exploits. However, it is also well known that model checkers suffer from scalability problems, and there is good reason to doubt whether a model checker can handle directly a realistic set of exploits for even a modest-sized network. In this paper, we revisit the idea of attack graphs themselves, and argue that they represent more information explicitly than is necessary for the analyst. Instead, we propose a more compact and scalable representation. Although we show that it is possible to produce attack trees from our representation, we argue that more useful information can be produced, for larger networks, while bypassing the attack tree step. Our approach relies on an explicit assumption of monotonicity, which, in essence, states that the precondition of a given exploit is never invalidated by the successful application of another exploit. In other words, the attacker never needs to backtrack. The assumption reduces the complexity of the analysis problem from exponential to polynomial, thereby bringing even very large networks within reach of analysis


title: Parkinson's disease detection using ensemble techniques and genetic algorithm

abstract: Parkinson's disease (PD) is a neurological disorder which progress by time. People suffering from PD experience shortage of Dopamine which is a chemical present in brain nerve cells. The symptoms of PD are tremor, rigidity, and slowness of movements and people with PD experience more severity by time progress. Therefore, the automation in early detection of PD is an important issue. In the literature, different classification methods have been proposed. Also, due to the high dimension of extracted features of voice, many feature selection algorithms have been developed. In this paper, we aim to propose a method for early detection of PD from voice recordings. The Genetic algorithm is used to select the optimal set of features which can reduce feature vector dimension from 22 to 6 features. We have achieved 96.55% and 98.28% detection rate by employing AdaBoost and Bagging algorithms for classification process, respectively.


title: Fuzzy Based Blood Image Segmentation for Automated Leukemia Detection

abstract: Acute lymphoblastic leukemia (ALL) are a group of hematological neoplasia of childhood which is characterized by a large number of lymphoid blasts in the blood stream. ALL makes around 80% of childhood leukemia and it mostly occur in the age group of 3-7. The nonspecific nature of the signs and symptoms of ALL often leads to wrong diagnosis. Diagnostic confusion is also posed due to imitation of similar signs by other disorders. Careful microscopic examination of stained blood smear or bone marrow aspirate is the only way to effective diagnosis of leukemia. Techniques such as fluorescence in situ hybridization (FISH), immunophenotyping, cytogenetic analysis and cytochemistry are also employed for specific leukemia detection. The need for automation of leukemia detection arises since the above specific tests are time consuming and costly. Morphological analysis of blood slides are influenced by factors such as hematologists experience and tiredness, resulting in non standardized reports. A low cost and efficient solution is to use image analysis for quantitative examination of stained blood microscopic images for leukemia detection. A fuzzy based two stage color segmentation strategy is employed for segregating leukocytes or white blood cells (WBC) from other blood components. Discriminative features i.e. nucleus shape, texture are used for final detection of leukemia. In the present paper two novel shape features i.e., Hausdorff Dimension and contour signature is implemented for classifying a lymphocytic cell nucleus. Support Vector Machine (SVM) is employed for classification. A total of 108 blood smear images were considered for feature extraction and final performance evaluation is validated with the results of a hematologist.


title: Visuo-haptic object-related activation in the ventral visual pathway

abstract: The ventral pathway is involved in primate visual object recognition. In humans, a central stage in this pathway is an occipito‐temporal region termed the lateral occipital complex (LOC), which is preferentially activated by visual objects compared to scrambled images or textures. However, objects have characteristic attributes (such as three-dimensional shape) that can be perceived both visually and haptically. Therefore, object-related brain areas may hold a representation of objects in both modalities. Using fMRI to map object-related brain regions, we found robust and consistent somatosensory activation in the occipito‐temporal cortex. This region showed clear preference for objects compared to textures in both modalities. Most somatosensory object-selective voxels overlapped a part of the visual object-related region LOC. Thus, we suggest that neuronal populations in the occipito‐temporal cortex may constitute a multimodal object-related network.


