User experiences and expectations of vibrotactile, thermal and squeeze feedback in interpersonal communication

Katja Suhonen Tampere University of Technology, Human-Centered Technology, P.O.Box 589, 33101 Tampere, Finland katja.suhonen@tut.fi Kaisa Väänänen-Vainio-Mattila Tampere University of Technology, Human-Centered Technology, P.O.Box 589, 33101 Tampere, Finland kaisa.vaananen-vainiomattila@tut.fi Kalle Mäkelä Tampere University of Technology, Department of Electronics, P.O.Box 692, 33101 Tampere, Finland kalle.makela@tut.fi


Differences in kinematics and electromyographic activity between men and women during the single-legged squat.

BACKGROUND
Numerous factors have been identified as potentially increasing the risk of anterior cruciate ligament injury in the female athlete. However, differences between the sexes in lower extremity coordination, particularly hip control, are only minimally understood.

HYPOTHESIS
There is no difference in kinematic or electromyographic data during the single-legged squat between men and women.

STUDY DESIGN
Descriptive comparison study.

METHODS
We kinematically and electromyographically analyzed the single-legged squat in 18 intercollegiate athletes (9 male, 9 female). Subjects performed five single-legged squats on their dominant leg, lowering themselves as far as possible and then returning to a standing position without losing balance.

RESULTS
Women demonstrated significantly more ankle dorsiflexion, ankle pronation, hip adduction, hip flexion, hip external rotation, and less trunk lateral flexion than men. These factors were associated with a decreased ability of the women to maintain a varus knee position during the squat as compared with the men. Analysis of all eight tested muscles demonstrated that women had greater muscle activation compared with men. When each muscle was analyzed separately, the rectus femoris muscle activation was found to be statistically greater in women in both the area under the linear envelope and maximal activation data.

CONCLUSIONS
Under a physiologic load in a position commonly assumed in sports, women tend to position their entire lower extremity and activate muscles in a manner that could increase strain on the anterior cruciate ligament.


Procedural Content Generation: Goals, Challenges and Actionable Steps

This chapter discusses the challenges and opportunities of procedural content generation (PCG) in games. It starts with defining three grand goals of PCG, namely multi-level multi-content PCG, PCG-based game design and generating complete games. The way these goals are defined, they are not feasible with current technology. Therefore we identify nine challenges for PCG research. Work towards meeting these challenges is likely to take us closer to realising the three grand goals. In order to help researchers get started, we also identify five actionable steps, which PCG researchers could get started working on immediately. 1998 ACM Subject Classification I.2.1 Applications and Expert Systems: Games


Time-to-Digital Converter Using a Tuned-Delay Line Evaluated in 28-, 40-, and 45-nm FPGAs

This paper proposes a bin-width tuning method for a field-programmable gate array (FPGA)-based delay line for a time-to-digital converter (TDC). Changing the hit transitions and sampling patterns of the carry chain considering delays of the sum and carry-out bins can improve the bin-width uniformity and thus measurement precision. The proposed sampling method was evaluated and compared with the ordinary tapped-delay-line (TDL) method in three different types of FPGAs: Kintex-7, Virtex-6, and Spartan-6. The linearity, equivalent bin width, and measurement precision improved for all the evaluated FPGAs by adopting the proposed method. The measurement precision obtained using the simple TDL architecture is comparable with other complex TDC architectures. In addition, the proposed method improves bin-width uniformity and measurement precision while maintaining the advantages of TDL TDCs, that is, fast conversion rate and small resource usage. Furthermore, the enhanced linearity of the delay line can also improve other carry-chain-based FPGA-TDCs.


Control structure design for complete chemical plants

Control structure design deals with the structural decisions of the control system, including what to control and how to pair the variables to form control loops. Although these are very important issues, these decisions are in most cases made in an ad hoc fashion, based on experience and engineering insight, without considering the details of each problem. In the paper, a systematic procedure for control structure design for complete chemical plants (plantwide control) is presented. It starts with carefully defining the operational and economic objectives, and the degrees of freedom available to fulfill them. Other issues, discussed in the paper, include inventory and production rate control, decentralized versus multivariable control, loss in performance by bottom-up design, and a definition of a the ‘‘complexity number’’ for the control system. # 2003 Elsevier Ltd. All rights reserved.


Reducing Uncertainty of Low-Sampling-Rate Trajectories

The increasing availability of GPS-embedded mobile devices has given rise to a new spectrum of location-based services, which have accumulated a huge collection of location trajectories. In practice, a large portion of these trajectories are of low-sampling-rate. For instance, the time interval between consecutive GPS points of some trajectories can be several minutes or even hours. With such a low sampling rate, most details of their movement are lost, which makes them difficult to process effectively. In this work, we investigate how to reduce the uncertainty in such kind of trajectories. Specifically, given a low-sampling-rate trajectory, we aim to infer its possible routes. The methodology adopted in our work is to take full advantage of the rich information extracted from the historical trajectories. We propose a systematic solution, History based Route Inference System (HRIS), which covers a series of novel algorithms that can derive the travel pattern from historical data and incorporate it into the route inference process. To validate the effectiveness of the system, we apply our solution to the map-matching problem which is an important application scenario of this work, and conduct extensive experiments on a real taxi trajectory dataset. The experiment results demonstrate that HRIS can achieve higher accuracy than the existing map-matching algorithms for low-sampling-rate trajectories.


Towards reliable traffic sign recognition

The demand for reliable traffic sign recognition (TSR) increases with the development of safety driven advanced driver assistance systems (ADAS). Emerging technologies like brake-by-wire or steer-by-wire pave the way for collision avoidance and threat identification systems. Obviously, decision making in such critical situations requires high reliability of the information base. Especially for comfort systems, we need to take into account that the user tends to trust the information provided by the ADAS [1]. In this paper, we present a robust system architecture for the reliable recognition of circular traffic signs. Our system employs complementing approaches for the different stages of current TSR systems. This introduces the application of local SIFT features for content-based traffic sign detection along with widely applied shape-based approaches. We further add a technique called contracting curve density (CCD) to refine the localization of the detected traffic sign candidates and therefore increase the performance of the subsequent classification module. Finally, the recognition stage based on SIFT and SURF descriptions of the candidates executed by a neural net provides a robust classification of structured image content like traffic signs. By applying these steps we compensate the weaknesses of the utilized approaches, and thus, improve the system's performance.


3-Dimensional Analysis on the GIDL Current of Body-tied Triple Gate FinFET

Triple gate FinFET is emerging as a promising candidate for the future CMOS device structures because of its immunity to short-channel effect. However, the suppression of GIDL is a significant challenge for its application. In this paper, we discuss the characteristics of GIDL on FinFET and extensively analyze the influence of the device technology on GIDL. The analysis is expected to give guidelines to the future development of triple gate FinFET


Perspectives on the Productivity Dilemma

Editor’s note The authors of this paper presented an All-academy session at the 2008 Academy of Management annual meeting in Anaheim, California. We were excited by the dynamic nature of the debate and felt that it related closely to critical issues in the areas of operations management, strategy, product development and international business. We thus invited the authors to write an article offering their individual and joint views on the productivity dilemma. We trust you will find it to be stimulating and thought provoking. We invite you to add your voice to the discussion by commenting on this article at the Operations and Supply Chain (OSM) Forum at http://www.journaloperationsmanagement.org/OSM.asp. – Kenneth K. Boyer and Morgan L. Swink


Applying the Waek Learning Framework to Understand and Improve C4.5

There has long been a chasm between theoretical mod els of machine learning and practical machine learn ing algorithms For instance empirically successful algorithms such as C and backpropagation have not met the criteria of the PAC model and its vari ants Conversely the algorithms suggested by com putational learning theory are usually too limited in various ways to nd wide application The theoreti cal status of decision tree learning algorithms is a case in point while it has been proven that C and all reasonable variants of it fails to meet the PAC model criteria other recently proposed decision tree algo rithms that do have non trivial performance guaran tees unfortunately require membership queries Two recent developments have narrowed this gap between theory and practice not for the PAC model but for the related model known as weak learning or boosting First an algorithm called Adaboost was proposed that meets the formal criteria of the boosting model and is also competitive in practice Second the basic algorithms underlying the popular C and CART programs have also very recently been shown to meet the formal criteria of the boosting model Thus it seems plausible that the weak learning frame work may provide a setting for interaction between formal analysis and machine learning practice that is lacking in other theoretical models Our aim in this paper is to push this interaction further in light of these recent developments In par ticular we perform experiments suggested by the for mal results for Adaboost and C within the weak learning framework We concentrate on two particu larly intriguing issues First the theoretical boosting results for top down decision tree algorithms such as C suggest that a new splitting criterion may result in trees that are smaller and more accurate than those obtained using the usual information gain We con rm this suggestion experimentally Second a super cial interpretation of the theo retical results suggests that Adaboost should vastly outperform C This is not the case in practice and we argue through experimental results that the theory must be understood in terms of a measure of a boosting algorithm s behavior called its advantage sequence We compare the advantage sequences for C and Adaboost in a number of experiments We nd that these sequences have qualitatively dif ferent behavior that explains in large part the dis crepancies between empirical performance and the theoretical results Brie y we nd that although C and Adaboost are both boosting algorithms Adaboost creates successively harder ltered dis tributions while C creates successively easier ones in a sense that will be made precise


Sex similarities and differences in preferences for short-term mates: what, whether, and why.

