title: Sublayer-Specific Coding Dynamics during Spatial Navigation and Learning in Hippocampal Area CA1

abstract: Summary The mammalian hippocampus is critical for spatial information processing and episodic memory. Its primary output cells, CA1 pyramidal cells (CA1 PCs), vary in genetics, morphology, connectivity, and electrophysiological properties. It is therefore possible that distinct CA1 PC subpopulations encode different features of the environment and differentially contribute to learning. To test this hypothesis, we optically monitored activity in deep and superficial CA1 PCs segregated along the radial axis of the mouse hippocampus and assessed the relationship between sublayer dynamics and learning. Superficial place maps were more stable than deep during head-fixed exploration. Deep maps, however, were preferentially stabilized during goal-oriented learning, and representation of the reward zone by deep cells predicted task performance. These findings demonstrate that superficial CA1 PCs provide a more stable map of an environment, while their counterparts in the deep sublayer provide a more flexible representation that is shaped by learning about salient features in the environment. Video Abstract


title: Robust Voice Activity Detection using Cepstral Features.

abstract: This paper reviews algorithms which rely on the analysis of time domain samples to provide energy and zero-crossing rates, together with more recent algorithms that use different methods for speech detection. We then examine a different approach using cepstral analysis, showing a high degree of amplitude and noise level independence. We show that a cepstral based algorithm exhibits a high degree of independence to levels of background noise and successful speech end-pointing can be achieved via thresholding cepstral distance measures. Through the use of a noise code-book we are able to provide a successful reference for Euclidean distance measures in the voice detection algorithm. >


title: Ultrawide-band coplanar waveguide-fed rectangular slot antenna

abstract: An ultrawide-band coplanar waveguide (CPW) fed slot antenna is presented. A rectangular slot antenna is excited by a 50-/spl Omega/ CPW with a U-shaped tuning stub. The impedance bandwidth, from both measurement and simulation, is about 110% (S11<-10 dB). The antenna radiates bi-directionally. The radiation patterns obtained from simulations are found to be stable across the matching band and experimental verification is provided at the high end of the band.


title: DSSD : Deconvolutional Single Shot Detector

abstract: The main contribution of this paper is an approach for introducing additional context into state-of-the-art general object detection. To achieve this we first combine a state-of-the-art classifier (Residual-101[14]) with a fast detection framework (SSD[18]). We then augment SSD+Residual-101 with deconvolution layers to introduce additional large-scale context in object detection and improve accuracy, especially for small objects, calling our resulting system DSSD for deconvolutional single shot detector. While these two contributions are easily described at a high-level, a naive implementation does not succeed. Instead we show that carefully adding additional stages of learned transformations, specifically a module for feed-forward connections in deconvolution and a new output module, enables this new approach and forms a potential way forward for further detection research. Results are shown on both PASCAL VOC and COCO detection. Our DSSD with $513 \times 513$ input achieves 81.5% mAP on VOC2007 test, 80.0% mAP on VOC2012 test, and 33.2% mAP on COCO, outperforming a state-of-the-art method R-FCN[3] on each dataset.


title: A Multi-Pass Sieve for Coreference Resolution

abstract: Most coreference resolution models determine if two mentions are coreferent using a single function over a set of constraints or features. This approach can lead to incorrect decisions as lower precision features often overwhelm the smaller number of high precision ones. To overcome this problem, we propose a simple coreference architecture based on a sieve that applies tiers of deterministic coreference models one at a time from highest to lowest precision. Each tier builds on the previous tier's entity cluster output. Further, our model propagates global information by sharing attributes (e.g., gender and number) across mentions in the same cluster. This cautious sieve guarantees that stronger features are given precedence over weaker ones and that each decision is made using all of the information available at the time. The framework is highly modular: new coreference modules can be plugged in without any change to the other modules. In spite of its simplicity, our approach outperforms many state-of-the-art supervised and unsupervised models on several standard corpora. This suggests that sieve-based approaches could be applied to other NLP tasks.


Cognitive impairment in women with joint hypermobility syndrome/Ehlers-Danlos syndrome hypermobility type


title: A universal statistical test for random bit generators

abstract: A new statistical test for random bit generators is presented which, in contrast to presently used statistical tests, is universal in the sense that it can detect any significant deviation of a device's output statistics from the statistics of a truly random bit source when the device can be modeled as an ergodic stationary source with finite memory but arbitrary (unknown) state transition probabilities. The test parameter is closely related to the device's per-bit entropy which is shown to be the correct quality measure for a secret-key source in a cryptographic application. The test hence measures the cryptographic badness of a device's possible defect. The test is easy to implement and very fast and thus well suited for practical applications. A sample program listing is provided.


title: Cloud robotics: Current status and open issues

abstract: With the development of cloud computing, big data, and other emerging technologies, the integration of cloud technology and multi-robot systems makes it possible to design multi-robot systems with improved energy efficiency, high real-time performance, and low cost. In order to address the potential of clouds in enhancing robotics for industrial systems, this paper describes the basic concepts and development process of cloud robotics and the overall architecture of these systems. Then, the major driving forces behind the development of cloud robotics are carefully analyzed from the point of view of cloud computing, big data, open source resources, robot cooperative learning, and network connectivity. Subsequently, the key issues and challenges in the current cloud robotic systems are proposed, and some possible solutions are also given. Finally, the potential value of cloud robotic systems in different practical applications is discussed.


title: The jabberwocky programming environment for structured social computing

abstract: We present Jabberwocky, a social computing stack that consists of three components: a human and machine resource management system called Dormouse, a parallel programming framework for human and machine computation called ManReduce, and a high-level programming language on top of ManReduce called Dog. Dormouse is designed to enable cross-platform programming languages for social computation, so, for example, programs written for Mechanical Turk can also run on other crowdsourcing platforms. Dormouse also enables a programmer to easily combine crowdsourcing platforms or create new ones. Further, machines and people are both first-class citizens in Dormouse, allowing for natural parallelization and control flows for a broad range of data-intensive applications. And finally and importantly, Dormouse includes notions of real identity, heterogeneity, and social structure. We show that the unique properties of Dormouse enable elegant programming models for complex and useful problems, and we propose two such frameworks. ManReduce is a framework for combining human and machine computation into an intuitive parallel data flow that goes beyond existing frameworks in several important ways, such as enabling functions on arbitrary communication graphs between human and machine clusters. And Dog is a high-level procedural language written on top of ManReduce that focuses on expressivity and reuse. We explore two applications written in Dog: bootstrapping product recommendations without purchase data, and expert labeling of medical images.


title: Cortical connectivity and sensory coding

abstract: The sensory cortex contains a wide array of neuronal types, which are connected together into complex but partially stereotyped circuits. Sensory stimuli trigger cascades of electrical activity through these circuits, causing specific features of sensory scenes to be encoded in the firing patterns of cortical populations. Recent research is beginning to reveal how the connectivity of individual neurons relates to the sensory features they encode, how differences in the connectivity patterns of different cortical cell classes enable them to encode information using different strategies, and how feedback connections from higher-order cortex allow sensory information to be integrated with behavioural context.


Identifying News from Tweets


title: On the localness of software

abstract: The n-gram language model, which has its roots in statistical natural language processing, has been shown to successfully capture the repetitive and predictable regularities (“naturalness") of source code, and help with tasks such as code suggestion, porting, and designing assistive coding devices. However, we show in this paper that this natural-language-based model fails to exploit a special property of source code: localness. We find that human-written programs are localized: they have useful local regularities that can be captured and exploited. We introduce a novel cache language model that consists of both an n-gram and an added “cache" component to exploit localness. We show empirically that the additional cache component greatly improves the n-gram approach by capturing the localness of software, as measured by both cross-entropy and suggestion accuracy. Our model’s suggestion accuracy is actually comparable to a state-of-the-art, semantically augmented language model; but it is simpler and easier to implement. Our cache language model requires nothing beyond lexicalization, and thus is applicable to all programming languages.


title: A Logic Level Design Methodology for a Secure DPA Resistant ASIC or FPGA Implementation

abstract: This paper describes a novel design methodology to implement a secure DPA resistant crypto processor. The methodology is suitable for integration in a common automated standard cell ASIC or FPGA design flow. The technique combines standard building blocks to make 'new' compound standard cells, which have a close to constant power consumption. Experimental results indicate a 50 times reduction in the power consumption fluctuations.


title: Explaining International Migration in the Skype Network: The Role of Social Network Features

abstract: In recent years, several new ways have appeared for quantifying human migration such as location based smartphone applications and tracking user activity from websites. To show usefulness of these new approaches, we present the results of a study of cross-country migration as observed via login events in the Skype network. We explore possibility to extract human migration and correlate it with institutional statistics. The study demonstrates that a number of social network features are strongly related to net migration from and to a given country, as well as net migration between pairs of countries. Specifically, we find that percentage of international calls, percentage of international links and foreign logins in a country, complemented by gross domestic product, can be used as relatively accurate proxies for estimating migration.


title: Face Detection

abstract: In this paper we present a comprehensive and critical survey of face detection algorithms. Face detection is a necessary first-step in face recognition systems, with the purpose of localizing and extracting the face region from the background. It also has several applications in areas such as content-based image retrieval, video coding, video conferencing, crowd surveillance, and intelligent human?computer interfaces. However, it was not until recently that the face detection problem received considerable attention among researchers. The human face is a dynamic object and has a high degree of variability in its apperance, which makes face detection a difficult problem in computer vision. A wide variety of techniques have been proposed, ranging from simple edge-based algorithms to composite high-level approaches utilizing advanced pattern recognition methods. The algorithms presented in this paper are classified as either feature-based or image-based and are discussed in terms of their technical approach and performance. Due to the lack of standardized tests, we do not provide a comprehensive comparative evaluation, but in cases where results are reported on common datasets, comparisons are presented. We also give a presentation of some proposed applications and possible application areas.


