title: Defect engineering of two-dimensional transition metal dichalcogenides

abstract: Two-dimensional transition metal dichalcogenides (TMDs), an emerging family of layered materials, have provided researchers a fertile ground for harvesting fundamental science and emergent applications. TMDs can contain a number of different structural defects in their crystal lattices which significantly alter their physico-chemical properties. Having structural defects can be either detrimental or beneficial, depending on the targeted application. Therefore, a comprehensive understanding of structural defects is required. Here we review different defects in semiconducting TMDs by summarizing: (i) the dimensionalities and atomic structures of defects; (ii) the pathways to generating structural defects during and after synthesis and, (iii) the effects of having defects on the physico-chemical properties and applications of TMDs. Thus far, significant progress has been made, although we are probably still witnessing the tip of the iceberg. A better understanding and control of defects is important in order to move forward the field of Defect Engineering in TMDs. Finally, we also provide our perspective on the challenges and opportunities in this emerging field.


title: Verification of timed-arc Petri nets

abstract: Timed-Arc Petri Nets (TAPN) are an extension of the classical P/T nets with continuous time. Tokens in TAPN carry an age and arcs between places and transitions are labelled with time intervals restricting the age of tokens available for transition firing. The TAPN model posses a number of interesting theoretical properties distinguishing them from other time extensions of Petri nets. We shall give an overview of the recent theory developed in the verification of TAPN extended with features like read/transport arcs, timed inhibitor arcs and age invariants. We will examine in detail the boundaries of automatic verification and the connections between TAPN and the model of timed automata. Finally, we will mention the tool TAPAAL that supports modelling, simulation and verification of TAPN and discuss a small case study of alternating bit protocol.


title: Error-Driven Incremental Learning in Deep Convolutional Neural Network for Large-Scale Image Classification

abstract: Supervised learning using deep convolutional neural network has shown its promise in large-scale image classification task. As a building block, it is now well positioned to be part of a larger system that tackles real-life multimedia tasks. An unresolved issue is that such model is trained on a static snapshot of data. Instead, this paper positions the training as a continuous learning process as new classes of data arrive. A system with such capability is useful in practical scenarios, as it gradually expands its capacity to predict increasing number of new classes. It is also our attempt to address the more fundamental issue: a good learning system must deal with new knowledge that it is exposed to, much as how human do. We developed a training algorithm that grows a network not only incrementally but also hierarchically. Classes are grouped according to similarities, and self-organized into levels. The newly added capacities are divided into component models that predict coarse-grained superclasses and those return final prediction within a superclass. Importantly, all models are cloned from existing ones and can be trained in parallel. These models inherit features from existing ones and thus further speed up the learning. Our experiment points out advantages of this approach, and also yields a few important open questions.


MODELLING AND VALIDATION OF A MULTILAYERED COMPOSITE FINITE ELEMENT WITH DAMPING CAPABILITY


title: An Expectation Propagation Perspective on Approximate Message Passing

abstract: An alternative derivation for the well-known approximate message passing (AMP) algorithm proposed by Donoho is presented in this letter. Compared with the original derivation, which exploits central limit theorem and Taylor expansion to simplify belief propagation (BP), our derivation resorts to expectation propagation (EP) and the neglect of high-order terms in large system limit. This alternative derivation leads to a different yet provably equivalent form of message passing, which explicitly establishes the intrinsic connection between AMP and EP, thereby offering some new insights in the understanding and improvement of AMP.


title: Social media and crisis management: CERC, search strategies, and Twitter content

abstract: The current manuscript explores Twitter use and content in the precrisis stages of a major weather event in the northeast. A multi-level content analysis of tweets collected in the lead up to landfall suggests that emergency management agencies largely underutilized the medium, and that actionable information was easier to find when searching along localized hashtags. The findings are discussed in terms of the Crisis and Emergency Risk Communication (CERC) model of crisis management and implications for emergency management agencies.


title: Analysing The Impact Of A DDoS Attack Announcement On Victim Stock Prices

abstract: DDoS attacks are increasingly used by 'hackers' and 'hacktivists' for various purposes. A number of on-line tools are available to launch an attack of significant intensity. These attacks lead to a variety of losses at the victim's end. We analyse the impact of Distributed Denial-of-Service (DDoS) attack announcements over a period of 5 years on the stock prices of the victim firms. We propose a method for event studies that does not assume the cumulative abnormal returns to be normally distributed, instead we use the empirical distribution for testing purposes. In most cases we find no significant impact on the stock returns but in cases where a DDoS attack creates an interruption in the services provided to the customer, we find a significant negative impact.


Risk Prediction with Electronic Health Records: A Deep Learning Approach


title: An Efficient Method for Traffic Sign Recognition Based on Extreme Learning Machine

abstract: This paper proposes a computationally efficient method for traffic sign recognition (TSR). This proposed method consists of two modules: 1) extraction of histogram of oriented gradient variant (HOGv) feature and 2) a single classifier trained by extreme learning machine (ELM) algorithm. The presented HOGv feature keeps a good balance between redundancy and local details such that it can represent distinctive shapes better. The classifier is a single-hidden-layer feedforward network. Based on ELM algorithm, the connection between input and hidden layers realizes the random feature mapping while only the weights between hidden and output layers are trained. As a result, layer-by-layer tuning is not required. Meanwhile, the norm of output weights is included in the cost function. Therefore, the ELM-based classifier can achieve an optimal and generalized solution for multiclass TSR. Furthermore, it can balance the recognition accuracy and computational cost. Three datasets, including the German TSR benchmark dataset, the Belgium traffic sign classification dataset and the revised mapping and assessing the state of traffic infrastructure (revised MASTIF) dataset, are used to evaluate this proposed method. Experimental results have shown that this proposed method obtains not only high recognition accuracy but also extremely high computational efficiency in both training and recognition processes in these three datasets.


title: Chinese sentence segmentation as comma classification

abstract: We describe a method for disambiguating Chinese commas that is central to Chinese sentence segmentation. Chinese sentence segmentation is viewed as the detection of loosely coordinated clauses separated by commas. Trained and tested on data derived from the Chinese Treebank, our model achieves a classification accuracy of close to 90% overall, which translates to an F1 score of 70% for detecting commas that signal sentence boundaries.


title: Automatic segmentation of crop leaf spot disease images by integrating local threshold and seeded region growing

abstract: At present, the region growing algorithm has been used as a segmentation technique of digital images. Most region growing algorithms are using fixed or determinate criterions to distinguish disease spots from leaf image with gray level differences between leaf and disease spot. But in practice, the objects in the disease leaf image have fuzziness and uncertainty, and edges of the objects are unclear. What's more, the color of leaf and disease spots is uneven, and the gray level is overlapping, so it is difficult to use fixed threshold or determinate criteria to determine the uncertain objects in leaf disease spot images accurately. In order to improve the crop leaf spot disease image segmentation accuracy, an adaptive segmentation algorithm by integrating local threshold and seeded region growing (LTSRG) is proposed. The algorithm was implemented on VC6.0. The segmentation algorithm uses the pixels of which the R-channel gray level is more than the G-channel gray level as initial seed points (pixels), and then local threshold C i is calculated for each connected seed region by Otsu. New seed pixels are included and the threshold C is re-calculated until no new seed pixel can be included. The results of LTSRG are compared with the results of threshold-based Otsu and clustering -based EM. The experiments show: The adapted segmentation method is satisfactory and highly efficient to separate disease spots from normal part of corn leaves. LTSRG algorithm is easy to realize, and can improve the precision of crop disease spot segmentation. Its image segmentation results have good region consistency and high efficiency. It is an adapting algorithm for image segmentation.


title: Reliable Gender Prediction Based on Users’ Video Viewing Behavior

abstract: With the growth of the digital advertising market, it has become more important than ever to target the desired audiences. Among various demographic traits, gender information plays a key role in precisely targeting the potential consumers in online advertising and ecommerce. However, such personal information is generally unavailable to digital media sellers. In this paper, we propose a novel task-specific multi-task learning algorithm to predict users' gender information from their video viewing behaviors. To detect as many desired users as possible, while controlling the Type I error rate at a user-specified level, we further propose Bayes testing and decision procedures to efficiently identify male and female users, respectively. Comprehensive experiments have justified the effectiveness and reliability of our framework.


title: A comparison of the Two One-Sided Tests Procedure and the Power Approach for assessing the equivalence of average bioavailability

abstract: The statistical test of hypothesis of no difference between the average bioavailabilities of two drug formulations, usually supplemented by an assessment of what the power of the statistical test would have been if the true averages had been inequivalent, continues to be used in the statistical analysis of bioavailability/bioequivalence studies. In the present article, this Power Approach (which in practice usually consists of testing the hypothesis of no difference at level 0.05 and requiring an estimated power of 0.80) is compared to another statistical approach, the Two One-Sided Tests Procedure, which leads to the same conclusion as the approach proposed by Westlake based on the usual (shortest) 1-2 alpha confidence interval for the true average difference. It is found that for the specific choice of alpha = 0.05 as the nominal level of the one-sided tests, the two one-sided tests procedure has uniformly superior properties to the power approach in most cases. The only cases where the power approach has superior properties when the true averages are equivalent correspond to cases where the chance of concluding equivalence with the power approach when the true averages are not equivalent exceeds 0.05. With appropriate choice of the nominal level of significance of the one-sided tests, the two one-sided tests procedure always has uniformly superior properties to the power approach. The two one-sided tests procedure is compared to the procedure proposed by Hauck and Anderson.


title: Beyond the basic emotions: what should affective computing compute?

