title: Improving high level synthesis optimization opportunity through polyhedral transformations

abstract: High level synthesis (HLS) is an important enabling technology for the adoption of hardware accelerator technologies. It promises the performance and energy efficiency of hardware designs with a lower barrier to entry in design expertise, and shorter design time. State-of-the-art high level synthesis now includes a wide variety of powerful optimizations that implement efficient hardware. These optimizations can implement some of the most important features generally performed in manual designs including parallel hardware units, pipelining of execution both within a hardware unit and between units, and fine-grained data communication. We may generally classify the optimizations as those that optimize hardware implementation within a code block (intra-block) and those that optimize communication and pipelining between code blocks (inter-block). However, both optimizations are in practice difficult to apply. Real-world applications contain data-dependent blocks of code and communicate through complex data access patterns. Existing high level synthesis tools cannot apply these powerful optimizations unless the code is inherently compatible, severely limiting the optimization opportunity. In this paper we present an integrated framework to model and enable both intra- and inter-block optimizations. This integrated technique substantially improves the opportunity to use the powerful HLS optimizations that implement parallelism, pipelining, and fine-grained communication. Our polyhedral model-based technique systematically defines a set of data access patterns, identifies effective data access patterns, and performs the loop transformations to enable the intra- and inter-block optimizations. Our framework automatically explores transformation options, performs code transformations, and inserts the appropriate HLS directives to implement the HLS optimizations. Furthermore, our framework can automatically generate the optimized communication blocks for fine-grained communication between hardware blocks. Experimental evaluation demonstrates that we can achieve an average of 6.04X speedup over the high level synthesis solution without our transformations to enable intra- and inter-block optimizations.


Data Analytics: Industrial Perspective & Solutions for Streaming Data


title: Implementation of wireless temperature and humidity monitoring on an embedded device

abstract: This paper presents an implementation of wireless temperature and humidity monitoring on a Raspberry Pi. The objective of this project is to design a monitoring temperature and humidity reading kit which implemented on Raspberry Pi. The study focuses on the embedded project for sensing the temperature and humidity in room and library area. The method of analysis is performed on Raspberry Pi implementation with interfacing of a web server, Thingspeak that has an API and posting and reviewing the data to the channel. Python language is encoded in Raspberry Pi Linux OS with DHT11 temperature and humidity sensor for retrieving the data. Hence, the temperature and humidity reading implemented on Raspberry Pi has been designed and it can be concluded that the increasing in temperature indicates to decreasing of humidity levels based on the result collected.


title: SensePods: A ZigBee-Based Tangible Smart Home Interface

abstract: Low-cost sensors and ubiquitous wireless networking is enabling novel ways in which homeowners can interact with their smart homes. Many complementary approaches like using voice commands, direct interaction by using touch or weight, or by using body gestures are emerging. This paper shows the design and implementation of a novel low-power, low-cost, hand-held wireless device called a SensePod. SensePods can be used by a consumer to interact with a smart home using simple gestures like rubbing, taping or rolling the device on any home surface like a dining table. The device is only 4.5 cm long, forms an ad-hoc wireless network using the ZigBee protocol, and can be easily interfaced to existing home management systems using a universal serial bus port. The gestures in each device can be programmed to control various objects of a smart home like smart curtains, for example. Hidden Markov models were used to train the device to recognize various gestures. The device was tested with a variety of gestures and has a recognition rate of over 99.7%, and a response time of less than two milliseconds.


title: Rhinoplasty: Surface Aesthetics and Surgical Techniques

abstract: Surface aesthetics of the attractive nose are created by certain lines, shadows, and highlights, with specific proportions and breakpoints. Our evaluation of the nasal surface aesthetics is achieved using the concept of geometric polygons as aesthetic subunits, both to define the existing deformity and the aesthetic goals. Surgical techniques have been developed and modified to achieve the desired surface appearance, and those are detailed in this article. The principles of geometric polygons allow the surgeon to analyze the deformities of the nose, to define an operative plan to achieve specific goals, and to select the appropriate operative technique. These aesthetic concepts and surgical techniques were used in 257 consecutive rhinoplasties performed in the past 3 years by the principal author (B.C.).


Therapeutic Efficacy of Conventional Treatment Combined with Manuka Honey in the Treatment of Patients with Diabetic Foot Ulcers : A Randomized Controlled Study


title: Using optical tracking for kinematic testing of medical robots

abstract: BACKGROUND ::: In image-guided robotic interventions, an error component is related to the positioning error of the manipulator. Therefore, measuring the kinematic error is required during robot development. However, no specialized measurement device exists for this task. This study explores the possibility of using optical tracking for robot measurement. ::: ::: ::: METHODS ::: A CNC machine is used to position an optical marker, generating a gold standard reference. Repeated position measurements are acquired with an NDI Polaris Hybrid® optical tracker at each static location, and averaged. These measurements are compared to the reference set. ::: ::: ::: RESULTS ::: Averaging repeated static position measurements improves precision (200-500 samples). Measurement accuracy ranges between 44 μm and 137 μm in close proximity of the tracker. ::: ::: ::: CONCLUSIONS ::: Repeated static position measurements in the near field of view enable the optical tracker to outperform its general-purpose accuracy specification. Optical tracking may be used to test robot kinematics with a high degree of accuracy.


title: Let's go to the whiteboard: how and why software developers use drawings

abstract: Software developers are rooted in the written form of their code, yet they often draw diagrams representing their code. Unfortunately, we still know little about how and why they create these diagrams, and so there is little research to inform the design of visual tools to support developers' work. This paper presents findings from semi-structured interviews that have been validated with a structured survey. Results show that most of the diagrams had a transient nature because of the high cost of changing whiteboard sketches to electronic renderings. Diagrams that documented design decisions were often externalized in these temporary drawings and then subsequently lost. Current visualization tools and the software development practices that we observed do not solve these issues, but these results suggest several directions for future research.


title: The influence of winding's pole pairs on position error of linear resolvers

abstract: Using linear position sensors is necessary in linear motion control systems. In this paper the influence of winding's pole pairs on the accuracy of linear resolver is studied. 2-D and 3-D time stepping finite element method (FEM) is used to calculate the performance characteristics of the resolver. The effect of finite mover's length, transverse edge effect and the end windings length are discussed in detail. Then a prototype of the studied resolver along with its experimental test setup is built. Good correlation between simulation and experimental results verified the simulation process.


VUE: A CONCEPT MAPPING TOOL FOR DIGITAL CONTENT


title: Improving Video Game Development: Facilitating Heterogeneous Team Collaboration through Flexible Software Processes

abstract: Based on our observations of Austrian video game software development (VGSD) practices we identified a lack of systematic processes/method support and inefficient collaboration between various involved disciplines, i.e. engineers and artists. VGSD includes heterogeneous disciplines, e.g. creative arts, game/content design, and software. Nevertheless, improving team collaboration and process support is an ongoing challenge to enable a comprehensive view on game development projects. Lessons learned from software engineering practices can help game developers to increase game development processes within a heterogeneous environment. Based on a state of the practice survey in the Austrian games industry, this paper presents (a) first results with focus on process/method support and (b) suggests a candidate flexible process approach based on Scrum to improve VGSD and team collaboration. Results showed (a) a trend to highly flexible software processes involving various disciplines and (b) identified the suggested flexible process approach as feasible and useful for project application.


title: Handwritten-Digit Recognition by Hybrid Convolutional Neural Network based on HfO2 Memristive Spiking-Neuron

abstract: Although there is a huge progress in complementary-metal-oxide-semiconductor (CMOS) technology, construction of an artificial neural network using CMOS technology to realize the functionality comparable with that of human cerebral cortex containing 1010-1011 neurons is still of great challenge. Recently, phase change memristor neuron has been proposed to realize a human-brain level neural network operating at a high speed while consuming a small amount of power and having a high integration density. Although memristor neuron can be scaled down to nanometer, integration of 1010-1011 neurons still faces many problems in circuit complexity, chip area, power consumption, etc. In this work, we propose a CMOS compatible HfO2 memristor neuron that can be well integrated with silicon circuits. A hybrid Convolutional Neural Network (CNN) based on the HfO2 memristor neuron is proposed and constructed. In the hybrid CNN, one memristive neuron can behave as multiple physical neurons based on the Time Division Multiplexing Access (TDMA) technique. Handwritten digit recognition is demonstrated in the hybrid CNN with a memristive neuron acting as 784 physical neurons. This work paves the way towards substantially shrinking the amount of neurons required in hardware and realization of more complex or even human cerebral cortex level memristive neural networks.


title: Tapered slot antenna for UWB applications

abstract: Tapered slot antennas are a class of traveling wave antenna having promising application due to their wide bandwidth and symmetric radiation patterns. This paper discusses the characteristics of a tapered slot antenna in antipodal configuration and printed with elliptically shaped radiators. Simulation and experimental studies show good radiation and impedance characteristics and its usefulness for pulsed application.


