title: Market power and efficiency in a computational electricity market with discriminatory double-auction pricing

abstract: This study reports experimental market power and efficiency outcomes for a computational wholesale electricity market operating in the short run under systematically varied concentration and capacity conditions. The pricing of electricity is determined by means of a clearinghouse double auction with discriminatory midpoint pricing. Buyers and sellers use a modified Roth Erev individual reinforcement learning algorithm to determine their price and quantity offers in each auction round. It is shown that high market efficiency is generally attained, and that market microstructure is strongly predictive for the relative market power of buyers and sellers independently of the values set for the reinforcement learning parameters. Results are briefly compared against results from an earlier study in which buyers and sellers instead engage in social mimicry learning via genetic algorithms.


title: Mining e-mail content for author identification forensics

abstract: We describe an investigation into e-mail content mining for author identification, or authorship attribution, for the purpose of forensic investigation. We focus our discussion on the ability to discriminate between authors for the case of both aggregated e-mail topics as well as across different e-mail topics. An extended set of e-mail document features including structural characteristics and linguistic patterns were derived and, together with a Support Vector Machine learning algorithm, were used for mining the e-mail content. Experiments using a number of e-mail documents generated by different authors on a set of topics gave promising results for both aggregated and multi-topic author categorisation.


title: Multi-Category RFID Estimation

abstract: This paper concerns the practically important problem of multi-category radio frequency identification (RFID) estimation: given a set of RFID tags, we want to quickly and accurately estimate the number of tags in each category. However, almost all the existing RFID estimation protocols are dedicated to the estimation problem on a single set, regardless of tag categories. A feasible solution is to separately execute the existing estimation protocols on each category. The execution time of such a serial solution is proportional to the number of categories, and cannot satisfy the delay-stringent application scenarios. Simultaneous RIFD estimation over multiple categories is desirable, and hence, this paper proposes an approach called simultaneous estimation for multi-category RFID systems (SEM). SEM exploits the Manchester-coding mechanism, which is supported by the ISO 18000-6 RFID standard, to decode the combined signals, thereby simultaneously obtaining the reply status of tags from each category. As a result, multiple bit vectors are decoded from just one physical slotted frame. Built on our SEM, many existing excellent estimation protocols can be used to estimate the tag cardinality of each category in a simultaneous manner. To ensure the predefined accuracy, we calculate the variance of the estimate in one round, as well as the variance of the average estimate in multiple rounds. To find the optimal frame size, we propose an efficient binary search-based algorithm. To address significant variance in category sizes, we propose an adaptive partitioning (AP) strategy to group categories of similar sizes together and execute the estimation protocol for each group separately. Compared with the existing protocols, our approach is much faster, meanwhile satisfying the predefined estimation accuracy. For example, with 20 categories, the proposed SEM+AP is about seven times faster than prior estimation schemes. Moreover, our approach is the only one whose normalized estimation time (i.e., time per category) decreases as the number of categories increases.


title: Tacit Knowledge and Knowledge Management: The Keys to Sustainable Competitive Advantage

abstract: B competitive advantage is increasingly found in knowing how to do things, rather than in having special access to resources and markets, knowledge and intellectual capital have become both the primary bases of core competencies and the key to superior performance. This article explores how companies can best grow their knowledge resources to create not simply competitive advantage, but sustainable competitive advantage. In recent years, the development of “hypercompetition” and shortened product lifecycles have reduced the degree to which much special knowledge can provide companies with sustained competitive advantage. Shrinking logistical and communication costs, along with new organizational designs, have enabled multinational corporations (MNCs) to function as truly global companies, rather than as conglomerations of national ones. Global companies introduce their newest products worldwide and effectively share knowledge across country units. Fueling hypercompetition, many industries now have several MNCs competing against each other on a worldwide basis, rather than a few local companies and only one MNC competing in each market. New product innovation has become the key to competing successfully. MNCs use their deep pockets to fund research and development (R&D), and they reverse engineer each other’s products and turn to consultants to learn about best practices in their industry. Companies develop or acquire new knowledge so rapidly that having special knowledge is no longer a basis for sustainable competitive advantage. To provide sustained competitive advantage, one needs knowledge that is difficult for outsiders to copy as well as the ability to rapidly develop new knowledge. In a graphic demonstration of the transient value of much knowledge, Michael Tushman began his courses at Columbia Business School by asking what the following list of products had in common: watches, cars, cameras, color TVs, hand tools, radial tires, industrial robots, machine tools, electric motors, financial services, food processors, microwave ovens, stereo equipment, athletic equipment, computer chips, optical equipment, medical equipment, and consulting services. The answer: each of these industries was initially dominated by a company with specialized knowledge, but that company rapidly lost its lead as other companies acquired the knowledge required to compete. Also illustrating the situation, fewer than 40% of the Fortune 500 companies of 1970 still existed in their original form by 1991. In today’s environment, much of the Organizational Dynamics, Vol. 29, No. 4, pp. 164–178, 2001 ISSN 0090-2616/01/$–see frontmatter © 2001 Elsevier Science, Inc. PII S0090-2616(01)00026-2


title: Chapter 7 REAL-TIME DATA ANALYTICS IN SENSOR NETWORKS

abstract: The proliferation of Wireless Sensor Networks (WSNs) in the past decade has provided the bridge between the physical and digital worlds, enabling the monitoring and study of physical phenomena at a granularity and level of detail that was never before possible. In this study, we review the efforts of the research community with respect to two important problems in the context of WSNs: real-time collection of the sensed data, and real-time processing of these data series.


title: Affective Ludology, Flow and Immersion in a First- Person Shooter: Measurement of Player Experience

abstract: Gameplay research about experiential phenomena is a challenging undertaking, given the variety of experiences that gamers encounter when playing and which currently do not have a formal taxonomy, such as flow, immersion, boredom, and fun. These informal terms require a scientific explanation. Ludologists also acknowledge the need to understand cognition, emotion, and goal- oriented behavior of players from a psychological perspective by establishing rigorous methodologies. This paper builds upon and extends prior work in an area for which we would like to coin the term"affective ludology."The area is concerned with the affective measurement of player-game interaction. The experimental study reported here investigated different traits of gameplay experience using subjective (i.e., questionnaires) and objective (i.e., psychophysiological) measures. Participants played three Half-Life 2 game level design modifications while measures such as electromyography (EMG), electrodermal activity (EDA) were taken and questionnaire responses were collected. A level designed for combat-oriented flow experience demonstrated significant high-arousal positive affect emotions. This method shows that emotional patterns emerge from different level designs, which has great potential for providing real-time emotional profiles of gameplay that may be generated together with self- reported subjective player experience descriptions.


Title Multi-hop Relay System Evaluation Methodology (Channel Model and Performance Metric) Date Submitted Source(s)


Targeted Sentiment to Understand Student Comments


title: IoT Smart Home Adoption: The Importance of Proper Level Automation

abstract: The word “smart” has been used in various fields and is widely accepted to mean intelligence. Smart home service, one of the representative emerging technologies in the IoT era, has changed house equipment into being more intelligent, remote controllable, and interconnected. However, the intelligence and controllability of a smart home service are contradictory concepts, under certain aspects. In addition, the level of intelligence or controllability of a smart home service that users want may differ according to the user. As potential users of smart home services have diversified in recent years, providing the appropriate functions and features is critical to the diffusion of the service. Thus, this study examines the smart home service features that current users require and empirically evaluates the relationship between the critical factors and the adoption behavior with 216 samples from Korea. The moderating effect of personal characteristics on behavior is also tested. The results of the analysis provide various theoretical and practical implications.


title: Security Concerns Towards Security Operations Centers

abstract: A security operations center (SOC) is a centralized place for monitoring and frequently managing the safety and security of the company's status. The primary purpose of SOC is to enable better incident detection, investigation, and response capabilities by using data from endpoint devices, logs, security systems, and network flows. Moreover, an efficient SOC can help the organizations enhance the ability of situational awareness and increase the deployment of enterprise resources to mitigate the security issues. However, the cyber threats are the significant challenges for SOC. In this paper, the authors expressed some security threats towards SOC and several solutions to fight against them.


title: Real time streaming pattern detection for eCommerce

abstract: Pattern detection over streams of events is gaining more and more attention, especially in the field of eCommerce. Our industrial partner Cdiscount, which is one of the largest eCommerce companies in France, wants to use pattern detection for real-time customer behavior analysis. The main challenges to consider are efficiency and scalability, as the detection of customer behavior must be achieved within a few seconds, while millions of unique customers visit the website every day, each performing hundreds of actions. In this paper, we present our approach to large-scale and efficient pattern detection for eCommerce. It relies on a domain-specific language to define behavior patterns. Patterns are then compiled into deterministic finite automata, which are run on a Big Data streaming platform to carry out the detection work. Our evaluation shows that our approach is efficient and scalable, and fits the requirements of Cdiscount.


title: New Sinogram Filter Design Utilizing Sinusoidal Trajectories

abstract: Stackgram filtering has recently been found to be very promising way of positron emission tomography (PET) data filtering providing quantitatively better results than common radial sinogram domain filtering. Stackgrams allow separate processing of each sinusoidal trajectory signal but the practical use of stackgrams is restricted by their large size. In this paper we introduce new angular sinogram domain filters approximating the stackgram filters. The structure of these filters gives new insight into the area of sinogram filtering. The filtering results of the proposed sinogram filters are shown to be very close to the results obtained by corresponding stackgram filters and quantitatively better than results obtained by common radial sinogram filtering.