title: Facebook's affordances, visible culture, and anti-anonymity

abstract: This paper outlines and analyzes a selection of Facebook's affordances to argue that the site compels users to become as visible as possible, thus refusing their right to be anonymous. This promoted visible culture is primarily guided by two affordance categories: the "real you" policy and photographs. The related interface affordances for each category are catalogued and focus groups with emerging adult users (n=45) are analyzed to better understand how users interact with the space while attempting to maintain and broadcast the self. Findings indicate that users (1) do not question the "real you" policy because they view Facebook, like the Census or an application, as an "official" space and (2) value photographs of corporeal selves to the point of ostracizing offline friends on the site when they have chosen "non-traditional" profile pictures.


title: A Description Logics Tableau Reasoner in Prolog

abstract: Description Logics (DLs) are gaining a widespread adoption as the popularity of the Semantic Web increases. Traditionally, reasoning algorithms for DLs have been implemented in procedural languages such as Java or C++. In this paper, we present the system TRILL for “Tableau Reasoner for descrIption Logics in proLog”. TRILL answers queries to SHOIN (D) knowledge bases using a tableau algorithm. Prolog nondeterminism is used for easily handling non-deterministic expansion rules that produce more than one tableau. Moreover, given a query, TRILL is able to return instantiated explanations for the query, i.e., instantiated minimal sets of axioms that allow the entailment of the query. The Thea2 library is exploited by TRILL for parsing ontologies and for the internal Prolog representation of DL axioms.


title: AutoPlait: automatic mining of co-evolving time sequences

abstract: Given a large collection of co-evolving multiple time-series, which contains an unknown number of patterns of different durations, how can we efficiently and effectively find typical patterns and the points of variation? How can we statistically summarize all the sequences, and achieve a meaningful segmentation? In this paper we present AutoPlait, a fully automatic mining algorithm for co-evolving time sequences. Our method has the following properties: (a) effectiveness: it operates on large collections of time-series, and finds similar segment groups that agree with human intuition; (b) scalability: it is linear with the input size, and thus scales up very well; and (c) AutoPlait is parameter-free, and requires no user intervention, no prior training, and no parameter tuning. Extensive experiments on 67GB of real datasets demonstrate that AutoPlait does indeed detect meaningful patterns correctly, and it outperforms state-of-the-art competitors as regards accuracy and speed: AutoPlait achieves near-perfect, over 95% precision and recall, and it is up to 472 times faster than its competitors.


Good News vs. Bad News: What are they talking about?


title: PERSPECTIVES ON PSYCHOLOGICAL SCIENCE Measured Gene-Environment Interactions in Psychopathology Concepts, Research Strategies, and Implications for Research, Intervention, and Public Understanding of

abstract: There is much curiosity about interactions between genes and environmental risk factors for psychopathology, but this interest is accompanied by uncertainty. This article aims to address this uncertainty. First, we explain what is and is not meant by gene-environment interaction. Second, we discuss reasons why such interactions were thought to be rare in psychopathology, and argue instead that they ought to be common. Third, we summarize emerging evidence about gene-environment interactions in mental disorders. Fourth, we argue that research on gene-environment interactions should be hypothesis driven, and we put forward strategies to guide future studies. Fifth, we describe potential benefits of studying measured gene-environment interactions for basic neuroscience, gene hunting, intervention, and public understanding of genetics. We suggest that information about nurture might be harnessed to make new discoveries about the nature of psychopathology.


title: A Multi-Cloud Framework for Measuring and Describing Performance Aspects of Cloud Services Across Different Application Types

abstract: Cloud services have emerged as an innovative IT provisioning model in the recent years. However, after ::: ::: their usage severe considerations have emerged with regard to their varying performance due to ::: ::: multitenancy and resource sharing issues. These issues make it very difficult to provide any kind of ::: ::: performance estimation during application design or deployment time. The aim of this paper is to present a mechanism and process for measuring the performance of various cloud services and describing this information in machine understandable format. The framework is responsible for organizing the execution and can support multiple cloud providers. Furthermore we present approaches for measuring service performance with the usage of specialized metrics for ranking the services according to a weighted combination of cost, performance and workload.