Analysis of Performing Secure Remote Vehicle Diagnostics


title: Necklace: An efficient cuckoo hashing scheme for cloud storage services

abstract: With the rapid growth of data, query performance is an important concern in cloud storage applications. To reduce the query response time, cuckoo hashing via d hash functions has been adopted to achieve O(1) query efficiency. However, in practice the cuckoo hashing consumes a large amount of system resources, since an item insertion may suffer from frequent “kick-out” operations or even endless loops. In order to address this problem, we propose an efficient loop-oblivious scheme, called Necklace, in the cloud. The idea behind Necklace is to minimize data migration among servers and alleviate the collisions in the insertion operation. We identify the shortest path via indexing the auxiliary table, without retrieving the actual storage contents or carrying out the tentative “kick-out” operations. We have implemented Necklace in a real cloud system and examined the performance by using a real-world trace. Extensive experimental results demonstrate the efficiency and efficacy of Necklace.


title: Security assessment for Personal Health Data Management System

abstract: In this paper, we explore and discuss the data security issues in a real-time remote patient health monitoring system, called the Personal Health Data Management System (PHDMS). The health monitoring system examined for this study consists of multiple wireless sensors, central data coordinator called the Wireless Intelligent Personal Communication Node (W-iPCN), an Android smartphone, and a remotely located central medical server. The PHDMS transmits raw sensor data from the W-iPCN to the Android smartphone using the Bluetooth connection. In addition, the system transmits raw sensor data using the Wi-Fi or cellular broadband connection between the Android smartphone and the central medical server. As a case study, we present a security protection scheme to encrypt and decrypt raw sensor data using multiple cryptologic algorithms. We applied this security protection scheme on the W-iPCN, Android smartphone and the central medical server to test their ability to process and transmit sensor data in real-time. This security assessment aims to observe the performance of data encryption and decryption, and real-time feasibility to remotely monitor patients' health.


title: Nondissipative turn-off snubber alleviates switching power dissipation, second-breakdown stress and VCE overshoot: Analysis, design procedure and experimental verification

abstract: A nondissipative, resonant turn-off snubber eliminates the power dissipation of the traditional R-C-diode snubber, while providing protection against transistor breakdown from energy stored in the power-transformer leakage inductance. Four operating modes are analyzed. Complete design procedures are given, with experimental verification.


title: Multi-document abstractive summarization using ILP based multi-sentence compression

abstract: Abstractive summarization is an ideal form of summarization since it can synthesize information from multiple documents to create concise informative summaries. In this work, we aim at developing an abstractive summarizer. First, our proposed approach identifies the most important document in the multi-document set. The sentences in the most important document are aligned to sentences in other documents to generate clusters of similar sentences. Second, we generate K-shortest paths from the sentences in each cluster using a word-graph structure. Finally, we select sentences from the set of shortest paths generated from all the clusters employing a novel integer linear programming (ILP) model with the objective of maximizing information content and readability of the final summary. Our ILP model represents the shortest paths as binary variables and considers the length of the path, information score and linguistic quality score in the objective function. Experimental results on the DUC 2004 and 2005 multi-document summarization datasets show that our proposed approach outperforms all the baselines and state-of-the-art extractive summarizers as measured by the ROUGE scores. Our method also outperforms a recent abstractive summarization technique. In manual evaluation, our approach also achieves promising results on informativeness and readability.


title: iForensics: Forensic Analysis of Instant Messaging on Smart Phones

abstract: Smart phones with Internet capability are growing in popularity, due to many of their useful capabilities. Among other handy features of smart phones, Instant Messaging (IM) is very popular due to the level of convenience it provides in interpersonal communications. As the usage of IM on smart phone is increasing rapidly, it is important to take measures in advance from forensic standpoint forecasting the potential use of it in cyber crimes such as the cyber stalking and cyber bullying. Although, current IM applications for smart phones are in most cases a downsized version of the one used on traditional computers, diverse structure of file systems and storage device on different smart phones pose unique challenges to forensic examiners for recovering digital evidences of a conversation under investigation. In this work, we study and report the forensic analysis of three different IMs: AIM, Yahoo! Messenger and Google Talk, (both client based and web based version) on Apple iPhone. Our results show that the forensic analysis of IMs on smart phones has significant value and needs further attention.


title: Small-footprint Deep Neural Networks with Highway Connections for Speech Recognition

abstract: For speech recognition, deep neural networks (DNNs) have significantly improved the recognition accuracy in most of benchmark datasets and application domains. However, compared to the conventional Gaussian mixture models, DNN-based acoustic models usually have much larger number of model parameters, making it challenging for their applications in resource constrained platforms, e.g., mobile devices. In this paper, we study the application of the recently proposed highway network to train small-footprint DNNs, which are thinner and deeper, and have significantly smaller number of model parameters compared to conventional DNNs. We investigated this approach on the AMI meeting speech transcription corpus which has around 80 hours of audio data. The highway neural networks constantly outperformed their plain DNN counterparts, and the number of model parameters can be reduced significantly without sacrificing the recognition accuracy.


title: Development of propulsion mechanism for Robot Manta Ray

abstract: Manta rays are very efficient swimmers which are able to travel at high speed over large distances despite their large sizes. This paper covers the design, experiment and prototype testing of a propulsive mechanism for Robot Manta Ray. The propulsive mechanism and the pectoral fins of the Robot Manta Ray were designed based on some of the hydrodynamic concepts of manta ray locomotion. The designs focus on simplicity and utilize passive flexibility to generate high forward thrust while achieving high energy efficiency. In order to find out the effects of flexibility on the fins, experimental tests were conducted with fin materials of various thicknesses. Also, experimental tests were performed to find out the effects of changing amplitude and frequency of flapping. A prototype of the Robot Manta Ray was designed and fabricated in order to test the swimming capability of the propulsion mechanism. The prototype was tested in a swimming pool to obtain its swimming speed.


title: Fusing LIDAR and images for pedestrian detection using convolutional neural networks

abstract: In this paper, we explore various aspects of fusing LIDAR and color imagery for pedestrian detection in the context of convolutional neural networks (CNNs), which have recently become state-of-art for many vision problems. We incorporate LIDAR by up-sampling the point cloud to a dense depth map and then extracting three features representing different aspects of the 3D scene. We then use those features as extra image channels. Specifically, we leverage recent work on HHA [9] (horizontal disparity, height above ground, and angle) representations, adapting the code to work on up-sampled LIDAR rather than Microsoft Kinect depth maps. We show, for the first time, that such a representation is applicable to up-sampled LIDAR data, despite its sparsity. Since CNNs learn a deep hierarchy of feature representations, we then explore the question: At what level of representation should we fuse this additional information with the original RGB image channels? We use the KITTI pedestrian detection dataset for our exploration. We first replicate the finding that region-CNNs (R-CNNs) [8] can outperform the original proposal mechanism using only RGB images, but only if fine-tuning is employed. Then, we show that: 1) using HHA features and RGB images performs better than RGB-only, even without any fine-tuning using large RGB web data, 2) fusing RGB and HHA achieves the strongest results if done late, but, under a parameter or computational budget, is best done at the early to middle layers of the hierarchical representation, which tend to represent midlevel features rather than low (e.g. edges) or high (e.g. object class decision) level features, 3) some of the less successful methods have the most parameters, indicating that increased classification accuracy is not simply a function of increased capacity in the neural network.


title: The Structure of the Relational Database Model

abstract: The book presents an overview of the most fundamental aspects of the theory that underlies the Relational Database Model. It provides exact definitions of widely accepted concepts, and also covers some recent research topics, making the book very suitable in classes on database theory.


title: FACTO: A Fact Lookup Engine Based on Web Tables

abstract: Recently answers for fact lookup queries have appeared on major search engines. For example, for the query Barack Obama date of birth Google directly shows "4 August 1961" above its regular results. In this paper, we describe FACTO, an end-to-end system for answering fact lookup queries for web search. FACTO extracts structured data from tables on the web, aggregates and cleans such data and stores them in a database. Given a web search query, FACTO will decide if it asks for facts in this database, and provides the most confident answer when possible. FACTO achieves higher precision and comparable coverage comparing with the fact lookup engines by Google and Ask.com, although FACTO is developed by a very small team. We present the challenges and our solutions in developing every component of FACTO. Some solutions are based on existing technologies, and many others are novel approaches proposed by us.


title: Learning to Recommend Point-of-Interest with the Weighted Bayesian Personalized Ranking Method in LBSNs

abstract: Point-of-interest (POI) recommendation has been well studied in recent years. However, most of the existing methods focus on the recommendation scenarios where users can provide explicit feedback. In most cases, however, the feedback is not explicit, but implicit. For example, we can only get a user’s check-in behaviors from the history of what POIs she/he has visited, but never know how much she/he likes and why she/he does not like them. Recently, some researchers have noticed this problem and began to learn the user preferences from the partial order of POIs. However, these works give equal weight to each POI pair and cannot distinguish the contributions from different POI pairs. Intuitively, for the two POIs in a POI pair, the larger the frequency difference of being visited and the farther the geographical distance between them, the higher the contribution of this POI pair to the ranking function. Based on the above observations, we propose a weighted ranking method for POI recommendation. Specifically, we first introduce a Bayesian personalized ranking criterion designed for implicit feedback to POI recommendation. To fully utilize the partial order of POIs, we then treat the cost function in a weighted way, that is give each POI pair a different weight according to their frequency of being visited and the geographical distance between them. Data analysis and experimental results on two real-world datasets demonstrate the existence of user preference on different POI pairs and the effectiveness of our weighted ranking method.


title: Texture indexes and gray level size zone matrix. Application to cell nuclei classification

abstract: In this paper, we present a study on the characterization and the classification of textures. This study is performed using a set of values obtained by the computation of indexes. To obtain these indexes, we extract a set of data with two techniques: the computation of matrices which are statistical representations of the texture and the computation of "measures". These matrices and measures are subsequently used as parameters of a function bringing real or discrete values which give information about texture features. A model of texture characterization is built based on this numerical information, for example to classify textures. An application is proposed to classify cells nuclei in order to diagnose patients affected by the Progeria disease.