title: Cognitive Mimetics for Designing Intelligent Technologies

abstract: Design mimetics is an important method of creation in technology design. Here, we review design mimetics as a plausible approach to address the problem of how to design generally intelligent technology. We argue that design mimetics can be conceptually divided into three levels based on the source of imitation. Biomimetics focuses on the structural similarities between systems in nature and technical solutions for solving design problems. In robotics, the sensory-motor systems of humans and animals are a source of design solutions. At the highest level, we introduce the concept of cognitive mimetics, in which the source for imitation is human information processing. We review and discuss some historical examples of cognitive mimetics, its potential uses, methods, levels, and current applications, and how to test its success. We conclude by a practical example showing how cognitive mimetics can be a highly valuable complimentary approach for pattern matching and machine learning based design of artificial intelligence (AI) for solving specific human-AI interaction design problems.


title: Sentence Simplification with Deep Reinforcement Learning

abstract: Sentence simplification aims to make sentences easier to read and understand. Most recent approaches draw on insights from machine translation to learn simplification rewrites from monolingual corpora of complex and simple sentences. We address the simplification problem with an encoder-decoder model coupled with a deep reinforcement learning framework. Our model, which we call {\sc Dress} (as shorthand for {\bf D}eep {\bf RE}inforcement {\bf S}entence {\bf S}implification), explores the space of possible simplifications while learning to optimize a reward function that encourages outputs which are simple, fluent, and preserve the meaning of the input. Experiments on three datasets demonstrate that our model outperforms competitive simplification systems.


title: Motion blur identification from image gradients

abstract: Restoration of a degraded image from motion blurring is highly dependent on the estimation of the blurring kernel. Most of the existing motion deblurring techniques model the blurring kernel with a shift-invariant box filter, which holds true only if the motion among images is of uniform velocity. In this paper, we present a spectral analysis of image gradients, which leads to a better configuration for identifying the blurring kernel of more general motion types (uniform velocity motion, accelerated motion and vibration). Furthermore, we introduce a hybrid Fourier-Radon transform to estimate the parameters of the blurring kernel with improved robustness to noise over available techniques. The experiments on both simulated images and real images show that our algorithm is capable of accurately identifying the blurring kernel for a wider range of motion types.


title: Off-line flux injection test probe for screening defective rotors in squirrel cage induction machines

abstract: The recent trend in squirrel cage induction motor manufacturing is to replace fabricated copper rotors with aluminum die-cast rotors to reduce manufacturing cost to stay competitive in the global market. Porosity in aluminum die-cast squirrel cage rotors is inevitably introduced during the die cast process. Porosity can cause degradation in motor performance and can lead to a forced outage causing irreversible damage if it is extreme. Many off-line and on-line quality assurance test methods have been developed and applied for assessment of rotor quality. However, years of experience with the existing test methods revealed that they are not suitable for quality testing or capable of providing a quantitative assessment of rotor condition with sufficient sensitivity. In this paper, a new off-line test method capable of providing sensitive assessment of rotor porosity is proposed. It is shown that rotors with minor and distributed porosity that are difficult to detect with other tests can be screened out during manufacturing. The proposed method is verified through a 3 dimensional finite element analysis and experimental testing on closed and semi-open slot aluminum die cast rotors of 5.5 kW induction motors with porosity.


title: Formal verification of a realistic compiler

abstract: This paper reports on the development and formal verification (proof of semantic preservation) of CompCert, a compiler from Clight (a large subset of the C programming language) to PowerPC assembly code, using the Coq proof assistant both for programming the compiler and for proving its correctness. Such a verified compiler is useful in the context of critical software and its formal verification: the verification of the compiler guarantees that the safety properties proved on the source code hold for the executable compiled code as well.


title: The Effect of Organizational Climate on Counterproductive Behaviors: An Empirical Study on the Employees of Manufacturing Enterprises

abstract: This study investigates the impact of organizational climate on counterproductive behaviors. In organizational behavior studies, organizational climate is suggested as an important determinant or precursor of counterproductive behaviors. However, in the literature, there are limited studies on the relationship between counterproductive behaviors and organizational climate. In this respect, significant findings have been collected as a result of a research conducted on 204 employees of two enterprises in the field of metal and solar energy. Based on the findings, significant and negative relationships have been observed between counterproductive behaviors and dimensions of organizational climate such as reward, warmth, support/commitment, organizational structure and organizational standards. Moreover, warmth relationship environment, support/commitment and organizational standards dimensions are found out to have effect on counterproductive behaviors.


title: Knowledge-Based Integrated Aircraft Design: An Applied Approach from Design to Concept Demonstration

abstract: The design and development of new aircraft are becoming increasingly expensive and timeconsuming. To assist the design process in reducing the development cost, time, and late design changes, the c ...


title: Visual analysis of code security

abstract: To help increase the confidence that software is secure, researchers and vendors have developed different kinds of automated software security analysis tools. These tools analyze software for weaknesses and vulnerabilities, but the individual tools catch different vulnerabilities and produce voluminous data with many false positives. This paper describes a system that brings together the results of disparate software analysis tools into a visual environment to support the triage and exploration of code vulnerabilities. Our system allows software developers to explore vulnerability results to uncover hidden trends, triage the most important code weaknesses, and show who is responsible for introducing software vulnerabilities. By correlating and normalizing multiple software analysis tools' data, the overall vulnerability detection coverage of software is increased. A visual overview and powerful interaction allows the user to focus attention on the most pressing vulnerabilities within huge volumes of data, and streamlines the secure software development workflow through integration with development tools.


title: A fast profiler for compilation of multi-threaded applications on a hybrid memory system

abstract: This paper proposes a fast memory access profiling tool called HMTool for multi-threaded applications. The proposed profiling tool extracts memory access patterns rapidly to optimize a multi-threaded application by allocating variables properly on a hybrid memory system with DRAM and non-volatile memory. To enhance the profiling performance, this paper proposes a static analysis and a caching technique for profiling. The proposed tool is implemented based on LLVM compiler framework. Experiment results show that the proposed profiling techniques improve the profiling performance by 5.1 times compared with an existing memory profiling tool.


title: Blepharo-cheilo-dontic (BCD) syndrome: expanding the phenotype, case report and review of literature.

abstract: The combination of lagophthalmia, euryblepharon, ectropion of lower eyelids, distichiasis, bilateral cleft lip and palate, and oligodontia comprises the blepharo-cheilo-dontic (BCD) syndrome. This combination has been found sporadically or with positive family history and inherited as an autosomal dominant condition with variable expression. We described a Saudi boy with the cardinal signs consistent with the BCD syndrome. In addition to the common components of BCD syndrome that involve eyelids, lip, and teeth abnormalities, this patient is the third reported BCD case with imperforate anus, the second with thyroid agenesis, and the first with lumbosacral meningomyelocele.


title: Cost of maternal health services in low and middle-income countries: protocol for a systematic review

abstract: There is substantial evidence that maternal health services across the continuum of care are effective in reducing morbidities and mortalities associated with pregnancy and childbirth. There is also consensus regarding the need to invest in the delivery of these services towards the global goal of achieving Universal Health Coverage in low- and middle-income countries (LMICs). However, there is limited evidence on the costs of providing these services. This protocol describes the methods and analytical framework to be used in conducting a systematic review of costs of providing maternal health services in LMICs.


title: Semi-supervised Learning for RGB-D Object Recognition

abstract: Conventional supervised object recognition methods have been investigated for many years. Despite their successes, there are still two suffering limitations: (1) various information of an object is represented by artificial features only derived from RGB images, (2) lots of manually labeled data is required by supervised learning. To address those limitations, we propose a new semi-supervised learning framework based on RGB and depth (RGB-D) images to improve object recognition. In particular, our framework has two modules: (1) RGB and depth images are represented by convolutional-recursive neural networks to construct high level features, respectively, (2) co-training is exploited to make full use of unlabeled RGB-D instances due to the existing two independent views. Experiments on the standard RGB-D object dataset demonstrate that our method can compete against with other state-of-the-art methods with only 20% labeled data.


Large Language Models in Machine Translation


title: Dual-beam radiation from a grid array antenna

abstract: A grid array antenna (GAA) printed on a dielectric substrate is investigated to obtain dual-beam radiation. For the analysis of the GAA, the method of moments (MoM) is applied to an integral equation expressed by Sommerfeld-type integrals. It is revealed that the maximum beam direction, gain, and half-power beam width in the principal plane are changed with frequency. The dual-beam radiation is obtained, if the operating frequency is appropriately chosen.


title: Vital nodes identification in complex networks

abstract: Abstract Real networks exhibit heterogeneous nature with nodes playing far different roles in structure and function. To identify vital nodes is thus very significant, allowing us to control the outbreak of epidemics, to conduct advertisements for e-commercial products, to predict popular scientific publications, and so on. The vital nodes identification attracts increasing attentions from both computer science and physical societies, with algorithms ranging from simply counting the immediate neighbors to complicated machine learning and message passing approaches. In this review, we clarify the concepts and metrics, classify the problems and methods, as well as review the important progresses and describe the state of the art. Furthermore, we provide extensive empirical analyses to compare well-known methods on disparate real networks, and highlight the future directions. In spite of the emphasis on physics-rooted approaches, the unification of the language and comparison with cross-domain methods would trigger interdisciplinary solutions in the near future.