title: Performance of IEC 61850-9-2 Process Bus and Corrective Measure for Digital Relaying

abstract: International Electrotechnical Commission (IEC) standard 61850 proposes the Ethernet-based communication networks for protection and automation within the power substation. Major manufacturers are currently developing products for the process bus in compliance with IEC 61850 part 9-2. For the successful implementation of the IEC 61850-9-2 process bus, it is important to analyze the performance of time-critical messages for the substation protection and control functions. This paper presents the performance evaluation of the IEC 61850-9-2 process bus for a typical 345 kV/230 kV substation by studying the time-critical sampled value messages delay and loss by using the OPNET simulation tool in the first part of this paper. In the second part, this paper presents a corrective measure to address the issues with the several sampled value messages lost and/or delayed by proposing the sampled value estimation algorithm for any digital substation relaying. Finally, the proposed sampled value estimation algorithm has been examined for various power system scenarios with the help of PSCAD/EMTDC and MATLAB simulation tools.


title: Stability and performance analysis with double-graph model of vehicle formations

abstract: In this paper, we treat the string stability as a kind of performance of a linear multi-vehicle system with acyclic formation structures. By using a double-graph model, we can describe information flows and formulate the string stability. This paper provides a systematic way to design the local controller and the system control strategy with the performance constrains. We also present the connection failure tolerance problem and give some essential conclusions.


Study of induced amphidiploid derivatives of Vigna radiata x Vigna mungo


title: Efficient trajectory optimization using a sparse model

abstract: The “timed elastic band” approach optimizes robot trajectories by subsequent modification of an initial trajectory generated by a global planner. The objectives considered in the trajectory optimization include but are not limited to the overall path length, trajectory execution time, separation from obstacles, passing through intermediate way points and compliance with the robots dynamic, kinematic and geometric constraints. “Timed elastic bands” explicitly consider spatial-temporal aspects of the motion in terms of dynamic constraints such as limited robot velocities and accelerations. The trajectory planning operates in real time such that “timed elastic bands” cope with dynamic obstacles and motion constraints. The “timed elastic band problem” is formulated as a scalarized multi-objective optimization problem. Most objectives are local and relate to only a small subset of parameters as they only depend on a few consecutive robot states. This local structure results in a sparse system matrix, which allows the utilization of fast and efficient optimization techniques such as the open-source framework “g2o” for solving “timed elastic band” problems. The “g2o” sparse system solvers have been successfully applied to VSLAM problems. This contribution describes the application and adaptation of the g2o-framework in the context of trajectory modification with the “timed elastic band”. Results from simulations and experiments with a real robot demonstrate that the implementation is robust and computationally efficient.


title: Myelin and oligodendrocyte lineage cells in white matter pathology and plasticity after traumatic brain injury

abstract: Abstract Impact to the head or rapid head acceleration–deceleration can cause traumatic brain injury (TBI) with a characteristic pathology of traumatic axonal injury (TAI) and secondary damage in white matter tracts. Myelin and oligodendrocyte lineage cells have significant roles in the progression of white matter pathology after TBI and in the potential for plasticity and subsequent recovery. The myelination pattern of specific brain regions, such as frontal cortex, may also increase susceptibility to neurodegeneration and psychiatric symptoms after TBI. White matter pathology after TBI depends on the extent and distribution of axon damage, microhemorrhages and/or neuroinflammation. TAI occurs in a pattern of damaged axons dispersed among intact axons in white matter tracts. TAI accompanied by bleeding and/or inflammation produces focal regions of overt tissue destruction, resulting in loss of both axons and myelin. White matter regions with TAI may also exhibit demyelination of intact axons. Demyelinated axons that remain viable have the potential for remyelination and recovery of function. Indeed, animal models of TBI have demonstrated demyelination that is associated with evidence of remyelination, including oligodendrocyte progenitor cell proliferation, generation of new oligodendrocytes, and formation of thinner myelin. Changes in neuronal activity that accompany TBI may also involve myelin remodeling, which modifies conduction efficiency along intact myelinated fibers. Thus, effective remyelination and myelin remodeling may be neurobiological substrates of plasticity in neuronal circuits that require long-distance communication. This perspective integrates findings from multiple contexts to propose a model of myelin and oligodendrocyte lineage cell relevance in white matter injury after TBI. This article is part of the Special Issue entitled ‘Oligodendrocytes in Health and Disease’.


title: Characterization of a RS-LiDAR for 3D Perception

abstract: High precision 3D LiDARs are still expensive and hard to acquire. This paper presents the characteristics of RS-LiDAR, a model of low-cost LiDAR with sufficient supplies, in comparison with VLP-16. The paper also provides a set of evaluations to analyze the characterizations and performances of LiDARs sensors. This work analyzes multiple properties, such as drift effects, distance effects, color effects and sensor orientation effects, in the context of 3D perception. By comparing with Velodyne LiDAR, we found RS-LiDAR as a cheaper and acquirable substitute of VLP-16 with similar efficiency.


Anisha Syal et al. / International Journal of Computer Science Engineering (IJCSE) A Survey of Computer Vision Methods for Counting Fruits and Yield Prediction


title: Dr.Fill: Crosswords and an Implemented Solver for Singly Weighted CSPs

abstract: We describe Dr. Fill, a program that solves American-style crossword puzzles. From a technical perspective, Dr. Fill works by converting crosswords to weighted csps, and then using a variety of novel techniques to find a solution. These techniques include generally applicable heuristics for variable and value selection, a variant of limited discrepancy search, and postprocessing and partitioning ideas. Branch and bound is not used, as it was incompatible with postprocessing and was determined experimentally to be of little practical value. Dr. Fill's performance on crosswords from the American Crossword Puzzle Tournament suggests that it ranks among the top fifty or so crossword solvers in the world.


title: Development of a Web-Based Control Laboratory for Automation Technicians: The Three-Tank System

abstract: This paper introduces a Web-based control laboratory for experimentation on a nonlinear multiple-input-multiple-output (MIMO) system: the three-tank plant. Using this application, automation technician students can learn in a practical way many fundamental aspects of control processes. The client-side of the virtual laboratory has been developed using the programming support provided by Easy Java Simulations (EJS), an open-source tool for teachers with limited programming skills who want to create Java applications and applets. The lab has been integrated in the collaborative Web-based experimentation environment eMersion which provides more flexibility for the students performing the laboratory experiments.


Randomized Hough Transform: Improved Ellipse Detection with Comparison


title: Categorical Metadata Representation for Customized Text Classification

abstract: The performance of text classification has improved tremendously using intelligently engineered neural-based models, especially those injecting categorical metadata as additional information, e.g., using user/product information for sentiment classification. These information have been used to modify parts of the model (e.g., word embeddings, attention mechanisms) such that results can be customized according to the metadata. We observe that current representation methods for categorical metadata, which are devised for human consumption, are not as effective as claimed in popular classification methods, outperformed even by simple concatenation of categorical features in the final layer of the sentence encoder. We conjecture that categorical features are harder to represent for machine use, as available context only indirectly describes the category, and even such context is often scarce (for tail category). To this end, we propose to use basis vectors to effectively incorporate categorical metadata on various parts of a neural-based model. This additionally decreases the number of parameters dramatically, especially when the number of categorical features is large. Extensive experiments on various datasets with different properties are performed and show that through our method, we can represent categorical metadata more effectively to customize parts of the model, including unexplored ones, and increase the performance of the model greatly.


title: Overview for the Second Shared Task on Language Identification in Code-Switched Data

abstract: We present an overview of the first shared task on language identification on codeswitched data. The shared task included code-switched data from four language pairs: Modern Standard ArabicDialectal Arabic (MSA-DA), MandarinEnglish (MAN-EN), Nepali-English (NEPEN), and Spanish-English (SPA-EN). A total of seven teams participated in the task and submitted 42 system runs. The evaluation showed that language identification at the token level is more difficult when the languages present are closely related, as in the case of MSA-DA, where the prediction performance was the lowest among all language pairs. In contrast, the language pairs with the higest F-measure where SPA-EN and NEP-EN. The task made evident that language identification in code-switched data is still far from solved and warrants further research.


title: Development of Mobile Mapping System for 3D Road Asset Inventory

abstract: Asset Management is an important component of an infrastructure project. A significant cost is involved in maintaining and updating the asset information. Data collection is the most time-consuming task in the development of an asset management system. In order to reduce the time and cost involved in data collection, this paper proposes a low cost Mobile Mapping System using an equipped laser scanner and cameras. First, the feasibility of low cost sensors for 3D asset inventory is discussed by deriving appropriate sensor models. Then, through calibration procedures, respective alignments of the laser scanner, cameras, Inertial Measurement Unit and GPS (Global Positioning System) antenna are determined. The efficiency of this Mobile Mapping System is experimented by mounting it on a truck and golf cart. By using derived sensor models, geo-referenced images and 3D point clouds are derived. After validating the quality of the derived data, the paper provides a framework to extract road assets both automatically and manually using techniques implementing RANSAC plane fitting and edge extraction algorithms. Then the scope of such extraction techniques along with a sample GIS (Geographic Information System) database structure for unified 3D asset inventory are discussed.