abstract: One of the primary goals of Affective Computing (AC) is to develop computer interfaces that automatically detect and respond to users' emotions. Despite significant progress, "basic emotions" (e.g., anger, disgust, sadness) have been emphasized in AC at the expense of other non-basic emotions. The present paper questions this emphasis by analyzing data from five studies that systematically tracked both basic and non-basic emotions. The results indicate that engagement, boredom, confusion, and frustration (all non-basic emotions) occurred at five times the rate of basic emotions after generalizing across tasks, interfaces, and methodologies. Implications of these findings for AC are discussed


title: Recent advances in the automatic recognition of audio-visual speech

abstract: Visual speech information from the speaker's mouth region has been successfully shown to improve noise robustness of automatic speech recognizers, thus promising to extend their usability in the human computer interface. In this paper, we review the main components of audiovisual automatic speech recognition (ASR) and present novel contributions in two main areas: first, the visual front-end design, based on a cascade of linear image transforms of an appropriate video region of interest, and subsequently, audiovisual speech integration. On the latter topic, we discuss new work on feature and decision fusion combination, the modeling of audiovisual speech asynchrony, and incorporating modality reliability estimates to the bimodal recognition process. We also briefly touch upon the issue of audiovisual adaptation. We apply our algorithms to three multisubject bimodal databases, ranging from small- to large-vocabulary recognition tasks, recorded in both visually controlled and challenging environments. Our experiments demonstrate that the visual modality improves ASR over all conditions and data considered, though less so for visually challenging environments and large vocabulary tasks.


title: Motion Planning Among Dynamic, Decision-Making Agents with Deep Reinforcement Learning

abstract: Robots that navigate among pedestrians use collision avoidance algorithms to enable safe and efficient operation. Recent works present deep reinforcement learning as a framework to model the complex interactions and cooperation. However, they are implemented using key assumptions about other agents' behavior that deviate from reality as the number of agents in the environment increases. This work extends our previous approach to develop an algorithm that learns collision avoidance among a variety of types of dynamic agents without assuming they follow any particular behavior rules. This work also introduces a strategy using LSTM that enables the algorithm to use observations of an arbitrary number of other agents, instead of previous methods that have a fixed observation size. The proposed algorithm outperforms our previous approach in simulation as the number of agents increases, and the algorithm is demonstrated on a fully autonomous robotic vehicle traveling at human walking speed, without the use of a 3D Lidar.


title: The ”best k” for entropy-based categorical data clustering

abstract: With the growing demand on cluster analysis for categorical data, a handful of categorical clustering algorithms have been developed. Surprisingly, to our knowledge, none has satisfactorily addressed the important problem for categorical clustering – how can we determine the best K number of clusters for a categorical dataset? Since the categorical data does not have the inherent distance function as the similarity measure, the traditional cluster validation techniques based on the geometry shape and density distribution cannot be applied to answer this question. In this paper, we investigate the entropy property of the categorical data and propose a BkPlot method for determining a set of candidate “best Ks”. This method is implemented with a hierarchical clustering algorithm HierEntro. The experimental result shows that our approach can effectively identify the significant clustering structures. keywords Categorical Data Clustering, Entropy, Cluster Validation


title: Edge Computing: Vision and Challenges

abstract: The proliferation of Internet of Things (IoT) and the success of rich cloud services have pushed the horizon of a new computing paradigm, edge computing, which calls for processing the data at the edge of the network. Edge computing has the potential to address the concerns of response time requirement, battery life constraint, bandwidth cost saving, as well as data safety and privacy. In this paper, we introduce the definition of edge computing, followed by several case studies, ranging from cloud offloading to smart home and city, as well as collaborative edge to materialize the concept of edge computing. Finally, we present several challenges and opportunities in the field of edge computing, and hope this paper will gain attention from the community and inspire more research in this direction.


title: InputFinder: Reverse Engineering Closed Binaries using Hardware Performance Counters

abstract: The effectiveness of many dynamic program analysis techniques depends heavily on the completeness of the test suite applied during the analysis process. Test suites are often composed by developers and aim at testing all of the functionality of a software system. However, test suites may not be complete, if they exist at all. To date, only two methods exist for automatically generating test input for closed binaries: fuzzing and symbolic execution. Despite previous successes of these methods in identifying bugs, both techniques have limitations. In this paper, we propose a new method for autonomously generating valid input and identifying protocols for closed x86 binaries. The method presented can be used as a standalone tool or can be combined with other techniques for improved results. To assess its effectiveness, we test InputFinder, the implementation of our method, against binaries from the DARPA Cyber Grand Challenge example set. Our evaluations show that our method is not only effective in finding input and determining whether a protocol is expected but can also find unexpected control flow paths.


title: Analysis of a Probabilistic Model of Redundancy in Unsupervised Information Extraction

abstract: Unsupervised Information Extraction (UIE) is the task of extracting knowledge from text without the use of hand-labeled training examples. Because UIE systems do not require human intervention, they can recursively discover new relations, attributes, and instances in a scalable manner. When applied to massive corpora such as the Web, UIE systems present an approach to a primary challenge in artificial intelligence: the automatic accumulation of massive bodies of knowledge. A fundamental problem for a UIE system is assessing the probability that its extracted information is correct. In massive corpora such as the Web, the same extraction is found repeatedly in different documents. How does this redundancy impact the probability of correctness? We present a combinatorial ''balls-and-urns'' model, called Urns, that computes the impact of sample size, redundancy, and corroboration from multiple distinct extraction rules on the probability that an extraction is correct. We describe methods for estimating Urns's parameters in practice and demonstrate experimentally that for UIE the model's log likelihoods are 15 times better, on average, than those obtained by methods used in previous work. We illustrate the generality of the redundancy model by detailing multiple applications beyond UIE in which Urns has been effective. We also provide a theoretical foundation for Urns's performance, including a theorem showing that PAC Learnability in Urns is guaranteed without hand-labeled data, under certain assumptions.


Thermal Stresses Associated with Part Overhang Geometry in Electron Beam Additive Manufacturing : Process Parameter Effects


A buck-boost converter-based bipolar pulse generator


title: Recurrent Slice Networks for 3D Segmentation of Point Clouds

abstract: Point clouds are an efficient data format for 3D data. However, existing 3D segmentation methods for point clouds either do not model local dependencies \cite{pointnet} or require added computations \cite{kd-net,pointnet2}. This work presents a novel 3D segmentation framework, RSNet\footnote{Codes are released here https://github.com/qianguih/RSNet}, to efficiently model local structures in point clouds. The key component of the RSNet is a lightweight local dependency module. It is a combination of a novel slice pooling layer, Recurrent Neural Network (RNN) layers, and a slice unpooling layer. The slice pooling layer is designed to project features of unordered points onto an ordered sequence of feature vectors so that traditional end-to-end learning algorithms (RNNs) can be applied. The performance of RSNet is validated by comprehensive experiments on the S3DIS\cite{stanford}, ScanNet\cite{scannet}, and ShapeNet \cite{shapenet} datasets. In its simplest form, RSNets surpass all previous state-of-the-art methods on these benchmarks. And comparisons against previous state-of-the-art methods \cite{pointnet, pointnet2} demonstrate the efficiency of RSNets.


title: Affective Video Content Analysis: A Multidisciplinary Insight

abstract: In our present society, the cinema has become one of the major forms of entertainment providing unlimited contexts of emotion elicitation for the emotional needs of human beings. Since emotions are universal and shape all aspects of our interpersonal and intellectual experience, they have proved to be a highly multidisciplinary research field, ranging from psychology, sociology, neuroscience, etc., to computer science. However, affective multimedia content analysis work from the computer science community benefits but little from the progress achieved in other research fields. In this paper, a multidisciplinary state-of-the-art for affective movie content analysis is given, in order to promote and encourage exchanges between researchers from a very wide range of fields. In contrast to other state-of-the-art papers on affective video content analysis, this work confronts the ideas and models of psychology, sociology, neuroscience, and computer science. The concepts of aesthetic emotions and emotion induction, as well as the different representations of emotions are introduced, based on psychological and sociological theories. Previous global and continuous affective video content analysis work, including video emotion recognition and violence detection, are also presented in order to point out the limitations of affective video content analysis work.


title: Autonomic nervous system activity in emotion: A review

abstract: Autonomic nervous system (ANS) activity is viewed as a major component of the emotion response in many recent theories of emotion. Positions on the degree of specificity of ANS activation in emotion, however, greatly diverge, ranging from undifferentiated arousal, over acknowledgment of strong response idiosyncrasies, to highly specific predictions of autonomic response patterns for certain emotions. A review of 134 publications that report experimental investigations of emotional effects on peripheral physiological responding in healthy individuals suggests considerable ANS response specificity in emotion when considering subtypes of distinct emotions. The importance of sound terminology of investigated affective states as well as of choice of physiological measures in assessing ANS reactivity is discussed.


