title: Frank-Wolfe Algorithms for Saddle Point Problems

abstract: We extend the Frank-Wolfe (FW) optimization algorithm to solve constrained smooth convex-concave saddle point (SP) problems. Remarkably, the method only requires access to linear minimization oracles. Leveraging recent advances in FW optimization, we provide the first proof of convergence of a FW-type saddle point solver over polytopes, thereby partially answering a 30 year-old conjecture. We also survey other convergence results and highlight gaps in the theoretical underpinnings of FW-style algorithms. Motivating applications without known efficient alternatives are explored through structured prediction with combinatorial penalties as well as games over matching polytopes involving an exponential number of constraints.


title: Centaur: A Framework for Hybrid CPU-FPGA Databases

abstract: Accelerating relational databases in general and SQL in particular has become an important topic given thechallenges arising from large data collections and increasinglycomplex workloads. Most existing work, however, has beenfocused on either accelerating a single operator (e.g., a join) orin data reduction along the data path (e.g., from disk to CPU). In this paper we focus instead on the system aspects of accelerating a relational engine in hybrid CPU-FPGA architectures. In particular, we present Centaur, a framework running on theFPGA that allows the dynamic allocation of FPGA operatorsto query plans, pipelining these operators among themselveswhen needed, and the hybrid execution of operator pipelinesrunning on the CPU and the FPGA. Centaur is fully compatiblewith relational engines as we demonstrate through its seamlessintegration with MonetDB, a popular column store database. Inthe paper, we describe how this integration is achieved, andempirically demonstrate the advantages of such an approach. The main contribution of the paper is to provide a realisticsolution for accelerating SQL that is compatible with existingdatabase architectures, thereby opening up the possibilities forfurther exploration of FPGA based data processing.


title: Dental metal allergy in patients with oral, cutaneous, and genital lichenoid reactions.

abstract: BACKGROUND ::: The subject of lichen planus (LP) and dental metal allergy long has been debated. An overwhelming majority of the existing literature focuses on mercury and gold salts in relation to oral lichen planus. ::: ::: ::: OBJECTIVE ::: Our objective was to expand current knowledge regarding LP and lichenoid lesions (LL) and dental metal allergy by investigating more metals and investigating cutaneous and genital disease in addition to oral disease. ::: ::: ::: METHODS ::: Fifty-one patients with known LP or LL were patch tested to a series of dental metals. Patients chose to replace their dental metals or make no revision. A telephone survey was conducted after 1 year to determine disease state. ::: ::: ::: RESULTS ::: Thirty-eight of 51 patients (74.5%) had at least 1 positive reaction. Twenty-five of 51 patients (49.0%) showed sensitivity to at least 1 mercurial allergen. Prevalence data for patients patch tested by the North American Contact Dermatitis Group (NACDG) from 1996 to 1998 was available for chromate, cobalt, gold, nickel, and thimerosal. The prevalence of positive reactions was higher in our group than in the NACDG group for all 5 of these allergens, and statistical significance was achieved for chromate (P = .028), gold (P = .041), and thimerosal (P = .005). Of patients who had a positive patch test reaction to 1 or more metals, 100% (9 of 9) reported improvement after metal replacement, whereas 62.5% (15 of 24) reported improvement without metal replacement. ::: ::: ::: CONCLUSION ::: Sensitization to dental metals is more common among LP and LL patients than in routinely tested patients, and might be an etiologic or triggering factor in the disease.


Coarse-Grained Network Simulation for Wide-Area Distributed Systems


title: Effects of noise exposure on click detection and the temporal resolution ability of the goldfish auditory system

abstract: Hearing specialist fishes investigated so far revealed excellent temporal resolution abilities, enabling them to accurately process temporal patterns of sounds. Because noise is a growing environmental problem, we investigated how it affects the temporal resolution ability of goldfish. Auditory evoked potentials (AEPs) in response to clicks and double clicks were recorded before exposing, immediately after exposing the fish to white noise of 158 dB re 1 microPa for 24 h, and after 3, 7 and 14 days of recovery. Immediately after noise exposure, hearing sensitivity to clicks was reduced on average by 21 dB and recovered within 1 week. Amplitudes of the AEPs decreased by about 71% while latencies increased by 0.63 ms. Both AEP characteristics returned to baseline values within 2 weeks. Analysis of the response to double clicks showed that the minimum click period resolvable by the auditory system increased significantly from 1.25 to 2.08 ms immediately after noise exposure. After a recovery period of 3 days, this minimum period returned to pre-exposure values. The present study revealed that noise exposure affects the detection of short transient signals and the temporal resolution ability. Because acoustic information is primarily encoded via temporal patterns of sounds in fishes, environmental noise could severely impair acoustic orientation and communication.


title: Comparative text analytics via topic modeling in banking

abstract: In this paper, we compare and evaluate multiple topic modeling approaches and their effectiveness in analyzing a large set of SEC filings by US public banks. More specifically, we apply four major topic modeling methods to a corpus of 8-K and 10-K filings, from the years 2005–2016, of 578 bank holding companies. These methods include Principal Component Analysis, Non-negative Matrix Factorization, Latent Dirichlet Allocation and KATE, a novel k-competitive autoencoder for text documents. Separately for 8-K and 10-K, the usefulness and effectiveness of these methods is evaluated by comparing their performances on two classification tasks: (i) predicting which section each document corresponds to, where we consider each section within an 8-K or 10-K filing as an individual document, and (ii) detecting text from a bank's year of failure, a task for which we use bank failure data from the 2008 financial crisis. In addition, we qualitatively compare the topics discovered by the different methods. We conclude that topic modeling can be an effective tool in financial decision making and risk management.


title: Multimodal Machine Translation with Reinforcement Learning

abstract: Multimodal machine translation is one of the applications that integrates computer vision and language processing. It is a unique task given that in the field of machine translation, many state-of-the-arts algorithms still only employ textual information. In this work, we explore the effectiveness of reinforcement learning in multimodal machine translation. We present a novel algorithm based on the Advantage Actor-Critic (A2C) algorithm that specifically cater to the multimodal machine translation task of the EMNLP 2018 Third Conference on Machine Translation (WMT18). We experiment our proposed algorithm on the Multi30K multilingual English-German image description dataset and the Flickr30K image entity dataset. Our model takes two channels of inputs, image and text, uses translation evaluation metrics as training rewards, and achieves better results than supervised learning MLE baseline models. Furthermore, we discuss the prospects and limitations of using reinforcement learning for machine translation. Our experiment results suggest a promising reinforcement learning solution to the general task of multimodal sequence to sequence learning.


A Load-Balanced Deterministic Runtime for Pipeline Parallelism


title: Randomized General Regression Network for Identification of Defect Patterns in Semiconductor Wafer Maps

abstract: Defect detection and classification in semiconductor wafers has received an increasing attention from both industry and academia alike. Wafer defects are a serious problem that could cause massive losses to the companies’ yield. The defects occur as a result of a lengthy and complex fabrication process involving hundreds of stages, and they can create unique patterns. If these patterns were to be identified and classified correctly, then the root of the fabrication problem can be recognized and eventually resolved. Machine learning (ML) techniques have been widely accepted and are well suited for such classification-/identification problems. However, none of the existing ML model’s performance exceeds 96% in identification accuracy for such tasks. In this paper, we develop a state-of-the-art classifying algorithm using multiple ML techniques, relying on a general-regression-network-based consensus learning model along with a powerful randomization technique. We compare our proposed method with the widely used ML models in terms of model accuracy, stability, and time complexity. Our method has proved to be more accurate and stable as compared to any of the existing algorithms reported in the literature, achieving its accuracy of 99.8%, stability of 1.128, and TBM of 15.8 s.


title: Review-based measurement of customer satisfaction in mobile service: Sentiment analysis and VIKOR approach

abstract: With the rapid growth and dissemination of mobile services, enhancement of customer satisfaction has emerged as a core issue. Customer reviews are recognized as fruitful information sources for monitoring and enhancing customer satisfaction levels, particularly as they convey the real voices of actual customers expressing relatively unambiguous opinions. As a methodological means of customer review analysis, sentiment analysis has come to the fore. Although several sentiment analysis approaches have proposed extraction of the emotional information from customer reviews, however, a lacuna remains as to how to effectively analyze customer reviews for the purpose of monitoring customer satisfaction with mobile services. In response, the present study developed a new framework for measurement of customer satisfaction for mobile services by combining VIKOR (in Serbian: ViseKriterijumsa Optimizacija I Kompromisno Resenje) and sentiment analysis. With VIKOR, which is a compromise ranking method of the multicriteria decision making (MCDM) approach, customer satisfaction for mobile services can be accurately measured by a sentiment-analysis scheme that simultaneously considers maximum group utility and individual regret. The suggested framework consists mainly of two stages: data collection and preprocessing, and measurement of customer satisfaction. In the first, data collection and preprocessing stage, text mining is utilized to compile customer-review-based dictionaries of attributes and sentiment words. Then, using sentiment analysis, sentiment scores for attributes are calculated for each mobile service. In the second stage, levels of customer satisfaction are measured using VIKOR. For the purpose of illustration, an empirical case study was conducted on customer reviews of mobile application services. We believe that the proposed customer-review-based approach not only saves time and effort in measuring customer satisfaction, but also captures the real voices of customers.


