title: Protoplast Fusion in Banana (Musa spp.): Comparison of Chemical (PEG: Polyethylene Glycol) and Electrical Procedure

abstract: Optimization of protoplast fusion parameters is a prerequisite for the establishment of somatic fusion technology for banana breeding. In the present investigations, we compared the most frequently used fusion methods: the electrofusion technique and chemical procedure (polyethylene glycol). With regard to frequency of binary fusion, protoplast fusion with the fusogen polyethylene glycol was best. Conversely, electric fusion was found to be better with respect to mitotic activities, somatic embryogenesis and plantlet regeneration rate.


title: A low-cost and efficient method of determining the best frequency band for video leaking signal reconstruction

abstract: Determining the best frequency band for reconstructing display video leaking signal is the key problem in TEMPEST, which is the technology of electromagnetic leaking research. To solve such problem, a novel algorithm based on spectral centroid has been developed. Using the property that spectral centroid can accurately identify the signal energy center in frequency domain, the proposed algorithm can find best frequency band automatically. The uniformity degree of spectral centroid spacing distribution is defined to find the best frequency band which has the highest signal to noise ratio (SNR). Thus the video leaking signal reconstruction can be realized efficiently without expensive equipment.


title: Security Threats in Online Social Networks

abstract: While the popularity of using online social networks is rising every day, the security threats to the users of these networks also increased dramatically. Many people find Social networks very interesting, because they offer wide range of online services for socializing between friends and people that have similar interests. However sharing these interests online and using them without considering the security factor can lead a user to become victim of a hacker. There are many different types of threats exists that might put the social network's users at cyber security risk. In this study we discussed different common types of social network threats and provided solutions to mitigate the security risks of using social networks.


title: Mathematical methods in solutions of the problems from the Third International Students' Olympiad in Cryptography

abstract: The mathematical problems and their solutions of the Third International Students' Olympiad in Cryptography NSUCRYPTO'2016 are presented. We consider mathematical problems related to the construction of algebraic immune vectorial Boolean functions and big Fermat numbers, problems about secrete sharing schemes and pseudorandom binary sequences, biometric cryptosystems and the blockchain technology, etc. Two open problems in mathematical cryptography are also discussed and a solution for one of them proposed by a participant during the Olympiad is described. It was the first time in the Olympiad history.


title: A Foundation for Capturing and Querying Complex Multidimensional Data

abstract: Abstract On-line analytical processing (OLAP) systems considerably improve data analysis and are finding wide-spread use. OLAP systems typically employ multidimensional data models to structure their data. This paper identifies 11 modeling requirements for multidimensional data models. These requirements are derived from an assessment of complex data found in real-world applications. A survey of 14 multidimensional data models reveals shortcomings in meeting some of the requirements. Existing models do not support many-to-many relationships between facts and dimensions, lack built-in mechanisms for handling change and time, lack support for imprecision, and are generally unable to insert data with varying granularities. This paper defines an extended multidimensional data model and algebraic query language that address all 11 requirements. The model reuses the common multidimensional concepts of dimension hierarchies and granularities to capture imprecise data. For queries that cannot be answered precisely due to the imprecise data, techniques are proposed that take into account the imprecision in the grouping of the data, in the subsequent aggregate computation, and in the presentation of the imprecise result to the user. In addition, alternative queries unaffected by imprecision are offered. The data model and query evaluation techniques discussed in this paper can be implemented using relational database technology. The approach is also capable of exploiting multidimensional query processing techniques like pre-aggregation. This yields a practical solution with low computational overhead.


title: Threshold-Independent QRS Detection Using the Dynamic Plosion Index

abstract: Detection of QRS serves as a first step in many automated ECG analysis techniques. Motivated by the strong similarities between the signal structures of an ECG signal and the integrated linear prediction residual (ILPR) of voiced speech, an algorithm proposed earlier for epoch detection from ILPR is extended to the problem of QRS detection. The ECG signal is pre-processed by high-pass filtering to remove the baseline wandering and by half-wave rectification to reduce the ambiguities. The initial estimates of the QRS are iteratively obtained using a non-linear temporal feature, named the dynamic plosion index suitable for detection of transients in a signal. These estimates are further refined to obtain a higher temporal accuracy. Unlike most of the high performance algorithms, this technique does not make use of any threshold or differencing operation. The proposed algorithm is validated on the MIT-BIH database using the standard metrics and its performance is found to be comparable to the state-of-the-art algorithms, despite its threshold independence and simple decision logic.


title: A New Change of Variables for Efficient BRDF Representation

abstract: We describe an idea for making decomposition of Bidirectional Reflectance Distribution Functions into basis functions more efficient, by performing a change-of-variables transformation on the BRDFs. In particular, we propose a reparameterization of the BRDF as a function of the halfangle (i.e. the angle halfway between the directions of incidence and reflection) and a difference angle instead of the usual parameterization in terms of angles of incidence and reflection. Because features in common BRDFs, including specular and retroreflective peaks, are aligned with the transformed coordinate axes, the change of basis reduces storage requirements for a large class of BRDFs. We present results derived from analytic BRDFs and measured data.


title: Interactive Multiscale Tensor Reconstruction for Multiresolution Volume Visualization

abstract: Large scale and structurally complex volume datasets from high-resolution 3D imaging devices or computational simulations pose a number of technical challenges for interactive visual analysis. In this paper, we present the first integration of a multiscale volume representation based on tensor approximation within a GPU-accelerated out-of-core multiresolution rendering framework. Specific contributions include (a) a hierarchical brick-tensor decomposition approach for pre-processing large volume data, (b) a GPU accelerated tensor reconstruction implementation exploiting CUDA capabilities, and (c) an effective tensor-specific quantization strategy for reducing data transfer bandwidth and out-of-core memory footprint. Our multiscale representation allows for the extraction, analysis and display of structural features at variable spatial scales, while adaptive level-of-detail rendering methods make it possible to interactively explore large datasets within a constrained memory footprint. The quality and performance of our prototype system is evaluated on large structurally complex datasets, including gigabyte-sized micro-tomographic volumes.


title: An HMM-Based Anomaly Detection Approach for SCADA Systems

abstract: We describe the architecture of an anomaly detection system based on the Hidden Markov Model (HMM) for intrusion detection in Industrial Control Systems (ICS) and especially in SCADA systems interconnected using TCP/IP. The proposed system exploits the unique characteristics of ICS networks and protocols to efficiently detect multiple attack vectors. We evaluate the proposed system in terms of detection accuracy using as reference datasets made available by other researchers. These datasets refer to real industrial networks and contain a variety of identified attack vectors. We benchmark our findings against a large set of machine learning algorithms and demonstrate that our proposal exhibits superior performance characteristics.


title: Vortex twins and anti-twins supported by multiring gain landscapes.

abstract: We address the properties of multivortex soliton complexes supported by multiring gain landscapes in focusing Kerr nonlinear media with strong two-photon absorption. Stable complexes incorporating two, three, or four vortices featuring opposite or identical topological charges are shown to exist. In the simplest geometries with two amplifying rings vortex twins with equal topological charges exhibit asymmetric intensity distributions, while vortex anti-twins may be symmetric or asymmetric, depending on the gain level and separation between rings.


title: Named Entity Sequence Classification

abstract: Named Entity Recognition (NER) aims at locating and classifying named entities in text. In some use cases of NER, including cases where detected named entities are used in creating content recommendations, it is crucial to have a reliable confidence level for the detected named entities. In this work we study the problem of finding confidence levels for detected named entities. We refer to this problem as Named Entity Sequence Classification (NESC). We frame NESC as a binary classification problem and we use NER as well as recurrent neural networks to find the probability of candidate named entity is a real named entity. We apply this approach to Tweet texts and we show how we could find named entities with high confidence levels from Tweets.


title: Bifilar Helix GNSS Antenna for Unmanned Aerial Vehicle Applications

abstract: A wideband Global Navigation Satellite System (GNSS) bifilar helix antenna for unmanned aerial vehicle (UAV) applications is presented here. The antenna is designed to fit inside the vertical stabilizer of the Dakota UAV. The antenna is fabricated using a novel direct write thermal spray technology. A feeding network insert is designed incorporating a hybrid providing a balanced 200- Ω output to the antenna.


title: Radio Transmitter Fingerprinting: A Steady State Frequency Domain Approach

abstract: We present a novel technique for radio transmitter identification based on frequency domain characteristics. Our technique detects the unique features imbued in a signal as it passes through a transmit chain. We are the first to propose the use of discriminatory classifiers based on steady state spectral features. In laboratory experiments, we achieve 97% accuracy at 30 dB SNR and 66% accuracy at OdB SNR based on eight identical universal software radio peripherals (USRP) transmitters. Our technique can be implemented using today's low cost high-volume receivers and requires no manual performance tuning.


title: VALUES, EMPATHY, AND FAIRNESS ACROSS SOCIAL BARRIERS What Motivates Participation in Violent Political Action Selective Incentives or Parochial Altruism?

abstract: In standard models of decision making, participation in violent political action is understood as the product of instrumentally rational reasoning. According to this line of thinking, instrumentally rational individuals will participate in violent political action only if there are selective incentives that are limited to participants. We argue in favor of an alternate model of political violence where participants are motivated by moral commitments to collective sacred values. Correlative and experimental empirical evidence in the context of the Israeli–Palestinian conflict strongly supports this alternate view.


