title: Madeus: Database Live Migration Middleware under Heavy Workloads for Cloud Environment

abstract: Database-as-a-service has been gaining popularity in cloud computing because multitenant databases can reduce costs by sharing off-the-shelf resources. However, due to heavy workloads, resource sharing often causes a hot spot; one node is overloaded even while others are not. Unfortunately, a hot spot can lead to violation of service level agreements and destroy customer satisfaction. To efficiently address the hot spot problem, we propose a middleware approach called Madeus that conducts database live migration. To make efficient database live migration possible, we also introduce the lazy snapshot isolation rule (LSIR) that enables concurrently propagating syncsets, which are the datasets needed to synchronize slave with master databases. Madeus provides efficient database live migration by implementing the LSIR under snapshot isolation. Unlike current approaches, Madeus is pure middleware that is transparent to the database management system and based on commodity hardware and software. To demonstrate the superiority of our approach over current approaches, we experimentally evaluated Madeus by using PostgreSQL with the TPC-W benchmark. The results indicate that Madeus achieves more efficient live migration than three other types of middleware approaches, especially under heavy workloads; therefore, it can effectively resolve hot spots.


title: Automated feedback generation for introductory programming assignments

abstract: We present a new method for automatically providing feedback for introductory programming problems. In order to use this method, we need a reference implementation of the assignment, and an error model consisting of potential corrections to errors that students might make. Using this information, the system automatically derives minimal corrections to student's incorrect solutions, providing them with a measure of exactly how incorrect a given solution was, as well as feedback about what they did wrong. We introduce a simple language for describing error models in terms of correction rules, and formally define a rule-directed translation strategy that reduces the problem of finding minimal corrections in an incorrect program to the problem of synthesizing a correct program from a sketch. We have evaluated our system on thousands of real student attempts obtained from the Introduction to Programming course at MIT (6.00) and MITx (6.00x). Our results show that relatively simple error models can correct on average 64% of all incorrect submissions in our benchmark set.


title: Low latency live video streaming using HTTP chunked encoding

abstract: Hypertext transfer protocol (HTTP) based streaming solutions for live video and video on demand (VOD) applications have become available recently. However, the existing HTTP streaming solutions cannot provide a low latency experience due to the fact that inherently in all of them, latency is tied to the duration of the media fragments that are individually requested and obtained over HTTP. We propose a low latency HTTP streaming approach using HTTP chunked encoding, which enables the server to transmit partial fragments before the entire video fragment is published. We develop an analytical model to quantify and compare the live latencies in three HTTP streaming approaches. Then, we present the details of our experimental setup and implementation. Both the analysis and experimental results show that the chunked encoding approach is capable of reducing the live latency to one to two chunk durations and that the resulting live latency is independent of the fragment duration.


title: On Real-Time Synthetic Primate Vision

abstract: The primate vision system exhibits numerous capabilities. Some important basic visual competencies include: 1) a consistent representation of visual space across eye movements; 2) egocentric spatial perception; 3) coordinated stereo fixation upon and pursuit of dynamic objects; and 4) attentional gaze deployment. We present a synthetic vision system that incorporates these competencies.We hypothesize that similarities between the underlying synthetic system model and that of the primate vision system elicit accordingly similar gaze behaviors. Psychophysical trials were conducted to record human gaze behavior when free-viewing a reproducible, dynamic, 3D scene. Identical trials were conducted with the synthetic system. A statistical comparison of synthetic and human gaze behavior has shown that the two are remarkably similar.


title: A fast patch-dictionary method for whole image recovery

abstract: Various algorithms have been proposed for dictionary learning. Among those for image processing, many use image patches to form dictionaries. This paper focuses on whole-image recovery from corrupted linear measurements. We address the open issue of representing an image by overlapping patches: the overlapping leads to an excessive number of dictionary coefficients to determine. With very few exceptions, this issue has limited the applications of image-patch methods to the local kind of tasks such as denoising, inpainting, cartoon-texture decomposition, super-resolution, and image deblurring, for which one can process a few patches at a time. Our focus is global imaging tasks such as compressive sensing and medical image recovery, where the whole image is encoded together, making it either impossible or very ineffective to update a few patches at a time. ::: Our strategy is to divide the sparse recovery into multiple subproblems, each of which handles a subset of non-overlapping patches, and then the results of the subproblems are averaged to yield the final recovery. This simple strategy is surprisingly effective in terms of both quality and speed. In addition, we accelerate computation of the learned dictionary by applying a recent block proximal-gradient method, which not only has a lower per-iteration complexity but also takes fewer iterations to converge, compared to the current state-of-the-art. We also establish that our algorithm globally converges to a stationary point. Numerical results on synthetic data demonstrate that our algorithm can recover a more faithful dictionary than two state-of-the-art methods. ::: Combining our whole-image recovery and dictionary-learning methods, we numerically simulate image inpainting, compressive sensing recovery, and deblurring. Our recovery is more faithful than those of a total variation method and a method based on overlapping patches.