Are there sex differences in criteria for sexual relationships? The answer depends on what question a researcher asks. Data suggest that, whereas the sexes differ in whether they will enter short-term sexual relationships, they are more similar in what they prioritize in partners for such relationships. However, additional data and context of other findings and theory suggest different underlying reasons. In Studies 1 and 2, men and women were given varying "mate budgets" to design short-term mates and were asked whether they would actually mate with constructed partners. Study 3 used a mate-screening paradigm. Whereas women have been found to prioritize status in long-term mates, they instead (like men) prioritize physical attractiveness much like an economic necessity in short-term mates. Both sexes also show evidence of favoring well-rounded long- and short-term mates when given the chance. In Studies 4 and 5, participants report reasons for having casual sex and what they find physically attractive. For women, results generally support a good genes account of short-term mating, as per strategic pluralism theory (S. W. Gangestad & J. A. Simpson, 2000). Discussion addresses broader theoretical implications for mate preference, and the link between method and theory in examining social decision processes.


Interpersonal connectedness: conceptualization and directions for a measurement instrument

Interpersonal connectedness is the sense of belonging based on the appraisal of having sufficient close social contacts. This feeling is regarded as one of the major outcomes of successful (mediated) social interaction and as such an important construct for HCI. However, the exact nature of this feeling, how to achieve it, and how to assess it remain unexplored to date. In the current paper we start the theoretical conceptualization of this phenomenon by exploring its basic origins in psychological literature and simultaneously formulate requirements for a measurement instrument to be developed in the service of exploring and testing CMC applications, in particular awareness technologies.


Security challenges in software defined network and their solutions

The main purpose of Software Defined Networking (SDN) is to allow network engineers to respond quickly to changing network industrial requirements. This network technology focuses on making network as adaptable and active as virtual server. SDN is physical separation of Control plane from Data plane and control plane is centralized to manage underlying infrastructure. Hence, the SDN permit network administrator to adjust wide traffic flow from centralized control console without having to touch Switches and Routers, and can provide the services to wherever they are needed in the network. As in SDN the control plane is disassociated from underlying forwarding plane, however, susceptible to many security challenges like Denial of Service (DoS) attack, Distributed DoS (DDoS) attack, Volumetric attack. In this paper, we highlight some security challenges and evaluate some security solutions.


The Seventh PASCAL Recognizing Textual Entailment Challenge

This paper presents the Seventh Recognizing Textual Entailment (RTE-7) challenge. This year’s challenge replicated the exercise proposed in RTE-6, consisting of a Main Task, in which Textual Entailment is performed on a real corpus in the Update Summarization scenario; a Main subtask aimed at detecting novel information; and a KBP Validation Task, in which RTE systems had to validate the output of systems participating in the KBP Slot Filling Task. Thirteen teams participated in the Main Task (submitting 33 runs) and 5 in the Novelty Detection Subtask (submitting 13 runs). The KBP Validation Task was undertaken by 2 participants which submitted 5 runs. The ablation test experiment, introduced in RTE-5 to evaluate the impact of knowledge resources used by the systems participating in the Main Task and extended also to tools in RTE-6, was also repeated in RTE-7.


Mobile applications for weight management: theory-based content analysis.

BACKGROUND
The use of smartphone applications (apps) to assist with weight management is increasingly prevalent, but the quality of these apps is not well characterized.

PURPOSE
The goal of the study was to evaluate diet/nutrition and anthropometric tracking apps based on incorporation of features consistent with theories of behavior change.

METHODS
A comparative, descriptive assessment was conducted of the top-rated free apps in the Health and Fitness category available in the iTunes App Store. Health and Fitness apps (N=200) were evaluated using predetermined inclusion/exclusion criteria and categorized based on commonality in functionality, features, and developer description. Four researchers then evaluated the two most popular apps in each category using two instruments: one based on traditional behavioral theory (score range: 0-100) and the other on the Fogg Behavioral Model (score range: 0-6). Data collection and analysis occurred in November 2012.

RESULTS
Eligible apps (n=23) were divided into five categories: (1) diet tracking; (2) healthy cooking; (3) weight/anthropometric tracking; (4) grocery decision making; and (5) restaurant decision making. The mean behavioral theory score was 8.1 (SD=4.2); the mean persuasive technology score was 1.9 (SD=1.7). The top-rated app on both scales was Lose It! by Fitnow Inc.

CONCLUSIONS
All apps received low overall scores for inclusion of behavioral theory-based strategies.


Constructions from Dots and Lines

Marko A. Rodriguez is graph systems architect at AT&T Interactive. He can be reached at marko<at>markorodriguez.com. Peter Neubauer is chief operating officer of Neo Technology. He can be reached at peter.neubauer<at>neotechnology.com A graph is a data structure composed of dots (i.e., vertices) and lines (i.e., edges). The dots and lines of a graph can be organized into intricate arrangements. A graph’s ability to denote objects and their relationships to one another allows for a surprisingly large number of things to be modeled as graphs. From the dependencies that link software packages to the wood beams that provide the framing to a house, most anything has a corresponding graph representation. However, just because it is possible to represent something as a graph does not necessarily mean that its graph representation will be useful. If a modeler can leverage the plethora of tools and algorithms that store and process graphs, then such a mapping is worthwhile. This article explores the world of graphs in computing and exposes situations in which graphical models are beneficial.


Hilbert Space Embeddings of Hidden Markov Models

Hidden Markov Models (HMMs) are important tools for modeling sequence data. However, they are restricted to discrete latent states, and are largely restricted to Gaussian and discrete observations. And, learning algorithms for HMMs have predominantly relied on local search heuristics, with the exception of spectral methods such as those described below. We propose a nonparametric HMM that extends traditional HMMs to structured and non-Gaussian continuous distributions. Furthermore, we derive a localminimum-free kernel spectral algorithm for learning these HMMs. We apply our method to robot vision data, slot car inertial sensor data and audio event classification data, and show that in these applications, embedded HMMs exceed the previous state-of-the-art performance.


Closing the learning-planning loop with predictive state representations

A central problem in artificial intelligence is that of planning to maximize future reward under uncertainty in a partially observable environment. In this paper we propose and demonstrate a novel algorithm which accurately learns a model of such an environment directly from sequences of action-observation pairs. We then close the loop from observations to actions by planning in the learned model and recovering a policy which is near-optimal in the original environment. Specifically, we present an efficient and statistically consistent spectral algorithm for learning the parameters of a Predictive State Representation (PSR). We demonstrate the algorithm by learning a model of a simulated high-dimensional, vision-based mobile robot planning task, and then perform approximate point-based planning in the learned PSR. Analysis of our results shows that the algorithm learns a state space which efficiently captures the essential features of the environment. This representation allows accurate prediction with a small number of parameters, and enables successful and efficient planning.


A Spectral Algorithm for Learning Hidden Markov Models

Hidden Markov Models (HMMs) are one of the most fundamental a nd widely used statistical tools for modeling discrete time series. Typically, they are learned using sea rch heuristics (such as the Baum-Welch / EM algorithm), which suffer from the usual local optima issues. While in gen eral these models are known to be hard to learn with samples from the underlying distribution, we provide t h first provably efficient algorithm (in terms of sample and computational complexity) for learning HMMs under a nat ur l separation condition. This condition is roughly analogous to the separation conditions considered for lear ning mixture distributions (where, similarly, these model s are hard to learn in general). Furthermore, our sample compl exity results do not explicitly depend on the number of distinct (discrete) observations — they implicitly depend on this number through spectral properties of the underlyin g HMM. This makes the algorithm particularly applicable to se ttings with a large number of observations, such as those in natural language processing where the space of observati on is sometimes the words in a language. Finally, the algorithm is particularly simple, relying only on a singula r value decomposition and matrix multiplications.


Hilbert space embeddings of conditional distributions with applications to dynamical systems

In this paper, we extend the Hilbert space embedding approach to handle conditional distributions. We derive a kernel estimate for the conditional embedding, and show its connection to ordinary embeddings. Conditional embeddings largely extend our ability to manipulate distributions in Hilbert spaces, and as an example, we derive a nonparametric method for modeling dynamical systems where the belief state of the system is maintained as a conditional embedding. Our method is very general in terms of both the domains and the types of distributions that it can handle, and we demonstrate the effectiveness of our method in various dynamical systems. We expect that conditional embeddings will have wider applications beyond modeling dynamical systems.


Knowledge tracing: Modeling the acquisition of procedural knowledge

This paper describes an effort to model students' changing knowledge state during skill acquisition. Students in this research are learning to write short programs with the ACT Programming Tutor (APT). APT is constructed around a production rule cognitive model of programming knowledge, called theideal student model. This model allows the tutor to solve exercises along with the student and provide assistance as necessary. As the student works, the tutor also maintains an estimate of the probability that the student has learned each of the rules in the ideal model, in a process calledknowledge tracing. The tutor presents an individualized sequence of exercises to the student based on these probability estimates until the student has ‘mastered’ each rule. The programming tutor, cognitive model and learning and performance assumptions are described. A series of studies is reviewed that examine the empirical validity of knowledge tracing and has led to modifications in the process. Currently the model is quite successful in predicting test performance. Further modifications in the modeling process are discussed that may improve performance levels.