pyannote.metrics: A Toolkit for Reproducible Evaluation, Diagnostic, and Error Analysis of Speaker Diarization Systems


title: Cache-conscious data placement

abstract: As the gap between memory and processor speeds continues to widen, cache eficiency is an increasingly important component of processor performance. Compiler techniques have been used to improve instruction cache pet$ormance by mapping code with temporal locality to different cache blocks in the virtual address space eliminating cache conflicts. These code placement techniques can be applied directly to the problem of placing data for improved data cache pedormance.In this paper we present a general framework for Cache Conscious Data Placement. This is a compiler directed approach that creates an address placement for the stack (local variables), global variables, heap objects, and constants in order to reduce data cache misses. The placement of data objects is guided by a temporal relationship graph between objects generated via profiling. Our results show that profile driven data placement significantly reduces the data miss rate by 24% on average.


title: Morpho–Sedimentary Monitoring in a Coastal Area, from 1D to 2.5D, Using Airborne Drone Imagery

abstract: Coastal areas are among the most endangered places in the world, due to their exposure to both marine and terrestrial hazards. Coastal areas host more than two-thirds of the world’s population, and will become increasingly affected by global changes, in particular, rising sea levels. Monitoring and protecting the coastlines have impelled scientists to develop adequate tools and methods to spatially monitor morpho-sedimentary coastal areas. This paper presents the capabilities of the aerial drone, as an “all-in-one” technology, to drive accurate morpho-sedimentary investigations in 1D, 2D and 2.5D at very high resolution. Our results show that drone-related fine-resolution, high accuracies and point density outperform the state-of-the-science manned airborne passive and active methods for shoreline position tracking, digital elevation model as well as point cloud creation. We further discuss the reduced costs per acquisition campaign, the increased spatial and temporal resolution, and demonstrate the potentialities to carry out diachronic and volumetric analyses, bringing new perspectives for coastal scientists and managers.


title: A study of statistical techniques and performance measures for genetics-based machine learning: accuracy and interpretability

abstract: The experimental analysis on the performance of a proposed method is a crucial and necessary task to carry out in a research. This paper is focused on the statistical analysis of the results in the field of genetics-based machine Learning. It presents a study involving a set of techniques which can be used for doing a rigorous comparison among algorithms, in terms of obtaining successful classification models. Two accuracy measures for multi-class problems have been employed: classification rate and Cohen’s kappa. Furthermore, two interpretability measures have been employed: size of the rule set and number of antecedents. We have studied whether the samples of results obtained by genetics-based classifiers, using the performance measures cited above, check the necessary conditions for being analysed by means of parametrical tests. The results obtained state that the fulfillment of these conditions are problem-dependent and indefinite, which supports the use of non-parametric statistics in the experimental analysis. In addition, non-parametric tests can be satisfactorily employed for comparing generic classifiers over various data-sets considering any performance measure. According to these facts, we propose the use of the most powerful non-parametric statistical tests to carry out multiple comparisons. However, the statistical analysis conducted on interpretability must be carefully considered.


title: Phoneme recognition using neural networks

abstract: A phoneme recognition system using an anti-symmetric multi-stage filter bank structure is presented. In a filter bank the input signal is convolved with digital filters having different cut-off frequencies so that the signal is analysed at different frequencies with different resolutions. The percentage energy content in each signal decomposition level is calculated and used as input to the artificial neural network for phoneme recognition.


title: A new framework for peer-to-peer energy sharing and coordination in the energy internet

abstract: Compared with the traditional power grid, Energy Internet is motivated by the concept of intelligent energy sharing and coordination, achieving higher penetration of renewable energy and economic saving. In this paper, we propose a new framework for the time-slotted Peer-to-Peer (P2P) energy sharing and coordination in Energy Internet, which aims to achieve flexible and efficient distributed energy management and control. In this framework, users are equipped with distributed generators (DGs), distributed energy storage systems (DESs) and smart meters; the P2P energy sharing fashion is supported, where users can buy/sell electric from/to utility company and their neighboring users. The energy sharing and coordination problem is formulated as a convex optimization problem with the objective to minimize the economic cost of users. Then, a distributed algorithm is proposed, in combination with alternating direction method of multipliers (ADMM). On the basis of a real-world dataset of renewable energy and real-time electricity price, both analytical and numerical results show the effectiveness of the proposed framework and algorithm in terms of not only fast convergence in a time slot but also economic saving prominently for a long time application.


title: Reversible Architectures for Arbitrarily Deep Residual Neural Networks

abstract: Recently, deep residual networks have been successfully applied in many computer vision and natural language processing tasks, pushing the state-of-the-art performance with deeper and wider architectures. In this work, we interpret deep residual networks as ordinary differential equations (ODEs), which have long been studied in mathematics and physics with rich theoretical and empirical success. From this interpretation, we develop a theoretical framework on stability and reversibility of deep neural networks, and derive three reversible neural network architectures that can go arbitrarily deep in theory. The reversibility property allows a memory-efficient implementation, which does not need to store the activations for most hidden layers. Together with the stability of our architectures, this enables training deeper networks using only modest computational resources. We provide both theoretical analyses and empirical results. Experimental results demonstrate the efficacy of our architectures against several strong baselines on CIFAR-10, CIFAR-100 and STL-10 with superior or on-par state-of-the-art performance. Furthermore, we show our architectures yield superior results when trained using fewer training data.


title: The prevalence of antibiotic resistance genes among Aeromonas species in aquatic environments

abstract: The global rise in antimicrobial resistance (AMR) among bacteria causing infectious diseases is well documented, and the associated risks for human health are well known. There is much less research on AMR with regard to environmental strains, both opportunistic and pathogenic ones. The genus Aeromonas is widely distributed in the environment and causes many variable diseases in fish and humans. Infections in humans are predominantly caused by Aeromonas veronii, A. hydrophila and A. caviae (A. punctata) in a form of bacteremia, gastroenteritis or even septicaemia in immunocompetent and immunocompromised individuals. Different groups of antibiotics are used in the treatment, but studies indicate that fluoroquinolones and cefotaxime are the most efficient. A disturbing consequence of antibiotic overuse is an increasing number of detection of various antibiotic resistance genes (ARG) within this genus. The water environment is one of the major modes of transmission of resistant bacteria from animals to humans, and, thus, the dissemination of antibiotic resistance genes, particularly those located in mobile genetic elements (MGE) occurs in such as plasmids and transposons. This review summarizes recently published information on the type, distribution, and transmission of ARG by MGE, widespread in Aeromonas strains living in various aquatic environments, including wastewater, natural water, aquaculture and urban drinking water. The data available indicate that the opportunistic pathogens like Aeromonas spp. might serve as important vectors of ARG for clinically relevant pathogens present in such bodies of water .


title: Applying Artificial Intelligence to Virtual Reality: Intelligent Virtual Environments

abstract: Research into virtual environments on the one hand and artificial intelligence and artificial life on the other has largely been carried out by two different groups of people with different preoccupation and interests, but some convergence is now apparent between the two fields. Applications in which activity independent of the user takes place- involving crowds or other agents- are beginning to be tackled, while synthetic agents, virtual humans, and computer pets are all areas in which techniques from the two fields require strong integration. The two communities have much to learn from each other if wheels are not to be reinvented on both sides. This paper reviews the issues arising from combining artificial intelligence and artificial life techniques with those of virtual environments to produce just such intelligent virtual environments. The discussion is illustrated with examples that include environments providing knowledge to direct or assist the user rather than relying entirely on the user's know...


title: ARMORED: CPU-Bound Encryption for Android-Driven ARM Devices

abstract: As recently shown by attacks against Android-driven smart phones, ARM devices are vulnerable to cold boot attacks. At the end of 2012, the data recovery tool FROST was released which exploits the remanence effect of RAM to recover user data from a smart phone, at worst its disk encryption key. Disk encryption is supported in Android since version 4.0 and is today available on many smart phones. With ARMORED, we demonstrate that Android's disk encryption feature can be improved to withstand cold boot attacks by performing AES entirely without RAM. ARMORED stores necessary keys and intermediate values of AES inside registers of the ARM microprocessor architecture without involving main memory. As a consequence, cold boot attacks on encryption keys in RAM appear to be futile. We developed our implementation on a Panda Board and tested it successfully on real phones. We also present a security and a performance analysis for ARMORED.