title: Access Point Localization Using Local Signal Strength Gradient

abstract: Many previous studies have examined the placement of access points (APs) to improve the community's understanding of the deployment and behavioral characteristics of wireless networks. A key implicit assumption in these studies is that one can estimate the AP location accurately from wardriving-like measurements. However, existing localization algorithms exhibit high error because they over-simplify the complex nature of signal propagation. In this work, we propose a novel approach that localizes APs using directional information derived from local signal strength variations. Our algorithm only uses signal strength information, and improves localization accuracy over existing techniques. Furthermore, the algorithm is robust to the sampling biases and non-uniform shadowing, which are common in wardriving measurements.


title: What We Know About Leadership

abstract: This article reviews the empirical literature on personality, leadership, and organizational effectiveness to make 3 major points. First, leadership is a real and vastly consequential phenomenon, perhaps the single most important issue in the human sciences. Second, leadership is about the performance of teams, groups, and organizations. Good leadership promotes effective team and group performance, which in turn enhances the well-being of the incumbents; bad leadership degrades the quality of life for everyone associated with it. Third, personality predicts leadership—who we are is how we lead—and this information can be used to select future leaders or improve the performance of current incumbents. A very smart political scientist friend used to say, “The fundamental question in human affairs is, who shall rule?” We think the fundamental question is, “who should rule?” Leadership is one of the most important topics in the human sciences and historically one of the more poorly understood; it is important for two reasons. First, leadership solves the problem of how to organize collective effort; consequently, it is the key to organizational effectiveness. With good leadership, organizations (governments, corporations, universities, hospitals, armies) thrive and prosper. When organizations succeed, the financial and psychological well


Complexity of growing patterns in cellular automata


title: Non-Axiomatic Reasoning System -- Exploring the Essence of Intelligence

abstract: Every artificial-intelligence research project needs a working definition of "intelligence", on which the deepest goals and assumptions of the research are based. In the project described in the following chapters, "intelligence" is defined as the capacity to adapt under insufficient knowledge and resources. Concretely, an intelligent system should be finite and open, and should work in real time. ::: If these criteria are used in the design of a reasoning system, the result is NARS, a non-axiomatic reasoning system. ::: NARS uses a term-oriented formal language, characterized by the use of subject-predicate sentences. The language has an experience-grounded semantics, according to which the truth value of a judgment is determined by previous experience, and the meaning of a term is determined by its relations with other terms. Several different types of uncertainty, such as randomness, fuzziness, and ignorance, can be represented in the language in a single way. ::: The inference rules of NARS are based on three inheritance relations between terms. With different combinations of premises, revision, deduction, induction, abduction, exemplification, comparison, and analogy can all be carried out in a uniform format, the major difference between these types of inference being that different functions are used to calculate the truth value of the conclusion from the truth values of the premises. ::: Since it has insufficient space-time resources, the system needs to distribute them among its tasks very carefully, and to dynamically adjust the distribution as the situation changes. This leads to a "controlled concurrency" control mechanism, and a "bag-based" memory organization. ::: A recent implementation of the NARS model, with examples, is discussed. The system has many interesting properties that are shared by human cognition, but are absent from conventional computational models of reasoning. ::: This research sheds light on several notions in artificial intelligence and cognitive science, including symbol-grounding, induction, categorization, logic, and computation. These are discussed to show the implications of the new theory of intelligence. ::: Finally, the major results of the research are summarized, a preliminary evaluation of the working definition of intelligence is given, and the limitations and future extensions of the research are discussed.


title: Design of Multi-mode E-commerce Recommendation System

abstract: At present, most e-commerce recommendation systems act only as just a single tool and provide a single recommendation model. However, due to the complexity of e-commerce system itself, different occasions and customers of different identities require different types of recommendation services. In the paper, we study the architecture of complex e-commerce which can collect multiple types of data, use a variety of recommendation algorithms order to meet the needs of different types of recommendation.


title: ℋ∞ almost synchronization for homogeneous networks of non-introspective SISO agents under external disturbances

abstract: This paper addresses the problem of H∞ almost synchronization” for networks of identical, linear agents under directed communication topologies. Agents are presumed to be non-introspective; i.e. agents are not aware of their own state or output, and every agent is only provided with a linear combination of its own output relative to that of the neighbors. Providing the solvability conditions, a family of dynamic protocols is developed such that the impact of disturbances on the synchronization error dynamics, expressed in terms of the H∞ norm of the corresponding closed-loop transfer function, is reduced to any arbitrarily small value. Thus, synchronization with any desired accuracy can be achieved.


title: Machine Learning Of Temporal Relations

abstract: This paper investigates a machine learning approach for temporally ordering and anchoring events in natural language texts. To address data sparseness, we used temporal reasoning as an over-sampling method to dramatically expand the amount of training data, resulting in predictive accuracy on link labeling as high as 93% using a Maximum Entropy classifier on human annotated data. This method compared favorably against a series of increasingly sophisticated baselines involving expansion of rules derived from human intuitions.


title: The State of Artificial Intelligence

abstract: Abstract Artificial intelligence has been an active branch of research for computer scientists and psychologists for 50 years. The concept of mimicking human intelligence in a computer fuels the public imagination and has led to countless academic papers, news articles and fictional works. However, public expectations remain largely unfulfilled, owing to the incredible complexity of everyday human behavior. A wide range of tools and techniques have emerged from the field of artificial intelligence, many of which are reviewed here. They include rules, frames, model-based reasoning, case-based reasoning, Bayesian updating, fuzzy logic, multiagent systems, swarm intelligence, genetic algorithms, neural networks, and hybrids such as blackboard systems. These are all ingenious, practical, and useful in various contexts. Some approaches are pre-specified and structured, while others specify only low-level behavior, leaving the intelligence to emerge through complex interactions. Some approaches are based on the use of knowledge expressed in words and symbols, whereas others use only mathematical and numerical constructions. It is proposed that there exists a spectrum of intelligent behaviors from low-level reactive systems through to high-level systems that encapsulate specialist expertise. Separate branches of research have made strides at both ends of the spectrum, but difficulties remain in devising a system that spans the full spectrum of intelligent behavior, including the difficult areas in the middle that include common sense and perception. Artificial intelligence is increasingly appearing in situated systems that interact with their physical environment. As these systems become more compact they are likely to become embedded into everyday equipment. As the 50th anniversary approaches of the Dartmouth conference where the term ‘artificial intelligence’ was first published, it is concluded that the field is in good shape and has delivered some great results. Yet human thought processes are incredibly complex, and mimicking them convincingly remains an elusive challenge.


title: Eye-tracking verification of the strategy used to analyse algorithms expressed in a flowchart and pseudocode

abstract: The results of qualitative and quantitative investigations conducted with individuals who learned algorithms in school are presented in this article. In these investigations, eye-tracking technology was used to follow the process of solving algorithmic problems. The algorithmic problems were presented in two comparable variants: in a pseudocode and flowchart. The eye-tracking data resulted in both qualitative (films registering the gaze paths) and quantitative measures, which allowed the detection and interpretation of the differences in the task-solving strategies between those who found the correct answer and those that did not. The results confirmed a hypothesis that use of the formal notation characteristic of a programming language for presenting algorithms is often a practical difficulty in the process of solving even simple tasks. This study opens a new direction of research; they show that eye-tracking technology can be used to optimise the educational process of learning programming.


title: Food image analysis: The big data problem you can eat!

abstract: Six of the ten leading causes of death in the United States can be directly linked to diet. Measuring accurate dietary intake, the process of determining what someone eats is considered to be an open research problem in the nutrition and health fields. We are developing image-based tools in order to automatically obtain accurate estimates of what foods a user consumes. We have developed a novel food record application using the embedded camera in a mobile device. This paper describes the current status of food image analysis and overviews problems that still need to be addressed.


title: CAT-SLAM: probabilistic localisation and mapping using a continuous appearance-based trajectory

abstract: This paper describes a new system, dubbed Continuous Appearance-based Trajectory Simultaneous Localisation and Mapping (CAT-SLAM), which augments sequential appearance-based place recognition with local metric pose filtering to improve the frequency and reliability of appearance-based loop closure. As in other approaches to appearance-based mapping, loop closure is performed without calculating global feature geometry or performing 3D map construction. Loop-closure filtering uses a probabilistic distribution of possible loop closures along the robot's previous trajectory, which is represented by a linked list of previously visited locations linked by odometric information. Sequential appearance-based place recognition and local metric pose filtering are evaluated simultaneously using a Rao-Blackwellised particle filter, which weights particles based on appearance matching over sequential frames and the similarity of robot motion along the trajectory. The particle filter explicitly models both the likelihood of revisiting previous locations and exploring new locations. A modified resampling scheme counters particle deprivation and allows loop-closure updates to be performed in constant time for a given environment. We compare the performance of CAT-SLAM with FAB-MAP (a state-of-the-art appearance-only SLAM algorithm) using multiple real-world datasets, demonstrating an increase in the number of correct loop closures detected by CAT-SLAM.


title: Multi-biometric continuous authentication: A trust model for an asynchronous system

abstract: Biometric technologies are used to grant specific users access to services and data. The access control is usually performed at the start of a session that spans over a period of time. Continuous authentication aims at insuring the identity of the user over this period of time, and not only at its start. Multi-biometrics aims at increasing the accuracy, robustness and usability of biometrics systems. This work presents a multi-biometric continuous authentication solution that includes information from the face images and the keystroke dynamics of the user. A database representing a realistic scenario was collected to develop and evaluate the presented solution. A multi-biometric trust model was designed to cope with the asynchronous nature induced by the different biometric characteristics. A set of performance metrics are discussed and a comparison is presented between the performances of the single characteristic solutions and the fused solution.