Purely Sequence-Trained Neural Networks for ASR Based on Lattice-Free MMI.


title: Metric properties of the Tower of Hanoi graphs and Stern's diatomic sequence

abstract: It is known that in the Tower of Hanoi graphs there are at most two different shortest paths between any fixed pair of vertices. A formula is given that counts, for a given vertex v, the number of vertices u such that there are two shortest u, v-paths. The formula is expressed in terms of Stern's diatomic sequence b(n) (n ≥ 0) and implies that only for vertices of degree two this number is zero. Plane embeddings of the Tower of Hanoi graphs are also presented that provide an explicit description of b(n) as the number of elements of the sets of vertices of the Tower of Hanoi graphs intersected by certain lines in the plane.


title: Robust Large Margin Deep Neural Networks

abstract: The generalization error of deep neural networks via their classification margin is studied in this paper. Our approach is based on the Jacobian matrix of a deep neural network and can be applied to networks with arbitrary nonlinearities and pooling layers, and to networks with different architectures such as feed forward networks and residual networks. Our analysis leads to the conclusion that a bounded spectral norm of the network's Jacobian matrix in the neighbourhood of the training samples is crucial for a deep neural network of arbitrary depth and width to generalize well. This is a significant improvement over the current bounds in the literature, which imply that the generalization error grows with either the width or the depth of the network. Moreover, it shows that the recently proposed batch normalization and weight normalization reparametrizations enjoy good generalization properties, and leads to a novel network regularizer based on the network's Jacobian matrix. The analysis is supported with experimental results on the MNIST, CIFAR-10, LaRED, and ImageNet datasets.


title: Mobile commerce: framework, applications and networking support

abstract: Advances in e-commerce have resulted in significant progress towards strategies, requirements, and development of e-commerce applications. However, nearly all e-commerce applications envisioned and developed so far assume fixed or stationary users with wired infrastructure. We envision many new e-commerce applications that will be possible and significantly benefit from emerging wireless and mobile networks. To allow designers, developers, and researchers to strategize and create mobile commerce applications, we propose a four-level integrated framework for mobile commerce. Since there are potentially an unlimited number of mobile commerce applications, we attempt to identify several important classes of applications such as mobile financial applications, mobile inventory management, proactive service management, product location and search, and wireless re-engineering. We discuss how to successfully define, architect, and implement the necessary hardware/software infrastructure in support of mobile commerce. Also, to make mobile commerce applications a reality, we address networking requirements, discuss support from wireless carriers, and present some open research problems.


Permanent is hard to compute even on a good day


title: Risk Management: Coordinating Corporate Investment and Financing Policies

abstract: This paper develops a general framework for analyzing corporate risk management policies. We begin by observing that if external sources of finance are more costly to corporations than internally generated funds, there will typically be a benefit to hedging: hedging adds value to the extent that it helps ensure that a corporation has sufficient internal funds available to take advantage of attractive investment opportunities. We then argue that this simple observation has wide-ranging implications for the design of risk management strategies. We delineate how these strategies should depend on such factors as shocks to investment and financing opportunities. We also discuss exchange-rate hedging strategies for multinationals. as well as strategies involving "nonlinear" instruments like options.


title: Finding without seeking: the information encounter in the context of reading for pleasure

abstract: Abstract This paper examines nongoal oriented transactions with texts in order to investigate the information encounter in the context of daily living. Findings are reported from a larger research project based on intensive interviews with 194 committed readers who read for pleasure. The paper analyses interview responses that illuminate two aspects of the readers' experience of reading for pleasure: (1) how readers choose books to read for pleasure; and (2) books that have made a significant difference in readers' lives. The paper concludes with five themes emerging from this analysis that have implications for the information search process: the active engagement of the reader/searcher in constructing meaning from texts; the role of the affective dimension; ‘trustworthiness’; the social context of information seeking; and the meta-knowledge used by experienced readers in making judgments about texts.


title: Feedback and Reflection: Teaching Methods for Clinical Settings

abstract: Feedback and reflection are two basic teaching methods used in clinical settings. In this article, the authors explore the distinctions between, and the potential impact of, feedback and reflection in clinical teaching. Feedback is the heart of medical education; different teaching encounters call for different types of feedback. Although most clinicians are familiar with the principles of giving feedback, many clinicians probably do not recognize the many opportunities presented to them for using feedback as a teaching tool. Reflection in medicine—the consideration of the larger context, the meaning, and the implications of an experience and action—allows the assimilation and reordering of concepts, skills, knowledge, and values into pre-existing knowledge structures. When used well, reflection will promote the growth of the individual. While feedback is not used often enough, reflection is probably used even less. Acad. Med. 2002;77:1185‐1188.


title: XNORBIN: A 95 TOp/s/W hardware accelerator for binary convolutional neural networks

abstract: Deploying state-of-the-art CNNs requires power-hungry processors and off-chip memory. This precludes the implementation of CNNs in low-power embedded systems. Recent research shows CNNs sustain extreme quantization, binarizing their weights and intermediate feature maps, thereby saving 8-32\x memory and collapsing energy-intensive sum-of-products into XNOR-and-popcount operations. We present XNORBIN, an accelerator for binary CNNs with computation tightly coupled to memory for aggressive data reuse. Implemented in UMC 65nm technology XNORBIN achieves an energy efficiency of 95 TOp/s/W and an area efficiency of 2.0 TOp/s/MGE at 0.8 V.


title: FPGA-optimised high-quality uniform random number generators

abstract: This paper introduces a method of constructing random numbergenerators from four of the basic primitives provided by FPGAs: Flip-Flips, Lookup-Tables, Shift Registers, and RAMs. The construction methodis designed to ensure maximum clock rates, while using the minimum of resources, and providing statistical quality at the level of the best software generators. In all platforms tested, the generators are limited in speed only by the clock distribution network or the maximum clockspeed of the underlying RAM primitives, using a platform independent VHDL description with no placement or other hints. The area utilisation is also very low, with a Virtex-5 generator requiring just one Block-RAMand 41 slices to produce 48Gb/s at 550MHz: over 14 times faster than the commonly used Mersenne-Twister RNG on an Opteron at 2.2GHz, while providing the same level of quality


title: From Robotic Hands to Human Hands : A Visualization and Simulation Engine for Grasping Research

abstract: Purpose – Robotic hands are still a long way from matching the grasping and manipulation capability of their human counterparts, but computer simulation may help us understand this disparity. We present our publicly available simulator, and describe our research projects involving the system including the development of a human hand model derived from experimental measurements.Design/methodology/approach – Unlike other simulation systems, our system was built specifically to analyze grasps. It can import a wide variety of robot designs by using standard descriptions of the kinematics and link geometries. Various components support the analysis of grasps, visualization of results, dynamic simulation of grasping tasks, and grasp planning.Findings – The simulator has been used in several grasping research problems and can be used to plan grasps for an actual robot. With the aid of a vision system, we have shown that these grasps can be executed by a robot.Research limitations/implications – We are currently ...


title: Window-Aware Load Shedding for Aggregation Queries over Data Streams

abstract: Data stream management systems may be subject to higher input rates than their resources can handle. When overloaded, the system must shed load in order to maintain low-latency query results. In this paper, we describe a load shedding technique for queries consisting of one or more aggregate operators with sliding windows. We introduce a new type of drop operator, called a "Window Drop". This operator is aware of the window properties (i.e., window size and window slide) of its downstream aggregate operators in the query plan. Accordingly, it logically divides the input stream into windows and probabilistically decides which windows to drop. This decision is further encoded into tuples by marking the ones that are disallowed from starting new windows. Unlike earlier approaches, our approach preserves integrity of windows throughout a query plan, and always delivers subsets of original query answers with minimal degradation in result quality.


title: Robust surface reconstruction via dictionary learning

abstract: Surface reconstruction from point cloud is of great practical importance in computer graphics. Existing methods often realize reconstruction via a few phases with respective goals, whose integration may not give an optimal solution. In this paper, to avoid the inherent limitations of multi-phase processing in the prior art, we propose a unified framework that treats geometry and connectivity construction as one joint optimization problem. The framework is based on dictionary learning in which the dictionary consists of the vertices of the reconstructed triangular mesh and the sparse coding matrix encodes the connectivity of the mesh. The dictionary learning is formulated as a constrained e2,q-optimization (0


title: 3D SLAM using IMU and its observability analysis

abstract: This paper investigates 3-dimensional Simultaneous Localization and Mapping (SLAM) and the corresponding observability analysis by fusing data from landmark sensors and a strap-down Inertial Measurement Unit (IMU) in an adaptive Kalman filter (KF). In addition to the vehicle's states and landmark positions, the self-tuning filter estimates the IMU calibration parameters as well as the covariance of the measurement noise. Examining the observability of the 3D SLAM system leads to the the conclusion that the system remains observable provided that the line connecting the two known landmarks is not collinear with the vector of total acceleration, i.e., the sum of gravitational and inertial accelerations.