Differential evolution algorithm with multiple mutation strategies based on roulette wheel selection

In this paper, we propose a differential evolution (DE) algorithm variant with a combination of multiple mutation strategies based on roulette wheel selection, which we call MMRDE. We first propose a new, reflection-based mutation operation inspired by the reflection operations in the Nelder–Mead method. We design an experiment to compare its performance with seven mutation strategies, and we prove its effectiveness at balancing exploration and exploitation of DE. Although our reflection-based mutation strategy can balance exploration and exploitation of DE, it is still prone to premature convergence or evolutionary stagnation when solving complex multimodal optimization problems. Therefore, we add two basic strategies to help maintain population diversity and increase the robustness. We use roulette wheel selection to arrange mutation strategies based on their success rates for each individual. MMRDE is tested with some improved DE variants based on 28 benchmark functions for real-parameter optimization that have been recommended by the Institute of Electrical and Electronics Engineers CEC2013 special session. Experimental results indicate that the proposed algorithm shows its effectiveness at cooperative work with multiple strategies. It can obtain a good balance between exploration and exploitation. The proposed algorithm can guide the search for a global optimal solution with quick convergence compared with other improved DE variants.


Managing the knowledge lifecycle: A integrated knowledge management process model

The main purpose of this study is to propose an integrated conceptual model for exploring knowledge management process. Reviewing literature on knowledge management and tracing its historical background, definitions and dominant paradigms and analysis 32 frameworks of KM, a relatively integrated model of KM was presented in this paper. The study found many similarities of models and its presents a model which combine all the best features of the models analyzed. A 10-stage integrated model was proposed. These stages include some activities such as knowledge goal setting, identification, acquisition, creation, organization, sharing, evaluation, preservation, retention and updating and KM effectiveness evaluation. The findings can be used by managers in all organizational environments to implementation and auditing of KM practices.


An Augmented Reality museum guide

Recent years have seen advances in many enabling augmented reality technologies. Furthermore, much research has been carried out on how augmented reality can be used to enhance existing applications. This paper describes our experiences with an AR-museum guide that combines some of the latest technologies. Amongst other technologies, markerless tracking, hybrid tracking, and an ultra-mobile-PC were used. Like existing audio guides, the AR-guide can be used by any museum visitor, during a six-month exhibition on Islamic art. We provide a detailed description of the museumpsilas motivation for using AR, of our experiences in developing the system, and the initial results of user surveys. Taking this information into account, we can derive possible system improvements.


Security in Cloud Computing Using Cryptographic Algorithms Miss .

Cloud computing is the concept implemented to decipher the Daily Computing Problems. Cloud computing is basically virtual pool of resources and it provides these resources to users via internet. Cloud computing is the internet based development and used in computer technology. The prevalent problem associated with cloud computing is data privacy, security, anonymity and reliability etc. But the most important between them is security and how cloud provider assures it. To secure the Cloud means secure the treatments (calculations) and storage (databases hosted by the Cloud provider). In this paper we analyses different security issues to cloud and different cryptographic algorithms adoptable to better security for the cloud. Keywords— Cloud Computing, Cryptographic Algorithm, Internet, Security Algorithms, Security Attacks, Security Issue


CEFAM: Comprehensive Evaluation Framework for Agile Methodologies

Agile software development is regarded as an effective and efficient approach, mainly due to its ability to accommodate rapidly changing requirements, and to cope with modern software development challenges. There is therefore a strong tendency to use agile software development methodologies where applicable; however, the sheer number of existing agile methodologies and their variants hinders the selection of an appropriate agile methodology or method chunk. Methodology evaluation tools address this problem through providing detailed evaluations, yet no comprehensive evaluation framework is available for agile methodologies. We introduce the comprehensive evaluation framework for agile methodologies (CEFAM) as an evaluation tool for project managers and method engineers. The hierarchical (and mostly quantitative) evaluation criterion set introduced in this evaluation framework enhances the usability of the framework and provides results that are precise enough to be useful for the selection, adaptation and construction of agile methodologies.


Design and operation of Wi-Fi agribot integrated system

Robotics in agriculture is not a new concept; in controlled environments (green houses), it has a history of over 20 years. Research has been performed to develop harvesters for cherry tomatoes, cucumbers, mushrooms, and other fruits. In horticulture, robots have been introduced to harvest citrus and apples. In this paper autonomous robot for agriculture (AgriBot) is a prototype and implemented for performing various agricultural activities like seeding, weeding, spraying of fertilizers, insecticides. AgriBot is controlled with a Arduino Mega board having At mega 2560 microcontroller. The powerful Raspberry Pi a mini computer is used to control and monitor the working of the robot. The Arduino Mega is mounted on a robot allowing for access to all of the pins for rapid prototyping. Its hexapod body can autonomously walk in any direction, avoiding objects with its ultrasonic proximity sensor. Its walking algorithms allow it to instantly change direction and walk in any new direction without turning its body. An underbody sensory array allows the robot to know if a seed has been planted in the area at the optimal spacing and depth. AgriBot can then dig a hole, plant a seed in the hole, cover the seed with soil, and apply any pre-emergence fertilizers and/or herbicides along with the marking agent. AgriBot can then signal to other robots in the immediate proximity that it needs help planting in that area or that this area has been planted and to move on by communicating through Wi-Fi.


A practical VEP-based brain-computer interface

This paper introduces the development of a practical brain-computer interface at Tsinghua University. The system uses frequency-coded steady-state visual evoked potentials to determine the gaze direction of the user. To ensure more universal applicability of the system, approaches for reducing user variation on system performance have been proposed. The information transfer rate (ITR) has been evaluated both in the laboratory and at the Rehabilitation Center of China, respectively. The system has been proved to be applicable to >90% of people with a high ITR in living environments.


Projector Calibration by "Inverse Camera Calibration"

The accuracy of 3-D reconstructions depends substantially on the accuracy of active vision system calibration. In this work, the problem of video projector calibration is solved by inverting the standard camera calibration work flow. The calibration procedure requires a single camera, which does not need to be calibrated and which is used as the sensor whether projected dots and calibration pattern landmarks, such as the checkerboard corners, coincide. The method iteratively adjusts the projected dots to coincide with the landmarks and the final coordinates are used as inputs to a camera calibration method. The otherwise slow iterative adjustment is accelerated by estimating a plane homography between the detected landmarks and the projected dots, which makes the calibration method fast.


FEEL: Featured Event Embedding Learning

Statistical script learning is an effective way to acquire world knowledge which can be used for commonsense reasoning. Statistical script learning induces this knowledge by observing event sequences generated from texts. The learned model thus can predict subsequent events, given earlier events. Recent approaches rely on learning event embeddings which capture script knowledge. In this work, we suggest a general learning model–Featured Event Embedding Learning (FEEL)–for injecting event embeddings with fine grained information. In addition to capturing the dependencies between subsequent events, our model can take into account higher level abstractions of the input event which help the model generalize better and account for the global context in which the event appears. We evaluated our model over three narrative cloze tasks, and showed that our model is competitive with the most recent state-of-the-art. We also show that our resulting embedding can be used as a strong representation for advanced semantic tasks such as discourse parsing and sentence semantic relatedness.


Attribute-based encryption with non-monotonic access structures

We construct an Attribute-Based Encryption (ABE) scheme that allows a user's private key to be expressed in terms of any access formula over attributes. Previous ABE schemes were limited to expressing only monotonic access structures. We provide a proof of security for our scheme based on the Decisional Bilinear Diffie-Hellman (BDH) assumption. Furthermore, the performance of our new scheme compares favorably with existing, less-expressive schemes.


Browser Fingerprinting as user tracking technology

The Web has become an indispensable part of our society and is currently most commonly used mode of information delivery. Millions of users access the free services provided by the websites on daily basis and while providing these free services websites track and profile their web users. In this environment, the ability to track users and their online habits can be very lucrative for advertising companies, yet very intrusive for the privacy of users. The objective of this paper is to study about the increasingly common yet hardly discussed technique of identifying individual Web users and tracking them across multiple websites known as “Browser Fingerprinting”. A unique browser fingerprint is derived by the unique pattern of information visible whenever a computer visits a website. The permutations thus collected are sufficiently distinct that they can be used as a tool for tracking. Unlike cookies, Fingerprints are generated on server side and are difficult for a user to influence. The main objective of this research is study about how the fingerprinting was evolved, its positives and negatives, what threat it poses to users' online privacy and what countermeasures could be used to prevent it. This paper will also analyse which different properties the browsers send to the server, allowing a unique fingerprint of those browsers to be created.


Zone Divisional Hierarchical Routing Protocol for Wireless Sensor Network

Clustering prolongs energy resources, improves scalability and preserves communication bandwidth. Clustering is either classified as static and dynamic or as equal and unequal. In cluster based routing protocols that employ multi-hop communication, imbalanced energy consumption among the nodes results in hot-spots. Unequal clustering overcomes hot-spots but requires a high overhead and is prone to connectivity issues. To offer guaranteed connectivity and alleviate the hot-spot problem, a zone divisional hierarchical routing protocol has been proposed in this paper. The network is divided into equal sized static rectangular clusters which are assigned to two zones namely near zone and far zone. The zone facing the base station is known as the near zone and rest of the network space makes up the far zone which is further divided into sub-zones. Dual cluster heads approach for sharing the reception, aggregation and forwarding tasks is proposed. The performance evaluation of the proposed protocol against the existing protocols reveals that the method offers energy-efficient multi-hop communication support, uses negligible overhead, prevents creation of hot-spots, avoids early death of nodes, uses balanced energy consumption across the network and maximizes the network lifetime.