title: The design and implementation of microdrivers

abstract: Device drivers commonly execute in the kernel to achieve high performance and easy access to kernel services. However, this comes at the price of decreased reliability and increased programming difficulty. Driver programmers are unable to use user-mode development tools and must instead use cumbersome kernel tools. Faults in kernel drivers can cause the entire operating system to crash. User-mode drivers have long been seen as a solution to this problem, but suffer from either poor performance or new interfaces that require a rewrite of existing drivers. This paper introduces the Microdrivers architecture that achieves high performance and compatibility by leaving critical path code in the kernel and moving the rest of the driver code to a user-mode process. This allows data-handling operations critical to I/O performance to run at full speed, while management operations such as initialization and configuration run at reduced speed in user-level. To achieve compatibility, we present DriverSlicer, a tool that splits existing kernel drivers into a kernel-level component and a user-level component using a small number of programmer annotations. Experiments show that as much as 65% of driver code can be removed from the kernel without affecting common-case performance, and that only 1-6 percent of the code requires annotations.


title: Learning to look up: Realtime monocular gaze correction using machine learning

abstract: We revisit the well-known problem of gaze correction and present a solution based on supervised machine learning. At training time, our system observes pairs of images, where each pair contains the face of the same person with a fixed angular difference in gaze direction. It then learns to synthesize the second image of a pair from the first one. After learning, the system gets the ability to redirect the gaze of a previously unseen person by the same angular difference as in the training set. Unlike many previous solutions to gaze problem in videoconferencing, ours is purely monocular, i.e. it does not require any hardware apart from an in-built web-camera of a laptop. Being based on efficient machine learning predictors such as decision forests, the system is fast (runs in real-time on a single core of a modern laptop). In the paper, we demonstrate results on a variety of videoconferencing frames and evaluate the method quantitatively on the hold-out set of registered images. The supplementary video shows example sessions of our system at work.


title: Robots for Use in Autism Research

abstract: Autism spectrum disorders are a group of lifelong disabilities that affect people's ability to communicate and to understand social cues. Research into applying robots as therapy tools has shown that robots seem to improve engagement and elicit novel social behaviors from people (particularly children and teenagers) with autism. Robot therapy for autism has been explored as one of the first application domains in the field of socially assistive robotics (SAR), which aims to develop robots that assist people with special needs through social interactions. In this review, we discuss the past decade's work in SAR systems designed for autism therapy by analyzing robot design decisions, human-robot interactions, and system evaluations. We conclude by discussing challenges and future trends for this young but rapidly developing research area.


title: ISO 9241-11 Revised: What Have We Learnt About Usability Since 1998?

abstract: A revision is currently being undertaken of ISO 9241-11, published in 1998 to provide guidance on usability. ISO-9241-11 defines usability in terms of effectiveness, efficiency and satisfaction in a particular context of use. The intention was to emphasise that usability is an outcome of interaction rather than a property of a product. This is now widely accepted. However, the standard also places emphasis on usability measurement and it is now appreciated that there is more to usability evaluation than measurement. Other developments include an increasing awareness of the importance of the individual user’s emotional experience as discretionary usage of complex consumer products and use of the World Wide Web have became more widespread. From an organisational perspective, it is now appreciated that usability plays an important role in managing the potentials risks that can arise from inappropriate outcomes of interaction. The revision of ISO 9241-11 takes account of these issues and other feedback.