The Effects of Gamification on Driver Behavior: An Example from a Free Float Carsharing Service


title: Abstraction in perceptual symbol systems

abstract: After reviewing six senses of abstraction, this article focuses on abstractions that take the form of summary representations. Three central properties of these abstractions are established: ( i ) type-token interpretation; (ii) structured representation; and (iii) dynamic realization. Traditional theories of representation handle interpretation and structure well but are not sufficiently dynamical. Conversely, connectionist theories are exquisitely dynamic but have problems with structure. Perceptual symbol systems offer an approach that implements all three properties naturally. Within this framework, a loose collection of property and relation simulators develops to represent abstractions. Type-token interpretation results from binding a property simulator to a region of a perceived or simulated category member. Structured representation results from binding a configuration of property and relation simulators to multiple regions in an integrated manner. Dynamic realization results from applying different subsets of property and relation simulators to category members on different occasions. From this standpoint, there are no permanent or complete abstractions of a category in memory. Instead, abstraction is the skill to construct temporary online interpretations of a category's members. Although an infinite number of abstractions are possible, attractors develop for habitual approaches to interpretation. This approach provides new ways of thinking about abstraction phenomena in categorization, inference, background knowledge and learning.


title: A new approach towards IoT by using health care-IoT and food distribution IoT

abstract: Many areas of today's living standard are covered by global sensing techniques using wireless sensor network approaches. It is the network that consist physical objects embedded with software, sensors, electronics. It provides connectivity that is used to enable things to exchange data with the manufacturer and other operator device which is based on the international global standards. It creates the capability to compute, conclude and understand environmental measure, from elegant ecologies. We can create IoT by distributing the work of devices in the communicating network. Using this approach we can get the way to combine actuators and sensing devices in a smooth way and common operating picture (COP) are developed by sharing information over the platforms. In this paper we show the vision, provocation, possible usage scenarios and the steps to build the Internet of Things. IP stacks and web servers technological developments are used for smart every day objects. This paper describes how IOT can be implemented in the realistic way to invoke the vision. The contents which we can use in IOT can come into picture from a big area of devices such as heart monitoring implants, biochip transponders, electric clams in coastal water automobiles with the help of built in sensors among them. If we want to develop a fully integrated future internet then IOT is steeped out by enable and using the technologies which is based on wireless such as RFID tokens and attached from nodes of actuator and sensing devices. If we talk about the present scenario then the present generation of technology of the Internet-of-Things (IoT) offers two applications Distribution of Food hierarchy and Healthcare to involve human health and well-being. These two applications show the technologies and architectures of the IoT and to solve the problems arisen in the wireless sensor network architectures and the architectures to integrate the system. At the system level, we have improved integration of distributed devices and their technologies; we also have EIS and integration of information in architectures such as prediction of shelf life and real time distribution hierarchy for food distribution IOT and distribution of health care at home.


title: A Lightweight Tool for Detecting Web Server Attacks

abstract: We present an intrusion-detection tool aimed at protecting web servers, and justify why such a tool is needed. We describe several interesting features, such as the ability to run in real time and to keep track of suspicious hosts. The design is flexible and the signatures used to detect malicious behavior are not limited to simple pattern matching of dangerous cgi scripts. The tool includes mechanisms to reduce the number of false alarms. We conclude with a discussion of the information gained from deploying the tool


title: Bringing science to digital forensics with standardized forensic corpora

abstract: Progress in computer forensics research has been limited by the lack of a standardized data sets-corpora-that are available for research purposes. We explain why corpora are needed to further forensic research, present a taxonomy for describing corpora, and announce the availability of several forensic data sets.


title: Predator's attack-induced phase-like transition in prey flock

abstract: Abstract As a flock of prey escapes from a predator, a phase-like transition was observed in the prey flock. This occurred from a state of the flock moving in two separated directions to a state of the prey flock moving a non-separated direction at a predator's attack angle θ = θ c . Additionally, regardless of the predator's attack speed κ, the near identical scaling of the θ c / κ against a predation risk R was present.


title: Band-gap shrinkage of semiconductors

abstract: Direct experimental evidence of band-gap shrinkage due to the presence of impurities in semiconductors is given. Experimental results are obtained using a piezomodulation-spectroscopy technique over the (4-300)\ifmmode^\circ\else\textdegree\fi{}K temperature range. They are interpreted in the linear-screened-potential and effective-mass approximations. Coupling spectroscopic and helium-temperature magnetotransport measurements allowed us to obtain in this model a good description of the transition edges over the entire temperaure range. We studied different GaSb samples with impurity concentration allowing us to observe band-gap shrinkage with one and two types of carriers. This is easily obtained in the GaSb case whose band structure presents $\ensuremath{\Gamma}$ and $L$ minima located very near in energy. On heavily doped samples, the low-temperature values of the band-gap shrinkage are used to obtain further informations concerning subsidiary minima. On lightly doped semiconductors, non-$k$ -conserving transitions initiating on the residual acceptor level are clearly shown.


title: Skene’s gland cyst excision

abstract: INTRODUCTION AND HYPOTHESIS ::: We review our experience with long-term outcome after Skene's gland cyst excision. ::: ::: ::: MATERIALS AND METHODS ::: After receiving institutional review board approval, we reviewed a surgical database of all procedures performed by two surgeons at one institution for Skene's gland cyst excision. Parameters evaluated include presenting symptoms, preoperative evaluation, excision site, perioperative complications, and clinical outcomes. The technique of surgical excision is presented in the accompanying video, and includes cystoscopy, dissection of cyst wall from the floor of the urethra, complete removal of the cyst wall, and primary vaginal-wall closure. ::: ::: ::: RESULTS ::: From 2001 to 2013, ten women underwent Skene's gland cyst excision. Mean follow-up was 3.5 years (range 3-96 months). Presenting symptoms were dyspareunia (4), urinary tract infections (4), vaginal mass (1), and voiding dysfunction (1). Five patients had more than one presenting symptom. To exclude urethral diverticulum, magnetic resonance imaging (MRI) was done in all patients and a voiding cystourethrogram in five. No perioperative complications were reported. A distal meatoplasty was done in two women. No recurrence occurred. Eight of ten women who were sexually active remained sexually active postoperatively. ::: ::: ::: CONCLUSIONS ::: Excision of Skene's gland cyst is a safe procedure with acceptable long-term functional outcomes.


Simulated Path Planning for Auburn ’ s Autonomous Lawnmower


title: Analysis of wireless geolocation in a non-line-of-sight environment

abstract: A process for drying organic waste, such as sewage sludge, by contacting the organic waste with hot vapors wherein the dried material is plasticized and extruded to form compacted granules having good flow characteristics and suitable for application by commercial fertilizer spreaders. A plasticizer and an extrusion aid are added to the dried material prior to extrusion.


title: Machine-Learning Algorithms to Automate Morphological and Functional Assessments in 2D Echocardiography.

abstract: Abstract Background Machine-learning models may aid cardiac phenotypic recognition by using features of cardiac tissue deformation. Objectives This study investigated the diagnostic value of a machine-learning framework that incorporates speckle-tracking echocardiographic data for automated discrimination of hypertrophic cardiomyopathy (HCM) from physiological hypertrophy seen in athletes (ATH). Methods Expert-annotated speckle-tracking echocardiographic datasets obtained from 77 ATH and 62 HCM patients were used for developing an automated system. An ensemble machine-learning model with 3 different machine-learning algorithms (support vector machines, random forests, and artificial neural networks) was developed and a majority voting method was used for conclusive predictions with further K -fold cross-validation. Results Feature selection using an information gain (IG) algorithm revealed that volume was the best predictor for differentiating between HCM ands. ATH (IG = 0.24) followed by mid-left ventricular segmental (IG = 0.134) and average longitudinal strain (IG = 0.131). The ensemble machine-learning model showed increased sensitivity and specificity compared with early-to-late diastolic transmitral velocity ratio (p 13 mm. In this subgroup analysis, the automated model continued to show equal sensitivity, but increased specificity relative to early-to-late diastolic transmitral velocity ratio, e′, and strain. Conclusions Our results suggested that machine-learning algorithms can assist in the discrimination of physiological versus pathological patterns of hypertrophic remodeling. This effort represents a step toward the development of a real-time, machine-learning–based system for automated interpretation of echocardiographic images, which may help novice readers with limited experience.


title: Sample Complexity of Episodic Fixed-Horizon Reinforcement Learning

abstract: Recently, there has been significant progress in understanding reinforcement learning in discounted infinite-horizon Markov decision processes (MDPs) by deriving tight sample complexity bounds. However, in many real-world applications, an interactive learning agent operates for a fixed or bounded period of time, for example tutoring students for exams or handling customer service requests. Such scenarios can often be better treated as episodic fixed-horizon MDPs, for which only looser bounds on the sample complexity exist. A natural notion of sample complexity in this setting is the number of episodes required to guarantee a certain performance with high probability (PAC guarantee). In this paper, we derive an upper PAC bound $\tilde O(\frac{|\mathcal S|^2 |\mathcal A| H^2}{\epsilon^2} \ln\frac 1 \delta)$ and a lower PAC bound $\tilde \Omega(\frac{|\mathcal S| |\mathcal A| H^2}{\epsilon^2} \ln \frac 1 {\delta + c})$ that match up to log-terms and an additional linear dependency on the number of states $|\mathcal S|$. The lower bound is the first of its kind for this setting. Our upper bound leverages Bernstein's inequality to improve on previous bounds for episodic finite-horizon MDPs which have a time-horizon dependency of at least $H^3$.


title: Graph-Laplacian PCA: Closed-Form Solution and Robustness

abstract: Principal Component Analysis (PCA) is a widely used to learn a low-dimensional representation. In many applications, both vector data X and graph data W are available. Laplacian embedding is widely used for embedding graph data. We propose a graph-Laplacian PCA (gLPCA) to learn a low dimensional representation of X that incorporates graph structures encoded in W. This model has several advantages: (1) It is a data representation model. (2) It has a compact closed-form solution and can be efficiently computed. (3) It is capable to remove corruptions. Extensive experiments on 8 datasets show promising results on image reconstruction and significant improvement on clustering and classification.