MOBA: a New Arena for Game AI

Games have always been popular testbeds for Artificial Intelligence (AI). In the last decade, we have seen the rise of the Multiple Online Battle Arena (MOBA) games, which are the most played games nowadays. In spite of this, there are few works that explore MOBA as a testbed for AI Research. In this paper we present and discuss the main features and opportunities offered by MOBA games to Game AI Research. We describe the various challenges faced along the game and also propose a discrete model that can be used to better understand and explore the game. With this, we aim to encourage the use of MOBA as a novel research platform for Game AI.


A 20-GHz phase-locked loop for 40-gb/s serializing transmitter in 0.13-/spl mu/m CMOS

A 20-GHz phase-locked loop with 4.9 ps/sub pp//0.65 ps/sub rms/ jitter and -113.5 dBc/Hz phase noise at 10-MHz offset is presented. A half-duty sampled-feedforward loop filter that simply replaces the resistor with a switch and an inverter suppresses the reference spur down to -44.0 dBc. A design iteration procedure is outlined that minimizes the phase noise of a negative-g/sub m/ oscillator with a coupled microstrip resonator. Static frequency dividers made of pulsed latches operate faster than those made of flip-flops and achieve near 2:1 frequency range. The phase-locked loop fabricated in a 0.13-/spl mu/m CMOS operates from 17.6 to 19.4GHz and dissipates 480mW.


Color Balance and Fusion for Underwater Image Enhancement

We introduce an effective technique to enhance the images captured underwater and degraded due to the medium scattering and absorption. Our method is a single image approach that does not require specialized hardware or knowledge about the underwater conditions or scene structure. It builds on the blending of two images that are directly derived from a color-compensated and white-balanced version of the original degraded image. The two images to fusion, as well as their associated weight maps, are defined to promote the transfer of edges and color contrast to the output image. To avoid that the sharp weight map transitions create artifacts in the low frequency components of the reconstructed image, we also adapt a multiscale fusion strategy. Our extensive qualitative and quantitative evaluation reveals that our enhanced images and videos are characterized by better exposedness of the dark regions, improved global contrast, and edges sharpness. Our validation also proves that our algorithm is reasonably independent of the camera settings, and improves the accuracy of several image processing applications, such as image segmentation and keypoint matching.


Rationale-Augmented Convolutional Neural Networks for Text Classification

We present a new Convolutional Neural Network (CNN) model for text classification that jointly exploits labels on documents and their constituent sentences. Specifically, we consider scenarios in which annotators explicitly mark sentences (or snippets) that support their overall document categorization, i.e., they provide rationales. Our model exploits such supervision via a hierarchical approach in which each document is represented by a linear combination of the vector representations of its component sentences. We propose a sentence-level convolutional model that estimates the probability that a given sentence is a rationale, and we then scale the contribution of each sentence to the aggregate document representation in proportion to these estimates. Experiments on five classification datasets that have document labels and associated rationales demonstrate that our approach consistently outperforms strong baselines. Moreover, our model naturally provides explanations for its predictions.


Informing Pedagogical Action: Aligning Learning Analytics With Learning Design

This article considers the developing field of learning analytics and argues that to move from small-scale practice to broad scale applicability, there is a need to establish a contextual framework that helps teachers interpret the information that analytics provides. The article presents learning design as a form of documentation of pedagogical intent that can provide the context for making sense of diverse sets of analytic data. We investigate one example of learning design to explore how broad categories of analytics—which we call checkpoint and process analytics—can inform the interpretation of outcomes from a learning design and facilitate pedagogical action.


Maternal depression and early childhood growth in developing countries: systematic review and meta-analysis.

OBJECTIVE
To investigate the relationship between maternal depression and child growth in developing countries through a systematic literature review and meta-analysis.

METHODS
Six databases were searched for studies from developing countries on maternal depression and child growth published up until 2010. Standard meta-analytical methods were followed and pooled odds ratios (ORs) for underweight and stunting in the children of depressed mothers were calculated using random effects models for all studies and for subsets of studies that met strict criteria on study design, exposure to maternal depression and outcome variables. The population attributable risk (PAR) was estimated for selected studies.

FINDINGS
Seventeen studies including a total of 13,923 mother and child pairs from 11 countries met inclusion criteria. The children of mothers with depression or depressive symptoms were more likely to be underweight (OR: 1.5; 95% confidence interval, CI: 1.2-1.8) or stunted (OR: 1.4; 95% CI: 1.2-1.7). Subanalysis of three longitudinal studies showed a stronger effect: the OR for underweight was 2.2 (95% CI: 1.5-3.2) and for stunting, 2.0 (95% CI: 1.0-3.9). The PAR for selected studies indicated that if the infant population were entirely unexposed to maternal depressive symptoms 23% to 29% fewer children would be underweight or stunted.

CONCLUSION
Maternal depression was associated with early childhood underweight and stunting. Rigorous prospective studies are needed to identify mechanisms and causes. Early identification, treatment and prevention of maternal depression may help reduce child stunting and underweight in developing countries.


Application of Artificial Neural Networks to Multiple Criteria Inventory Classification

Original scientific paper Inventory classification is a very important part of inventory control which represents the technique of operational research discipline. A systematic approach to the inventory control and classification may have a significant influence on company competitiveness. The paper describes the results obtained by investigating the application of neural networks in multiple criteria inventory classification. Various structures of a back-propagation neural network have been analysed and the optimal one with the minimum Root Mean Square error selected. The predicted results are compared to those obtained by the multiple criteria classification using the analytical hierarchy process.


Industrial & Engineering Chemistry Research is published by the American Chemical Society. 1155 Sixteenth Street N.W., Washington, DC 20036 Review Methods for Pretreatment of Lignocellulosic Biomass for Efficient Hydrolysis and Biofuel Production Parveen Kumar, Diane M. Barrett, Michael J. Delwiche, and Pieter Stroeve Ind. Eng. Chem. Res., Article ASAP • DOI: 10.1021/ie801542g • Publication Date (Web): 20 March 2009 Downloaded from http://pubs.acs.org on March 26, 2009


Automatic Game Design via Mechanic Generation

Game designs often center on the game mechanics— rules governing the logical evolution of the game. We seek to develop an intelligent system that generates computer games. As first steps towards this goal we present a composable and cross-domain representation for game mechanics that draws from AI planning action representations. We use a constraint solver to generate mechanics subject to design requirements on the form of those mechanics—what they do in the game. A planner takes a set of generated mechanics and tests whether those mechanics meet playability requirements—controlling how mechanics function in a game to affect player behavior. We demonstrate our system by modeling and generating mechanics in a role-playing game, platformer game, and combined role-playing-platformer game.


Central Retinal Artery Occlusion and Partial Ophthalmoplegia Caused by Hyaluronic Acid Injection Filler Cosmetic

Central retinal artery occlusion is a rare ocular emergency condition that can lead to a total blindness. Hereby we report a 30-year-old woman with a sudden visual loss of the left eye after being injected by hyaluronic acid filler at the dorsum nasal area for cosmetic purpose. Visual acuity was light perception while anterior segment and intraocular pressure were within normal limit. Partial ophthalmoplegia include restriction to the nasal gaze were found. Funduscopic examination revealed total retinal edema with a cherry red spot at the fovea. Ocular massage was performed as initial management followed by humor aqueous paracentesis. All procedures were done within 90 minutes. The patients discharge with the visual improvement to hand motion and the Partial ophthalmophlegia has been improved after being evaluated less than a month.


Critiquing-based recommenders: survey and emerging trends

Critiquing-based recommender systems elicit users’ feedback, called critiques, which they made on the recommended items. This conversational style of interaction is in contract to the standard model where users receive recommendations in a single interaction. Through the use of the critiquing feedback, the recommender systems are able to more accurately learn the users’ profiles, and therefore suggest better recommendations in the subsequent rounds. Critiquing-based recommenders have been widely studied in knowledge-, content-, and preference-based recommenders and are beginning to be tried in several online websites, such as MovieLens. This article examines the motivation and development of the subject area, and offers a detailed survey of the state of the art concerning the design of critiquing interfaces and development of algorithms for critiquing generation. With the help of categorization analysis, the survey reveals three principal branches of critiquing based recommender systems, using respectively natural language based, system-suggested, and user-initiated critiques. Representative example systems will be presented and analyzed for each branch, and their respective pros and cons will be discussed. Subsequently, a hybrid framework is developed to unify the advantages of different methods and overcome their respective limitations. Empirical findings from user studies are further presented, indicating how hybrid critiquing supports could effectively enable end-users to achieve more confident decisions. Finally, the article will point out several future trends to boost the advance of critiquing-based recommenders.


