title: MiniCPS: A Toolkit for Security Research on CPS Networks

abstract: In recent years, tremendous effort has been spent to modernizing communication infrastructure in Cyber-Physical Systems (CPS) such as Industrial Control Systems (ICS) and related Supervisory Control and Data Acquisition (SCADA) systems. While a great amount of research has been conducted on network security of office and home networks, recently the security of CPS and related systems has gained increased attention. Unfortunately, real-world CPS are often not open to security researchers, and as a result very few reference physical-layer processes, control systems and communication topologies are available. In this work, we present MiniCPS, a toolkit intended to alleviate this problem. The goal of MiniCPS is to create an extensible, reproducible research environment for network communications, control systems, and physical-layer interactions in CPS. Instead of focusing on a customized simulation settings for specific subsystems, the main goal is to establish a framework to connect together real CPS soft- and hardware, simulation scripts for such components, and physical-layer simulation engines. MiniCPS builds on Mininet to provide lightweight real-time network emulation, and extends Mininet with tools to simulate typical CPS components such as programmable logic controllers, which use industrial protocols (eg. EtherNet/IP, Modbus/TCP). To capture physical-layer interactions, MiniCPS defines a simple API to connect to physical-layer simulations. We demonstrate applications of MiniCPS in two example scenarios, and show how MiniCPS can be used to develop attacks and defenses that are directly applicable to real systems.


title: Supporting a Light-Weight Data Management Layer over HDF5

abstract: Scientific simulations are now being performed at finer temporal and spatial scales, leading to an explosion of the output data, and challenges in storing, managing, disseminating, analyzing, and visualizing these datasets. Tools commonly used today for disseminating and visualizing such data have inherent limitations, making it extremely hard to deal with larger datasets. We have developed a light-weight data management tool, which allows server-side sub setting and aggregation on scientific datasets stored in HDF5, one of the most popular scientific data formats. To support a variety of queries efficiently, our tool generates code for hyper slab selector and content-based filtering, and parallelizes selection and aggregation queries efficiently using novel algorithms. Additionally, our tool also supports certain most recent HDF5 features including dimension scale and compound data type. Through extensive evaluation, we show that our system is capable of efficiently supporting a variety of queries, scaling performance by parallelizing the queries, and reducing wide area data transfers through server-side data aggregation. We demonstrate that even for sub setting queries that are directly supported in OPeNDAP, a tool widely used by data dissemination portals, the sequential performance of our system is better.


title: Experimental Comparison of Sensor Fusion Algorithms for Attitude Estimation

abstract: Abstract Inertial Measurement Unit is commonly used in various applications especially as a low-cost system for localization and attitude estimation. Some applications are: real-time motion capture system, gait analysis for rehabilitation purposes, biomedical applications, advanced robotic applications such as mobile robot localization and Unmanned Aerial Vehicles (UAV) attitude estimation. In all the mentioned applications the accuracy and the fast response are the most important requirements, thus the research is focused on the design and the implementation of highly accurate hardware systems and fast sensor data fusion algorithms, named Attitude and Heading Reference System (AHRS), aimed at estimating the orientation of a rigid body with respect to a reference frame. A large number of different solutions can be found in the literature, and an experimental comparison of the most popular is presented in this work. In particular, the algorithm based on the gradient descent method and the algorithm based on a nonlinear complementary filter are compared to a standard Extended Kalman Filter (EKF) with the aim to show that a general method can easily compete with ad-hoc solutions and even outperform them in particular conditions. In order to validate the estimation accuracy a Kuka robot is used to compute the ground truth. Moreover, in order to estimate the computational burden, the algorithms are implemented on an ARM-Cortex M4-based evaluation board.


title: Automatic Gender Identification for Hindi Speech Recognition

abstract: paper presents the preliminary work done towards the development of a Gender Recognition System that can be incorporated into the Hindi Automatic Speech Recognition (ASR) System. Gender Recognition (GR) can help in the development of speaker-independent speech recognition systems. This paper presents a general approach to identifying feature vectors that effectively distinguish gender of a speaker from Hindi phoneme utterances. 10 vowels and 5 nasals of the Hindi language were studied for their effectiveness in identifying gender of the speaker. All the 10 vowel Phonemes performed well, while b) bZ) A) ,) ,s) vks and vkS showed excellent gender distinction performance. All five nasals ³) ´) .k) u and e which were tested, showed a recognition accuracy of almost 100%. The Mel Frequency Cepstral Coefficients (MFCC) are widely used in ASR. The choice of MFCC as features in Gender Recognition will avoid additional computation. The effect of the MFCC feature vector dimension on the GR accuracy was studied and the findings presented.


title: Is semen loss syndrome a psychological or physical illness? A case for conflict of interest

abstract: Young men presenting with sexual problems arising out of non-contact like semen loss syndrome are common in the Indian subcontinent. They usually present with depressive, anxiety symptoms, and non-specific somatic complaints. This has no medical explanation and is currently conceptualized as a culture bound neurotic disorder in the International Classification of Diseases-10 (ICD-10), clinical descriptions, and diagnostic guidelines. In this report, we present the case of a young male who developed delusions following concerns about semen loss. Conflicting explanations about his illness between traditional and allopathic practitioners led to problems in management. The importance of creating awareness among traditional practitioners regarding contemporary allopathic models of illness is stressed. Management strategies employed should reflect this shared understanding.


title: Fusion classification technique used to detect downy and Powdery Mildew grape leaf diseases

abstract: Grape constitutes one of the most widely grown fruit crop in the India. Manual observation of experts is used in practice for detection of leaf diseases, which takes more time for further control action. Without accurate disease diagnosis, proper control actions cannot be taken at appropriate time. This is where modern agriculture technique is required to detect and prevent the leaf from different diseases. This paper aims to introduce a new approach for detection of grape leaf diseases using image processing, which will minimize the loss and increase its profit due to automation. In this system, classification is done using Support Vector Machine (SVM) and Artificial Neural Network (ANN) classifies separately. A new classifier is proposed using fusion classification technique which ensembles classifiers from SVM and ANN to regenerate base classifier for grape leaf disease detection. Based on detection of disease the proper mixture of fungicides will be provided to the grape farmers.


title: Battery-Free Cellphone

abstract: We present the first battery-free cellphone design that consumes only a few micro-watts of power. Our design can sense speech, actuate the earphones, and switch between uplink and downlink communications, all in real time. Our system optimizes transmission and reception of speech while simultaneously harvesting power which enables the battery-free cellphone to operate continuously. The battery-free device prototype is built using commercial-off-the-shelf components on a printed circuit board. It can operate on power that is harvested from RF signals transmitted by a basestation 31 feet (9.4 m) away. Further, using power harvested from ambient light with tiny photodiodes, we show that our device can communicate with a basestation that is 50 feet (15.2 m) away. Finally, we perform the first Skype call using a battery-free phone over a cellular network, via our custom bridged basestation. This we believe is a major leap in the capability of battery-free devices and a step towards a fully functional battery-free cellphone.


Endpoint detection using grid long short-term memory networks for streaming speech recognition


Self-efficacy in the prediction of academic performance and perceived career options.


title: Stacked Generalization for Medical Concept Extraction from Clinical Notes

abstract: The goal of our research is to extract medical concepts from clinical notes containing patient information. Our research explores stacked generalization as a metalearning technique to exploit a diverse set of concept extraction models. First, we create multiple models for concept extraction using a variety of information extraction techniques, including knowledgebased, rule-based, and machine learning models. Next, we train a meta-classifier using stacked generalization with a feature set generated from the outputs of the individual classifiers. The meta-classifier learns to predict concepts based on information about the predictions of the component classifiers. Our results show that the stacked generalization learner performs better than the individual models and achieves state-of-the-art performance on the 2010 i2b2 data set.


title: Reconciling Ecological and Social Sustainability: The Case of the EcoDensity Initiative

abstract: This report assesses how the EcoDensity Initiative may impact housing affordability, which is a vital component of social sustainability. I use a combination of document assessments, selective informant interviews, and a review of the relevant academic literature to triangulate my findings. The report assesses key EcoDensity documents to determine the methods EcoDensity will use to improve housing affordability. I establish the initiative’s planning policy context and the context of the opposition to EcoDensity. The report outlines non market housing policy and the context of market housing in regards to housing affordability. Interview respondents indicated that the market has the potential to help with affordability through densification, but the market is hindered due to the influence of the economy and the desirability of Vancouver as a World Class City, which serves to maintain the growth in housing prices. In general, these conclusions are supported by the academic literature. Interview responses highlight the need for provincial and federal governments to provide an increased and permanent funding stream for non-market housing. The report finds that if EcoDensity makes Vancouver a more desirable urban environment, then there may be a tradeoff between ecological sustainability and housing affordability.


title: Artificial Neural Network and Genetic Algorithm Hybrid Intelligence for Predicting Thai Stock Price Index Trend

abstract: This study investigated the use of Artificial Neural Network ANN and Genetic Algorithm GA for prediction of Thailand’s SET50 index trend. ANN is a widely accepted machine learning method that uses past data to predict future trend, while GA is an algorithm that can find better subsets of input variables for importing into ANN, hence enabling more accurate prediction by its efficient feature selection. The imported data were chosen technical indicators highly regarded by stock analysts, each represented by 4 input variables that were based on past time spans of 4 different lengths: 3-, 5-, 10-, and 15-day spans before the day of prediction. This import undertaking generated a big set of diverse input variables with an exponentially higher number of possible subsets that GA culled down to a manageable number of more effective ones. SET50 index data of the past 6 years, from 2009 to 2014, were used to evaluate this hybrid intelligence prediction accuracy, and the hybrid’s prediction results were found to be more accurate than those made by a method using only one input variable for one fixed length of past time span.