title: Self-Protection against Insider Threats in DBMS through Policies Implementation

abstract: In today’s world, information security of an organization has become a major challenge as well as a critical business issue. Managing and mitigating these internal or external security related issues, organizations hire highly knowledgeable security expert persons. Insider threats in database management system (DBMS) are inherently a very hard problem to address. Employees within the organization carry out or harm organization data in a professional manner. To protect and monitor organization information from insider user in DBMS, the organization used different techniques, but these techniques are insufficient to secure their data. We offer an autonomous approach to self-protection architecture based on policy implementation in DBMS. This research proposes an autonomic model for protection that will enforce Access Control policies, Database Auditing policies, Encryption policies, user authentication policies, and database configuration setting policies in DBMS. The purpose of these policies to restrict insider user or Database Administrator (DBA) from malicious activities to protect data.


title: Fuzzy logic controller for hybrid renewable energy system with multiple types of storage

abstract: The objective of this work is to design a scheme to control the power flow of a hybrid renewable energy system with multiple renewable energy sources (solar energy and wind energy) and multiple energy storage systems. The use of energy storage is necessary due to the intermittency of the renewable energy sources and the consequent peak power shift between the sources and the load. In addition, the use of the energy storage can increase the overall system reliability and stability. In this work, batteries are used as the primary energy storage system for short to medium storage term, while hydrogen fuel cell is used as the long-term energy storage. A supervisory control system is designed to handle various changes in power supply and power demand by managing power intermittency, power peak shaving, and long-term energy storage. Since both power supply and demand are not fully predictable and they have time variant nonlinear behavior, computational intelligence is introduced to solve this issue and provide suitable control algorithm. This type of control, based on fuzzy logic, combines knowledge of the system and control. The proposed methodology can be optimized such that the system is able to adapt to its working environment and deliver best results under given circumstances. For more realistic simulation results, the designed model utilizes real solar power data collected from the University of Queensland in Australia, while real wind power and load data was collected by the Alberta Electric System Operator in Alberta, Canada. The model of the system is designed and simulated in MATLAB SimPowerSystems™ to verify the effectiveness of the proposed scheme.


title: Authenticated Diffie-Hellman Key Agreement Protocols

abstract: This paper surveys recent work on the design and analysis of key agreement protocols that are based on the intractability of the Diffie-Hellman problem. The focus is on protocols that have been standardized, or are in the process of being standardized, by organizations such as ANSI, IEEE, ISO/IEC, and NIST. The practical and provable security aspects of these protocols are discussed.


title: Underwater remotely operated vehicle for surveillance and marine study

abstract: Oceanic exploration has become an emerging field of research due to many resources located in the deep sea. Robots are used in sea exploration as it is very difficult for the human beings to carry out exploration work due to harsh sea environment. Development of underwater Remotely Operated Vehicles (ROVs) is an important area of research. This paper describes a simple ROV prototype based on low cost boards, Raspberry Pi and Arduino UNO. The underwater platform consists of simple control system, camera, gripper and basic equipment. The manoeuvrability consists of forward-reverse motion, submerge under surface motion. The ROV has been tested for the above basic operations.


title: Experimental security surveillance system for an Island-based facility

abstract: A test bed was developed for Plum Island, NY experimental multi-sensor security surveillance. The system includes broadband radars, cameras, an Automatic Identification System receiver, geophones and underwater passive acoustic sensors. The sensors were grouped into nodes connected to a Command and Control Center via a Wi-Fi network. Some sensors are COTS, some were developed and built by Stevens Institute of Technology. Sensor network was tested for the detection of potential surface and underwater intruders. This paper presents a system description and sensor performance estimations for various targets, including jet skis, small boats, kayaks, and divers. The system design performance versus cost considerations and methods of sensor integration are discussed.


Performance and Environmental Correction of a Low-Cost NDIR CO2 Sensor


title: Super 4PCS Fast Global Pointcloud Registration via Smart Indexing

abstract: Data acquisition in large-scale scenes regularly involves accumulating information across multiple scans. A common approach is to locally align scan pairs using Iterative Closest Point (ICP) algorithm (or its variants), but requires static scenes and small motion between scan pairs. This prevents accumulating data across multiple scan sessions and/or different acquisition modalities (e.g., stereo, depth scans). Alternatively, one can use a global registration algorithm allowing scans to be in arbitrary initial poses. The state-of-the-art global registration algorithm, 4PCS, however has a quadratic time complexity in the number of data points. This vastly limits its applicability to acquisition of large environments. We present Super 4PCS for global pointcloud registration that is optimal, i.e., runs in linear time (in the number of data points) and is also output sensitive in the complexity of the alignment problem based on the (unknown) overlap across scan pairs. Technically, we map the algorithm as an 'instance problem' and solve it efficiently using a smart indexing data organization. The algorithm is simple, memory-efficient, and fast. We demonstrate that Super 4PCS results in significant speedup over alternative approaches and allows unstructured efficient acquisition of scenes at scales previously not possible. Complete source code and datasets are available for research use at http://geometry.cs.ucl.ac.uk/projects/2014/super4PCS/.


Robust Audio Hashing for Content Identification


title: Cohort Comparison of Event Sequences with Balanced Integration of Visual Analytics and Statistics

abstract: Finding the differences and similarities between two datasets is a common analytics task. With temporal event sequence data, this task is complex because of the many ways single events and event sequences can differ between the two datasets (or cohorts) of records: the structure of the event sequences (e.g., event order, co-occurring events, or event frequencies), the attributes of events and records (e.g., patient gender), or metrics about the timestamps themselves (e.g., event duration). In exploratory analyses, running statistical tests to cover all cases is time-consuming and determining which results are significant becomes cumbersome. Current analytics tools for comparing groups of event sequences emphasize a purely statistical or purely visual approach for comparison. This paper presents a taxonomy of metrics for comparing cohorts of temporal event sequences, showing that the problem-space is bounded. We also present a visual analytics tool, CoCo (for "Cohort Comparison"), which implements balanced integration of automated statistics with an intelligent user interface to guide users to significant, distinguishing features between the cohorts. Lastly, we describe two early case studies: the first with a research team studying medical team performance in the emergency department and the second with pharmacy researchers.


title: MultiSE: multi-path symbolic execution using value summaries

abstract: Dynamic symbolic execution (DSE) has been proposed to effectively generate test inputs for real-world programs. Unfortunately, DSE techniques do not scale well for large realistic programs, because often the number of feasible execution paths of a program increases exponentially with the increase in the length of an execution path. In this paper, we propose MultiSE, a new technique for merging states incrementally during symbolic execution, without using auxiliary variables. The key idea of MultiSE is based on an alternative representation of the state, where we map each variable, including the program counter, to a set of guarded symbolic expressions called a value summary. MultiSE has several advantages over conventional DSE and conventional state merging techniques: value summaries enable sharing of symbolic expressions and path constraints along multiple paths and thus avoid redundant execution. MultiSE does not introduce auxiliary symbolic variables, which enables it to 1) make progress even when merging values not supported by the constraint solver, 2) avoid expensive constraint solver calls when resolving function calls and jumps, and 3) carry out most operations concretely. Moreover, MultiSE updates value summaries incrementally at every assignment instruction, which makes it unnecessary to identify the join points and to keep track of variables to merge at join points. We have implemented MultiSE for JavaScript programs in a publicly available open-source tool. Our evaluation of MultiSE on several programs shows that 1) value summaries are an eective technique to take advantage of the sharing of value along multiple execution path, that 2) MultiSE can run significantly faster than traditional dynamic symbolic execution and, 3) MultiSE saves a substantial number of state merges compared to conventional state-merging techniques.


title: Social Media Use and Mental Health among Young Adults

abstract: In recent years many parents, advocates and policy makers have expressed concerns regarding the potential negative impact of social media use. Some studies have indicated that social media use may be tied to negative mental health outcomes, including suicidality, loneliness and decreased empathy. Other studies have not found evidence for harm, or have indicated that social media use may be beneficial for some individuals. The current correlational study examined 467 young adults for their time spent using social media, importance of social media in their lives and tendency to engage in vaguebooking (posting unclear but alarming sounding posts to get attention). Outcomes considered included general mental health symptoms, suicidal ideation, loneliness, social anxiety and decreased empathy. Results indicated that social media use was not predictive of impaired mental health functioning. However, vaguebooking was predictive of suicidal ideation, suggesting this particular behavior could be a warning sign for serious issues. Overall, results from this study suggest that, with the exception of vaguebooking, concerns regarding social media use may be misplaced.


title: The Reinforcement Learning Competitions

abstract: This article reports on the reinforcement learning competitions, which have been held annually since 2006. In these events, researchers from around the world developed reinforcement learning agents to compete in domains of various complexity and difficulty. We focus on the 2008 competition, which employed fundamentally redesigned evaluation frameworks that aimed systematically to encourage the submission of robust learning methods. We describe the unique challenges of empirical evaluation in reinforcement learning and briefly review the history of the previous competitions and the evaluation frameworks they employed. We describe the novel frameworks developed for the 2008 competition as well as the software infrastructure on which they rely. Furthermore, we describe the six competition domains, present selected competition results, and discuss the implications of these results. Finally, we summarize the 2009 competition, which used the same evaluation framework but different events, and outline ideas for the future of the competition.