title: De-Noising By Soft-Thresholding

abstract: Donoho and Johnstone (1994) proposed a method for reconstructing an unknown function f on [0,1] from noisy data d/sub i/=f(t/sub i/)+/spl sigma/z/sub i/, i=0, ..., n-1,t/sub i/=i/n, where the z/sub i/ are independent and identically distributed standard Gaussian random variables. The reconstruction f/spl circ/*/sub n/ is defined in the wavelet domain by translating all the empirical wavelet coefficients of d toward 0 by an amount /spl sigma//spl middot//spl radic/(2log (n)/n). The authors prove two results about this type of estimator. [Smooth]: with high probability f/spl circ/*/sub n/ is at least as smooth as f, in any of a wide variety of smoothness measures. [Adapt]: the estimator comes nearly as close in mean square to f as any measurable estimator can come, uniformly over balls in each of two broad scales of smoothness classes. These two properties are unprecedented in several ways. The present proof of these results develops new facts about abstract statistical inference and its connection with an optimal recovery model. >


title: Micro-Objective Learning : Accelerating Deep Reinforcement Learning through the Discovery of Continuous Subgoals

abstract: Recently, reinforcement learning has been successfully applied to the logical game of Go, various Atari games, and even a 3D game, Labyrinth, though it continues to have problems in sparse reward settings. It is difficult to explore, but also difficult to exploit, a small number of successes when learning policy. To solve this issue, the subgoal and option framework have been proposed. However, discovering subgoals online is too expensive to be used to learn options in large state spaces. We propose Micro-objective learning (MOL) to solve this problem. The main idea is to estimate how important a state is while training and to give an additional reward proportional to its importance. We evaluated our algorithm in two Atari games: Montezuma's Revenge and Seaquest. With three experiments to each game, MOL significantly improved the baseline scores. Especially in Montezuma's Revenge, MOL achieved two times better results than the previous state-of-the-art model.


title: Community based innovation: How to integrate members of virtual communities into new product development

abstract: Online consumer groups represent a large pool of product know-how. Hence, they seem to be a promising source of innovation. At present, except for open source software, little is known about how to utilize this know-how for new product development. In this article we explore if and how members of virtual communities can be integrated into new product development. We explain how to identify and access online communities and how to interact with its members in order to get valuable input for new product development. This approach we term "Community Based Innovation." The Audi case illustrates the applicability of the method and underscores the innovative capability of consumers encountered in virtual communities.


title: A Large-Signal Millimeter-wave InP/GaInAs Phototransistor Model: Method of Parameters Extraction and Maximum Gain Investigation

abstract: A large signal opto-microwave modeling technique is developed for an InP/GaInAs heterojunction bipolar phototransistor. The DC and RF parameters as well as the optical parameters extraction methods are described. Then an investigation of the maximum available gain is made using three-port considerations to determine the optimal load impedances. Opto-microwave S-Parameters and an opto-microwave gain describing the transfer from the optical input to the electrical output of the phototransistor at millimeter-wave frequencies are defined. This study demonstrates the importance of the base load impedance on the opto-microwave transfer from the optical input to the collector output of the InP/GaInAs heterojunction bipolar phototransistor, and therefore on the phototransistor responsivity.


title: High Power Resonant Inverter with Simultaneous Dual-frequency Output

abstract: Induction surface hardening of parts with non-uniform cylindrical shape requires a multi-frequency process in order to obtain a uniform surface hardened depth. This paper presents an induction heating high power supply constituted of an only inverter circuit and a specially designed output resonant circuit. The whole circuit supplies both medium and high frequency power signals to the heating inductor simultaneously


title: Packaging and integration of DBC-based SiC hybrid power module in 379W/in3 DC/DC converter

abstract: A new DBC-based hybrid packaging and integration method is proposed in this paper. A multilayer power module is formed by a direct-bond-copper (DBC) and a window cutting printed circuit board (PCB). The SiC chips and PCB are placed and soldering on the DBC. Al bonding wires are used for connecting the chips and the PCB. A full SiC half-bridge power module is designed and fabricated in compact size. The parasitic inductance of power loop is only 3.38 nH. The passive device and gate drive circuit can be easily integrated on the PCB. Based on the idea, a highly integrated 2.5kW 300 kHz TCM synchronous rectification (SR) is designed. The power density achieves 379 W/in3 and maximum efficiency reaches 98.4%.


title: Security against hardware Trojan through a novel application of design obfuscation

abstract: Malicious hardware Trojan circuitry inserted in safety-critical applications is a major threat to national security. In this work, we propose a novel application of a key-based obfus-cation technique to achieve security against hardware Trojans. The obfuscation scheme is based on modifying the state transition function of a given circuit by expanding its reachable state space and enabling it to operate in two distinct modes — the normal mode and the obfuscated mode. Such a modification obfuscates the rareness of the internal circuit nodes, thus making it difficult for an adversary to insert hard-to-detect Trojans. It also makes some inserted Trojans benign by making them activate only in the obfuscated mode. The combined effect leads to higher Trojan detectability and higher level of protection against such attack. Simulation results for a set of benchmark circuits show that the scheme is capable of achieving high levels of security at modest design overhead. Categories and Subject Descriptors B.6.1 [Logic Design]: Design Styles-sequential circuits; K.6.5 [Management of Computing and Information Systems]: Security and Protection-physical security General Terms Design, Security


title: Short Answer Assessment: Establishing Links Between Research Strands

abstract: A number of different research subfields are concerned with the automatic assessment of student answers to comprehension questions, from language learning contexts to computer science exams. They share the need to evaluate free-text answers but differ in task setting and grading/evaluation criteria, among others. ::: ::: This paper has the intention of fostering synergy between the different research strands. It discusses the different research strands, details the crucial differences, and explores under which circumstances systems can be compared given publicly available data. To that end, we present results with the CoMiC-EN Content Assessment system (Meurers et al., 2011a) on the dataset published by Mohler et al. (2011) and outline what was necessary to perform this comparison. We conclude with a general discussion on comparability and evaluation of short answer assessment systems.


title: Web accessibility for older adults: effects of line spacing and text justification on reading web pages

abstract: Numerous guidelines for making websites more accessible for older users have been proposed, but few provide evidence from such users for their recommendations This study investigated effects of line spacing and text justification on younger (24--31 years) and older (65--78 years) adults' performance and preferences in web reading tasks. The three levels of line spacing (single, 1.5, and double) and 2 types of text justification (left only and left-right) were studied. Neither variable had a significant effect on performance measures, although both younger and older adults preferred 1.5 or double spacing over single spacing. There were no significant differences in preferences for left versus left-right justification. These results suggest that contrary to common recommendations, 1.5 or double spacing should be recommended for all users, not only older users and that no recommendation is needed on text justification.


title: Stacked convolutional auto-encoders for hierarchical feature extraction

abstract: We present a novel convolutional auto-encoder (CAE) for unsupervised feature learning. A stack of CAEs forms a convolutional neural network (CNN). Each CAE is trained using conventional on-line gradient descent without additional regularization terms. A max-pooling layer is essential to learn biologically plausible features consistent with those found by previous approaches. Initializing a CNN with filters of a trained CAE stack yields superior performance on a digit (MNIST) and an object recognition (CIFAR10) benchmark.


title: A ParaBoost Method to Image Quality Assessment

abstract: An ensemble method for full-reference image quality assessment (IQA) based on the parallel boosting (ParaBoost) idea is proposed in this paper. We first extract features from existing image quality metrics and train them to form basic image quality scorers (BIQSs). Then, we select additional features to address specific distortion types and train them to construct auxiliary image quality scorers (AIQSs). Both BIQSs and AIQSs are trained on small image subsets of certain distortion types and, as a result, they are weak performers with respect to a wide variety of distortions. Finally, we adopt the ParaBoost framework, which is a statistical scorer selection scheme for support vector regression (SVR), to fuse the scores of BIQSs and AIQSs to evaluate the images containing a wide range of distortion types. This ParaBoost methodology can be easily extended to images of new distortion types. Extensive experiments are conducted to demonstrate the superior performance of the ParaBoost method, which outperforms existing IQA methods by a significant margin. Specifically, the Spearman rank order correlation coefficients (SROCCs) of the ParaBoost method with respect to the LIVE, CSIQ, TID2008, and TID2013 image quality databases are 0.98, 0.97, 0.98, and 0.96, respectively.


title: Contexts for the Semantic Web

abstract: A central theme of the Semantic Web is that programs should be able to easily aggregate data from different sources. Unfortunately, even if two sites provide their data using the same data model and vocabulary, subtle differences in their use of terms and in the assumptions they make pose challenges for aggregation. Experiences with the TAP project reveal some of the phenomena that pose obstacles to a simplistic model of aggregation. Similar experiences have been reported by AI projects such as Cyc, which has led to the development and use of various context mechanisms. In this paper we report on some of the problems with aggregating independently published data and propose a context mechanism to handle some of these problems. We briefly survey the context mechanisms developed in AI and contrast them with the requirements of a context mechanism for the Semantic Web. Finally, we present a context mechanism for the Semantic Web that is adequate to handle the aggregation tasks, yet simple from both computational and model theoretic perspectives.


title: Deconstructing the human algorithms for exploration

abstract: Abstract The dilemma between information gathering (exploration) and reward seeking (exploitation) is a fundamental problem for reinforcement learning agents. How humans resolve this dilemma is still an open question, because experiments have provided equivocal evidence about the underlying algorithms used by humans. We show that two families of algorithms can be distinguished in terms of how uncertainty affects exploration. Algorithms based on uncertainty bonuses predict a change in response bias as a function of uncertainty, whereas algorithms based on sampling predict a change in response slope. Two experiments provide evidence for both bias and slope changes, and computational modeling confirms that a hybrid model is the best quantitative account of the data.