title: The influence of energy storage and return foot stiffness on walking mechanics and muscle activity in below-knee amputees

abstract: article i nfo Article history: Received 15 February 2011 Accepted 15 June 2011 Background: Below-knee amputees commonly experience asymmetrical gait patterns and develop comorbidities in their intact and residual legs. Carbon fiber prosthetic feet have been developed to minimize these asymmetries by utilizing elastic energy storage and return to provide body support, forward propulsion and leg swing initiation. However, how prosthetic foot stiffness influences walking characteristics is not well- understood. The purpose of this study was to identify the influence of foot stiffness on kinematics, kinetics, muscle activity, prosthetic energy storage and return, and mechanical efficiency during amputee walking. Methods: A comprehensive biomechanical analysis was performed on 12 unilateral below-knee amputees. Subjects walked overground at 1.2 m/s with three prosthetic feet of varying keel and heel stiffness levels, which were created using additive manufacturing. Findings: As stiffness decreased, peak residual and intact leg ankle angles and residual leg knee flexion angle increased. The residual and intact leg braking ground reaction forces and knee extensor moments, residual leg vastus and gluteus medius activity, and intact leg vastus and rectus femoris activity also increased. The second vertical ground reaction force peak and hamstring activity in the residual leg and first vertical ground reaction force peak in the intact leg decreased. In addition, prosthetic energy storage and return increased and mechanical efficiency decreased as stiffness decreased. Interpretation: Decreasing foot stiffness can increase prosthesis range of motion, mid-stance energy storage and late-stance energy return, but the net contributions to forward propulsion and swing initiation may be limited as additional muscle activity to provide body support becomes necessary.


title: EM performance upside of short BEOL interconnects in advanced process technologies: Electrical-thermal finite element simulations and silicon verifications

abstract: In this paper, thermal characteristics (Joule heating) induced by currents in short metal interconnect lines are studied. Electrical-thermal 3D-Finite Element Method (FEM) simulation is employed to model the property of temperature in short length metal lines and an empirical yet practical current model with metal line length effect is introduced. Consequently, the Irms current gain up to 25% in short length metal was achieved. This is attributed to the heat dissipation at the line end being much more effective in short metal lines compared to long metal lines. The material properties of interconnects for simulation was obtained using 64nm pitch BEOL process. The simulation results were verified with experiment silicon data using metal test structures.


title: Whole-Exome Sequencing in Searching for New Variants Associated With the Development of Parkinson’s Disease

abstract: Background: Parkinson’s disease is a complex disease with its monogenic forms accounting for less than 10% of all cases. Whole-exome sequencing technology has been used successfully to find mutations in large families. However, because of the late onset of the disease, only small families and unrelated patients are usually available. Whole-exome sequencing conducted in such cases yields in a large number of candidate variants. There are currently a number of imperfect software tools that allow the pathogenicity of variants to be evaluated. Objectives: We analyzed 48 unrelated patients with an alleged autosomal dominant familial form of Parkinson’s disease using whole-exome sequencing and developed a strategy for selecting potential pathogenetically significant variants using almost all available bioinformatics resources for the analysis of exonic areas. Methods: DNA sequencing of 48 patients with excluded frequent mutations was performed using an Illumina HiSeq 2500 platform. The possible pathogenetic significance of identified variants and their involvement in the pathogenesis of Parkinson’s disease was assessed using SVS, CADD, and REVEL software. Functional evaluation was performed using the Pathway Studio database. Results: A significant reduction in the search range from 7082 to 25 variants in 23 genes associated with Parkinson’s disease or neuronal function was achieved. Eight (FXN, MFN2, MYOC, NPC1, PSEN1, RET, SCN3A, and SPG7) were the most significant. Conclusions: The multistep approach developed made it possible to conduct an effective search for potential pathogenetically significant variants, presumably involved in the pathogenesis of Parkinson’s disease. The data obtained need to be further verified experimentally.