title: User experience rating scales with 7, 11, or 101 points: does it matter?

abstract: There is a large body of work on the topic of the optimal number of response options to use in multipoint items. The takeaways from the literature are not completely consistent, most likely due to variation in measurement contexts (e.g., clinical, market research, psychology) and optimization criteria (e.g., reliability, validity, sensitivity, ease-of-use). There is also considerable research literature on visual analog scales (VAS), which are endpoint-anchored lines on which respondents place a mark to provide a rating. Typically, a VAS is a 10-cm line with the marked position converted to a 101-point scale (0-100). ::: ::: Multipoint rating items are widely employed in user experience (UX) research. The use of the VAS, on the other hand, is relatively rare. It seems possible that the continuous structure of the VAS could offer some measurement advantages. Our objective for this study was to compare psychometric properties of individual items and multiitem questionnaires using 7- and 11-point Likert-type agreement items and the VAS in the context of UX research. ::: ::: Some characteristics (e.g., means and correlations) of the VAS were different from the Likert-style (7- and 11-point items), so the VAS does not appear to be interchangeable with the Likert-style items. There were no differences in the classical psychometric properties of reliability and concurrent validity. Thus, we did not find any particular measurement advantage associated with the use of 7-point, 11-point, or VAS items. With regard to measurement properties, it doesn't seem to matter (but the literature suggests multipoint items are easier to use).


title: Personalized Book Recommendation Based on Ontology and Collaborative Filtering Algorithm

abstract: Information recommendation service is one of important functions of digital library, aiming at the problem that book recommendation service exists the insufficient requirement mining of service object information in the current uni- versity library, personalized book recommendation method based on ontology information and collaborative filtering al- gorithm (abbreviated as OI-CFA algorithm) is proposed. Firstly, this paper discusses the necessity of collaborative rec- ommendation in digital library, introduces main methods and technology based on collaborative filtering recommendation system. However, there are several problems that are data sparse and new item forecast with collaborative filtering rec- ommendation method based on item. In order to solve these problems, this paper introduced an integrated similarity algo- rithms of structured semantic information based on OI-CFA. Extracting the semantic information of items including knowledge representation based on ontology, through ontology learning, the specified domain ontology is constructed. Compared with the traditional collaborative filtering algorithm and SVM, experimental results show that this method can not only solve the problems caused by the item-based collaborative filtering algorithm, but also improve the accuracy of recommendation.


title: Data hiding method in speech using echo embedding and voicing correction

abstract: Ste gano graphic systems are used for the transmission of hidden data in the original signal. The article describes the algorithm of the hidden data transmission using the speech signal as a carrier. The echo method is used for data embedding. In order to improve the decoding efficiency of embedded data, the procedure of voicing correction and mechanism of informed coding were developed and implemented. We checked the robustness of proposed steganographic system against the transmission of speech signal through VoIP, robustness against to steganoanalysis as well as the perception transparency of the embedded information in the original (host) signal.


title: DNW-Controllable triggered voltage of the integrated diode triggered SCR (IDT-SCR) ESD protection device

abstract: A novel Integrated Diode Triggered SCR (IDT-SCR), with low capacitance ( T1 is proposed for 1.8V application. The triggering is determined by the DNW biasing plus one integrated diode. The fail-current density is 16 times higher, and leakage 20 times lower, compared to a traditional capacitive-triggered bigFET.


title: Leadership and Knowledge Sharing

abstract: Knowledge processes and leadership and management have both been central to discussions within the academic literature. While the number of empirical studies looking at the interaction between these dimensions has been limited. Within this study structural equation modelling using SMART PLS is utilised to identify the implications when determining the most effective leadership styles for achieving knowledge transfer. It was found that a combined leadership approach was the most effective when considering knowledge processes through the SECI model. The study contributes to the literature by allowing us to identify specific leadership style attributes being matched to specific knowledge process stages for achieving maximum impact. The study also allowed us to test empirically the suitability of the leadership and knowledge constructs within the UAE.


title: VQA-E: Explaining, Elaborating, and Enhancing Your Answers for Visual Questions

abstract: Most existing works in visual question answering (VQA) are dedicated to improving the accuracy of predicted answers, while disregarding the explanations. We argue that the explanation for an answer is of the same or even more importance compared with the answer itself, since it makes the question and answering process more understandable and traceable. To this end, we propose a new task of VQA-E (VQA with Explanation), where the computational models are required to generate an explanation with the predicted answer. We first construct a new dataset, and then frame the VQA-E problem in a multi-task learning architecture. Our VQA-E dataset is automatically derived from the VQA v2 dataset by intelligently exploiting the available captions. We have conducted a user study to validate the quality of explanations synthesized by our method. We quantitatively show that the additional supervision from explanations can not only produce insightful textual sentences to justify the answers, but also improve the performance of answer prediction. Our model outperforms the state-of-the-art methods by a clear margin on the VQA v2 dataset.


title: Chaotic Artificial Bee Colony for Text Clustering

abstract: Text clustering is widely used for creating clusters of the digital documents. Selection of cluster centers plays an important role in creating clusters of the documents. In this paper, we use artificial bee colony algorithm (hereinafter referred to as ABC) to select an appropriate cluster centers for text documents. The ABC is a swarm intelligence based algorithm inspired by intelligent foraging behavior of real honey bees. The ABC provides good exploration of the search space at a cost of exploitation. To address this issue, we use the chaotic map as a local search paradigm to improve its exploitation capability. The proposed algorithm chaotic artificial bee colony (hereinafter referred to as ChABC) is tested on two benchmark text datasets namely Reuters-21,578 and Classic4, and the obtained results are compared with k-means clustering, ABC, and a recent variant of ABC namely gbest guided ABC (hereinafter referred to as GABC). The comparisons show that the ChABC offers the better clustering quality and faster convergence among all the competitive algorithms in all cases.


title: Development of control circuit for single phase inverter using Atmel Microcontroller

abstract: Organized by Kolej Universiti Kejuruteraan Utara Malaysia (KUKUM), 15-16 September 2006 at Pulau Langkawi, Malaysia.


title: Exoskeletal Spine and Shoulders for Full Body Exoskeletons in Health Care

abstract: Currently, full body exoskeletons still lack movability in their back and shoulder parts, resulting in limited applicability in, e.g., nursing care. An exoskeletal spine and shoulder mechanism called “exo-spine” has therefore been developed with the purpose of allowing 3 degree of freedom (DOF) spinal motion and 2 DOF shoulder girdle abduction. It consists of a mechanism of vertebras, shoulder blades, and two cables for actuation such that only one motor is required. Control equations were obtained through measurements and friction analysis. Experiments were performed with a subject wearing the exo-spine in a simplified full body exoskeleton. The wearer was able to lift up to 40 kg using all DOF of the system. From this we confirmed the exo-spine’s 5 DOF movability and lifting performance. Next, the exo-spine will be installed in HAL exoskeletons in order to increase their usability in health care settings


title: Mechanical Digital-To-Analog Converters

abstract: We have demonstrated a technique to create a digitalto-analog converter (DAC) from lever arms and actuator arrays. These DACs take digital electrical signals and produce mechanical displacements at the output. A 3-bit DAC with thermal actuator arrays operating on 5V signals has been demonstrated with 2 states of displacements. This DAC has a least significant bit (LSB) size of 0.74μm, an integral nonlinearity (INL) of ± 0.27LSB, and a differential nonlinearity (DNL) of ± 0.25LSB. The DAC was coupled to a hinged micro mirror to test the dynamic and transient response using a laser setup. Such devices could be used as digitally controlled actuators to drive mechanisms in micro optics, mechanical computing, and micro robotics.


title: Microwave Displacement Sensor for Hydraulic Devices

abstract: This paper presents the fundamentals, design, realization, and characterization of a state-of-the-art microwave based piston position detection sensor for position detection of stationary or moving pistons in industrial hydraulic devices. This function has a high importance in industrial applications to provide precise feeding in position controlled hydraulic drives (classical actuators, medium separators, and pressure accumulators). Microwave technology is established in many sectors except for factory automation. In this paper, we describe an initial minimally invasive and contactless implementation of this technology for the above mentioned industrial applications. It is suitable for small but also for large piston stroke ranges in rugged industrial environments and is able to fulfill the tough specific requirements.


title: The association between Internet addiction and psychiatric disorder: A review of the literature

abstract: Abstract Internet addiction is a newly emergent disorder. It has been found to be associated with a variety of psychiatric disorders. Information about such coexisting psychiatric disorders is essential to understand the mechanism of Internet addiction. In this review, we have recruited articles mentioning coexisting psychiatric disorders of Internet addiction from the PubMed database as at November 3, 2009. We describe the updated results for such disorders of Internet addiction, which include substance use disorder, attention-deficit hyperactivity disorder, depression, hostility, and social anxiety disorder. We also provide discussion for possible mechanisms accounting for the coexistence of psychiatric disorders and Internet addiction. The review might suggest that combined psychiatric disorders mentioned above should be evaluated and treated to prevent their deteriorating effect on the prognosis of Internet addiction. On the other hand, Internet addiction should be paid more attention to when treating people with these coexisting psychiatric disorders of Internet addiction. Additionally, we also suggest future necessary research directions that could provide further important information for the understanding of this issue.