Obstacle Detection and Collision Avoidance Using Ultrasonic Distance Sensors for an Autonomous Quadrocopter


Radar-Vision Fusion for Vehicle Detection


title: Free space range measurements with Semtech Lora™ technology

abstract: Although short range wireless communication explicitly targets local and very regional applications, range continues to be an extremely important issue. The range directly depends on the so called link budget, which can be increased by the choice of modulation and coding schemes. Especially, the recent transceiver generation comes with extensive and flexible support for Software Defined Radio (SDR). The SX127x family from Semtech Corp. is a member of this device class and promises significant benefits for range, robust performance, and battery lifetime compared to competing technologies. This contribution gives a short overview into the technologies to support Long Range (LoRa ™), describes the outdoor setup at the Laboratory Embedded Systems and Communication Electronics of Offenburg University of Applied Sciences, shows detailed measurement results and discusses the strengths and weaknesses of this technology.


title: Polymers in Carbon Dots: A Review

abstract: Carbon dots (CDs) have been widely studied since their discovery in 2004 as a green substitute of the traditional quantum dots due to their excellent photoluminescence (PL) and high biocompatibility. Meanwhile, polymers have increasingly become an important component for both synthesis and modification of CDs to provide polymeric matrix and enhance their PL property. Furthermore, critical analysis of composites of CDs and polymers has not been available. Herein, in this review, we summarized the use of polymers in the synthesis and functionalization of CDs, and the applications of these CDs in various fields.


title: Elixir: Effective object-oriented program repair

abstract: This work is motivated by the pervasive use of method invocations in object-oriented (OO) programs, and indeed their prevalence in patches of OO-program bugs. We propose a generate-and-validate repair technique, called ELIXIR designed to be able to generate such patches. ELIXIR aggressively uses method calls, on par with local variables, fields, or constants, to construct more expressive repair-expressions, that go into synthesizing patches. The ensuing enlargement of the repair space, on account of the wider use of method calls, is effectively tackled by using a machine-learnt model to rank concrete repairs. The machine-learnt model relies on four features derived from the program context, i.e., the code surrounding the potential repair location, and the bug report. We implement ELIXIR and evaluate it on two datasets, the popular Defects4J dataset and a new dataset Bugs.jar created by us, and against 2 baseline versions of our technique, and 5 other techniques representing the state of the art in program repair. Our evaluation shows that ELIXIR is able to increase the number of correctly repaired bugs in Defects4J by 85% (from 14 to 26) and by 57% in Bugs.jar (from 14 to 22), while also significantly out-performing other state-of-the-art repair techniques including ACS, HD-Repair, NOPOL, PAR, and jGenProg.


title: Phrase-Indexed Question Answering: A New Challenge for Scalable Document Comprehension

abstract: We formalize a new modular variant of current question answering tasks by enforcing complete independence of the document encoder from the question encoder. This formulation addresses a key challenge in machine comprehension by requiring a standalone representation of the document discourse. It additionally leads to a significant scalability advantage since the encoding of the answer candidate phrases in the document can be pre-computed and indexed offline for efficient retrieval. We experiment with baseline models for the new task, which achieve a reasonable accuracy but significantly underperform unconstrained QA models. We invite the QA research community to engage in Phrase-Indexed Question Answering (PIQA, pika) for closing the gap. The leaderboard is at: nlp.cs.washington.edu/piqa


title: ERIS: A NUMA-Aware In-Memory Storage Engine for Analytical Workload

abstract: The ever-growing demand for more computing power forces hardware vendors to put an increasing number of multiprocessors into a single server system, which usually exhibits a non-uniform memory access (NUMA). In-memory database systems running on NUMA platforms face several issues such as the increased latency and the decreased bandwidth when accessing remote main memory. To cope with these NUMA-related issues, NUMA-awareness has to be considered as a major design principle for the fundamental architecture of a database system. In this paper we present ERIS, a NUMA-aware inmemory storage engine that is based on a data-oriented architecture. In contrast to existing approaches that focus on transactional workloads on a disk-based DBMS, ERIS aims at tera-scale analytical workloads that are executed entirely in main memory. ERIS uses an adaptive partitioning approach that exploits the topology of the underlying NUMA platform and significantly reduces NUMA-related issues. We evaluate ERIS on widespread standard server systems as well as on a system consisting of 64 multiprocessors and 512 cores. On these platforms, we achieve a more than linear speedup for index lookups and scalable parallel scan operations that are only limited by the available local bandwidth of the multiprocessor. Moreover, we measured a performance gain of up to 200% (index lookups) respectively 660% (column scans) in the memory-bound case compared to a NUMA-agnostic storage subsystem.


title: Semantic interpretation of noun compounds using verbal and other paraphrases

abstract: We study the problem of semantic interpretation of noun compounds such as bee honey, malaria mosquito, apple cake, and stem cell. In particular, we explore the potential of using predicates that make explicit the hidden relation that holds between the nouns that form the noun compound. For example, mosquito that carries malaria is a paraphrase of the compound malaria mosquito in which the verb explicitly states the semantic relation between the two nouns. We study the utility of using such paraphrasing verbs, with associated weights, to build a representation of the semantics of a noun compound, for example, malaria mosquito can be represented as follows: carry (23), spread (16), cause (12), transmit (9), and so on. We also explore the potential of using multiple paraphrasing verbs as features for predicting abstract semantic relations such as CAUSE, and we demonstrate that using explicit paraphrases can help improve statistical machine translation.


title: Extinction Learning in Humans Role of the Amygdala and vmPFC

abstract: Understanding how fears are acquired is an important step in translating basic research to the treatment of fear-related disorders. However, understanding how learned fears are diminished may be even more valuable. We explored the neural mechanisms of fear extinction in humans. Studies of extinction in nonhuman animals have focused on two interconnected brain regions: the amygdala and the ventral medial prefrontal cortex (vmPFC). Consistent with animal models suggesting that the amygdala is important for both the acquisition and extinction of conditioned fear, amygdala activation was correlated across subjects with the conditioned response in both acquisition and early extinction. Activation in the vmPFC (subgenual anterior cingulate) was primarily linked to the expression of fear learning during a delayed test of extinction, as might have been expected from studies demonstrating this region is critical for the retention of extinction. These results provide evidence that the mechanisms of extinction learning may be preserved across species.


title: Characterizing privacy in online social networks

abstract: Online social networks (OSNs) with half a billion users have dramatically raised concerns on privacy leakage. Users, often willingly, share personal identifying information about themselves, but do not have a clear idea of who accesses their private information or what portion of it really needs to be accessed. In this study we examine popular OSNs from a viewpoint of characterizing potential privacy leakage. Our study identifies what bits of information are currently being shared, how widely, and what users can do to prevent such sharing. We also examine the role of third-party sites that track OSN users and compare with privacy leakage on popular traditional Web sites. Our long term goal is to identify the narrow set of private information that users really need to share to accomplish specific interactions on OSNs.


title: Deep Knowledge Tracing and Dynamic Student Classification for Knowledge Tracing

abstract: In Intelligent Tutoring System (ITS), tracing the student's knowledge state during learning has been studied for several decades in order to provide more supportive learning instructions. In this paper, we propose a novel model for knowledge tracing that i) captures students' learning ability and dynamically assigns students into distinct groups with similar ability at regular time intervals, and ii) combines this information with a Recurrent Neural Network architecture known as Deep Knowledge Tracing. Experimental results confirm that the proposed model is significantly better at predicting student performance than well known state-of-the-art techniques for student modelling.


title: An Empirical Comparison of Human Value Models

abstract: Over the past century, various value models have been proposed. To determine which value model best predicts prosocial behavior, mental health, and pro-environmental behavior, we subjected seven value models to a hierarchical regression analysis. A sample of University students (N = 271) completed the Portrait Value Questionnaire (Schwartz et al., 2012), the Basic Value Survey (Gouveia, Milfont, Fischer, & Santos, 2008) and the Social Value Orientation scale (Van Lange, De Bruin, Otten, & Joireman, 1997). Additionally, they completed the Values Survey Module (Hofstede & Minkov, 2013), Inglehart’s (1977) materialism – postmaterialism items, the Study of Values, 4th edition (Allport, Vernon, & Lindzey, 1960; Kopelman, Rovenpor, & Guan, 2003) and the Rokeach (1973) Value Survey. However, because the reliability of the latter measures was low, only the PVQ-RR, the BVS, and the SVO where entered into our analysis. Our results provide empirical evidence that the PVQ-RR is the strongest predictor of all three outcome variables, explaining variance above and beyond the other two instruments in almost all cases. The BVS significantly predicted prosocial and pro-environmental behavior, while the SVO only explained variance in pro-environmental behavior.


title: Walking in Facebook: A Case Study of Unbiased Sampling of OSNs

abstract: With more than 250 million active users, Facebook (FB) is currently one of the most important online social networks. Our goal in this paper is to obtain a representative (unbiased) sample of Facebook users by crawling its social graph. In this quest, we consider and implement several candidate techniques. Two approaches that are found to perform well are the Metropolis-Hasting random walk (MHRW) and a re-weighted random walk (RWRW). Both have pros and cons, which we demonstrate through a comparison to each other as well as to the "ground-truth" (UNI - obtained through true uniform sampling of FB userIDs). In contrast, the traditional Breadth-First-Search (BFS) and Random Walk (RW) perform quite poorly, producing substantially biased results. In addition to offline performance assessment, we introduce online formal convergence diagnostics to assess sample quality during the data collection process. We show how these can be used to effectively determine when a random walk sample is of adequate size and quality for subsequent use (i.e., when it is safe to cease sampling). Using these methods, we collect the first, to the best of our knowledge, unbiased sample of Facebook. Finally, we use one of our representative datasets, collected through MHRW, to characterize several key properties of Facebook.