Expert Level Control of Ramp Metering Based on Multi-Task Deep Reinforcement Learning

This paper shows how the recent breakthroughs in reinforcement learning (RL) that have enabled robots to learn to play arcade video games, walk, or assemble colored bricks, can be used to perform other tasks that are currently at the core of engineering cyberphysical systems. We present the first use of RL for the control of systems modeled by discretized non-linear partial differential equations (PDEs) and devise a novel algorithm to use non-parametric control techniques for large multi-agent systems. Cyberphysical systems (e.g., hydraulic channels, transportation systems, the energy grid, and electromagnetic systems) are commonly modeled by PDEs, which historically have been a reliable way to enable engineering applications in these domains. However, it is known that the control of these PDE models is notoriously difficult. We show how neural network-based RL enables the control of discretized PDEs whose parameters are unknown, random, and time-varying. We introduce an algorithm of mutual weight regularization (MWR), which alleviates the curse of dimensionality of multi-agent control schemes by sharing experience between agents while giving each agent the opportunity to specialize its action policy so as to tailor it to the local parameters of the part of the system it is located in. A discretized PDE, such as the scalar Lighthill–Whitham–Richards PDE can indeed be considered as a macroscopic freeway traffic simulator and which presents the most salient challenges for learning to control large cyberphysical system with multiple agents. We consider two different discretization procedures and show the opportunities offered by applying deep reinforcement for continuous control on both. Using a neural RL PDE controller on a traffic flow simulation based on a Godunov discretization of the San Francisco Bay Bridge, we are able to achieve precise adaptive metering without model calibration thereby beating the state of the art in traffic metering. Furthermore, with the more accurate BeATS simulator, we manage to achieve a control performance on par with ALINEA, a state-of-the-art parametric control scheme, and show how using MWR improves the learning procedure.


Design Issues and Challenges in Wireless Sensor Networks

Wireless Sensor Networks (WSNs) are composed self-organized wireless ad hoc networks which comprise of a large number of resource constrained sensor nodes. The major areas of research in WSN is going on hardware, and operating system of WSN, deployment, architecture, localization, synchronization, programming models, data aggregation and dissemination, database querying, architecture, middleware, quality of service and security. This paper study highlights ongoing research activities and issues that affect the design and performance of Wireless Sensor Network.


Deep Learning

Machine-learning technology powers many aspects of modern society: from web searches to content filtering on social networks to recommendations on e-commerce websites, and it is increasingly present in consumer products such as cameras and smartphones. Machine-learning systems are used to identify objects in images, transcribe speech into text, match news items, posts or products with users’ interests, and select relevant results of search. Increasingly, these applications make use of a class of techniques called deep learning. Conventional machine-learning techniques were limited in their ability to process natural data in their raw form. For decades, constructing a pattern-recognition or machine-learning system required careful engineering and considerable domain expertise to design a feature extractor that transformed the raw data (such as the pixel values of an image) into a suitable internal representation or feature vector from which the learning subsystem, often a classifier, could detect or classify patterns in the input. Representation learning is a set of methods that allows a machine to be fed with raw data and to automatically discover the representations needed for detection or classification. Deep-learning methods are representation-learning methods with multiple levels of representation, obtained by composing simple but non-linear modules that each transform the representation at one level (starting with the raw input) into a representation at a higher, slightly more abstract level. With the composition of enough such transformations, very complex functions can be learned. For classification tasks, higher layers of representation amplify aspects of the input that are important for discrimination and suppress irrelevant variations. An image, for example, comes in the form of an array of pixel values, and the learned features in the first layer of representation typically represent the presence or absence of edges at particular orientations and locations in the image. The second layer typically detects motifs by spotting particular arrangements of edges, regardless of small variations in the edge positions. The third layer may assemble motifs into larger combinations that correspond to parts of familiar objects, and subsequent layers would detect objects as combinations of these parts. The key aspect of deep learning is that these layers of features are not designed by human engineers: they are learned from data using a general-purpose learning procedure. Deep learning is making major advances in solving problems that have resisted the best attempts of the artificial intelligence community for many years. It has turned out to be very good at discovering intricate structures in high-dimensional data and is therefore applicable to many domains of science, business and government. In addition to beating records in image recognition and speech recognition, it has beaten other machine-learning techniques at predicting the activity of potential drug molecules, analysing particle accelerator data, reconstructing brain circuits, and predicting the effects of mutations in non-coding DNA on gene expression and disease. Perhaps more surprisingly, deep learning has produced extremely promising results for various tasks in natural language understanding, particularly topic classification, sentiment analysis, question answering and language translation. We think that deep learning will have many more successes in the near future because it requires very little engineering by hand, so it can easily take advantage of increases in the amount of available computation and data. New learning algorithms and architectures that are currently being developed for deep neural networks will only accelerate this progress.


A Persona-Based Neural Conversation Model

We present persona-based models for handling the issue of speaker consistency in neural response generation. A speaker model encodes personas in distributed embeddings that capture individual characteristics such as background information and speaking style. A dyadic speakeraddressee model captures properties of interactions between two interlocutors. Our models yield qualitative performance improvements in both perplexity and BLEU scores over baseline sequence-to-sequence models, with similar gains in speaker consistency as measured by human judges.


Intelligent Tutoring Systems with Conversational Dialogue

been developed during the last 20 years have proven to be quite successful, particularly in the domains of mathematics, science, and technology. They produce significant learning gains beyond classroom environments. They are capable of engaging most students’ attention and interest for hours. We have been working on a new generation of intelligent tutoring systems that hold mixedinitiative conversational dialogues with the learner. The tutoring systems present challenging problems and questions to the learner, the learner types in answers in English, and there is a lengthy multiturn dialogue as complete solutions or answers evolve. This article presents the tutoring systems that we have been developing. AUTOTUTOR is a conversational agent, with a talking head, that helps college students learn about computer literacy. ANDES, ATLAS, AND WHY2 help adults learn about physics. Instead of being mere information-delivery systems, our systems help students actively construct knowledge through conversations.


Deep Reinforcement Learning with Double Q-learning

The popular Q-learning algorithm is known to overestimate action values under certain conditions. It was not previously known whether, in practice, such overestimations are common, whether they harm performance, and whether they can generally be prevented. In this paper, we answer all these questions affirmatively. In particular, we first show that the recent DQN algorithm, which combines Q-learning with a deep neural network, suffers from substantial overestimations in some games in the Atari 2600 domain. We then show that the idea behind the Double Q-learning algorithm, which was introduced in a tabular setting, can be generalized to work with large-scale function approximation. We propose a specific adaptation to the DQN algorithm and show that the resulting algorithm not only reduces the observed overestimations, as hypothesized, but that this also leads to much better performance on several games. The goal of reinforcement learning (Sutton and Barto 1998) is to learn good policies for sequential decision problems, by optimizing a cumulative future reward signal. Q-learning (Watkins 1989) is one of the most popular reinforcement learning algorithms, but it is known to sometimes learn unrealistically high action values because it includes a maximization step over estimated action values, which tends to prefer overestimated to underestimated values. In previous work, overestimations have been attributed to insufficiently flexible function approximation (Thrun and Schwartz 1993) and noise (van Hasselt 2010, 2011). In this paper, we unify these views and show overestimations can occur when the action values are inaccurate, irrespective of the source of approximation error. Of course, imprecise value estimates are the norm during learning, which indicates that overestimations may be much more common than previously appreciated. It is an open question whether, if the overestimations do occur, this negatively affects performance in practice. Overoptimistic value estimates are not necessarily a problem in and of themselves. If all values would be uniformly higher then the relative action preferences are preserved and we would not expect the resulting policy to be any worse. Furthermore, it is known that sometimes it is good to be optimistic: optimism in the face of uncertainty is a well-known Copyright c © 2016, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. exploration technique (Kaelbling et al. 1996). If, however, the overestimations are not uniform and not concentrated at states about which we wish to learn more, then they might negatively affect the quality of the resulting policy. Thrun and Schwartz (1993) give specific examples in which this leads to suboptimal policies, even asymptotically. To test whether overestimations occur in practice and at scale, we investigate the performance of the recent DQN algorithm (Mnih et al. 2015). DQN combines Q-learning with a flexible deep neural network and was tested on a varied and large set of deterministic Atari 2600 games, reaching human-level performance on many games. In some ways, this setting is a best-case scenario for Q-learning, because the deep neural network provides flexible function approximation with the potential for a low asymptotic approximation error, and the determinism of the environments prevents the harmful effects of noise. Perhaps surprisingly, we show that even in this comparatively favorable setting DQN sometimes substantially overestimates the values of the actions. We show that the Double Q-learning algorithm (van Hasselt 2010), which was first proposed in a tabular setting, can be generalized to arbitrary function approximation, including deep neural networks. We use this to construct a new algorithm called Double DQN. This algorithm not only yields more accurate value estimates, but leads to much higher scores on several games. This demonstrates that the overestimations of DQN indeed lead to poorer policies and that it is beneficial to reduce them. In addition, by improving upon DQN we obtain state-of-the-art results on the Atari domain.


Attention with Intention for a Neural Network Conversation Model

In a conversation or a dialogue process, attention and intention play intrinsic roles. This paper proposes a neural network based approach that models the attention and intention processes. It essentially consists of three recurrent networks. The encoder network is a word-level model representing source side sentences. The intention network is a recurrent network that models the dynamics of the intention process. The decoder network is a recurrent network that produces responses to the input from the source side. It is a language model that is dependent on the intention and has an attention mechanism to attend to particular source side words, when predicting a symbol in the response. The model is trained end-toend without labeling data. Experiments show that this model generates natural responses to user inputs.