title: Learning and teaching with social network sites: A decade of research in K-12 related education

abstract: The increasingly widespread use of social network sites to expand and deepen one's social connections is a relatively new but potentially important phenomenon that has implications for teaching and learning and teacher education in the 21st century. This paper surveys the educational research literature to examine: How such technologies are perceived and used by K-12 learners and teachers with what impacts on pedagogy or students' learning. Selected studies were summarized and categorized according to the four types introduced by Roblyer (2005) as studies most needed to move the educational technology field forward. These include studies that establish the technology's effectiveness at improving student learning; investigate implementation strategies; monitor social impact; and report on common uses to shape the direction of the field. We found the most prevalent type of study conducted related to our focal topic was research on common uses. The least common type of study conducted was research that established the technology's effectiveness at improving student learning. Implications for the design of future research and teacher education initiatives are discussed.


title: BigHand2.2M Benchmark: Hand Pose Dataset and State of the Art Analysis

abstract: In this paper we introduce a large-scale hand pose dataset, collected using a novel capture method. Existing datasets are either generated synthetically or captured using depth sensors: synthetic datasets exhibit a certain level of appearance difference from real depth images, and real datasets are limited in quantity and coverage, mainly due to the difficulty to annotate them. We propose a tracking system with six 6D magnetic sensors and inverse kinematics to automatically obtain 21-joints hand pose annotations of depth maps captured with minimal restriction on the range of motion. The capture protocol aims to fully cover the natural hand pose space. As shown in embedding plots, the new dataset exhibits a significantly wider and denser range of hand poses compared to existing benchmarks. Current state-of-the-art methods are evaluated on the dataset, and we demonstrate significant improvements in cross-benchmark performance. We also show significant improvements in egocentric hand pose estimation with a CNN trained on the new dataset.


title: A GPU Laplacian solver for diffusion curves and Poisson image editing

abstract: We present a new Laplacian solver for minimal surfaces---surfaces having a mean curvature of zero everywhere except at some fixed (Dirichlet) boundary conditions. Our solution has two main contributions: First, we provide a robust rasterization technique to transform continuous boundary values (diffusion curves) to a discrete domain. Second, we define a variable stencil size diffusion solver that solves the minimal surface problem. We prove that the solver converges to the right solution, and demonstrate that it is at least as fast as commonly proposed multigrid solvers, but much simpler to implement. It also works for arbitrary image resolutions, as well as 8 bit data. We show examples of robust diffusion curve rendering where our curve rasterization and diffusion solver eliminate the strobing artifacts present in previous methods. We also show results for real-time seamless cloning and stitching of large image panoramas.


title: An efficient multi-gateway-based three-factor user authentication and key agreement scheme in hierarchical wireless sensor networks

abstract: User authentication in wireless sensor network WSN plays a very important role in which a legal registered user is allowed to access the real-time sensing information from the sensor nodes inside WSN. To allow such access, a user needs to be authenticated by the accessed sensor nodes as well as gateway nodes inside WSNs. Because of resource limitations and vulnerability to physical capture of some sensor nodes by an attacker, design of a secure user authentication in WSN continues to be an important and challenging research area in recent years. In this paper, we propose a new three-factor user authentication scheme based on the multi-gateway WSN architecture. Through the widely-accepted Burrows-Abadi-Needham logic, we prove that our scheme provides the secure mutual authentication. We then present the formal security verification of our proposed scheme using AVISPA tool, which is a powerful validation tool for network security applications, and show that our scheme is secure. In addition, the rigorous informal security analysis shows that our scheme is also secure against possible other known attacks including the sensor node capture attack. Furthermore, we present the additional functionality features that our scheme offers, which are efficient in communication and computation. Copyright © 2016 John Wiley & Sons, Ltd.


title: Impedance tomography as a new monitoring technique

abstract: Purpose of reviewElectrical impedance tomography (EIT) noninvasively creates images of the local ventilation and arguably lung perfusion distribution at bedside. Methodological and clinical aspects of EIT when used as a monitoring tool in the intensive care unit are reviewed and discussed.Recent fin


title: Personalised Query Suggestion for Intranet Search with Temporal User Profiling

abstract: Recent research has shown the usefulness of using collective user interaction data (e.g., query logs) to recommend query modification suggestions for Intranet search. However, most of the query suggestion approaches for Intranet search follow an ``one size fits all'' strategy, whereby different users who submit an identical query would get the same query suggestion list. This is problematic, as even with the same query, different users may have different topics of interest, which may change over time in response to the user's interaction with the system. We address the problem by proposing a personalised query suggestion framework for Intranet search. For each search session, we construct two temporal user profiles: a click user profile using the user's clicked documents and a query user profile using the user's submitted queries. We then use the two profiles to re-rank the non-personalised query suggestion list returned by a state-of-the-art query suggestion method for Intranet search. Experimental results on a large-scale query logs collection show that our personalised framework significantly improves the quality of suggested queries.


title: An architecture for building self-configurable systems

abstract: Developing wireless sensor networks can enable information gathering, information processing and reliable monitoring of a variety of environments for both civil and military applications. It is however necessary to agree upon a basic architecture for building sensor network applications. This paper presents a general classification of sensor network applications based on their network configurations and discusses some of their architectural requirements. We propose a generic architecture for a specific subclass of sensor applications which we define as self-configurable systems where a large number of sensors coordinate amongst themselves to achieve a large sensing task. Throughout this paper we assume a certain subset of the sensors to be immobile. This paper lists the general architectural and infra-structural components necessary for building this class of sensor applications. Given the various architectural components, we present an algorithm that self-organizes the sensors into a network in a transparent manner. Some of the basic goals of our algorithm include minimizing power utilization, localizing operations and tolerating node and link failures.


title: Discovering essential code elements in informal documentation

abstract: To access the knowledge contained in developer communication, such as forum posts, it is useful to determine automatically the code elements referred to in the discussions. We propose a novel traceability recovery approach to extract the code elements contained in various documents. As opposed to previous work, our approach does not require an index of code elements to find links, which makes it particularly well-suited for the analysis of informal documentation. When evaluated on 188 StackOverflow answer posts containing 993 code elements, the technique performs with average 0.92 precision and 0.90 recall. As a major refinement on traditional traceability approaches, we also propose to detect which of the code elements in a document are salient, or germane, to the topic of the post. To this end we developed a three-feature decision tree classifier that performs with a precision of 0.65-0.74 and recall of 0.30-0.65, depending on the subject of the document.


title: An IoT Endpoint System-on-Chip for Secure and Energy-Efficient Near-Sensor Analytics

abstract: Near-sensor data analytics is a promising direction for internet-of-things endpoints, as it minimizes energy spent on communication and reduces network load - but it also poses security concerns, as valuable data are stored or sent over the network at various stages of the analytics pipeline. Using encryption to protect sensitive data at the boundary of the on-chip analytics engine is a way to address data security issues. To cope with the combined workload of analytics and encryption in a tight power envelope, we propose Fulmine , a system-on-chip (SoC) based on a tightly-coupled multi-core cluster augmented with specialized blocks for compute-intensive data processing and encryption functions, supporting software programmability for regular computing tasks. The Fulmine SoC, fabricated in 65-nm technology, consumes less than 20mW on average at 0.8V achieving an efficiency of up to 70pJ/B in encryption, 50pJ/px in convolution, or up to 25MIPS/mW in software. As a strong argument for real-life flexible application of our platform, we show experimental results for three secure analytics use cases: secure autonomous aerial surveillance with a state-of-the-art deep convolutional neural network (CNN) consuming 3.16pJ per equivalent reduced instruction set computer operation, local CNN-based face detection with secured remote recognition in 5.74pJ/op, and seizure detection with encrypted data collection from electroencephalogram within 12.7pJ/op.


title: Shape Completion Using 3D-Encoder-Predictor CNNs and Shape Synthesis

abstract: We introduce a data-driven approach to complete partial 3D shapes through a combination of volumetric deep neural networks and 3D shape synthesis. From a partially-scanned input shape, our method first infers a low-resolution – but complete – output. To this end, we introduce a 3D-Encoder-Predictor Network (3D-EPN) which is composed of 3D convolutional layers. The network is trained to predict and fill in missing data, and operates on an implicit surface representation that encodes both known and unknown space. This allows us to predict global structure in unknown areas at high accuracy. We then correlate these intermediary results with 3D geometry from a shape database at test time. In a final pass, we propose a patch-based 3D shape synthesis method that imposes the 3D geometry from these retrieved shapes as constraints on the coarsely-completed mesh. This synthesis process enables us to reconstruct fine-scale detail and generate high-resolution output while respecting the global mesh structure obtained by the 3D-EPN. Although our 3D-EPN outperforms state-of-the-art completion method, the main contribution in our work lies in the combination of a data-driven shape predictor and analytic 3D shape synthesis. In our results, we show extensive evaluations on a newly-introduced shape completion benchmark for both real-world and synthetic data.