title: BIG DATA PROCESSING: BIG CHALLENGES AND OPPORTUNITIES

abstract: With the rapid growth of emerging applications like social network, semantic web, sensor networks and LBS (Location Based Service) applications, a variety of data to be processed continues to witness a quick increase. Effective management and processing of large-scale data poses an interesting but critical challenge. Recently, big data has attracted a lot of attention from academia, industry as well as government. This paper introduces several big data processing techniques from system and application aspects. First, from the view of cloud data management and big data processing mechanisms, we present the key issues of big data processing, including definition of big data, big data management platform, big data service models, distributed file system, data storage, data virtualization platform and distributed applications. Following the MapReduce parallel processing framework, we introduce some MapReduce optimization strategies reported in the literature. Finally, we discuss the open issues and challenges...


title: RSSI-Based Indoor Localization and Tracking Using Sigma-Point Kalman Smoothers

abstract: Solutions for indoor tracking and localization have become more critical with recent advancement in context and location-aware technologies. The accuracy of explicit positioning sensors such as global positioning system (GPS) is often limited for indoor environments. In this paper, we evaluate the feasibility of building an indoor location tracking system that is cost effective for large scale deployments, can operate over existing Wi-Fi networks, and can provide flexibility to accommodate new sensor observations as they become available. This paper proposes a sigma-point Kalman smoother (SPKS)-based location and tracking algorithm as a superior alternative for indoor positioning. The proposed SPKS fuses a dynamic model of human walking with a number of low-cost sensor observations to track 2-D position and velocity. Available sensors include Wi-Fi received signal strength indication (RSSI), binary infra-red (IR) motion sensors, and binary foot-switches. Wi-Fi signal strength is measured using a receiver tag developed by Ekahau, Inc. The performance of the proposed algorithm is compared with a commercially available positioning engine, also developed by Ekahau, Inc. The superior accuracy of our approach over a number of trials is demonstrated.


title: Template Adaptation for Face Verification and Identification

abstract: Abstract Face recognition performance evaluation has traditionally focused on one-to-one verification, popularized by the Labeled Faces in the Wild data set [1] for imagery and the YouTubeFaces data set [2] for videos. In contrast, the newly released IJB-A face recognition data set [3] unifies evaluation of one-to-many face identification with one-to-one face verification over templates, or sets of imagery and videos for a subject. In this paper, we study the problem of template adaptation, a form of transfer learning to the set of media in a template. Extensive performance evaluations on IJB-A show a surprising result, that perhaps the simplest method of template adaptation, combining deep convolutional network features with template specific linear SVMs, outperforms the state-of-the-art by a wide margin. We study the effects of template size, negative set construction and classifier fusion on performance, then compare template adaptation to convolutional networks with metric learning, 2D and 3D alignment. Our unexpected conclusion is that these other methods, when combined with template adaptation, all achieve nearly the same top performance on IJB-A for template-based face verification and identification.


title: Advancing PICO Element Detection in Biomedical Text via Deep Neural Networks

abstract: In evidence-based medicine (EBM), defining a clinical question in terms of the specific patient problem aids the physicians to efficiently identify appropriate resources and search for the best available evidence for medical treatment. In order to formulate a well-defined, focused clinical question, a framework called PICO is widely used, which identifies the sentences in a given medical text that belong to the four components typically reported in clinical trials: Participants/Problem (P), Intervention (I), Comparison (C) and Outcome (O). In this work, we propose a novel deep learning model for recognizing PICO elements in biomedical abstracts. Based on the previous state-of-the-art bidirectional long-short term memory (biLSTM) plus conditional random field (CRF) architecture, we add another layer of biLSTM upon the sentence representation vectors so that the contextual information from surrounding sentences can be gathered to help infer the interpretation of the current one. In addition, we propose two methods to further generalize and improve the model: adversarial training and unsupervised pre-training over large corpora. We tested our proposed approach over two benchmark datasets. One is the PubMed-PICO dataset, where our best results outperform the previous best by 5.5%, 7.9%, and 5.8% for P, I, and O elements in terms of F1 score, respectively. And for the other dataset named NICTA-PIBOSO, the improvements for P/I/O elements are 2.4%, 13.6%, and 1.0% in F1 score, respectively. Overall, our proposed deep learning model can obtain unprecedented PICO element detection accuracy while avoiding the need for any manual feature selection.


title: Semantic Aware Attention Based Deep Object Co-segmentation

abstract: Object co-segmentation is the task of segmenting the same objects from multiple images. In this paper, we propose the Attention Based Object Co-Segmentation for object co-segmentation that utilize a novel attention mechanism in the bottleneck layer of deep neural network for the selection of semantically related features. Furthermore, we take the benefit of attention learner and propose an algorithm to segment multi-input images in linear time complexity. Experiment results demonstrate that our model achieves state of the art performance on multiple datasets, with a significant reduction of computational time.


title: A non-intrusive Short-Time Objective Intelligibility measure

abstract: We propose a non-intrusive intelligibility measure for noisy and non-linearly processed speech, i.e. a measure which can predict intelligibility from a degraded speech signal without requiring a clean reference signal. The proposed measure is based on the Short-Time Objective Intelligibility (STOI) measure. In particular, the non-intrusive STOI measure estimates clean signal amplitude envelopes from the degraded signal. Subsequently, the STOI measure is evaluated by use of the envelopes of the degraded signal and the estimated clean envelopes. The performance of the proposed measure is evaluated on a dataset including speech in different noise types, processed with binary masks. The measure is shown to predict intelligibility well in all tested conditions, with the exception of those including a single competing speaker. While the measure does not perform as well as the original (intrusive) STOI measure, it is shown to outperform existing non-intrusive measures.


title: A New Visual Speech Recognition Approach for RGB-D Cameras

abstract: Visual speech recognition remains a challenging topic due to various speaking characteristics. This paper proposes a new approach for lipreading to recognize isolated speech segments (words, digits, phrases, etc.) using both of 2D image and depth data. The process of the proposed system is divided into three consecutive steps, namely, mouth region tracking and extraction, motion and appearance descriptors (HOG and MBH) computing, and classification using the Support Vector Machine (SVM) method. To evaluate the proposed approach, three public databases (MIRALC, Ouluvs, and CUAVE) were used. Speaker dependent and speaker independent settings were considered in the evaluation experiments. The obtained recognition results demonstrate that lipreading can be performed effectively, and the proposed approach outperforms recent works in the literature for the speaker dependent setting while being competitive for the speaker independent setting.


title: ExerLink: enabling pervasive social exergames with heterogeneous exercise devices

abstract: We envision that diverse social exercising games, or exergames, will emerge, featuring much richer interactivity with immersive game play experiences. Further, the recent advances of mobile devices and wireless networking will make such social engagement more pervasive - people carry portable exergame devices (e.g., jump ropes) and interact with remote users anytime, anywhere. Towards this goal, we explore the potential of using heterogeneous exercise devices as game controllers for a multi-player social exergame; e.g., playing a boat paddling game with two remote exercisers (one with a jump rope, and the other with a treadmill). In this paper, we propose a novel platform called ExerLink that converts exercise intensity to game inputs and intelligently balances intensity/delay variations for fair game play experiences. We report the design considerations and guidelines obtained from the design and development processes of game controllers. We validate the efficacy of game controllers and demonstrate the feasibility of social exergames with heterogeneous exercise devices via extensive human subject studies.


title: Systolic blood pressure estimation using PPG and ECG during physical exercise.

abstract: In this work, a model to estimate systolic blood pressure (SBP) using photoplethysmography (PPG) and electrocardiography (ECG) is proposed. Data from 19 subjects doing a 40 min exercise was analyzed. Reference SBP was measured at the finger based on the volume-clamp principle. PPG signals were measured at the finger and forehead. After an initialization process for each subject at rest, the model estimated SBP every 30 s for the whole period of exercise. In order to build this model, 18 features were extracted from PPG signals by means of its waveform, first derivative, second derivative, and frequency spectrum. In addition, pulse arrival time (PAT) was derived as a feature from the combination of PPG and ECG. After evaluating four regression models, we chose multiple linear regression (MLR) to combine all derived features to estimate SBP. The contribution of each feature was quantified using its normalized weight in the MLR. To evaluate the performance of the model, we used a leave-one-subject-out cross validation. With the aim of exploring the potential of the model, we investigated the influences of the inclusion of PAT, regression models, measurement sites (finger and forehead), and posture change (lying, sitting, and standing). The results show that the inclusion of PAT reduced the standard deviation (SD) of the difference from 14.07 to 13.52 mmHg. There was no significant difference in the estimation performance between the model using finger- and forehead-derived PPG signals. Separate models are necessary for different postures. The optimized model using finger-derived PPG signals during physical exercise had a performance with a mean difference of 0.43 mmHg, an SD of difference of 13.52 mmHg, and median correlation coefficients of 0.86. Furthermore, we identified two groups of features that contributed more to SBP estimation compared to other features. One group consists of our proposed features depicting beat morphology. The other comprises existing features depicting the dicrotic notch. The present work demonstrates promising results of the SBP estimation model during physical exercise.


title: Improved recognition of contact names in voice commands

abstract: The recognition of contact names in mobile-device voice commands is a challenging problem. Some of the difficulties include potentially infinite vocabularies, low probability of contact tokens in the language model (LM), increased false triggering of contact voice commands when none are spoken, and very large and noisy contact name lists. In this paper we suggest solutions for each of these difficulties.