title: Anomaly Detection in Medical Wireless Sensor Networks

abstract: In this paper, we propose a new framework for anomaly detection in medical wireless sensor networks, which are used for remote monitoring of patient vital signs. The proposed framework performs sequential data analysis on a mini gateway used as a base station to detect abnormal changes and to cope with unreliable measurements in collected data without prior knowledge of anomalous events or normal data patterns. The proposed approach is based on the Mahalanobis distance for spatial analysis, and a kernel density estimator for the identification of abnormal temporal patterns. Our main objective is to distinguish between faulty measurements and clinical emergencies in order to reduce false alarms triggered by faulty measurements or ill-behaved sensors. Our experimental results on both real and synthetic medical datasets show that the proposed approach can achieve good detection accuracy with a low false alarm rate (less than 5.5%). Category: Embedded computing


title: Capabilities and performance in collective marketing: the importance of learning to cope with agency dilemmas.

abstract: This chapter focuses on the inherent challenges in collective action and the institutional arrangements or regulations that hold market-oriented farmer groups together. It operationalizes the measurement of the capacity to contain these inherent challenges by mapping the presence and effectiveness of internal rules and regulations, related to mechanisms of price-determination, payment systems, quality controls, task delegation, etc. Using data from 38 farmer organizations in Bolivia, we show that this capability is strongly correlated with the performance of the group. We illustrate the relevance of learning and experimentation to develop these capacities, and how the policy constrained room for maneuver to do so. We conclude with the importance of policies and legal frameworks that facilitate this search for effective solutions to agency dilemmas in collective marketing, and stress the importance of differentiating between market-oriented and community-oriented farmer organizations.


Convolutional Neural Networks


Connecting Language and Vision to Actions


Learning Semantic Representations for Unsupervised Domain Adaptation


title: The impact of different multi-walled carbon nanotubes on the X-band microwave absorption of their epoxy nanocomposites

abstract: AbstractBackgroundCarbon nanotube (CNT) characteristics, besides the processing conditions, can change significantly the microwave absorption behavior of CNT/polymer composites. In this study, we investigated the influence of three commercial multi-walled CNT materials with various diameters and length-to-diameter aspect ratios on the X-band microwave absorption of epoxy nanocomposites with CNT contents from 0.125 to 2 wt%, prepared by two dispersion methods, i.e. in solution with surfactant-aiding and via ball-milling.ResultsThe laser diffraction particle size and TEM analysis showed that both methods produced good dispersions at the microscopic level of CNTs. Both a high aspect ratio resulting in nanotube alignment trend and good infiltration of the matrix in the individual nanotubes, which was indicated by high Brookfield viscosities at low CNT contents of CNT/epoxy dispersions, are important factors to achieve composites with high microwave absorption characteristics. The multi-walled carbon nanotube (MWCNT) with the largest aspect ratio resulted in composites with the best X-band microwave absorption performance, which is considerably better than that of reported pristine CNT/polymer composites with similar or lower thicknesses and CNT loadings below 4 wt%.ConclusionsA high aspect ratio of CNTs resulting in microscopic alignment trend of nanotubes as well as a good level of micro-scale CNT dispersion resulting from good CNT-matrix interactions are crucial to obtain effective microwave absorption performance. This study demonstrated that effective radar absorbing MWCNT/epoxy nanocomposites having small matching thicknesses of 2–3 mm and very low filler contents of 0.25-0.5 wt%, with microwave energy absorption in the X-band region above 90% and maximum absorption peak values above 97%, could be obtained via simple processing methods, which is promising for mass production in industrial applications. ::: Graphical AbstractComparison of the X-band microwave reflection loss of epoxy composites of various commercial multi-walled carbon nanotube materials.


title: Temporal Logic Motion Planning for Dynamic Robots

abstract: In this paper, we address the temporal logic motion planning problem for mobile robots that are modeled by second order dynamics. Temporal logic specifications can capture the usual control specifications such as reachability and invariance as well as more complex specifications like sequencing and obstacle avoidance. Our approach consists of three basic steps. First, we design a control law that enables the dynamic model to track a simpler kinematic model with a globally bounded error. Second, we built a robust temporal logic specification that takes into account the tracking errors of the first step. Finally, we solve the new robust temporal logic path planning problem for the kinematic model using automata theory and simple local vector fields. The resulting continuous time trajectory is provably guaranteed to satisfy the initial user specification.


title: Answering Science Exam Questions Using Query Rewriting with Background Knowledge

abstract: Open-domain question answering (QA) is an important problem in AI and NLP that is emerging as a bellwether for progress on the generalizability of AI methods and techniques. Much of the progress in open-domain QA systems has been realized through advances in information retrieval methods and corpus construction. In this paper, we focus on the recently introduced ARC Challenge dataset, which contains 2,590 multiple choice questions authored for grade-school science exams. These questions are selected to be the most challenging for current QA systems, and current state of the art performance is only slightly better than random chance. We present a system that rewrites a given question into queries that are used to retrieve supporting text from a large corpus of science-related text. Our rewriter is able to incorporate background knowledge from ConceptNet and -- in tandem with a generic textual entailment system trained on SciTail that identifies support in the retrieved results -- outperforms several strong baselines on the end-to-end QA task despite only being trained to identify essential terms in the original source question. We use a generalizable decision methodology over the retrieved evidence and answer candidates to select the best answer. By combining query rewriting, background knowledge, and textual entailment our system is able to outperform several strong baselines on the ARC dataset.


title: Energy-Aware Mobile Edge Computing and Routing for Low-Latency Visual Data Processing

abstract: New paradigms such as Mobile Edge Computing (MEC) are becoming feasible for use in, e.g., real-time decision-making during disaster incident response to handle the data deluge occurring in the network edge. However, MEC deployments today lack flexible IoT device data handling such as handling user preferences for real-time versus energy-efficient processing. Moreover, MEC can also benefit from a policy-based edge routing to handle sustained performance levels with efficient energy consumption. In this paper, we study the potential of MEC to address application issues related to energy management on constrained IoT devices with limited power sources, while also providing low-latency processing of visual data being generated at high resolutions. Using a facial recognition application that is important in disaster incident response scenarios, we propose a novel “offload decision-making” algorithm that analyzes the tradeoffs in computing policies to offload visual data processing (i.e., to an edge cloud or a core cloud) at low-to-high workloads. This algorithm also analyzes the impact on energy consumption in the decision-making under different visual data consumption requirements (i.e., users with thick clients or thin clients). To address the processing-throughput versus energy-efficiency tradeoffs, we propose a “Sustainable Policy-based Intelligence-Driven Edge Routing” algorithm that uses machine learning within Mobile Ad hoc Networks. This algorithm is energy aware and improves the geographic routing baseline performance (i.e., minimizes impact of local minima) for throughput performance sustainability, while also enabling flexible policy specification. We evaluate our proposed algorithms by conducting experiments on a realistic edge and core cloud testbed in the GENI Cloud infrastructure, and recreate disaster scenes of tornado damages within simulations. Our empirical results show how MEC can provide flexibility to users who desire energy conservation over low latency or vice versa in the visual data processing with a facial recognition application. In addition, our simulation results show that our routing approach outperforms existing solutions under diverse user preferences, node mobility, and severe node failure conditions.


title: A Secure System For Pervasive Social Network-Based Healthcare

abstract: Modern technologies of mobile computing and wireless sensing prompt the concept of pervasive social network (PSN)-based healthcare. To realize the concept, the core problem is how a PSN node can securely share health data with other nodes in the network. In this paper, we propose a secure system for PSN-based healthcare. Two protocols are designed for the system. The first one is an improved version of the IEEE 802.15.6 display authenticated association. It establishes secure links with unbalanced computational requirements for mobile devices and resource-limited sensor nodes. The second protocol uses blockchain technique to share health data among PSN nodes. We realize a protocol suite to study protocol runtime and other factors. In addition, human body channels are proposed for PSN nodes in some use cases. The proposed system illustrates a potential method of using blockchain for PSN-based applications.


title: A STRATIFIED APPROACH FOR SOUND SPATIALIZATION

abstract: We propose a multi-layer structure to mediate essential components in sound spatialization. This approach will facilitate artistic work with spatialization systems, a process which currently lacks structure, flexibility, and interoperability.