title: Comparison of alcoholic chlorhexidine and povidone–iodine cutaneous antiseptics for the prevention of central venous catheter-related infection: a cohort and quasi-experimental multicenter study

abstract: Purpose ::: Compare the effectiveness of different cutaneous antiseptics in reducing risk of catheter-related infection in intensive care unit (ICU) patients.


title: Entity Linking with Effective Acronym Expansion, Instance Selection, and Topic Modeling

abstract: Entity linking maps name mentions in the documents to entries in a knowledge base through resolving the name variations and ambiguities. In this paper, we propose three advancements for entity linking. Firstly, expanding acronyms can effectively reduce the ambiguity of the acronym mentions. However, only rule-based approaches relying heavily on the presence of text markers have been used for entity linking. In this paper, we propose a supervised learning algorithm to expand more complicated acronyms encountered, which leads to 15.1% accuracy improvement over state-of-the-art acronym expansion methods. Secondly, as entity linking annotation is expensive and labor intensive, to automate the annotation process without compromise of accuracy, we propose an instance selection strategy to effectively utilize the automatically generated annotation. In our selection strategy, an informative and diverse set of instances are selected for effective disambiguation. Lastly, topic modeling is used to model the semantic topics of the articles. These advancements give statistical significant improvement to entity linking individually. Collectively they lead the highest performance on KBP-2010 task.


title: Fog Computing Over IoT: A Secure Deployment and Formal Verification

abstract: Fog computing, being an extension to cloud computing has addressed some issues found in cloud computing by providing additional features, such as location awareness, low latency, mobility support, and so on. Its unique features have also opened a way toward security challenges, which need to be focused for making it bug-free for the users. This paper is basically focusing on overcoming the security issues encountered during the data outsourcing from fog client to fog node. We have added Shibboleth also known as security and cross domain access control protocol between fog client and fog node for improved and secure communication between the fog client and fog node. Furthermore to prove whether Shibboleth meets the security requirement needed to provide the secure outsourcing. We have also formally verified the protocol against basic security properties using high level Petri net.


title: Risk management in Agile software development: A comparative study

abstract: The main goal of agile development methods is to reduce risk thereby resulting in more successful and effective information systems. Indeed, Finding out, analyzing, prioritising, and planning risks are important activities in all development approaches, including Agile development. However, while there is an extensive body of academic literature on risk management, few little researches have attempted to study risk management in Agile development projects. This paper discusses the Risk Management (RM) activities in Agile software development, and shows how using Agile can help with traditional RM process. Finally, the paper sheds light on the benefits and limitations of some Agile methodologies from a RM perspective and highlights directions for future work in this topic.


title: Awaiwl of diioxin bioavailabitity and phamecokinstics in relation to cardiac therapy

abstract: Butler and Chen’ first reported on the preparation of digoxin-specific antibodies. Later, Smith and associates2s3 characterized the antibodies2 and developed a radioimmunoassay for digoxin in serum or plasma.3 Subsequently, it was shown that antibodies present in commerciaIly available antiserum were not specific for digoxin, but that digoxin, digoxigen-mono-digotoxoside, and digoxigen-bis-digitoxoside all reacted with the antibody to about the same degree.4*6 Digoxigenin also cross-reacted, but to a much lesser degree.“” However, earlier studies6s7 indicated that all the metabolites of both digoxin and digitoxin are cardioactive to varying degrees in both the guinea pig and the cat. Only the aglycones appear to have greatly reduced activity in vivo. Thus, what low levels of metabolites one would measure by application of the radioimmunoassay wduld mostly be cardioactive. Hence, from a clinical point of view, the radioimmunoassay procedure for digoxin is quite useful for monitoring individual patients, determining therapeutic and toxic plasma or serum levels, and in bioavailability studies where plasma or serum levels of dioxin and/or urinary excretion of digoxin are


title: The size, mass, and composition of plastic debris in the western North

abstract: This study reports the first inventory of physical properties of individual plastic debris in the North Atlantic. We analyzed 748 samples for size, mass, and material composition collected from surface net tows on 11 expeditions from Cape Cod, Massachusetts to the Caribbean Sea between 1991 and 2007. Particles were mostly fragments less than 10 mm in size with nearly all lighter than 0.05 g. Material densities ranged from 0.808 to 1.24 g ml � 1 , with about half between 0.97 and 1.04 g ml � 1 , a range not typically found in virgin plastics. Elemental analysis suggests that samples in this density range are consistent with polypropylene and polyethylene whose densities have increased, likely due to biofouling. Pelagic densities varied considerably from that of beach plastic debris, suggesting that plastic particles are modified during their residence at sea. These analyses provide clues in understanding particle fate and potential debris sources, and address ecological implications of pelagic plastic debris.


title: How online social interactions influence customer information contribution behavior in online social shopping communities: A social learning theory perspective

abstract: Online social shopping communities are transforming the way customers communicate and exchange product information with others. To date, the issue of customer participation in online social shopping communities has become an important but underexplored research area in the academic literature. In this study, we examined how online social interactions affect customer information contribution behavior. We also explored the moderating role of customer reputation in the relationship between observational learning and reinforcement learning as well as customer information contribution behavior. Analyses of panel data from 6,121 customers in an online social fashion platform revealed that they are significant factors affecting customer information contribution behavior and that reinforcement learning exhibits a stronger effect than observational learning. The results also showed that customer reputation has a significant negative moderating effect on the relationship between observational learning and customer information contribution behavior. This study not only enriched our theoretical understanding of information contribution behavior but also provided guidelines for online social shopping community administrators to better design their community features.


title: Immunotherapy and targeted therapy in brain metastases: emerging options in precision medicine.

abstract: Brain metastases (BM) continue to represent an unmet clinical need in oncology. Immunotherapy and targeted therapy hold great promise in the treatment of BM. Emerging data are confirming the activity of these agents in patients with BM. Genomic studies have confirmed that clinically actionable mutations are present in BM and they can be used in clinical studies to link targeted therapies with their genetic targets. Furthermore, as molecular signatures associated with sensitivity and resistance to immunotherapies are developed, we will better be able to select BM patients who will most benefit from these therapies. Understanding the genetic and immune evolution within BM should drive the next generation of immunotherapy and target therapy, as well as increase the accuracy of the selection process for these therapies.


title: Swing up control of inverted pendulum

abstract: A bang-bang-type state feedback control algorithm which can swing up a pendulum from a pendant position to an upright position is proposed. In the control system a conventional LQ control method is also employed around the upright position to maintain the pendulum in the upright position after the pendulum has been swung up to this position by the bang-bang control. The control system was constructed and the validity of the control method was examined experimentally. Since the pendulum is attached by a hinge to a rotating arm fixed to a direct drive motorshaft, it contains fewer uncertainties introduced by transmission mechanisms, e.g., a belt and gears in a linear-type inverted pendulum. The advantages of the designed pendulum are that it is small and saves experimental space, and that the robustness of control algorithms can be easily tested with it since known parasitics can be added as uncertainties in the control loops, e.g., a flexible arm can be used instead of a rigid arm. >


title: Types and tokens in visual processing : A double dissociation between the attentional blink and repetition blindness

abstract: The visual system is generally limited in the number of items it can process at any given time. Performance decrements can be observed for reporting multiple targets presented in a sequence. Using a technique where targets are embedded among distractors in rapid serial visual presentation (RSVP), previous studies have shown that correct identification of a target (T1) interferes with processing of a second target (T2) appearing within half a second. This effect on T2 has been called an attentional blink (AB; Raymond, Shapiro, & Amell, 1992). A similar deficit has been shown for a repeated stimulus appearing in RSVP, an effect called repetition blindness (RB; Kanwisher, 1987). The focus of this article is on examining the relationship between the two deficits. The AB occurs when two different targets are presented


title: StereoTrust: a group based personalized trust model

abstract: Trust plays important roles in diverse decentralized environments, including our society at large. Computational trust models help to, for instance, guide users' judgements in online auction sites about other users; or determine quality of contributions in web 2.0 sites. Most of the existing trust models, however, require historical information about past behavior of a specific agent being evaluated - information that is not always available. In contrast, in real life interactions among users, in order to make the first guess about the trustworthiness of a stranger, we commonly use our "instinct" - essentially stereotypes developed from our past interactions with "similar" people. We propose StereoTrust, a computational trust model inspired by real life stereotypes. A user forms stereotypes using her previous transactions with other agents. A stereotype contains certain features of agents and an expected outcome of the transaction. These features can be taken from agents' profile information, or agents' observed behavior in the system. When facing a stranger, the stereotypes matching stranger's profile are aggregated to derive his expected trust. Additionally, when some information about stranger's previous transactions is available, StereoTrust uses it to refine the stereotype matching. According to our experiments, StereoTrust compares favorably with existing trust models that use different kind of information and more complete historical information. Moreover, because evaluation is done according to user's personal stereotypes, the system is completely distributed and the result obtained is personalized. StereoTrust can be used as a complimentary mechanism to provide the initial trust value for a stranger, especially when there is no trusted, common third parties.