An electropalatographic, kinematic, and acoustic analysis of supralaryngeal correlates of word-level prominence contrasts in English.


title: A common database approach for OLTP and OLAP using an in-memory column database

abstract: When SQL and the relational data model were introduced 25 years ago as a general data management concept, enterprise software migrated quickly to this new technology. It is fair to say that SQL and the various implementations of RDBMSs became the backbone of enterprise systems. In those days. we believed that business planning, transaction processing and analytics should reside in one single system. Despite the incredible improvements in computer hardware, high-speed networks, display devices and the associated software, speed and flexibility remained an issue. The nature of RDBMSs, being organized along rows, prohibited us from providing instant analytical insight and finally led to the introduction of so-called data warehouses. This paper will question some of the fundamentals of the OLAP and OLTP separation. Based on the analysis of real customer environments and experience in some prototype implementations, a new proposal for an enterprise data management concept will be presented. In our proposal, the participants in enterprise applications, customers, orders, accounting documents, products, employees etc. will be modeled as objects and also stored and maintained as such. Despite that, the vast majority of business functions will operate on an in memory representation of their objects. Using the relational algebra and a column-based organization of data storage will allow us to revolutionize transactional applications while providing an optimal platform for analytical data processing. The unification of OLTP and OLAP workloads on a shared architecture and the reintegration of planning activities promise significant gains in application development while simplifying enterprise systems drastically. The latest trends in computer technology -- e.g. blade architecture, multiple CPUs per blade with multiple cores per CPU allow for a significant parallelization of application processes. The organization of data in columns supports the parallel use of cores for filtering and aggregation. Elements of application logic can be implemented as highly efficient stored procedures operating on columns. The vast increase in main memory combined with improvements in L1--, L2--, L3--caching, together with the high data compression rate column storage will allow us to support substantial data volumes on one single blade. Distributing data across multiple blades using a shared nothing approach provides further scalability.


title: Music summarization using key phrases

abstract: Systems to automatically provide a representative summary or 'key phrase' of a piece of music are described. For a 'rock' song with 'verse' and 'chorus' sections, we aim to return the chorus or in any case the most repeated and hence most memorable section. The techniques are less applicable to music with more complicated structure although possibly our general framework could still be used with different heuristics. Our process consists of three steps. First we parameterize the song into features. Next we use these features to discover the song structure, either by clustering fixed-length segments or by training a hidden Markov model (HMM) for the song. Finally, given this structure, we use heuristics to choose the key phrase. Results for summaries of 18 Beatles songs evaluated by ten users show that the technique based on clustering is superior to the HMM approach and to choosing the key phrase at random.


title: 1-MHz isolated bipolar half-bridge GaN gate driver for SiC MOSFETs

abstract: In this paper, 1-MHz isolated bipolar halfbridge SiC MOSFET gate drivers using GaN semiconductors are proposed. To realise the 1 MHz switching capability of the gate driver for SiC MOSFET, the half-bridge topology is used in conjunction with Digital Isolators. The design of the gate drivers takes into consideration the capability of driving SiC MOSFET at 1 MHz switching with less driving power loss and propagation delay. The proposed gate driver is designed and experimentally validated to drive SiC MOSFET at 1 MHz in the 1 kW boost converter. Satisfactory operation of the SiC gate drivers without significant self-heating and with less propagation delay at 1 MHz demonstrates their suitability for high-frequency, high-power applications.


Gestural interfaces: a step backward in usability


title: Image annotation by kNN-sparse graph-based label propagation over noisily tagged web images

abstract: In this article, we exploit the problem of annotating a large-scale image corpus by label propagation over noisily tagged web images. To annotate the images more accurately, we propose a novel kNN-sparse graph-based semi-supervised learning approach for harnessing the labeled and unlabeled data simultaneously. The sparse graph constructed by datum-wise one-vs-kNN sparse reconstructions of all samples can remove most of the semantically unrelated links among the data, and thus it is more robust and discriminative than the conventional graphs. Meanwhile, we apply the approximate k nearest neighbors to accelerate the sparse graph construction without loosing its effectiveness. More importantly, we propose an effective training label refinement strategy within this graph-based learning framework to handle the noise in the training labels, by bringing in a dual regularization for both the quantity and sparsity of the noise. We conduct extensive experiments on a real-world image database consisting of 55,615 Flickr images and noisily tagged training labels. The results demonstrate both the effectiveness and efficiency of the proposed approach and its capability to deal with the noise in the training labels.


title: Interactive learning for efficiently detecting errors in insurance claims

abstract: Many practical data mining systems such as those for fraud detection and surveillance deal with building classifiers that are not autonomous but part of a larger interactive system with an expert in the loop. The goal of these systems is not just to maximize the performance of the classifier but to make the experts more efficient at performing their task, thus maximizing the overall Return on Investment of the system. This paper describes an interactive system for detecting payment errors in insurance claims with claim auditors in the loop. We describe an interactive claims prioritization component that uses an online cost-sensitive learning approach (more-like-this) to make the system efficient. Our interactive prioritization component is built on top of a batch classifier that has been trained to detect payment errors in health insurance claims and optimizes the interaction between the classifier and the domain experts who are consuming the results of this system. The goal is to make these auditors more efficient and effective as well as improving the classification performance of the system. The result is both a reduction in time it takes for the auditors to review and label claims as well as improving the precision of the system in finding payment errors. We show results obtained from applying this system at two major US health insurance companies indicating significant reduction in claim audit costs and potential savings of $20-$26 million/year making the insurance providers more efficient and lowering their operating costs. Our system reduces the money being wasted by providers and insurers dealing with incorrectly processed claims and makes the healthcare system more efficient.


title: A Dual Approach to Scalable Verification of Deep Networks

abstract: This paper addresses the problem of formally verifying desirable properties of neural networks, i.e., obtaining provable guarantees that the outputs of the neural network will always behave in a certain way for a given class of inputs. Most previous work on this topic was limited in its applicability by the size of the network, network architecture and the complexity of properties to be verified. In contrast, our framework applies to much more general class of activation functions and specifications on neural network inputs and outputs. We formulate verification as an optimization problem and solve a Lagrangian relaxation of the optimization problem to obtain an upper bound on the verification objective. Our approach is anytime, i.e. it can be stopped at any time and a valid bound on the objective can be obtained. We develop specialized verification algorithms with provable tightness guarantees under special assumptions and demonstrate the practical significance of our general verification approach on a variety of verification tasks.


title: Apache Spark Streaming and HarmonicIO: A Performance and Architecture Comparison.

abstract: Studies have demonstrated that Apache Spark, Flink and related frameworks can perform stream processing at very high frequencies, whilst tending to focus on small messages with a computationally light `map' stage for each message; a common enterprise use case. We add to these benchmarks by broadening the domain to include loads with larger messages (leading to network-bound throughput), and that are computationally intensive (leading to CPU-bound throughput) in the map phase; in order to evaluate applicability of these frameworks to scientific computing applications. We present a performance benchmark comparison between Apache Spark Streaming (ASS) under both file and TCP streaming modes; and HarmonicIO, comparing maximum throughput over a broad domain of message sizes and CPU loads. We find that relative performance varies considerably across this domain, with the chosen means of stream source integration having a big impact. We offer recommendations for choosing and configuring the frameworks, and present a benchmarking toolset developed for this study.


title: Analyzing Sentiments in One Go: A Supervised Joint Topic Modeling Approach

abstract: In this work, we focus on modeling user-generated review and overall rating pairs, and aim to identify semantic aspects and aspect-level sentiments from review data as well as to predict overall sentiments of reviews. We propose a novel probabilistic supervised joint aspect and sentiment model (SJASM) to deal with the problems in one go under a unified framework. SJASM represents each review document in the form of opinion pairs, and can simultaneously model aspect terms and corresponding opinion words of the review for hidden aspect and sentiment detection. It also leverages sentimental overall ratings, which often come with online reviews, as supervision data, and can infer the semantic aspects and aspect-level sentiments that are not only meaningful but also predictive of overall sentiments of reviews. Moreover, we also develop efficient inference method for parameter estimation of SJASM based on collapsed Gibbs sampling. We evaluate SJASM extensively on real-world review data, and experimental results demonstrate that the proposed model outperforms seven well-established baseline methods for sentiment analysis tasks.


title: REAL-TIME LANE DETECTION FOR DRIVING SYSTEM USING IMAGE PROCESSING BASED ON EDGE DETECTION AND HOUGH TRANSFORM

abstract: Real-time Lane Detection for Driving System Using Image Processing Based on Edge Detection and Hough Transform has been developed to aid a driver in the lane departure decision-making, to reduce a loss of concentration and to prevent an accident while driving. In this paper, we propose a method for detecting the lane markers in real time by using a webcam camera to record the road as a video stream file (.avi). The Edge detection is a technique which used to identify points into a set of curved line segments called edge. Hough Transform method is used to detect the lanes in an image for increasing the accurate lane detection and the safety driving system. The results show the performance of the proposed lane detection and tracking algorithm in various lane road conditions on video clip that used for testing. According to the results, the driver can use this information to increase their safety driving, especially when making the decision of lane changing.


title: Motion Capture Data Manipulation and Reuse via B-splines

abstract: This paper presents an integrated set of tools to facilitate the manipulation and reuse of motion capture data. A functional representation in terms of nonuniform B-splines provides a compact and efficient description of motion sequences. A collection of B-spline curves attached to a hierarchical structure is used to represent the animation of articulated figures. A set of primitive operators acting on these curves and their application to motion editing is presented.We have successfully used these primitives to generate smooth transitions between motion sequences, motion interpolation and motion cyclification.


title: Bidirectional Current-Fed Half-Bridge (C) (LC)–(LC ) Configuration for Inductive Wireless Power Transfer System

abstract: This paper contributes to the analysis and development of a new power electronics system for bidirectional wireless power transfer (WPT). The major focus is the analysis and implementation of a new current-fed resonant topology with current-sharing and voltage-doubling features. A new bidirectional WPT system with current-fed half-bridge voltage-doubler circuit is proposed and analyzed with series–parallel and series resonant networks. Traditionally used parallel L–C resonant tank in transmitter circuit with current-fed WPT topology causes higher voltage stress across the inverter devices to compensate the reactive power consumed by the loosely coupled coil. In the proposed topology, this is mitigated by adding a suitably designed capacitor in series with the transmitter coil; thus, developing a series–parallel CLC tank. Detailed analysis and design is reported for both grid-to-vehicle and vehicle-to-grid operations. The power flow is controlled through variable frequency modulation. Soft switching of the devices is obtained irrespective of the load current. A proof-of-concept experimental hardware prototype rated at 1.2 kW is developed and tested. Experimental results are presented to verify the analysis and demonstrate the performance of the system with bidirectional power flow.


title: Network Flows: Theory, Algorithms, and Applications

abstract: A comprehensive introduction to network flows that brings together the classic and the contemporary aspects of the field, and provides an integrative view of theory, algorithms, and applications. presents in-depth, self-contained treatments of shortest path, maximum flow, and minimum cost flow problems, including descriptions of polynomial-time algorithms for these core models. emphasizes powerful algorithmic strategies and analysis tools such as data scaling, geometric improvement arguments, and potential function arguments. provides an easy-to-understand descriptions of several important data structures, including d-heaps, Fibonacci heaps, and dynamic trees. devotes a special chapter to conducting empirical testing of algorithms. features over 150 applications of network flows to a variety of engineering, management, and scientific domains. contains extensive reference notes and illustrations.