title: A lexicon based method to search for extreme opinions

abstract: Studies in sentiment analysis and opinion mining have been focused on many aspects related to opinions, namely polarity classification by making use of positive, negative or neutral values. However, most studies have overlooked the identification of extreme opinions (most negative and most positive opinions) in spite of their vast significance in many applications. We use an unsupervised approach to search for extreme opinions, which is based on the automatic construction of a new lexicon containing the most negative and most positive words.


title: Fast Abstractive Summarization with Reinforce-Selected Sentence Rewriting

abstract: Inspired by how humans summarize long documents, we propose an accurate and fast summarization model that first selects salient sentences and then rewrites them abstractively (i.e., compresses and paraphrases) to generate a concise overall summary. We use a novel sentence-level policy gradient method to bridge the non-differentiable computation between these two neural networks in a hierarchical way, while maintaining language fluency. Empirically, we achieve the new state-of-the-art on all metrics (including human evaluation) on the CNN/Daily Mail dataset, as well as significantly higher abstractiveness scores. Moreover, by first operating at the sentence-level and then the word-level, we enable parallel decoding of our neural generative model that results in substantially faster (10-20x) inference speed as well as 4x faster training convergence than previous long-paragraph encoder-decoder models. We also demonstrate the generalization of our model on the test-only DUC-2002 dataset, where we achieve higher scores than a state-of-the-art model.


title: In-Order Transition-based Constituent Parsing

abstract: Both bottom-up and top-down strategies have been used for neural transition-based constituent parsing. The parsing strategies differ in terms of the order in which they recognize productions in the derivation tree, where bottom-up strategies and top-down strategies take post-order and pre-order traversal over trees, respectively. Bottom-up parsers benefit from rich features from readily built partial parses, but lack lookahead guidance in the parsing process; top-down parsers benefit from non-local guidance for local decisions, but rely on a strong encoder over the input to predict a constituent hierarchy before its construction.To mitigate both issues, we propose a novel parsing system based on in-order traversal over syntactic trees, designing a set of transition actions to find a compromise between bottom-up constituent information and top-down lookahead information. Based on stack-LSTM, our psycholinguistically motivated constituent parsing system achieves 91.8 F1 on WSJ benchmark. Furthermore, the system achieves 93.6 F1 with supervised reranking and 94.2 F1 with semi-supervised reranking, which are the best results on the WSJ benchmark.


title: New Avenues in Opinion Mining and Sentiment Analysis

abstract: The Web holds valuable, vast, and unstructured information about public opinion. Here, the history, current use, and future of opinion mining and sentiment analysis are discussed, along with relevant techniques and tools.


title: Exploring Triangle-Free Dense Structures

abstract: Triadic closure is ubiquitous in social networks, which refers to the property among three individuals, A, B, and C, such that if there exist strong ties between A-B and A-C, then there must be a strong or weak tie between B-C. Related to triadic closure, the number of triangles has been extensively studied since it can be effectively used as a metric to analyze the structure and function of a network. In this paper, from a different viewpoint, we study triangle-free dense structures which have received little attention. We focus on $K_{3,3}$ where there are two subsets of three vertices, a vertex in a subset has an edge connected to every vertex in another subset while it does not have an edge to any other vertex in the same subset. Such $K_{n,n}$ in general implies a philosophy contradiction: (a) Any two individuals are friends if they have no common friends, and (b) Any two individuals are not friends if they have common friends. However, we find such induced $K_{3,3}$ does exist frequently, and they do not disappear over time over a real academic collaboration network. In addition, in the real datasets tested, nearly all edges appearing in $K_{3,3}$ appear in some triangles. We analyze the expected numbers of induced $K_{3,3}$ and triangles ( $\Delta$ ) in four representative random graph models, namely, Erdős-Renyi random graph model, Watts-Strogatz small-world model, Barabasi-Albert preferential attachment model, and configuration model, and give an algorithm to enumerate all distinct $K_{3,3}$ in an undirected social network. We conduct extensive experiments on both real and synthetic datasets to confirm our findings. As an application, such $K_{3,3}$ found helps to find new stars collaborated by well-known figures who themselves do not collaborate.