title: Towards a Semantic Extract-Transform-Load (ETL) Framework for Big Data Integration

abstract: Big Data has become the new ubiquitous term used to describe massive collection of datasets that are difficult to process using traditional database and software techniques. Most of this data is inaccessible to users, as we need technology and tools to find, transform, analyze, and visualize data in order to make it consumable for decision-making. One aspect of Big Data research is dealing with the Variety of data that includes various formats such as structured, numeric, unstructured text data, email, video, audio, stock ticker, etc. Managing, merging, and governing a variety of data is the focus of this paper. This paper proposes a semantic Extract-Transform-Load (ETL) framework that uses semantic technologies to integrate and publish data from multiple sources as open linked data. This includes - creation of a semantic data model to provide a basis for integration and understanding of knowledge from multiple sources, creation of a distributed Web of data using Resource Description Framework (RDF) as the graph data model, extraction of useful knowledge and information from the combined data using SPARQL as the semantic query language.


title: Bandwidth Distributed Denial of Service: Attacks and Defenses

abstract: The Internet is vulnerable to bandwidth distributed denial-of-service (BW-DDoS) attacks, wherein many hosts send a huge number of packets to cause congestion and disrupt legitimate traffic. So far, BW-DDoS attacks have employed relatively crude, inefficient, brute force mechanisms; future attacks might be significantly more effective and harmful. To meet the increasing threats, we must deploy more advanced defenses.


title: Intelligent Adaptive E-learning Model for Learning Management System

abstract: In this study we have proposed an Intelligent Adaptive e-Learning Model that incorporates the ability to intelligently classify learners. There is a need for learning to continue, whether learners are on- or off-line. This study emphasize on developing an agent-based personalized adaptive learning model. This model is deployed as a service using agent technology and not just as an application as is the case with all other available LMS. We tested Intelligent Adaptive e-Learning Model prototype that implements an adaptive presentation of course content under conditions of intermittent Internet connections on postgraduate students studying a networking course. The study found out that it is possible for learners to study under both off-line and on-line modes through adaptive learning and the Intelligent Adaptive E-Learning system successfully classified learners and the accuracy was 85%.


title: Switched inductor boost converter for PV applications

abstract: This paper introduces a boost converter with high dc gain as a solution for partial shading of photovoltaic (PV) module. Switched inductor boost converter (SIBC) is introduced by replacing the inductor of the boost converter with a switched inductor branch. As a result, the conversion gain ratio can be increased. The proposed converter is used as an interface between the PV system and the load. A Maximum Power Point Tracking (MPPT) control is applied to extract the maximum power of the PV module. Analyses, simulation, and experimental results are provided to validate the operation of the converter.


title: 20 Questions on Dialogue Act Taxonomies

abstract: There is currently a broad interest in dialogue acts and dialogue act taxonomies, and new uses, taxonomies, and standardization efforts continue to be proposed. This paper presents a discussion of issues that must be addressed in order to facilitate the shared understanding and use of taxonomies. The discussion is framed in terms of 20 questions, the answers to which will help make the meanings of taxonomy elements more clear to different communities of users. I INTRODUCTION When engaging in a study related to dialogue pragmatics, a researcher is confronted with a bewildering range of theories and taxonomies of dialogue acts1 to choose from. Moreover, specific deficits in any given theory often


title: Towards improving bug tracking systems with game mechanisms

abstract: Low bug report quality and human conflicts pose challenges to keep bug tracking systems productive. This work proposes to address these issues by applying game mechanisms to bug tracking systems. We investigate the use of game mechanisms in Stack Overflow, an online community organized to resolve computer programming related problems, for which the improvements we seek for bug tracking systems also turn out to be relevant. The results of our Stack Overflow investigation show that its game mechanisms could be used to address these issues by motivating contributors to increase contribution frequency and quality, by filtering useful contributions, and by creating an agile and dependable moderation system. We proceed by mapping these mechanisms to open-source bug tracking systems, and find that most benefits are applicable. Additionally, our results motivate tailoring a reward and reputation system and summarizing bug reports as future directions for increasing the benefits of game mechanisms in bug tracking systems.


Smarter Presentations: Exploiting Homography in Camera-Projector Systems


title: Hierarchical Spatial Hashing for Real-time Collision Detection

abstract: We present a new, efficient and easy to use collision detection scheme for real-time collision detection between highly deformable tetrahedral models. Tetrahedral models are a common representation of volumetric meshes which are often used in physically based simulations, e.g. in Virtual surgery. In a deformable models environment collision detection usually is a performance bottleneck since the data structures used for efficient intersection tests need to be rebuilt or modified frequently. Our approach minimizes the time needed for building a collision detection data structure. We employ an infinite hierarchical spatial grid in which for each single tetrahedron in the scene a well fitting grid cell size is computed. A hash function is used to project occupied grid cells into a finite ID hash table. Only primitives mapped to the same hash index indicate a possible collision and need to be checked for intersections. This results in a high performance collision detection algorithm which does not depend on user defined parameters and thus flexibly adapts to any scene setup.


title: Improved neural machine translation with SMT features

abstract: Neural machine translation (NMT) conducts end-to-end translation with a source language encoder and a target language decoder, making promising translation performance. However, as a newly emerged approach, the method has some limitations. An NMT system usually has to apply a vocabulary of certain size to avoid the time-consuming training and decoding, thus it causes a serious out-of-vocabulary problem. Furthermore, the decoder lacks a mechanism to guarantee all the source words to be translated and usually favors short translations, resulting in fluent but inadequate translations. In order to solve the above problems, we incorporate statistical machine translation (SMT) features, such as a translation model and an n-gram language model, with the NMT model under the log-linear framework. Our experiments show that the proposed method significantly improves the translation quality of the state-of-the-art NMT system on Chinese-to-English translation tasks. Our method produces a gain of up to 2.33 BLEU score on NIST open test sets.


Integrating Natural Language Processing and Software Engineering


title: Species diversity and seasonal abundance in tropical Sphingidae (Lepidoptera)

abstract: Synopsis ::: ::: An account is given of species diversity in two samples of Sphingidae obtained by different methods of trapping in Freetown, Sierra Leone. Seasonal abundance and resulting seasonal changes in diversity are discussed.


title: Chatty Tenants and the Cloud Network Sharing Problem

abstract: The emerging ecosystem of cloud applications leads to significant inter-tenant communication across a datacenter's internal network. This poses new challenges for cloud network sharing. Richer inter-tenant traffic patterns make it hard to offer minimum bandwidth guarantees to tenants. Further, for communication between economically distinct entities, it is not clear whose payment should dictate the network allocation. ::: ::: Motivated by this, we study how a cloud network that carries both intra-and inter-tenant traffic should be shared. We argue for network allocations to be dictated by the least-paying of communication partners. This, when combined with careful VM placement, achieves the complementary goals of providing tenants with minimum bandwidth guarantees while bounding their maximum network impact. Through a prototype deployment and large-scale simulations, we show that minimum bandwidth guarantees, apart from helping tenants achieve predictable performance, also improve overall datacenter throughput. Further, bounding a tenant's maximum impact mitigates malicious behavior.


title: Bounding and reducing memory interference in COTS-based multi-core systems

abstract: In multi-core systems, main memory is a major shared resource among processor cores. A task running on one core can be delayed by other tasks running simultaneously on other cores due to interference in the shared main memory system. Such memory interference delay can be large and highly variable, thereby posing a significant challenge for the design of predictable real-time systems. In this paper, we present techniques to reduce this interference and provide an upper bound on the worst-case interference on a multi-core platform that uses a commercial-off-the-shelf (COTS) DRAM system. We explicitly model the major resources in the DRAM system, including banks, buses, and the memory controller. By considering their timing characteristics, we analyze the worst-case memory interference delay imposed on a task by other tasks running in parallel. We find that memory interference can be significantly reduced by (i) partitioning DRAM banks, and (ii) co-locating memory-intensive tasks on the same processing core. Based on these observations, we develop a memory interference-aware task allocation algorithm for reducing memory interference. We evaluate our approach on a COTS-based multi-core platform running Linux/RK. Experimental results show that the predictions made by our approach are close to the measured worst-case interference under workloads with both high and low memory contention. In addition, our memory interference-aware task allocation algorithm provides a significant improvement in task schedulability over previous work, with as much as 96 % more tasksets being schedulable.


title: Impact of reviewer social interaction on online consumer review fraud detection

abstract: Online consumer reviews have become a baseline for new consumers to try out a business or a new product. The reviews provide a quick look into the application and experience of the business/product and market it to new customers. However, some businesses or reviewers use these reviews to spread fake information about the business/product. The fake information can be used to promote a relatively average product/business or can be used to malign their competition. This activity is known as reviewer fraud or opinion spam. The paper proposes a feature set, capturing the user social interaction behavior to identify fraud. The problem being solved is one of the characteristics that lead to fraud rather than detecting fraud. Neural network algorithm is used to evaluate the proposed feature set and compare it against the state-of-the-art feature sets in detecting fraud. The feature set considers the user’s social interaction on the Yelp platform to determine if the user is committing fraud. The neural network algorithm helps in comparing the feature set with other feature sets used to detect fraud. Any attempt to find the characteristics that lead to fraud has a prerequisite to be good enough to detect fraud as well. The F1 score obtained using neural networks is on par with all the well-known methods for detecting fraud, a value of 0.95. The effectiveness of the feature set is in rivaling the other approaches to fraud detection. A user’s social interaction on a digital platform such as Yelp is equally important in evaluating the user as social interaction is in real life. The characteristics that lead to fraud can be intuitively captured. The characteristics such as number of friends, number of followers and the number of times the user has provided a review which was helpful to multiple people provide the neural network with a base to form a relationship between opinion fraud and social interaction characteristics.


title: Visual attribute detction for pedestrian detection

abstract: Attributes are expected to narrow down the semantic gap between low-level visual features and high-level semantic meanings. Such superiority motivates us to explore pedestrian attributes which has became a critical problem to boost image understanding and improve the performance of pedestrian detection, retrieval, re-identification, etc. Based on the PETA dateset, we manually relabel two subset VIPeR and PRID as our experimental dataset. Moreover, we proposed an evaluation protocol for researchers to evaluate pedestrian attribute classification algorithms. In this paper, we utilized two baseline methods to to demonstrate the performance of the attribute in pedestrian detection. The first one directly uses color and texture features to train Support Vector Machine (SVM) classification while the other one uses DSIFT (Dense SIFT) with Bag-of-Visual-Words (BoVW) to train SVM classification. Finally, we report and discuss the baseline performance on the database following the proposed evaluation protocol.