title: Group-wise Deep Co-saliency Detection

abstract: In this paper, we propose an end-to-end group-wise deep co-saliency detection approach to address the co-salient object discovery problem based on the fully convolutional network (FCN) with group input and group output. The proposed approach captures the group-wise interaction information for group images by learning a semantics-aware image representation based on a convolutional neural network, which adaptively learns the group-wise features for co-saliency detection. Furthermore, the proposed approach discovers the collaborative and interactive relationships between group-wise feature representation and single-image individual feature representation, and model this in a collaborative learning framework. Finally, we set up a unified end-to-end deep learning scheme to jointly optimize the process of group-wise feature representation learning and the collaborative learning, leading to more reliable and robust co-saliency detection results. Experimental results demonstrate the effectiveness of our approach in comparison with the state-of-the-art approaches.


title: R-C3D: Region Convolutional 3D Network for Temporal Activity Detection

abstract: We address the problem of activity detection in continuous, untrimmed video streams. This is a difficult task that requires extracting meaningful spatio-temporal features to capture activities, accurately localizing the start and end times of each activity. We introduce a new model, Region Convolutional 3D Network (R-C3D), which encodes the video streams using a three-dimensional fully convolutional network, then generates candidate temporal regions containing activities, and finally classifies selected regions into specific activities. Computation is saved due to the sharing of convolutional features between the proposal and the classification pipelines. The entire model is trained end-to-end with jointly optimized localization and classification losses. R-C3D is faster than existing methods (569 frames per second on a single Titan X Maxwell GPU) and achieves state-of-the-art results on THUMOS'14. We further demonstrate that our model is a general activity detection framework that does not rely on assumptions about particular dataset properties by evaluating our approach on ActivityNet and Charades. Our code is available at this http URL.


title: Nuclear Medicine Imaging of Neuroendocrine Tumors.

abstract: An important role is reserved for nuclear imaging techniques in the imaging of neuroendocrine tumors (NETs). Somatostatin receptor scintigraphy (SRS) with (111)In-DTPA-octreotide is currently the most important tracer in the diagnosis, staging and selection for peptide receptor radionuclide therapy (PRRT). In the past decade, different positron-emitting tomography (PET) tracers have been developed. The largest group is the (68)Gallium-labeled somatostatin analogs ((68)Ga-SSA). Several studies have demonstrated their superiority compared to SRS in sensitivity and specificity. Furthermore, patient comfort and effective dose are favorable for (68)Ga-SSA. Other PET targets like β-[(11)C]-5-hydroxy-L-tryptophan ((11)C-5-HTP) and 6-(18)F-L-3,4-dihydroxyphenylalanine ((18)F-DOPA) were developed recently. For insulinomas, glucagon-like peptide-1 receptor imaging is a promising new technique. The evaluation of response after PRRT and other therapies is a challenge. Currently, the official follow-up is performed with radiological imaging techniques. The role of nuclear medicine may increase with the newest tracers for PET. In this review, the different nuclear imaging techniques and tracers for the imaging of NETs will be discussed.


title: Customer Lifetime Value Measurement

abstract: The measurement of customer lifetime value is important because it is used as a metric in evaluating decisions in the context of customer relationship management. For a firm, it is important to form some expectations as to the lifetime value of each customer at the time a customer starts doing business with the firm, and at each purchase by the customer. In this paper, we use a hierarchical Bayes approach to estimate the lifetime value of each customer at each purchase occasion by jointly modeling the purchase timing, purchase amount, and risk of defection from the firm for each customer. The data come from a membership-based direct marketing company where the times of each customer joining the membership and terminating it are known once these events happen. In addition, there is an uncertain relationship between customer lifetime and purchase behavior. Therefore, longer customer lifetime does not necessarily imply higher customer lifetime value. ::: ::: We compare the performance of our model with other models on a separate validation data set. The models compared are the extended NBD--Pareto model, the recency, frequency, and monetary value model, two models nested in our proposed model, and a heuristic model that takes the average customer lifetime, the average interpurchase time, and the average dollar purchase amount observed in our estimation sample and uses them to predict the present value of future customer revenues at each purchase occasion in our hold-out sample. The results show that our model performs better than all the other models compared both at predicting customer lifetime value and in targeting valuable customers. The results also show that longer interpurchase times are associated with larger purchase amounts and a greater risk of leaving the firm. Both male and female customers seem to have similar interpurchase time intervals and risk of leaving; however, female customers spend less compared with male customers.


title: Learning Fair Classifiers

abstract: Automated data-driven decision systems are ubiquitous across a wide variety of online services, from online social networking and e-commerce to e-government. These systems rely on complex learning methods and vast amounts of data to optimize the service functionality, satisfaction of the end user and profitability. However, there is a growing concern that these automated decisions can lead to user discrimination, even in the absence of intent, leading to a lack of fairness, i.e., their outcomes have a disproportionally large adverse impact on particular groups of people sharing one or more sensitive attributes (e.g., race, sex). In this paper, we introduce a flexible mechanism to design fair classifiers in a principled manner. Then, we instantiate this mechanism on three well-known classifiers -- logistic regression, hinge loss and linear and nonlinear support vector machines. Experiments on both synthetic and real-world data show that our mechanism allows for a fine-grained control of the level of fairness, often at a minimal cost in terms of accuracy, and it provides more flexibility than alternatives.


title: Quantifying Translation-Invariance in Convolutional Neural Networks

abstract: A fundamental problem in object recognition is the development of image representations that are invariant to common transformations such as translation, rotation, and small deformations. There are multiple hypotheses regarding the source of translation invariance in CNNs. One idea is that translation invariance is due to the increasing receptive field size of neurons in successive convolution layers. Another possibility is that invariance is due to the pooling operation. We develop a simple a tool, the translation-sensitivity map, which we use to visualize and quantify the translation-invariance of various architectures. We obtain the surprising result that architectural choices such as the number of pooling layers and the convolution filter size have only a secondary effect on the translation-invariance of a network. Our analysis identifies training data augmentation as the most important factor in obtaining translation-invariant representations of images using convolutional neural networks.


Exploring Text Links for Coherent Multi-Document Summarization


title: Flattened Convolutional Neural Networks for Feedforward Acceleration

abstract: We present flattened convolutional neural networks that are designed for fast feedforward execution. The redundancy of the parameters, especially weights of the convolutional filters in convolutional neural networks has been extensively studied and different heuristics have been proposed to construct a low rank basis of the filters after training. In this work, we train flattened networks that consist of consecutive sequence of one-dimensional filters across all directions in 3D space to obtain comparable performance as conventional convolutional networks. We tested flattened model on different datasets and found that the flattened layer can effectively substitute for the 3D filters without loss of accuracy. The flattened convolution pipelines provide around two times speed-up during feedforward pass compared to the baseline model due to the significant reduction of learning parameters. Furthermore, the proposed method does not require efforts in manual tuning or post processing once the model is trained.


title: Category learning and multiple memory systems

abstract: Categorization is a vitally important skill that people use every day. Early theories of category learning assumed a single learning system, but recent evidence suggests that human category learning may depend on many of the major memory systems that have been hypothesized by memory researchers. As different memory systems flourish under different conditions, an understanding of how categorization uses available memory systems will improve our understanding of a basic human skill, lead to better insights into the cognitive changes that result from a variety of neurological disorders, and suggest improvements in training procedures for complex categorization tasks.


title: Active frame selection for label propagation in videos

abstract: Manually segmenting and labeling objects in video sequences is quite tedious, yet such annotations are valuable for learning-based approaches to object and activity recognition. While automatic label propagation can help, existing methods simply propagate annotations from arbitrarily selected frames (e.g., the first one) and so may fail to best leverage the human effort invested. We define an active frame selection problem: select k frames for manual labeling, such that automatic pixel-level label propagation can proceed with minimal expected error. We propose a solution that directly ties a joint frame selection criterion to the predicted errors of a flow-based random field propagation model. It selects the set of k frames that together minimize the total mislabeling risk over the entire sequence. We derive an efficient dynamic programming solution to optimize the criterion. Further, we show how to automatically determine how many total frames k should be labeled in order to minimize the total manual effort spent labeling and correcting propagation errors. We demonstrate our method's clear advantages over several baselines, saving hours of human effort per video.


title: Allocation of Splitting Nodes in All-Optical Wavelength-Routed Networks

abstract: In this paper, we introduce the splitter placement problem in wavelength-routed networks (SP-WRN). Given a network topology, a set of multicast sessions, and a fixed number of multicast-capable cross-connects, the SP-WRN problem entails the placement of the multicast-capable cross-connects so that the blocking probability is minimized. The SP-WRN problem is NP-complete as it includes as a subproblem the routing and wavelength assignment problem which is NP-complete. To gain a deeper insight into the computational complexity of the SP-WRN problem, we define a graph-theoretic version of the splitter placement problem (SPG), and show that even SPG is NP-complete. We develop three heuristics for the SP-WRN problem with different degrees of trade-off between computation time and quality of solution. The first heuristic uses the CPLEX general solver to solve an integer-linear program (ILP) of the problem. The second heuristic is based on a greedy approach and is called most-saturated node first (MSNF). The third heuristic employs simulated annealing (SA) with route-coordination. Through numerical examples on a wide variety of network topologies we demonstrate that: (1) no more than 50% of the cross-connects need to be multicast-capable, (2) the proposed SA heuristic provides fast near-optimal solutions, and (3) it is not practical to use general solvers such as CPLEX for solving the SP-WRN problem.