title: Tree Measurement in Forest by 2D Laser Scanning

abstract: Measurement of tree parameters, particularly tree diameter and location, in connection to forest harvesters, is discussed. Diameters are determined from laser scanner measurements using a simple method based on edge points of a feature, in addition to the point with the shortest range to the scanner. The measurement height is equal to 1.3 m from the ground. The data for this research have been collected in a pine forest using a 2D laser scanner mounted on a mobile ATV platform. The error of the tree diameter calculations is 4% in average. The evaluation of the tree localization is based on manually measured reference data of the forest.


title: Dual- and multi-energy CT: approach to functional imaging

abstract: The energy spectrum of X-ray photons after passage through an absorber contains information about its elemental composition. Thus, tissue characterisation becomes feasible provided that absorption characteristics can be measured or differentiated. Dual-energy CT uses two X-ray spectra enabling material differentiation by analysing material-dependent photo-electric and Compton effects. Elemental concentrations can thereby be determined using three-material decomposition algorithms. In comparison to dual-energy CT used in clinical practice, recently developed energy-sensitive photon-counting detectors sample the material-specific attenuation curves at multiple energy levels and within narrow energy bands; the latter allows the detection of element-specific, k-edge discontinuities of the photo-electric cross section. Multi-energy CT imaging therefore is able to concurrently identify multiple materials with increased accuracy. These specific data on material distribution provide information beyond morphological CT, and approach functional imaging. This article reviews the principles of dual- and multi-energy CT imaging, hardware approaches and clinical applications.


title: Interactive Message-Locked Encryption and Secure Deduplication

abstract: This paper considers the problem of secure storage of outsourced data in a way that permits deduplication. We are for the first time able to provide privacy for messages that are both correlated and dependent on the public system parameters. The new ingredient that makes this possible is interaction. We extend the message-locked encryption (MLE) primitive of prior work to interactive message-locked encryption (iMLE) where upload and download are protocols. Our scheme, providing security for messages that are not only correlated but allowed to depend on the public system parameters, is in the standard model. We explain that interaction is not an extra assumption in practice because full, existing deduplication systems are already interactive.


title: Agile versus CMMI - process template selection and integration with microsoft team foundation server

abstract: Increasingly many software development organizations must pursue at least a level 3 CMMI certification to compete in the marketplace. Not all software organizations are under this constraint, but the CMMI model offers benefit beyond just a marketing advantage. Agile methods have taken hold in many of the same organizations that stand to gain benefit from CMMI. CMMI adds some stress to the Agile approach by nature of more detailed planning and tracking. Microsoft and other software development organizations have endeavored to automate the more tedious aspects of CMMI, thus making it easier to support agile approaches within more rigid frameworks. The Microsoft Solutions Framework (MSF) initially founded on Microsoft software best practices observed internally and externally has expanded to integrate automated software configuration for team projects. The Team Foundation Server (TFS) product leverages MSF and supports both CMMI and Agile as process templates and provides tools around these templates to automate the use of either approach. In this paper, we explore how Microsoft has integrated these approaches into TFS, the rationale and implications for selecting one approach over another and how agile practices can be integrated into a CMMI framework.


title: Data Augmentation in Emotion Classification Using Generative Adversarial Networks

abstract: It is a difficult task to classify images with multiple class labels using only a small number of labeled examples, especially when the label (class) distribution is imbalanced. Emotion classification is such an example of imbalanced label distribution, because some classes of emotions like \emph{disgusted} are relatively rare comparing to other labels like {\it happy or sad}. In this paper, we propose a data augmentation method using generative adversarial networks (GAN). It can complement and complete the data manifold and find better margins between neighboring classes. Specifically, we design a framework with a CNN model as the classifier and a cycle-consistent adversarial networks (CycleGAN) as the generator. In order to avoid gradient vanishing problem, we employ the least-squared loss as adversarial loss. We also propose several evaluation methods on three benchmark datasets to validate GAN's performance. Empirical results show that we can obtain 5%~10% increase in the classification accuracy after employing the GAN-based data augmentation techniques.


title: How a Traditional Company Seeded New Analytics Capabilities

abstract: textabstractDeveloping state-of-the art data analytics capabilities is a challenge for traditional companies with long-established processes and legacy systems. We describe how one such company (a Swiss electricity utility) conducted a seed project-a bottom-up initiative to develop an analytics ecosystem of business, organizational and technological capabilities. This project was an effective first step in growing the company's analytics capabilities. Based on this case, we provide four key lessons that can be used by other traditional companies seeking to develop new digital capabilities.


title: Gait analysis in children with cerebral palsy via inertial and magnetic sensors

abstract: 3D kinematic measurements in children with cerebral palsy (CP) to assess gait deviations can only be performed in gait laboratories using optoelectronic systems. Alternatively, an inertial and magnetic measurement system (IMMS) can be applied for ambulatory motion-tracking. A protocol named Outwalk has recently been developed to measure the 3D kinematics during gait with IMMS. This study preliminary validated the application of IMMS, based on the Outwalk protocol, in gait analysis of six children with CP and one typically developing child. Reference joint kinematics were simultaneously obtained from a laboratory-based system and protocol. On average, the root mean square error (RMSE) of Outwalk/IMMS, compared to the reference, was less than 17° in the transversal plane, and less than 10° in the sagittal and frontal planes. The greatest differences were found in offsets in the knee and ankle rotation, and in the hip flexion. These offset differences were mainly caused by a different anatomical calibration in the protocols. When removing the offsets, RMSE was always less than 4°. Therefore, IMMS is suitable for gait analysis of major joint angles in a laboratory-free setting. Further studies should focus on improvement of anatomical calibrations of IMMS that can be performed in children with CP.


A Survey of Indoor Localization Techniques


title: Visualization of the 3D structures of small organisms via LED-SIM

abstract: BACKGROUND ::: Innovative new techniques that aid in the visualization of microscopic anatomical structures have improved our understanding of organismal biology significantly. It is often challenging to observe internal 3D structures, despite the use of techniques such as confocal laser scanning microscopy (CLSM), micro-computed tomography (Micro-CT), magnetic resonance imaging (MRI), focused ion beam scanning electron microscopy (FIB-SEM) and others. In the current paper, we assess LED-SIM (DMD-based LED-illumination structured illumination microscopy), which facilitates the acquisition of nano- and micro-3D structures of small organisms in a high-resolution format (500 nm in the XY-plane and 930 nm along the Z-axis). ::: ::: ::: RESULTS ::: We compare other microstructural imaging techniques (involving conventional optical microscopy, CLSM and Micro-CT) with LED-SIM to assess the quality (e.g. resolution, penetration depth, etc.) of LED-SIM images, as well as to document the potential short-comings of LED-SIM. Based on these results we present an optimized set of protocols to ensure that LED-SIM arthropod and nematode samples with different cuticles or textures are prepared for analysis in an optimal manner. Six arthropod and nematode specimens were tested and shown to be suitable for LED-SIM imaging, which was found to yield high resolution 3D images. ::: ::: ::: CONCLUSIONS ::: Although LED-SIM still must be thoroughly tested before it is widely accepted and the Z-axis resolution still requires improvement, this technique offers distinct high quality 3D images. LED-SIM can be highly effective and may provide high quality 3D images for zoological studies following the guidelines of sample preparation presented in the current paper.


title: Surface Multi-Purposes Low Power Wireless Electromyography (EMG) system Design

abstract: The progress in the field of electronics and technology as well as the processing of signals coupled with advance in the use of computer technology has given the opportunity to record and analyze the bio-electric signals from the human body in real time that requires dealing with many challenges according to the nature of the signal and its frequency. This could be up to 1 kHz, in addition to the need to transfer data from more than one channel at the same time. Moreover, another challenge is a high sensitivity and low noise measurements of the acquired bio-electric signals which may be tens of micro volts in amplitude. For these reasons, a low power wireless Electromyography (EMG) data transfer system is designed in order to meet these challenging demands. In this work, we are able to develop an EMG analogue signal processing hardware, along with computer based supporting software. In the development of the EMG analogue signal processing hardware, many important issues have been addressed. Some of these issues include noise and artifact problems, as well as the bias DC current. The computer based software enables the user to analyze the collected EMG data and plot them on graphs for visual decision making. The work accomplished in this study enables users to use the surface EMG device for recording EMG signals for various purposes in movement analysis in medical diagnosis, rehabilitation sports medicine and ergonomics. Results revealed that the proposed system transmit and receive the signal without any losing in the information of signals.


title: Knowledge sharing: influences of trust, commitment and cost

abstract: Purpose – This paper’s aim is to examine the influence of perceived cost of sharing knowledge and ::: affective trust in colleagues on the relationship between affective commitment and knowledge sharing. ::: Design/methodology/approach – The methodology used was a survey of 496 employees from 15 ::: organizations across ten industries. ::: Findings – Affective trust in colleagues moderates the relationship between affective commitment and ::: knowledge sharing and the relationship between cost of knowledge sharing and knowledge sharing. ::: Research limitations/implications – Future researchers should operationalize the perceived cost of ::: knowledge sharing construct to include other potential group barriers; for instance, politics and ::: organizational barriers, management commitment and lack of trust. ::: Practical implications – The findings of this study suggest that employees who value social ::: relationships and social resources tend to view knowledge as a collectively owned commodity. As such, ::: their knowledge sharing behavior reflects the model of reciprocal social exchanges. ::: Social implications – The results of this study indicate that an organizational culture that encourages ::: affect-based trust between colleagues will facilitate knowledge sharing. ::: Originality/value – The paper bridges the gap between the literature on knowledge sharing, perceived ::: cost of knowledge sharing, affective organizational commitment and trust in a single model.