title: Metacognitive evaluation, self-relevance, and the right prefrontal cortex

abstract: Abstract The capability to foster metacognitive evaluations (MEs) of oneself and others represents a major component of conscious awareness. Separate emerging lines of brain activation research examining ME have converged on the medial prefrontal cortex as a common finding. The current functional magnetic resonance imaging (fMRI) study utilized a task that directly compared ME associated with two referentially discrete targets: oneself and a significant other (e.g., close friend or relative). Nineteen healthy young adult participants (mean age 24; 9 female, 10 male) were required to make yes/no decisions based on individually presented trait adjectives across two separate referential conditions and a nonreferential control condition: self-evaluation (SE), significant other-evaluation (OE), and semantic positivity-evaluation (SPE), respectively. Results of random-effects group analyses indicated a common area of medial prefrontal activation during the ME conditions of self- and other-evaluation versus the baseline semantic positivity-evaluation condition. A direct comparison of brain activation between the self and other evaluative conditions revealed a right dorsolateral prefrontal response that was significantly more active when making evaluations about the self. The present study extends upon the prior findings of separate research domains by directly comparing the cerebral response to ME about the self and others, and finding right PFC activation increases as a function of self-relevance.


title: Machine Learning in Adversarial Settings

abstract: Recent advances in machine learning have led to innovative applications and services that use computational structures to reason about complex phenomenon. Over the past several years, the security and machine-learning communities have developed novel techniques for constructing adversarial samples--malicious inputs crafted to mislead (and therefore corrupt the integrity of) systems built on computationally learned models. The authors consider the underlying causes of adversarial samples and the future countermeasures that might mitigate them.


title: The gist of maps - summarizing experience for lifelong localization

abstract: Robust, scalable place recognition is a core competency for many robotic applications. However, when revisiting places over and over, many state-of-the-art approaches exhibit reduced performance in terms of computation and memory complexity and in terms of accuracy. For successful deployment of robots over long time scales, we must develop algorithms that get better with repeated visits to the same environment, while still working within a fixed computational budget. This paper presents and evaluates an algorithm that alternates between online place recognition and offline map maintenance with the goal of producing the best performance with a fixed map size. At the core of the algorithm is the concept of a Summary Map, a reduced map representation that includes only the landmarks that are deemed most useful for place recognition. To assign landmarks to the map, we use a scoring function that ranks the utility of each landmark and a sampling policy that selects the landmarks for each place. The Summary Map can then be used by any descriptor-based inference method for constant-complexity online place recognition. We evaluate a number of scoring functions and sampling policies and show that it is possible to build and maintain maps of a constant size and that place-recognition performance improves over multiple visits.


title: A systematic review of the impact of the use of social networking sites on body image and disordered eating outcomes

abstract: Abstract A large body of literature has demonstrated mass media effects on body image and disordered eating. More recently, research in this area has turned to ‘new’ forms of media, such as the Internet, and particularly Social Networking Sites (SNSs). A systematic search for peer-reviewed articles on SNS use and body image and eating disorders resulted in 20 studies meeting specific inclusion criteria. As a whole, these articles demonstrated that use of SNSs is associated with body image and disordered eating. Specific SNS activities, such as viewing and uploading photos and seeking negative feedback via status updates, were identified as particularly problematic. A small number of studies also addressed underlying processes and found that appearance-based social comparison mediated the relationship between SNS use and body image and eating concerns. Gender was not found to be a moderating factor. It was concluded that, although there is a good deal of correlational research supporting the maladaptive effect of SNS use on body image and disordered eating, more longitudinal and experimental studies are needed.


title: Predicting hardware failure using machine learning

abstract: The Weibull distribution has historically been the Reliability Engineer's best tool for describing the probability of failures over time [1]. While this technique is very accurate at describing failure distributions for large populations of components, it works very poorly at predicting the time until failure of an individual component. The mean time until failure is often used to predict times until failure of individual components, but this value may vary greatly with actual times until failure. With the advent of machine learning techniques, the ability to learn from past behavior in order to predict future behavior makes it possible to predict an individual component's time until failure much more accurately. In this paper, we explore the predictive abilities of a machine learning technique to improve upon our ability to predict individual component times until failure in advance of actual failure. Once failure is predicted, an impending problem can be fixed before it actually occurs. This paper brings to light a machine learning approach for predicting individual component times until failure that we will show is far more accurate than the traditional MTBF approach. The algorithm built was able to monitor the health of 14 hardware samples and notify us of an impending failure well ahead of actual failure, providing adequate time to fix the problem before actual failure occurred.


title: Application of Data Mining In Marketing

abstract: One of the most important problems in modern finance is finding efficient ways to summarize and visualize the stock market data to give individuals or institutions useful information about the market behavior for investment decisions. The enormous amount of valuable data generated by the stock market has attracted researchers to explore this problem domain using different methodologies. Potential significant benefits of solving these problems motivated extensive research for years. The research in data mining has gained a high attraction due to the importance of its applications and the increasing generation information. This paper provides an overview of application of data mining techniques such as decision tree. Also, this paper reveals progressive applications in addition to existing gap and less considered area and determines the future works for researchers.


title: Automatic Question Generation from Queries

abstract: With increasing popularity of communitybased question answering services such as Yahoo! Answers, huge amounts of user generated questions are available. In this paper, we propose using these data along with search engine query logs to create a question generation shared task that aims to automatically generate questions given a query. At least two benefits could result from such a task. First, it can be used to rank possible candidate questions; therefore, it would enable search of question answer archives. Second, it could be used as a more informative and richer way of query expansion to guide or aid search.


title: Perception of internal auditor on the use of Generalized Audit Software

abstract: The objective of this research is to explore the perception of internal auditor in public companies on the use of Generalized Audit Software (GAS) to improve the transparency and accountability of financial report. Qualitative approach was used by conducting face-to-face semi structured interview with internal auditors from Indonesian public companies. This research uses Agency Theory and Technology Acceptance Model (TAM) as underpinning theories, therefore this research provides academic contribution not only in auditing area but also in information technology. The results indicated that not all participants agreed that GAS is helping them in producing a transparent and accountable financial report.


title: Nedocromil sodium in allergen-induced bronchial responses and airway hyperresponsiveness in allergic sheep.

abstract: We examined the effects of nedocromil sodium, a new drug developed for the treatment of reversible obstructive airway disease, on allergen-induced early and late bronchial responses and the development of airway hyperresponsiveness 24 h after challenge in nine allergic sheep. On occasions greater than 2 wk apart the sheep were treated with 1) placebo aerosol (buffered saline) before and 3 h after antigen challenge, 2) an aerosol of nedocromil sodium (1 mg/kg in 3 ml buffered saline) before antigen challenge and placebo 3 h after challenge, and 3) placebo aerosol before and nedocromil sodium aerosol 3 h after challenge. Early and late bronchial responses were determined by measuring specific lung resistance (sRL) before and periodically after challenge. Airway responsiveness was assessed by determining from dose-response curves the carbachol concentration (in % wt/vol) that increased sRL to 5 cmH2O/s. In the placebo trial, antigen challenge resulted in early and late increases in sRL over a base line of 353 +/- 32 and 131 +/- 17% (SE), respectively. Both early and late increases in sRL were blocked (P less than 0.05) when the sheep were pretreated with nedocromil sodium. When nedocromil was given after the early response, the late response was reduced significantly. Eight of nine sheep developed airway hyperresponsiveness 24 h after antigen challenge. In these eight sheep, carbachol concentration before antigen challenge was 2.6 +/- 0.3%, 24 h later carbachol concentration was significantly lower (1.8 +/- 0.3%). Both nedocromil sodium treatments blocked (P less than 0.05) this antigen-induced airway hyperresponsiveness.(ABSTRACT TRUNCATED AT 250 WORDS)


title: Statistics review 14: Logistic regression

abstract: This review introduces logistic regression, which is a method for modelling the dependence of a binary response variable on one or more explanatory variables. Continuous and categorical explanatory variables are considered.


title: Bundle-branch block as a risk factor in noncardiac surgery.

abstract: BACKGROUND ::: Despite extensive data examining perioperative risk in patients with coronary artery disease, little attention has been devoted to the implications of conduction system abnormalities. ::: ::: ::: OBJECTIVE ::: To define the clinical significance of bundle-branch block (BBB) as a perioperative risk factor. ::: ::: ::: METHODS ::: Retrospective, cohort-controlled study of all noncardiac, nonophthalmologic, adult patients with BBB seen in our preoperative evaluation center. Medical charts were reviewed for data regarding cardiovascular disease, surgical procedure, type of anesthesia, intravascular monitoring, and perioperative complications. ::: ::: ::: RESULTS ::: Bundle-branch block was present in 455 patients. Right BBB (RBBB) was more common than left BBB (LBBB) (73.8% vs 26.2%). Three patients with LBBB and 1 patient with RBBB died; 1 patient had a supraventricular tachyarrhythmia. Three of the 4 deaths were sepsis related. There were 2 (0.4%) deaths in the control group. There was no difference in mortality between BBB and control groups (P = .32). Subgroup analysis suggested an increased risk for death in patients with LBBB vs controls (P = .06; odds ratio, 6.0; 95% confidence interval, 1.2-100.0) and vs RBBB (P = .06; odds ratio, 8.7; 95% confidence interval, 1.2-100.0). ::: ::: ::: CONCLUSIONS ::: The presence of BBB is not associated with a high incidence of postoperative cardiac complications. Perioperative mortality is not increased in patients with RBBB and not directly attributable to cardiac complications in patients with LBBB. These data suggest that the presence of BBB does not significantly increase the likelihood of cardiac complications following surgery, but that patients with LBBB may not tolerate the stress of perioperative noncardiac complications.