title: Low voltage low power current comparator circuit

abstract: This paper presents a high speed, low voltage, low power current comparator. A modification to an existing current comparator proposed by Traff has been introduced. The circuit presents a two flipped voltage follower current mirrors (FVF) connected in parallel which replaces the input stage of the existing current comparator of Tang and Pun. This modification allows us to reach the higher speed, lower voltage and lower power dissipation. ELDO simulators using 0.18 μm, CMOS and TSMC parameters are performed to confirm the workability of this circuit. The proposed current comparator circuit is powered with a 1 V supply voltage. It is characterized by wide conversion frequency 2 GHz and a low power consumption that is about 0.208 mW.


title: Compact UWB-MIMO antenna array with a novel decoupling structure

abstract: In this paper, a compact planar Ultra-Wideband (UWB) Multiple-input Multiple-output (MIMO) antenna array is proposed. This UWB-MIMO antenna array consists of two identical monopole antenna elements with a novel decoupling structure etched on the ground plane. The antenna performs very well over the UWB frequency range of 3.1–10.6 GHz. The decoupling structure improves the isolation between the antennas over the complete frequency band which can only be achieved otherwise by increasing the separation between the antenna elements. The analysis of antenna performance with and without stub is provided to demonstrate the significance of adding the decoupling stub to the design. The proposed compact and cost efficient antenna array system measures 27 × 47 mm2 only.


title: Weighted citation: An indicator of an article's prestige

abstract: We propose using the technique of weighted citation to measure an article's prestige. The technique allocates a different weight to each reference by taking into account the impact of citing journals and citation time intervals. Weighted citation captures prestige, whereas citation counts capture popularity. We compare the value variances for popularity and prestige for articles published in the Journal of the American Society for Information Science and Technology from 1998 to 2007, and find that the majority have comparable status.


title: Similarity of relaxation in supercooled liquids and interacting arrays of oscillators.

abstract: Dielectric relaxation and dynamic light scattering of small molecule glass-forming liquids invariably show that the fractional exponent beta(alpha) of the Kohlrausch-Williams-Watts correlation function, exp[-(t/tau(alpha))(beta(alpha))], used to fit the alpha-relaxation data is temperature dependent, increasing towards the value of unity as temperature is raised and the relaxation time tau decreased. Comparing different glass formers, another property is the existence of a correlation between the value of (1-beta(alpha)) at the glass temperature, T(g), and the T(g)-scaled temperature dependence of tau(alpha). We analyze a system of interacting arrays of globally coupled nonlinear oscillators. Each array has its oscillators coupled among themselves with a coupling strength K. The coupling between arrays is characterized by the interarray coupling strength K'. The decay of the phase coherence r for each array is slowed down by the interarray coupling and its time dependence is well approximated at sufficiently long times by exp[-(t/tau)(beta)]. For a fixed K', on increasing K the results exhibit a decrease of tau and a concomitant increase of beta, similar to the properties of dielectric relaxation and dynamic light scattering of glass-forming liquids on increasing temperature. For each K' we define K(g) to be the value of K at which tau is equal to an arbitrarily chosen long time. We find that beta(K(g)) is correlated with the K(g)-scaled K dependence of tau. The results obtained in this manner at various fixed values of K' reproduce the relaxation properties and temperature dependencies of strong, intermediate, and fragile glass-forming liquids.


title: On the relationship between visual attributes and convolutional networks

abstract: One of the cornerstone principles of deep models is their abstraction capacity, i.e. their ability to learn abstract concepts from ‘simpler’ ones. Through extensive experiments, we characterize the nature of the relationship between abstract concepts (specifically objects in images) learned by popular and high performing convolutional networks (conv-nets) and established mid-level representations used in computer vision (specifically semantic visual attributes). We focus on attributes due to their impact on several applications, such as object description, retrieval and mining, and active (and zero-shot) learning. Among the findings we uncover, we show empirical evidence of the existence of Attribute Centric Nodes (ACNs) within a conv-net, which is trained to recognize objects (not attributes) in images. These special conv-net nodes (1) collectively encode information pertinent to visual attribute representation and discrimination, (2) are unevenly and sparsely distribution across all layers of the conv-net, and (3) play an important role in conv-net based object recognition.


title: Status epilepticus in the elderly

abstract: The status epilepticus in the elderly has not been considered specifically until the last few years. In this report we revised the epidemiological and clinical aspects as well as its etiology, prognostic and therapeutic implications. These group of patients have proper specific conditions. We emphasize the importance off an accurate diagnosis and specific treatment. Finally we present our personal data over pseudo-status and different types of status.