Deep Almond : A Deep Learning-based Virtual Assistant [ Language-to-code synthesis of Trigger-Action programs using Seq 2 Seq Neural Networks ]


title: A methodology to generate a synergetic land-cover map by fusion of different land-cover products

abstract: a b s t r a c t The main goal of this study is to develop a general framework for building a hybrid land-cover map by the synergistic combination of a number of land-cover classifications with different legends and spatial resolutions. The proposed approach assesses class-specific accuracies of datasets and establishes affinity between thematic legends using a common land-cover language such as the UN Land-Cover Classification System (LCCS). The approach is illustrated over a large region in Europe using four land-cover datasets (CORINE, GLC2000, MODIS and GlobCover), but it can be applied to any set of existing products. The multi-classification map is expected to improve the performance of individual classifications by recon- ciling their best characteristics while avoiding their main weaknesses. The intermap comparison reveals improved agreement of the hybrid map with all other land-cover products and therefore indicates the successful exploration of synergies between the different products. The approach offers also estimates for the classification confidence associated with the pixel label and flexibility to shift the balance between commission and omission errors, which are critical in order to obtain a desired reliable map.


title: An unsupervised framework of exploring events on twitter: filtering, extraction and categorization

abstract: Twitter, as a popular microblogging service, has become a new information channel for users to receive and exchange the most up-to-date information on current events. However, since there is no control on how users can publish messages on Twitter, finding newsworthy events from Twitter becomes a difficult task like "finding a needle in a haystack". ::: ::: In this paper we propose a general unsupervised framework to explore events from tweets, which consists of a pipeline process of filtering, extraction and categorization. To filter out noisy tweets, the filtering step exploits a lexicon-based approach to separate tweets that are event-related from those that are not. Then, based on these event-related tweets, the structured representations of events are extracted and categorized automatically using an unsupervised Bayesian model without the use of any labelled data. Moreover, the categorized events are assigned with the event type labels without human intervention. The proposed framework has been evaluated on over 60 millions tweets which were collected for one month in December 2010. A precision of 70.49% is achieved in event extraction, outperforming a competitive baseline by nearly 6%. Events are also clustered into coherence groups with the automatically assigned event type label.


title: Characterizing glottal jet turbulence

abstract: Air pressure associated with airflow from the lungs drives the vocal folds into oscillation and allows the air to exit the glottis as a turbulent jet, even though laminar flow may enter the glottis from the trachea. The separation of the turbulence from the deterministic portion of the glottal jet was investigated in the excised canine larynx model. The present study is methodological in that the main goal was to examine three methods of obtaining reasonable representations of both the deterministic signal and the residual turbulence portion: (a) smoothing, (b) wavelet denoising, and (c) ensemble averaging. Ensemble averaging resulted in a deterministic signal that disregarded gross cyclic alterations while exaggerating the turbulence intensity. Wavelet denoising can perform an excellent analysis and synthesis of the glottal velocity, but was problematic in determining which levels of analysis to choose to represent both the deterministic and turbulence appropriately. Smoothing appeared to be the most app...


title: One Million Sense-Tagged Instances for Word Sense Disambiguation and Induction

abstract: Supervised word sense disambiguation (WSD) systems are usually the best performing systems when evaluated on standard benchmarks. However, these systems need annotated training data to function properly. While there are some publicly available open source WSD systems, very few large annotated datasets are available to the research community. The two main goals of this paper are to extract and annotate a large number of samples and release them for public use, and also to evaluate this dataset against some word sense disambiguation and induction tasks. We show that the open source IMS WSD system trained on our dataset achieves stateof-the-art results in standard disambiguation tasks and a recent word sense induction task, outperforming several task submissions and strong baselines.


title: Dilated convolutions for image classification and object localization

abstract: Yu et al.[1] showed that dilated convolutions are very effective in dense prediction problems such as semantic segmentation. In this work, we propose a new ResNet[2] based convolutional neural network model using dilated convolutions and show that this model can achieve lower error rate for image classification than ResNet with reduction of the number of the parameters of the network by 94% and that this model has high ability to localize objects despite being trained on image-level labels. We evaluated this model on ImageNet[5] which has 50 class labels randomly selected from 1000 class labels.


title: Efficient control design for single phase grid tie inverter of PV system

abstract: In the past decade the utilization of renewable energy is ever increasing due to the scarcity of fossil fuels. The excessive power from renewable energy is considered mandatory in integration that improves the performance of the electric transmission system in terms of better reliability and eco green power generation. Extraction of abundant of power from the solar and interfacing to the electric grid through the power electronic components plays a significant role. However, the operation and control of the electric power system becomes very challenging during the integration. This paper uses single stage high performance maximum power point tracking (MPPT) for solar photovoltaic power generation. An efficient control design of current and voltage controllers is designed and a 3kW single phase grid connected PV inverter is proposed and validated using MATLAB/Simulink/PLECS.


title: A Review and Classification of Widely used Offline Brain Datasets

abstract: Brain Computer Interfaces (BCI) are a natural extension to Human Computer Interaction (HCI) technologies. BCI is especially useful for people suffering from diseases, such as Amyotrophic Lateral Sclerosis (ALS) which cause motor disabilities in patients. To evaluate the effectiveness of BCI in different paradigms, the need of benchmark BCI datasets is increasing rapidly. Although, such datasets do exist, a comparative study of such datasets is not available to the best of our knowledge. In this paper, we provided a comprehensive overview of various BCI datasets. We briefly describe the characteristics of these datasets and devise a classification scheme for them. The comparative study provides feature extractors and classifiers used for each dataset. Moreover, potential use-cases for each dataset are also provided.


title: Using ultra-low parasitic hybrid packaging method to reduce high frequency EMI noise for SiC power module

abstract: In order to reduce high frequency electromagnetic interference (EMI) noise for SiC devices, this paper presents a hybrid structure SiC power module with low parasitic inductance and low ground capacitance that can suppress EMI efficiently. To certify the improvement on EMI, two buck converters, using the same power loop and gate drive, are built for experiment. Two converters use the proposed power module and TO-247 packaged commercial devices respectively. The experimental results show that low parasitic inductance can increase switching speed considerably while have only slight impact on EMI. And low parasitic ground capacitance can decrease the ground current effectively. Considering soft switching can also reduce EMI, this paper further compares EMI noise of two converters under two conditions: continuous current modulation (CCM) and triangle current modulation (TCM). The result shows that the proposed power module can suppress the common mode (CM) noise effectively at both situations. Furthermore, this paper verifies the noise mode transformation from differential mode (DM) EMI to CM EMI.


title: A Comparative Study of Stereovision Algorithms

abstract: Stereo vision has been and continues to be one of the most researched domains of computer vision, having many applications, among them, allowing the depth extraction of a scene. This paper provides a comparative study of stereo vision and matching algorithms, used to solve the correspondence problem. The study of matching algorithms was followed by experiments on the Middlebury benchmarks. The tests focused on a comparison of 6 stereovision methods. In order to assess the performance, RMS and some statistics related were computed. In order to emphasize the advantages of each stereo algorithm considered, two-frame methods have been employed, both local and global. The experiments conducted have shown that the best results are obtained by Graph Cuts. Unfortunately, this has a higher computational cost. If high quality is not an issue in applications, local methods provide reasonable results within a much lower time-frame and offer the possibility of parallel implementations.


title: Frame Detection for Marginal Noise Removal from Scanned Documents

abstract: We describe and evaluate a method to robustly detect the page frame in document images, locating the actual page contents area and removing textual and non-textual noise along the page borders. We use a geometric matching algorithm to find the optimal page frame, which has the advantages of not assuming the existence of whitespace between noisy borders and actual page contents, and of giving a practical solution to the page frame detection problem without the need for parameter tuning. We define suitable performance measures and evaluate the algorithm on the UW-III database. The results show that the error rates are below 4% for each of the performance measures used. In addition, we demonstrate that the use of page frame detection reduces the optical character recognition (OCR) error rate by removing textual noise. Experiments using a commercial OCR system show that the error rate due to elements outside the page frame is reduced from 4.3% to 1.7% on the UW-III dataset.


title: Smartwatch-Based Keystroke Inference Attacks and Context-Aware Protection Mechanisms

abstract: Wearable devices, such as smartwatches, are furnished with state-of-the-art sensors that enable a range of context-aware applications. However, malicious applications can misuse these sensors, if access is left unaudited. In this paper, we demonstrate how applications that have access to motion or inertial sensor data on a modern smartwatch can recover text typed on an external QWERTY keyboard. Due to the distinct nature of the perceptible motion sensor data, earlier research efforts on emanation based keystroke inference attacks are not readily applicable in this scenario. The proposed novel attack framework characterizes wrist movements (captured by the inertial sensors of the smartwatch worn on the wrist) observed during typing, based on the relative physical position of keys and the direction of transition between pairs of keys. Eavesdropped keystroke characteristics are then matched to candidate words in a dictionary. Multiple evaluations show that our keystroke inference framework has an alarmingly high classification accuracy and word recovery rate. With the information recovered from the wrist movements perceptible by a smartwatch, we exemplify the risks associated with unaudited access to seemingly innocuous sensors (e.g., accelerometers and gyroscopes) of wearable devices. As part of our efforts towards preventing such side-channel attacks, we also develop and evaluate a novel context-aware protection framework which can be used to automatically disable (or downgrade) access to motion sensors, whenever typing activity is detected.