Low cost power failure protection for MLC NAND flash storage systems with PRAM/DRAM hybrid buffer

In the latest PRAM/DRAM hybrid MLC NAND flash storage systems (NFSS), DRAM is used to temporarily store file system data for system response time reduction. To ensure data integrity, super-capacitors are deployed to supply the backup power for moving the data from DRAM to NAND flash during power failures. However, the capacitance degradation of super-capacitor severely impairs system robustness. In this work, we proposed a low cost power failure protection scheme to reduce the energy consumption of power failure protection and increase the robustness of the NFSS with PRAM/DRAM hybrid buffer. Our scheme enables the adoption of the more reliable regular capacitor to replace the super capacitor as the backup power. The experimental result shows that our scheme can substantially reduce the capacitance budget of power failure protection circuitry by 75.1% with very marginal performance and energy overheads.


Latent Variable Analysis Growth Mixture Modeling and Related Techniques for Longitudinal Data

This chapter gives an overview of recent advances in latent variable analysis. Emphasis is placed on the strength of modeling obtained by using a flexible combination of continuous and categorical latent variables. To focus the discussion and make it manageable in scope, analysis of longitudinal data using growth models will be considered. Continuous latent variables are common in growth modeling in the form of random effects that capture individual variation in development over time. The use of categorical latent variables in growth modeling is, in contrast, perhaps less familiar, and new techniques have recently emerged. The aim of this chapter is to show the usefulness of growth model extensions using categorical latent variables. The discussion also has implications for latent variable analysis of cross-sectional data. The chapter begins with two major parts corresponding to continuous outcomes versus categorical outcomes. Within each part, conventional modeling using continuous latent variables will be described


Code quality analysis in open source software development

Proponents of open source style software development claim that better software is produced using this model compared with the traditional closed model. However, there is little empirical evidence in support of these claims. In this paper, we present the results of a pilot case study aiming: (a) to understand the implications of structural quality; and (b) to figure out the benefits of structural quality analysis of the code delivered by open source style development. To this end, we have measured quality characteristics of 100 applications written for Linux, using a software measurement tool, and compared the results with the industrial standard that is proposed by the tool. Another target of this case study was to investigate the issue of modularity in open source as this characteristic is being considered crucial by the proponents of open source for this type of software development. We have empirically assessed the relationship between the size of the application components and the delivered quality measured through user satisfaction. We have determined that, up to a certain extent, the average component size of an application is negatively related to the user satisfaction for this application.


A snake-based method for segmentation of intravascular ultrasound images and its in vivo validation.

Image segmentation for detection of vessel walls is necessary for quantitative assessment of vessel diseases by intravascular ultrasound. A new segmentation method based on gradient vector flow (GVF) snake model is proposed in this paper. The main characteristics of the proposed method include two aspects: one is that nonlinear filtering is performed on GVF field to reduce the critical points, change the morphological structure of the parallel curves and extend the capture range; the other is that balloon snake is combined with the model. Thus, the improved GVF and balloon snake can be automatically initialized and overcome the problem caused by local energy minima. Results of 20 in vivo cases validated the accuracy and stability of the segmentation method for intravascular ultrasound images.


Basket-Sensitive Personalized Item Recommendation

Personalized item recommendation is useful in narrowing down the list of options provided to a user. In this paper, we address the problem scenario where the user is currently holding a basket of items, and the task is to recommend an item to be added to the basket. Here, we assume that items currently in a basket share some association based on an underlying latent need, e.g., ingredients to prepare some dish, spare parts of some device. Thus, it is important that a recommended item is relevant not only to the user, but also to the existing items in the basket. Towards this goal, we propose two approaches. First, we explore a factorizationbased model called BFM that incorporates various types of associations involving the user, the target item to be recommended, and the items currently in the basket. Second, based on our observation that various recommendations towards constructing the same basket should have similar likelihoods, we propose another model called CBFM that further incorporates basket-level constraints. Experiments on three real-life datasets from different domains empirically validate these models against baselines based on matrix factorization and association rules.


A Review of Classic Edge Detectors

In this paper some of the classic alternatives for edge detection in digital images are studied. The main idea of edge detection algorithms is to find where abrupt changes in the intensity of an image have occurred. The first family of algorithms reviewed in this work uses the first derivative to find the changes of intensity, such as Sobel, Prewitt and Roberts. In the second reviewed family, second derivatives are used, for example in algorithms like Marr-Hildreth and Haralick. The obtained results are analyzed from a qualitative point of view (perceptual) and from a quantitative point of view (number of operations, execution time), considering different ways to convolve an image with a kernel (step required in some of the algorithms). Source Code For all the reviewed algorithms, an open source C implementation is provided which can be downloaded from the IPOL web page of this article1. An online demonstration is also available, where the user can test and reproduce our results.


An Analysis of the Accuracy of Bluetooth Low Energy for Indoor Positioning Applications

This study investigated the impact of Bluetooth Low Energy devices in advertising/beaconing mode on fingerprint-based indoor positioning schemes. Early experimentation demonstrated that the low bandwidth of BLE signals compared to WiFi is the cause of significant measurement error when coupled with the use of three BLE advertising channels. The physics underlying this behaviour is verified in simulation. A multipath mitigation scheme is proposed and tested. It is determined that the optimal positioning performance is provided by 10Hz beaconing and a 1 second multipath mitigation processing window size. It is determined that a steady increase in positioning performance with fingerprint size occurs up to 7 ±1, above this there is no clear benefit to extra beacon coverage.


Internet of Things: Vision, Applications and Challenges

The form of communication that we see now is either human-human or human-device, but the Internet of Things (IoT) promises a great future for the internet where the type of communication is machine-machine (M2M). It aims to unify everything in our world under a common infrastructure, giving us not only control of things around us, but also keeping us informed of the state of the things. This paper aims to provide a comprehensive overview of the IoT scenario and reviews its enabling technologies and the sensor networks. Also, it describes a six-layered architecture of IoT and points out the related key challenges. However, this manuscript will give good comprehension for the new researchers, who want to do research in this field of Internet of Things and facilitate knowledge accumulation in efficiently.


The architecture of virtual machines

A virtual machine can support individual processes or a complete system depending on the abstraction level where virtualization occurs. Some VMs support flexible hardware usage and software isolation, while others translate from one instruction set to another. Virtualizing a system or component -such as a processor, memory, or an I/O device - at a given abstraction level maps its interface and visible resources onto the interface and resources of an underlying, possibly different, real system. Consequently, the real system appears as a different virtual system or even as multiple virtual systems. Interjecting virtualizing software between abstraction layers near the HW/SW interface forms a virtual machine that allows otherwise incompatible subsystems to work together. Further, replication by virtualization enables more flexible and efficient and efficient use of hardware resources.


Parasitic-Aware Common-Centroid FinFET Placement and Routing for Current-Ratio Matching

The FinFET technology is regarded as a better alternative for modern high-performance and low-power integrated-circuit design due to more effective channel control and lower power consumption. However, the gate-misalignment problem resulting from process variation and the parasitic resistance resulting from interconnecting wires based on the FinFET technology becomes even more severe compared with the conventional planar CMOS technology. Such gate misalignment and unwanted parasitic resistance may increase the threshold voltage and decrease the drain current of transistors. When applying the FinFET technology to analog circuit design, the variation of drain currents can destroy current-ratio matching among transistors and degrade circuit performance. In this article, we present the first FinFET placement and routing algorithms for layout generation of a common-centroid FinFET array to precisely match the current ratios among transistors. Experimental results show that the proposed matching-driven FinFET placement and routing algorithms can obtain the best current-ratio matching compared with the state-of-the-art common-centroid placer.


A Domain Model for the Internet of Things

By bringing together the physical world of real objects with the virtual world of IT systems, the Internet of Things has the potential to significantly change both the enterprise world as well as society. However, the term is very much hyped and understood differently by different communities, especially because IoT is not a technology as such but represents the convergence of heterogeneous - often new - technologies pertaining to different engineering domains. What is needed in order to come to a common understanding is a domain model for the Internet of Things, defining the main concepts and their relationships, and serving as a common lexicon and taxonomy and thus as a basis for further scientific discourse and development of the Internet of Things. As we show, having such a domain model is also helpful in design of concrete IoT system architectures, as it provides a template and thus structures the analysis of use cases.


3D People Tracking with Gaussian Process Dynamical Models

We advocate the use of Gaussian Process Dynamical Models (GPDMs) for learning human pose and motion priors for 3D people tracking. A GPDM provides a lowdimensional embedding of human motion data, with a density function that gives higher probability to poses and motions close to the training data. With Bayesian model averaging a GPDM can be learned from relatively small amounts of data, and it generalizes gracefully to motions outside the training set. Here we modify the GPDM to permit learning from motions with significant stylistic variation. The resulting priors are effective for tracking a range of human walking styles, despite weak and noisy image measurements and significant occlusions.