title: Arrakis: The Operating System Is the Control Plane

abstract: Storage systems have grown to the point where failures are inevitable, and those who design systems must plan ahead so that precious data is not lost when failures occur. The core technology for protecting data from failures is erasure coding, which has a rich 50+ year history stemming communication systems, and as such, can be confusing to the storage systems community. In this article, I present a primer on erasure coding as it applies to storage systems, and I summarize some recent research on erasure coding.


title: A Dual-Carrier Modulation Technique for Half-Bridge Resonant Converter With Wide Soft-Switching Range

abstract: A dual-carrier modulation technique is proposed to improve the performance of the half-bridge resonant converter in this paper. By alternately using two different sawtooth waveforms as a carrier, the dual-carrier modulation combines the characteristics of both variable frequency modulation and fixed-frequency modulation. According to the structure of modulator, there are two kinds of dual-carrier modulation: type A and type B. Type A dual-carrier modulation is suitable for resonant converters operating in inductive region to achieve a wide zero voltage switching (ZVS) range, while type B dual-carrier modulation is preferred for resonant converters operating in capacitive region to obtain a wide zero current switching range. By utilizing both switching frequency modulation and duty ratio modulation, the proposed dual-carrier modulation not only extends soft-switching range of the resonant converter, but also reduces switching frequency variation range. In order to verify the benefits of the proposed modulation, a 500-W dual-carrier modulated LCC resonant converter operating in inductive region is designed and built. Simulation and experimental results show the prototype achieves ZVS soft-switching operation for 200–400 V input voltage and 100–500 W output power, with only about 10 kHz switching frequency variation.


title: On-the-Fly Adaptation of Regression Forests for Online Camera Relocalisation

abstract: Camera relocalisation is an important problem in computer vision, with applications in simultaneous localisation and mapping, virtual/augmented reality and navigation. Common techniques either match the current image against keyframes with known poses coming from a tracker, or establish 2D-to-3D correspondences between keypoints in the current image and points in the scene in order to estimate the camera pose. Recently, regression forests have become a popular alternative to establish such correspondences. They achieve accurate results, but must be trained offline on the target scene, preventing relocalisation in new environments. In this paper, we show how to circumvent this limitation by adapting a pre-trained forest to a new scene on the fly. Our adapted forests achieve relocalisation performance that is on par with that of offline forests, and our approach runs in under 150ms, making it desirable for real-time systems that require online relocalisation.


title: Q-CP: Learning Action Values for Cooperative Planning

abstract: Research on multi-robot systems has demonstrated promising results in manifold applications and domains. Still, efficiently learning an effective robot behaviors is very difficult, due to unstructured scenarios, high uncertainties, and large state dimensionality (e.g. hyper-redundant and groups of robot). To alleviate this problem, we present Q-CP a cooperative model-based reinforcement learning algorithm, which exploits action values to both (1) guide the exploration of the state space and (2) generate effective policies. Specifically, we exploit Q-learning to attack the curse-of-dimensionality in the iterations of a Monte-Carlo Tree Search. We implement and evaluate Q-CP on different stochastic cooperative (general-sum) games: (1) a simple cooperative navigation problem among 3 robots, (2) a cooperation scenario between a pair of KUKA YouBots performing hand-overs, and (3) a coordination task between two mobile robots entering a door. The obtained results show the effectiveness of Q-CP in the chosen applications, where action values drive the exploration and reduce the computational demand of the planning process while achieving good performance.


title: ACQUINE: aesthetic quality inference engine - real-time automatic rating of photo aesthetics

abstract: We present ACQUINE - Aesthetic Quality Inference Engine, a publicly accessible system which allows users to upload their photographs and have them rated automatically for aesthetic quality. The system integrates a support vector machine based classifier which extracts visual features on the fly and performs real-time classification and prediction. As the first publicly available tool for automatically determining the aesthetic value of an image, this work is a significant first step in recognizing human emotional reaction to visual stimulus. In this paper, we discuss fundamentals behind this system, and some of the challenges faced while creating it. We report statistics generated from over 140,000 images uploaded by Web users. The system is demonstrated at http://acquine.alipr.com.