title: Running enhances neurogenesis, learning, and long-term potentiation in mice

abstract: Running increases neurogenesis in the dentate gyrus of the hippocampus, a brain structure that is important for memory function. Consequently, spatial learning and long-term potentiation (LTP) were tested in groups of mice housed either with a running wheel (runners) or under standard conditions (controls). Mice were injected with bromodeoxyuridine to label dividing cells and trained in the Morris water maze. LTP was studied in the dentate gyrus and area CA1 in hippocampal slices from these mice. Running improved water maze performance, increased bromodeoxyuridine-positive cell numbers, and selectively enhanced dentate gyrus LTP. Our results indicate that physical activity can regulate hippocampal neurogenesis, synaptic plasticity, and learning.


title: Anomaly detection in ECG time signals via deep long short-term memory networks

abstract: Electrocardiography (ECG) signals are widely used to gauge the health of the human heart, and the resulting time series signal is often analyzed manually by a medical professional to detect any arrhythmia that the patient may have suffered. Much work has been done to automate the process of analyzing ECG signals, but most of the research involves extensive preprocessing of the ECG data to derive vectorized features and subsequently designing a classifier to discriminate between healthy ECG signals and those indicative of an Arrhythmia. This approach requires knowledge and data of the different types of Arrhythmia for training. However, the heart is a complex organ and there are many different and new types of Arrhythmia that can occur which were not part of the original training set. Thus, it may be more prudent to adopt an anomaly detection approach towards analyzing ECG signals. In this paper, we utilize a deep recurrent neural network architecture with Long Short Term Memory (LSTM) units to develop a predictive model for healthy ECG signals. We further utilize the probability distribution of the prediction errors from these recurrent models to indicate normal or abnormal behavior. An added advantage of using LSTM networks is that the ECG signal can be directly fed into the network without any elaborate preprocessing as required by other techniques. Also, no prior information about abnormal signals is needed by the networks as they were trained only on normal data. We have used the MIT-BIH Arrhythmia Database to obtain ECG time series data for both normal periods and for periods during four different types of Arrhythmias, namely Premature Ventricular Contraction (PVC), Atrial Premature Contraction (APC), Paced Beats (PB) and Ventricular Couplet (VC). Results are promising and indicate that Deep LSTM models may be viable for detecting anomalies in ECG signals.


title: LH-CAM: Logic-Based Higher Performance Binary CAM Architecture on FPGA

abstract: Binary content-addressable memory (BiCAM) is a popular high speed search engine in hardware, which provides output typically in one clock cycle. But speed of CAM comes at the cost of various disadvantages, such as high latency, low storage density, and low architectural scalability. In addition, field-programmable gate arrays (FPGAs), which are used in many applications because of its advantages, do not have hard IPs for CAM. Since FPGAs have embedded IPs for random-access memories (RAMs), several RAM-based CAM architectures on FPGAs are available in the literature. However, these architectures are especially targeted for ternary CAMs, not for BiCAMs; thus, the available RAM-based CAMs may not be fully beneficial for BiCAMs in terms of architectural design. Since modern FPGAs are enriched with logical resources, why not to configure them to design BiCAM on FPGA? This letter presents a logic-based high performance BiCAM architecture (LH-CAM) using Xilinx FPGA. The proposed CAM is composed of CAM words and associated comparators. A sample of LH-CAM of size ${64\times 36}$ is implemented on Xilinx Virtex-6 FPGA. Compared with the latest prior work, the proposed CAM is much simpler in architecture, storage efficient, reduces power consumption by 40.92%, and improves speed by 27.34%.


title: Achievement goals and achievement emotions: Testing a model of their joint relations with academic performance.

abstract: The authors propose a theoretical model linking achievement goals and achievement emotions to academic performance. This model was tested in a prospective study with undergraduates (N = 213), using exam-specific assessments of both goals and emotions as predictors of exam performance in an introductory-level psychology course. The findings were consistent with the authors' hypotheses and supported all aspects of the proposed model. In multiple regression analysis, achievement goals (mastery, performance approach, and performance avoidance) were shown to predict discrete achievement emotions (enjoyment, boredom, anger, hope, pride, anxiety, hopelessness, and shame), achievement emotions were shown to predict performance attainment, and 7 of the 8 focal emotions were documented as mediators of the relations between achievement goals and performance attainment. All of these findings were shown to be robust when controlling for gender, social desirability, positive and negative trait affectivity, and scholastic ability. The results are discussed with regard to the underdeveloped literature on discrete achievement emotions and the need to integrate conceptual and applied work on achievement goals and achievement emotions.


title: Evaluation of Road Marking Feature Extraction

abstract: This paper proposes a systematic approach to evaluate algorithms for extracting road marking features from images. This specific topic is seldom addressed in the literature while many road marking detection algorithms have been proposed. Most of them can be decomposed into three steps: extracting road marking features, estimating a geometrical marking model, tracking the parameters of the geometrical model along an image sequence. The present work focuses on the first step, i.e. feature extraction. A reference database containing over 100 images of natural road scenes was built with corresponding manually labeled ground truth images (available at http://www.lcpc.fr/en/produits/ride/). This database enables to evaluate and compare extractors in a systematic way. Different road marking feature extraction algorithm representing different classes of techniques are evaluated: thresholding, gradient analysis, and convolution. As a result of this analysis, recommendations are given on which extractor to choose according to a specific application.


title: Low-Cost Inkjet-Printed Fully Passive RFID Tags for Calibration-Free Capacitive/Haptic Sensor Applications

abstract: A fully passive, compact, and low-cost capacitive wireless radio frequency identification (RFID)-enabled sensing system for capacitive sensing and other Internet of Things applications is proposed. This calibration-free sensor utilizes a dual-tag topology, which consists of two closely spaced RFID tags with dipole antennas and printed capacitive sensor component connected to one of the tags. A series LC resonator is used to both reduce the antenna size and improve the isolation between the two antennas and the design/optimization steps are discussed in detail. All components except for the RFID chips are inkjet printed on an off-the-shelf photopaper using a silver nanoparticle ink. The complete sensor dimension is $84~\textrm {mm} \times 95~\textrm {mm}$ and the sensor is compatible with EPC Class 1 Gen 2 (UHF) standard reader technology at 915 MHz.


title: Smartphone PDR positioning in large environments employing WiFi, particle filter, and backward optimization

abstract: The evolution of an existing offline algorithm for smartphone based indoor localization is outlined, discussed and evaluated. The existing algorithm performs dead reckoning using accelerometer-, compass- and gyroscope data. Accelerometer data is used to detect step events. Compass and gyroscope are used for heading estimation. Drift compensation and step length estimation is performed by a particle filter using map information. Floor detection is based on RSSI (Radio signal strength indicator) evaluation. In an offline scenario where positions are determined on recorded datasets, a backtracking algorithm allows a considerable increase in accuracy by elimination of unfeasible traces. The evolution comprises a radio map generation algorithm, the WiFi RSSI position determination, the inclusion of WiFi- and floor change information into the particle filter of the dead reckoning algorithm, as well as outlier identification in the RSSI position- and floor estimation. The modifications are described and discussed. The performance is evaluated against a publicly available database in the frame of a competition.


title: Online Consumer Review: Word-of-Mouth as a New Element of Marketing Communication Mix

abstract: As a new type of word-of-mouth information, online consumer product review is an emerging market phenomenon that is playing an increasingly important role in consumers' purchase decisions. This paper argues that online consumer review, a type of product information created by users based on personal usage experience, can serve as a new element in the marketing communications mix and work as free “sales assistants” to help consumers identify the products that best match their idiosyncratic usage conditions. ::: ::: This paper develops a normative model to address several important strategic issues related to consumer reviews. First, we show when and how the seller should adjust its own marketing communication strategy in response to consumer reviews. Our results reveal that if the review information is sufficiently informative, the two types of product information, i.e., the seller-created product attribute information and buyer-created review information, will interact with each other. For example, when the product cost is low and/or there are sufficient expert (more sophisticated) product users, the two types of information are complements, and the seller's best response is to increase the amount of product attribute information conveyed via its marketing communications after the reviews become available. However, when the product cost is high and there are sufficient novice (less sophisticated) product users, the two types of information are substitutes, and the seller's best response is to reduce the amount of product attribute information it offers, even if it is cost-free to provide such information. We also derive precise conditions under which the seller can increase its profit by adopting a proactive strategy, i.e., adjusting its marketing strategies even before consumer reviews become available. Second, we identify product/market conditions under which the seller benefits from facilitating such buyer-created information (e.g., by allowing consumers to post user-based product reviews on the seller's website). Finally, we illustrate the importance of the timing of the introduction of consumer reviews available as a strategic variable and show that delaying the availability of consumer reviews for a given product can be beneficial if the number of expert (more sophisticated) product users is relatively large and cost of the product is low.


title: Nonparametric Inference for Auto-Encoding Variational Bayes

abstract: We would like to learn latent representations that are low-dimensional and highly interpretable. A model that has these characteristics is the Gaussian Process Latent Variable Model. The benefits and negative of the GP-LVM are complementary to the Variational Autoencoder, the former provides interpretable low-dimensional latent representations while the latter is able to handle large amounts of data and can use non-Gaussian likelihoods. Our inspiration for this paper is to marry these two approaches and reap the benefits of both. In order to do so we will introduce a novel approximate inference scheme inspired by the GP-LVM and the VAE. We show experimentally that the approximation allows the capacity of the generative bottle-neck (Z) of the VAE to be arbitrarily large without losing a highly interpretable representation, allowing reconstruction quality to be unlimited by Z at the same time as a low-dimensional space can be used to perform ancestral sampling from as well as a means to reason about the embedded data.