Larva migrans: an overview.


title: Invertebrates in Permanent Wetlands (Long-Hydroperiod Marshes and Shallow Lakes)

abstract: Invertebrate communities in permanent wetlands are influenced by many abiotic factors, but longer hydroperiods and greater depths in these habitats increase the importance of biotic interactions. Fish have strong effects on invertebrates here via direct predation and also by influencing whether wetlands will be submerged macrophyte-dominated, or in an alternative phytoplankton-dominated state. However, fish presence and abundance varies dramatically among permanent wetlands, and reflects landscape-level features, especially connectivity among waterbodies. Resulting invertebrate communities in permanent wetlands are controlled by a complex suite of interacting biotic and abiotic factors that include water chemistry, fish predation, alternative stable states, and landscape features.


title: MistForm: Adaptive Shape Changing Fog Screens

abstract: We present MistForm, a shape changing fog display that can support one or two users interacting with either 2D or 3D content. Mistform combines affordances from both shape changing interfaces and mid-air displays. For example, a concave display can maintain content in comfortable reach for a single user, while a convex shape can support several users engaged on individual tasks. MistForm also enables unique interaction possibilities by exploiting the synergies between shape changing interfaces and mid-air fog displays. For instance, moving the screen will affect the brightness and blurriness of the screen at specific locations around the display, creating spaces with similar (collaboration) or different visibility (personalized content). We describe the design of MistForm and analyse its inherent challenges, such as image distortion and uneven brightness on dynamic curved surfaces. We provide a machine learning approach to characterize the shape of the screen and a rendering algorithm to remove aberrations. We finally explore novel interactive possibilities and reflect on their potential and limitations.


title: Online anonymity for personalized web services

abstract: To receive personalized web services, the user has to provide personal information and preferences, in addition to the query itself, to the web service. However, detailed personal information could identify the sender of sensitive queries, thus compromise user privacy. We propose the notion of online anonymity to enable users to issue personalized queries to an untrusted web service while with their anonymity preserved. The challenge for providing online anonymity is dealing with unknown and dynamic web users who can get online and offline at any time. We define this problem, discuss its implications and differences from the problems in the literature, and propose a solution.


title: Unseen noise estimation using separable deep auto encoder for speech enhancement

abstract: Unseen noise estimation is a key yet challenging step to make a speech enhancement algorithm work in adverse environments. At worst, the only prior knowledge we know about the encountered noise is that it is different from the involved speech. Therefore, by subtracting the components which cannot be adequately represented by a well defined speech model, the noises can be estimated and removed. Given the good performance of deep learning in signal representation, a deep auto encoder (DAE) is employed in this work for accurately modeling the clean speech spectrum. In the subsequent stage of speech enhancement, an extra DAE is introduced to represent the residual part obtained by subtracting the estimated clean speech spectrum (by using the pre-trained DAE) from the noisy speech spectrum. By adjusting the estimated clean speech spectrum and the unknown parameters of the noise DAE, one can reach a stationary point to minimize the total reconstruction error of the noisy speech spectrum. The enhanced speech signal is thus obtained by transforming the estimated clean speech spectrum back into time domain. The above proposed technique is called separable deep auto encoder (SDAE). Given the under-determined nature of the above optimization problem, the clean speech reconstruction is confined in the convex hull spanned by a pre-trained speech dictionary. New learning algorithms are investigated to respect the non-negativity of the parameters in the SDAE. Experimental results on TIMIT with 20 noise types at various noise levels demonstrate the superiority of the proposed method over the conventional baselines.


title: Learning shape correspondence with anisotropic convolutional neural networks

abstract: Establishing correspondence between shapes is a fundamental problem in geometry processing, arising in a wide variety of applications. The problem is especially difficult in the setting of non-isometric deformations, as well as in the presence of topological noise and missing parts, mainly due to the limited capability to model such deformations axiomatically. Several recent works showed that invariance to complex shape transformations can be learned from examples. In this paper, we introduce an intrinsic convolutional neural network architecture based on anisotropic diffusion kernels, which we term Anisotropic Convolutional Neural Network (ACNN). In our construction, we generalize convolutions to non-Euclidean domains by constructing a set of oriented anisotropic diffusion kernels, creating in this way a local intrinsic polar representation of the data (`patch'), which is then correlated with a filter. Several cascades of such filters, linear, and non-linear operators are stacked to form a deep neural network whose parameters are learned by minimizing a task-specific cost. We use ACNNs to effectively learn intrinsic dense correspondences between deformable shapes in very challenging settings, achieving state-of-the-art results on some of the most difficult recent correspondence benchmarks.


title: An initialization method to simultaneously find initial cluster centers and the number of clusters for clustering categorical data

abstract: The leading partitional clustering technique, k-modes, is one of the most computationally efficient clustering methods for categorical data. However, in the k-modes-type algorithms, the performance of their clustering depends on initial cluster centers and the number of clusters needs be known or given in advance. This paper proposes a novel initialization method for categorical data which is implemented to the k-modes-type algorithms. The proposed method can not only obtain the good initial cluster centers but also provide a criterion to find candidates for the number of clusters. The performance and scalability of the proposed method has been studied on real data sets. The experimental results illustrate that the proposed method is effective and can be applied to large data sets for its linear time complexity with respect to the number of data points.


title: Learning to Search: Functional Gradient Techniques for Imitation Learning

abstract: Programming robot behavior remains a challenging task. While it is often easy to abstractly define or even demonstrate a desired behavior, designing a controller that embodies the same behavior is difficult, time consuming, and ultimately expensive. The machine learning paradigm offers the promise of enabling "programming by demonstration" for developing high-performance robotic systems. Unfortunately, many "behavioral cloning" (Bain and Sammut in Machine intelligence agents. London: Oxford University Press, 1995; Pomerleau in Advances in neural information processing systems 1, 1989; LeCun et al. in Advances in neural information processing systems 18, 2006) approaches that utilize classical tools of supervised learning (e.g. decision trees, neural networks, or support vector machines) do not fit the needs of modern robotic systems. These systems are often built atop sophisticated planning algorithms that efficiently reason far into the future; consequently, ignoring these planning algorithms in lieu of a supervised learning approach often leads to myopic and poor-quality robot performance. ::: ::: While planning algorithms have shown success in many real-world applications ranging from legged locomotion (Chestnutt et al. in Proceedings of the IEEE-RAS international conference on humanoid robots, 2003) to outdoor unstructured navigation (Kelly et al. in Proceedings of the international symposium on experimental robotics (ISER), 2004; Stentz et al. in AUVSI's unmanned systems, 2007), such algorithms rely on fully specified cost functions that map sensor readings and environment models to quantifiable costs. Such cost functions are usually manually designed and programmed. Recently, a set of techniques has been developed that explore learning these functions from expert human demonstration. These algorithms apply an inverse optimal control approach to find a cost function for which planned behavior mimics an expert's demonstration. ::: ::: The work we present extends the Maximum Margin Planning (MMP) (Ratliff et al. in Twenty second international conference on machine learning (ICML06), 2006a) framework to admit learning of more powerful, non-linear cost functions. These algorithms, known collectively as LEARCH (LEArning to seaRCH), are simpler to implement than most existing methods, more efficient than previous attempts at non-linearization (Ratliff et al. in NIPS, 2006b), more naturally satisfy common constraints on the cost function, and better represent our prior beliefs about the function's form. We derive and discuss the framework both mathematically and intuitively, and demonstrate practical real-world performance with three applied case-studies including legged locomotion, grasp planning, and autonomous outdoor unstructured navigation. The latter study includes hundreds of kilometers of autonomous traversal through complex natural environments. These case-studies address key challenges in applying the algorithm in practical settings that utilize state-of-the-art planners, and which may be constrained by efficiency requirements and imperfect expert demonstration.


title: FX trading via recurrent reinforcement learning

abstract: This study investigates high frequency currency trading with neural networks trained via recurrent reinforcement learning (RRL). We compare the performance of single layer networks with networks having a hidden layer and examine the impact of the fixed system parameters on performance. In general, we conclude that the trading systems may be effective, but the performance varies widely for different currency markets and this variability cannot be explained by simple statistics of the markets. Also we find that the single layer network outperforms the two layer network in this application.


Frequency Domain Enhancement Algorithm Based on Log-Gabor Filter in FFT Domain By Mrs


title: Experimental Study of Thermal Runaway Process of 18650 Lithium-Ion Battery

abstract: This study addresses the effects of the SOC (State of Charge) and the charging–discharging process on the thermal runaway of 18650 lithium-ion batteries. A series of experiments were conducted on an electric heating and testing apparatus. The experimental results indicate that 6 W is the critical heating power for 40% SOC. With a 20 W constant heating rate, the thermal runaway initial temperature of the lithium-ion battery decreases with the increasing SOC. The final thermal runaway temperature increases with the SOC when the SOC is lower than 80%. However, a contrary conclusion was obtained when the SOC was higher than 80%. Significant mass loss, accompanied by an intense exothermic reaction, took place under a higher SOC. The critical charging current, beyond which the thermal runaway occurs, was found to be 2.6 A. The thermal runaway initial temperature decreases with the increasing charging current, while the intensity of the exothermic reaction varies inversely. Mass ejection of gas and electrolytes exists during thermal runaway when the charging current is higher than 10.4 A, below which only a large amount of gas is released. The thermal runaway initial temperature of discharging is higher than that of non-discharging.