title: Social-Aware Stateless Routingin Pocket Switched Networks

abstract: Existing social-aware routing protocols for pocket switched networks make use of the information about the social structure of the network deduced by state information of nodes (e.g., history of past encounters) to optimize routing. Although these approaches are shown to have superior performance to social-oblivious, stateless routing protocols (BinarySW, Epidemic), the improvement comes at the cost of considerable storage overhead required on the nodes. In this paper we present SANE, the first routing mechanism that combines the advantages of both social-aware and stateless approaches. SANE is based on the observation—that we validate on a real-world trace—that individuals with similar interests tend to meet more often. In SANE, individuals (network members) are characterized by their interest profile, a compact representation of their interests. By implementing a simple routing rule based on interest profile similarity, SANE is free of network state information, thus overcoming the storage capacity problem with existing social-aware approaches. Through thorough experiments, we show the superiority of SANE over existing approaches, both stateful, social-aware and stateless, social-oblivious. We discuss the statelessness of our approach in the supplementary file, which can be found on the Computer Society Digital Library at http://doi.ieeecomputersociety.org/10.1109/TPDS.2014.2307857, of this manuscript. Our interest-based approach easily enables innovative networking services, such as interest-casting. An interest-casting protocol is also introduced in this paper, and evaluated through experiments based on both real-world and synthetic mobility traces.


title: A Low-Cost Low-Power Ring Oscillator-Based Truly Random Number Generator for Encryption on Smart Cards

abstract: The design of a low-cost low-power ring oscillator-based truly random number generator (TRNG) macrocell, which is suitable to be integrated in smart cards, is presented. The oscillator sampling technique is exploited, and a tetrahedral oscillator with large jitter has been employed to realize the TRNG. Techniques to improve the statistical quality of the ring oscillator-based TRNGs' bit sequences have been presented and verified by simulation and measurement. A postdigital processor is added to further enhance the randomness of the output bits. Fabricated in the HHNEC 0.13- $\mu\text{m}$ standard CMOS process, the proposed TRNG has an area as low as 0.005 mm2. Powered by a single 1.8-V supply voltage, the TRNG has a power consumption of 40 $\mu\text{W}$ . The bit rate of the TRNG after postprocessing is 100 kb/s. The proposed TRNG has been made into an IP and successfully applied in an SD card for encryption application. The proposed TRNG has passed the National Institute of Standards and Technology tests and Diehard tests.


Multi-Domain Aspect Extraction Using Support Vector Machines.


title: Improved K-means Algorithm in User Behavior Analysis

abstract: College Network Center has accumulated large number of user's online behavior data. We tried to analyze these data in order to grasp the user surfing habits and potential relationship, and then summarize an effective strategy for user online management. First of all, we taken a university as the example, and collected the user online data to extract related fields for sampling. In addition, we proposed a figure to present the number of Internet users in different time. After that, we use k-means algorithm to cluster the records, improved the algorithm by considering the element of user information. Finally, we apply the improved algorithm on a dataset and get the conclusion.


title: Gender Differences in ADHD: A Meta-Analysis and Critical Review

abstract: ABSTRACT Objective To quantitatively review and critically evaluate literature examining gender differences in attention-deficit hyperactivity disorder (ADHD). Method A meta-analysis of relevant research based on 18 studies meeting inclusion criteria was performed. Domains evaluated included primary symptomatology, intellectual and academic functioning, comorbid behavior problems, social behavior, and family variables. Results Gender differences were not found in impulsivity, academic performance, social functioning, fine motor skills, parental education, or parental depression. However, compared with ADHD boys, ADHD girls displayed greater intellectual impairment, lower levels of hyperactivity, and lower rates of other externalizing behaviors; it was not possible to evaluate the extent to which referral bias affected these findings. Some gender differences were clearly mediated by the effects of referral source; among children with ADHD identified from nonreferred populations, girls with ADHD displayed lower levels of inattention, internalizing behavior, and peer aggression than boys with ADHD, while girls and boys with ADHD identified from clinic-referred samples displayed similar levels of impairment on these variables. Conclusions The need for future research examining gender differences in ADHD is strongly indicated, with attention to methodological limitations of the current literature, including the potential confounding effects of referral bias, comorbidity, developmental patterns, diagnostic procedures, and rater source.


