title: Comparison of miniscalpel-needle release, acupuncture needling, and stretching exercise to trigger point in myofascial pain syndrome.

abstract: Objective: Myofascial pain syndrome (MPS) is one of the most common causes of chronic musculoskeletal pain. Several methods have been recommended for the inactivation of trigger points (TrPs). We carried out this study to investigate the effectiveness of miniscalpel-needle (MSN) release and acupuncture needling and self neck-stretching exercises on myofascial TrPs of the upper trapezius muscle. Methods: Eighty-three TrPs in 43 patients with MPS were treated and randomly assigned to 3 groups: group 1 received MSN release in conjunction with self neck-stretching exercises; group 2 received acupuncture needling treatment and performed self neck-stretching exercises; and group 3, the control group, was assigned self neck-stretching exercises only. The therapeutic effectiveness was evaluated using subjective pain intensity (PI) with a visual analog scale, pressure pain threshold (PPT), and contralateral bending range of motion (ROM) of cervical spine at pretreatment, 2 weeks, and 3 months after treatment. Results: The improvement of PI, PPT, and contralateral bending ROM of cervical spine was significantly greater in group 1 and 2 than that in control group at 2 weeks and 3 months follow-up. Compared with group 2, patients in group 1 had a statistically significant reduction in PI, an increase in PPT, contralateral bending ROM of cervical spine at 3 months follow-up. Discussion: The effectiveness of MSN release for MPS is superior to that of acupuncture needling treatment or self neck-stretching exercises alone. The MSN release is also safe, without severe side effects in treatment of MPS.


title: Social stream marketing on Facebook: a case study

abstract: Recently, social network marketing has gained tremendously in popularity. There are increasing numbers of web-based companies that focus their marketing strategies on social network platforms such as Facebook, Twitter, etc. Even if social network marketing is perceived as a huge success, little or nothing is known about how well such social network-based marketing campaigns perform. To contribute to this field of research, we present in this paper the results of an ad-driven social network-based marketing campaign centred on the social network Facebook. In particular, we demonstrate in the extent to which ads placed on the user's social stream and generated by the Facebook tools and applications can increase the number of visits, profit or ROI of a web-based platform called VirWoX. In addition to these findings, we present an analysis of a number of simple real-time measures to detect the most 'valuable' users on Facebook. To the best of our knowledge, this is the first work that provides such detailed results of a social stream marketing campaign using Facebook. This paper is relevant for engineers, researchers or teachers who are interested in the field of social media marketing.


title: Researches of Fractional Composition of Protein of Amaranth

abstract: It is widely known that Amaranth is a highly nutritious plant, and contains all the essential amino acids. It provides an amazing amount of magnesium, calcium and dietary fiber for general health. The literature provides that there are numerous citations about the quality and composition of amaranth seeds, but not much information about amaranth protein in the green mass. Furthermore, amaranths green mass has a fast and high yield, making it a great source of protein. For this reason, the protein amount, as well as fractional composition in different kind of amaranth, was investigated. It was revealed that amaranth differs in its content of protein, with its maximal accumulation in the blossoming phase (17.2-32.6% from dry weight for various samples), within various species. It was confirmed that in the ontogenesis of amaranth, the share of albumins and globulins is reduced while the share of glutelins and gliadins is increased. A low content of reserved proteins in the seeds of amaranth (42% from the sum of protein) in comparison with wheat and other cereals has been observed.


Reducing Duplicate Filters in Deep Neural Networks


title: A novel type induction-heating single-ended resonant inverter using new bipolar Darlington-Transistor

abstract: Demonstrated is a novel type of single-ended resonant inverter using a new Bipolar Darlington-Transistor that operates efficiently in the frequency range of 20kHz to about 50kHz. The inverter implementation for an induction-heating range includes a unique power control strategy. The analysis and performance evaluations for circuit design are carried out herein.


title: Effect of aqueous extract of Arctium lappa L. (burdock) roots on the sexual behavior of male rats

abstract: Arctium lappa L. root has traditionally been recommended as an aphrodisiac agent. It is used to treat impotence and sterility in China, and Native Americans included the root in herbal preparations for women in labor. However, its use has not been scientifically validated. The present study therefore investigated the effects of aqueous extract of Arctium lappa L. roots on sexual behavior in normal male rats. Seventy-five albino male rats were randomly divided into five groups of 15 rats each. Rats in group 1 (control) were administered 10 mL⁄kg body weight distilled water (vehicle), group 2 received 60 mg/kg body weight sildenafil citrate (Viagra), while those in groups 3, 4, and 5 were given 300, 600, and 1,200 mg/kg body weight, respectively, of aqueous extract of Arctium lappa L. roots in the same volume. Female albino rats were made receptive by hormonal treatment. Sexual behavior parameters in male rats were monitored on days 3, 7 and 15 by pairing with receptive females (1:3). Male serum testosterone concentrations and potency were also determined. Oral administration of Arctium lappa L. roots extract at 600 and 1,200 mg/kg body weight significantly increased the frequencies of mount, intromission, and ejaculation frequency (p < 0.05). The latencies of mount and intromission were significantly reduced and ejaculation latency was prolonged. Administration of the extract also reduced the post-ejaculatory interval. The standard drug (Viagra) was more effective than the extract. The extract significantly increased the frequencies of all components of penile reflexes as well as serum testosterone levels, compared with the distilled water controls. The results of this study demonstrate that aqueous extract of Arctium lappa L. roots enhances sexual behavior in male rats. The aphrodisiac effects of the plant extract may be related to the presence of flavonoids, saponins, lignans and alkaloids, acting via a multitude of central and peripheral mechanisms. These results thus support the traditional use of Arctium lappa L. root extract for treating impotence and sterility.


title: Deep Count: Fruit Counting Based on Deep Simulated Learning

abstract: Recent years have witnessed significant advancement in computer vision research based on deep learning. Success of these tasks largely depends on the availability of a large amount of training samples. Labeling the training samples is an expensive process. In this paper, we present a simulated deep convolutional neural network for yield estimation. Knowing the exact number of fruits, flowers, and trees helps farmers to make better decisions on cultivation practices, plant disease prevention, and the size of harvest labor force. The current practice of yield estimation based on the manual counting of fruits or flowers by workers is a very time consuming and expensive process and it is not practical for big fields. Automatic yield estimation based on robotic agriculture provides a viable solution in this regard. Our network is trained entirely on synthetic data and tested on real data. To capture features on multiple scales, we used a modified version of the Inception-ResNet architecture. Our algorithm counts efficiently even if fruits are under shadow, occluded by foliage, branches, or if there is some degree of overlap amongst fruits. Experimental results show a 91% average test accuracy on real images and 93% on synthetic images.


title: Novel amidases of two Aminobacter sp. strains: Biotransformation experiments and elucidation of gene sequences

abstract: The amidase activities of two Aminobacter sp. strains (DSM24754 and DSM24755) towards the aryl-substituted substrates phenylhydantoin, indolylmethyl hydantoin, D,L-6-phenyl-5,6-dihydrouracil (PheDU) and para-chloro-D,L-6-phenyl-5,6-dihydrouracil were compared. Both strains showed hydantoinase and dihydropyrimidinase activity by hydrolyzing all substrates to the corresponding N-carbamoyl-α- or N-carbamoyl-β-amino acids. However, carbamoylase activity and thus a further degradation of these products to α- and β-amino acids was not detected. Additionally, the genes coding for a dihydropyrimidinase and a carbamoylase of Aminobacter sp. DSM24754 were elucidated. For Aminobacter sp. DSM24755 a dihydropyrimidinase gene flanked by two genes coding for putative ABC transporter proteins was detected. The deduced amino acid sequences of both dihydropyrimidinases are highly similar to the well-studied dihydropyrimidinase of Sinorhizobium meliloti CECT4114. The latter enzyme is reported to accept substituted hydantoins and dihydropyrimidines as substrates. The deduced amino acid sequence of the carbamoylase gene shows a high similarity to the very thermostable enzyme of Pseudomonas sp. KNK003A.


title: Big data team process methodologies: A literature review and the identification of key factors for a project's success

abstract: This paper reports on our review of published research relating to how teams work together to execute Big Data projects. Our findings suggest that there is no agreed upon standard for executing these projects but that there is a growing research focus in this area and that an improved process methodology would be useful. In addition, our synthesis also provides useful suggestions to help practitioners execute their projects, specifically our identified list of 33 important success factors for executing Big Data efforts, which are grouped by our six identified characteristics of a mature Big Data organization.


title: Emerging Frontiers of Neuroengineering: A Network Science of Brain Connectivity

abstract: Neuroengineering is faced with unique challenges in repairing or replacing complex neural systems that are composed of many interacting parts. These interactions form intricate patterns over large spatiotemporal scales and produce emergent behaviors that are difficult to predict from individual elements. Network science provides a particularly appropriate framework in which to study and intervene in such systems by treating neural elements (cells, volumes) as nodes in a graph and neural interactions (synapses, white matter tracts) as edges in that graph. Here, we review the emerging discipline of network neuroscience, which uses and develops tools from graph theory to better understand and manipulate neural systems from micro- to macroscales. We present examples of how human brain imaging data are being modeled with network analysis and underscore potential pitfalls. We then highlight current computational and theoretical frontiers and emphasize their utility in informing diagnosis and monitoring, brain–m...


title: Reasoning with Temporal Logic on Truncated Paths

abstract: We consider the problem of reasoning with linear temporal logic on truncated paths. A truncated path is a path that is finite, but not necessarily maximal. Truncated paths arise naturally in several areas, among which are incomplete verification methods (such as simulation or bounded model checking) and hardware resets. We present a formalism for reasoning about truncated paths, and analyze its characteristics.


title: Detection of faults in electrical panels using deep learning method

abstract: In the image analysis, a big trend within the field of artificial intelligence is using the Deep Learning method, which is an upgrade of the existing neural network adaptive architecture (ANN). Deep Learning is a major new field in machine learning that encompasses a wide range of neural network architectures designed to perform various tasks. In the thermography energy sector, examples that are processed on a daily basis are sampling of active energy components, focus segmentation, and fault classification. The most popular network architecture for Deep Learning in image analysis is the convolution neural network (CNN), where traditional machine learning methods require determination and calculation, from which the algorithm training comes. Deep Learning approach captures important features as well as the appropriate weight of these attributes to make decision for new data. This paper describes a method and tool that are available to build and conduct an effective analysis of the Deep Learning Methodfor electrical components.