title: Neural correlates of superior intelligence: Stronger recruitment of posterior parietal cortex

abstract: Received 20 April 2005; revised 21 July 2005; accepted 22 July 2005 General intelligence (g) is a common factor in diverse cognitive abilities and a major influence on life outcomes. Neuroimaging studies in adults suggest that the lateral prefrontal and parietal cortices play a crucial role in related cognitive activities including fluid reasoning, the control of attention, and working memory. Here, we investigated the neural bases for intellectual giftedness (superior-g) in adolescents, using fMRI. The participants consisted of a superior-g group (n = 18, mean RAPM = 33.9 T 0.8, >99%) from the national academy for gifted adolescents and the control group (n = 18, mean RAPM = 22.8 T 1.6, 60%) from local high schools in Korea (mean age = 16.5 T 0.8). fMRI data were acquired while they performed two reasoning tasks with high and low g-loadings. In both groups, the high g-loaded tasks specifically increased regional activity in the bilateral fronto-parietal network including the lateral prefrontal, anterior cingulate, and posterior parietal cortices. However, the regional activations of the superior-g group were significantly stronger than those of the control group, especially in the posterior parietal cortex. Moreover, regression analysis revealed that activity of the superior and intraparietal cortices (BA 7/40) strongly covaried with individual differences in g (r = 0.71 to 0.81). A correlated vectors analysis implicated bilateral posterior parietal areas in g. These results suggest that superior-g may not be due to the recruitment of additional brain regions but to the functional facilitation of the fronto-parietal network particularly driven by the


title: Bio 2 RDF : Towards A Mashup To Build Bioinformatics Knowledge System

abstract: Presently, there are numerous bioinformatics databases available on different websites. Although RDF was proposed as a standard format for the web, these databases are still available in various formats. With the increasing popularity of the semantic web technologies and the ever growing number of databases in bioinformatics, there is a pressing need to develop mashup systems to help the process of bioinformatics knowledge integration. Bio2RDF is such a system, built from rdfizer programs written in JSP, the Sesame open source triplestore technology and an OWL ontology. With Bio2RDF, documents from public bioinformatics databases such as Kegg, PDB, MGI, HGNC and several of NCBI's databases can now be made available in RDF format through a unique URL in the form of http://bio2rdf.org/namespace:id. The Bio2RDF project has successfully applied the semantic web technology to publicly available databases by creating a knowledge space of RDF documents linked together with normalized URIs and sharing a common ontology. Bio2RDF is based on a three-step approach to build mashups of bioinformatics data. The present article details this new approach and illustrates the building of a mashup used to explore the implication of four transcription factor genes in Parkinson's disease. The Bio2RDF repository can be queried at http://bio2rdf.org.


title: Antimicrobial Activities of Leaf Extracts of Guava (Psidium guajava L.) on Two Gram-Negative and Gram-Positive Bacteria

abstract: Aim. To determine the antimicrobial potential of guava (Psidium guajava) leaf extracts against two gram-negative bacteria (Escherichia coli and Salmonella enteritidis) and two gram-positive bacteria (Staphylococcus aureus and Bacillus cereus) which are some of foodborne and spoilage bacteria. The guava leaves were extracted in four different solvents of increasing polarities (hexane, methanol, ethanol, and water). The efficacy of these extracts was tested against those bacteria through a well-diffusion method employing 50 μL leaf-extract solution per well. According to the findings of the antibacterial assay, the methanol and ethanol extracts of the guava leaves showed inhibitory activity against gram-positive bacteria, whereas the gram-negative bacteria were resistant to all the solvent extracts. The methanol extract had an antibacterial activity with mean zones of inhibition of 8.27 and 12.3 mm, and the ethanol extract had a mean zone of inhibition of 6.11 and 11.0 mm against B. cereus and S. aureus, respectively. On the basis of the present finding, guava leaf-extract might be a good candidate in the search for a natural antimicrobial agent. This study provides scientific understanding to further determine the antimicrobial values and investigate other pharmacological properties.


The roles of Jaks and Stats in cytokine signaling.


title: Resonator-Loaded Broadband Antenna for LTE700/GSM850/GSM900 Base Stations

abstract: A resonator-loaded broadband antenna is proposed, which is composed of a resonator-loaded dipole, a balun, a U-shaped strip feed, and a box-shaped reflector. The resonator beside the dipole is adopted for impedance bandwidth enhancement. The higher resonate modes can be moved lower by using a resonator, and wide bandwidth can be achieved by moving this mode close to the main resonating mode of the dipole. A prototype of the proposed antenna is designed, fabricated, and tested. Measured results show that the antenna achieves an impedance bandwidth of 37.5% (0.67-0.98 GHz) for |S ::: 11 ::: | <; -15 dB.


title: Software radio on smartphones: feasible?

abstract: In this work-in-progress paper, we explore the expected impacts, the reality, and the technical issues of making MAC/PHY protocols downloadable softwares (like apps) for smartphones. The notion of software radio on a small number of fixed stations has been investigated for decades, but moving its platform to billions of smartphones and even making it downloadable as an app has never been attempted. We firmly believe that such attempt could open new fronts for the software radio research. Beyond considering its possible impacts and ramifications, we demonstrate through measurements that today's smartphone hardware is already capable enough to support the real-time execution of down-loadable MAC/PHY software. It is certainly true for low-speed technologies such as ZigBee, and is close to providing real-time operation of higher-speed technologies like Wi-Fi. In our study, we also find that different application processor architectures lead to different bottlenecks in the MAC/PHY processing chain. This implies that the design of protocols that might be executed in software on general-purpose platforms should factor in the processor architecture like multiple cores and SIMD instruction sets, as it will facilitate the softwarization of wireless communication protocols in the future.


title: Channel Estimation for OFDM

abstract: Orthogonal frequency division multiplexing (OFDM) has been widely adopted in modern wireless communication systems due to its robustness against the frequency selectivity of wireless channels. For coherent detection, channel estimation is essential for receiver design. Channel estimation is also necessary for diversity combining or interference suppression where there are multiple receive antennas. In this paper, we will present a survey on channel estimation for OFDM. This survey will first review traditional channel estimation approaches based on channel frequency response (CFR). Parametric model (PM)-based channel estimation, which is particularly suitable for sparse channels, will be also investigated in this survey. Following the success of turbo codes and low-density parity check (LDPC) codes, iterative processing has been widely adopted in the design of receivers, and iterative channel estimation has received a lot of attention since that time. Iterative channel estimation will be emphasized in this survey as the emerging iterative receiver improves system performance significantly. The combination of multiple-input multiple-output (MIMO) and OFDM has been widely accepted in modern communication systems, and channel estimation in MIMO-OFDM systems will also be addressed in this survey. Open issues and future work are discussed at the end of this paper.


title: Application of Data Mining in Customer Relationship Management

abstract: With economic globalization and the rapid development of e-commerce, customer relationship management (CRM) has become the core of growth of the company. Data mining, as a powerful data analysis tool, extracts critical information supporting the company to make better decisions by processing a large number of data in commercial databases. This paper introduced the basic concepts of data mining and CRM, and described the process how to use data mining for CRM. At last, the paper described the applications of several main data mining methods in CRM, such as clustering, classification and association rule.


title: JCLEC : a Java framework for evolutionary computation

abstract: In this paper we describe JCLEC, a Java software system for the development of evolutionary computation applications. This system has been designed as a framework, applying design patterns to maximize its reusability and adaptability to new paradigms with a minimum of programming effort. JCLEC architecture comprises three main modules: the core contains all abstract type definitions and their implementation; experiments runner is a scripting environment to run algorithms in batch mode; finally, GenLab is a graphical user interface that allows users to configure an algorithm, to execute it interactively and to visualize the results obtained. The use of JCLEC system is illustrated though the analysis of one case study: the resolution of the 0/1 knapsack problem by means of evolutionary algorithms.


ADAPTIVE HARMONIC COMPONENTS DETECTION AND FORECASTING IN WAVE NON-PERIODIC TIME SERIES USING NEURAL NETWORKS


title: Extractive Summarisation Based on Keyword Profile and Language Model

abstract: We present a statistical framework to extract information-rich citation sentences that summarise the main contributions of a scientific paper. In a first stage, we automatically discover salient keywords from a paper’s citation summary, keywords that characterise its main contributions. In asecond stage, exploitingthe results of the first stage, we identify citation sentences that best capture the paper’s main contributions. Experimental results show that our approach using methods rooted in quantitative statistics and information theory outperforms the current state-of-the-art systems in scientific paper summarisation.


title: miR-29b moulds the tumour microenvironment to repress metastasis

abstract: Tumour cells are influenced by their microenvironment, which can promote uncontrolled growth, invasion and metastasis. The GATA3 transcription factor is now shown to regulate the tumour microenvironment by inducing the expression of miR-29b in cancer cells. This microRNA in turn inhibits the expression of genes involved in angiogenesis and extracellular matrix signalling and remodelling to suppress metastasis.


title: De-noising of Contrast-Enhanced MRI Sequences by an Ensemble of Expert Deep Neural Networks

abstract: Dynamic contrast-enhanced MRI (DCE-MRI) is an imaging protocol where MRI scans are acquired repetitively throughout the injection of a contrast agent. The analysis of dynamic scans is widely used for the detection and quantification of blood brain barrier (BBB) permeability. Extraction of the pharmacokinetic (PK) parameters from the DCE-MRI washout curves allows quantitative assessment of the BBB functionality. Nevertheless, curve fitting required for the analysis of DCE-MRI data is error-prone as the dynamic scans are subject to non-white, spatially-dependent and anisotropic noise that does not fit standard noise models. The two existing approaches i.e. curve smoothing and image de-noising can either produce smooth curves but cannot guaranty fidelity to the PK model or cannot accommodate the high variability in noise statistics in time and space.