title: Agent-oriented supply-chain management

abstract: The supply chain is a worldwide network of suppliers, factories, warehouses, distribution centers, and retailers through which raw materials are acquired, transformed, and delivered to customers. In recent years, a new software architecture for managing the supply chain at the tactical and operational levels has emerged. It views the supply chain as composed of a set of intelligent software agents, each responsible for one or more activities in the supply chain and each interacting with other agents in the planning and execution of their responsibilities. This paper investigates issues and presents solutions for the construction of such an agent-oriented software architecture. The approach relies on the use of an agent building shell, providing generic, reusable, and guaranteed components and services for communicative-act-based communication, conversational coordination, role-based organization modeling, and others. Using these components, we show two nontrivial agent-based supply-chain architectures able to support complex cooperative work and the management of perturbation caused by stochastic events in the supply chain.


title: [Skin pigmentation induced by meropenem and levofloxacin].

abstract: BACKGROUND ::: Various cutaneous side-effects, including, exanthema, pruritus, urticaria and Lyell or Stevens-Johnson syndrome, have been reported with meropenem (carbapenem), a rarely-prescribed antibiotic. Levofloxacin (fluoroquinolone), a more frequently prescribed antibiotic, has similar cutaneous side-effects, as well as photosensitivity. We report a case of cutaneous hyperpigmentation induced by meropenem and levofloxacin. ::: ::: ::: PATIENTS AND METHODS ::: A 67-year-old male was treated with meropenem (1g×4 daily), levofloxacin (500mg twice daily) and amikacin (500mg daily) for 2 weeks, followed by meropenem, levofloxacin and rifampicin (600mg twice daily) for 4 weeks for osteitis of the fifth metatarsal. Three weeks after initiation of antibiotic therapy, dark hyperpigmentation appeared on the lower limbs, predominantly on the anterior aspects of the legs. Histology revealed dark, perivascular and interstitial deposits throughout the dermis, which stained with both Fontana-Masson and Perls stains. Infrared microspectroscopy revealed meropenem in the dermis of involved skin. After withdrawal of the antibiotics, the pigmentation subsided slowly. ::: ::: ::: DISCUSSION ::: Similar cases of cutaneous hyperpigmentation have been reported after use of minocycline. In these cases, histological examination also showed iron and/or melanin deposits within the dermis, but the nature of the causative pigment remains unclear. In our case, infrared spectroscopy enabled us to identify meropenem in the dermis. Two cases of cutaneous hyperpigmentation have been reported following use of levofloxacin, and the results of histological examination were similar. This is the first case of cutaneous hyperpigmentation induced by meropenem.


title: Aberrant miRNA promoter methylation and EMT-involving miRNAs in breast cancer metastasis: Diagnosis and therapeutic implications.

abstract: Breast cancer (BC) is the most prevalent cancer in women worldwide. Although extensive studies are ongoing concerning its intricate molecular mechanisms, development of novel therapies and more accurate diagnostic and prognostic approaches is still a challenge. Epithelial-mesenchymal transition (EMT) enables the invasion of metastatic cancer cells and has recently been highlighted in a Cancer Stem Cell (CSC) model of BC. Epigenetic events as well as miRNA expression are the master regulators of tumorigenesis and add a further layer to the complexity of BC pathogenesis. The miRNAs are related to epigenetic event and additionally affect epigenetic pathways. Recent evidence demonstrates that epigenetic mechanisms such as DNA methylation may control miRNA expression. Because each miRNA may regulate several target genes, dysregulation of miRNA caused by aberrant DNA methylation patterns of the locus may influence important downstream pathways. Furthermore, some miRNAs is believed to regulate important DNA methylator factors. Any disruption or modification of this intricate network can contribute to the disease process; thus, it is essential to understand these changes. Advancements in new sequencing technologies to detect DNA methylation patterns has provided the opportunity to determine differentially methylated regions (DMRs) of the miRNA locus and their effect on expression profiles to improve BC diagnosis and treatment. The current review examines the interplay of DNA methylation mechanisms and miRNA function in invasive tumorigenesis, specifically EMT and CSC of BC, to highlight its potential for advancements on BC etiology, diagnosis, and therapy.


title: Reuters tracer: Toward automated news production using large scale social media data

abstract: To deal with the sheer volume of information and gain competitive advantage, the news industry has started to explore and invest in news automation. In this paper, we present Reuters Tracer, a system that automates end-to-end news production using Twitter data. It is capable of detecting, classifying, annotating, and disseminating news in real time for Reuters journalists without manual intervention. In contrast to other similar systems, Tracer is topic and domain agnostic. It has a bottom-up approach to news detection, and does not rely on a predefined set of sources or subjects. Instead, it identifies emerging conversations from 12+ million tweets per day and selects those that are news-like. Then, it contextualizes each story by adding a summary and a topic to it, estimating its newsworthiness, veracity, novelty, and scope, and geotags it. Designing algorithms to generate news that meets the standards of Reuters journalists in accuracy and timeliness is quite challenging. But Tracer is able to achieve competitive precision, recall, timeliness, and veracity on news detection and delivery. In this paper, we reveal our key algorithm designs and evaluations that helped us achieve this goal, and lessons learned along the way.


title: A 95-dBA DR Digital Audio Class-D Amplifier Using a Calibrated Digital-to-Pulse Converter

abstract: A digital class-D amplifier (CDA) converts an audio digital stream into sound directly and power-efficiently. It first encodes the pulse-code-modulated audio input into a digital pulse-width-modulated (PWM) signal. It needs a digital-to-pulse converter (DPC) to translate this digital PWM signal into a series of analog binary pulses accurately. We report a 5-3 segmented DPC that includes both a counter and a delay line for pulse width conversion. The timing skews along the delay line are detected using a zero-crossing detection scheme and corrected in the digital domain. This calibration can operate continuously in the background. A digital CDA prototype was fabricated using a 65-nm CMOS technology. It includes the aforementioned PWM modulator and DPC. It also integrated an open-loop switching driver to deliver the DPC’s output to a speaker. This digital CDA consumes $875~ {\mu }\text {W}$ under a 1-V supply when the input is zero and no output power is transferred to the external load. It can deliver 13.3 mW to a $32~ {\Omega }$ resistive load in the H-bridge topology with 89% power efficiency. For a 1-kHz sine-wave input, it achieves 95 dBA dynamic range, 93.6 dBA peak SNR, 86.4 dBA peak SNDR, and 0.006% THD at -2-dBFS input level. The core area of the chip is $ {0.87\times 0.5\,\,\text {mm}^{2}}$ .


title: Design and Analysis of a Hardware-Efficient Compressed Sensing Architecture for Data Compression in Wireless Sensors

abstract: This work introduces the use of compressed sensing (CS) algorithms for data compression in wireless sensors to address the energy and telemetry bandwidth constraints common to wireless sensor nodes. Circuit models of both analog and digital implementations of the CS system are presented that enable analysis of the power/performance costs associated with the design space for any potential CS application, including analog-to-information converters (AIC). Results of the analysis show that a digital implementation is significantly more energy-efficient for the wireless sensor space where signals require high gain and medium to high resolutions. The resulting circuit architecture is implemented in a 90 nm CMOS process. Measured power results correlate well with the circuit models, and the test system demonstrates continuous, on-the-fly data processing, resulting in more than an order of magnitude compression for electroencephalography (EEG) signals while consuming only 1.9 μW at 0.6 V for sub-20 kS/s sampling rates. The design and measurement of the proposed architecture is presented in the context of medical sensors, however the tools and insights are generally applicable to any sparse data acquisition.


title: Tetrasomy 15q25.2 → qter identified with SNP microarray in a patient with multiple anomalies including complex cardiovascular malformation

abstract: We report on a male neonate with prenatally diagnosed mosaicism for a supernumerary marker chromosome and multiple congenital anomalies. Prenatal ultrasound imaging revealed a heart defect, pleural effusion, clubbed feet, and absent right kidney. Clinical cytogenetic analysis of amniocytes identified a marker chromosome present in 10 out of 15 cells analyzed. Clinical evaluation of the neonate revealed distinct facial features, complex heart defects, solitary left kidney, and arachnodactyly. Chromosome analysis of lymphocytes demonstrated an abnormal male karyotype with a marker chromosome present in all 24 cells examined. To identify the marker chromosome, SNP microarray analysis was performed which detected the presence of a two copy gain of 17.7 Mb of DNA from the distal long arm of chromosome 15 (15q25.2-qter). FISH analysis using a probe specific to the 15q26.3 region showed one signal on each normal 15q and two signals, one on each arm of the marker chromosome resulting in four copies. Distal tetrasomy 15q is rare. Only 11 cases have been described in the literature, all due to a supernumerary analphoid marker chromosome consisting of an inverted duplication of the distal long arm of chromosome 15. We report on a unique patient with tetrasomy 15q with complex cardiovascular malformation (CVM) involving progressive diffuse pulmonary vein stenosis (PVS). We propose overexpression of three genes, ADAMTSL3, MESP1, and MESP2 as a potential mechanism for cardiac and vessel malformations associated with tetrasomy 15q. Finally, we believe cardiac defects with this genetic syndrome are a poor prognostic finding associated with high mortality.


title: Development of a buoyancy control device for Autonomous Underwater Vehicle

abstract: The development of Autonomous Underwater Vehicle (AUV) or known as unmanned underwater vehicle increase exponentially in these recent years. This is due to the increase of demand in many applications such as prospecting on the seafloor and other underwater tasks without human interference and supervision. This vehicle can be used to perform underwater missions such as surveys, monitoring, image capturing and other applications. In achieving given tasks, AUVs must capable to control its depth level. Thus, a buoyancy control device (BCD) is developed to assist AUV in controlling its depth level. This paper is focusing in developing a volume based BCD. A pair of rubber bellow is simulated as the main hardware to vary the volume of BCD while keeping a constant mass. To execute this method, an input (depth level) is required from user through a Graphical User Interface (GUI) provided. Water level transducer is used to measure the current level of the BCD and compare with the user input. By this comparison, BCD will compensate with water density, in order to achieve positive, negative or neutral buoyancy based on the desired input value. This system is a close loop system with a Proportional Integral Derivatives (PID) controller integrated for the controller. PID controller will continuously calculates the error value as the difference between the desired (user input) and the actual value measured. PID controller is designed to reduce the oscillation occurred in the system during autonomous adjustments with a suitable gain values. This paper proved that by controlling the volume of the bladder, depth level of BCD also can be controlled accordingly with 47% maximum and 2% minimum error occurred.