Automatic sense disambiguation using machine readable dictionaries: how to tell a pine cone from an ice cream cone


title: Gated Multimodal Units for Information Fusion

abstract: This paper presents a novel model for multimodal learning based on gated neural networks. The Gated Multimodal Unit (GMU) model is intended to be used as an internal unit in a neural network architecture whose purpose is to find an intermediate representation based on a combination of data from different modalities. The GMU learns to decide how modalities influence the activation of the unit using multiplicative gates. It was evaluated on a multilabel scenario for genre classification of movies using the plot and the poster. The GMU improved the macro f-score performance of single-modality approaches and outperformed other fusion strategies, including mixture of experts models. Along with this work, the MM-IMDb dataset is released which, to the best of our knowledge, is the largest publicly available multimodal dataset for genre prediction on movies.


title: Music lessons enhance IQ

abstract: The idea that music makes you smarter has received considerable attention from scholars and the media. The present report is the first to test this hypothesis directly with random assignment of a large sample of children (N = 144) to two different types of music lessons (keyboard or voice) or to control groups that received drama lessons or no lessons. IQ was measured before and after the lessons. Compared with children in the control groups, children in the music groups exhibited greater increases in full-scale IQ. The effect was relatively small, but it generalized across IQ subtests, index scores, and a standardized measure of academic achievement. Unexpectedly, children in the drama group exhibited substantial pre- to post-test improvements in adaptive social behavior that were not evident in the music groups.


title: Solid-Duty-Control Technique for Alleviating the Right-Half-Plane Zero Effect in Continuous Conduction Mode Boost Converters

abstract: This paper proposes a solid-duty-control (SDC) technique for application in boost converters to maintain a constant duty value and reduce dip voltage during load transient periods. Fast transient response was also achieved because of the variable transient enhancement controller. The proposed SDC technique can provide a stable and regulated output for edge-lit light-emitting diode backlight systems. This converter was used in a 0.25-μm CMOS process. Experimental results show that compared with a conventional design without a fast transient technique, the proposed approach yields about 30% and 80% improvement in undershoot voltage and recovery time, respectively, as load current changes from 50 to 250 mA.


title: City traffic forecasting using taxi GPS data: A coarse-grained cellular automata model

abstract: City traffic is a dynamic system of enormous complexity. Modeling and predicting city traffic flow remains to be a challenge task and the main difficulties are how to specify the supply and demands and how to parameterize the model. In this paper we attempt to solve these problems with the help of large amount of floating car data. We propose a coarse-grained cellular automata model that simulates vehicles moving on uniform grids whose size are much larger compared with the microscopic cellular automata model. The car-car interaction in the microscopic model is replaced by the coupling between vehicles and coarse-grained state variables in our model. To parameterize the model, flux-occupancy relations are fitted from the historical data at every grids, which serve as the coarse-grained fundamental diagrams coupling the occupancy and speed. To evaluate the model, we feed it with the historical travel demands and trajectories obtained from the floating car data and use the model to predict road speed one hour into the future. Numerical results show that our model can capture the traffic flow pattern of the entire city and make reasonable predictions. The current work can be considered a prototype for a model-based forecasting system for city traffic.


title: A complete analytical solution to the inverse kinematics of the Pioneer 2 robotic arm

abstract: For robotic manipulators that are redundant or with high degrees of freedom (dof), an analytical solution to the inverse kinematics is very difficult or impossible. Pioneer 2 robotic arm (P2Arm) is a recently developed and widely used 5-dof manipulator. There is no effective solution to its inverse kinematics to date. This paper presents a first complete analytical solution to the inverse kinematics of the P2Arm, which makes it possible to control the arm to any reachable position in an unstructured environment. The strategies developed in this paper could also be useful for solving the inverse kinematics problem of other types of robotic arms.


title: Comparison of cathode ray tube and liquid crystal display stimulators for use in multifocal VEP

abstract: Purpose ::: To compare the modified signal-to-noise ratio (SNR*) of multifocal visual evoked potential (mfVEP) responses elicited by a cathode ray tube (CRT) and liquid crystal display (LCD) monitor in normal subjects.


title: A STUDY OF X-RAY MACHINE IMAGE LOCAL SEMANTIC FEATURES EXTRACTION MODEL BASED ON BAG-OFWORDS FOR AIRPORT SECURITY

abstract: The aviation security at the airport has been faced with increasingly severe situations since the 9-11 event. It’s of utmost importance to train airport X-ray machine screeners image recognition competency. So they can prevent terrorists from bringing dangerous articles in their carry-on or checked bags. However, usually the luggages are placed in different positions and the density & volume of articles differ greatly. As a result, dangerous articles show a variety of X-ray image features. It’s easy for the confused screeners to miss or incorrectly detect dangerous articles. This has been a hidden danger for civil aviation safety. For image recognition improvement, the researcher analyzed the visual semantics of dangerous goods images and applied a local semantic features extraction method. After classification and summarization, the method was used to train the screeners for particular image recognition. The comparison showed the improved accuracy and efficiency of image recognition for the screeners and demonstrated a satisfactory effect


title: Rapid and robust human detection and tracking based on omega-shape features

abstract: This paper proposes a novel method for rapid and robust human detection and tracking based on the omega-shape features of people's head-shoulder parts. There are two modules in this method. In the first module, a Viola-Jones type classifier and a local HOG (Histograms of Oriented Gradients) feature based AdaBoost classifier are combined to detect head-shoulders rapidly and effectively. Then, in the second module, each detected head-shoulder is tracked by a particle filter tracker using local HOG features to model target's appearance, which shows great robustness in scenarios of crowding, background distractors and partial occlusions. Experimental results demonstrate the effectiveness and efficiency of the proposed approach.


title: Next-generation sequencing technologies and their impact on microbial genomics

abstract: Next-generation sequencing technologies have had a dramatic impact in the field of genomic research through the provision of a low cost, high-throughput alternative to traditional capillary sequencers. These new sequencing methods have surpassed their original scope and now provide a range of utility-based applications, which allow for a more comprehensive analysis of the structure and content of microbial genomes than was previously possible. With the commercialization of a third generation of sequencing technologies imminent, we discuss the applications of current next-generation sequencing methods and explore their impact on and contribution to microbial genome research.


title: Social and collaborative aspects of interaction with a service robot

abstract: To an increasing extent, robots are being designed to become a part of the lives of ordinary people. This calls for new models of the interaction between humans and robots, taking advantage of huma ...


title: A Uniform Approach to Analogies, Synonyms, Antonyms, and Associations

abstract: Recognizing analogies, synonyms, antonyms, and associations appear to be four distinct tasks, requiring distinct NLP algorithms. In the past, the four tasks have been treated independently, using a wide variety of algorithms. These four semantic classes, however, are a tiny sample of the full range of semantic phenomena, and we cannot afford to create ad hoc algorithms for each semantic phenomenon; we need to seek a unified approach. We propose to subsume a broad range of phenomena under analogies. To limit the scope of this paper, we restrict our attention to the subsumption of synonyms, antonyms, and associations. We introduce a supervised corpus-based machine learning algorithm for classifying analogous word pairs, and we show that it can solve multiple-choice SAT analogy questions, TOEFL synonym questions, ESL synonym-antonym questions, and similar-associated-both questions from cognitive psychology.


title: Design and workspace analysis of a 6-6 cable-suspended parallel robot

abstract: In this paper, we study the design and workspace of a 6-6 cable-suspended parallel robot. The workspace volume is characterized as the set of points where the centroid of the MP (MP) can reach with tensions in all suspension cables at a constant orientation. This paper attempts to tackle some aspects of optimal design of a 6DOF cable robot by addressing the variations of the workspace volume and the accuracy of the robot using different geometric configurations, different sizes and orientations of the MP. The global condition index is used as a performance index of a robot with respect to the force and velocity transmission over the whole workspace. The results are used for design analysis of the cable-robot for a specific motion of the MP.


title: Recurrent Neural Networks With Limited Numerical Precision

abstract: Recurrent Neural Networks (RNNs) produce state-of-art performance on many machine learning tasks but their demand on resources in terms of memory and computational power are often high. Therefore, there is a great interest in optimizing the computations performed with these models especially when considering development of specialized low-power hardware for deep networks. One way of reducing the computational needs is to limit the numerical precision of the network weights and biases. This has led to different proposed rounding methods which have been applied so far to only Convolutional Neural Networks and Fully-Connected Networks. This paper addresses the question of how to best reduce weight precision during training in the case of RNNs. We present results from the use of different stochastic and deterministic reduced precision training methods applied to three major RNN types which are then tested on several datasets. The results show that the weight binarization methods do not work with the RNNs. However, the stochastic and deterministic ternarization, and pow2-ternarization methods gave rise to low-precision RNNs that produce similar and even higher accuracy on certain datasets therefore providing a path towards training more efficient implementations of RNNs in specialized hardware.