26 – Disorders of the Paranasal Sinuses


title: Using Indirect Encoding of Multiple Brains to Produce Multimodal Behavior

abstract: An important challenge in neuroevolution is to evolve complex neural networks with multiple modes of behavior. Indirect encodings can potentially answer this challenge. Yet in practice, indirect encodings do not yield effective multimodal controllers. Thus, this paper introduces novel multimodal extensions to HyperNEAT, a popular indirect encoding. A previous multimodal HyperNEAT approach called situational policy geometry assumes that multiple brains benefit from being embedded within an explicit geometric space. However, experiments here illustrate that this assumption unnecessarily constrains evolution, resulting in lower performance. Specifically, this paper introduces HyperNEAT extensions for evolving many brains without assuming geometric relationships between them. The resulting Multi-Brain HyperNEAT can exploit human-specified task divisions to decide when each brain controls the agent, or can automatically discover when brains should be used, by means of preference neurons. A further extension called module mutation allows evolution to discover the number of brains, enabling multimodal behavior with even less expert knowledge. Experiments in several multimodal domains highlight that multi-brain approaches are more effective than HyperNEAT without multimodal extensions, and show that brains without a geometric relation to each other outperform situational policy geometry. The conclusion is that Multi-Brain HyperNEAT provides several promising techniques for evolving complex multimodal behavior.


title: A Novel Triband E-Shaped Printed Monopole Antenna for MIMO Application

abstract: A novel multiband E-shaped printed monopole antenna for multiple-input-multiple-output (MIMO) system is presented. The proposed E-shaped monopole antenna can create a single resonance within the WLAN range. Placement of two slots within the E-shaped monopole antenna creates two extra resonances whose center frequencies can be adjusted by the E-shaped monopole and the slots parameters. The antenna parameters for WLAN application, covering 2.4, 5.4, and 5.8 GHz, are given. Various configurations of array of this E-shaped monopole antenna for MIMO application are also studied. It is shown that good MIMO performance can be achieved even for small element spacing. Simulation and measurement results are compared and discussed.


title: From amateurs to connoisseurs: modeling the evolution of user expertise through online reviews

abstract: Recommending products to consumers means not only understanding their tastes, but also understanding their level of experience. For example, it would be a mistake to recommend the iconic film Seven Samurai simply because a user enjoys other action movies; rather, we might conclude that they will eventually enjoy it---once they are ready. The same is true for beers, wines, gourmet foods---or any products where users have acquired tastes: the `best' products may not be the most 'accessible'. Thus our goal in this paper is to recommend products that a user will enjoy now, while acknowledging that their tastes may have changed over time, and may change again in the future. We model how tastes change due to the very act of consuming more products---in other words, as users become more experienced. We develop a latent factor recommendation system that explicitly accounts for each user's level of experience. We find that such a model not only leads to better recommendations, but also allows us to study the role of user experience and expertise on a novel dataset of fifteen million beer, wine, food, and movie reviews.


title: Three-flap umbilicoplasty: a novel and preliminary method of laparoendoscopic single-site transumbilical surgical approach for urachal remnants

abstract: Purpose ::: Symptomatic urachal remnants are rare benign anomalies affecting young adults. For these patients, both curative treatment and acceptable postoperative aesthetic outcomes are important. Here, we describe a novel transumbilical approach, using Y-shaped incisions and three-flap umbilicoplasty techniques, to improve repair and aesthetic outcomes.


title: A scalable peer-to-peer control plane architecture for Software Defined Networks

abstract: Control plane scalability is one of the major concerns in Software Defined Networking (SDN) deployment. Although the centralization of the control plane by decoupling it from the data plane facilitates ease of network management, however, it introduces new challenges. One of these challenges is to maintain performance, consistency, and scalability while minimizing the corresponding overheads. In this paper, we propose an architecture that allows the control plane to evolve at a hyper-scale level as well as address important performance and reliability issues. A hierarchical control plane architecture with peer-to-peer communication among logically distributed controllers is designed with the goal of achieving optimum performance and consistency gains while mitigating overheads. A root controller is deployed at the top layer of the hierarchy to maintain global network view. The proposed model is helpful in improving network robustness against failures and supporting a desired level of reliability. To evaluate our model, we developed a realistic emulation platform using ONOS, FlowVisor, Mininet, and Open vSwitch. The proposed architecture is compared with earlier solutions and experimental results are presented to demonstrate the effectiveness of the proposed model.


title: The development of malaria diagnostic techniques: a review of the approaches with focus on dielectrophoretic and magnetophoretic methods

abstract: The large number of deaths caused by malaria each year has increased interest in the development of effective malaria diagnoses. At the early-stage of infection, patients show non-specific symptoms or are asymptomatic, which makes it difficult for clinical diagnosis, especially in non-endemic areas. Alternative diagnostic methods that are timely and effective are required to identify infections, particularly in field settings. This article reviews conventional malaria diagnostic methods together with recently developed techniques for both malaria detection and infected erythrocyte separation. Although many alternative techniques have recently been proposed and studied, dielectrophoretic and magnetophoretic approaches are among the promising new techniques due to their high specificity for malaria parasite-infected red blood cells. The two approaches are discussed in detail, including their principles, types, applications and limitations. In addition, other recently developed techniques, such as cell deformability and morphology, are also overviewed in this article.


title: Linear positive control systems on time scales ; controllability

abstract: Positive linear systems on arbitrary time scales are studied. The theory developed in the paper unifies and extends concepts and results known for continuous-time and discrete-time systems. A necessary and sufficient condition for a linear system on a time scale to be positive is presented. Properties of positive reachable sets are investigated and characterizations of various controllability properties are presented. A modified Gram matrix of the system is used to state necessary and sufficient condition of positive reachability of a positive system on an arbitrary time scale.


title: Calibration-less Nauta OTA operating at 0.25-V power supply in a 130-nm digital CMOS process

abstract: This work presents an architecture for Nauta fully differential operational transconductance amplifier that requires no additional circuitry for calibration. This design operates with a power supply of 0.25-V, implemented in a 130-nm digital CMOS process. By operating in the weak inversion region and using the transistor array approach, this OTA shows voltage reduction and stable gain using halo implanted transistors. The proposed OTA can operate at a voltage range from 0.15-V to 0.35-V, consuming only 6-nW to 333-nW of power and presenting a measured transconductance of 2.24-μS at 0.25-V. This makes it suitible for low-power G m -C filters, such as biquad, which needs a wider range of frequencies (30-Hz to 6-kHz).


title: Observations of artefacts in the x-ray ptychography method

abstract: X-ray ptychography, a scanning coherent diffraction imaging method, was used to reconstruct images of a “Siemens star” test pattern with amplitude and phase contrast. While studying how the use of illumination with an increased bandwidth results in clear improvements in the quality of image reconstructions, we found that an artificial change in the overall distance scale factor of the algorithm leads to a systematic response in the image, which is reproduced with an incorrect number of spokes. This pathology is explained by the conflict between the length scales set by the scan and by the diffraction patterns on the detector.


title: Designing for imaginary friends: information architecture, personas and the politics of user-centered design

abstract: This article considers the problematic relationship between new media designers and users in current texts written about user-centered design (UCD) techniques. To better understand and solidify the importance of the user within the technological artifact, these designers often create ‘personas’ — prototypical users with names, faces, interests and preferences. Personas serve as boundary objects used as conceptual stand-ins for users when team members make design decisions. This article traces the discursive construction of the ‘user’ within web design texts and how these texts describe the persona technique. The analysis suggests that the use of personas is motivated as much by political realities within new media organizations, as it is by the desire to address user needs. In addition, it is argued that personas serve to reinscribe the conceptual separation between the user and designer despite technological developments (like Web 2.0) that blur this boundary.


title: Event detection on large social media using temporal analysis

abstract: Social media networks are now considered as one of the major news channels that breaks news as they fold. The problem of event detection based on social media has attracted researchers' attention recently because of the enormous popularity of social media. Existing approaches focus on features that don't reflect full characteristics of the social network. For the purpose of this research, we define an event as an occurrence that has enough force and momentum that could create an observable change of the context of a social network. Such a definition provides us with a wider perspective through which we can view the big picture of the social network. In this research, we propose a novel framework for detecting events on social media. We introduce a temporal approach to detect structural change of the social network that reflects an occurrence of an event using machine learning algorithms. In this study, we show that processing temporal social networks captures the complete complexity of the social network, which results in a higher accuracy of event detection.


title: An under-actuated mechanism for a robotic finger

abstract: Robotic hands can be applied in different applications such as prosthesis, humanoid robots, industrial robotic manipulators and other kinds of robotic arms. Introduction of robotic technology into the field of prosthesis has resulted a higher quality of life for amputees. In this paper an under-actuated mechanism which has the self-adaptation ability is proposed to be used in the fingers of the hand prosthesis. The mechanism is a modification of the cross bar mechanism and it shows grasping adaptation ability for different geometries. In addition the mechanism is capable to generate dexterous grasping patterns making a paradigm shift from the conventional linkage mechanisms used for fingers which only has the capability for the grasping. Kinematic analysis, mathematical simulation and computer simulations were carried out to evaluate the effectiveness of the mechanism. Furthermore a new parameter, degree of adaptation is introduced to evaluate the performance of the under-actuated finger mechanisms.


title: Security of CCTV and Video Surveillance Systems: Threats, Vulnerabilities, Attacks, and Mitigations

abstract: Video surveillance, closed-circuit TV and IP-camera systems became virtually omnipresent and indispensable for many organizations, businesses, and users. Their main purpose is to provide physical security, increase safety, and prevent crime. They also became increasingly complex, comprising many communication means, embedded hardware and non-trivial firmware. However, most research to date focused mainly on the privacy aspects of such systems, and did not fully address their issues related to cyber-security in general, and visual layer (i.e., imagery semantics) attacks in particular. In this paper, we conduct a systematic review of existing and novel threats in video surveillance, closed-circuit TV and IP-camera systems based on publicly available data. The insights can then be used to better understand and identify the security and the privacy risks associated with the development, deployment and use of these systems. We study existing and novel threats, along with their existing or possible countermeasures, and summarize this knowledge into a comprehensive table that can be used in a practical way as a security checklist when assessing cyber-security level of existing or new CCTV designs and deployments. We also provide a set of recommendations and mitigations that can help improve the security and privacy levels provided by the hardware, the firmware, the network communications and the operation of video surveillance systems. We hope the findings in this paper will provide a valuable knowledge of the threat landscape that such systems are exposed to, as well as promote further research and widen the scope of this field beyond its current boundaries.