title: Lane-Level Localization Using an AVM Camera for an Automated Driving Vehicle in Urban Environments

abstract: This paper describes a lane-level localization algorithm based on a map-matching method for application to automated driving in urban environments. The lane-level localization implies localizing the vehicle with centimeter-level accuracy. In order to achieve a satisfactory level of position accuracy with a low-cost GPS, a sensor fusion approach is essential for lane-level localization. The proposed sensor fusion approach for the lane-level localization of a vehicle uses an around view monitoring (AVM) module and vehicle sensors. The proposed algorithm consists of three parts: lane detection, position correction, and localization filter. In order to detect lanes, a commercialized AVM module is used. Since this module can acquire an image around the vehicle, it is possible to obtain accurate position information of the lanes. With this information, the vehicle position can be corrected by the iterative closest point (ICP) algorithm. This algorithm estimates the rigid transformation between the lane map and lanes obtained by AVM in real-time. The vehicle position corrected by this transformation is fused with the information of vehicle sensors based on an extended Kalman filter. For higher accuracy, the covariance of the ICP is estimated using Haralick's method. The performance of the proposed localization algorithm is verified via vehicle tests on a proving ground. Test results show that the proposed method can achieve localization centimeter-level accuracy. The proposed algorithm will be useful in the implementation of automated driving control.


title: Fine-Grained Car Detection for Visual Census Estimation

abstract: Targeted socioeconomic policies require an accurate understanding of a country's demographic makeup. To that end, the United States spends more than 1 billion dollars a year gathering census data such as race, gender, education, occupation and unemployment rates. Compared to the traditional method of collecting surveys across many years which is costly and labor intensive, data-driven, machine learning driven approaches are cheaper and faster--with the potential ability to detect trends in close to real time. In this work, we leverage the ubiquity of Google Street View images and develop a computer vision pipeline to predict income, per capita carbon emission, crime rates and other city attributes from a single source of publicly available visual data. We first detect cars in 50 million images across 200 of the largest US cities and train a model to predict demographic attributes using the detected cars. To facilitate our work, we have collected the largest and most challenging fine-grained dataset reported to date consisting of over 2600 classes of cars comprised of images from Google Street View and other web sources, classified by car experts to account for even the most subtle of visual differences. We use this data to construct the largest scale fine-grained detection system reported to date. Our prediction results correlate well with ground truth income data (r=0.82), Massachusetts department of vehicle registration, and sources investigating crime rates, income segregation, per capita carbon emission, and other market research. Finally, we learn interesting relationships between cars and neighborhoods allowing us to perform the first large scale sociological analysis of cities using computer vision techniques.


title: Massively parallel KD-tree construction and nearest neighbor search algorithms

abstract: This paper presents parallel algorithms for the construction of k dimensional tree (KD-tree) and nearest neighbor search (NNS) on massively parallel architecture (MPA) of graphics processing unit (GPU). Unlike previous parallel algorithms for KD-tree, for the first time, our parallel algorithms integrate high dimensional KD-tree construction and NNS on an MPA platform. The proposed massively parallel algorithms are of comparable quality as traditional sequential counterparts on CPU, while achieve high speedup performance on both low and high dimensional KD-tree. Low dimensional KD-tree construction and NNS algorithms, presented in this paper, outperform their serial CPU counterparts by a factor of up to 24 and 218, respectively. For high dimensional KD-tree, the speedup factors are even higher, raising to 30 and 242, respectively. Our implementations will potentially benefit real time three-dimensional (3D) image registration and high dimensional descriptor matching.


title: UAVSim: A simulation testbed for unmanned aerial vehicle network cyber security analysis

abstract: Increased use of unmanned systems in various tasks enables users to complete important missions without risking human lives. Nonetheless, these systems pose a huge threat if the operational cyber security is not handled properly, especially for the unmanned aerial vehicle systems (UAVS), which can cause catastrophic damages. Therefore, it is important to check the impact of various attack attempts on the UAV system. The most economical and insightful way to do this is to simulate operational scenarios of UAVs in advance. In this paper, we introduce UAVSim, a simulation testbed for Unmanned Aerial Vehicle Networks cyber security analysis. The testbed allows users to easily experiment by adjusting different parameters for the networks, hosts and attacks. In addition, each UAV host works on well-defined mobility framework and radio propagation models, which resembles real-world scenarios. Based on the experiments performed in UAVSim, we evaluate the impact of Jamming attacks against UAV networks and report the results to demonstrate the necessity and usefulness of the testbed.


A Hybrid Cloud Approach for Secure Authorized Deduplication


title: Temporal and regional patterns of Smad activation in the rat hippocampus following global ischemia

abstract: In this study, we examined the temporal and regional patterns of Smad activation in the rat hippocampus following global ischemia. We also examined the association between Smad activation and ischemia-induced pathology in the hippocampus. We found that 1) Smad1, -2, -3, and -5 proteins were detected in the rat hippocampus by means of western blot and immunohistochemistry; 2) after 5 min of ischemia, Smad2 and Smad3 proteins accumulated in the nuclei of pyramidal cells in the CA1 region, which is vulnerable to ischemia; 3) after 3 min of ischemia, which was non-lethal, there was no such nuclear accumulation of Smad2 and Smad3 in the CA1 region; 4) following injection of activin A, nuclear accumulation of Smad2 and Smad3 was induced not only in pyramidal cells of the CA1 region, but also in pyramidal cells of the CA3 region as well as in granule cells of the DG region; 5) activin A-induced nuclear accumulation of Smad2 and Smad3 neither caused degeneration of hippocampal neurons nor prevented degeneration induced by ischemia. These results suggest that in the hippocampus, ischemia-induced activation of Smad2 and Smad3 is associated with the response to stress but is not related to neuronal survival or death.


title: SCADAVT-A framework for SCADA security testbed based on virtualization technology

abstract: Supervisory Control and Data Acquisition (SCADA) systems monitor and control infrastructures and industrial processes such as smart grid power and water distribution systems. Recently, such systems have been attacked, and traditional security solutions have failed to provide an appropriate level of protection. Therefore, it is important to develop security solutions tailored to SCADA systems. However, it is impractical to evaluate such solutions on actual live systems. This paper proposes a SCADA security testbed based on virtualization technology, and introduces a server which is used as a surrogate for water distribution systems. In addition, this paper presents a case study of two malicious attacks to demonstrate how the testbed can easily monitor and control any automatised processes, and also to show how malicious attacks can disrupt supervised processes.


title: Abnormal Structural Networks Characterize Major Depressive Disorder: A Connectome Analysis

abstract: Background Major depressive disorder (MDD) has been shown to be associated with a disrupted topological organization of functional brain networks. However, little is known regarding whether these changes have a structural basis. Diffusion tensor imaging (DTI) enables comprehensive whole-brain mapping of the white matter tracts that link regions distributed throughout the entire brain, the so-called human connectome. Methods We examined whole-brain structural networks in a cohort of 95 MDD outpatients and 102 matched control subjects. Structural networks were represented by an 84 × 84 connectivity matrix representing probabilistic white matter connections between 84 parcellated cortical and subcortical regions using DTI tractography. Network-based statistics were used to assess differences in the interregional connectivity matrix between the two groups, and graph theory was used to examine overall topological organization. Results Our network-based statistics analysis demonstrates lowered structural connectivity within two distinct brain networks that are present in depression: the first primarily involves the regions of the default mode network and the second comprises the frontal cortex, thalamus, and caudate regions that are central in emotional and cognitive processing. These two altered networks were observed in the context of an overall preservation of topology as reflected as no significant group differences for the graph-theory measures. Conclusions This is the first report to use DTI to show the structural connectomic alterations present in MDD. Our findings highlight that altered structural connectivity between nodes of the default mode network and the frontal-thalamo-caudate regions are core neurobiological features associated with MDD.


title: GABA concentrations in forebrain areas of suicide victims

abstract: Concentrations of GABA and seven other amino acids, including the neurotransmitters or neuromodulators taurine, glycine, aspartate, and glutamate, were determined in postmortem brain samples from suicide victims and normal controls. The five brain areas (caudate nucleus, nucleus accumbens, frontal cortex, amygdala, and hypothalamus) contained very similar concentrations of the amino acids in both groups. The only significant difference between the groups was a low glutamine concentration in the hypothalamus of suicide victims. Even when the data were adjusted for differences in tryptophan concentration, a putative biochemical index for agonal and postmortem changes of brain tissue, no new differences emerged in the concentrations of neuroactive amino acids between suicide victims and control subjects.


title: Inverse dynamics and trajectory tracking control of a new six degrees of freedom spatial 3-RPRS parallel manipulator

abstract: Abstract. This paper presents the complete dynamic model of a new six degrees of freedom (DOF) spatial 3-RPRS parallel manipulator. The geometry parameters of the manipulator are optimized for a given constant orientation workspace. Further, a robust task-space trajectory tracking control is also designed for the manipulator along with a nonlinear disturbance observer. To demonstrate the efficacy and show the complete performance of the proposed controller, virtual prototype experiments are executed using one of the multibody dynamics software namely MSC Adams. The computer-based virtual prototype experiment results show that the manipulator tracking performance is satisfactory with the proposed control scheme. In addition, the controller parameter sensitivity and robustness analyses are also accomplished.