title: Design and simulation dual-band PIFA antenna for GSM systems

abstract: In this paper, a new design of a dual band Planner Inverted-F Antenna (PIFA), for cellular telephone systems and other applications is presented. The PIFA antenna is composed of ground plane, patch antenna, feeding post and shorting plate connected to the ground plane. The designed antenna has been simulated using the CST 2010 software. The simulated results for the resonant frequency, return loss, radiation pattern and gain are presented and discussed. The bandwidths of 114MHz and 176MHz for GSM900 and GSM1900 respectively are achieved on the basis of ≤ −6 dB return loss as an acceptable reference in mobile phones applications. A gain of 1.933dB and 4.74dB is obtained in lower and higher frequency respectively.


title: Cookie Synchronization: Everything You Always Wanted to Know But Were Afraid to Ask

abstract: User data is the primary input of digital advertising, fueling the free Internet as we know it. As a result, web companies invest a lot in elaborate tracking mechanisms to acquire user data that can sell to data markets and advertisers. However, with same-origin policy and cookies as a primary identification mechanism on the web, each tracker knows the same user with a different ID. To mitigate this, Cookie Synchronization (CSync) came to the rescue, facilitating an information sharing channel between 3rd-parties that may or not have direct access to the website the user visits. In the background, with CSync, they merge user data they own, but also reconstruct a user's browsing history, bypassing the same origin policy. In this paper, we perform a first to our knowledge in-depth study of CSync in the wild, using a year-long weblog from 850 real mobile users. Through our study, we aim to understand the characteristics of the CSync protocol and the impact it has on web users' privacy. For this, we design and implement CONRAD, a holistic mechanism to detect CSync events at real time, and the privacy loss on the user side, even when the synced IDs are obfuscated. Using CONRAD, we find that 97% of the regular web users are exposed to CSync: most of them within the first week of their browsing, and the median userID gets leaked, on average, to 3.5 different domains. Finally, we see that CSync increases the number of domains that track the user by a factor of 6.75.


Shared phylogeographical breaks in a Caribbean coral reef sponge and its invertebrate commensals


title: Specification-based IDS for securing RPL from topology attacks

abstract: This paper focuses on the security aspect of RPL (Routing Protocol for Low-power and lossy network) by introducing a new type of threat — the topology attack, which changes the node operation for breaking the optimised network topology, and designing a specification-based IDS for detecting it. We present two novel RPL attacks of this type: the rank attack and local repair attack. We also propose an IDS architecture using network monitor backbone, and describe its monitoring mechanisms through a RPL finite state machine implemented in each monitor node. We show that our system can effectively detect these routing operation threats with a reasonable overhead.


title: Evaluation of the interference of hemoglobin, bilirubin, and lipids on Roche Cobas 6000 assays.

abstract: BACKGROUND ::: Pre-analytical error accounts for major total laboratory errors. We assessed the impacts of hemolysis, icterus, and lipemia on laboratory tests on Roche Cobas 6000. ::: ::: ::: METHODS ::: Various concentrations of hemoglobin, bilirubin, or Intralipid® were added into the plasma to simulate hemolytic, icteric, or lipemic samples. The analytes were then measured on Roche Cobas 6000 and the change of the analyte concentrations was determined. ::: ::: ::: RESULTS ::: For most of the chemistry assays, our data were in a good agreement with Roche package inserts. However some assays had significant interference at lower index values while others were affected at higher index than the Roche package inserts indicated. In addition, we observed the positive interference by hemolysis on ALT, lipase, total protein, potassium, and iron. Negative interference was noted on calcium and CK. Most of the immunoassays were not affected by hemoglobin, bilirubin, and lipids although there were a few exceptions. Several therapeutic drugs were affected either positively or negatively by hemolysis, icterus, or lipemia to a certain extent. ::: ::: ::: CONCLUSIONS ::: We have demonstrated some test interferences which have not been reported previously on the Cobas 6000. The implementation of the cut-off indices on Cobas 6000 would provide more accurate test result reporting.


title: Efficient mixture model for clustering of sparse high dimensional binary data

abstract: Clustering is one of the fundamental tools for preliminary analysis of data. While most of the clustering methods are designed for continuous data, sparse high-dimensional binary representations became very popular in various domains such as text mining or cheminformatics. The application of classical clustering tools to this type of data usually proves to be very inefficient, both in terms of computational complexity as well as in terms of the utility of the results. In this paper we propose a mixture model, SparseMix, for clustering of sparse high dimensional binary data, which connects model-based with centroid-based clustering. Every group is described by a representative and a probability distribution modeling dispersion from this representative. In contrast to classical mixture models based on the EM algorithm, SparseMix: is specially designed for the processing of sparse data; can be efficiently realized by an on-line Hartigan optimization algorithm; describes every cluster by the most representative vector. We have performed extensive experimental studies on various types of data, which confirmed that SparseMix builds partitions with a higher compatibility with reference grouping than related methods. Moreover, constructed representatives often better reveal the internal structure of data.


title: Permittivity and Measurements

abstract: Dielectric properties of food, agricultural products, biological tissues, resins, plastics, ceramics, soils, and wood at RF and microwave frequencies are considered. The influence of three main factors—frequency, temperature, and moisture content—on complex dielectric constant behavior of lossy materials is analyzed. The most popular measurement techniques—the open-ended coaxial probe, transmission-line, and perturbation methods—are described. ::: ::: ::: Keywords: ::: ::: dielectric constant; ::: frequency; ::: temperature; ::: measurement instrumentation; ::: electromagnetic field


title: Blockchain based permission delegation and access control in Internet of Things (BACI)

abstract: Abstract Access control with permission delegation mechanism allows fine granular access to secure resources. In the literature, existing architectures for permission delegation and access control are either event-based or query-based. These previous works assume a single trusted delegation service, which however is likely biased or fails to service. Also, they fail to allow users to verify delegation service operations, as such cannot be directly applied to IoT (Internet of Things) due to low power, low-bandwidth, ad-hoc and decentralized nature. This paper proposes a novel decentralized architecture for permission delegation and access control for IoT application, with demands on event and query base permission delegation. We further apply Blockchain (BC) technology to make delegation services secure, trusted, verifiable and decentralized. We investigate our proposed approach in Simple PROMELA INterpreter (SPIN) model checker using PROMELA (Process Meta Language). The “Platform Verification”, “Delegation”, “Mutual Exclusion” properties written in Linear Temporal Logic (LTL) are also verified against the PROMELA model.


title: A literature review on optimum and preferred joint angles in automotive sitting posture.

abstract: In this study, a survey of the scientific literature in the field of optimum and preferred human joint angles in automotive sitting posture was conducted by referring to thirty different sources published between 1940 and today. The strategy was to use only sources with numerical angle data in combination with keywords. The aim of the research was to detect commonly used joint angles in interior car design. The main analysis was on data measurement, usability and comparability of the different studies. In addition, the focus was on the reasons for the differently described results. It was found that there is still a lack of information in methodology and description of background. Due to these reasons published data is not always usable to design a modern ergonomic car environment. As a main result of our literature analysis we suggest undertaking further research in the field of biomechanics and ergonomics to work out scientific based and objectively determined "optimum" joint angles in automotive sitting position.


title: Reconstructing metastatic seeding patterns of human cancers

abstract: Reconstructing the evolutionary history of metastases is critical for understanding their basic biological principles and has profound clinical implications. Genome-wide sequencing data has enabled modern phylogenomic methods to accurately dissect subclones and their phylogenies from noisy and impure bulk tumour samples at unprecedented depth. However, existing methods are not designed to infer metastatic seeding patterns. Here we develop a tool, called Treeomics, to reconstruct the phylogeny of metastases and map subclones to their anatomic locations. Treeomics infers comprehensive seeding patterns for pancreatic, ovarian, and prostate cancers. Moreover, Treeomics correctly disambiguates true seeding patterns from sequencing artifacts; 7% of variants were misclassified by conventional statistical methods. These artifacts can skew phylogenies by creating illusory tumour heterogeneity among distinct samples. In silico benchmarking on simulated tumour phylogenies across a wide range of sample purities (15–95%) and sequencing depths (25-800 × ) demonstrates the accuracy of Treeomics compared with existing methods. Tumours frequently metastasize to multiple anatomical sites and understanding how these different metastases evolve may be important for therapy. Here, the authors develop a method—Treeomics—that can construct phylogenies from multiple metastases from next-generation sequencing data.


title: Cyber Network Mission Dependencies

abstract: Abstract : Common Cyber Environment Representation (CCER) is an ontology for describing operationally relevant, and technically representative, cyber range event environments. Third-party ontology developers, as well as in-house ontology developers, contributed to CCER Ontology. Since the cyber range environments continue to evolve and expand, we expect the CCER Ontology also to evolve and expand. Therefore, an easy to comprehend versioning scheme that will support a systematic ontology evolution is needed. This document describes the requirements for such a versioning scheme, and its design. This document also describes how to assign version numbers under different ontology evolution situations, and provides guidelines for evaluating the impact of the version changes.


title: A survey of variants and extensions of the location-routing problem

abstract: This is a review of the literature on variants and extensions of the standard location-routing problem published since the last survey, by Nagy and Salhi, appeared in 2006. We propose a classification of problem variants, provide concise paper excerpts that convey the central ideas of each work, discuss recent developments in the field, and list promising topics for further research.


title: Clustering-based joint feature selection for semantic attribute prediction

abstract: Semantic attributes have been proposed to bridge the semantic gap between low-level feature representation and high-level semantic understanding of visual objects. Obtaining a good representation of semantic attributes usually requires learning from high-dimensional low-level features, which not only significantly increases the time and space requirement but also degrades the performance due to numerous irrelevant features. Since multiattribute prediction can be generalized as an multitask learning problem, sparse-based multi-task feature selection approaches have been introduced, utilizing the relatedness among multiple attributes. However, such approaches either do not investigate the pattern of the relatedness among attributes, or require prior knowledge about the pattern. In this paper, we propose a novel feature selection approach which embeds attribute correlation modeling in multi-attribute joint feature selection. Experiments on both synthetic dataset and multiple public benchmark datasets demonstrate that the proposed approach effectively captures the correlation among multiple attributes and significantly outperforms the state-of-the-art approaches.