title: Material and design engineering of (Al)GaN for high-performance avalanche photodiodes and intersubband applications

abstract: Numerous applications in scientific, medical, and military areas demand robust, compact, sensitive, and fast ultraviolet (UV) detection. Our (Al)GaN photodiodes pose high avalanche gain and single-photon detection efficiency that can measure up to these requirements. Inherit advantage of back-illumination in our devices offers an easier integration and layout packaging via flip-chip hybridization for UV focal plane arrays that may find uses from space applications to hostile-agent detection. Thanks to the recent (Al)GaN material optimization, III-Nitrides, known to have fast carrier dynamics and short relaxation times, are employed in (Al)GaN based superlattices that absorb in near-infrared regime. In this work, we explain the origins of our high performance UV APDs, and employ our (Al)GaN material knowledge for intersubband applications. We also discuss the extension of this material engineering into the far infrared, and even the terahertz (THz) region.


title: Tutorial Dialogue as Adaptive Collaborative Learning Support

abstract: In this paper we investigate the role of reflection in simulation based learning by manipulating two independent factors that each separately lead to significant learning effects, namely whether students worked alone or in pairs, and what type of support students were provided with. Our finding is that in our simulation based learning task, students learned significantly more when they worked in pairs than when they worked alone. Furthermore, dynamic support implemented with tutorial dialogue agents lead to significantly more learning than no support, while static support was not statistically distinguishable from either of the other two conditions. The largest effect size in comparison with the control condition of individuals working alone with no support was Pairs+Dynamic support, with an effect size of 1.24 standard deviations.


title: Human Activity Recognition with Smartphone Inertial Sensors Using Bidir-LSTM Networks

abstract: Owing to its extensive applications in solving real-life, human-centric problems, human activity recognition (HAR) has become an important research area in pervasive computing. Most Human activity recognition researches are based on multi-sensor based approaches which use three or more sensors attached on the different parts of human body. However, many sensors were redundant for specific motions. The more sensors being used, the less convenience the users have. The current generation of portable mobile devices incorporates various types of sensors that open up new areas for the analysis of human behavior. Smartphone can be used for the activity recognition of rehabilitation, aged care and so on. In this paper, we propose a bidirectional LSTM structure for human activity recognition using time series, collected from a smartphone (with the accelerometer and gyroscope embedded on the phone) worn on the waist of human, the best recognition accuracy of which can reach 93.79%.


title: Aesthetic capital: what makes london look beautiful, quiet, and happy?

abstract: In the 1960s, Lynch's 'The Image of the City' explored what impression US city neighborhoods left on its inhabitants. The scale of urban perception studies until recently was considerably constrained by the limited number of study participants. We here present a crowdsourcing project that aims to investigate, at scale, which visual aspects of London neighborhoods make them appear beautiful, quiet, and/or happy. We collect votes from over 3.3K individuals and translate them into quantitative measures of urban perception. In so doing, we quantify each neighborhood's aesthetic capital. By then using state-of-the-art image processing techniques, we determine visual cues that may cause a street to be perceived as being beautiful, quiet, or happy. We identify effects of color, texture and visual words. For example, the amount of greenery is the most positively associated visual cue with each of three qualities; by contrast, broad streets, fortress-like buildings, and council houses tend to be associated with the opposite qualities (ugly, noisy, and unhappy).


title: Big Data Processing for Full-Text Search and Visualization with Elasticsearch

abstract: In this paper, the task of using Big Data to identify specific individuals on the indirect grounds of their interaction with information resources is considered. Possible sources of Big Data and problems related to its processing are analyzed. Existing means of data clustering are considered. Available software for full-text search and data visualization is analyzed, and a system based on Elasticsearch engine and MapReduce model is proposed for the solution of user verification problem.