Collaborative Departure Queue Management An Example of Airport Collaborative Decision Making in the United States


title: Types for Units-of-Measure : Theory and Practice

abstract: Units-of-measure are to science what types are to programming. In science and engineering, dimensional and unit consistency provides a first check on the correctness of an equation or formula, just as in programming the validation of a program by the type-checker eliminates one possible reason for failure.


title: A co-simulation framework for Smart Grid wide-area monitoring networks

abstract: The next generation of the electrical power grid, called the Smart Grid, overcomes the deficiencies of the existing system using several Information and Communication Technology (ICT) mechanisms. One such mechanism is wide-area grid monitoring based on phasor measurement units (PMU). The data from these units are communicated to several applications (such as control, market brokerage, etc.) using a communication network framework called the North American Synchro-Phasor Initiative Network (NASPInet). To understand the performance and to guide design decisions of wide-area monitoring based Smart Grid applications, there is a need for a simulator that has both power system and communication system simulation capabilities. In this paper, we present a co-simulation framework developed using two existing public-domain simulators (OpenDSS and OMNET++ respectively for power systems and communication networks simulation). The former is based on continuous-time and the latter on discrete-time simulation. The proposed framework also deals with the time synchronization between these two systems. This framework has been implemented at IIT Madras for public use. A hybrid state estimation application, a voltage monitoring application and a renewable energy integration application are implemented over this framework. The results obtained from the applications verify their field correctness and also validate the correctness of the co-simulation framework.


title: Towards zero-shot learning for human activity recognition using semantic attribute sequence model

abstract: Understanding human activities is important for user-centric and context-aware applications. Previous studies showed promising results using various machine learning algorithms. However, most existing methods can only recognize the activities that were previously seen in the training data. In this paper, we present a new zero-shot learning framework for human activity recognition that can recognize an unseen new activity even when there are no training samples of that activity in the dataset. We propose a semantic attribute sequence model that takes into account both the hierarchical and sequential nature of activity data. Evaluation on datasets in two activity domains show that the proposed zero-shot learning approach achieves 70-75% precision and recall recognizing unseen new activities, and outperforms supervised learning with limited labeled data for the new classes.


title: Fragile online relationship: a first look at unfollow dynamics in twitter

abstract: We analyze the dynamics of the behavior known as 'unfollow' in Twitter. We collected daily snapshots of the online relationships of 1.2 million Korean-speaking users for 51 days as well as all of their tweets. We found that Twitter users frequently unfollow. We then discover the major factors, including the reciprocity of the relationships, the duration of a relationship, the followees' informativeness, and the overlap of the relationships, which affect the decision to unfollow. We conduct interview with 22 Korean respondents to supplement the quantitative results. They unfollowed those who left many tweets within a short time, created tweets about uninteresting topics, or tweeted about the mundane details of their lives. To the best of our knowledge, this work is the first systematic study of the unfollow behavior in Twitter.


title: Perceptions of Parental Involvement and Autonomy Support: Their Relations with Self-Regulation, Academic Performance, Substance Use and Resilience among Adolescents

abstract: Three important developmental tasks for adolescents in the U.S. are doing reasonably well in schools, behaving in a manner that does not disrupt others, and resisting the urge to use alcohol and other drugs (Masten, 2001; Masten & Reed, 2002). This study examined how parental involvement and parental autonomy support were related to these three psychosocial outcomes in adolescence, i.e., academic performance, disruptive behavior and substance use. Additionally, this study tested whether self-regulation mediated the effects of parental involvement and autonomy support on these outcomes. Moreover, this study examined the relations among parenting practices, self-regulation and resilience, i.e., good adaptation in spite of adversity. Past research has found that perception of parental involvement and autonomy support are positively related to academic performance (Grolnick, Ryan, & Deci, 1991; Grolnick & Slowiaczek, 1994; Soenens & Vansteenkiste, 2005). Parental involvement is the extent to which parents are interested in, knowledgeable about, and willing to take an active role in the day-to-day activities of their children. Parental autonomy support is the extent to which parents value and use techniques that facilitate independent problem solving, choice, and self-determination in their children. Specifically, perception of greater parental involvement is associated with higher standardized achievement scores, higher teacher rated competence, and better school grade (Grolnick et al., 1991; Grolnick & Slowiaczek, 1994). Perception of greater parental autonomy support is related to higher grade point average, higher teacher-rated competence, more active job search behavior, and a strong vocational identity (Grolnick et al., 1991; Soenens & Vansteenkiste, 2005). This study examined the effects of perceived parental involvement and autonomy support on academic performance, classroom disruptive behavior and substance use in adolescence. Classroom disruptive behavior and substance use are two important psychosocial outcomes that have not been studied in previous research on perceived parental involvement and autonomy support (Grolnick et al., 1991; Grolnick & Slowiaczek, 1994; Soenens & Vansteenkiste, 2005). Additionally, this study examined how perceived parental involvement and autonomy support were related to resilience, i.e., positive adaptation in spite of adversity (Luthar, Cicchetti, & Becker, 2000; Masten, 2001). I compared the effects of perceived parental involvement and autonomy support on psychosocial outcomes between high-risk and low-risk students: those who were in challenging circumstances (i.e., having low parental education and low parental acculturation) and those who were not. Past research has shown that low parental education and acculturation were correlated with a number of poor outcomes in children and adolescents such as low academic performance (Blair, Blair & Madamba, 1999; Farver, Bhadha, & Narang, 2002), more substance use (Conway, Swendsen, Dierker, Canino & Merikangas, 2007; Rodriguez, Henderson, Rowe, 2007) and a high frequency of sexual behavior (Wills, Gibbons, Gerrard, Murray & Brody, 2003). Parenting practices and characteristics of parent-child interactions have been linked to behavioral problems, substance use (Hawkins, Catalano, & Miller, 1992; Petraitis, Flay, & Miller, 1995) and resilience (Luthar & Zelazo, 2003; Masten & Reed, 2002). Factors such as parents' permissiveness, inconsistent and unclear behavioral limits, unrealistic parental expectations, negative communication patterns, and lack of parental monitoring were associated with earlier initiation and higher rates of drug use (Baumrind, 1991; Chilcoat & Anthony, 1996). Greater perceived parental support and responsiveness together with high parental expectations were associated with resilience in children and youth (Forgatch & DeGarno, 1999; Masten & Reed, 2002). …


title: Using hidden Markov models for iterative non-intrusive appliance monitoring.

abstract: Non-intrusive appliance load monitoring is the process of breaking down a household’s total electricity consumption into its contributing appliances. In this paper we propose an approach by which individual appliances are iteratively separated from the aggregate load. Our approach does not require training data to be collected by sub-metering individual appliances. Instead, prior models of general appliance types are tuned to specific appliance instances using only signatures extracted from the aggregate load. The tuned appliance models are used to estimate each appliance’s load, which is subsequently subtracted from the aggregate load. We evaluate our approach using the REDD data set, and show that it can disaggregate 35% of a typical household’s total energy consumption to an accuracy of 83% by only disaggregating three of its highest energy consuming appliances.


title: Multimodal fusion for multimedia analysis: a survey

abstract: This survey aims at providing multimedia researchers with a state-of-the-art overview of fusion strategies, which are used for combining multiple modalities in order to accomplish various multimedia analysis tasks. The existing literature on multimodal fusion research is presented through several classifications based on the fusion methodology and the level of fusion (feature, decision, and hybrid). The fusion methods are described from the perspective of the basic concept, advantages, weaknesses, and their usage in various analysis tasks as reported in the literature. Moreover, several distinctive issues that influence a multimodal fusion process such as, the use of correlation and independence, confidence level, contextual information, synchronization between different modalities, and the optimal modality selection are also highlighted. Finally, we present the open issues for further research in the area of multimodal fusion.


title: Analyses of Rotational Torque and Suspension Force in a Permanent Magnet Synchronous Bearingless Motor with Short-pitch Winding

abstract: This paper presents the analyzed results of rotational torque and suspension force in a bearingless motor with the short-pitch winding, which are based on the computation by finite element method (FEM). The bearingless drive technique is applied to a conventional brushless DC motor, in which the stator windings are arranged at the short-pitch, and encircle only a single stator tooth. At first, the winding arrangement in the stator core, the principle of suspension force generation and the magnetic suspension control method are shown in the bearingless motor with brushless DC structure. The torque and suspension force are computed by FEM using a machine model with the short-pitch winding arrangement, and the computed results are compared between the full-pitch and short-pitch winding arrangements. The advantages of short-pitch winding arrangement are found on the basis of computed results and discussion.


Design and Field Test of a WSN Platform Prototype for Long-Term Environmental Monitoring


title: Flexible-Latency DRAM: Understanding and Exploiting Latency Variation in Modern DRAM Chips

abstract: This article summarizes key results of our work on experimental characterization and analysis of latency variation and latency-reliability trade-offs in modern DRAM chips, which was published in SIGMETRICS 2016, and examines the work's significance and future potential. The goal of this work is to (i) experimentally characterize and understand the latency variation across cells within a DRAM chip for these three fundamental DRAM operations, and (ii) develop new mechanisms that exploit our understanding of the latency variation to reliably improve performance. To this end, we comprehensively characterize 240 DRAM chips from three major vendors, and make six major new observations about latency variation within DRAM. Notably, we find that (i) there is large latency variation across the cells for each of the three operations; (ii) variation characteristics exhibit significant spatial locality: slower cells are clustered in certain regions of a DRAM chip; and (iii) the three fundamental operations exhibit different reliability characteristics when the latency of each operation is reduced. Based on our observations, we propose Flexible-LatencY DRAM (FLY-DRAM), a mechanism that exploits latency variation across DRAM cells within a DRAM chip to improve system performance. The key idea of FLY-DRAM is to exploit the spatial locality of slower cells within DRAM, and access the faster DRAM regions with reduced latencies for the fundamental operations. Our evaluations show that FLY-DRAM improves the performance of a wide range of applications by 13.3%, 17.6%, and 19.5%, on average, for each of the three different vendors' real DRAM chips, in a simulated 8-core system.