title: Metabolizable Energy in Four Seeds Fed to Cardinals

abstract: Metabolizable energy was determined for four seeds fed to cardinals (Cardinalis cardinalis) confined in an environmental chamber under simulated winter conditions. Gross energy of the seeds used ranged from 4.94 to 6.92 kcal/g and metabolizable energy ranged from 2.66 to 5.86 kcal/g. The efficiency of cardinals to metabolize the energy contained in the different seeds ranged from 53.9 to 84.7%. For energetic studies of wild granivorous avifauna to be meaningful, they must be accompanied by detailed food habit studies and determinations of seed-specific metabolic efficiencies. Gross energy contents of many seeds eaten by birds have been determined and published (Golley, 1959, Kendeigh and West, 1965, Johnson and Robel, 1968, Robel, 1973), but records of metabolizable energy in seeds are less common. Willson and Harmeson (1973) determined the metabolizable energy in six seeds fed to cardinals (Cardinalis cardinalis). We determined metabolic efficiency of cardinals for four additional seeds. Wild adult cardinals were mist-netted within 10 km of Manhattan, Kansas, in October, placed in an aviary for two weeks, then individually confined in cages in an environmental chamber at 5?C, 50% R.H., and 10L: 14D photoperiod. Cages, 33 x 21 x 24 cm, were fitted with removable floors of 13mm hardware cloth raised 2 cm above the cage bottom to minimize coprophagy. Seeds of roundhead lespedeza (Lespedeza capitata), thickspike gayfeather (Liatrus pycnostachya), maximilian sunflower (Helianthus maximiliani), and sawtooth sunflower (H. grosseserratus) were obtained from the Manhattan Plant Materials Center of the U.S. Soil Conservation Service. These plants were being propogated by the Soil Conservation Service for use in 1 Contribution 79-254-J, Division of Biology, Kansas Agricultural Experiment Station, Kansas State University, Manhattan, Kansas 66506. 2 Present address: Oregon Institute of Marine Biology, University of Oregon, Charleston, Oregon 97420. This content downloaded from 157.55.39.26 on Sun, 04 Sep 2016 06:00:22 UTC All use subject to http://about.jstor.org/terms 116 TRANSACTIONS OF THE KANSAS ACADEMY OF SCIENCES Table 1. Metabolizable-energy data from four feeding trials with indicated seeds each fed to two adult cardinals separately. Energy Energy MetaboEnergy ingested excreted Metabolic lizable content (kcall (kcalt efficiency energy Seed (kcal/g) bird/day) bird/day) (%) (kcal/g) Roundhead lespedeza 4.94 6.99 3.22 53.9 2.66 Thickspike gayfeather 5.94 8.61 2.02 76.6 4.55 Sawtooth sunflower 6.53 38.76 7.17 81.5 5.33 Maximilian sunflower 6.92 39.76 6.08 84.7 5.86 wildlife plantings. An ad libitum ration of each seed was provided separately to two weight-stable cardinals during two consecutive 2-day periods. Energy data (feed and excreta) were collected at the end of each 2-day period and processed according to procedures described by Case and Robel (1974:639). Energetic data were adjusted for body weight loss by subtracting 0.356 x weight loss (g) from the excretory energy (Clement 1970:25). Data were analyzed by analysis of variance and Duncan's New Multiple Range Test (a = 0.05). Pre-feeding trial weights of cardinals averaged 42 g. Cardinals fed roundhead lespedeza lost 5.4 g/day (13% of pre-trial weight) and those fed thickspike gayfeather lost 5.1 g/day (12% of pre-trial weight). That contrasts with weight gains by cardinals fed sunflower seeds, e.g., 0.4 g/day increase for birds on maximilian sunflower and 0.6 g/day for birds fed sawtooth sunflower seeds. Weight losses by cardinals on lespedeza and gayfeather seeds were caused by low intake of these seeds during the feeding trials. Cardinals consumed an average of only 1.4 g/day of lespedeza or gayfeather seeds, significantly less (P 0.05) from the 84.7% for maximilian seeds. The metabolizable energy (kcal/g) in lespedeza or gayfeather seeds was lower (P < 0.05) than in seeds of sawtooth or maximilian sunflowers (Table 1). Our study was not designed to measure feeding preferences of cardinals, however, birds on ad libitum rations of roundhead lespedeza or thickspike gayfeather seeds consumed about 75% less food than birds offered sunflowThis content downloaded from 157.55.39.26 on Sun, 04 Sep 2016 06:00:22 UTC All use subject to http://about.jstor.org/terms VOLUME 84, NUMBER 2 117 er seeds. From this we conclude that cardinals prefer sunflower seeds to seeds of roundhead lespedeza or thickspike gayfeather. Data from our study, and the study of Willson and Harmeson (1973) suggest that different species of birds might metabolize the same seeds with differing efficiency. For example, our cardinals used 81 to 84% of the energy in sunflower seeds, and Willson and Harmeson (1973) reported an 80% metabolic efficiency for sunflower seeds by cardinals. However, Robel et al. (1979) found that bobwhites (Colinus virginianus) used only 61% of the energy in sunflower seeds. Willson and Harmeson (1973) report 73% utilization of hemp (Cannabis sativa) seed energy by cardinals compared with 45% reported by Robel et al. (1979) for bobwhites. For other seeds, different species of birds appear to have similar utilization levels, e.g., Robel et al. (1979) reported 76% utilization of energy in ragweed seeds by bobwhites and Willson and Harmeson (1973) reported 77% by cardinals. Some of the differences and agreements in energetic variables could result from differences in handling data. We took weight losses into account, but Willson and Harmeson (1973) did not. Significant error can result by dietderived metabolic wastes in the excreta being supplemented by exogenous excretory energy from the metabolism of body reserves. Much of the variability in the metabolic energy efficiencies reported for cardinals by Willson and Harmeson (1973) may have stemmed from their not considering weight losses in their calculations. More work needs to be done to standardize methodology so comparable data will result. Our studies, and those of others, indicate that for energetic studies of wild granivorous avifauna to be meaningful, they must be accompanied by detailed food habit studies. Also, it may be necessary to determine seedspecific metabolic efficiencies of different bird species to add precision to field studies of avian energetics.


title: Resolver-to-digital converter with synchronous demodulation for FPGA based low-latency control loops

abstract: This paper deals with a synchronous demodulation scheme to be used to obtain the speed and position from resolver position sensor. Proposed algorithm is intended to be implemented on a FPGA to provide the elaborated information for very low latency control loops. Moreover, resolver excitation circuit has been simplified working directly with a square wave signal. Resolver frequency behavior has been also taken into account due to the non-sinusoidal excitation.


community2vec: Vector representations of online communities encode semantic relationships


title: Multi-value Classification of Very Short Texts

abstract: We introduce a new stacking-like approach for multi-value classification. We apply this classification scheme using Naive Bayes, Rocchio and kNN classifiers on the well-known Reuters dataset. We use part-of-speech tagging for stopword removal. We show that our setup performs almost as well as other approaches that use the full article text even though we only classify headlines. Finally, we apply a Rocchio classifier on a dataset from a Web 2.0 site and show that it is suitable for semi-automated labelling (often called tagging) of short texts and is faster than other approaches.


title: Storage and Querying of E-Commerce Data

abstract: New generation of e-commerce applications require data schemas that are constantly evolving and sparsely populated. The conventional horizontal row representation fails to meet these requirements. We represent objects in a vertical format storing an object as a set of tuples. Each tuple consists of an object identifier and attribute name-value pair. Schema evolution is now easy. However, writing queries against this format becomes cumbersome. We create a logical horizontal view of the vertical representation and transform queries on this view to the vertical table. We present alternative implementations and performance results that show the effectiveness of the vertical representation for sparse data. We also identify additional facilities needed in database systems to support these applications well.


title: The Role of Text Analytics and Information Retrieval in the Accounting Domain

abstract: ABSTRACT: Textual documents proliferate throughout accounting practice, and a wide variety of groups make financial decisions based on written guidance. The Generally Accepted Accounting Principles (GAAP), along with annual corporate financial statements and other reporting narratives, are important sources of such guidance and information. This paper examines the literature in two major areas relevant to text analytics and information retrieval in the accounting domain: (1) the manual and computational content analysis of accounting narratives, accounting readability studies, and related text-mining work, and (2) the information retrieval literature stream that addresses the extraction of both text elements as well as quantities imbedded in text from accounting documents, and includes the impact of understanding the accounting lexicon upon retrieval from digital accounting documents. We use the goals in developing the GAAP Codification, as expressed by the Financial Accounting Standards Board (FASB) in t...


title: Linear-time algorithms for testing the satisfiability of propositional horn formulae

abstract: New algorithms for deciding whether a (propositional) Horn formula is satisfiable are presented. If the Horn formula A contains K distinct propositional letters and if it is assumed that they are exactly P1,…, PK, the two algorithms presented in this paper run in time O(N), where N is the total number of occurrences of literals in A. By representing a Horn proposition as a graph, the satisfiability problem can be formulated as a data flow problem, a certain type of pebbling. The difference between the two algorithms presented here is the strategy used for pebbling the graph. The first algorithm is based on the principle used for finding the set of nonterminals of a context-free grammar from which the empty string can be derived. The second algorithm is a graph traversal and uses a “call-by-need” strategy. This algorithm uses an attribute grammar to translate a propositional Horn formula to its corresponding graph in linear time. Our formulation of the satisfiability problem as a data flow problem appears to be new and suggests the possibility of improving efficiency using parallel processors.


title: An opponent-process theory of motivation: I. Temporal dynamics of affect.