Towards Conversational Human-Computer Interaction


title: Global Norm-Aware Pooling for Pose-Robust Face Recognition at Low False Positive Rate

abstract: In this paper, we propose a novel Global Norm-Aware Pooling (GNAP) block, which reweights local features in a convolutional neural network (CNN) adaptively according to their L2 norms and outputs a global feature vector with a global average pooling layer. Our GNAP block is designed to give dynamic weights to local features in different spatial positions without losing spatial symmetry. We use a GNAP block in a face feature embedding CNN to produce discriminative face feature vectors for pose-robust face recognition. The GNAP block is of very cheap computational cost, but it is very powerful for frontal-profile face recognition. Under the CFP frontal-profile protocol, the GNAP block can not only reduce EER dramatically but also boost TPR@FPR=0.1% (TPR i.e. True Positive Rate, FPR i.e. False Positive Rate) substantially. Our experiments show that the GNAP block greatly promotes pose-robust face recognition over the base model especially at low false positive rate.


title: Multimodal Entity Coreference for Cervical Dysplasia Diagnosis

abstract: Cervical cancer is the second most common type of cancer for women. Existing screening programs for cervical cancer, such as Pap Smear, suffer from low sensitivity. Thus, many patients who are ill are not detected in the screening process. Using images of the cervix as an aid in cervical cancer screening has the potential to greatly improve sensitivity, and can be especially useful in resource-poor regions of the world. In this paper, we develop a data-driven computer algorithm for interpreting cervical images based on color and texture. We are able to obtain 74% sensitivity and 90% specificity when differentiating high-grade cervical lesions from low-grade lesions and normal tissue. On the same dataset, using Pap tests alone yields a sensitivity of 37% and specificity of 96%, and using HPV test alone gives a 57% sensitivity and 93% specificity. Furthermore, we develop a comprehensive algorithmic framework based on Multimodal Entity Coreference for combining various tests to perform disease classification and diagnosis. When integrating multiple tests, we adopt information gain and gradient-based approaches for learning the relative weights of different tests. In our evaluation, we present a novel algorithm that integrates cervical images, Pap, HPV, and patient age, which yields 83.21% sensitivity and 94.79% specificity, a statistically significant improvement over using any single source of information alone.


title: Introduction to astroML: Machine Learning for Astrophysics

abstract: Astronomy and astrophysics are witnessing dramatic increases in data volume as detectors, telescopes and computers become ever more powerful. During the last decade, sky surveys across the electromagnetic spectrum have collected hundreds of terabytes of astronomical data for hundreds of millions of sources. Over the next decade, the data volume will enter the petabyte domain, and provide accurate measurements for billions of sources. Astronomy and physics students are not traditionally trained to handle such voluminous and complex data sets. In this paper we describe astroML; an initiative, based on python and scikit-learn, to develop a compendium of machine learning tools designed to address the statistical needs of the next generation of students and astronomical surveys. We introduce astroML and present a number of example applications that are enabled by this package.


title: A way forward: enabling operating system innovation in the cloud

abstract: Cloud computing has not resulted in a fundamental change to the underlying operating systems. Rather, distributed applications are built over middleware that provides high-level abstractions to exploit the cloud’s scale and elasticity. This middleware conjoins many general purpose OS instances. Others have demonstrated that a new operating system built specifically for the cloud can achieve increased efficiency, scale and functionality [11, 14]. However, this work does not take into account the way applications are being deployed in cloud environments. In particular, entire physical or virtual machines are being dedicated to run a single application, rather than concurrently supporting many users and multiple applications. In this paper we introduce a new model for distributed applications that embraces a reduced role of the OS in the cloud. It allows for the construction of application-driven compositions of OS functionality wherein each application can employ its own customized operating system.