A tutorial for Discriminant Analysis of Principal Components (DAPC) using adegenet 1.3-6


title: User's Web Page Aesthetics Opinion: A Matter of Low-Level Image Descriptors Based on MPEG-7

abstract: Analyzing a user's first impression of a Web site is essential for interface designers, as it is tightly related to their overall opinion of a site. In fact, this early evaluation affects user navigation behavior. Perceived usability and user interest (e.g., revisiting and recommending the site) are parameters influenced by first opinions. Thus, predicting the latter when creating a Web site is vital to ensure users’ acceptance. In this regard, Web aesthetics is one of the most influential factors in this early perception. We propose the use of low-level image parameters for modeling Web aesthetics in an objective manner, which is an innovative research field. Our model, obtained by applying a stepwise multiple regression algorithm, infers a user's first impression by analyzing three different visual characteristics of Web site screenshots—texture, luminance, and color—which are directly derived from MPEG-7 descriptors. The results obtained over three wide Web site datasets (composed by 415, 42, and 6 Web sites, respectively) reveal a high correlation between low-level parameters and the users’ evaluation, thus allowing a more precise and objective prediction of users’ opinion than previous models that are based on other image characteristics with fewer predictors. Therefore, our model is meant to support a rapid assessment of Web sites in early stages of the design process to maximize the likelihood of the users’ final approval.


title: Steganalysis and payload estimation of embedding in pixel differences using neural networks

abstract: In this paper a steganalysis technique is proposed for pixel-value differencing method. This steganographic method, which is immune against conventional attacks, performs the embedding in the difference of the values of pixel pairs. Therefore, the histogram of the differences of an embedded image is different as compared with a cover image. A number of characteristics are identified in the difference histogram that show meaningful alterations when an image is embedded. Five distinct multilayer perceptrons neural networks are trained to detect different levels of embedding. Every image is fed to all networks and a voting system categorizes the image as stego or cover. The implementation results indicate 88.6% success in correct categorization of the test images that contained more than 20% embedding. Furthermore, using a neural network an estimator is presented which gives an estimate of the amount of the MPVD embedding in an image. Implementation of the estimator showed an average accuracy of 88.3% in the estimation of the amount of embedding.


title: End-to-End Delay Minimization for Scientific Workflows in Clouds under Budget Constraint

abstract: Next-generation e-Science features large-scale, compute-intensive workflows of many computing modules that are typically executed in a distributed manner. With the recent emergence of cloud computing and the rapid deployment of cloud infrastructures, an increasing number of scientific workflows have been shifted or are in active transition to cloud environments. As cloud computing makes computing a utility, scientists across different application domains are facing the same challenge of reducing financial cost in addition to meeting the traditional goal of performance optimization. We develop a prototype generic workflow system by leveraging existing technologies for a quick evaluation of scientific workflow optimization strategies. We construct analytical models to quantify the network performance of scientific workflows using cloud-based computing resources, and formulate a task scheduling problem to minimize the workflow end-to-end delay under a user-specified financial constraint. We rigorously prove that the proposed problem is not only NP-complete but also non-approximable. We design a heuristic solution to this problem, and illustrate its performance superiority over existing methods through extensive simulations and real-life workflow experiments based on proof-of-concept implementation and deployment in a local cloud testbed.


title: The Digital Divide: Current and Future Research Directions

abstract: The digital divide refers to the separation between those who have access to digital information and communications technology (ICT) and those who do not. Many believe that universal access to ICT would bring about a global community of interaction, commerce, and learning resulting in higher standards of living and improved social welfare. However, the digital divide threatens this outcome leading many public policy makers to debate the best way to bridge the divide. Much of the research on the digital divide focuses on first order effects regarding who has access to the technology, but some work addresses the second order effects of inequality in the ability to use the technology among those who do have access. In this paper, we examine both first and second order effects of the digital divide at three levels of analysis  the individual level, the organizational level, and the global level. At each level, we survey the existing research noting the theoretical perspective taken in the work, the research methodology employed, and the key results that were obtained. We then suggest a series of research questions at each level of analysis to guide researchers seeking to further examine the digital divide and how it impacts citizens, managers, and economies.


title: Performance analysis of cart and C5.0 using sampling techniques

abstract: Data mining is the process of extracting the hidden predictive model from large databases. It has various methods and algorithms. Classification is a supervised method, which builds a model for predicting the new instances. Different algorithms like decision tree, neural networks, support vector machines, k nearest neighbour, Bayesian classification are available for the classification. Decision tree is the simple and most commonly used algorithm among the classification algorithms. It constructs a tree based model on the values of feature and generates rules for decision making. Samples are used for classification in order to train the model and predict the new instances. Unbiased samples can improve the performance of classification. This paper analyses the performance of CART and C5.0 algorithms using sampling techniques.


title: Scalable distributed DNN training using commodity GPU cloud computing.

abstract: We introduce a new method for scaling up distributed Stochastic Gradient Descent (SGD) training of Deep Neural Networks (DNN). The method solves the well-known communication bottleneck problem that arises for data-parallel SGD because compute nodes frequently need to synchronize a replica of the model. We solve it by purposefully controlling the rate of weight-update per individual weight, which is in contrast to the uniform update-rate customarily imposed by the size of a mini-batch. It is shown empirically that the method can reduce the amount of communication by three orders of magnitude while training a typical DNN for acoustic modelling. This reduction in communication bandwidth enables efficient scaling to more parallel GPU nodes than any other method that we are aware of, and it can be achieved with neither loss in convergence rate nor accuracy in the resulting DNN. Furthermore, the training can be performed on commodity cloud infrastructure and networking.


title: Tackling the Poor Assumptions of Naive Bayes Text Classifiers

abstract: Naive Bayes is often used as a baseline in text classification because it is fast and easy to implement. Its severe assumptions make such efficiency possible but also adversely affect the quality of its results. In this paper we propose simple, heuristic solutions to some of the problems with Naive Bayes classifiers, addressing both systemic issues as well as problems that arise because text is not actually generated according to a multinomial model. We find that our simple corrections result in a fast algorithm that is competitive with state-of-the-art text classification algorithms such as the Support Vector Machine.


title: Accurate Modeling of Thermal Resistance for On-Wafer SiGe HBTs Using Average Thermal Conductivity

abstract: An accurate analytic model is proposed for estimating the junction temperature and thermal resistance in silicon–germanium heterojunction bipolar transistors (SiGe HBTs) including the back-end-of-line (BEOL) metal layers. The model uses an average value of thermal conductivity in order to include the temperature dependence of thermal resistance. The parameters corresponding to the thermal conductivity and the BEOL thermal resistance used in the model are extracted following a recently reported methodology. The proposed model is scalable in nature and verification with experimental data shows an excellent accuracy across different emitter geometries of SiGe HBTs fabricated in STMicroelectronics B9MW technology. Compact model simulations show that the proposed model simulates around 23% faster compared with an existing state-of-the-art iterative method.


title: Multi-chart Generative Surface Modeling

abstract: This paper introduces a 3D shape generative model based on deep neural networks. A new image-like (i.e., tensor) data representation for genus-zero 3D shapes is devised. It is based on the observation that complicated shapes can be well represented by multiple parameterizations (charts), each focusing on a different part of the shape. The new tensor data representation is used as input to Generative Adversarial Networks for the task of 3D shape generation. ::: ::: The 3D shape tensor representation is based on a multi-chart structure that enjoys a shape covering property and scale-translation rigidity. Scale-translation rigidity facilitates high quality 3D shape learning and guarantees unique reconstruction. The multi-chart structure uses as input a dataset of 3D shapes (with arbitrary connectivity) and a sparse correspondence between them. The output of our algorithm is a generative model that learns the shape distribution and is able to generate novel shapes, interpolate shapes, and explore the generated shape space. The effectiveness of the method is demonstrated for the task of anatomic shape generation including human body and bone (teeth) shape generation.