Can We Predict a Riot? Disruptive Event Detection Using Twitter

In recent years, there has been increased interest in real-world event detection using publicly accessible data made available through Internet technology such as Twitter, Facebook, and YouTube. In these highly interactive systems, the general public are able to post real-time reactions to “real world” events, thereby acting as social sensors of terrestrial activity. Automatically detecting and categorizing events, particularly small-scale incidents, using streamed data is a non-trivial task but would be of high value to public safety organisations such as local police, who need to respond accordingly. To address this challenge, we present an end-to-end integrated event detection framework that comprises five main components: data collection, pre-processing, classification, online clustering, and summarization. The integration between classification and clustering enables events to be detected, as well as related smaller-scale “disruptive events,” smaller incidents that threaten social safety and security or could disrupt social order. We present an evaluation of the effectiveness of detecting events using a variety of features derived from Twitter posts, namely temporal, spatial, and textual content. We evaluate our framework on a large-scale, real-world dataset from Twitter. Furthermore, we apply our event detection system to a large corpus of tweets posted during the August 2011 riots in England. We use ground-truth data based on intelligence gathered by the London Metropolitan Police Service, which provides a record of actual terrestrial events and incidents during the riots, and show that our system can perform as well as terrestrial sources, and even better in some cases.


An Improved Algorithm for Incremental Induction of Decision Trees

This paper presents an algorithm for incremental induction of decision trees that is able to handle both numeric and symbolic variables. In order to handle numeric variables, a new tree revision operator called`slewing' is introduced. Finally, a non-incremental method is given for nding a decision tree based on a direct metric of a candidate tree.


Sentiment Analysis and Summarization of Twitter Data

Sentiment Analysis (SA) and summarization has recently become the focus of many researchers, because analysis of online text is beneficial and demanded in many different applications. One such application is product-based sentiment summarization of multi-documents with the purpose of informing users about pros and cons of various products. This paper introduces a novel solution to target-oriented (i.e. aspect-based) sentiment summarization and SA of short informal texts with a main focus on Twitter posts known as "tweets". We compare different algorithms and methods for SA polarity detection and sentiment summarization. We show that our hybrid polarity detection system not only outperforms the unigram state-of-the-art baseline, but also could be an advantage over other methods when used as a part of a sentiment summarization system. Additionally, we illustrate that our SA and summarization system exhibits a high performance with various useful functionalities and features.


Data Representation Based on Interval-Sets for Anomaly Detection in Time Series

Anomaly detection in time series is a popular topic focusing on a variety of applications, which achieves a wealth of results. However, there are many cases of missing anomaly and increased false alarm in most of the existing works. Inspired by the concept of interval-sets, this paper proposes an anomaly detection algorithm called a fuzzy interval-set and tries to detect the potential value of the time series from a new perspective. In the proposed algorithm, a time series will be divided into several subsequences. Each subsequence is regarded as an interval-set depending on its value space and boundary of the subsequence. The similarity measurements between interval sets adopt interval operations and point probability distributions of the interval bounds. In addition, an anomaly score is defined based on similarity results. The experimental results on synthetic and real data sets indicate that the proposed algorithm has better discriminative performance than the piecewise aggregate approximation method and reduces the false alarm rate significantly.


Bridging the divide in financial market forecasting: machine learners vs. financial economists

Financial time series forecasting is a popular application of machine learning methods. Previous studies report that advanced forecasting methods predict price changes in financial markets with high accuracy and that profit can be made trading on these predictions. However, financial economists point to the informational efficiency of financial markets, which questions price predictability and opportunities for profitable trading. The objective of the paper is to resolve this contradiction. To this end, we undertake an extensive forecasting simulation, based on data from thirty-four financial indices over six years. These simulations confirm that the best machine learning methods produce more accurate forecasts than the best econometric methods. We also examine the methodological factors that impact the predictive accuracy of machine learning forecasting experiments. The results suggest that the predictability of a financial market and the feasibility of profitable model-based trading are significantly influenced by the maturity of the market, the forecasting method employed, the horizon for which it generates predictions and the methodology used to assess the model and simulate model-based trading. We also find evidence against the informational value of indicators from the field of technical analysis. Overall, we confirm that advanced forecasting methods can be used to predict price changes in some financial markets and we discuss whether these results question the prevailing view in the financial economics literature that financial markets are efficient.


Algorithms for asymptotic extrapolation ∗

Consider a power series f ∈ R[[z]], which is obtained by a precise mathematical construction. For instance, f might be the solution to some differential or functional initial value problem or the diagonal of the solution to a partial differential equation. In cases when no suitable method is beforehand for determining the asymptotics of the coefficients fn, but when many such coefficients can be computed with high accuracy, it would be useful if a plausible asymptotic expansion for fn could be guessed automatically. In this paper, we will present a general scheme for the design of such “asymptotic extrapolation algorithms”. Roughly speaking, using discrete differentiation and techniques from automatic asymptotics, we strip off the terms of the asymptotic expansion one by one. The knowledge of more terms of the asymptotic expansion will then allow us to approximate the coefficients in the expansion with high accuracy.


Knowledge Engineering with Markov Logic Networks : A Review

Within the realm of statistical relational knowledge representation formalisms, Markov logic is perhaps one of the most flexible and general languages, for it generalises both first-order logic (for finite domains) and probabilistic graphical models. Knowledge engineering with Markov logic is, however, not a straightforward task. In particular, modelling approaches that are too firmly rooted in the principles of logic often tend to produce unexpected results in practice. In this paper, I collect a number of issues that are relevant to knowledge engineering practice: I describe the fundamental semantics of Markov logic networks and explain how simple probabilistic properties can be represented. Furthermore, I discuss fallacious modelling assumptions and summarise conditions under which generalisation across domains may fail. As a collection of fundamental insights, the paper is primarily directed at knowledge engineers who are new to Markov logic.


Effective Crowd Annotation for Relation Extraction

Can crowdsourced annotation of training data boost performance for relation extraction over methods based solely on distant supervision? While crowdsourcing has been shown effective for many NLP tasks, previous researchers found only minimal improvement when applying the method to relation extraction. This paper demonstrates that a much larger boost is possible, e.g., raising F1 from 0.40 to 0.60. Furthermore, the gains are due to a simple, generalizable technique, Gated Instruction, which combines an interactive tutorial, feedback to correct errors during training, and improved screening.


A Hardware Framework for Yield and Reliability Enhancement in Chip Multiprocessors

Device reliability and manufacturability have emerged as dominant concerns in end-of-road CMOS devices. An increasing number of hardware failures are attributed to manufacturability or reliability problems. Maintaining an acceptable manufacturing yield for chips containing tens of billions of transistors with wide variations in device parameters has been identified as a great challenge. Additionally, today’s nanometer scale devices suffer from accelerated aging effects because of the extreme operating temperature and electric fields they are subjected to. Unless addressed in design, aging-related defects can significantly reduce the lifetime of a product. In this article, we investigate a micro-architectural scheme for improving yield and reliability of homogeneous chip multiprocessors (CMPs). The proposed solution involves a hardware framework that enables us to utilize the redundancies inherent in a multicore system to keep the system operational in the face of partial failures. A micro-architectural modification allows a faulty core in a CMP to use another core’s resources to service any instruction that the former cannot execute correctly by itself. This service improves yield and reliability but may cause loss of performance. The target platform for quantitative evaluation of performance under degradation is a dual-core and a quad-core chip multiprocessor with one or more cores sustaining partial failure. Simulation studies indicate that when a large, high-latency, and sparingly used unit such as a floating-point unit fails in a core, correct execution may be sustained through outsourcing with at most a 16% impact on performance for a floating-point intensive application. For applications with moderate floating-point load, the degradation is insignificant. The performance impact may be mitigated even further by judicious selection of the cores to commandeer depending on the current load on each of the candidate cores. The area overhead is also negligible due to resource reuse.


Data processing and calibration of a cross-pattern stripe projector

Dense surface acquisition is one of the most challenging tasks for optical 3-D measurement systems in applications such as inspection of industrial parts, reverse engineering, digitization of virtual reality models and robot guidance. In order to achieve high accuracy we need good hardware equipment, as well as sophisticated data processing methods. Since measurements should be feasible under real world conditions, accuracy, reliability and consistency are a major issue. Based on the experiences with a previous system, a detailed analysis of the performance was carried out, leading to a new hardware setup. On the software side, we improved our calibration procedure and replaced the phase shift technique previously used by a new processing scheme which we call line shift processing. This paper describes our new approach. Results are presented and compared to results derived from the previous system.


Anomaly network traffic detection algorithm based on information entropy measurement under the cloud computing environment

In recent years, there are more and more abnormal activities in the network, which greatly threaten network security. Hence, it is of great importance to collect the data which indicate the running statement of the network, and distinguish the anomaly phenomena of the network in time. In this paper, we propose a novel anomaly network traffic detection algorithm under the cloud computing environment. Firstly, the framework of the anomaly network traffic detection system is illustrated, and six type of network traffic features are consider in this work, that is, (1) number of source IP address, (2) number of source port number, (3) number of destination IP address, (4) number of destination port number, (5) Number of packet type, and (6) number of network packets. Secondly, we propose a novel hybrid information entropy and SVM model to tackle the proposed problem by normalizing values of network features and exploiting SVM detect anomaly network behaviors. Finally, experimental results demonstrate that the proposed algorithm can detect anomaly network traffic with high accuracy and it can also be used in the large scale dataset.