Lexical and Syntactic cues to identify Reference Scope of Citance


title: Authenticating Location-Based Skyline Queries in Arbitrary Subspaces

abstract: With the ever-increasing use of smartphones and tablet devices, location-based services (LBSs) have experienced explosive growth in the past few years. To scale up services, there has been a rising trend of outsourcing data management to Cloud service providers, which provide query services to clients on behalf of data owners. However, in this data-outsourcing model, the service provider can be untrustworthy or compromised, thereby returning incorrect or incomplete query results to clients, intentionally or not. Therefore, empowering clients to authenticate query results is imperative for outsourced databases. In this paper, we study the authentication problem for location-based arbitrary-subspace skyline queries (LASQs), which represent an important class of LBS applications. We propose a basic Merkle Skyline R-tree method and a novel Partial S4-tree method to authenticate one-shot LASQs. For the authentication of continuous LASQs, we develop a prefetching-based approach that enables clients to compute new LASQ results locally during movement, without frequently contacting the server for query re-evaluation. Experimental results demonstrate the efficiency of our proposed methods and algorithms under various system settings.


title: Direct torque control for Linear Induction Motor

abstract: This paper presents a direct torque control for a Single-sided linear induction motor (SLIM). In the last years, in the field of research on linear induction drives, a lot of efforts have been oriented to improve the dynamics performances of these machines by using direct torque control. In order to insure a high transient behavior of the drive and to avoid the draw back of the parametric perturbations in the implemented control, the direct torque control is considered in this work.


title: The generation of environmental map based on a NDT grid mapping -Proposal of convergence calculation corresponding to high resolution grid-

abstract: The paper presents a new convergence calculation method of the normal distributions transform (NDT) scan matching for high resolution grid map. The proposed method employs interactive closest point (ICP) algorithm to find corresponding point, even if the input scan points are in the outside of the reference grid area. It also enlarges the convergence area by modifying the eigenvalue of normal distribution so that the evaluation value can be driven effectively by all the pairing data for improving convergence speed. In the normal NDT scan matching algorithm, the grid size especially has much effect on the capability of convergence area and speed. Thus, it is difficult to generate the detailed map with small grids. The proposed convergence approach can solve the problem by the phased adjustment of convergence area. Experimental result shows the feasibility of the detailed environmental map generated by the NDT grid mapping.


title: An Analysis of Deep Neural Network Models for Practical Applications

abstract: Since the emergence of Deep Neural Networks (DNNs) as a prominent technique in the field of computer vision, the ImageNet classification challenge has played a major role in advancing the state-of-the-art. While accuracy figures have steadily increased, the resource utilisation of winning models has not been properly taken into account. In this work, we present a comprehensive analysis of important metrics in practical applications: accuracy, memory footprint, parameters, operations count, inference time and power consumption. Key findings are: (1) power consumption is independent of batch size and architecture; (2) accuracy and inference time are in a hyperbolic relationship; (3) energy constraint are an upper bound on the maximum achievable accuracy and model complexity; (4) the number of operations is a reliable estimate of the inference time. We believe our analysis provides a compelling set of information that helps design and engineer efficient DNNs.


title: Predicting Financial Markets: Comparing Survey, News, Twitter and Search Engine Data

abstract: Financial market prediction on the basis of online sentiment tracking has drawn a lot of attention recently. However, most results in this emerging domain rely on a unique, particular combination of data sets and sentiment tracking tools. This makes it difficult to disambiguate measurement and instrument effects from factors that are actually involved in the apparent relation between online sentiment and market values. In this paper, we survey a range of online data sets (Twitter feeds, news headlines, and volumes of Google search queries) and sentiment tracking methods (Twitter Investor Sentiment, Negative News Sentiment and Tweet & Google Search volumes of financial terms), and compare their value for financial prediction of market indices such as the Dow Jones Industrial Average, trading volumes, and market volatility (VIX), as well as gold prices. We also compare the predictive power of traditional investor sentiment survey data, i.e. Investor Intelligence and Daily Sentiment Index, against those of the mentioned set of online sentiment indicators. Our results show that traditional surveys of Investor Intelligence are lagging indicators of the financial markets. However, weekly Google Insight Search volumes on financial search queries do have predictive value. An indicator of Twitter Investor Sentiment and the frequency of occurrence of financial terms on Twitter in the previous 1-2 days are also found to be very statistically significant predictors of daily market log return. Survey sentiment indicators are however found not to be statistically significant predictors of financial market values, once we control for all other mood indicators as well as the VIX.


title: Incorporating Entities in News Topic Modeling

abstract: News articles express information by concentrating on named entities like who, when, and where in news. Whereas, extracting the relationships among entities, words and topics through a large amount of news articles is nontrivial. Topic modeling like Latent Dirichlet Allocation has been applied a lot to mine hidden topics in text analysis, which have achieved considerable performance. However, it cannot explicitly show relationship between words and entities. In this paper, we propose a generative model, Entity-Centered Topic Model(ECTM) to summarize the correlation among entities, words and topics by taking entity topic as a mixture of word topics. Experiments on real news data sets show our model of a lower perplexity and better in clustering of entities than state-of-the-art entity topic model(CorrLDA2). We also present analysis for results of ECTM and further compare it with CorrLDA2.


title: A fast algorithm for multilevel thresholding

abstract: Otsu reference proposed a criterion for maximizing the between-class variance of pixel intensity to perform picture thresholding. However, Otsu's method for image segmentation is very time-consuming because of the inefficient formulation of the be- tween-class variance. In this paper, a faster version of Otsu's method is proposed for improving the efficiency of computation for the optimal thresholds of an image. First, a criterion for maximizing a modified between-class variance that is equivalent to the criterion of maximizing the usual between-class variance is proposed for image segmen- tation. Next, in accordance with the new criterion, a recursive algorithm is designed to efficiently find the optimal threshold. This procedure yields the same set of thresholds as the original method. In addition, the modified between-class variance can be pre-computed and stored in a look-up table. Our analysis of the new criterion clearly shows that it takes less computation to compute both the cumulative probability (zeroth order moment) and the mean (first order moment) of a class, and that determining the modified between-class variance by accessing a look-up table is quicker than that by performing mathematical arithmetic operations. For example, the experimental results of a five-level threshold selection show that our proposed method can reduce down the processing time from more than one hour by the conventional Otsu's method to less than 107 seconds.


title: Systems thinking and predictive analytics to improve veteran healthcare scheduling

abstract: As a culture, the United States acknowledges that the delivery of veteran services to those who risked their lives and suffered to protect our nation is a top national priority. Over the past several years, however, the media have reported stark examples of how these services are lacking, particularly in the case of medical appointment scheduling. At the same time, the Veterans Health Administration is plagued by strikingly high no-show rates at its medical outpatient clinics and a resulting handicap in resource allocation. We bring to bear systems thinking to address these issues. As a result, we developed a model for a dynamic overbooking system that receives the probability of a patient arriving on-time for their appointment from the patient's phone and couples this real-time probability with prior probability derived from existing VA data. Note that the system protects patient privacy by never transmitting nor sharing location data. When the arrival probability of a patient falls below a given threshold, an algorithm can automatically cancel a patient's appointment and re-assign it to another patient drawn from a pool of wait-list and other patients with high arrival probabilities given their current location. In this presentation, we share the progress to date on our approach, and our proposals for future work and implementation.