Guidelines for Conducting Mixed-methods Research: An Extension and Illustration


title: Principal geodesic analysis for the study of nonlinear statistics of shape

abstract: A primary goal of statistical shape analysis is to describe the variability of a population of geometric objects. A standard technique for computing such descriptions is principal component analysis. However, principal component analysis is limited in that it only works for data lying in a Euclidean vector space. While this is certainly sufficient for geometric models that are parameterized by a set of landmarks or a dense collection of boundary points, it does not handle more complex representations of shape. We have been developing representations of geometry based on the medial axis description or m-rep. While the medial representation provides a rich language for variability in terms of bending, twisting, and widening, the medial parameters are not elements of a Euclidean vector space. They are in fact elements of a nonlinear Riemannian symmetric space. In this paper, we develop the method of principal geodesic analysis, a generalization of principal component analysis to the manifold setting. We demonstrate its use in describing the variability of medially-defined anatomical objects. Results of applying this framework on a population of hippocampi in a schizophrenia study are presented.


title: Semantic Communication for Simple Goals is Equivalent to On-line Learning ⋆

abstract: Previous works [11, 6] introduced a model of semantic communication between a "user" and a "server," in which the user attempts to achieve a given goal for communication. They show that whenever the user can sense progress, there exist universal user strategies that can achieve the goal whenever it is possible for any other user to reliably do so. A drawback of the actual constructions is that the users are inefficient: they enumerate protocols until they discover one that is successful, leading to the potential for exponential overhead in the length of the desired protocol. Goldreich et al. [6] conjectured that this overhead could be reduced to a polynomial dependence if we restricted our attention to classes of sufficiently simple user strategies and goals. In this work, we are able to obtain such universal strategies for some reasonably general special cases by establishing an equivalence between these special cases and the usual model of mistake-bounded on-line learning [3, 15]. This equivalence also allows us to see the limits of constructing universal users based on sensing and motivates the study of sensing with richer kinds of feedback. Along the way, we also establish a new lower bound for the "beliefs model" [12], which demonstrates that constructions of efficient users in that framework rely on the existence of a common "belief" under which all of the servers in a class are designed to be efficient.


title: Segregation of Tactile Input Features in Neurons of the Cuneate Nucleus

abstract: Our tactile perception of external objects depends on skin-object interactions. The mechanics of contact dictates the existence of fundamental spatiotemporal input features-contact initiation and cessation, slip, and rolling contact-that originate from the fact that solid objects do not interpenetrate. However, it is unknown whether these features are represented within the brain. We used a novel haptic interface to deliver such inputs to the glabrous skin of finger/digit pads and recorded from neurons of the cuneate nucleus (the brain's first level of tactile processing) in the cat. Surprisingly, despite having similar receptive fields and response properties, each cuneate neuron responded to a unique combination of these inputs. Hence, distinct haptic input features are encoded already at subcortical processing stages. This organization maps skin-object interactions into rich representations provided to higher cortical levels and may call for a re-evaluation of our current understanding of the brain's somatosensory systems.


title: Hidden conditional random fields for gait recognition

abstract: Gait is a recent important research field among the computer vision community. It aims identifying humans by analyzing their walk. It has different advantage comparing to others biometrics technologies such as face recognition, iris recognition and fingerprint. It can be performed at distance and without subject cooperation. Also, it doesn't need high resolution of image. In this paper, we present a new discriminative method for gait recognition using hybrid conditional random fields (CRF). We use a Hidden CRF model to combine two classifiers; a spatial classifier which assigns a label to a local feature (SURF descriptors) and temporal classifier which uses a motion History Image (MHI). The proposed framework, firstly extracts the human silhouette. Secondly, it takes out spatial and temporal cues from each frame. Then, it applies the MLP classification to the two set of features to obtain the Hidden CRF input; the final step is recognizing person with HCRF. Experimental results showed the superiority of our proposed method over several state of arts.


Learning User Embeddings from Emails


title: Time Follower's Vision: a teleoperation interface with past images

abstract: Time-Followers's Vision is a mixed-reality-based visual presentation system that captures a robotic vehicle's size, position, and environment, allowing even inexperienced operators to easily control it. The technique produces a virtual image using mixed reality technology and presents the vehicle's surrounding environment and status to the operator. Therefore, even for inexperienced operators, the vehicle's position and orientation and the surrounding situation can be readily understood. The authors implement a prototype system and evaluate its feasibility. This article is available with a short video documentary on CD-ROM.


title: RFID based student monitoring and attendance tracking system

abstract: India has many numbers of colleges and teaching is one of the major activities providing employment to number of people who like to give knowledge to the people. Today many colleges of rural area are facing common problem like bunking the college lectures also meet with the accidents. Indian department of education arises question to the department for their irresponsibility. Education department also seeks records of all the students which are very difficult to maintain. This describes a prototype development of maintain the record of all the students titled RFID based Tracking & Attendance with GSM Module exclusively catering the need of Indian teachers.


title: Two-channel monopulse tracking receiver for onboard antenna tracking system

abstract: The advance communication satellites, having narrow spot beams, require an automatic antenna pointing mechanism to maintain the spot beam coverage over the specified area. The monopulse tracking system is a popular choice, where the required tracking accuracy is better than 1/10th of antenna beam width. The two-channel monopulse tracking receiver is an essential subsystem of two-channel monopulse tracking system. The receiver is realized using coherent detection technique for better noise performance. The phase lock loop (PLL), and automatic gain control (AGC) loop are the two major building blocks of the receiver. The receiver has been developed and tested with the tracking chain of GSAT-4 satellite. This paper presents specifications, design, implementation issues and performance of the receiver.


title: Dynamic Channel Pruning: Feature Boosting and Suppression

abstract: Making deep convolutional neural networks more accurate typically comes at the cost of increased computational and memory resources. In this paper, we exploit the fact that the importance of features computed by convolutional layers is highly input-dependent, and propose feature boosting and suppression (FBS), a new method to predictively amplify salient convolutional channels and skip unimportant ones at run-time. FBS introduces small auxiliary connections to existing convolutional layers. In contrast to channel pruning methods which permanently remove channels, it preserves the full network structures and accelerates convolution by dynamically skipping unimportant input and output channels. FBS-augmented networks are trained with conventional stochastic gradient descent, making it readily available for many state-of-the-art CNNs. We compare FBS to a range of existing channel pruning and dynamic execution schemes and demonstrate large improvements on ImageNet classification. Experiments show that FBS can accelerate VGG-16 by $5\times$ and improve the speed of ResNet-18 by $2\times$, both with less than $0.6\%$ top-5 accuracy loss.


title: Image recognition method based on deep learning

abstract: Deep learning algorithms are a subset of the machine learning algorithms, which aim at discovering multiple levels of distributed representations. Recently, numerous deep learning algorithms have been proposed to solve traditional artificial intelligence problems. This work aims to review the state-of-the-art in deep learning algorithms in computer vision by highlighting the contributions and challenges from recent research papers. It first gives an overview of various deep learning approaches and their recent developments, and then briefly describes their applications in diverse vision tasks. Finally, the paper summarizes the future trends and challenges in designing and training deep neural networks.


title: 3D Object Reconstruction from a Single Depth View with Adversarial Learning

abstract: In this paper, we propose a novel 3D-RecGAN approach, which reconstructs the complete 3D structure of a given object from a single arbitrary depth view using generative adversarial networks. Unlike the existing work which typically requires multiple views of the same object or class labels to recover the full 3D geometry, the proposed 3D-RecGAN only takes the voxel grid representation of a depth view of the object as input, and is able to generate the complete 3D occupancy grid by filling in the occluded/missing regions. The key idea is to combine the generative capabilities of autoencoders and the conditional Generative Adversarial Networks (GAN) framework, to infer accurate and fine-grained 3D structures of objects in high-dimensional voxel space. Extensive experiments on large synthetic datasets show that the proposed 3D-RecGAN significantly outperforms the state of the art in single view 3D object reconstruction, and is able to reconstruct unseen types of objects. Our code and data are available at: this https URL


title: Individual Tree Segmentation from LiDAR Point Clouds for Urban Forest Inventory

abstract: The objective of this study is to develop new algorithms for automated urban forest inventory at the individual tree level using LiDAR point cloud data. LiDAR data contain three-dimensional structure information that can be used to estimate tree height, base height, crown depth, and crown diameter. This allows precision urban forest inventory down to individual trees. Unlike most of the published algorithms that detect individual trees from a LiDAR-derived raster surface, we worked directly with the LiDAR point cloud data to separate individual trees and estimate tree metrics. Testing results in typical urban forests are encouraging. Future works will be oriented to synergize LiDAR data and optical imagery for urban tree characterization through data fusion techniques.


title: The Father of Ethology and the Foster Mother of Ducks: Konrad Lorenz as Expert on Motherhood

abstract: ABSTRACT Konrad Lorenz's popularity in the United States has to be understood in the context of social concern about the mother‐infant dyad after World War II. Child analysts David Levy, Rene Spitz, Margarethe Ribble, Therese Benedek, and John Bowlby argued that many psychopathologies were caused by a disruption in the mother‐infant bond. Lorenz extended his work on imprinting to humans and argued that maternal care was also instinctual. The conjunction of psychoanalysis and ethology helped shore up the view that the mother‐child dyad rests on an instinctual basis and is the cradle of personality formation. Amidst the Cold War emphasis on rebuilding an emotionally sound society, these views received widespread attention. Thus Lorenz built on the social relevance of psychoanalysis, while analysts gained legitimacy by drawing on the scientific authority of biology. Lorenz's work was central in a rising discourse that blamed the mother for emotional degeneration and helped him recast his eugenic fears in a s...