abstract: A new theory of motivation is described along with its applications to addiction and aversion. The theory assumes that many hedonic, affective, or emotional states are automatically opposed by central nervous system mechanisms which reduce the intensity of hedonic feelings, both pleasant and aversive. The opponent processes for most hedonic states are strengthened by use and are weakened by disuse. These simple assumptions lead to deductions of many known facts about acquired motivation. In addition, the theory suggests several new lines of research on motivation. It argues that the establishment of some types of acquired motivation does not depend on conditioning and is nonassociative in nature. The relationships between conditioning processes and postulated opponent processes are discussed. Finally, it is argued that the data on several types of acquired motivation, arising from either pleasurable or aversive stimulation, can be fruitfully reorganized and understood within the framework provided by the opponent-process model.


title: Classification of facebook news feeds and sentiment analysis

abstract: As recently seen in Google's Gmail, the messages in inbox are classified into primary, social and promotions, which makes it easy for the users to differentiate the messages which they are looking for from the bulk of messages. Similarly, a users wall in facebook is usually flooded with huge amount of data which makes it annoying for the users to view the important news feeds among the rest. Thus we aim to focuses on classification of facebook news feeds. In this paper, we attempt to classify the users news feeds into various categories using classifiers to provide a better representation of data on users wall. News feeds collected from facebook are dynamically classified into various classes such as friends posts and liked pages posts. Friends posts are further categorized into life events posts and entertainment posts. Posts or updates from pages which are liked by the users are grouped as liked pages posts. Posts from friends are tagged as friends posts and those regarding the events occurring in their lives are said to be life event posts and the rest are tagged as entertainment posts. This helps users to find “important news feeds” from “live news feeds”. Sentiments are important as they depict the opinions and expressions of the user. Hence, detecting the sentiments of users from the life event posts also becomes an essential task. We also propose a system for automatic detection of sentiments from the life event posts and categorize based on sentiments into happy, neutral and bad feelings posts. This paper looks towards applying the classification methods from the literature to our dataset with the objective of evaluating methods of automatic news feeds classification and sentiment analysis which in future can provide facebook page a well organized and more appealing look.


title: A theory of self-calibration of a moving camera

abstract: There is a close connection between the calibration of a single camera and the epipolar transformation obtained when the camera undergoes a displacement. The epipolar transformation imposes two algebraic constraints on the camera calibration. If two epipolar transformations, arising from different camera displacements, are available then the compatible camera calibrations are parameterized by an algebraic curve of genus four. The curve can be represented either by a space curve of degree seven contained in the intersection of two cubic surfaces, or by a curve of degree six in the dual of the image plane. The curve in the dual plane has one singular point of order three and three singular points of order two.


title: Utilizing contextual ontological user profiles for personalized recommendations

abstract: As users may have different needs in different situations and contexts, it is increasingly important to consider user context data when filtering information. In the field of web personalization and recommender systems, most of the studies have focused on the process of modelling user profiles and the personalization process in order to provide personalized services to the user, but not on contextualized services. Rather limited attention has been paid to investigate how to discover, model, exploit and integrate context information in personalization systems in a generic way. In this paper, we aim at providing a novel model to build, exploit and integrate context information with a web personalization system. A context-aware personalization system (CAPS) is developed which is able to model and build contextual and personalized ontological user profiles based on the user’s interests and context information. These profiles are then exploited in order to infer and provide contextual recommendations to users. The methods and system developed are evaluated through a user study which shows that considering context information in web personalization systems can provide more effective personalization services and offer better recommendations to users.


title: CMD: A convincing mechanism for MITM detection in SDN

abstract: There are many security problems in network environment. Among them, man-in-the-middle (MITM) attack, as a typical network attack, greatly threatens the communication of network and damages victims. Until now, solutions to reduce the MITM attacks still have many limits. In this paper, we proposed a proactive detection mechanism CMD to detect MITM attacks in SDN (Software Defined Network). The topology and connection characteristics of network traffic are utilized by CMD, without the analysis of packet contents. Through the theoretical analysis and experimental verification, CMD shows good time and accuracy performance.


title: Circle recognition through a 2D Hough Transform and radius histogramming Abstract

abstract: We present a two-step algorithm for the recognition of circles. The first step uses a 2D Hough Transform for the detection of the centres of the circles and the second step validates their existence by radius histogramming. The 2D Hough Transform technique makes use of the property that every chord of a circle passes through its centre. We present results of experiments with synthetic data demonstrating that our method is more robust to noise than standard gradient based methods. The promise of the method is demonstrated with its application on a natural image and on a digitized mammogram.


title: Dataset, Ground-Truth and Performance Metrics for Table Detection Evaluation

abstract: Table detection is an important task in the field of document analysis. It has been extensively studied since a couple of decades. Various kinds of document mediums are involved, from scanned images to web pages, from plain texts to PDF files. Numerous algorithms published bring up a challenging issue: how to evaluate algorithms in different context. Currently, most work on table detection conducts experiments on their in-house dataset. Even the few sources of online datasets are targeted at image documents only. Moreover, Precision and recall measurement are usual practice in order to account performance based on human evaluation. In this paper, we provide a dataset that is representative, large and most importantly, publicly available. The compatible format of the ground truth makes evaluation independent of document medium. We also propose a set of new measures, implement them, and open the source code. Finally, three existing table detection algorithms are evaluated to demonstrate the reliability of the dataset and metrics.


title: Joint Emoji Classification and Embedding Learning

abstract: Under conversation scenarios, emoji is widely used to express humans’ feelings, which greatly enriches the representation of plain text. Plentiful utterances with emoji are produced by humans manually in social media platforms every day, which make emoji great influence on the human life. For the academic community, researchers are always with the help of utterances including emoji as annotated data to work on sentiment analysis, yet lack of adequate attention to emoji itself. The challenges lie in how to discriminate so many different kinds of emoji, especially for those with similar meanings, which make this problem quite different from traditional sentiment analysis. In this paper, in order to gain an insight into emoji, we propose a matching architecture using deep neural networks to jointly learn emoji embeddings and make classification. In particular, we use a convolutional neural network to get the embedding of the utterance and match it with the embedding of the corresponding emoji, to obtain its best classification, and otherwise also train the emoji embeddings. Experiments based on a massive dataset demonstrate the effectiveness of our proposed approach better than traditional softmax methods in terms of p@1, p@5 and MRR evaluation metrics. Then a test of human experience shows the performance could meet the requirement of practice systems.


title: Imaging Through the Atmosphere at Terahertz Frequencies

abstract: There has been significant recent interest in the application of millimeter wave (MMW), sub-MMW, and THz technology to develop imaging systems that can "see" though either adverse weather environments or through materials such as clothing or baggage. There are numerous challenges to realizing systems operating in these frequency bands which have direct impact on the requirements on the sensor components. The atmosphere and materials exhibit attenuating characteristics that can vary markedly with frequency. These phenomenological issues must be traded off with component performance, architectural designs, as well as resolution requirements. This paper will investigate our current understanding of the phenomenological concerns and there impact on requirements for electronic components and imaging architectures that will produce sensors capable of detecting, and perhaps identifying, concealed objects that may form a security risk. Some conclusions with respect to further technology developments are presented.


Domain Specific and Model Based Systems Engineering in the Smart Grid as Prerequesite for Security by Design


title: Design and Development of a Real-Time Monitoring System for Multiple Lead–Acid Batteries Based on Internet of Things

abstract: In this paper, real-time monitoring of multiple lead-acid batteries based on Internet of things is proposed and evaluated. Our proposed system monitors and stores parameters that provide an indication of the lead acid battery’s acid level, state of charge, voltage, current, and the remaining charge capacity in a real-time scenario. To monitor these lead–acid battery parameters, we have developed a data acquisition system by building an embedded system, i.e., dedicated hardware and software. The wireless local area network is used as the backbone network. The information collected from all the connected battery clients in the system is analyzed in an asynchronous transmission control protocol/user datagram protocol-based C♯ server program running on a personal computer (server) to determine important parameters like the state of charge of the individual battery, and if required, appropriate action can be taken in advance to prevent excessive impairment to the battery. Further, data are also displayed on an Android mobile device and are stored in an SQL server database. We have developed a real prototype to devise an end product for our proposed system.


title: Fault Detection of Roller-Bearings Using Signal Processing and Optimization Algorithms

abstract: This study presents a fault detection of roller bearings through signal processing and optimization techniques. After the occurrence of scratch-type defects on the inner race of bearings, variations of kurtosis values are investigated in terms of two different data processing techniques: minimum entropy deconvolution (MED), and the Teager-Kaiser Energy Operator (TKEO). MED and the TKEO are employed to qualitatively enhance the discrimination of defect-induced repeating peaks on bearing vibration data with measurement noise. Given the perspective of the execution sequence of MED and the TKEO, the study found that the kurtosis sensitivity towards a defect on bearings could be highly improved. Also, the vibration signal from both healthy and damaged bearings is decomposed into multiple intrinsic mode functions (IMFs), through empirical mode decomposition (EMD). The weight vectors of IMFs become design variables for a genetic algorithm (GA). The weights of each IMF can be optimized through the genetic algorithm, to enhance the sensitivity of kurtosis on damaged bearing signals. Experimental results show that the EMD-GA approach successfully improved the resolution of detectability between a roller bearing with defect, and an intact system.


title: Digitally controlled oscillator (DCO)-based architecture for RF frequency synthesis in a deep-submicrometer CMOS process

abstract: A novel digitally controlled oscillator (DCO)-based architecture for frequency synthesis in wireless RF applications is proposed and demonstrated. It deliberately avoids any use of an analog tuning voltage control line. Fine frequency resolution is achieved through high-speed /spl Sigma//spl Delta/ dithering. Other imperfections of analog circuits are compensated through digital means. The presented ideas enable the employment of fully-digital frequency synthesizers using sophisticated signal processing algorithms, realized in the most advanced deep-submicrometer digital CMOS processes which allow almost no analog extensions. They also promote cost-effective integration with the digital back-end onto a single silicon die. The demonstrator test chip has been fabricated in a digital 0.13-/spl mu/m CMOS process together with a DSP, which acts as a digital baseband processor with a large number of digital gates in order to investigate noise coupling. The phase noise is -112 dBc/Hz at 500-kHz offset. The close-in spurious tones are below -62 dBc, while the far-out spurs are below -80 dBc. The presented ideas have been incorporated in a commercial Bluetooth transceiver.


title: Substrate integrated waveguide 180-degree narrow-wall directional coupler

abstract: Substrate integrated waveguide (SIW) is a new type of guided wave structure which could be implemented in both microwave- and millimeter wave integrated circuits. The study combines narrow-wall directional coupler with slow wave structure in SIW technique and achieved an over 5.4% bandwidth with /spl plusmn/0.3dB power equality, -20 dB isolation and 180 degrees phase shift between the output- and coupled ports as well for a coupler of length of 0.59. Its low cost and small size make it attractive for many narrowband applications.