Normalization of Social Media Text using Deep Neural Networks.


title: Characterisation of Tactile Sensors based on Fibre Bragg gratings Towards Temperature Independent Pressure Sensing

abstract: Abstract This paper presents the development of flexible pressure sensing sheets, based on fibre Bragg grating sensing elements. The devices feature optical wavelength-encoded sensing signals and show promise in applications such as pressure mapping and tactile sensing. FBGs inscribed in highly-birefringent microstructured fibres, reflecting two separate Bragg peaks, are specially employed. Prototypes with FBGs embedded in polymer sheets were produced and characterised for temperature and pressure sensitivities. An improved sensor structure was implemented aiming at temperature independent tactile sensing with Bragg peak separation as the sensing signal.


title: A regulated transitive reduction (RTR) for longer memory race recording

abstract: Now at VMware. Multithreaded deterministic replay has important applications in cyclic debugging, fault tolerance and intrusion analysis. Memory race recording is a key technology for multithreaded deterministic replay. In this paper, we considerably improve our previous always-on Flight Data Recorder (FDR) in four ways: •Longer recording by reducing the log size growth rate to approximately one byte per thousand dynamic instructions. •Lower hardware cost by reducing the cost to 24 KB per processor core. •Simpler design by modifying only the cache coherence protocol, but not the cache. •Broader applicability by supporting both Sequential Consistency (SC) and Total Store Order (TSO) memory consistency models (existing recorders support only SC).These improvements stem from several ideas: (1) a Regulated Transitive Reduction (RTR) recording algorithm that creates stricter and vectorizable dependencies to reduce the log growth rate; (2) a Set/LRU timestamp approximation method that better approximates timestamps of uncached memory locations to reduce the hardware cost; (3) an order-value-hybrid recording methodthat explicitly logs the value of potential SC-violating load instructions to support multiprocessor systems with TSO.


Mining, Pruning and Visualizing Frequent Patterns for Temporal Event Sequence Analysis


Impact of E-Service Quality on Consumer Purchase Behaviour in an On-line Shopping


title: EncKV: An Encrypted Key-value Store with Rich Queries

abstract: Distributed data stores have been rapidly evolving to serve the needs of large-scale applications such as online gaming and real-time targeting. In particular, distributed key-value stores have been widely adopted due to their superior performance. However, these systems do not guarantee to provide strong protection of data confidentiality, and as a result fall short of addressing serious privacy concerns raised from massive data breaches. In this paper, we introduce EncKV, an encrypted key-value store with secure rich query support. First, EncKV stores encrypted data records with multiple secondary attributes in the form of encrypted key-value pairs. Second, it leverages the latest practical primitives for searching over encrypted data, i.e., searchable symmetric encryption and order-revealing encryption, and provides encrypted indexes with guaranteed security to support exact-match and range-match queries via secondary attributes of data records. Third, it carefully integrates these indexes into a distributed index framework to facilitate secure query processing in parallel. To mitigate recent inference attacks on encrypted database systems, EncKV protects the order information during range queries, and presents an interactive batch query mechanism to further hide the associations across data values on different attributes. We implement an EncKV prototype on a Redis cluster, and conduct an extensive set of performance evaluations on the Amazon EC2 public cloud platform. Our results show that EncKV effectively preserves the efficiency and scalability of plaintext distributed key-value stores.


title: Generative Adversarial Style Transfer Networks for Face Aging

abstract: How somebody looked like when younger? What could a person look like when 10 years older? In this paper we look at the problem of face aging, which relates to processing an image of a face to change its apparent age. This task involves synthesizing images and modeling the aging process, which both are problems that have recently enjoyed much research interest in the field of face and gesture recognition. We propose to look at the problem from the perspective of image style transfer, where we consider the age of the person as the underlying style of the image. We show that for large age differences, convincing face aging can be achieved by formulating the problem with a pairwise training of Cycle-consistent Generative Adversarial Networks (CycleGAN) over age groups. Furthermore, we propose a variant of CycleGAN which directly incorporates a pre-trained age prediction model, which performs better when the desired age difference is smaller. The proposed approaches are complementary in strengths and their fusion performs well for any desired level of aging effect. We quantitatively evaluate our proposed method through a user study and show that it outperforms prior state-of-the-art techniques for face aging.


title: Dissecting Contributions of Prefrontal Cortex and Fusiform Face Area to Face Working Memory

abstract: Interactions between prefrontal cortex (PFC) and stimulus-specific visual cortical association areas are hypothesized to mediate visual working memory in behaving monkeys. To clarify the roles for homologous regions in humans, event-related fMRI was used to assess neural activity in PFC and fusiform face area (FFA) of subjects performing a delay-recognition task for faces. In both PFC and FFA, activity increased parametrically with memory load during encoding and maintenance of face stimuli, despite quantitative differences in the magnitude of activation. Moreover, timing differences in PFC and FFA activation during memory encoding and retrieval implied a context dependence in the flow of neural information. These results support existing neurophysiological models of visual working memory developed in the nonhuman primate.


title: Double nerve intraneural interface implant on a human amputee for robotic hand control

abstract: Objectives: The principle underlying this project is that, despite nervous reorganization following upper limb amputation, original pathways and CNS relays partially maintain their function and can be exploited for interfacing prostheses.


title: FSM-H: Frequent Subgraph Mining Algorithm in Hadoop

abstract: Frequent subgraph mining (FSM) is an important task for exploratory data analysis on graph data. Over the years, many algorithms have been proposed to solve this task. These algorithms assume that the data structure of the mining task is small enough to fit in the main memory of a computer. However, as the real-world graph data grows, both in size and quantity, such an assumption does not hold any longer. To overcome this, some graph database-centric methods have been proposed in recent years for solving FSM, however, a distributed solution using MapReduce paradigm has not been explored extensively. Since, MapReduce is becoming the de-facto paradigm for computation on massive data, an efficient FSM algorithm on this paradigm is of huge demand. In this work, we propose a frequent subgraph mining algorithm called FSM-H which uses an iterative MapReduce based framework. FSM-H is complete as it returns all the frequent subgraphs for a given user-defined support, and it is efficient as it applies all the optimizations that the latest FSM algorithms adopt. Our experiments with real life and large synthetic datasets validate the effectiveness of FSM-H for mining frequent subgraphs from large graph datasets. The source code of FSM-H is available from www.cs.iupui.edu/~alhasan/software/


title: Bridging Java and AspectJ through Explicit Join Points ABSTRACT

abstract: Through AspectJ, aspect-oriented programming (AOP) is becoming of increasing interest and availability to Java programmers as it matures as a methodology for improved software modularity via the separation of cross-cutting concerns. AOP proponents often advocate a development strategy where Java programmers write the main application, ignoring cross-cutting concerns, and then AspectJ programmers, domain experts in their specific concerns, weave in the logic for these more specialized cross-cutting concerns. However, several authors have recently debated the merits of this strategy by empirically showing certain drawbacks. The proposed solutions paint a different development strategy where base code and aspect programmers are aware of each other (to varying degrees) and interactions between cross-cutting concerns are planned for early on. Herein we explore new possibilities in the language design space that open up when the base code is aware of cross-cutting aspects. Using our insights from this exploration we concretize these new possibilities by extending AspectJ with concise yet powerful constructs, while maintaining full backwards compatibility. These new constructs allow base code and aspects to cooperate in ways that were previously not possible: arbitrary blocks of code can be advised, advice can be explicitly parameterized, base code can guide aspects in where to apply advice, and aspects can statically enforce new constraints upon the base code that they advise. These new techniques allow aspect modularity and program safety to increase. We illustrate the value of our extensions through an example based on transactions.


title: A survey of graph layout problems

abstract: Graph layout problems are a particular class of combinatorial optimization problems whose goal is to find a linear layout of an input graph in such way that a certain objective cost is optimized. This survey considers their motivation, complexity, approximation properties, upper and lower bounds, heuristics and probabilistic analysis on random graphs. The result is a complete view of the current state of the art with respect to layout problems from an algorithmic point of view.


title: Game-theoretic Resource Allocation Methods for Device-to-Device (D2D) Communication

abstract: Device-to-device communication underlaying cellular networks allows mobile devices such as smartphones and tablets to use the licensed spectrum allocated to cellular services for direct peer-to-peer transmission. D2D communication can use either one-hop transmission (i.e. D2D direct communication) or multi-hop clusterbased transmission (i.e. in D2D local area networks). The D2D devices can compete or cooperate with each other to reuse the radio resources in D2D networks. Therefore, resource allocation and access for D2D communication can be treated as games. The theories behind these games provide a variety of mathematical tools to effectively model and analyze the individual or group behaviors of D2D users. In addition, game models can provide distributed solutions to the resource allocation problems for D2D communication. The aim of this article is to demonstrate the applications of game-theoretic models to study the radio resource allocation issues in D2D communication. The article also outlines several key open research directions.


title: Visitor Identification - Elaborating Real Time

abstract: In this paper requirements and conditions for the visitor identification system are outlined and an example system is proposed. Two main subsystems: face detection and face recognition are described. Algorithm for face detection integrates skin-colour, mask analysis, facial features (fast and effective way of eyes localization is presented), reductors, knowledge and template matching. For face recognition a three stage algorithm is proposed. It utilizes well known methods connected in a sequential mode. To improve accuracy and speed some modifications to original methods were proposed and new one presented. The aim was to build a visitor identification system which would be able to operate in mode with a camera and present results in real-time. The emphasis on speed and accuracy was stressed.


title: Texture Modelling with Nested High-order Markov-Gibbs Random Fields

abstract: Currently, Markov-Gibbs random field (MGRF) image models which include high-order interactions are almost always built by modelling responses of a stack of local linear filters. Actual interaction structure is specified implicitly by the filter coefficients. In contrast, we learn an explicit high-order MGRF structure by considering the learning process in terms of general exponential family distributions nested over base models, so that potentials added later can build on previous ones. We relatively rapidly add new features by skipping over the costly optimisation of parameters. We introduce the use of local binary patterns as features in MGRF texture models, and generalise them by learning offsets to the surrounding pixels. These prove effective as high-order features, and are fast to compute. Several schemes for selecting high-order features by composition or search of a small subclass are compared. Additionally we present a simple modification of the maximum likelihood as a texture modelling-specific objective function which aims to improve generalisation by local windowing of statistics. The proposed method was experimentally evaluated by learning high-order MGRF models for a broad selection of complex textures and then performing texture synthesis, and succeeded on much of the continuum from stochastic through irregularly structured to near-regular textures. Learning interaction structure is very beneficial for textures with large-scale structure, although those with complex irregular structure still provide difficulties. The texture models were also quantitatively evaluated on two tasks and found to be competitive with other works: grading of synthesised textures by a panel of observers; and comparison against several recent MGRF models by evaluation on a constrained inpainting task.


title: A Probabilistic Trust and Reputation Model for Supply Chain Management

abstract: My thesis contributes to the field of multi-agent systems by proposing a novel trust-based decision model for supply chain management.