title: On standardizing the MR image intensity scale

abstract: The lack of a standard image intensity scale in MRI causes many difficulties in image display and analysis. A two-step postprocessing method is proposed for standardizing the intensity scale in such a way that for the same MR protocol and body region, similar intensities will have similar tissue meaning. In the first step, the parameters of the standardizing transformation are "learned" from a set of images. In the second step, for each MR study these parameters are used to map their histogram into the standardized histogram. The method was tested quantitatively on 90 whole-brain studies of multiple sclerosis patients for several protocols and qualitatively for several other protocols and body regions. Measurements using mean squared difference showed that the standardized image intensities have statistically significantly (P < 0.01) more consistent range and meaning than the originals. Fixed gray level windows can be established for the standardized images and used for display without the need of per case adjustment. Preliminary results also indicate that the method facilitates improving the degree of automation of image segmentation. Magn Reson Med 42:1072-1081, 1999.


title: A new nucleoporin-like protein interacts with both HIV-1 Rev nuclear export signal and CRM-1.

abstract: The HIV-1 Rev is a shuttling protein required for the nuclear export of unspliced and partially spliced viral mRNA. In this study, we have identified a new Rev-interacting protein, that specifically interacts with the Rev nuclear export signal both in yeast and mammalian cells. This protein has features found in nucleoporins including many phenylalanine-glycine repeats, a very high serine content, a putative zinc finger, and a coiled-coil domain; we thus called it NLP-1 (nucleoporin-like protein 1). In addition, gene expression analysis and wheat germ agglutinin chromatography experiments suggested that NLP-1 is an ubiquitous O-glycosylated nuclear protein. Recently, a cellular factor called CRM-1 has been shown to be an essential nuclear export factor interacting directly with nuclear export signals including the Rev nuclear export signal in a RanGTP-dependent manner. We show here that NLP-1, like the previously described Rev-interacting protein hRIP/Rab and several nucleoporins, also interacts with CRM-1 both in yeast and mammalian cells.


title: Occupy the cloud: distributed computing for the 99%

abstract: Distributed computing remains inaccessible to a large number of users, in spite of many open source platforms and extensive commercial offerings. While distributed computation frameworks have moved beyond a simple map-reduce model, many users are still left to struggle with complex cluster management and configuration tools, even for running simple embarrassingly parallel jobs. We argue that stateless functions represent a viable platform for these users, eliminating cluster management overhead, fulfilling the promise of elasticity. Furthermore, using our prototype implementation, PyWren, we show that this model is general enough to implement a number of distributed computing models, such as BSP, efficiently. Extrapolating from recent trends in network bandwidth and the advent of disaggregated storage, we suggest that stateless functions are a natural fit for data processing in future computing environments.


title: SC-DCNN: Highly-Scalable Deep Convolutional Neural Network using Stochastic Computing

abstract: With the recent advance of wearable devices and Internet of Things (IoTs), it becomes attractive to implement the Deep Convolutional Neural Networks (DCNNs) in embedded and portable systems. Currently, executing the software-based DCNNs requires high-performance servers, restricting the widespread deployment on embedded and mobile IoT devices. To overcome this obstacle, considerable research efforts have been made to develop highly-parallel and specialized DCNN accelerators using GPGPUs, FPGAs or ASICs. Stochastic Computing (SC), which uses a bit-stream to represent a number within [-1, 1] by counting the number of ones in the bit-stream, has high potential for implementing DCNNs with high scalability and ultra-low hardware footprint. Since multiplications and additions can be calculated using AND gates and multiplexers in SC, significant reductions in power (energy) and hardware footprint can be achieved compared to the conventional binary arithmetic implementations. The tremendous savings in power (energy) and hardware resources allow immense design space for enhancing scalability and robustness for hardware DCNNs. This paper presents SC-DCNN, the first comprehensive design and optimization framework of SC-based DCNNs, using a bottom-up approach. We first present the designs of function blocks that perform the basic operations in DCNN, including inner product, pooling, and activation function. Then we propose four designs of feature extraction blocks, which are in charge of extracting features from input feature maps, by connecting different basic function blocks with joint optimization. Moreover, the efficient weight storage methods are proposed to reduce the area and power (energy) consumption. Putting all together, with feature extraction blocks carefully selected, SC-DCNN is holistically optimized to minimize area and power (energy) consumption while maintaining high network accuracy. Experimental results demonstrate that the LeNet5 implemented in SC-DCNN consumes only 17 mm2 area and 1.53 W power, achieves throughput of 781250 images/s, area efficiency of 45946 images/s/mm2, and energy efficiency of 510734 images/J.


title: An Experimental Comparison of Multiobjective Algorithms: NSGA-II and OMOPSO

abstract: The optimization of multi objective problems is currently an area of important research and development. The importance of type of problems has allowed the development of multiple metaheuristics for their solution. To determine which multi objective metaheuristic has the best performance with respect to a problem, in this article an experimental comparison between two of them: Sorting Genetic Algorithm No dominated-II (NSGA-II) and Multi Objective Particle Swarm Optimization (MOPS) using ZDT test functions is made. The results obtained by both algorithms are compared and analyzed based on different performance metrics that evaluate both the dispersion of the solutions on the Pareto front, and its proximity to it.


title: Quasar: Datasets for Question Answering by Search and Reading

abstract: We present two new large-scale datasets aimed at evaluating systems designed to comprehend a natural language query and extract its answer from a large corpus of text. The Quasar-S dataset consists of 37000 cloze-style (fill-in-the-gap) queries constructed from definitions of software entity tags on the popular website Stack Overflow. The posts and comments on the website serve as the background corpus for answering the cloze questions. The Quasar-T dataset consists of 43000 open-domain trivia questions and their answers obtained from various internet sources. ClueWeb09 serves as the background corpus for extracting these answers. We pose these datasets as a challenge for two related subtasks of factoid Question Answering: (1) searching for relevant pieces of text that include the correct answer to a query, and (2) reading the retrieved text to answer the query. We also describe a retrieval system for extracting relevant sentences and documents from the corpus given a query, and include these in the release for researchers wishing to only focus on (2). We evaluate several baselines on both datasets, ranging from simple heuristics to powerful neural models, and show that these lag behind human performance by 16.4% and 32.1% for Quasar-S and -T respectively. The datasets are available at this https URL .


title: Cloud-fog computing for information-centric Internet-of-Things applications

abstract: This work develops a cloud-fog computing architecture for information-centric Internet-of-Things (IoT) applications with job classification and resource scheduling functions. The two designed functions support quality of service (QoS) by classifying IoT applications and scheduling computing resources. An innovative scheduling mechanism is developed to optimize the dispatch of cloud and fog resources at minimum cost in a cloud-fog computing environment. One cloud, three fogs, four types of IoT application and 2500 IoT tasks are designed in the cloud-fog simulation environment. The simulation results reveal that the proposed computing architecture outperforms computing without scheduling by reducing the cloud computing load by approximately 20.47%. The QoS of real-time IoT applications is guaranteed in the proposed mechanism.


title: A Systematic Literature Review of Requirements Negotiation Methods from 2010 till 2015

abstract: This paper carries out a systematic literature review of requirements negotiation methods (RNM) from 2010 till 2015. We provide advice to researchers and practitioners. To researchers we provide advice in which fields of study further research is needed. For practitioners we scored the methods for different requirement conflict (RC) types. In total, we found 12 new RNM. For these methods, we identified the type of RC that the methods resolve. In the last five years, more methods are created than before. Requirements contradiction conflicts and quality attribute conflicts are better covered in methods now than before 2010.


title: Traffic Road Sign Detection and Recognition for Automotive Vehicles

abstract: Traffic road sign detection and recognition is important to transport system with a robotic eyes or camera while driving in the road. This paper presents and overview the traffic road sign detection and recognition, we developed and implemented the procedure to extract the road sign from a natural complex image. The main objective of this paper is to design and construct a computer based system which can automatically detect the direction of the road sign. This paper is based upon a major approach to detect the direction. In this paper, we will demonstrate the basic idea of how detect the area and extract it. This system will play an important role for the detection purpose of specific domains like island, schools, traffic sign, universities, hospitals, offices etc.


title: Mindfulness-based cognitive therapy for the treatment of headache pain: a pilot study.

abstract: OBJECTIVE ::: This pilot study reports the findings of a randomized controlled trial (RCT) investigating the feasibility, tolerability, acceptability, and initial estimates of efficacy of mindfulness-based cognitive therapy (MBCT) compared to a delayed treatment (DT) control for headache pain. It was hypothesized that MBCT would be a viable treatment approach and that compared to DT, would elicit significant improvement in primary headache pain-related outcomes and secondary cognitive-related outcomes. ::: ::: ::: MATERIALS AND METHODS ::: RCT methodology was employed and multivariate analysis of variance models were conducted on daily headache diary data and preassessment and postassessment data for the intent-to-treat sample (N=36), and on the completer sample (N=24). ::: ::: ::: RESULTS ::: Patient flow data and standardized measures found MBCT for headache pain to be feasible, tolerable, and acceptable to participants. Intent-to-treat analyses showed that compared to DT, MBCT patients reported significantly greater improvement in self-efficacy (P=0.02, d=0.82) and pain acceptance (P=0.02, d=0.82). Results of the completer analyses produced a similar pattern of findings; additionally, compared to DT, MBCT completers reported significantly improved pain interference (P<0.01, d=-1.29) and pain catastrophizing (P=0.03, d=-0.94). Change in daily headache diary outcomes was not significantly different between groups (P's>0.05, d's≤-0.24). ::: ::: ::: DISCUSSION ::: This study empirically examined MBCT for the treatment of headache pain. Results indicated that MBCT is a feasible, tolerable, acceptable, and potentially efficacious intervention for patients with headache pain. This study provides a research base for future RCTs comparing MBCT to attention control, and future comparative effectiveness studies of MBCT and cognitive-behavioral therapy.