title: Static Scheduling of Synchronous Data Flow Programs for Digital Signal Processing

abstract: Large grain data flow (LGDF) programming is natural and convenient for describing digital signal processing (DSP) systems, but its runtime overhead is costly in real time or cost-sensitive applications. In some situations, designers are not willing to squander computing resources for the sake of programmer convenience. This is particularly true when the target machine is a programmable DSP chip. However, the runtime overhead inherent in most LGDF implementations is not required for most signal processing systems because such systems are mostly synchronous (in the DSP sense). Synchronous data flow (SDF) differs from traditional data flow in that the amount of data produced and consumed by a data flow node is specified a priori for each input and output. This is equivalent to specifying the relative sample rates in signal processing system. This means that the scheduling of SDF nodes need not be done at runtime, but can be done at compile time (statically), so the runtime overhead evaporates. The sample rates can all be different, which is not true of most current data-driven digital signal processing programming methodologies. Synchronous data flow is closely related to computation graphs, a special case of Petri nets. This self-contained paper develops the theory necessary to statically schedule SDF programs on single or multiple processors. A class of static (compile time) scheduling algorithms is proven valid, and specific algorithms are given for scheduling SDF systems onto single or multiple processors.


title: Color map design for visualization in flood risk assessment

abstract: Visualizations of flood maps from simulation models are widely used for assessing the likelihood of flood hazards in spatial planning. The choice of a suitable type of visualization as well as efficient color maps is critical to avoid errors or bias when interpreting the data. Based on a review of previous flood uncertainty visualization techniques, this paper identifies areas of improvements and suggests criteria for the design of a task-specific color scale in flood map visualization. We contribute a novel color map design for visualizing probabilities and uncertainties from flood simulation ensembles. A user study encompassing 83 participants was carried out to evaluate the effects of this new color map on user’s decisions in a spatial planning task. We found that the type of visualization makes a difference when it comes to identification of non-hazardous sites in the flood risk map and when accepting risks in more uncertain areas. In comparison with two other existing visualization techniques, we observed that the new design was superior both in terms of task compliance and efficiency. In regions with uncertain flood statuses, users were biased toward accepting less risky locations with our new color map design.


title: BUZZ: testing context-dependent policies in stateful networks

abstract: Checking whether a network correctly implements intended policies is challenging even for basic reachability policies (Can X talk to Y?) in simple stateless networks with L2/L3 devices. In practice, operators implement more complex context-dependent policies by composing stateful network functions; e.g., if the IDS flags X for sending too many failed connections, then subsequent packets from X must be sent to a deep-packet inspection device. Unfortunately, existing approaches in network verification have fundamental expressiveness and scalability challenges in handling such scenarios. To bridge this gap, we present BUZZ, a practical model-based testing framework. BUZZ's design makes two key contributions: (1) Expressive and scalable models of the data plane, using a novel high-level traffic unit abstraction and by modeling complex network functions as an ensemble of finite-state machines; and (2) A scalable application of symbolic execution to tackle state-space explosion. We show that BUZZ generates test cases for a network with hundreds of network functions within two minutes (five orders of magnitude faster than alternative designs). We also show that BUZZ uncovers a range of both new and known policy violations in SDN/NFV systems.


title: The musician's brain as a model of neuroplasticity

abstract: Studies of experience-driven neuroplasticity at the behavioural, ensemble, cellular and molecular levels have shown that the structure and significance of the eliciting stimulus can determine the neural changes that result. Studying such effects in humans is difficult, but professional musicians represent an ideal model in which to investigate plastic changes in the human brain. There are two advantages to studying plasticity in musicians: the complexity of the eliciting stimulus — music — and the extent of their exposure to this stimulus. Here, we focus on the functional and anatomical differences that have been detected in musicians by modern neuroimaging methods.


title: Assessment of Requirement Elicitation Tools and Techniques by Various Parameters

abstract: Software development cycle is based on the software requirements. Requirement elicitation is first and most important activity in the requirement engineering. If the requirements are efficiently elicited and managed then the stakeholders application can be accomplished within time and budget. If the requirements are not complete, consistent and correct then projects fail because they not meet the user needs. Requirement is define as the needs of customer. Requirement elicitation techniques and tools are used for correct and complete requirement gathering. Many requirement elicitation techniques and tools are available. This study based on the understanding of requirement gathering techniques and tools. The paper also summarizes the comparison of the requirement elicitation techniques and software requirement tools. Comparison of requirement elicitation techniques and tools are based on the various parameters.


title: Ontology-based distributed intrusion detection system

abstract: In this paper we discussed about utilizing methods and techniques of semantic web in the Intrusion Detection Systems. To extract semantic relations between computer attacks and intrusions in a Distributed Intrusion Detection System, we use ontology. Protege software is our selected software for building ontology. In addition, we utilized Jena framework to make interaction between MasterAgent and attacks ontology. Our Distributed Intrusion Detection System is a network which contains some IDSagents and a special MasterAgent. MasterAgent contains our proposed attacks ontology. Every time a IDSagent detects an attack or new suspected condition, it sends detection's report for MasterAgent. Therefore, it can extract the semantic relationship among computer attacks and suspected situations in the network with proposed ontology. Finally, the experience shows that the pruposed system reduced the rate of false positive and false negative.


title: Magnetization Reversal Mechanism and Magnetic Properties of Co/Pd Multilayers

abstract: To study the magnetization reversal mechanism and the magnetic properties of Co/Pd multilayers fabricated at various Ar sputtering pressures, we first demagnetized the samples using the field-demagnetization method, then measured initial and magnetization curves and minor loops. The coercivity and the perpendicular magnetic anisotropy were obtained from the parallel and perpendicular magnetization curves measured at different temperatures. We interpret our experimental results by applying several qualitative methods. From this study, we found that the domain wall pinning with a small pinning site is the dominant magnetization reversal mechanism.


title: How does Disagreement Help Generalization against Label Corruption?

abstract: Learning with noisy labels is one of the hottest problems in weakly-supervised learning. Based on memorization effects of deep neural networks, training on small-loss instances becomes very promising for handling noisy labels. This fosters the state-of-the-art approach "Co-teaching" that cross-trains two deep neural networks using the small-loss trick. However, with the increase of epochs, two networks converge to a consensus and Co-teaching reduces to the self-training MentorNet. To tackle this issue, we propose a robust learning paradigm called Co-teaching+, which bridges the "Update by Disagreement" strategy with the original Co-teaching. First, two networks feed forward and predict all data, but keep prediction disagreement data only. Then, among such disagreement data, each network selects its small-loss data, but back propagates the small-loss data from its peer network and updates its own parameters. Empirical results on benchmark datasets demonstrate that Co-teaching+ is much superior to many state-of-the-art methods in the robustness of trained models.


title: A New Type of Neurons for Machine Learning

abstract: In machine learning, the use of an artificial neural network is the mainstream approach. Such a network consists of layers of neurons. These neurons are of the same type characterized by the two features: (1) an inner product of an input vector and a matching weighting vector of trainable parameters and (2) a nonlinear excitation function. Here we investigate the possibility of replacing the inner product with a quadratic function of the input vector, thereby upgrading the 1st order neuron to the 2nd order neuron, empowering individual neurons, and facilitating the optimization of neural networks. Also, numerical examples are provided to illustrate the feasibility and merits of the 2nd order neurons. Finally, further topics are discussed.


title: Post-traumatic stress disorder: the neurobiological impact of psychological trauma

abstract: The classic fight-or-flight response to perceived threat is a reflexive nervous phenomenon thai has obvious survival advantages in evolutionary terms. However, the systems that organize the constellation of reflexive survival behaviors following exposure to perceived threat can under some circumstances become dysregulated in the process. Chronic dysregulation of these systems can lead to functional impairment in certain individuals who become “psychologically traumatized” and suffer from post-traumatic stress disorder (PTSD), A body of data accumulated over several decades has demonstrated neurobiological abnormalities in PTSD patients. Some of these findings offer insight into the pathophysiology of PTSD as well as the biological vulnerability of certain populations to develop PTSD, Several pathological features found in PTSD patients overlap with features found in patients with traumatic brain injury paralleling the shared signs and symptoms of these clinical syndromes.


title: A Survey of Technologies for Climbing Robots Adhesion to Surfaces

abstract: Climbing robots are being developed for applications ranging from cleaning to inspection of difficult to reach constructions. These machines should be capable of travelling on different types of surfaces (such as floors, walls, ceilings) and to walk between such surfaces. Furthermore, these machines should adapt and reconfigure for various environment conditions and should be self-contained. Regarding the adhesion to the surface, they should be able to produce a secure gripping force using a light-weight mechanism. Bearing these facts in mind, this paper presents a survey of different technologies used for climbing robots adhesion to surfaces.


title: Euro Banknote Recognition System Using a Three - layered Perceptron and RBF Networks

abstract: We propose an Euro banknote recognition system using two types of neural networks; a three-layered perceptron and a Radial Basis Function (RBF) network. A three-layered perceptron is well known method for pattern recognition and is also a very effective tool for classifing banknotes. An RBF network has a potential to reject invalid data because it estimates the probability distribution of the sample data. We use a three-layered perceptron for classification and several RBF networks for validation. The proposed system has two advantages over the system using only one RBF network. The feature extraction area can be simply defined, and the calculation cost does not increase when the number of classes increases. We also propose to use infra-red (IR) and visible images as input data to the system since Euro banknotes have quite significant features in IR images. We have tested our system in terms of acceptance rates for valid banknotes and rejection rates for invalid data.


title: Equidistant Fish-Eye Calibration and Rectification by Vanishing Point Extraction

abstract: In this paper, we describe a method to photogrammetrically estimate the intrinsic and extrinsic parameters of fish-eye cameras using the properties of equidistance perspective, particularly vanishing point estimation, with the aim of providing a rectified image for scene viewing applications. The estimated intrinsic parameters are the optical center and the fish-eye lensing parameter, and the extrinsic parameters are the rotations about the world axes relative to the checkerboard calibration diagram.