An Intelligent Approach for Anti-Spoofing in a Multimodal Biometric System


Linking WordNet to 3 D Shapes


title: Women at the top: Powerful leaders define success as work + family in a culture of gender.

abstract: How do women rise to the top of their professions when they also have significant family care responsibilities? This critical question has not been addressed by existing models of leadership. In a review of recent research, we explore an alternative model to the usual notion of a Western male as the prototypical leader. The model includes (a) relationship-oriented leadership traits, (b) the importance of teamwork and consensus building, and (c) an effective work-family interface that women with family care responsibilities create and use to break through the glass ceiling. We adopted a cross-cultural perspective to highlight the importance of relational orientation and work-family integration in collectivistic cultures, which supplements models of leadership based on Western men. Our expanded model of leadership operates in the context of a "culture of gender" that defines expectations for women and men as leaders. This complex model includes women in diverse global contexts and enriches our understanding of the interplay among personal attributes, processes, and environments in leadership.


title: Ubiquitous Smart Home System Using Android Application

abstract: This paper presents a flexible standalone, low-cost smart home system, which is based on the Android app communicating with the micro-web server providing more than the switching functionalities. The Arduino Ethernet is used to eliminate the use of a personal computer (PC) keeping the cost of the overall system to a minimum while voice activation is incorporated for switching functionalities. Devices such as light switches, power plugs, temperature sensors, humidity sensors, current sensors, intrusion detection sensors, smoke/gas sensors and sirens have been integrated in the system to demonstrate the feasibility and effectiveness of the proposed smart home system. The smart home app is tested and it is able to successfully perform the smart home operations such as switching functionalities, automatic environmental control and intrusion detection, in the later case where an email is generated and the siren goes on.


title: A comparison of feature selection approach between greedy, IG-ratio, Chi-square, and mRMR in educational mining

abstract: Educational data mining is a widely interesting issue in data mining research field. One of the topics is feature selection method to reduce a feature set. The main purpose of this study is to compare feature selection methods for the efficiency of student performance prediction improvement. In this research, we proposed 4 feature selection methods: greedy algorithm, Information gain ratio, chi-square, and mRMR that combine with 4 classification models. The example data were 6,882 engineering students in Rajamangala University of Technology Thanyaburi, Thailand from year 2004 to 2010. The experiments demonstrate the effectiveness of the feature selection method in classification of student performance prediction. The result shows that greedy forward selection with neural network classification model presents the best efficiency couple with 91.16% accuracy.


Cross-lingual Parser Selection for Low-resource Languages


title: The protégé project: a look back and a look forward

abstract: Few funded research projects in computer science go on for decades. It’s even questionable whether any project should be sustained that long. Nevertheless, the Protege project at Stanford University began in the 1980s and is still going strong, helping developers to construct reusable ontologies and to build knowledge-based systems. The recognition of our work at the International Semantic Web Conference in October 2014 with the “Ten Years” Award was a great honor. The award has provided an opportunity f o r reflection—both on the Protege project itself and on the need for computational infrastructure in the AI community. ::: ::: Protege has become the most widely used software for building and maintaining ontologies. It is by no means the only solution, and there are known problems with Protege, but we appreciate that the system is extremely popular. Although his study is now a bit dated, Cardoso (2007) surveyed the Semantic Web community and found that two-thirds of his respondents used Protege. To date, more than 250,000 people have registered to use the software. Many Fortune 500 companies use Protege to build their ontologies. Important government projects, such as the development of the National Cancer Institute Thesaurus (Noy et al., 2008; Figure 1) and the World Health Organization’s International Classification of Diseases (ICD-11; Tudorache et al., 2010) depend on the software. ::: ::: ::: ::: Figure 1 ::: ::: The Protege 5 Desktop System. The figure shows the most recent version of the Protege system used to edit the National Cancer Institute Thesaurus. In the Figure, the user has selected the class Antigen Gene. The visualization ... ::: ::: ::: ::: Protege currently exists in a variety of frameworks. A desktop system (Protege 5) supports many advanced features to enable the construction and management of OWL ontologies (see Figure 1). A Web-based system (WebProtege) offers distributed access over the Internet using any Web browser and, by design, is much simpler to use for many ontology-engineering tasks (Figure 2). The Web-based version has become extremely popular, and it recently exceeded the desktop-client in its degree of usage. It is extremely handy to be able to point a Web browser to an appropriate server and to begin editing. Like a Google doc, a WebProtege ontology can be easily shared with a distributed group of users who can engage in collaborative authoring activities from wherever they happen to be logged in. The development environment used by the World Health Organization to manage ICD-11 is based on WebProtege (Tudorache et al., 2010). ::: ::: ::: ::: Figure 2 ::: ::: WebProtege. The Web-based version of Protege offers users and their collaborators the opportunity to share and edit ontologies online, much like a Google doc. Here we see the Ontology for Parasite Lifecycle (OPL), an ontology ... ::: ::: ::: ::: Older versions of the Protege desktop system have included support for editing ontologies represented in a frame language (namely, in the OKBC framework developed by the DARPA Knowledge Sharing Initiative in the 1990s; Neches et al., 1991). Comparable functionality has not yet been migrated to current versions of Protege, however. All versions of Protege may be downloaded from the project’s Web site (http://protege.stanford.edu). They are available under an open-source license. ::: ::: The paper for which the Protege team won the “Ten Years” Award (Knublauch et al., 2004) describes the first Protege system to support the World Wide Web Consortium’s recommended Web Ontology Language (OWL). We had been tracking the emerging OWL specification, and Holger Knublauch, then a post-doctoral fellow in our laboratory, worked with the rest of the team to extend Protege to create what was, at the time, the only ontology-development platform that could accommodate nearly the complete OWL specification. Protege’s support for OWL has been enhanced over the years, particularly through a very successful collaboration with Alan Rector’s CO-ODE project at the University of Manchester, and recent versions of Protege fully support the latest OWL 2.0 specification. ::: ::: When people think of Protege, they think of an editor for ontologies, and they think of OWL. Protege did not start out this way, however. Gennari et al. (2003) traced the history of the first 15 years of the Protege project, documenting the many shifts in perspective as Protege progressed from a student project for a Ph.D. dissertation that focused on problems of building knowledge-based systems (Musen, 1989) to a major open-source platform supported by a huge community of diverse users (Musen, 2005).


title: Textual resource acquisition and engineering

abstract: A key requirement for high-performing question-answering (QA) systems is access to high-quality reference corpora from which answers to questions can be hypothesized and evaluated. However, the topic of source acquisition and engineering has received very little attention so far. This is because most existing systems were developed under organized evaluation efforts that included reference corpora as part of the task specification. The task of answering Jeopardy!™ questions, on the other hand, does not come with such a well-circumscribed set of relevant resources. Therefore, it became part of the IBM Watson™ effort to develop a set of well-defined procedures to acquire high-quality resources that can effectively support a high-performing QA system. To this end, we developed three procedures, i.e., source acquisition, source transformation, and source expansion. Source acquisition is an iterative development process of acquiring new collections to cover salient topics deemed to be gaps in existing resources based on principled error analysis. Source transformation refers to the process in which information is extracted from existing sources, either as a whole or in part, and is represented in a form that the system can most easily use. Finally, source expansion attempts to increase the coverage in the content of each known topic by adding new information as well as lexical and syntactic variations of existing information extracted from external large collections. In this paper, we discuss the methodology that we developed for IBM Watson for performing acquisition, transformation, and expansion of textual resources. We demonstrate the effectiveness of each technique through its impact on candidate recall and on end-to-end QA performance.


title: Design and control of a multifunction myoelectric hand with new adaptive grasping and self-locking mechanisms

abstract: This paper presents a multifunction myoelectric hand that is designed with underactuated mechanisms. The linger design allows an adaptive grasp, including adaptation between lingers and phalanges with respect to the shape of an object. In addition, a self-lock is embedded in the metacarpophalangeal joint to prevent back driving when external forces act on the lingers. The thumb design also provides adaptation between phalanges and adds an intermittent rotary motion to the carpometacarpal joint. As a result, the hand can perform versatile grasping motions using only two motors, and is capable of natural and stable grasping without complex sensor and servo systems. Moreover, the adaptive grasping capabilities reduce the requirements of electromyogram pattern recognition, as analogous motions, such as cylindrical and tip grasps, can be classified as one motion.


title: A Reformulation of the Dual Career Conceptual Model for Analysis in an Organizational Scope: Revealing new Aspects

abstract: Couples who live a dual career, in general, are characterized by their continuing professional engagement and their desire for personal growth together. It is a synergy between career aspirations and family sphere, so that they co-exist; reflecting nowadays, a challenge for people who seek to live this duality. Not exempt from it, it is possible to understand the need for management models of people who are in harmony with the desires of dual career couples who are part of organizations. If in the 1980s the existence of dual career couples was not so common in Brazil, nowadays organizations increasingly receive these couples, which impacts the need for people management models to keep up with these social changes. Therefore, the model recognizes that the personal dimension (impacts) on the organizational context cannot be avoided, and also that other factors affect both spheres (personal and organizational) when referring to the normative roles that permeate these areas. The main intention of this essay is to construct a theoretical model of dual career to consider the factor - organization, as vital to understand (and accept) the need to consider other dimensions on the dual career analytical perspective. The first evidences of dual career studies in Brazil revealed that the look at this movement only from the individual's margin is limited. This way, to consider the existence of other dimensions and consequently the influences they may cause, favors an expansion of the perspective, and also brings a detailing about the external factors (organization, society and culture) that influence the dual career couple. To consider that this couple, as well as having personal challenges in the relationship between work and family, is subject to the culture that regulates their roles (men and women) and that directly influences how organizations will handle that topic reveals the merit of this study. This, in turn, draws attention to the organizational sphere and shows that it cannot be detached from this reality. Considering that people management policies, that include dual career couples, are emerging.