DeepPointSet : A Point Set Generation Network for 3 D Object Reconstruction from a Single Image Supplementary Material

We conducted human study to provide reference to our current CD and EMD values reported on the rendered dataset. We provided the human subject with a GUI tool to create a triangular mesh from the image. The tool (see Fig 1) enables the user to edit the mesh in 3D and to align the modeled object back to the input image. In total 16 models are created from the input images of our validation set. N = 1024 points are sampled from each model.


Machine Learning for Real-Time Prediction of Damaging Straight-Line Convective Wind

Thunderstorms in theUnited States cause over 100 deaths and $10 billion (U.S. dollars) in damage per year, much of which is attributable to straight-line (nontornadic) wind. This paper describes a machine-learning system that forecasts the probability of damaging straight-line wind ($50 kt or 25.7m s) for each storm cell in the continental United States, at distances up to 10 km outside the storm cell and lead times up to 90min. Predictors are based on radar scans of the storm cell, storm motion, storm shape, and soundings of the nearstorm environment. Verification data come from weather stations and quality-controlled storm reports. The system performs very well on independent testing data. The area under the receiver operating characteristic (ROC) curve ranges from 0.88 to 0.95, the critical success index (CSI) ranges from 0.27 to 0.91, and the Brier skill score (BSS) ranges from 0.19 to 0.65 (.0 is better than climatology). For all three scores, the best value occurs for the smallest distance (inside storm cell) and/or lead time (0–15min), while the worst value occurs for the greatest distance (5–10 km outside storm cell) and/or lead time (60–90min). The system was deployed during the 2017 Hazardous Weather Testbed.


Predicting Depth, Surface Normals and Semantic Labels with a Common Multi-scale Convolutional Architecture

In this paper we address three different computer vision tasks using a single basic architecture: depth prediction, surface normal estimation, and semantic labeling. We use a multiscale convolutional network that is able to adapt easily to each task using only small modifications, regressing from the input image to the output map directly. Our method progressively refines predictions using a sequence of scales, and captures many image details without any superpixels or low-level segmentation. We achieve state-of-the-art performance on benchmarks for all three tasks.


ParseNet: Looking Wider to See Better

We present a technique for adding global context to deep convolutional networks for semantic segmentation. The approach is simple, using the average feature for a layer to augment the features at each location. In addition, we study several idiosyncrasies of training, significantly increasing the performance of baseline networks (e.g. from FCN [19]). When we add our proposed global feature, and a technique for learning normalization parameters, accuracy increases consistently even over our improved versions of the baselines. Our proposed approach, ParseNet, achieves state-of-the-art performance on SiftFlow and PASCAL-Context with small additional computational cost over baselines, and near current state-of-theart performance on PASCAL VOC 2012 semantic segmentation with a simple approach. Code is available at https: //github.com/weiliu89/caffe/tree/fcn.


Coupled depth learning

In this paper we propose a method for estimating depth from a single image using a coarse to fine approach. We argue that modeling the fine depth details is easier after a coarse depth map has been computed. We express a global (coarse) depth map of an image as a linear combination of a depth basis learned from training examples. The depth basis captures spatial and statistical regularities and reduces the problem of global depth estimation to the task of predicting the input-specific coefficients in the linear combination. This is formulated as a regression problem from a holistic representation of the image. Crucially, the depth basis and the regression function are coupled and jointly optimized by our learning scheme. We demonstrate that this results in a significant improvement in accuracy compared to direct regression of depth pixel values or approaches learning the depth basis disjointly from the regression function. The global depth estimate is then used as a guidance by a local refinement method that introduces depth details that were not captured at the global level. Experiments on the NYUv2 and KITTI datasets show that our method outperforms the existing state-of-the-art at a considerably lower computational cost for both training and testing.


Shape, Illumination, and Reflectance from Shading

A fundamental problem in computer vision is that of inferring the intrinsic, 3D structure of the world from flat, 2D images of that world. Traditional methods for recovering scene properties such as shape, reflectance, or illumination rely on multiple observations of the same scene to overconstrain the problem. Recovering these same properties from a single image seems almost impossible in comparison-there are an infinite number of shapes, paint, and lights that exactly reproduce a single image. However, certain explanations are more likely than others: surfaces tend to be smooth, paint tends to be uniform, and illumination tends to be natural. We therefore pose this problem as one of statistical inference, and define an optimization problem that searches for the most likely explanation of a single image. Our technique can be viewed as a superset of several classic computer vision problems (shape-from-shading, intrinsic images, color constancy, illumination estimation, etc) and outperforms all previous solutions to those constituent problems.


Multi-Scale Context Aggregation by Dilated Convolutions

State-of-the-art models for semantic segmentation are based on adaptations of convolutional networks that had originally been designed for image classification. However, dense prediction problems such as semantic segmentation are structurally different from image classification. In this work, we develop a new convolutional network module that is specifically designed for dense prediction. The presented module uses dilated convolutions to systematically aggregate multiscale contextual information without losing resolution. The architecture is based on the fact that dilated convolutions support exponential expansion of the receptive field without loss of resolution or coverage. We show that the presented context module increases the accuracy of state-of-the-art semantic segmentation systems. In addition, we examine the adaptation of image classification networks to dense prediction and show that simplifying the adapted network can increase accuracy.


An overview on crime prediction methods

In the recent past, crime analyses are required to reveal the complexities in the crime dataset. This process will help the parties that involve in law enforcement in arresting offenders and directing the crime prevention strategies. The ability to predict the future crimes based on the location, pattern and time can serve as a valuable source of knowledge for them either from strategic or tactical perspectives. Nevertheless, to predict future crime accurately with a better performance, it is a challenging task because of the increasing numbers of crime in present days. Therefore, crime prediction method is important to identify the future crime and reduces the numbers of crime. Currently, some researchers have been conducted a study to predict crime based on particular inputs. The performance of prediction models can be evaluated using a variety of different prediction methods such as support vector machine, multivariate time series and artificial neural network. However, there are still some limitations on their findings to provide an accurate prediction for the location of crimes. A large number of research papers on this topic have already been published previously. Thus, in this paper, we thoroughly review each of them and summarized the outcomes. Our objective is to identify current implementations of crime prediction method and the possibility to enhance it for future needs.


Organizational Use of Big Data and Competitive Advantage - Exploration of Antecedents

The use of Big Data can hold considerable benefits for organizations, but there is as yet little research on the impacts of Big Data use on key variables such as competitive advantage. The antecedents to Big Data use aiming at competitive advantage are also poorly understood. Drawing from prior research on the ICT-competitiveness link, this research examined the relationship of organizational use of Big Data and competitive advantage – as well as certain hypothesized antecedents to this relationship. Data was collected from a nationwide survey in Japan which was addressed to Big Data users in private companies. The result shows that the competitiveness is influenced by the systematic and extensive use of Big Data in the organization, and top management understanding for Big Data use. Antecedents of systematic and extensive Big Data use were found to be the cross-functional activities of analysts, proactive attitude of users, secured data management, and human resources. Future research should strive to verify these respondent opinions using longitudinal approaches based on objective financial data.


Back to the Future: Current-Mode Processor in the Era of Deeply Scaled CMOS

This paper explores the use of MOS current-mode logic (MCML) as a fast and low noise alternative to static CMOS circuits in microprocessors, thereby improving the performance, energy efficiency, and signal integrity of future computer systems. The power and ground noise generated by an MCML circuit is typically 10-100× smaller than the noise generated by a static CMOS circuit. Unlike static CMOS, whose dominant dynamic power is proportional to the frequency, MCML circuits dissipate a constant power independent of clock frequency. Although these traits make MCML highly energy efficient when operating at high speeds, the constant static power of MCML poses a challenge for a microarchitecture that operates at the modest clock rate and with a low activity factor. To address this challenge, a single-core microarchitecture for MCML is explored that exploits the C-slow retiming technique, and operates at a high frequency with low complexity to save energy. This design principle contrasts with the contemporary multicore design paradigm for static CMOS that relies on a large number of gates operating in parallel at the modest speeds. The proposed architecture generates 10-40× lower power and ground noise, and operates within 13% of the performance (i.e., 1/ExecutionTime) of a conventional, eight-core static CMOS processor while exhibiting 1.6× lower energy and 9% less area. Moreover, the operation of an MCML processor is robust under both systematic and random variations in transistor threshold voltage and effective channel length.


Marine Litter Distribution and Density in European Seas, from the Shelves to Deep Basins

Anthropogenic litter is present in all marine habitats, from beaches to the most remote points in the oceans. On the seafloor, marine litter, particularly plastic, can accumulate in high densities with deleterious consequences for its inhabitants. Yet, because of the high cost involved with sampling the seafloor, no large-scale assessment of distribution patterns was available to date. Here, we present data on litter distribution and density collected during 588 video and trawl surveys across 32 sites in European waters. We found litter to be present in the deepest areas and at locations as remote from land as the Charlie-Gibbs Fracture Zone across the Mid-Atlantic Ridge. The highest litter density occurs in submarine canyons, whilst the lowest density can be found on continental shelves and on ocean ridges. Plastic was the most prevalent litter item found on the seafloor. Litter from fishing activities (derelict fishing lines and nets) was particularly common on seamounts, banks, mounds and ocean ridges. Our results highlight the extent of the problem and the need for action to prevent increasing accumulation of litter in marine environments.