title: PV Isolated Three-Port Converter and Energy-Balancing Control Method for PV-Battery Power Supply Applications

abstract: A photovoltaic (PV)-based stand-alone power system is usually used to manage the energy supplied from several power sources such as PV solar arrays and battery and deliver a continuous power to the users in an appropriate form. Traditionally, three different dc/dc converters would have been used. To reduce the cost and improve the power density of the power system, an integrated solution of PV isolated dc/dc three-port converter (TPC) is proposed in this paper. Zero current switching can be achieved for all main diodes and MOSFETs to improve the efficiency, and a continuous input current of solar array is maintained by adding a magnetic switch derived from a fourth winding of the half-bridge transformer. Based on the energy-balancing part formed by boost, the control methods for the single module to realize maximum power point tracking (MPPT), battery charge control, and main bus regulation are proposed. The power system control method for multimodules in parallel is also derived, and the operation of the TPC power system can be transited between conductance mode and MPPT mode automatically. Finally, the experimental results verify that the proposed TPC, together with the proposed control method, meets the requirements of a high-power-density stand-alone PV-battery power system.


title: CHAOS: an SDN-based Moving Target Defense System

abstract: Moving target defense (MTD) has provided a dynamic and proactive network defense to reduce or move the attack surface that is available for exploitation. However, traditional network is difficult to realize dynamic and active security defense effectively and comprehensively. Software-defined networking (SDN) points out a brand-new path for building dynamic and proactive defense system. In this paper, we propose CHAOS, an SDN-based MTD system. Utilizing the programmability and flexibility of SDN, CHAOS obfuscates the attack surface including host mutation obfuscation, ports obfuscation, and obfuscation based on decoy servers, thereby enhancing the unpredictability of the networking environment. We propose the Chaos Tower Obfuscation (CTO) method, which uses the Chaos Tower Structure (CTS) to depict the hierarchy of all the hosts in an intranet and define expected connection and unexpected connection. Moreover, we develop fast CTO algorithms to achieve a different degree of obfuscation for the hosts in each layer. We design and implement CHAOS as an application of SDN controller. Our approach makes it very easy to realize moving target defense in networks. Our experimental results show that a network protected by CHAOS is capable of decreasing the percentage of information disclosure effectively to guarantee the normal flow of traffic.


title: User-defined gestures for surface computing

abstract: Many surface computing prototypes have employed gestures created by system designers. Although such gestures are appropriate for early investigations, they are not necessarily reflective of user behavior. We present an approach to designing tabletop gestures that relies on eliciting gestures from non-technical users by first portraying the effect of a gesture, and then asking users to perform its cause. In all, 1080 gestures from 20 participants were logged, analyzed, and paired with think-aloud data for 27 commands performed with 1 and 2 hands. Our findings indicate that users rarely care about the number of fingers they employ, that one hand is preferred to two, that desktop idioms strongly influence users' mental models, and that some commands elicit little gestural agreement, suggesting the need for on-screen widgets. We also present a complete user-defined gesture set, quantitative agreement scores, implications for surface technology, and a taxonomy of surface gestures. Our results will help designers create better gesture sets informed by user behavior.


title: A Critical Discourse Analysis of Political Ideology and Control Factors in News Translation

abstract: There are some factors in translating a text which influence the result of translation process, one of which is political ideology and control factors which impose some limitations and give direction to the translator. Through this paper an attempt has been made to substantiate these imposing factors as well as describing the ways translators might apply. Based on critical discourse analysis (CDA) approach to textual analysis and following Lefevere's (1992a; 1992b) concept of patronage, it has been tried to compare some newspaper texts with their related translations so as to depict the role of imposing factors in the process of translation. Results of the survey and analysis of the factors behind the translational aspects indicate that ideologies and political issues are important stimuli which can control the materials being translated, and then presented to the mass through news texts.


SUper Team at SemEval-2016 Task 3: Building a Feature-Rich System for Community Question Answering


title: Learning Fashion Compatibility with Bidirectional LSTMs

abstract: The ubiquity of online fashion shopping demands effective recommendation services for customers. In this paper, we study two types of fashion recommendation: (i) suggesting an item that matches existing components in a set to form a stylish outfit (a collection of fashion items), and (ii) generating an outfit with multimodal (images/text) specifications from a user. To this end, we propose to jointly learn a visual-semantic embedding and the compatibility relationships among fashion items in an end-to-end fashion. More specifically, we consider a fashion outfit to be a sequence (usually from top to bottom and then accessories) and each item in the outfit as a time step. Given the fashion items in an outfit, we train a bidirectional LSTM (Bi-LSTM) model to sequentially predict the next item conditioned on previous ones to learn their compatibility relationships. Further, we learn a visual-semantic space by regressing image features to their semantic representations aiming to inject attribute and category information as a regularization for training the LSTM. The trained network can not only perform the aforementioned recommendations effectively but also predict the compatibility of a given outfit. We conduct extensive experiments on our newly collected Polyvore dataset, and the results provide strong qualitative and quantitative evidence that our framework outperforms alternative methods.


title: A Bidirectional Nonisolated Multi-Input DC–DC Converter for Hybrid Energy Storage Systems in Electric Vehicles

abstract: To process the power in hybrid energy systems using a reduced part count, researchers have proposed several multiinput dc–dc power converter topologies to transfer power from different input voltage sources to the output. This paper proposes a novel bidirectional nonisolated multi-input converter (MIC) topology for hybrid systems to be used in electric vehicles composed of energy storage systems (ESSs) with different electrical characteristics. The proposed converter has the ability to control the power of ESSs by allowing active power sharing. The voltage levels of utilized ESSs can be higher or lower than the output voltage. The inductors of the converter are connected to a single switch; therefore, the converter requires only one extra active switch for each input, unlike its counterparts, hence resulting in reduced element count. The proposed MIC topology is compared with its counterparts concerning various parameters. It is analyzed in detail, and then, this analysis is validated by simulation and through a 1-kW prototype based on a battery/ultracapacitor hybrid ESS.


title: A Low-Power RF Direct-Conversion Receiver/Transmitter for 2.4-GHz-Band IEEE 802.15.4 Standard in 0.18- $\mu{\hbox {m}}$ CMOS Technology

abstract: This paper presents a low-power RF receiver/transmitter front-end for 2.4-GHz-band IEEE 802.15.4 standard in 0.18-mum CMOS technology. An RF receiver comprises a single-ended low-noise amplifier, a quadrature passive mixer, and a transimpedance amplifier. A current-mode passive mixer showing a very good 1/f noise performance is adopted to convert an RF signal directly to a baseband signal. Moreover, this type of passive mixer shows high-linearity performance, leading to overall RF receiver linearity improvement. A low-power, high-linearity transmitter front-end is implemented by using a passive mixer and two-stage driver amplifier in which the first stage is a conventional cascode amplifier and the second stage uses a folded cascode one. The receiver front-end achieves 30-dB voltage conversion gain, 7.3-dB noise figure with 1/f noise corner frequency of 70 kHz, -8-dBm input third-order intercept point, and +40-dBm input second-order intercept point. The transmitter front-end shows 12-dB power conversion gain, 0-dBm output power with 10-dBm output third-order intercept point, and -30-dB local-oscillator suppression. The receiver and transmitter front-end dissipate 3.5 and 3 mA from a 1.8-V supply, respectively


title: Network coding approach for vehicle-to-vehicle communication: Principles, protocols and benefits

abstract: Information dissemination in vehicular networks is becoming a hot topic of research in the field of Vehicle-to-Vehicle (V2V) communications, since efficient data exchange is needed for most crucial applications (road accident warning, traffic congestion warning …). Many protocols have been then proposed for the sake of disseminating information with high reachability and low end-to-end delay in limited bandwidth. In this work, we propose to review the design of some of dissemination protocols based on the network coding technique. Newly adopted in wireless ad hoc networks, network coding paradigm seems to be a promising solution for data dissemination in vehicular environment. The goal of this work is then to classify the different network coding based protocols appeared in the literature and reveal the gain achieved once this technique is applied. To this end, a summary table for a comparative study of the numerous retained protocols is presented.


title: Upper-limb tremor suppression with a 7DOF exoskeleton power-assist robot

abstract: A tremor which is one of the involuntary motions is somewhat rhythmic motion that may occur in various body parts. Although there are several kinds of the tremor, an essential tremor is the most common tremor disorder of the arm. The essential tremor is a disorder of unknown cause, and it is common in the elderly. The essential tremor interferes with a patient's daily living activity, because it may occur during a voluntary motion. If a patient of an essential tremor uses an EMG-based controlled power-assist robot, the robot might misunderstand the user's motion intention because of the effect of the essential tremor. In that case, upper-limb power-assist robots must carry out tremor suppression as well as power-assist, since a person performs various precise tasks with certain tools by the upper-limb in daily living. Therefore, it is important to suppress the tremor at the hand and grasped tool. However, in the case of the tremor suppression control method which suppressed the vibrations of the hand and the tip of the tool, vibration of other part such as elbow might occur. In this paper, the tremor suppression control method for upper-limb power-assist robot is proposed. In the proposed method, the vibration of the elbow is suppressed in addition to the hand and the tip of the tool. The validity of the proposed method was verified by the experiments.