CLASSIFICATION OF DEFECTS IN SOFTWARE USING DECISION TREE ALGORITHM


title: ForkBase: An Efficient Storage Engine for Blockchain and Forkable Applications

abstract: Existing data storage systems offer a wide range of functionalities to accommodate an equally diverse range of applications. However, new classes of applications have emerged, e.g., blockchain and collaborative analytics, featuring data versioning, fork semantics, tamper-evidence or any combination thereof. They present new opportunities for storage systems to efficiently support such applications by embedding the above requirements into the storage. ::: ::: In this paper, we present ForkBase, a storage engine designed for blockchain and forkable applications. By integrating core application properties into the storage, ForkBase not only delivers high performance but also reduces development effort. The storage manages multiversion data and supports two variants of fork semantics which enable different fork worklflows. ForkBase is fast and space efficient, due to a novel index class that supports efficient queries as well as effective detection of duplicate content across data objects, branches and versions. We demonstrate ForkBase's performance using three applications: a blockchain platform, a wiki engine and a collaborative analytics application. We conduct extensive experimental evaluation against respective state-of-the-art solutions. The results show that ForkBase achieves superior performance while significantly lowering the development effort.


title: Private spatial data aggregation in the local setting

abstract: With the deep penetration of the Internet and mobile devices, privacy preservation in the local setting has become increasingly relevant. The local setting refers to the scenario where a user is willing to share his/her information only if it has been properly sanitized before leaving his/her own device. Moreover, a user may hold only a single data element to share, instead of a database. Despite its ubiquitousness, the above constraints make the local setting substantially more challenging than the traditional centralized or distributed settings. In this paper, we initiate the study of private spatial data aggregation in the local setting, which finds its way in many real-world applications, such as Waze and Google Maps. In response to users' varied privacy requirements that are natural in the local setting, we propose a new privacy model called personalized local differential privacy (PLDP) that allows to achieve desirable utility while still providing rigorous privacy guarantees. We design an efficient personalized count estimation protocol as a building block for achieving PLDP and give theoretical analysis of its utility, privacy and complexity. We then present a novel framework that allows an untrusted server to accurately learn the user distribution over a spatial domain while satisfying PLDP for each user. This is mainly achieved by designing a novel user group clustering algorithm tailored to our problem. We confirm the effectiveness and efficiency of our framework through extensive experiments on multiple real benchmark datasets.


title: Application of Stereo Vision on Determination of End-Effector Position and Orientation of Manipulators

abstract: the main idea of this paper is to present application of stereo vision approach to determine position and orientation of end-effector of mechanical manipulators. Stereo vision is a technique based on inferring depth of an object from two cameras. A stereo vision test set up that is made of two parallel CMOS cameras is provided and USB port is used to transmit image data to the computer. Experimental tests are exerted for 6R mechanical manipulator where the end-effector moves on circular trajectory and Scout mobile robot to move a predefined optimal trajectory. The image processing algorithm, matching algorithm and triangulation calculations are optimized to decrease time required for matching procedure. The results illustrate that the measurement error is less than 1 mm. Because of rapid image processing algorithm; proposed method can be used to detect end-effector in dynamic trajectories. This stereo vision set up can be applied easily as measuring unit in a closed loop control system. Keywords-Manipulators; End-Effector; Positioning; Orientation; Stereo Vision


title: A novel end-to-end classifier using domain transferred deep convolutional neural networks for biomedical images

abstract: Abstract Background and objectives Highly accurate classification of biomedical images is an essential task in the clinical diagnosis of numerous medical diseases identified from those images. Traditional image classification methods combined with hand-crafted image feature descriptors and various classifiers are not able to effectively improve the accuracy rate and meet the high requirements of classification of biomedical images. The same also holds true for artificial neural network models directly trained with limited biomedical images used as training data or directly used as a black box to extract the deep features based on another distant dataset. In this study, we propose a highly reliable and accurate end-to-end classifier for all kinds of biomedical images via deep learning and transfer learning. Methods We first apply domain transferred deep convolutional neural network for building a deep model; and then develop an overall deep learning architecture based on the raw pixels of original biomedical images using supervised training. In our model, we do not need the manual design of the feature space, seek an effective feature vector classifier or segment specific detection object and image patches, which are the main technological difficulties in the adoption of traditional image classification methods. Moreover, we do not need to be concerned with whether there are large training sets of annotated biomedical images, affordable parallel computing resources featuring GPUs or long times to wait for training a perfect deep model, which are the main problems to train deep neural networks for biomedical image classification as observed in recent works. Results With the utilization of a simple data augmentation method and fast convergence speed, our algorithm can achieve the best accuracy rate and outstanding classification ability for biomedical images. We have evaluated our classifier on several well-known public biomedical datasets and compared it with several state-of-the-art approaches. Conclusions We propose a robust automated end-to-end classifier for biomedical images based on a domain transferred deep convolutional neural network model that shows a highly reliable and accurate performance which has been confirmed on several public biomedical image datasets.


title: Single- and Dual-Band Transparent Circularly Polarized Patch Antennas With Metamaterial Loading

abstract: In this letter, we present circularly polarized (CP) patch antennas that achieve up to 70% optical transparency by using a wire-mesh patch and by cutting the same grid pattern through the substrate. Glass or quartz wafers are therefore not needed, and our antennas may be fabricated on standard ceramic substrates, reducing cost and simplifying construction. Metamaterial loading is incorporated in one patch to control the relative phase difference between the patch’s orthogonal modes, which in turn produces dual-band CP radiation. Our dual-band transparent antenna operates at 2.35 and 2.73 GHz with peak radiation efficiencies of 70% and 78%, respectively.


title: Elastic Stream Computing with Clouds

abstract: Stream computing, also known as data stream processing, has emerged as a new processing paradigm that processes incoming data streams from tremendous numbers of sensors in a real-time fashion. Data stream applications must have low latency even when the incoming data rate fluctuates wildly. This is almost impossible with a local stream computing environment because its computational resources are finite. To address this kind of problem, we have devised a method and an architecture that transfers data stream processing to a Cloud environment as required in response to the changes of the data rate in the input data stream. Since a trade-off exists between application's latency and the economic costs when using the Cloud environment, we treat it as an optimization problem that minimizes the economic cost of using the Cloud. We implemented a prototype system using Amazon EC2 and an IBM System S stream computing system to evaluate the effectiveness of our approach. Our experimental results show that our approach reduces the costs by 80% while keeping the application's response latency low.


title: On laplacians of random complexes

abstract: Eigenvalues associated to graphs are a well-studied subject. In particular the spectra of the adjacency matrix and of the Laplacian of random graphs G(n,p) are known quite precisely. We consider generalizations of these matrices to simplicial complexes of higher dimensions and study their eigenvalues for the Linial--Meshulam model Xk(n,p) of random k-dimensional simplicial complexes on n vertices. We show that for p=Ω(log n/n), the eigenvalues of both, the higher-dimensional adjacency matrix and the Laplacian, are a.a.s.~sharply concentrated around two values. In a second part of the paper, we discuss a possible higher-dimensional analogue of the Discrete Cheeger Inequality. This fundamental inequality expresses a close relationship between the eigenvalues of a graph and its combinatorial expansion properties; in particular, spectral expansion (a large eigenvalue gap) implies edge expansion. Recently, a higher-dimensional analogue of edge expansion for simplicial complexes was introduced by Gromov, and independently by Linial, Meshulam and Wallach and by Newman and Rabinovich. It is natural to ask whether there is a higher-dimensional version of Cheeger's inequality. We show that the most straightforward version of a higher-dimensional Cheeger inequality fails: for every k>1, there is an infinite family of k-dimensional complexes that are spectrally expanding (there is a large eigenvalue gap for the Laplacian) but not combinatorially expanding.


title: An exploration of fractal art in fashion design

abstract: Fractals have always been associated with the term chaos. To many chaologists, the study of chaos and fractals is more than just a new field in science which fascinates the researchers to work on a theory that unifies mathematics, physics, art, and computer science - it is a revolution in science. Nature has used fractal designs for at least hundreds of millions of years and here we are focusing on the work done on fractal art in fashion design. This review represents a survey of the significant advances on fractal art in fashion design, both practical and theoretical, since the early publication of 1918. Results of our initial work in same direction are also shown in paper. This art can be used to generate unimaginable patterns which can increase the creativity of designers and give promising results to the fashion industry.