title: Content sharing in a social broadcasting environment: evidence from twitter

abstract: The rise of social broadcasting technologies has greatly facilitated open access to information worldwide, not only by powering decentralized information production and consumption, but also by expediting information diffusion through social interactions like content sharing. Voluntary information sharing by users in the context of Twitter, the predominant social broadcasting site, is studied by modeling both the technology and user behavior. A detailed data set about the official content-sharing function on Twitter, called retweet, is collected and the statistical relationships between users' social network characteristics and their retweeting acts are documented. A two-stage consumption-sharing model is then estimated using the conditional maximum likelihood estimatio (MLE) method. The empirical results convincingly support our hypothesis that weak ties (in the form of unidirectional links) are more likely to engage in the social exchange process of content sharing. Specifically, we find that after a median quality tweet (as defined in the sample) is consumed, the likelihood that a unidirectional follower will retweet is 3.1 percentage point higher than the likelihood that a bidirectional follower will do so.


title: System Dynamics in Software Project Management: towards the development of a formal integrated framework

abstract: Successful software development is becoming increasingly important to many companies. However, most projects fail to meet their targets, highlighting the inadequacies of traditional project management techniques in this unique setting. Despite breakthroughs in software engineering, management methodologies have not improved, and the major opportunities for better results are now in this area. Poor strategic management and related human factors have been cited as a major cause for failures, which traditional techniques cannot incorporate explicitly. System dynamics (SD) aims to model the behaviour of complex socio-economic systems; there has been a number of applications to software project management. SD provides an alternative view in which the major project influences are considered and quantified explicitly. Grounded on a holistic perspective it avoids consideration of the detail required by traditional tools, looking at the key aspects of the general project behaviour. However, if SD is to play a key role in software project management it needs to be embedded within the traditional decision-making framework. The authors developed a conceptual integrated model, the SYDPIM, which has been tested and improved within a large on-going software project. Such a framework specifies the roles of SD models, how they are to be used within the traditional management process, how they exchange information with the traditional models, and a general method to support model development.


Predicting Moves in Chess using Convolutional Neural Networks


title: Gene transfer of truncated IkappaBalpha prevents tubulointerstitial injury.

abstract: BACKGROUND ::: Severe proteinuria not only indicates the presence of progressive glomerular disease, but also causes tubular epithelial cells to produce inflammatory mediators leading to tubulointerstitial (TI) injury. We investigated the role of nuclear factor-kappaB (NF-kappaB) in tubular epithelial cells in the development of proteinuria-induced TI injury. ::: ::: ::: METHODS ::: To specifically inhibit NF-kappaB activation, a recombinant adenovirus vector expressing a truncated form of IkappaBalpha (AdexIkappaBDeltaN) was injected into renal arteries of protein-overloaded rats, a model of TI injury characterized by infiltration of mononuclear cells and fibrosis. ::: ::: ::: RESULTS ::: Activation of NF-kappaB in the renal cortex, observed in protein-overloaded rats treated with a control vector, recombinant lacZ adenovirus, was prevented in AdexIkappaBDeltaN-injected rats. Microscopic examination revealed AdexIkappaBDeltaN treatment to markedly attenuate proteinuria-induced TI injury. Increased immunostaining of vascular cell adhesion molecule-1, transforming growth factor-beta, and fibronectin in TI lesions also was suppressed by AdexIkappaBDeltaN injection. ::: ::: ::: CONCLUSIONS ::: These findings provide evidence of the critical role of NF-kappaB activation in TI injury and suggest the therapeutic potential of adenovirus-mediated IkappaBDeltaN gene transfer into the kidney as a means of interrupting the process of TI damage.


title: Indoor positioning of mobile devices with agile iBeacon deployment

abstract: Position of mobile devices and their users provides a great amount of added value and opportunities. The penetration of tracking devices with sensory such as GPS devices, accelerators and specifically smart phones has impacted human lives extensively. Nowadays, many applications on smart phones and mobile devices exploit different techniques and inputs for positioning. Wireless positioning is generally divided into two categories: outdoor positioning and indoor positioning, depending on not only where they are used but also how they work. Powerful as it is, indoor positing is still a challenging problem because satellite-based approaches do not work properly inside buildings. Therefore, for indoor positioning, we need to use other technologies creatively. iBeacon, the focus of this paper, is a new technology which provides a higher level of location awareness in indoor positioning. iBeacon is a built-in, cross-platform technology for Android and iOS devices, which utilizes Bluetooth Low Energy (BLE) for long-last services. This technology has significant advantages compared to other types of indoor positioning technologies, such as less expensive hardware, less energy consumption, needless to internet connection, and being capable of receiving notifications in background. This technology will provide huge benefits for future location awareness applications. It will change the way retailers, event organizers, and educational institutions communicate with people indoors. In this paper, we aim to provide a more accurate, cost efficient approach to indoor positioning of mobile devices using iBeacon.


title: Flowdometry: An Optical Flow and Deep Learning Based Approach to Visual Odometry

abstract: Visual odometry is a challenging task related to simultaneous localization and mapping that aims to generate a map traveled from a visual data stream. Based on one or two cameras, motion is estimated from features and pixel differences between frames. Because of the frame rate of the cameras, there are generally small, incremental changes between subsequent frames where optical flow can be assumed to be proportional to the physical distance moved by an egocentric reference, such as a camera on a vehicle. In this paper, a visual odometry system called Flowdometry is proposed based on optical flow and deep learning. Optical flow images are used as input to a convolutional neural network, which calculates a rotation and displacement for each image pixel. The displacements and rotations are applied incrementally to construct a map of where the camera has traveled. The proposed system is trained and tested on the KITTI visual odometry dataset, and accuracy is measured by the difference in distances between ground truth and predicted driving trajectories. Different convolutional neural network architecture configurations are tested for accuracy, and then results are compared to other state-of-the-art monocular odometry systems using the same dataset. The average translation error from the Flowdometry system is 10.77% and the average rotation error is 0.0623 degrees per meter. The total execution time of the system per optical flow frame is 0.633 seconds, which offers a 23.796x speedup over state-of-the-art methods using deep learning.


title: Structure Analysis of Magnetic Coupling Resonant for Wireless Power Transmission System

abstract: Using magnetically coupled resonators coils in a strongly coupled near field was suggested for wireless power transfer. A circuit model is presented along with a derivation of key system concepts such as frequency splitting, the maximum operating distance (critical coupling). Here, a simple circuit topological structure, the series-shunt-mixed resonant coupling, is presented with better performance in the transfer distance and efficiency. The theoretical analysis and experimental verifications of several resonant coupling structures have been investigated by several groups. In the experimental verification, a pair of resonant coils with 15 cm diameter was used. The experimental results show that high efficiency of 85% was achieved at a distance of 10 cm and 45% efficiency at 20 cm. Through the developed model, it is seen that the system can transfer power over a midrange of a few meters and that impedance matching is important to achieve high efficiency.


title: Selective Sampling Using the Query by Committee Algorithm

abstract: We analyze the “query by committee” algorithm, a method for filtering informative queries from a random stream of inputs. We show that if the two-member committee algorithm achieves information gain with positive lower bound, then the prediction error decreases exponentially with the number of queries. We show that, in particular, this exponential decrease holds for query learning of perceptrons.


title: Identifying First-Person Camera Wearers in Third-Person Videos

abstract: We consider scenarios in which we wish to perform joint scene understanding, object tracking, activity recognition, and other tasks in scenarios in which multiple people are wearing body-worn cameras while a third-person static camera also captures the scene. To do this, we need to establish person-level correspondences across first-and third-person videos, which is challenging because the camera wearer is not visible from his/her own egocentric video, preventing the use of direct feature matching. In this paper, we propose a new semi-Siamese Convolutional Neural Network architecture to address this novel challenge. We formulate the problem as learning a joint embedding space for first-and third-person videos that considers both spatial-and motion-domain cues. A new triplet loss function is designed to minimize the distance between correct first-and third-person matches while maximizing the distance between incorrect ones. This end-to-end approach performs significantly better than several baselines, in part by learning the first-and third-person features optimized for matching jointly with the distance measure itself.


title: A Sorted Neighborhood Approach for Detecting Duplicated Regions in Image Forgeries Based on DWT and SVD

abstract: The presence of duplicated regions in the image can be considered as a tell-tale sign for image forgery, which belongs to the research field of digital image forensics. In this paper, a blind forensics approach based on DWT (discrete wavelet transform) and SVD (singular value decomposition) is proposed to detect the specific artifact. Firstly, DWT is applied to the image, and SVD is used on fixed-size blocks of low-frequency component in wavelet sub-band to yield a reduced dimension representation. Then the SV vectors are then lexicographically sorted and duplicated image blocks will be close in the sorted list, and therefore will be compared during the detection steps. The experimental results demonstrate that the proposed approach can not only decrease computational complexity, but also localize the duplicated regions accurately even when the image was highly compressed or edge processed.