title: A new optimum topology switching DC-to-DC converter

abstract: A novel switching dc-to-dc converter is presented, which has the same general conversion property (increase or decrease of the input dc voltage) as does the conventional buck-boost converter, and which offers through its new optimum topology higher efficiency, lower output voltage ripple, reduced EMI, smaller size and weight, and excellent dynamics response. One of its most significant advantages is that both input and output current are not pulsating but are continuous (essentially dc with small superimposed switching current ripple), this resulting in a close approximation to the ideal physically nonrealizable dc-to-dc transformer. The converter retains the simplest possible structure with the minimum number of components which, when interconnected in its optimum topology, yield the maximum performance. The new converter is extensively experimentally verified, and both the steady state (dc) and the dynamic (ac) theoretical model are correlated well with theexperimental data. both theoretical and experimental comparisons with the conventional buck-boost converter, to which an input filter has been added, demonstrate the significant advantages of the new optimum topology switching dc-to-dc converter.


title: Mining Sequential Patterns : Generalizations and Performance Improvements

abstract: The problem of mining sequential patterns was recently introduced in [3]. We are given a database of sequences, where each sequence is a list of transactions ordered by transaction-time, and each transaction is a set of items. The problem is to discover all sequential patterns with a user-specified minimum support, where the support of a pattern is the number of data-sequences that contain the pattern. An example of a sequential pattern is“5% of customers bought ‘Foundation’ and ‘Ringworld’ in one transaction, followed by ‘Second Foundation’ in a later transaction”. We generalize the problem as follows. First, we add time constraints that specify a minimum and/or maximum time period between adjacent elements in a pattern. Second, we relax the restriction that the items in an element of a sequential pattern must come from the same transaction, instead allowing the items to be present in a set of transactions whose transaction-times are within a user-specified time window. Third, given a user-defined taxonomy (is-a hierarchy) on items, we allow sequential patterns to include items across all levels of the taxonomy.


title: Vector space model adaptation and pseudo relevance feedback for content-based image retrieval

abstract: Image retrieval is an important problem for researchers in computer vision and content-based image retrieval (CBIR) fields. Over the last decades, many image retrieval systems were based on image representation as a set of extracted low-level features such as color, texture and shape. Then, systems calculate similarity metrics between features in order to find similar images to a query image. The disadvantage of this approach is that images visually and semantically different may be similar in the low level feature space. So, it is necessary to develop tools to optimize retrieval of information. Integration of vector space models is one solution to improve the performance of image retrieval. In this paper, we present an efficient and effective retrieval framework which includes a vectorization technique combined with a pseudo relevance model. The idea is to transform any similarity matching model (between images) to a vector space model providing a score. A study on several methodologies to obtain the vectorization is presented. Some experiments have been undertaken on Wang, Oxford5k and Inria Holidays datasets to show the performance of our proposed framework.


title: Robust Road Marking Detection and Recognition Using Density-Based Grouping and Machine Learning Techniques

abstract: This paper presents a robust approach for road marking detection and recognition from images captured by an embedded camera mounted on a car. Our method is designed to cope with illumination changes, shadows, and harsh meteorological conditions. Furthermore, the algorithm can effectively group complex multi-symbol shapes into an individual road marking. For this purpose, the proposed technique relies on MSER features to obtain candidate regions which are further merged using density-based clustering. Finally, these regions of interest are recognized using machine learning approaches. Worth noting, the algorithm is versatile since it does not utilize any prior information about lane position or road space. The proposed method compares favorably to other existing works through a large number of experiments on an extensive road marking dataset.


title: An Anti-Islanding Protection Method Based on Reactive Power Injection and ROCOF

abstract: All distributed-generation units need to be equipped with an anti-islanding protection (AIP) scheme in order to avoid unintentional islanding. Unfortunately, most AIP methods fail to detect islanding if the demand in the islanded circuit matches the production in the island. Another concern is that many active AIP schemes cause power-quality problems. This paper proposes an AIP method which is based on the combination of a reactive power versus frequency droop and rate of change of frequency (ROCOF). The method is designed so that the injection of reactive power is of minor scale during normal operating conditions. Yet, the method can rapidly detect islanding which is verified by PSCAD/EMTDC simulations.