Black Hat Visualization


title: Wisture: RNN-based Learning of Wireless Signals for Gesture Recognition in Unmodified Smartphones

abstract: This paper introduces Wisture, a new online machine learning solution for recognizing touch-less dynamic hand gestures on a smartphone. Wisture relies on the standard Wi-Fi Received Signal Strength (RSS) using a Long Short-Term Memory (LSTM) Recurrent Neural Network (RNN), thresholding filters and traffic induction. Unlike other Wi-Fi based gesture recognition methods, the proposed method does not require a modification of the smartphone hardware or the operating system, and performs the gesture recognition without interfering with the normal operation of other smartphone applications. ::: We discuss the characteristics of Wisture, and conduct extensive experiments to compare its performance against state-of-the-art machine learning solutions in terms of both accuracy and time efficiency. The experiments include a set of different scenarios in terms of both spatial setup and traffic between the smartphone and Wi-Fi access points (AP). The results show that Wisture achieves an online recognition accuracy of up to 94% (average 78%) in detecting and classifying three hand gestures.


title: An improved implementation of brain tumor detection using segmentation based on soft computing

abstract: Image segmentation is an important and challenging factor in the medical image segmentation. This paper describes segmentation method consisting of two phases. In the first phase, the MRI brain image is acquired from patients’ database, In that film, artifact and noise are removed after that HSom is applied for image segmentation. The HSom is the extension of the conventional self organizing map used to classify the image row by row. In this lowest level of weight vector, a higher value of tumor pixels, computation speed is achieved by the HSom with vector quantization.


title: Skin-Color Modeling and Adaptation

abstract: This paper studies a statistical skin-color model and its adaptation. It is revealed that (1) human skin colors cluster in a small region in a color space; (2) the variance of a skin color cluster can be reduced by intensity normalization, and (3) under a certain lighting condition, a skin-color distribution can be characterized by a multivariate normal distribution in the normalized color space. We then propose an adaptive model to characterize human skin-color distributions for tracking human faces under different lighting conditions. The parameters of the model are adapted based on the maximum likelihood criterion. The model has been successfully applied to a real-time face tracker and other applications.


title: A differential ISFET pH sensor

abstract: In this paper, we present a simple differential CMOS ISFET based pH sensor. The design uses two different sensing areas. The resulting sensor reduces complexity and power consumption making it suitable for implantable biomedical applications. The design is implemented in a standard 130nm process and the design constraints on sensitivity are explored. Analytical results based on theory show that sensor sensitivity increases with increasing transistor areas, biasing current levels and maximized difference in the two sensing areas. The designed differential single-chip ISFET pH sensor provides satisfactory performance for a 1–14 pH range with power consumption of 1.2 μW for bias current of 1 μA, simple structure, and sensor size of 160 μm × 80 μm.


title: A novel microstrip triplexer based on meandered loop resonators

abstract: In this paper, a novel microstrip triplexer is designed by using meandered dual-mode loop resonators. Proposed resonators are coupled to input and output ports by means of lumped capacitors in order to simplify the coupling paths. Thus, feedlines for the input and three output ports are not required. Center frequencies of channels can be controlled depending on the electrical lengths of the resonators. The designed triplexer has three channels at 2.5 GHz, 2.8 GHz and 3.5 GHz and it has also been fabricated and measured to validate the proposed approach. Measurements are in a good agreement with the simulations. Insertion losses have been obtained as better than 3 dB at three channels and isolations between the output ports have been observed as better than 20 dB.


title: Anger and core beliefs in the eating disorders

abstract: Objective ::: The link between emotion and eating pathology has long been established, but relatively little is known about the role of anger, partly because the existing literature has tended to concentrate on anger as a unitary construct. Nor is there any understanding of the cognitive factors that drive this affect in the eating disorders. This study had two aims: to determine levels of different facets of anger across eating disorder diagnoses and behaviors; and to investigate whether facets of anger are related to the individual's negative core beliefs. ::: ::: Method ::: The sample consisted of 140 women who met DSM-IV criteria for eating disorders, and 50 female control participants (university undergraduates). The women completed self-report questionnaires of anger levels and unhealthy core beliefs, and the presence of bulimic behaviors was recorded at assessment. ::: ::: Results ::: The eating-disordered women had higher levels of state anger and anger suppression, particularly if the diagnosis included bulimic symptoms. Different aspects of anger were associated with specific bulimic behaviors. Unhealthy core beliefs were associated with higher levels of trait anger in both groups but with anger suppression in the clinical women only. ::: ::: Discussion ::: Suggestions are made regarding ways in which state anger and anger suppression might be understood and treated in women with eating disorders. © 2003 by Wiley Periodicals, Inc. Int J Eat Disord 34: 118–124, 2003.


title: #Earthquake: Twitter as a Distributed Sensor System

abstract: Social media feeds are rapidly emerging as a novel avenue for the contribution and dissemination of information that is often geographic. Their content often includes references to events occurring at, or affecting specific locations. Within this article we analyze the spatial and temporal characteristics of the twitter feed activity responding to a 5.8 magnitude earthquake which occurred on the East Coast of the United States (US) on August 23, 2011. We argue that these feeds represent a hybrid form of a sensor system that allows for the identification and localization of the impact area of the event. By contrasting this with comparable content collected through the dedicated crowdsourcing ‘Did You Feel It?’ (DYFI) website of the U.S. Geological Survey we assess the potential of the use of harvested social media content for event monitoring. The experiments support the notion that people act as sensors to give us comparable results in a timely manner, and can complement other sources of data to enhance our situational awareness and improve our understanding and response to such events.


title: EvoDroid: segmented evolutionary testing of Android apps

abstract: Proliferation of Android devices and apps has created a demand for applicable automated software testing techniques. Prior research has primarily focused on either unit or GUI testing of Android apps, but not their end-to-end system testing in a systematic manner. We present EvoDroid, an evolutionary approach for system testing of Android apps. EvoDroid overcomes a key shortcoming of using evolutionary techniques for system testing, i.e., the inability to pass on genetic makeup of good individuals in the search. To that end, EvoDroid combines two novel techniques: (1) an Android-specific program analysis technique that identifies the segments of the code amenable to be searched independently, and (2) an evolutionary algorithm that given information of such segments performs a step-wise search for test cases reaching deep into the code. Our experiments have corroborated EvoDroid’s ability to achieve significantly higher code coverage than existing Android testing tools.


title: Influential Neighbours Selection for Information Diffusion in Online Social Networks

abstract: The problem of maximizing information diffusion through a network is a topic of considerable recent interest. A conventional problem is to select a set of any arbitrary $k$ nodes as the initial influenced nodes so that they can effectively disseminate the information to the rest of the network. However, this model is usually unrealistic in online social networks since we cannot typically choose arbitrary nodes in the network as the initial influenced nodes. From the point of view of an individual user who wants to spread information as much as possible, a more reasonable model is to try to initially share the information with only some of its neighbours rather than a set of any arbitrary nodes; but how can these neighbours be effectively chosen? We empirically study how to design more effective neighbours selection strategies to maximize information diffusion. Our experimental results through intensive simulation on several real- world network topologies show that an effective neighbours selection strategy is to use node degree information for short-term propagation while a naive random selection is also adequate for long-term propagation to cover more than half of a network. We also discuss the effects of the number of initial activated neighbours. If we particularly select the highest degree nodes as initial activated neighbours, the number of initial activated neighbours is not an important factor at least for long-term propagation of information.


title: Early experiences with participation in persuasive technology design

abstract: Persuasive technology, designed to change behaviors and attitudes, stands on uneasy moral ground. A key concern is the appropriateness of the means of persuasion and the intent to persuade. Engaging with those who will use the persuasive technology can ensure that it aligns with their own desires for change. This paper presents an early case study applying participatory design methods to persuasive technology in the context of a college EcoHouse. After presenting the methods and results, I synthesize lessons learned for the intersection of participatory design and persuasive technology design: begin with participants who want change, attend to power relations, promote reflection, start with simple behaviors, use examples to educate and inspire, explore designs in parallel, and be open to not designing technology. Finally, I identify challenges for future work: designing an effective design process, negotiating tensions between effectiveness and reflectiveness, and evaluating the impact of participation.


title: A high-efficiency PWM DC-DC buck converter with a novel DCM control under light-load

abstract: This paper presents a high-efficiency PWM DC-DC buck converter with a novel discontinuous-conduction mode (DCM) control under light-load condition. It is designed by using TSMC 1.8/3.3V 0.18µm CMOS technology. The proposed PWM buck converter with DCM operation not only can reach high efficiency in heavy-load (over 95% at 100mA loading), but also has great improvement in light-load efficiency (36.14% higher than the conventional PWM converter at 10mA loading) without using any PFM techniques. The output accuracy and the output ripples are better than PFM converters under light-load, and as good as a normal PWM converter under heavy-load. The proposed PWM converter is designed at 1.8V supply and 1V output voltage, and the maximum output power is 500mW.