title: CRSM: a practical crowdsourcing-based road surface monitoring system

abstract: Detecting road potholes and road roughness levels is key to road condition monitoring, which impacts transport safety and driving comfort. We propose a crowdsourcing-based road surface monitoring system, simply called CRSM. CRSM can effectively detect road potholes and evaluate road roughness levels using hardware modules mounted on distributed vehicles. These modules use low-end accelerometers and GPS devices to obtain vibration patterns, locations, and vehicle velocities. Considering the high cost of onboard storage and wireless transmission, a novel light-weight data mining algorithm is proposed to detect road surface events and transmit potential pothole information to a central server. The central server gathers reports from multiple vehicles, and makes a comprehensive evaluation on road surface quality. We have implemented a product-quality system, and have deployed it on 100 taxies in the Shenzhen urban area. The results show that CRSM can detect road potholes with 90 % accuracy, with nearly zero false alarms. CRSM can also evaluate road roughness levels correctly, even with some interferences from small bumps or potholes.


title: Increased Growth Factors Play a Role in Wound Healing Promoted by Noninvasive Oxygen-Ozone Therapy in Diabetic Patients with Foot Ulcers

abstract: Management of diabetic foot ulcers (DFUs) is a great challenge for clinicians. Although the oxygen-ozone treatment improves the diabetic outcome, there are few clinical trials to verify the efficacy and illuminate the underlying mechanisms of oxygen-ozone treatment on DFUs. In the present study, a total of 50 type 2 diabetic patients complicated with DFUs, Wagner stage 2~4, were randomized into control group treated by standard therapy only and ozone group treated by standard therapy plus oxygen-ozone treatment. The therapeutic effects were graded into 4 levels from grade 0 (no change) to grade 3 (wound healing). The wound sizes were measured at baseline and day 20, respectively. Tissue biopsies were performed at baseline and day 11. The expressions of vascular endothelial growth factor (VEGF), transforming growth factor-β (TGF-β), and platelet-derived growth factor (PDGF) proteins in the pathologic specimens were determined by immunohistochemical examinations. The effective rate of ozone group was significantly higher than that of control group (92% versus 64%, ). The wound size reduction was significantly more in ozone group than in control group (). After treatment, the expressions of VEGF, TGF-β, and PDGF proteins at day 11 were significantly higher in ozone group than in control group. Ozone therapy promotes the wound healing of DFUs via potential induction of VEGF, TGF-β, and PDGF at early stage of the treatment. (Clinical trial registry number is ChiCTR-TRC-14004415).


title: How to use , and influence , consumer social communications to improve business performance , reputation , and profit

abstract: How to use, and influence, consumer social communications to improve business performance, reputation, and profit.


title: Judgment of Information Quality and Cognitive Authority in the Web

abstract: In the WWW, people are engaging in interaction with more, and more diverse information than ever before, so there is an increasing need for information “filtering.” But because of the diversity of information resources, and the lack of traditional quality control on the WWW, the criteria of authority and quality of information that people have used for this purpose in past contexts may no longer be relevant. This paper reports on a study of people’s decision making with respect to quality and authority in the WWW. Seven facets of judgment of information quality were identified: source, content, format, presentation, currency, accuracy, and speed of loading. People mentioned source credibility with two levels: institutional level and individual level. Authority was identified as a underlying theme in source credibility. Institutional authority involved: institutional domain identified by URL; institution type; and institution reputation recognized by names. Individual authority involved: identification of creator/author; creator/author affiliation; and creator/author’s name. People were more or less concerned with evaluating information quality depending upon: the consequence of use of information; act or commitment based on information; and the focus of inquiry. It was also found that people believed that the web, as an institution, was less authoritative and less credible than other types of information systems.


title: The Influence of Early Respondents: Information Cascade Effects in Online Event Scheduling

abstract: Sequential group decision-making processes, such as online event scheduling, can be subject to social influence if the decisions involve individuals? subjective preferences and values. Indeed, prior work has shown that scheduling polls that allow respondents to see others' answers are more likely to succeed than polls that hide other responses, suggesting the impact of social influence and coordination. In this paper, we investigate whether this difference is due to information cascade effects in which later respondents adopt the decisions of earlier respondents. Analyzing more than 1.3 million Doodle polls, we found evidence that cascading effects take place during event scheduling, and in particular, that early respondents have a larger influence on the outcome of a poll than people who come late. Drawing on simulations of an event scheduling model, we compare possible interventions to mitigate this bias and show that we can optimize the success of polls by hiding the responses of a small percentage of low availability respondents.


title: Learning Latent Dynamics for Planning from Pixels

abstract: Planning has been very successful for control tasks with known environment dynamics. To leverage planning in unknown environments, the agent needs to learn the dynamics from interactions with the world. However, learning dynamics models that are accurate enough for planning has been a long-standing challenge, especially in image-based domains. We propose the Deep Planning Network (PlaNet), a purely model-based agent that learns the environment dynamics from images and chooses actions through fast online planning in latent space. To achieve high performance, the dynamics model must accurately predict the rewards ahead for multiple time steps. We approach this using a latent dynamics model with both deterministic and stochastic transition components. Moreover, we propose a multi-step variational inference objective that we name latent overshooting. Using only pixel observations, our agent solves continuous control tasks with contact dynamics, partial observability, and sparse rewards, which exceed the difficulty of tasks that were previously solved by planning with learned models. PlaNet uses substantially fewer episodes and reaches final performance close to and sometimes higher than strong model-free algorithms.


title: A Star Shaped Superwide Band Fractal Antenna for 5G Applications

abstract: A superwide band (SWB) microstrip fractal antenna for 5G communication is proposed in this paper and a snowflake like structure is achieved using star shaped fractal geometry. The configuration of the proposed design has a substrate of Rogers RT5880 having thickness of 0.787mm, dielectric constant of 2.2 and loss tangent of 0.0009. The presented antenna is simulated using CST Microwave studio and parameters of the proposed structure are optimized so that it is suitable to be operated in the frequency range from 17.22GHz to 180GHz with the fractional bandwidth of 165%. The overall dimension of the antenna is just $20\text{mm}\times 20\text{mm}$ which is very compact. The peak gain of 10dBi and average gain of 6.4dBi is acquired and over all appreciated gain is maintained throughout the entire range. Result curves such as S-parameter, VSWR, radiation pattern, gain and Input impedance have been depicted and discussed in the paper.


title: Fluctuation-Aware and Predictive Workflow Scheduling in Cost-Effective Infrastructure-as-a-Service Clouds

abstract: Cloud computing is becoming an increasingly popular platform for the execution of scientific applications such as scientific workflows. In contrast to grids and other traditional high-performance computing systems, clouds provide a customizable infrastructure where scientific workflows can provision desired resources ahead of the execution and set up a required software environment on virtual machines (VMs). Nevertheless, various challenges, especially its quality-of-service prediction and optimal scheduling, are yet to be addressed. Existing studies mainly consider workflow tasks to be executed with VMs having time-invariant, stochastic, or bounded performance and focus on minimizing workflow execution time or execution cost while meeting the quality-of-service requirements. This work considers time-varying performance and aims at minimizing the execution cost of workflow deployed on Infrastructure-as-a-Service clouds while satisfying Service-Level-Agreements with users. We employ time-series-based approaches to capture dynamic performance fluctuations, feed a genetic algorithm with predicted performance of VMs, and generate schedules at run-time. A case study based on real-world third-party IaaS clouds and some well-known scientific workflows show that our proposed approach outperforms traditional approaches, especially those considering time-invariant or bounded performance only.


title: Comparative Studies of Detecting Abusive Language on Twitter

abstract: The context-dependent nature of online aggression makes annotating large collections of data extremely difficult. Previously studied datasets in abusive language detection have been insufficient in size to efficiently train deep learning models. Recently, Hate and Abusive Speech on Twitter, a dataset much greater in size and reliability, has been released. However, this dataset has not been comprehensively studied to its potential. In this paper, we conduct the first comparative study of various learning models on Hate and Abusive Speech on Twitter, and discuss the possibility of using additional features and context data for improvements. Experimental results show that bidirectional GRU networks trained on word-level features, with Latent Topic Clustering modules, is the most accurate model scoring 0.805 F1.


title: A general framework for describing diversity within systems and similarity between systems with applications in informetrics

abstract: Building on the ideas of Stirling (J R Soc Interface, 4(15), 707–719, 2007) and Rafols and Meyer (Scientometrics, 82(2), 263–287, 2010), we borrow models of genetic distance based on gene diversity and propose a general conceptual framework to investigate the diversity within and among systems and the similarity between systems. This framework can be used to reveal the relationship of systems weighted by the similarity of the corresponding categories. Application of the framework to scientometrics is explored to evaluate the balance of national disciplinary structures, and the homogeneity of disciplinary structures between countries.


title: Cross-Document Coreference Resolution Using Latent Features

abstract: Over the last years, entity detection approaches which combine named entity recognition and entity linking have been used to detect mentions of RDF resources from a given reference knowledge base in unstructured data. In this paper, we address the problem of assigning a single URI to named entities which stand for the same real-object across documents but are not yet available in the reference knowledge base. This task is known as cross-document co-reference resolution and has been addressed by manifold approaches in the past. We present a preliminary study of a novel take on the task based on the use of latent features derived from matrix factorizations combined with parameter-free graph clustering. We study the influence of different parameters (window size, rank, hardening) on our approach by comparing the F-measures we achieve on the N3 benchmark. Our results suggest that using latent features leads to higher F-measures with an increase of up to 20.5% on datasets of the N3 collection.