title: Relations Among Loneliness, Social Anxiety, and Problematic Internet Use

abstract: The model of problematic Internet use advanced and tested in the current study proposes that individuals' psychosocial well-being, along with their beliefs about interpersonal communication (both face-to-face and online) are important cognitive predictors of negative outcomes arising from Internet use. The study examined the extent to which social anxiety explains results previously attributed to loneliness as a predictor of preference for online social interaction and problematic Internet use. The results support the hypothesis that the relationship between loneliness and preference for online social interaction is spurious, and that social anxiety is the confounding variable.


title: Using a Room Metaphor to Ease Transitions in Groupware

abstract: Many groupware systems contain gaps that hinder or block natural social interaction or that does not let people easily move between different styles of work. We believe that the adoption of a room metaphor can ease people’s transitions across these gaps, allowing them to work together more naturally. Using the TeamWave Workplace system as an example, we show how particular gaps are removed. First, we ease a person’s transition between single user and groupware applications by making rooms suitable for both individual and group activity. Second, people can move fluidly between asynchronous and synchronous work because room artifacts persist. People can leave messages, documents and annotations for others, or work on them together when occupying the room at the same time. Third, we ease the difficulty of initiating real time work by providing people with awareness of others who may be available for real-time interactions, and by automatically establishing connections as users enter a common room. Fourth, we discuss how a technical space can be transformed into a social place by describing how a group crafts meaning into a room. We also argue that a room metaphor’s seamless support of everyday activities will foster an environment where groups naturally share their expertise.


Implementing the Belief Propagation Algorithm in MATLAB


Insider Trading in Credit Derivatives


title: DeePM: A Deep Part-Based Model for Object Detection and Semantic Part Localization

abstract: In this paper, we propose a deep part-based model (DeePM) for symbiotic object detection and semantic part localization. For this purpose, we annotate semantic parts for all 20 object categories on the PASCAL VOC 2012 dataset, which provides information on object pose, occlusion, viewpoint and functionality. DeePM is a latent graphical model based on the state-of-the-art R-CNN framework, which learns an explicit representation of the object-part configuration with flexible type sharing (e.g., a sideview horse head can be shared by a fully-visible sideview horse and a highly truncated sideview horse with head and neck only). For comparison, we also present an end-to-end Object-Part (OP) R-CNN which learns an implicit feature representation for jointly mapping an image ROI to the object and part bounding boxes. We evaluate the proposed methods for both the object and part detection performance on PASCAL VOC 2012, and show that DeePM consistently outperforms OP R-CNN in detecting objects and parts. In addition, it obtains superior performance to Fast and Faster R-CNNs in object detection.


title: Support Vector Machines for TCP traffic classification

abstract: Support Vector Machines (SVM) represent one of the most promising Machine Learning (ML) tools that can be applied to the problem of traffic classification in IP networks. In the case of SVMs, there are still open questions that need to be addressed before they can be generally applied to traffic classifiers. Having being designed essentially as techniques for binary classification, their generalization to multi-class problems is still under research. Furthermore, their performance is highly susceptible to the correct optimization of their working parameters. In this paper we describe an approach to traffic classification based on SVM. We apply one of the approaches to solving multi-class problems with SVMs to the task of statistical traffic classification, and describe a simple optimization algorithm that allows the classifier to perform correctly with as little training as a few hundred samples. The accuracy of the proposed classifier is then evaluated over three sets of traffic traces, coming from different topological points in the Internet. Although the results are relatively preliminary, they confirm that SVM-based classifiers can be very effective at discriminating traffic generated by different applications, even with reduced training set sizes.


title: Infrastructure and API Extensions for Elastic Execution of MPI Applications

abstract: Dynamic Processes support was added to MPI in version 2.0 of the standard. This feature of MPI has not been widely used by application developers in part due to the performance cost and limitations of the spawn operation. In this paper, we propose an extension to MPI that consists of four new operations. These operations allow an application to be initialized in an elastic mode of execution and enter an adaptation window when necessary, where resources are incorporated into or released from the application's world communicator. A prototype solution based on the MPICH library and the SLURM resource manager is presented and evaluated alongside an elastic scientific application that makes use of the new MPI extensions. The cost of these new operations is shown to be negligible due mainly to the latency hiding design, leaving the application's time for data redistribution as the only significant performance cost.