title: An application of analytic hierarchy process (AHP) to investment portfolio selection in the banking sector of the Nigerian capital market

abstract: The importance of investment to the individual, a nation and the world economy cannot be over emphasized. Investment involves the sacrifice of immediate consumption to achieve greater consumption in the future. The Nigerian banking sector has made tremendous success in the recent past. All the banks that approached the capital market through public offers and right issues to raise their capital base recorded huge success. Investors in bank stocks have enjoyed high returns on investment, despite the slow growth in the nation’s economy. However, the recent financial crisis that started in America, which has caused economy meltdown in many nations of the world and sudden fall in share prices, has brought about a higher risk than envisaged on investors, particularly those investing in bank stocks. This study underlines the importance of different criteria, factors and alternatives that are essential to successful investment decisions by applying the Analytic Hierarchy Process (AHP) in the selection of investment in bank stocks in the circumstances of financial crisis. The study provides recommendation on strategic investment decision options. ::: ::: ::: ::: Key words: Application, analytic hierarchy process, investment, portfolio selection, capital market.


title: Natural Language Processing for Conceptual Modeling

abstract: A semi-automated approach for the design of databases in enhanced-ERD notation is presented. It focuses on the very early stage of the database development which is the stage of user requirement analysis. It is supposed to be used between the requirements determination stage and analysis. The approach provides the opportunity of using natural language text documents as a source of knowledge for semi-automated generation of a conceptual data model. The system performs information extraction by parsing the syntax of the sentences and semantically analyzing their content.


title: Fast spectral methods for ratio cut partitioning and clustering

abstract: Partitioning of circuit netlists in VLSI design is considered. It is shown that the second smallest eigenvalue of a matrix derived from the netlist gives a provably good approximation of the optimal ratio cut partition cost. It is also demonstrated that fast Lanczos-type methods for the sparse symmetric eigenvalue problem are a robust basis for computing heuristic ratio cuts based on the eigenvector of this second eigenvalue. Effective clustering methods are an immediate by-product of the second eigenvector computation and are very successful on the difficult input classes proposed in the CAD literature. The intersection graph representation of the circuit netlist is considered, as a basis for partitioning, a heuristic based on spectral ratio cut partitioning of the netlist intersection graph is proposed. The partitioning heuristics were tested on industry benchmark suites, and the results were good in terms of both solution quality and runtime. Several types of algorithmic speedups and directions for future work are discussed. >


title: Reinforcement Learning for Pivoting Task

abstract: In this work we propose an approach to learn a robust policy for solving the pivoting task. Recently, several model-free continuous control algorithms were shown to learn successful policies withou ...


title: A Self-Modifying Cellular Automaton Model of Historical Urbanization in the San Francisco Bay Area

abstract: In this paper we describe a cellular automaton (CA) simulation model developed to predict urban growth as part of a project for estimating the regional and broader impact of urbanization on the San Francisco Bay area's climate. The rules of the model are more complex than those of a typical CA and involve the use of multiple data sources, including topography, road networks, and existing settlement distributions, and their modification over time. In addition, the control parameters of the model are allowed to self-modify: that is, the CA adapts itself to the circumstances it generates, in particular, during periods of rapid growth or stagnation. In addition, the model was written to allow the accumulation of probabilistic estimates based on Monte Carlo methods. Calibration of the model has been accomplished by the use of historical maps to compare model predictions of urbanization, based solely upon the distribution in year 1900, with observed data for years 1940, 1954, 1962, 1974, and 1990. The complexity of this model has made calibration a particularly demanding step. Lessons learned about the methods, measures, and strategies developed to calibrate the model may be of use in other environmental modeling contexts. With the calibration complete, the model is being used to generate a set of future scenarios for the San Francisco Bay area along with their probabilities based on the Monte Carlo version of the model. Animated dynamic mapping of the simulations will be used to allow visualization of the impact of future urban growth.


title: Fluoroquinolone resistance: mechanisms, impact on bacteria, and role in evolutionary success

abstract: Quinolone and fluoroquinolone antibiotics are potent, broad-spectrum agents commonly used to treat a range of infections. Resistance to these agents is multifactorial and can be via one or a combination of target-site gene mutations, increased production of multidrug-resistance (MDR) efflux pumps, modifying enzymes, and/or target-protection proteins. Fluoroquinolone-resistant clinical isolates of bacteria have emerged readily and recent data have shown that resistance to this class of antibiotics can have diverse, species-dependent impacts on host-strain fitness. Here we outline the impacts of quinolone-resistance mutations in relation to the fitness and evolutionary success of mutant strains.


title: Break Ames room illusion: depth from general single images

abstract: Photos compress 3D visual data to 2D. However, it is still possible to infer depth information even without sophisticated object learning. We propose a solution based on small-scale defocus blur inherent in optical lens and tackle the estimation problem by proposing a non-parametric matching scheme for natural images. It incorporates a matching prior with our newly constructed edgelet dataset using a non-local scheme, and includes semantic depth order cues for physically based inference. Several applications are enabled on natural images, including geometry based rendering and editing.


title: Determinants of consumer food waste behaviour: Two routes to food waste

abstract: Approximately one quarter of the food supplied for human consumption is wasted across the food supply chain. In the high income countries, the food waste generated at the household level represents about half of the total food waste, making this level one of the biggest contributors to food waste. Yet, there is still little evidence regarding the determinants of consumers' food waste behaviour. The present study examines the effect of psycho-social factors, food-related routines, household perceived capabilities and socio-demographic characteristics on self-reported food waste. Survey data gathered among 1062 Danish respondents measured consumers' intentions not to waste food, planning, shopping and reuse of leftovers routines, perceived capability to deal with household food-related activities, injunctive and moral norms, attitudes towards food waste, and perceived behavioural control. Results show that perceived behavioural control and routines related to shopping and reuse of leftovers are the main drivers of food waste, while planning routines contribute indirectly. In turn, the routines are related to consumers' perceived capabilities to deal with household related activities. With regard to intentional processes, injunctive norms and attitudes towards food waste have an impact while moral norms and perceived behavioural control make no significant contribution. Implications of the study for initiatives aimed at changing consumers' food waste behaviour are discussed.


title: Feature fusion for fake Indian currency detection

abstract: Fake currency detection is a serious issue worldwide, affecting the economy of almost every country including India. The possible solutions are to use either chemical properties of the currency or to use its physical appearance. The approach presented in this paper is based upon physical appearance of the Indian currency. Image processing algorithms have been adopted to extract the features such as security thread, intaglio printing(RBI logo) and identification mark, which have been adopted as security features of Indian currency. To make the system more robust and accurate, the decisive score of all the three features has been fused to differentiate between real and fake currencies. The fake currency detection accuracy of the proposed system is 100%. Another parameter used to measure the performance of the proposed system is mean square error, which is approximately 1%. It may be adopted by the common people as well, who quite often face the problem of differentiating between real and fake currencies.


title: An architecture for a text simplification system

abstract: We present a pipelined architecture for a text simplification system and describe our implementation of the three stages-analysis, transformation and regeneration. Our architecture allows each component to be developed and evaluated independently. We lay particular emphasis on the discourse level aspects of syntactic simplification as these are crucial to the process and have not been dealt with by previous research in the field. These aspects include generating referring expressions, deciding determiners, deciding sentence order and preserving rhetorical and anaphoric structure.


title: Role of cortical cell type and morphology in subthreshold and suprathreshold uniform electric field stimulation in vitro

abstract: Background The neocortex is the most common target of subdural electrotherapy and noninvasive brain stimulation modalities, including transcranial magnetic stimulation (TMS) and transcranial current simulation (TCS). Specific neuronal elements targeted by cortical stimulation are considered to underlie therapeutic effects, but the exact cell type(s) affected by these methods remains poorly understood. Objective We determined whether neuronal morphology or cell type predicted responses to subthreshold and suprathreshold uniform electric fields. Methods We characterized the effects of subthreshold and suprathreshold electrical stimulation on identified cortical neurons in vitro . Uniform electric fields were applied to rat motor cortex brain slices, while recording from interneurons and pyramidal cells across cortical layers, using a whole-cell patch clamp. Neuron morphology was reconstructed after intracellular dialysis of biocytin. Based solely on volume-weighted morphology, we developed a parsimonious model of neuronal soma polarization by subthreshold electric fields. Results We found that neuronal morphology correlated with somatic subthreshold polarization. Based on neuronal morphology, we predict layer V pyramidal neuronal soma to be individually the most sensitive to polarization by optimally oriented subthreshold fields. Suprathreshold electric field action potential threshold was shown to reflect both direct cell polarization and synaptic (network) activation. Layer V/VI neuron absolute electric field action potential thresholds were lower than layer II/III pyramidal neurons and interneurons. Compared with somatic current injection, electric fields promoted burst firing and modulated action potential firing times. Conclusions We present experimental data indicating that cortical neuron morphology relative to electric fields and cortical cell type are factors in determining sensitivity to sub- and supra-threshold brain stimulation.