title: Comparison of Q-Switched 1064-nm Nd: YAG laser and fractional CO2 laser efficacies on improvement of atrophic facial acne scar*

abstract: BACKGROUND ::: Acne scarring is treatable by a variety of modalities. Ablative carbon dioxide laser (ACL), while effective, is associated with undesirable side effect profiles. Newer modalities using the principles of fractional photothermolysis (FP) produce modest results than traditional carbon dioxide (CO(2)) lasers but with fewer side effects. A novel ablative CO(2) laser device use a technique called ablative fractional resurfacing (AFR), combines CO(2) ablation with a FP system. This study was conducted to compare the efficacy of Q-switched 1064-nm Nd: YAG laser and that of fractional CO(2) laser in the treatment of patients with moderate to severe acne scarring. ::: ::: ::: METHODS ::: Sixty four subjects with moderate to severe facial acne scars were divided randomly into two groups. Group A received Q-Switched 1064-nm Nd: YAG laser and group B received fractional CO(2) laser. Two groups underwent four session treatment with laser at one month intervals. Results were evaluated by patients based on subjective satisfaction and physicians' assessment and photo evaluation by two blinded dermatologists. Assessments were obtained at baseline and at three and six months after final treatment. ::: ::: ::: RESULTS ::: Post-treatment side effects were mild and transient in both groups. According to subjective satisfaction (p = 0.01) and physicians' assessment (p < 0.001), fractional CO(2) laser was significantly more effective than Q- Switched 1064- nm Nd: YAG laser. ::: ::: ::: CONCLUSIONS ::: Fractional CO2 laser has the most significant effect on the improvement of atrophic facial acne scars, compared with Q-Switched 1064-nm Nd: YAG laser.


title: Tuning of Barium Strontium Titanate (BST) Thin Film Materials Employing High Resistive Thin Indium Tin Oxide (ITO) Layer

abstract: The rapid growth of communication systems demands for wide tunable low loss microwave components. Barium strontium titanate (BST) voltage dependent permittivity has shown a great potential impact for the fabrication of frequency agile components. In this work, the indium tin oxide (ITO) high resistive thin layer is used as a bottom electrode to tune BST based structures such as parallel plate capacitor and CPW line. The tunability is reduced due to voltage drop in the ITO layer which exhibits a high dc resistance. The effect of using ITO thin layer in microwave device is explored.


title: A generic reconfigurable neural network architecture as a network on chip

abstract: Neural networks are widely used in pattern recognition, security applications and data manipulation. We propose a hardware architecture for a generic neural network, using network on chip (NoC) interconnect. The proposed architecture allows for expandability, mapping of more than one logical unit onto a single physical unit, and dynamic reconfiguration based on application-specific demands. Simulation results show that this architecture has significant performance benefits over existing architectures.


title: The Economics of Privacy

abstract: This article summarizes and draws connections among diverse streams of theoretical and empirical research on the economics of privacy. We focus on the economic value and consequences of protecting and disclosing personal information, and on consumers' understanding and decisions regarding the trade-offs associated with the privacy and the sharing of personal data. We highlight how the economic analysis of privacy evolved over time, as advancements in information technology raised increasingly nuanced and complex issues. We find and highlight three themes that connect diverse insights from the literature. First, characterizing a single unifying economic theory of privacy is hard, because privacy issues of economic relevance arise in widely diverse contexts. Second, there are theoretical and empirical situations where the protection of privacy can both enhance and detract from individual and societal welfare. Third, in digital economies, consumers' ability to make informed decisions about their privacy is severely hindered because consumers are often in a position of imperfect or asymmetric information regarding when their data is collected, for what purposes, and with what consequences. We conclude the article by highlighting some of the ongoing issues in the privacy debate of interest to economists.


ENTERPRISE SERVICES ON THE CLOUD BY A TECHNIQUE TO MAINTAIN VARIABILITY


title: Bitwise data parallelism in regular expression matching

abstract: A new parallel algorithm for regular expression matching is developed and applied to the classical grep (global regular expression print) problem. Building on the bitwise data parallelism previously applied to the manual implementation of token scanning in the Parabix XML parser, the new algorithm represents a general solution to the problem of regular expression matching using parallel bit streams. On widely-deployed commodity hardware using 128-bit SSE2 SIMD technology, our algorithm implementations can substantially outperform traditional grep implementations based on NFAs, DFAs or backtracking. 5X or better performance advantage against the best of available competitors is not atypical. The algorithms are also designed to scale with the availability of additional parallel resources such as the wider SIMD facilities (256-bit) of Intel AVX2 or future 512-bit extensions. Our AVX2 implementation showed dramatic reduction in instruction count and significant improvement in speed. Our GPU implementations show further acceleration.


title: Multi-user interaction using handheld projectors

abstract: Recent research on handheld projector interaction has expanded the display and interaction space of handheld devices by projecting information onto the physical environment around the user, but has mainly focused on single-user scenarios. We extend this prior single-user research to co-located multi-user interaction using multiple handheld projectors. We present a set of interaction techniques for supporting co-located collaboration with multiple handheld projectors, and discuss application scenarios enabled by them.


title: A wearable low-cost device based upon Force-Sensing Resistors to detect single-finger forces

abstract: In rehabilitation robotics it is highly desirable to find novel human-machine interfaces for the disabled, in particular to substitute or augment surface electromyography (sEMG), trying to keep at the same time its easiness of use, precision and non-invasiveness. In this paper we design and demonstrate one such device, based upon Force-Sensing Resistors (FSRs). An array of 10 FSRs was wrapped around the proximal section of the forearm of ten intact subjects engaged in pressing on an accurate force sensor with their fingers (this includes the rotation of the thumb). The FSRs would detect the forearm surface deformations induced by muscle activity; the signals provided by the FSRs were then matched to the recorded forces. The experimental results show that finger forces can be predicted using this device with the same accuracy obtained in literature using sEMG. The device, even as an academic prototype, weighs about 65 grams and costs around 50 EUR. Thus, it is remarkably light and cheap in comparison to standard sEMG electrode arrays.


title: From cognitive to neural models of working memory

abstract: Working memory refers to the temporary retention of information that was just experienced or just retrieved from long-term memory but no longer exists in the external environment. These internal representations are short-lived, but can be stored for longer periods of time through active maintenance or rehearsal strategies, and can be subjected to various operations that manipulate the information in such a way that makes it useful for goal-directed behaviour. Empirical studies of working memory using neuroscientific techniques, such as neuronal recordings in monkeys or functional neuroimaging in humans, have advanced our knowledge of the underlying neural mechanisms of working memory. This rich dataset can be reconciled with behavioural findings derived from investigating the cognitive mechanisms underlying working memory. In this paper, I review the progress that has been made towards this effort by illustrating how investigations of the neural mechanisms underlying working memory can be influenced by cognitive models and, in turn, how cognitive models can be shaped and modified by neuroscientific data. One conclusion that arises from this research is that working memory can be viewed as neither a unitary nor a dedicated system. A network of brain regions, including the prefrontal cortex (PFC), is critical for the active maintenance of internal representations that are necessary for goal-directed behaviour. Thus, working memory is not localized to a single brain region but probably is an emergent property of the functional interactions between the PFC and the rest of the brain.


title: Emotion detection state of the art

abstract: Emotion recognition and analysis has been extensively researched in neuroscience, psychology, cognitive science and computer science. In the absence of verbal and facial cues commonly associated with recognizing emotions, can emotions be detected via intelligently analyzing textual information? Our main goal is to answer this question by presenting a state-of-the-art overview of studies that have been carried out in the domain of text based emotion detection. To this end, we will explore how emotions are detected from textual sources. We begin with a discussion of currently prevailing emotion theories that provide the basis for text based emotion detection. To achieve an in depth and scientifically structured evaluation, we study the underlying structure of several approaches in the literature and design an evaluation framework. Existing approaches are critically evaluated against the proposed evaluation framework. In particular, the results of various systems are discussed and contrasted. The outcome of such a detailed analysis highlights the current gaps in scientific literature in the field of automated emotion detection.


title: Bayesian Face Sketch Synthesis

abstract: Exemplar-based face sketch synthesis has been widely applied to both digital entertainment and law enforcement. In this paper, we propose a Bayesian framework for face sketch synthesis, which provides a systematic interpretation for understanding the common properties and intrinsic difference in different methods from the perspective of probabilistic graphical models. The proposed Bayesian framework consists of two parts: the neighbor selection model and the weight computation model. Within the proposed framework, we further propose a Bayesian face sketch synthesis method. The essential rationale behind the proposed Bayesian method is that we take the spatial neighboring constraint between adjacent image patches into consideration for both aforementioned models, while the state-of-the-art methods neglect the constraint either in the neighbor selection model or in the weight computation model. Extensive experiments on the Chinese University of Hong Kong face sketch database demonstrate that the proposed Bayesian method could achieve superior performance compared with the state-of-the-art methods in terms of both subjective perceptions and objective evaluations.


The Processing Tomato Cultivar ‘Natsunoshun’ is Susceptible to an Excess or Lack of Soil Moisture after the Flowering Stage


Performance Analysis of Chess Board Protocol in RoF


Budget optimization for online advertising campaigns with carryover effects