title: A Kernel Method for the Two-Sample Problem

abstract: We propose a framework for analyzing and comparing distributions, allowing us to design statistical tests to determine if two samples are drawn from different distributions. Our test statistic is the largest difference in expectations over functions in the unit ball of a reproducing kernel Hilbert space (RKHS). We present two tests based on large deviation bounds for the test statistic, while a third is based on the asymptotic distribution of this statistic. The test statistic can be computed in quadratic time, although efficient linear time approximations are available. Several classical metrics on distributions are recovered when the function space used to compute the difference in expectations is allowed to be more general (eg. a Banach space). We apply our two-sample tests to a variety of problems, including attribute matching for databases using the Hungarian marriage method, where they perform strongly. Excellent performance is also obtained when comparing distributions over graphs, for which these are the first such tests.


title: Doppelgänger Finder: Taking Stylometry to the Underground

abstract: Stylometry is a method for identifying anonymous authors of anonymous texts by analyzing their writing style. While stylometric methods have produced impressive results in previous experiments, we wanted to explore their performance on a challenging dataset of particular interest to the security research community. Analysis of underground forums can provide key information about who controls a given bot network or sells a service, and the size and scope of the cybercrime underworld. Previous analyses have been accomplished primarily through analysis of limited structured metadata and painstaking manual analysis. However, the key challenge is to automate this process, since this labor intensive manual approach clearly does not scale. We consider two scenarios. The first involves text written by an unknown cybercriminal and a set of potential suspects. This is standard, supervised stylometry problem made more difficult by multilingual forums that mix l33t-speak conversations with data dumps. In the second scenario, you want to feed a forum into an analysis engine and have it output possible doppelgangers, or users with multiple accounts. While other researchers have explored this problem, we propose a method that produces good results on actual separate accounts, as opposed to data sets created by artificially splitting authors into multiple identities. For scenario 1, we achieve 77% to 84% accuracy on private messages. For scenario 2, we achieve 94% recall with 90% precision on blogs and 85.18% precision with 82.14% recall for underground forum users. We demonstrate the utility of our approach with a case study that includes applying our technique to the Carders forum and manual analysis to validate the results, enabling the discovery of previously undetected doppelganger accounts.


title: Feedforward reversed nested Miller compensation techniques for three-stage amplifiers

abstract: Two novel reversed nested Miller compensation (RNMC) techniques for low-voltage three-stage amplifiers are proposed in this contribution: Nested Feedforward RNMC (NFRNMC) and Crossed Feedforward RNMC (CFRNMC). Both techniques employ double feedforward paths to remove the right-half-plane zero. The second architecture generates a left-half-plane zero to further improve the phase margin. To demonstrate advantages of the new RNMC techniques over the traditional RNMC architecture, two three-stage amplifiers are designed employing the proposed techniques in a standard 0.5 /spl mu/m CMOS process. Simulation results show that, with the same gain-bandwidth product, the NFRNMC and CFRNMC amplifiers have improved stabilities over the conventional RNMC amplifiers by more than 150 and 200 in the phase margin, respectively. They both dissipate less than 0.4 mW of power with a 1 V supply.


title: Debulking Surgery for Elephantiasis Nostras With Large Ectatic Podoplanin-Negative Lymphatic Vessels in Patients With Lipo-Lymphedema

abstract: Lymphedema is a chronic, progressing, debilitating disorder either due to primary malformation of lymphatic vessels or due to trauma, malignancies, filariasis, relapsing erysipelas, surgery, and/or radiotherapy leading to lymphatic insufficiency.1 The backbone of treatment is decongestive lymphatic therapy (DLT) that consists of manual lymphatic drainage, compression, mobilization, and hygienic and skin care measures.2-5 ::: ::: Lipedema is a disorder of abnormal growth and distribution of adipose tissue mostly of the proximal extremities. Lipedema is associated with bruising and pain. The disease affects females during or after puberty. Although an increased body mass index can be observed, the disease is different from obesity. They differ not only by the distribution of adipose tissue (peripheral vs central). In addition, lipedema does not respond to diets, drugs, and physical activities. It can lead to severe impairment of joint mobility (in particular of the knees) and it involves the risk of developing metabolic syndrome.6,7 The only available treatment to improve the long-term outcome is microcannular liposuction in tumescent anesthesia.8-12 ::: ::: Although lipedema primarily is not associated with disturbances of lymphatic flow, advanced cases may develop a secondary lymphedema resulting in an overlap disease known as lipo-lymphedema.6 ::: ::: Elephantiasis characterizes a disfiguring end-stage of lymphatic disease (stage III according to the International Lymphatic Society) with profound but circumscribed changes in soft tissue. Tissue fibrosis, fat deposition, hyperpigmentation, cutaneous acanthosis, and papillomatosis are present. The skin develops a cobble-stone surface pattern. The disease is slowly progressive. These patients possess an increased risk for soft tissue infections that can aggravate the condition. Debulking surgery is an option for selected cases.1,13 ::: ::: We report on 2 female patients with elephantiasis nostras developing secondary on lipo-lymphedema of the legs, who were treated by microcannular liposuction and debulking surgery.


title: Speaker diarization through speaker embeddings

abstract: This paper proposes to learn a set of high-level feature representations through deep learning, referred to as Speaker Embeddings, for speaker diarization. Speaker Embedding features are taken from the hidden layer neuron activations of Deep Neural Networks (DNN), when learned as classifiers to recognize a thousand speaker identities in a training set. Although learned through identification, speaker embeddings are shown to be effective for speaker verification in particular to recognize speakers unseen in the training set. In particular, this approach is applied to speaker diarization. Experiments, conducted on the corpus of French broadcast news ETAPE, show that this new speaker modeling technique decreases DER by 1.67 points (a relative improvement of about 8% DER).


Sweeping the Floor or Putting a Man on the Moon: How to Define and Measure Meaningful Work


Location Sensitive Deep Convolutional Neural Networks for Segmentation of White Matter Hyperintensities


title: A Middleware for Fast and Flexible Sensor Network Deployment

abstract: A key problem in current sensor network technology is the heterogeneity of the available software and hardware platforms which makes deployment and application development a tedious and time consuming task. To minimize the unnecessary and repetitive implementation of identical functionalities for different platforms, we present our Global Sensor Networks (GSN) middleware which supports the flexible integration and discovery of sensor networks and sensor data, enables fast deployment and addition of new platforms, provides distributed querying, filtering, and combination of sensor data, and supports the dynamic adaption of the system configuration during operation. GSN's central concept is the virtual sensor abstraction which enables the user to declaratively specify XML-based deployment descriptors in combination with the possibility to integrate sensor network data through plain SQL queries over local and remote sensor data sources. In this demonstration, we specifically focus on the deployment aspects and allow users to dynamically reconfigure the running system, to add new sensor networks on the fly, and to monitor the effects of the changes via a graphical interface. The GSN implementation is available from http://globalsn.sourceforge.net/.


title: Concurrent plan recognition and execution for human-robot teams

abstract: There is a strong demand for robots to work in environments, such as aircraft manufacturing, where they share tasks with humans and must quickly adapt to each other's needs. To do so, a robot must both infer the intent of humans, and must adapt accordingly. The literature to date has made great progress on these two tasks - recognition and adaptation - but largely as separate research activities. In this paper, we present a unified approach to these two problems, in which recognition and adaptation occur concurrently and holistically. Key to our approach is a task representation that uses choice to represent alternative plans for both the human and robot, allowing a single set of algorithms to simultaneously achieve recognition and adaptation. To achieve such fluidity, a labeled propagation mechanism is used where decisions made by the human and robot during execution are propagated to relevant future open choices, as determined by causal link analysis, narrowing the possible options that the human would reasonably take (hence achieving intent recognition) as well as the possible actions the robot could consistently take (adaptation). This paper introduces Pike, an executive for human-robot teamwork that quickly adapts and infers intent based on the preconditions of actions in the plan, temporal constraints, unanticipated disturbances, and choices made previously (by either robot or human). We evaluate Pike's performance and demonstrate it on a household task in a human-robot team testbed.


title: Clinical Benefits After the Implementation of a Protocol of Restricted Perioperative Intravenous Crystalloid Fluids in Major Abdominal Operations

abstract: Background ::: Perioperative fluid replacement is a challenging issue in surgical care. The purpose of the present study was to investigate the effect of two different perioperative hydration protocols on the outcome in patients undergoing major abdominal operations.


title: Hybrid-IoT: Hybrid Blockchain Architecture for Internet of Things - PoW Sub-Blockchains

abstract: From its early days the Internet of Things (IoT) has evolved into a decentralized system of cooperating smart objects with the requirement, among others, of achieving distributed consensus. Yet, current IoT platform solutions are centralized cloud based computing infrastructures, manifesting a number of significant disadvantages, such as, among others, high cloud server maintenance costs, weakness for supporting time-critical IoT applications, security and trust issues. Enabling blockchain technology into IoT can help to achieve a proper distributed consensus based IoT system that overcomes those disadvantages. While this is an ideal match, it is still a challenging endeavor. In this paper we take a first step towards that goal by designing Hybrid-IoT, a hybrid blockchain architecture for IoT. In Hybrid-IoT, subgroups of IoT devices form PoW blockchains, referred to as PoW sub-blockchains. Then, the connection among the PoW sub-blockchains employs a BFT inter-connector framework, such as Polkadot or Cosmos. In this paper we focus on the PoW sub-blockchains formation, guided by a set of guidelines based on a set of dimensions, metrics and bounds. In order to prove the validity of the approach we carry a performance and security evaluation.