title: The e-puck, a robot designed for education in engineering

abstract: Mobile robots have the potential to become the ideal tool to teach a broad range of engineering disciplines. Indeed, mobile robots are getting increasingly complex and accessible. They embed elements from diverse ﬁelds such as mechanics, digital electronics, automatic control, signal processing, embedded programming, and energy management. Moreover, they are attractive for students which increases their motivation to learn. However, the requirements of an effective education tool bring new constraints to robotics. This article presents the e-puck robot design, which specifically targets engineering education at university level. Thanks to its particular design, the e-puck can be used in a large spectrum of teaching activities, not strictly related to robotics. Through a systematic evaluation by the students, we show that the e-puck ﬁts this purpose and is appreciated by 90 percent of a large sample of students.


title: Metric-based data quality assessment - Developing and evaluating a probability-based currency metric

abstract: Data quality assessment has been discussed intensively in the literature and is critical in business. The importance of using up-to-date data in business, innovation, and decision-making processes has revealed the need for adequate metrics to assess the currency of data in information systems. In this paper, we propose a data quality metric for currency that is based on probability theory. Our metric allows for a reproducible configuration and a high level of automation when assessing the currency of attribute values. The metric values represent probabilities and can be integrated into a decision calculus (e.g., based on decision theory) to support decision-making. The evaluation of our metric consists of two main steps: (1) we define an instantiation of the metric for a real-use situation of a German mobile services provider to demonstrate both the applicability and the practical benefit of the approach; (2) we use publicly available real world data provided by the Federal Statistical Office of Germany and the German Institute of Economic Research to demonstrate its feasibility by defining an instantiation of the metric and to evaluate its strength (compared to existing approaches). We propose a well-founded probability-based data quality metric for currency.The metric values can be used in a decision calculus to support decision-making.The metric has successfully been applied in several real-use situations.The metric has significant advantages and can yield substantial practical benefit.


title: Hepta-band CPW bow-tie aperture antenna for WiFi (2.4 GHz and 5.5 GHz) and other bands

abstract: A Hepta-band Bowtie Antenna is proposed to function at two Wi-Fi bands and other bands. The following seven frequency bands are operational for the proposed antenna: 2.4, 3.9, 5.5, 6.7, 8.4, 10.2 and 12.8 GHz. The antenna structure consists of six loaded metallic strips in addition to interconnected loaded parasitic rectangular components near to the edge of the left side of the antenna. The loaded metallic strips resulted in five frequency bands and the interconnected parasitic components resulted in the remaining additional two frequency bands. The four metallic strips that are near the center of the antenna are symmetric while the remaining two metallic strips have different heights. All the metallic strips have the same width. The hepta-band antenna is simulated with HFSS to obtain the results of the return loss, radiation patterns and other antenna parameters. It will be shown from the simulation results that the antenna has seven operational frequency bands including the two Wi-Fi bands 2.4 and 5.5 GHz.


title: UPS Optimizes Its Air Network

abstract: Operations research specialists at UPS and the Massachusetts Institute of Technology (MIT) created a system to optimize the design of service networks for delivering express packages. The system simultaneously determines aircraft routes, fleet assignments, and package routings to ensure overnight delivery at minimal cost. It has become central to the UPS planning process, fundamentally transforming the process and the underlying planning assumptions. Planners now use the system's solutions and insights to improve plans. UPS management credits the system with identifying operational changes that have saved over $87 million between 2000 and 2002. Anticipated future savings are expected to be in the hundreds of millions of dollars.


title: Memory performance-related dynamic brain connectivity indicates pathological burden and genetic risk for Alzheimer’s disease

abstract: Background ::: The incidence of Alzheimer’s disease (AD) strongly relates to advanced age and progressive deposition of cerebral amyloid-beta (Aβ), hyperphosphorylated tau, and iron. The purpose of this study was to investigate the relationship between cerebral dynamic functional connectivity and variability of long-term cognitive performance in healthy, elderly subjects, allowing for local pathology and genetic risk.


title: Topological Methods for Flow Visualization

abstract: For visualization purposes, the depiction of the topology results in synthetic representations that transcribe the fundamental characteristics of the data. Further, topology-based visualization results in a dramatic decrease in the amount of data required for interpretation, which makes it very appealing for the analysis of large-scale datasets. This chapter introduces to the mathematical foundations of the topological approach to flow visualization along with a survey of existing techniques in this domain. The focus is on methods directly related to the depiction and analysis of the flow topology. The chapter considers vector fields and introduces basic theoretical notions. Theoretical notions result from the qualitative theory of dynamical systems. Nonlinear and parameter-dependent topologies are discussed in the chapter, along with the fundamental concept of bifurcation. The chapter also treats tensor fields and considers the topology of the eigenvector fields of symmetric, second-order tensor fields.


title: A wireless surveillance and safety system for mine workers based on Zigbee

abstract: This paper addresses a cost-effective, flexible solution of underground mine workers' safety. A module of MEMS based sensors are used for underground environment monitoring and automating progression of measurement data through digital wireless communication technique is proposed with high accuracy, smooth control and reliability. A microcontroller is used for collecting data and making decision, based on which the mine worker is informed through alarm as well as voice system. The voice system with both microphone and speaker, transforms into digital signal and effectively communicate wirelessly with the ground control centre computer. ZigBee, based on IEEE 802.15.4 standard is used for this short distance transmission between the hardware fitted with the mine worker and the ground control centre.


PAC-optimal, Non-parametric Algorithms and Bounds for Exploration in Concurrent MDPs with Delayed Updates


Systems and WBANs for Controlling Obesity


title: Ichthyosis: mechanisms of disease.

abstract: The disorders of cornification (ichthyoses) comprise acquired and inherited disorders characterized clinically by generalized scaling and histologically by hyperkeratosis. They may arise through defects in the production or maintenance of a normal cornified cell compartment, or both. The stratum corneum is composed of protein-enriched and lipid-depleted corneocytes ("bricks") surrounded by an intercellular domain ("mortar") composed of hydrophobic, lipid-enriched membrane bilayers, and containing desmosomes and a limited array of hydrolytic enzymes. Mechanisms whereby a genetic defect involving either the bricks or the mortar may result in abnormal stratum corneum retention are discussed using ichthyosis vulgaris and recessive X-linked ichthyosis as examples. In addition, epidermal hyperproliferation, which floods the cornified cell compartment with incompletely formed units, results in hyperkeratosis. To date, no primary disorders of epidermal hyperproliferation have been defined. Recent work, however, demonstrates that stratum corneum barrier function regulates epidermal DNA synthesis. For example, in essential fatty acid deficiency, barrier dysfunction is responsible at least in part ror the epidermal hyperproliferation. Defective barrier function due to defective lamellar body secretion may also underlie the phenotypic changes after birth in harlequin ichthyosis; that is, from the massive, constrictive hyperkeratosis of the newborn to an exfoliative erythroderma in survivors. The mechanisms whereby specific defects in cornification result in generalized scaling disease are only beginning to be defined. Yet, even at this early stage, the view of the stratum corneum as a tightly organized structure whose function is highly regulated is emerging. Hence, the disorders of cornification should provide important insights into stratum corneum structure and function.


title: Auction-based resource allocation in OpenFlow multi-tenant networks

abstract: In this paper, we investigate the allocation of network resources (such as FlowTable entries and bandwidth) in multi-tenant Software-Defined Networks (SDNs) that are managed by a FlowVisor. This resource allocation problem is modeled as an auction where the FlowVisor acts as the auctioneer and the network Controllers act as the bidders. The problem is analyzed by means of non-cooperative game theory, and it is shown that the auction admits a unique Nash Equilibrium (NE) under suitable conditions. Furthermore, a novel distributed learning procedure is provided that allows each Controller to reach the games unique NE in a few iterations by exploiting only locally available information. An implementation in OpenFlow-compliant SDNs is also proposed in a way that exploits native procedures already offered by OpenFlow. Finally, simulation results show that the proposed auction-based resource management scheme leads to significant improvements in network performance (for instance, achieving gains of up to 5 reduction in transmission delays).


title: Smart parking systems by using espresso lite 2.0

abstract: There are more and more vehicles on the road in every country, which have to be parked in spaces that are becoming increasingly packed. This is a big challenge especially for city planners, architects, and building owners. To meet this demand, the innovative and space saving-parking system is created. This is the new way of technology that can be applied to all users and it is more efficient than the previous parking systems. All buildings including hospitals, government building, shopping complex can access and apply this technology. This paper proposed a program that combined with the iOS system and connected through wireless connection. In addition, it will be connected to the mobile phones using this technology. Before the data can be displayed, the mobile phone has to be installed with BLINK's application that can be downloaded from the apple store. After this application is installed, then apps will show the available parking slot in the specific parking area. Any parking spaces can be assessed anywhere and anytime as long as users have internet connection to their smartphones. This paper proposed to develop the iOS apps that can show and update the parking spaces that are available and the hardware is designed by using ESPresso Lite 2.0 board.


title: Design and implementation of a door lock control based on a near field communication of a smartphone

abstract: In this paper we propose an integration design of both a near field communication (NFC) and a smartphone to achieve a door lock control system. This design consists of a built-in NFC capabilities of a smartphone combined with a dedicated application deemed to be a key to open the door by means of the logical link control protocol (LLCP) exchange together with a time stamp to match the user's own set of password information to verify who is a permissions user or not. When verified the specific door which is secured by this door lock control system immediately opens.