title: Approaches for Sentiment Analysis on Twitter: A State-of-Art study

abstract: Microbloging is an extremely prevalent broadcast medium amidst the Internet fraternity these days. People share their opinions and sentiments about variety of subjects like products, news, institutions, etc., every day on microbloging websites. Sentiment analysis plays a key role in prediction systems, opinion mining systems, etc. Twitter, one of the microbloging platforms allows a limit of 140 characters to its users. This restriction stimulates users to be very concise about their opinion and twitter an ocean of sentiments to analyze. Twitter also provides developer friendly streaming API for data retrieval purpose allowing the analyst to search real time tweets from various users. In this paper, we discuss the state-of-art of the works which are focused on Twitter, the online social network platform, for sentiment analysis. We survey various lexical, machine learning and hybrid approaches for sentiment analysis on Twitter.


title: Geometric features for voxel-based surface recognition

abstract: We introduce a library of geometric voxel features for CAD surface recognition/retrieval tasks. Our features include local versions of the intrinsic volumes (the usual 3D volume, surface area, integrated mean and Gaussian curvature) and a few closely related quantities. We also compute Haar wavelet and statistical distribution features by aggregating raw voxel features. We apply our features to object classification on the ESB data set and demonstrate accurate results with a small number of shallow decision trees.


title: Synthesizing database programs for schema refactoring

abstract: Many programs that interact with a database need to undergo schema refactoring several times during their life cycle. Since this process typically requires making significant changes to the program's implementation, schema refactoring is often non-trivial and error-prone. Motivated by this problem, we propose a new technique for automatically synthesizing a new version of a database program given its original version and the source and target schemas. Our method does not require manual user guidance and ensures that the synthesized program is equivalent to the original one. Furthermore, our method is quite efficient and can synthesize new versions of database programs (containing up to 263 functions) that are extracted from real-world web applications with an average synthesis time of 69.4 seconds.


title: Intercomparison of UAV, Aircraft and Satellite Remote Sensing Platforms for Precision Viticulture

abstract: Precision Viticulture is experiencing substantial growth thanks to the availability of improved and cost-effective instruments and methodologies for data acquisition and analysis, such as Unmanned Aerial Vehicles (UAV), that demonstrated to compete with traditional acquisition platforms, such as satellite and aircraft, due to low operational costs, high operational flexibility and high spatial resolution of imagery. In order to optimize the use of these technologies for precision viticulture, their technical, scientific and economic performances need to be assessed. The aim of this work is to compare NDVI surveys performed with UAV, aircraft and satellite, to assess the capability of each platform to represent the intra-vineyard vegetation spatial variability. NDVI images of two Italian vineyards were acquired simultaneously from different multi-spectral sensors onboard the three platforms, and a spatial statistical framework was used to assess their degree of similarity. Moreover, the pros and cons of each technique were also assessed performing a cost analysis as a function of the scale of application. Results indicate that the different platforms provide comparable results in vineyards characterized by coarse vegetation gradients and large vegetation clusters. On the contrary, in more heterogeneous vineyards, low-resolution images fail in representing part of the intra-vineyard variability. The cost analysis showed that the adoption of UAV platform is advantageous for small areas and that a break-even point exists above five hectares; above such threshold, airborne and then satellite have lower imagery cost.


title: Octree-based Point-Cloud Compression

abstract: In this paper we present a progressive compression method for point sampled models that is specifically apt at dealing with densely sampled surface geometry. The compression is lossless and therefore is also suitable for storing the unfiltered, raw scan data. Our method is based on an octree decomposition of space. The point-cloud is encoded in terms of occupied octree-cells. To compress the octree we employ novel prediction techniques that were specifically designed for point sampled geometry and are based on local surface approximations to achieve high compression rates that outperform previous progressive coders for point-sampled geometry. Moreover we demonstrate that additional point attributes, such as color, which are of great importance for point-sampled geometry, can be well integrated and efficiently encoded in this framework.


features Pendulum Pattern Perception *


title: A New Convolutional Network-in-Network Structure and Its Applications in Skin Detection, Semantic Segmentation, and Artifact Reduction

abstract: The inception network has been shown to provide good performance on image classification problems, but there are not much evidences that it is also effective for the image restoration or pixel-wise labeling problems. For image restoration problems, the pooling is generally not used because the decimated features are not helpful for the reconstruction of an image as the output. Moreover, most deep learning architectures for the restoration problems do not use dense prediction that need lots of training parameters. From these observations, for enjoying the performance of inception-like structure on the image based problems we propose a new convolutional network-in-network structure. The proposed network can be considered a modification of inception structure where pool projection and pooling layer are removed for maintaining the entire feature map size, and a larger kernel filter is added instead. Proposed network greatly reduces the number of parameters on account of removed dense prediction and pooling, which is an advantage, but may also reduce the receptive field in each layer. Hence, we add a larger kernel than the original inception structure for not increasing the depth of layers. The proposed structure is applied to typical image-to-image learning problems, i.e., the problems where the size of input and output are same such as skin detection, semantic segmentation, and compression artifacts reduction. Extensive experiments show that the proposed network brings comparable or better results than the state-of-the-art convolutional neural networks for these problems.


title: Using Virtual Machine Allocation Policies to Defend against Co-Resident Attacks in Cloud Computing

abstract: Cloud computing enables users to consume various IT resources in an on-demand manner, and with low management overhead. However, customers can face new security risks when they use cloud computing platforms. In this paper, we focus on one such threat—the co-resident attack, where malicious users build side channels and extract private information from virtual machines co-located on the same server. Previous works mainly attempt to address the problem by eliminating side channels. However, most of these methods are not suitable for immediate deployment due to the required modifications to current cloud platforms. We choose to solve the problem from a different perspective, by studying how to improve the virtual machine allocation policy, so that it is difficult for attackers to co-locate with their targets. Specifically, we (1) define security metrics for assessing the attack; (2) model these metrics, and compare the difficulty of achieving co-residence under three commonly used policies; (3) design a new policy that not only mitigates the threat of attack, but also satisfies the requirements for workload balance and low power consumption; and (4) implement, test, and prove the effectiveness of the policy on the popular open-source platform OpenStack.


title: Independent Comparative Study of PCA, ICA, and LDA on the FERET Data Set

abstract: Face recognition is one of the most successful applica- tions of image analysis and understanding and has gained much attention in recent years. Various algorithms were proposed and research groups across the world reported different and often contra- dictory results when comparing them. The aim of this paper is to present an independent, comparative study of three most popular appearance-based face recognition projection methods (PCA, ICA, and LDA) in completely equal working conditions regarding prepro- cessing and algorithm implementation. We are motivated by the lack of direct and detailed independent comparisons of all possible algo- rithm implementations (e.g., all projection-metric combinations) in available literature. For consistency with other studies, FERET data set is used with its standard tests (gallery and probe sets). Our results show that no particular projection-metric combination is the best across all standard FERET tests and the choice of appropriate projec- tion-metric combination can only be made for a specific task. Our results are compared to other available studies and some discrepan- cies are pointed out. As an additional contribution, we also introduce our new idea of hypothesis testing across all ranks when comparing


title: Association between Physical Activity Levels and Physical Symptoms or Illness among University Students in Korea

abstract: Background: Low levels of physical activity can cause various physical symptoms or illness. However, few studies on this association have been conducted in young adults. The aim of this study was to investigate the association between physical activity levels and physical symptoms or illness in young adults. Methods: Subjects were university students who participated in a web-based self-administered questionnaire in a university in Seoul in 2013. We obtained information on physical activities and physical symptoms or illness in the past year. Independent variables were defined as symptoms or illness which were associated with decreased academic performance. Logistic regression was performed to calculate odds ratios (ORs) and 95% confidence intervals (CIs) of each physical symptom or illness with adjustment for covariables. Results: A total of 2,201 participants were included in the study. The main physical symptoms or illness among participants were severe fatigue (64.2%), muscle or joint pain (46.3%), gastrointestinal problems (43.1%), headache or dizziness (38.6%), frequent colds (35.1%), and sleep problems (33.3%). Low physical activity levels were significantly associated with high ORs of physical symptoms or illness. Multivariable-adjusted ORs (95% CIs) in the lowest vs. highest tertile of physical activity were 1.45 (1.14–1.83) for severe fatigue, 1.35 (1.07–1.70) for frequent colds, and 1.29 (1.02–1.63) for headaches or dizziness. We also found that lower levels of physical activity were associated with more physical symptoms or bouts of illness. Conclusion: Low physical activity levels were significantly associated with various physical symptoms or illness among university students. Also, individuals in the lower levels of physical activity were more likely to experience more physical symptoms or bouts of illness than those in the highest tertile of physical activity.


title: Learning Hierarchical Features for Scene Labeling

abstract: Scene labeling consists of labeling each pixel in an image with the category of the object it belongs to. We propose a method that uses a multiscale convolutional network trained from raw pixels to extract dense feature vectors that encode regions of multiple sizes centered on each pixel. The method alleviates the need for engineered features, and produces a powerful representation that captures texture, shape, and contextual information. We report results using multiple postprocessing methods to produce the final labeling. Among those, we propose a technique to automatically retrieve, from a pool of segmentation components, an optimal set of components that best explain the scene; these components are arbitrary, for example, they can be taken from a segmentation tree or from any family of oversegmentations. The system yields record accuracies on the SIFT Flow dataset (33 classes) and the Barcelona dataset (170 classes) and near-record accuracy on Stanford background dataset (eight classes), while being an order of magnitude faster than competing approaches, producing a 320×240 image labeling in less than a second, including feature extraction.