title: Question Answering System for the French Language

abstract: This paper describes our first participation in the QA@CLEF monolingual and bilingual task, where our objective was to propose a question answering system designed to respond to French queries submitted to search French documents. We wanted to combine a classic information retrieval model (based on the Okapi probabilistic model) with a linguistic approach based mainly on syntactic analysis. In order to utilize our monolingual system in the bilingual task, we automatically translated into French queries written in seven other source languages, namely Dutch, German, Italian, Portuguese, Spanish, English and Bulgarian.


title: AN AGENT BASED ARCHITECTURE (USINGPLANNING) FOR DYNAMIC AND SEMANTIC WEB SERVICES COMPOSITION IN AN EBXML CONTEXT

abstract: process-based semantic composition of Web Services is gaining a considerable momentum as an approach for the effective integration of distributed, heterogeneous, and autonomous applications. To compose Web Services semantically, we need an ontology. There are several ways of inserting semantics in Web Services. One of them consists of using description languages like OWL-S. In this paper, we introduce our work which consists in the proposition of a new model and the use of semantic matching technology for semantic and dynamic composition of ebXML business processes.


title: A probabilistic method to detect regulatory modules

abstract: Motivation: The discovery of cis-regulatory modules in metazoan genomes is crucial for understanding the connection between genes and organism diversity. Results: We develop a computational method that uses Hidden Markov Models and an Expectation Maximization algorithm to detect such modules, given the weight matrices of a set of transcription factors known to work together. Two novel features of our probabilistic model are: (i) correlations between binding sites, known to be required for module activity, are exploited, and (ii) phylogenetic comparisons among sequences from multiple species are made to highlight a regulatory module. The novel features are shown to improve detection of modules, in experiments on synthetic as well as biological data. Availability: The source code for the programs is available upon request from the authors.


title: Optimal spatial adaptation to inhomogeneous smoothness: an approach based on kernel estimates with variable bandwidth selectors

abstract: A new v~ria~~,E_<:.~_~1~!~_s,~~~,!?E_0r estimation is proposed. The application-of this bandwidth selector leads to kernel estimates that achieve optimal rates of convergence over B£~~.£~:3.sses. This implies that the procedure adapts to spatially inhomogeneous s-;noothness. In particu lar, the estimates share optimality properties with wavelet estimates based on thresholding of empirical wavelet coefficients. 1. Introduction. In nonparametric curve estimation the statistical anal ysis may focus on the inference of the qualitative structure of the analyzed curve. Often, interesting features of the curve are connected with spatially inhomogeneous smoothness. In this case, curve estimates that are -spatially adaptlve'ar'e'approprIaIe':"'A variety of such procedures have been proposed in the literature. In Breiman, Friedman, Olshen and Stone (1983) piecewise constant least squares estimates are considered with a data adaptive choice of the pieces (CART). More generally, Friedman and Silverman (1989), Friedman (1991) and Luo and Wahba (1995) use variable knot splines (MARS). Knot points are added, removed and allocated recursively using cross-validation techniques. These methods have shown good performance in simulations and real data examples. However, no asymptotic theory is available. Mammen and van de Geer (1997) discuss penalized least squares curve estimation for spatially inhomogeneous curves. They propose penalty terms which allow more spatial inhomogeneity than the usual L2-norms of deriva tives of the curve. The estimates turn out to be variable knot splines [see also Mammen (1991)]. Results on rates of convergence and a pointwise asymptotic distribution theory are given. Miiller and Stadtmiiller (1987), Staniswalis (1989) and Brockmann, Gasser and Hermann (1993) propose kernel estimation with locally variable band width selectors. The calculation of local bandwidths is based on pilot estima tion of local smoothness characteristics. An asymptotic analysis is available


title: An Empirical Study of Geographic User Activity Patterns in Foursquare

abstract: We present a large-scale study of user behavior in Foursquare, conducted on a dataset of about 700 thousand users that spans a period of more than 100 days. We analyze user checkin dynamics, demonstrating how it reveals meaningful spatio-temporal patterns and offers the opportunity to study both user mobility and urban spaces. Our aim is to inform on how scientific researchers could utilise data generated in Location-based Social Networks to attain a deeper understanding of human mobility and how developers may take advantage of such systems to enhance applications such as recommender systems.


title: Derivatives of Buckling Loads and Vibration Frequencies With Respect to Stiffness and Initial Strain Parameters

abstract: A uniform variational approach to sensitivity analysis of vibration frequencies and bifurcation loads of nonlinear structures is developed. Two methods of calculating the sensitivities of bifurcation buckling loads and vibration frequencies of nonlinear structures, with respect to stiffness and initial strain parameters, are presented. A direct method requires calculation of derivatives of the prebuckling state with respect to these parameters. An adjoint method bypasses the need for these derivatives by using instead the strain field associated with the second-order post-buckling state. The derivative computations are easily implemented in structure analysis programs


title: Analysis of EMG signals of patients with essential tremor focusing on the change of tremor frequency

abstract: Essential tremor is a disorder that causes involuntary oscillations in patients while engaging in actions or while maintaining a posture. ET patients have serious difficulties in performing activities of daily living such as eating food, drinking water, and writing. We have thus been developing an EMG-controlled exoskeletal robot to suppress tremors. The EMG signal of ET patients involves a mix of voluntary movement and tremor signals. To control the exoskeletal robot accurately, tremor signals must be removed from the patient's EMG signal. To date, we have been developing a filter to remove tremor signals from the patient's EMG. The design of this filter was based on the hypothesis that the rectified tremor signals are able to be approximated by a powered sine wave. This filter was found to have a large effect on removing tremor signals. However, tremor signals are generated both while performing voluntary movement and while maintaining a posture, and the filter was attenuating both signals. To control this robot accurately, the signal generated while performing voluntary movement is expected not to be attenuated. To accomplish this, we try to use a parameter that reflects a state of the patient's movement, performing a voluntary movement or maintaining a posture, as a switch to activate the powered sine filter. This paper provides an analysis of the favorable parameters. We focus on two parameters: the peak-to-peak interval of the rectified EMG signal, and the interval of the flat and low amplitude area of the rectified EMG signal. Through evaluation, it is affirmed that both parameters change with the state of the patient's movement. However, the latter parameter is superior to the former in terms of variability, which indicates that the interval of the flat and low amplitude area of the rectified EMG signal is a more favorable parameter to promote control of the exoskeletal robot. As a future work, we will mount the parameter to the algorithm and evaluate the robotic system.


title: Compact Proofs of Retrievability

abstract: In a proof-of-retrievability system, a data storage center must prove to a verifier that he is actually storing all of a client's data. The central challenge is to build systems that are both efficient and provably secure--that is, it should be possible to extract the client's data from any prover that passes a verification check. In this paper, we give the first proof-of-retrievability schemes with full proofs of security against arbitrary adversaries in the strongest model, that of Juels and Kaliski. ::: ::: Our first scheme, built from BLS signatures and secure in the random oracle model, features a proof-of-retrievability protocol in which the client's query and server's response are both extremely short. This scheme allows public verifiability: anyone can act as a verifier, not just the file owner. Our second scheme, which builds on pseudorandom functions (PRFs) and is secure in the standard model, allows only private verification. It features a proof-of-retrievability protocol with an even shorter server's response than our first scheme, but the client's query is long. Both schemes rely on homomorphic properties to aggregate a proof into one small authenticator value.


When one cue is better than two : Lexical vs . Syntactic Cues to Verb Learning


title: Three-phase dual-output six-switch inverter

abstract: A novel dual-output six-switch inverter topology is proposed in this study for independently supplying two three-phase loads. This inverter employs less number of semiconductor devices compared with former dual-output inverters proposed so far in the literature. Reducing the number of switches and hence drive and control circuits may result in a reduction in overall cost of the system, may reduce its semiconductor power loss and as a result increases efficiency and reliability especially in applications with low and medium voltage and power ratings. The new configuration is introduced and carrier-based pulse-width modulation (PWM) schemes for its two defined modes of operation (constant frequency and variable frequency modes) are developed. Analysis of sizing of the DC-link capacitors is also performed so as to minimise the DC-link voltage ripple and to reduce the value of DC-link capacitors. Furthermore, loss profile of the system is studied and the results are compared with counterpart topologies. Simulation and experimental results are presented to verify the authenticity of the theoretical model.


title: RPL-based multipath Routing Protocols for Internet of Things on Wireless Sensor Networks

abstract: In the last few years, Wireless Sensor Network (WSN) emerges and appears as an essential platform for prominent concept of Internet of Things (IoT). Their application ranges from so-called “smart cities”, “smart homes” over environmental monitoring. The connectivity in IoT mainly relies on RPL (IPv6 Routing Protocol for Low Power and Lossy Network) - a routing algorithm that constructs and maintains DODAGs (Destination Oriented Directed Acyclic Graph) to transmit data from sensors to root over a single path. However, due to the resource constraints of sensor nodes and the unreliability of wireless links, single-path routing approaches cannot be considered effective techniques to meet the performance demands of various applications. In order to overcome these problems, many individuals and group research focuses on multi-path solutions for RPL routing protocol. In this paper, we propose three multipath schemes based on RPL (Energy Load Balancing-ELB, Fast Local Repair-FLR and theirs combination-ELB-FLR) and integrate them in a modified IPv6 communication stack for IoT. These schemes are implemented in OMNET++ simulator and the experiment outcomes show that our approaches have achieved better energy efficiency, better end-to-end delay, packet delivery rate and network load balance compared to traditional solution of RPL.