title: Comparison of the Levels of Presence and Anxiety in an Acrophobic Environment Viewed via HMD or CAVE

abstract: People who suffer from acrophobia fear any situation that involves heights. Several virtual reality systems have been presented to treat this problem. This paper presents a comparison study of the levels of presence and anxiety in an acrophobic environment that is viewed using a computer automatic virtual environment (CAVE) and a head-mounted display (HMD). In this environment, the floor fell away and the walls rose up. To determine whether either of the two visualization systems induced a greater sense of presence/anxiety in non-phobic users, an experiment comparing the two visualization systems was carried out. Twenty-five participants took part in this study. After using each visualization system (HMD or CAVE), the participants were asked to fill out an adapted Slater et al. questionnaire (Slater, Usoh, & Steed, 1994), and a Student t test was applied to the data obtained. The CAVE induces a high level of presence in users. The mean score was 5.01 (where 7 is the maximum value), which was higher than the score obtained using the HMD which was 3.59. The Student t test indicates that there are significant statistical differences. The level of anxiety was also examined at different times during the experiment. The results indicate that both visualization systems provoke anxiety, but that the CAVE provokes more anxiety than the HMD. The animation in which the floor falls away is the one that provoked the most anxiety. The results from the correlation between the anxiety and the level of presence at the three times indicated a significant correlation between the two measures.


Stars and Models : How to Build and Maintain Star Schemas Using SAS ® Data Integration Server in SAS ® 9


title: Wiki-ly Supervised Part-of-Speech Tagging

abstract: Despite significant recent work, purely unsupervised techniques for part-of-speech (POS) tagging have not achieved useful accuracies required by many language processing tasks. Use of parallel text between resource-rich and resource-poor languages is one source of weak supervision that significantly improves accuracy. However, parallel text is not always available and techniques for using it require multiple complex algorithmic steps. In this paper we show that we can build POS-taggers exceeding state-of-the-art bilingual methods by using simple hidden Markov models and a freely available and naturally growing resource, the Wiktionary. Across eight languages for which we have labeled data to evaluate results, we achieve accuracy that significantly exceeds best unsupervised and parallel text methods. We achieve highest accuracy reported for several languages and show that our approach yields better out-of-domain taggers than those trained using fully supervised Penn Treebank.


title: Evolving the Topology of Large Scale Deep Neural Networks

abstract: In the recent years Deep Learning has attracted a lot of attention due to its success in difficult tasks such as image recognition and computer vision. Most of the success in these tasks is merit of Convolutional Neural Networks (CNNs), which allow the automatic construction of features. However, designing such networks is not an easy task, which requires expertise and insight. In this paper we introduce DENSER, a novel representation for the evolution of deep neural networks. In concrete we adapt ideas from Genetic Algorithms (GAs) and Grammatical Evolution (GE) to enable the evolution of sequences of layers and their parameters. We test our approach in the well-known image classification CIFAR-10 dataset. The results show that our method: (i) outperforms previous evolutionary approaches to the generations of CNNs; (ii) is able to create CNNs that have state-of-the-art performance while using less prior knowledge (iii) evolves CNNs with novel topologies, unlikely to be designed by hand. For instance, the best performing CNN obtained during evolution has an unexpected structure using six consecutive dense layers. On the CIFAR-10 the best model reports an average error of 5.87% on test data.


title: Computational Design and Construction of Notch-Free Reciprocal Frame Structures

abstract: A reciprocal frame (RF) is a self-standing 3D structure typically formed by a complex grillage created as an assembly of simple atomic RF-units, which are in turn made up of three or more sloping rods forming individual units. While RF-structures are attractive given their simplicity, beauty, and ease of deployment; creating such structures, however, is difficult and cumbersome. In this work, we present an interactive computational framework for designing and assembling RF-structures around a 3D reference surface. Targeting notch-free assemblies, wherein individual rods or sticks are simply tied together, we focus on simplifying both the process of exploring the space of aesthetic designs and also the actual assembly process. By providing computational support to simplify the design and assembly process, our tool enables novice users to interactivity explore a range of design variations, and assists them to construct the final RF-structure design. We use the proposed framework to design a range of RF-structures of varying complexity and also physically construct a selection of the models.


title: Moral character in the workplace.

abstract: In two three-month diary studies and a large cross-sectional survey, we identified distinguishing features of adults with low versus high levels of moral character. Adults with high levels of moral character tend to consider the needs and interests of others and how their actions affect other people (e.g., they have high levels of Honesty-Humility, empathic concern, guilt proneness), regulate their behavior effectively, specifically with reference to behaviors that have positive short-term consequences but negative long-term consequences (e.g., they have high levels of Conscientiousness, self-control, consideration of future consequences), and value being moral (e.g., they have high levels of moral identity-internalization). Cognitive moral development, Emotionality, and social value orientation were found to be relatively undiagnostic of moral character. Studies 1 and 2 revealed that low-moral-character employees committed harmful work behaviors more frequently and helpful work behaviors less frequently than high-moral-character employees, according to their own admissions and coworkers’ observations. Study 3 revealed that low-moral-character adults committed more delinquent behavior and had more lenient attitudes toward unethical negotiation tactics as compared to high-moral-character adults. By showing that individual differences have consistent, meaningful effects on employees’ behaviors, after controlling for demographic variables (e.g., gender, age, income) and basic attributes of the work setting (e.g., enforcement of an ethics code), our results contest situationist perspectives that de-emphasize the importance of personality. Moral people can be identified by self-reports in surveys, and these self-reports predict consequential behaviors months after the initial assessment.


title: Municipal solid waste management in Indian cities - A review.

abstract: Municipal solid waste management (MSWM) is one of the major environmental problems of Indian cities. Improper management of municipal solid waste (MSW) causes hazards to inhabitants. Various studies reveal that about 90% of MSW is disposed of unscientifically in open dumps and landfills, creating problems to public health and the environment. In the present study, an attempt has been made to provide a comprehensive review of the characteristics, generation, collection and transportation, disposal and treatment technologies of MSW practiced in India. The study pertaining to MSWM for Indian cities has been carried out to evaluate the current status and identify the major problems. Various adopted treatment technologies for MSW are critically reviewed, along with their advantages and limitations. The study is concluded with a few fruitful suggestions, which may be beneficial to encourage the competent authorities/researchers to work towards further improvement of the present system.


title: From computational thinking to computational participation in K--12 education

abstract: Seeking to reframe computational thinking as computational participation.


title: An Automated Image Processing Method for Segmentation and Quantification of Rust Disease in Maize Leaves

abstract: $A$ In the agro-ecological domain, maize is one of the most dominantcrops of the world. The disease of the maize plantation not only affect the nutritional balance but also the economics related to the crop. In this paper, an automated image processing method is proposed to identify the rust affected maize leaves and differentiate them from the healthy maize leaves. Segmentation and quantification of the rusted portion from the images of the maize leaf is done. The rust affected portion is accurately quantified of the maize leaf using morphological operations and area based thresholding to make the algorithm computationally efficient. Quantification of the segmented rusted spots is done to measure the degree of damage done by the crop disease. The results obtained from the proposed methodology are encouraging and can be used in agricultural industry for some real-time detection of diseases affecting the productivity of crops.


title: Reconstructing building mass models from UAV images

abstract: We present an automatic reconstruction pipeline for large scale urban scenes from aerial images captured by a camera mounted on an unmanned aerial vehicle. Using state-of-the-art Structure from Motion and Multi-View Stereo algorithms, we first generate a dense point cloud from the aerial images. Based on the statistical analysis of the footprint grid of the buildings, the point cloud is classified into different categories (i.e., buildings, ground, trees, and others). Roof structures are extracted for each individual building using Markov random field optimization. Then, a contour refinement algorithm based on pivot point detection is utilized to refine the contour of patches. Finally, polygonal mesh models are extracted from the refined contours. Experiments on various scenes as well as comparisons with state-of-the-art reconstruction methods demonstrate the effectiveness and robustness of the proposed method. Graphical abstractDisplay Omitted HighlightsA novel framework for automatic reconstruction of large scale urban scenes from UAV images.An object level point cloud segmentation algorithm and a roof extraction algorithm based on a regularized Markov random field formulation.An effective contour refinement method based on pivot point detection.


Understanding Presence


title: Exploring Design Factors for Transforming Passive Vibration Signals into Smartwear Interactions

abstract: Vibrational signals that are generated when a finger is swept over an uneven surface can be reliably detected via low-cost sensors that are in proximity to the interaction surface. Such interactions provide an alternative to touchscreens by enabling always-available input. In this paper we demonstrate that Inertial Measurement Units (known as IMUs) embedded in many off-the-shelf smartwear are well suited for capturing vibrational signals generated by a user's finger swipes, even when the IMU appears in a smartring or smartwatch. In comparison to acoustic based approaches for capturing vibrational signals, IMUs are sensitive to a vast number of factors, both, in terms of the surface and swipe properties, when the interaction is carried out. We contribute by examining the impact of these surface and swipe properties, including surface or bump height and density, surface stability, sensor location, swipe style, and swipe direction. Based on our results, we present a number of usage scenarios to demonstrate how this approach can be used to provide always-available input for digital interactions.