title: Keyword extraction for social snippets

abstract: Today, a huge amount of text is being generated for social purposes on social networking services on the Web. Unlike traditional documents, such text is usually extremely short and tends to be informal. Analysis of such text benefit many applications such as advertising, search, and content filtering. In this work, we study one traditional text mining task on such new form of text, that is extraction of meaningful keywords. We propose several intuitive yet useful features and experiment with various classification models. Evaluation is conducted on Facebook data. Performances of various features and models are reported and compared.


title: The Sample Complexity of Dictionary Learning

abstract: A large set of signals can sometimes be described sparsely using a dictionary, that is, every element can be represented as a linear combination of few elements from the dictionary. Algorithms for various signal processing applications, including classification, denoising and signal separation, learn a dictionary from a given set of signals to be represented. Can we expect that the error in representing by such a dictionary a previously unseen signal from the same source will be of similar magnitude as those for the given examples? We assume signals are generated from a fixed distribution, and study these questions from a statistical learning theory perspective. We develop generalization bounds on the quality of the learned dictionary for two types of constraints on the coefficient selection, as measured by the expected L2 error in representation when the dictionary is used. For the case of l1 regularized coefficient selection we provide a generalization bound of the order of O(√np ln(mλ)/m), where n is the dimension, p is the number of elements in the dictionary, λ is a bound on the l1 norm of the coefficient vector and m is the number of samples, which complements existing results. For the case of representing a new signal as a combination of at most k dictionary elements, we provide a bound of the order O(√np ln(mk)/m) under an assumption on the closeness to orthogonality of the dictionary (low Babel function). We further show that this assumption holds for most dictionaries in high dimensions in a strong probabilistic sense. Our results also include bounds that converge as 1/m, not previously known for this problem. We provide similar results in a general setting using kernels with weak smoothness requirements.


title: A survey about user requirements for biometric authentication on smartphones

abstract: The increasing number of smartphone users stresses the need for an improved protection of users' personal data, such as health information, personal identifiers and financial data. One trend to address this need is the adoption of biometric authentication techniques such as fingerprint and iris recognition. We report on a study that examines user requirements and preferences for biometric authentication on smartphones with the focus on iris recognition. We surveyed users about their perception of different biometric authentication techniques in terms of trust, information security, and data privacy. Furthermore, we assessed tradeoffs that users are willing to accept for additional security. We also examined usability requirements for iris recognition and identified trends between different demographic groups. We discuss the findings and emphasize the need for an improved, data-driven, understanding of the emerging requirements for such biometric mobile systems.


title: The effectiveness of bulk gasket and shield cover on the reduction of EMI radiation in LCD TV

abstract: In this paper, the effectiveness of bulk gasket and shield cover is studied. Those materials are widely used in LCD TV, because they strengthen the PCB ground and suppress EMI radiation. In this paper, shielding and grounding effect by those materials is examined by EM field simulation and the result is compared with test one. Finally, it is verified that the simulation result is in good agreement with test result.


title: Modeling, simulation, and construction of a furuta pendulum test-bed

abstract: This paper presents a Furuta pendulum as a test-bed to experimentally validate automatic control strategies or theoretical concepts associated with nonlinear systems. Herein, the modeling, simulation, and construction of a Furuta pendulum test-bed are introduced step-by-step. The development of the Furuta pendulum mathematical model is achieved by using Lagrange equations of motion. In contrast with other works, the development of this model includes an analysis of the system kinematics. Also, a numerical simulation of the mathematical model is performed via Matlab-Simulink. Furthermore, with the purpose of presenting a different tool for the modeling and simulation of dynamic systems, a graphical model and a graphical simulation of the Furuta pendulum are carried out by means of Working Model 3D. Moreover, a computer aided design of the system under study is accomplished through SolidWorks, based on such a design a test-bed is built. With the intention of verifying that the test-bed built behaves according to the models (numerical one and graphical one) presented herein, experiments with the test-bed in open-loop are carried out by using Matlab-Simulink, ControlDesk, and a DS1104 board from dSPACE.


title: An unfermented gel component of psyllium seed husk promotes laxation as a lubricant in humans.

abstract: BACKGROUND ::: In addition to increasing stool weight, supplements of psyllium seed husk produce stools that are slick and gelatinous. ::: ::: ::: OBJECTIVE ::: Our purpose was to test the hypothesis that a gel-forming fraction of psyllium escapes microbial fermentation and is responsible for the characteristics that enhance laxation. ::: ::: ::: DESIGN ::: Fifteen healthy adults consumed controlled diets for two 7-d periods, one of which included 8.8 g dietary fiber provided by 15 g/d of a psyllium seed husk preparation. All stools were collected and evaluated and diet was monitored throughout. ::: ::: ::: RESULTS ::: Psyllium significantly increased the apparent viscosity of an aqueous stool extract, stool moisture, and wet and dry stool weights. A very viscous fraction, not present in low-fiber stool and containing predominantly 2 sugars that are also found in abundance in psyllium husk, was isolated from psyllium stool. ::: ::: ::: CONCLUSIONS ::: In contrast with other viscous fibers that are fermented completely in the colon, a component of psyllium is not fermented. This gel provided lubrication that facilitated propulsion of colon contents and produced a stool that was bulkier and more moist than were stools resulting with use of comparable amounts of other bowel-regulating fiber sources.


title: Wearable Monitoring Unit for Swimming Performance Analysis

abstract: BIOSWIM (Body Interface System based on Wearable Integration Monitoring) is a joint multidisciplinary effort involving a number of Portuguese R&D teams. It seeks a pervasive monitoring solution for physiological and biomechanical signals from a swimmer under normal training conditions, both in and out of the water. A wearable inertial monitoring unit (WIMU) was developed in order to serve as the biomechanical data processing unit of the system. The preliminary version of the WIMU has a commercially available microcontroller and transceiver set, as well as a tri-axial accelerometer and a bi-axial gyroscope serving as sensors. Testing in and out of the water has provided promising data and contributed to design modifications. These also took into account input from athletes, trainers, and physicians. Future work includes the integration of the WIMU within the complete BIOSWIM swimsuit system, complemented by truly integrated EMG and ECG textile sensors and a chemical monitoring unit.


title: Glaucoma Detection From Fundus Image Using Opencv

abstract: This study proposes a semi automated method for glaucoma detection using CDR and ISNT ratio of a fundus image. CDR (Cup to Disc Ratio) is ratio of area of Optic Cup to area of Optic Disc. For a patient with glaucoma Optic Cup size increases while the Optic Disc size remains same and hence CDR will be high for glaucoma patient than normal fundus image. The ROI of green plane is taken and K-Means clustering technique is recursively applied and Optic Disc and Optic Cup is segmented. Through elliptic fiiting, area of Optic Disc and Cup is determined and hence CDR is calculated. ISNT is another parameter used for the diagnosis of glaucoma which is determined through the ratio of area of blood vessels in Inferior Superior to Nasal Temporal side. Blood vessels will shift to Nasal side for glaucoma patients, hence value will be less for glaucoma patient than normal fundus image. Matched filter and Local entropy thresholding is applied to extract blood vessels. The code is programmed in C++ using OpenCV library functions. OpenCV (Open Source Computer Vision Library) is a library of programming functions developed by Intel. Core, highgui, imgproc, ml are the main libraries used from OpenCV. The optimized functions in OpenCV increase the speed of operation and is very much suitable for real time mass screening purpose. A batch of 50 retinal images (25 normal set and 25 abnormal set) obtained from the Aravind Eye Hospital, is used to assess the performance of the proposed system.


title: Heavy Metal Contamination in the Surface Layer of Bottom Sediments in a Flow-Through Lake: A Case Study of Lake Symsar in Northern Poland

abstract: River-lake systems most often behave as hydrographic units, which undergo complex interactions, especially in the contact zone. One such interaction pertains to the role of a river in the dispersal of trace elements carried into and out of a lake. In this study, we aimed to assess the impact of rivers on the accumulation of heavy metals in bottom sediments of natural lakes comprised in postglacial river-lake systems. The results showed that a river flowing through a lake is a key factor responsible for the input of the majority of available fraction of heavy metals (Zn, Mn, Cd and Ni) into the water body and for their accumulation along the flow of river water in the lake. The origin of other accumulated elements were the linear and point sources in catchments. In turn, the Pb content was associated with the location of roads in the direct catchment, while the sediment structure (especially size of fraction and density) could have affected the accumulation of Cr and Zn, which indicated correlations between these metals and fine fraction. Our results suggest that lakes act as filters and contribute to the self-purification of water that flows through them. As a result, the content of most metals in lake sediments showed a decrease by approx. 75% between the upstream (inflow) and downstream (outflow) sections. The increased content of two metals only, such as chromium and cadmium (higher by 2.0 and 2.5 times, respectively, after passing through the lake), was due to the correlation of the metals with fine sand. Both the content and distribution pattern of heavy metals in lake sediments are indicative of the natural response of aquatic ecosystems to environmental stressors, such as pollutant import with river water or climate change. The complex elements creating the water ecosystem of each lake can counteract stress by temporarily removing pollutants such as toxic metals form circulation and depositing them mostly around the delta.