title: Brain-inspired computing exploiting carbon nanotube FETs and resistive RAM: Hyperdimensional computing case study

abstract: We demonstrate an end-to-end brain-inspired hyperdimensional (HD) computing nanosystem, effective for cognitive tasks such as language recognition, using heterogeneous integration of multiple emerging nanotechnologies. It uses monolithic 3D integration of carbon nanotube field-effect transistors (CNFETs, an emerging logic technology with significant energy-delay product (EDP) benefit vs. silicon CMOS [1]) and Resistive RAM (RRAM, an emerging memory that promises dense non-volatile and analog storage [2]). Due to their low fabrication temperature ( 20,000 sentences (6.4 million characters) per language pair. 2. One-shot learning (i.e., learning from few examples) using one text sample (∼100,000 characters) per language. 3. Resilient operation (98% accuracy) despite 78% hardware errors (circuit outputs stuck at 0 or 1). Our HD nanosystem consists of 1,952 CNFETs integrated with 224 RRAM cells.


title: Lightweight, High-Force Gripper Inspired by Chuck Clamping Devices

abstract: In this letter, we present a novel gripper, whose design was inspired by chuck clamping devices, for transferring heavy objects and assembling parts precisely in industrial applications. The developed gripper is lightweight (0.9 kg), can manipulate heavy payloads (over 23 kgf), and can automatically align its position and posture via a grasping motion. A fingertip design criterion is presented for the position alignment, while a control strategy is presented for the posture alignment. With one actuator, this gripper realized the above features. This letter describes the mathematical analyses and experiments used to validate these key metrics.


title: Do humans have two systems to track beliefs and belief-‐like states

abstract: The lack of consensus on how to characterize humans' capacity for belief reasoning has been brought into sharp focus by recent research. Children fail critical tests of belief reasoning before 3 to 4 years of age (H. Wellman, D. Cross, & J. Watson, 2001; H. Wimmer & J. Perner, 1983), yet infants apparently pass false-belief tasks at 13 or 15 months (K. H. Onishi & R. Baillargeon, 2005; L. Surian, S. Caldi, & D. Sperber, 2007). Nonhuman animals also fail critical tests of belief reasoning but can show very complex social behavior (e.g., J. Call & A Tomasello, 2005). Fluent social interaction in adult humans implies efficient processing of beliefs, yet direct tests suggest that belief reasoning is cognitively demanding, even for adults (e.g., I. A. Apperly, D. Samson, & G. W. Humphreys, 2009). The authors interpret these findings by drawing an analogy with the domain of number cognition, where similarly contrasting results have been observed. They propose that the success of infants and nonhuman animals on some belief reasoning tasks may be best explained by a cognitively efficient but inflexible capacity for tracking belief-like states. In humans, this capacity persists in parallel with a later-developing, more flexible but more cognitively demanding theory-of-mind abilities.


title: Overview of BioNLP Shared Task 2013

abstract: The BioNLP Shared Task 2013 is the third edition of the BioNLP Shared Task series that is a community-wide effort to address fine-grained, structural information extraction from biomedical literature. The BioNLP Shared Task 2013 was held from January to April 2013. Six main tasks were proposed. 38 final submissions were received, from 22 teams. The results show advances in the state of the art and demonstrate that extraction methods can be successfully generalized in various aspects.


title: Automotive radar sensor for ultra short range applications

abstract: A small form factor, 79 GHz, MIMO radar sensor with 2D angle of arrival estimation capabilities was designed for automotive applications. It offers a 0.05 m distance resolution required to make small minimum distance measurements. The radar dimensions are 42×44×20 mm3 enabling installation in novel side locations. This aspect, combined with a wide field of view, creates a coverage that compliments the near range coverage gaps of existing long and medium range radars. Therefore, this radar supports novel radar applications such as parking aid and can be used to create a 360 degrees safety cocoon around the car.


title: Convolutional neural networks for chemical-disease relation extraction are improved with character-based word embeddings

abstract: We investigate the incorporation of character-based word representations into a standard CNN-based relation extraction model. We experiment with two common neural architectures, CNN and LSTM, to learn word vector representations from character embeddings. Through a task on the BioCreative-V CDR corpus, extracting relationships between chemicals and diseases, we show that models exploiting the character-based word representations improve on models that do not use this information, obtaining state-of-the-art result relative to previous neural approaches.


title: Use of virtual reality technique for the training of motor control in the elderly

abstract: Virtual augmented exercise, an emerging technology that can help to promote physical activity and combine the strengths of indoor and outdoor exercise, has recently been proposed as having the potential to increase exercise behavior in older adults. By creating a strong presence in a virtual, interactive environment, distraction can be taken to greater levels while maintaining the benefits of indoor exercises which may result in a shift from negative to positive thoughts about exercise. Recent findings on young participants show that virtual reality training enhances mood, thus, increasing enjoyment and energy. For older adults virtual, interactive environments can influence postural control and fall events by stimulating the sensory cues that are responsible in maintaining balance and orientation. However, the potential of virtual reality training has yet to be explored for older adults. This manuscript describes the potential of dance pad training protocols in the elderly and reports on the theoretical rationale of combining physical game-like exercises with sensory and cognitive challenges in a virtual environment.


title: Habits as knowledge structures: Automaticity in goal-directed behavior.

abstract: This study tested the idea of habits as a form of goal-directed automatic behavior. Expanding on the idea that habits are mentally represented as associations between goals and actions, it was proposed that goals are capable of activating the habitual action. More specific, when habits are established (e.g., frequent cycling to the university), the very activation of the goal to act (e.g., having to attend lectures at the university) automatically evokes the habitual response (e.g., bicycle). Indeed, it was tested and confirmed that, when behavior is habitual, behavioral responses are activated automatically. In addition, the results of 3 experiments indicated that (a) the automaticity in habits is conditional on the presence of an active goal (cf. goal-dependent automaticity; J. A. Bargh, 1989), supporting the idea that habits are mentally represented as goal-action links, and (b) the formation of implementation intentions (i.e., the creation of a strong mental link between a goal and action) may simulate goal-directed automaticity in habits.


title: Structured Sparse Convolutional Autoencoder

abstract: This paper aims to improve the feature learning in Convolutional Networks (Convnet) by capturing the structure of objects. A new sparsity function is imposed on the extracted featuremap to capture the structure and shape of the learned object, extracting interpretable features to improve the prediction performance. The proposed algorithm is based on organizing the activation within and across featuremap by constraining the node activities through $\ell_{2}$ and $\ell_{1}$ normalization in a structured form.


title: Automatically Annotated Turkish Corpus for Named Entity Recognition and Text Categorization using Large-Scale Gazetteers

abstract: Turkish Wikipedia Named-Entity Recognition and Text Categorization (TWNERTC) dataset is a collection of automatically categorized and annotated sentences obtained from Wikipedia. We constructed large-scale gazetteers by using a graph crawler algorithm to extract relevant entity and domain information from a semantic knowledge base, Freebase. The constructed gazetteers contains approximately 300K entities with thousands of fine-grained entity types under 77 different domains. Since automated processes are prone to ambiguity, we also introduce two new content specific noise reduction methodologies. Moreover, we map fine-grained entity types to the equivalent four coarse-grained types: person, loc, org, misc. Eventually, we construct six different dataset versions and evaluate the quality of annotations by comparing ground truths from human annotators. We make these datasets publicly available to support studies on Turkish named-entity recognition (NER) and text categorization (TC).


title: EXPLORING ERP SYSTEM OUTCOMES IN SMEs: A MULTIPLE CASE STUDY

abstract: The purpose of this paper is to investigate Enterprise Resource Planning (ERP) system outcomes in the context of small and medium-sized enterprises (SMEs). Most of the former research on ERP outcomes is based on data from large enterprises, and this study examines how the SME context affects the ERP system outcomes and the related evaluation practices. The paper reports findings from a multiple case study of ERP implementation in four SMEs. The case companies put more emphasis on system and information quality improvements from the ERP systems, compared to individual and organizational outcomes. This can be related to the lack of a strategic perspective on ERP implementation in these companies, with replacement of the legacy systems serving as the main motivation for the implementation projects. Moreover, the findings indicate that the identified lack of ERP system evaluation practice in these SMEs can be explained by ownership type, resource constraints, limited IT competence, and status of the IT legacy systems in SMEs.


Implementation Independent Architectural Comparison


Breaking CAPTCHAs with Convolutional Neural Networks


title: Natural scene text detection based on SWT, MSER and candidate classification

abstract: This paper presents a novel scene text detection algorithm based on Stroke Width Transform (SWT), Maximally Extremal Regions (MSER) and candidate classification. Firstly, utilize the SWT and MSER to extract the candidate characters at the same time. Secondly, preliminary filtering the candidate connected components based on heuristic rules. Thirdly, using mutual verification and integration to class all candidate into two categories: strong candidates, weak candidates. If the weak candidate has similar properties with strong candidate, then the weak candidate is changed into strong candidate. Finally, the text area is aggregated into text lines by text line aggregation algorithm. The experiment results on public datasets show that the proposed method can detect text lines effectively.


title: Homicidal Maniacs and Narcissisfic Parasites: Stigmatization of Mentally Ill Persons in the Movies

abstract: The portrayal of mentally ill persons in movies and television programs has an important and underestimated influence on public perceptions of their condition and care. Movie stereotypes that contribute to the stigmatization of mentally ill persons include the mental patient as rebellious free spirit, homicidal maniac, seductress, enlightened member of society, narcissistic parasite, and zoo specimen. The authors suggest that mental health professionals can fight this source of stigma by increasing their collaboration with patient advocacy groups in monitoring negative portrayals of mentally ill people, using public information campaigns such as Mental illness Awareness Week to call attention to theprocess of stigmatization, and supporting accurate dramatic and documentary depictions of mental illness.