Why Model Versioning Research is Needed ! ? An Experience Report ?


title: A non-local algorithm for image denoising

abstract: We propose a new measure, the method noise, to evaluate and compare the performance of digital image denoising methods. We first compute and analyze this method noise for a wide class of denoising algorithms, namely the local smoothing filters. Second, we propose a new algorithm, the nonlocal means (NL-means), based on a nonlocal averaging of all pixels in the image. Finally, we present some experiments comparing the NL-means algorithm and the local smoothing filters.


title: Clutchable series-elastic actuator: Design of a robotic knee prosthesis for minimum energy consumption

abstract: The cyclic and often linear torque-angle relationship of locomotion presents the opportunity to innovate on the design of traditional series-elastic actuators (SEAs). In this paper, a novel modification to the SEA architecture was proposed by adding a clutch in parallel with the motor within the SEA--denoted as a CSEA. This addition permits bimodal dynamics where the system is characterized by an SEA when the clutch is disengaged and a passive spring when the clutch is engaged. The purpose of the parallel clutch was to provide the ability to store energy in a tuned series spring, while requiring only reactionary torque from the clutch. Thus, when the clutch is engaged, a tuned elastic relationship can be achieved with minimal electrical energy consumption. The state-based model of the CSEA is introduced and the implementation of the CSEA mechanism in a powered knee prosthesis is detailed. The series elasticity was optimized to fit the spring-like torque-angle relationship of early stance phase knee flexion and extension during level ground walking. In simulation, the CSEA knee required 70% less electrical energy than a traditional SEA. Future work will focus on the mechanical implementation of the CSEA knee and an empirical demonstration of reduced electrical energy consumption during walking.


title: Model-Predictive Motion Planning: Several Key Developments for Autonomous Mobile Robots

abstract: A necessary attribute of a mobile robot planning algorithm is the ability to accurately predict the consequences of robot actions to make informed decisions about where and how to drive. It is also important that such methods are efficient, as onboard computational resources are typically limited and fast planning rates are often required. In this article, we present several practical mobile robot motion planning algorithms for local and global search, developed with a common underlying trajectory generation framework for use in model-predictive control. These techniques all center on the idea of generating informed, feasible graphs at scales and resolutions that respect computational and temporal constraints of the application. Connectivity in these graphs is provided by a trajectory generator that searches in a parameterized space of robot inputs subject to an arbitrary predictive motion model. Local search graphs connect the currently observed state-to-states at or near the planning or perception horizon. Global search graphs repeatedly expand a precomputed trajectory library in a uniformly distributed state lattice to form a recombinant search space that respects differential constraints. In this article, we discuss the trajectory generation algorithm, methods for online or offline calibration of predictive motion models, sampling strategies for local search graphs that exploit global guidance and environmental information for real-time obstacle avoidance and navigation, and methods for efficient design of global search graphs with attention to optimality, feasibility, and computational complexity of heuristic search. The model-invariant nature of our approach to local and global motions planning has enabled a rapid and successful application of these techniques to a variety of platforms. Throughout the article, we also review experiments performed on planetary rovers, field robots, mobile manipulators, and autonomous automobiles and discuss future directions of the article.


title: Why do people buy virtual goods? Attitude toward virtual good purchases versus game enjoyment

abstract: Game enjoyment is negatively associated with purchase intentions for virtual goods.Attitude is positively associated with purchase intentions for virtual goods.Social influence is positively associated with purchase intentions for virtual goods.Intention to continue playing is positively associated with purchase intentions. In this study we investigate purchase behavior for virtual goods in three free-to-play game environments. In the modern free games, publishers sell virtual goods in order to generate revenue. However, game publishers face dire negative attitudes toward the business model as it can entice publishers to degrade the enjoyment of the game in order to sell more virtual goods that address the artificial gaps in the game. This study focuses on this looming question in the game industry whether people buy virtual goods because they enjoy the game and want to keep on playing it or rather because their attitudes toward virtual goods are favorable and they believe it is also accepted in the peer-group. Player responses (N=2791) were gathered from three different game types: social virtual world (Habbo) (n=2156), first-person shooters (n=398), and social networking games (Facebook games) (n=237). The results support both main hypotheses (1) enjoyment of the game reduces the willingness to buy virtual goods while at the same time it increases the willingness to play more of the game. Continued use, however, does positively predict purchase intentions for virtual goods. (2) Attitude toward virtual goods and the beliefs about peers' attitudes strongly increase the willingness to purchase virtual goods. Beyond these interesting results the paper points to several further lines of inquiry.


title: Analysis of Solid State Transformer based microgrid system

abstract: The contribution of this paper has been focused on investigating a new microgrid architecture that integrates the electronic transformer (SST, the acronym Solid State Transformer) with microgrids system. The design of the converters required for implementation of the electronic transformer are dealt. The work to provide an infrastructure for creating an intelligent energy system, which will enable the integration of smart grid concepts, microrrede generation distribute and distribution of direct current. The advantages of SST, listed the reduction of the volume and weight as well as fault isolation capability, voltage regulation, harmonic filtering, reactive power compensation and power factor correction. The work presents the advantages of applying the SST in a microgrid system. Therefore, simulation results in MATLAB/Simulink software are presented.


The power of global biodiversity indicators to predict future policy outcomes


title: Incentives for expressing opinions in online polls

abstract: Prediction markets efficiently extract and aggregate the private information held by individuals about events and facts that can be publicly verified. However, facts such as the effects of raising or lowering interest rates can never be publicly verified, since only one option will be implemented. Online opinion polls can still be used to extract and aggregate private information about such questions. This paper addresses incentives for truthful reporting in online opinion polls. The challenge lies in designing reward schemes that do not require a-priori knowledge of the participants' beliefs. We survey existing solutions, analyze their practicality and propose a new mechanism that extracts accurate information from rational participants.


title: Sketch it, make it: sketching precise drawings for laser cutting

abstract: Sketch It, Make It (SIMI) is a modeling tool that enables non-experts to design items for fabrication with laser cutters. SIMI recognizes rough, freehand input as a user iteratively edits a structured vector drawing. The tool combines the strengths of sketch-based interaction with the power of constraint-based modeling. Several interaction techniques are combined to present a coherent system that makes it easier to make precise designs for laser cutters.


title: A neural network memory prefetcher using semantic locality

abstract: Accurate memory prefetching is paramount for processor performance, and modern processors employ various techniques to identify and prefetch different memory access patterns. While most modern prefetchers target spatio-temporal patterns by matching memory addresses that are accessed in close proximity (either in space or time), the recently proposed concept of semantic locality views locality as an artifact of the algorithmic level and searches for correlations between memory accesses and program state. While this approach was shown to be effective, capturing semantic locality requires significant associative learning capabilities. In this paper we utilize neural networks for this task. We leverage recent advances in machine learning to propose a neural network prefetcher. We show that by observing program context, this prefetcher can learn distinct memory access patterns that cannot be covered by other state-of-the-art prefetchers. We evaluate the neural network prefetcher over SPEC2006, Graph500, and several microbenchmarks. We show that the prefetcher can deliver an average speedup of 30% for SPEC2006 (up to 2.7x) and up to 4.6x over kernels. We also present a high-level design of our prefetcher, explore the power, energy and area limitations, and propose several optimizations for feasibility. We believe that this line of research can further improve the efficiency of such neural networks and allow harnessing them for additional micro-architectural predictions.


title: A Control-Theoretic Approach for Dynamic Adaptive Video Streaming over HTTP

abstract: User-perceived quality-of-experience (QoE) is critical in Internet video applications as it impacts revenues for content providers and delivery systems. Given that there is little support in the network for optimizing such measures, bottlenecks could occur anywhere in the delivery system. Consequently, a robust bitrate adaptation algorithm in client-side players is critical to ensure good user experience. Previous studies have shown key limitations of state-of-art commercial solutions and proposed a range of heuristic fixes. Despite the emergence of several proposals, there is still a distinct lack of consensus on: (1) How best to design this client-side bitrate adaptation logic (e.g., use rate estimates vs. buffer occupancy); (2) How well specific classes of approaches will perform under diverse operating regimes (e.g., high throughput variability); or (3) How do they actually balance different QoE objectives (e.g., startup delay vs. rebuffering). To this end, this paper makes three key technical contributions. First, to bring some rigor to this space, we develop a principled control-theoretic model to reason about a broad spectrum of strategies. Second, we propose a novel model predictive control algorithm that can optimally combine throughput and buffer occupancy information to outperform traditional approaches. Third, we present a practical implementation in a reference video player to validate our approach using realistic trace-driven emulations.


abstract: This paper presents the first version of the NIST Cloud Computing Reference Architecture (RA). This is a vendor neutral conceptual model that concentrates on the role and interactions of the identified actors in the cloud computing sphere. Five primary actors were identified - Cloud Service Consumer, Cloud Service Provider, Cloud Broker, Cloud Auditor and Cloud Carrier. Their roles and activities are discussed in this report. A primary goal for generating this model was to give the United States Government (USG) a method for understanding and communicating the components of a cloud computing system for Federal IT executives, Program Managers and IT procurement officials.