Collaborative Filtering using Weighted BiPartite Graph Projection


title: Effects of Inhaled Rosemary Oil on Subjective Feelings and Activities of the Nervous System

abstract: Rosemary oil is one of the more famous essential oils widely used in aromatherapy. However, the effects of rosemary oil on the human body, in particular the nervous system, have not been sufficiently studied. This study investigates the effects of the inhalation of rosemary oil on test subjects’ feelings, as well as its effects on various physiological parameters of the nervous system. Twenty healthy volunteers participated in the experiment. All subjects underwent autonomic nervous system (ANS) recording. This consisted of measurements of skin temperature; heart rate; respiratory rate; blood pressure; evaluations of the subjects’ mood states; and electroencephalography (EEG) recordings in the pre-, during treatment, and post-rosemary inhalation periods as compared with control conditions. Our results showed significant increases in blood pressure, heart rate, and respiratory rate after rosemary oil inhalation. After the inhalation treatments, subjects were found to have become more active and stated that they felt “fresher”. The analysis of EEGs showed a reduction in the power of alpha1 (8–10.99 Hz) and alpha2 (11–12.99 Hz) waves. Moreover, an increment in the beta wave (13–30 Hz) power was observed in the anterior region of the brain. These results confirm the stimulatory effects of rosemary oil and provide supporting evidence that brain wave activity, autonomic nervous system activity, as well as mood states are all affected by the inhalation of the rosemary oil.


title: Building an IoT Framework for Connected Dairy

abstract: Heat stress (HS) causes cows to produce less milk with the same nutritional input, which effectively increases farmers' production costs. The economic toll due to higher-temperature, heat stress is a $1 billion annual problems. Not only in the United States, but also around the globe heat stress causes an adverse impact on dairy productivity. The opportunities, however, for the dairy industry is to electronically monitor cattle temperature and implement appropriate measures so that the impact of HS can be minimized. The U.S. Department of Agriculture estimates nearly $2.4 billion a year in losses from animal illnesses that lead to death can be prevented by electronically checking on cattle's' vital signs. This research paper recommends the most innovative electronic monitor framework, the 'Smart Connected Objects", aka, 'the Internet of Things (IoT)', that enables dairies to minimize the economic impact of HS and, at the same, capture the higher Return on Assets (ROA) a Return on Investment (ROI) by improving operational efficiencies. Happy Cow, more importantly, means happier, more profitable, dairy industry and richer and creamer dairy products. The proposed framework supports both offline and online dairy IoT. This paper presents a prototyping solution design as well as its application and certain experimental results.


title: Identification of Conditional Interventional Distributions

abstract: The subject of this paper is the elucidation of effects of actions from causal assumptions represented as a directed graph, and statistical knowledge given as a probability distribution. In particular, we are interested in predicting distributions on post-action outcomes given a set of measurements. We provide a necessary and sufficient graphical condition for the cases where such distributions can be uniquely computed from the available information, as well as an algorithm which performs this computation whenever the condition holds. Furthermore, we use our results to prove completeness of do-calculus [Pearl, 1995] for the same identification problem, and show applications to sequential decision making.


title: Quality of Life and Objective Measures of Diabetic Neuropathy in a Prospective Placebo-Controlled Trial of Ruboxistaurin and Topiramate

abstract: Background:The Norfolk Quality of Life Questionnaire-Diabetic Neuropathy (Norfolk QOL-DN) is a validated comprehensive questionnaire designed to capture the entire spectrum of DN related to large fiber, small fiber, and autonomic neuropathy not captured in existing instruments. We aimed to determine if the Norfolk QOL-DN could be used to capture changes in QOL that correlate with nerve fiber-specific objective measures in a placebo-controlled trial of two agents that affect different nerve fibers.Methods:Sixty patients with DN were allocated to treatment on ruboxistaurin (RBX) (n = 18), topiramate (TPX) (n = 18), or placebo (n = 18). QOL-DN was administered and objective measures of nerve function were performed at entry and end of the study period.Results:Total QOL scores improved significantly in the active treatment groups (RBX −9.56 ± 4.13; TPX −12.22 ± 2.76) but not in placebo (−5.56 ± 3.49). There were differences in nerve function improvement between treatments. Neurological symptom scores (NSS) im...


title: Is it possible to cure Internet addiction with the Internet?

abstract: Significant technological advancements over the last two decades have led to enhanced accessibility to computing devices and the Internet. Our society is experiencing an ever-growing integration of the Internet into everyday lives, and this has transformed the way we obtain and exchange information, communicate and interact with one another as well as conduct business. However, the term ‘Internet addiction’ (IA) has emerged from problematic and excessive Internet usage which leads to the development of addictive cyber-behaviours, causing health and social problems. The most commonly used intervention treatments such as motivational interviewing, cognitive-behavioural therapy, and retreat or inpatient care mix a variety of psychotherapy theories to treat such addictive behaviour and try to address underlying psychosocial issues that are often coexistent with IA, but the efficacy of these approaches is not yet proved. The aim of this paper is to address the question of whether it is possible to cure IA with the Internet. After detailing the current state-of-the-art including various IA definitions, risk factors, assessment methods and IA treatments, we outline the main research challenges that need to be solved. Moreover, we propose an Internet-based IA Recovery Framework (IARF) which uses AI to closely observe, visualize and analyse patient’s Internet usage behaviour for possible staged intervention. The proposal to use smart Internet-based systems to control IA can be expected to be controversial. This paper is intended to stimulate further discussion and research in IA recovery through Internet-based frameworks.


title: Experimental Evaluations on Ship Autonomous Navigation and Collision Avoidance by Intelligent Guidance

abstract: Experimental evaluations on autonomous navigation and collision avoidance of ship maneuvers by intelligent guidance are presented in this paper. These ship maneuvers are conducted on an experimental setup that consists of a navigation and control platform and a vessel model, in which the mathematical formulation presented is actually implemented. The mathematical formulation of the experimental setup is presented under three main sections: vessel traffic monitoring and information system, collision avoidance system, and vessel control system. The physical system of the experimental setup is presented under two main sections: vessel model and navigation and control platform. The vessel model consists of a scaled ship that has been used in this study. The navigation and control platform has been used to control the vessel model and that has been further divided under two sections: hardware structure and software architecture. Therefore, the physical system has been used to conduct ship maneuvers in autonomous navigation and collision avoidance experiments. Finally, several collision avoidance situations with two vessels are considered in this study. The vessel model is considered as the vessel (i.e., own vessel) that makes collision avoidance decisions/actions and the second vessel (i.e., target vessel) that does not take any collision avoidance actions is simulated. Finally, successful experimental results on several collision avoidance situations with two vessels are also presented in this study.


Illiquidity and Stock Returns: Cross-section and Time-series Effects


title: Create Your Own Internet of Things: A survey of IoT platforms.

abstract: We live in the age of After Google (AG), where information is just one click away and talking just one touch away. The near future of the AG age is the Internet of Things (IoT), where physical things connected over a network will take part in Internet activities to exchange information about themselves and their surroundings. In other words, the IoT is nothing but a computing concept in which everyday objects with embedded hardware/devices are connected to a network or are simply online.


title: Characterization of structural variants with single molecule and hybrid sequencing approaches

abstract: Motivation: Structural variation is common in human and cancer genomes. High-throughput DNA sequencing has enabled genome-scale surveys of structural variation. However, the short reads produced by these technologies limit the study of complex variants, particularly those involving repetitive regions. Recent ‘third-generation’ sequencing technologies provide single-molecule templates and longer sequencing reads, but at the cost of higher per-nucleotide error rates. ::: ::: Results: We present MultiBreak-SV, an algorithm to detect structural variants (SVs) from single molecule sequencing data, paired read sequencing data, or a combination of sequencing data from different platforms. We demonstrate that combining low-coverage third-generation data from Pacific Biosciences (PacBio) with high-coverage paired read data is advantageous on simulated chromosomes. We apply MultiBreak-SV to PacBio data from four human fosmids and show that it detects known SVs with high sensitivity and specificity. Finally, we perform a whole-genome analysis on PacBio data from a complete hydatidiform mole cell line and predict 1002 high-probability SVs, over half of which are confirmed by an Illumina-based assembly. ::: ::: Availability and implementation: MultiBreak-SV is available at http://compbio.cs.brown.edu/software/. ::: ::: Contact: ude.tv@ztiranna or ude.nworb.sc@leahparb ::: ::: Supplementary information: Supplementary data are available at Bioinformatics online.


title: A Recursive TF-ISF Based Sentence Retrieval Method with Local Context

abstract: Sentence retrieval consists of retrieving relevant sentences from a document base in response to a query. Question answering, novelty detection, summarization, opinion mining and information provenance make use of sentence retrieval. Most of the sentence retrieval methods are trivial adaptations of document retrieval methods. However some newer sentence retrieval methods based on the language modeling framework successfully use some kind of context of sentences. Unlike that there is no successful improvement of the TF-ISF method that takes into account the context of sentences. In this paper we propose a recursive TF-ISF based method that takes into account the local context of a sentence. The context is considered the previous and next sentence of current sentence. We compared the new method to the TF-ISF baseline and to an earlier unsuccessful method that also incorporates a similar context into TF-ISF. We got statistically significant improvements of the results in comparison to both of the methods. Additional benefit of our method is the clear explicit model of the context that will allow us to automatically generate a document representation with context suitable for sentence retrieval which is important for our future work.