title: Learning Human-Object Interactions by Graph Parsing Neural Networks

abstract: This paper addresses the task of detecting and recognizing human-object interactions (HOI) in images and videos. We introduce the Graph Parsing Neural Network (GPNN), a framework that incorporates structural knowledge while being differentiable end-to-end. For a given scene, GPNN infers a parse graph that includes i) the HOI graph structure represented by an adjacency matrix, and ii) the node labels. Within a message passing inference framework, GPNN iteratively computes the adjacency matrices and node labels. We extensively evaluate our model on three HOI detection benchmarks on images and videos: HICO-DET, V-COCO, and CAD-120 datasets. Our approach significantly outperforms state-of-art methods, verifying that GPNN is scalable to large datasets and applies to spatial-temporal settings. The code is available at this https URL.


title: Real-time rendering and editing of vector-based terrains, in "Eurographics, Grèce Hersonissos

abstract: We present a method to populate very large terrains with very detailed features such as roads, rivers, lakes and fields. These features can be interactively edited, and the landscape can be explored in real time at any altitude from flight view to car view. We use vector descriptions of linear and areal features, with associated shaders to specify their appearance (terrain color and material), their footprint (effect on terrain shape), and their associated objects (bridges, hedges, etc.). In order to encompass both very large terrains and very fine details we rely on a view dependent quadtree refinement scheme. New quads are generated when needed and cached on the GPU. For each quad we produce on the GPU an appearance texture, a footprint texture, and some object meshes, based on the features vector description and their associated shaders. Adaptive refinement, procedural vector features and a mipmap pyramid provide three LOD mechanisms for small, medium and large scale quads. Our results and attached video show high performance with high visual quality.


title: Clickbait detection using deep learning

abstract: Clickbaits, in social media, are exaggerated headlines whose main motive is to mislead the reader to “click” on them. They create a nuisance in the online experience by creating a lure towards poor content. Online content creators are utilizing more of them to get increased page views and thereby more ad revenue without providing the backing content. This paper proposes a model for detection of clickbait by utilizing convolutional neural networks and presents a compiled clickbait corpus. We create a corpus using multiple social media platforms and utilize deep learning for learning features rather than undergoing the long and complex process of feature engineering. Our model achieves high performance in identification of clickbaits.


title: The assessment of the body fat percentage by skinfold thickness measurements in childhood and young adolescence.

abstract: The relationship of skinfold thicknesses to body density and of skinfold thicknesses to densitometrically determined body fat was evaluated in a group of 378 boys and girls, aged 7-20 years. According to their maturation level, they were divided into a prepubertal, a pubertal and a post-pubertal group. In each maturation group boys were older, had higher body-weights and body heights, higher body densities, lower percentage body fat, higher waist: hips ratios and higher trunk: total skinfolds ratios than girls. Body density in each maturation level could be quite precisely predicted by skinfold thicknesses. In prepubertal and pubertal boys and girls but not in post-pubertal boys and girls, age was also an important predicting variable for body density. The assessment of percentage body fat from skinfold thicknesses had a prediction error of 3-5 %, which was highest in the prepubertal children. The prediction error is comparable to the prediction error of percentage body fat from skinfold thicknesses in adults, as reported in the literature. Only in post-pubertal boys and girls was the waist:hip ratio correlated with measures of body fatness. Moreover, only in the post-pubertal boys and in the pubertal and post-pubertal girls was the waist: hips ratio correlated with another measure of body fat distribution, the trunk :total skinfold ratio. The relative amount of internal body fat was found to be higher in the younger maturation groups. It is concluded that at younger ages the waist: hips ratio is a poor indicator of body fat distribution.


title: An offline system for handwritten signature recognition

abstract: Automatic online and offline signature recognition and verification is becoming ubiquitous in person identification and authentication problems, in various domains requiring different levels of security. There has recently been an increasing interest in developing such systems, with several views on which are the best discriminator features. This paper presents a new offline signature verification system, which considers a new combination of previously used features and introduces two new distance-based ones. A new feature grouping is presented. We have experimented with two classification methods and two feature selection techniques. The best performance so far was obtained with the Naive Bayes classifier on the reduced feature set (through feature selection).


title: Story extraction from the Web: A case study in security informatics

abstract: Open source intelligence is becoming more and more important in security-related applications. Effective extraction of valuable information from these intelligence sources so as to understand the content of intelligence is one of the central issues in security domain. To address this key problem, this paper studies how to extract domain events and generate story representation of the events. We implement a story extraction platform (SEP) which applies pattern matching to extract events from Web news and organizes events based on theme using narrative structure. We also design extraction rules, use domain-specific features and employ ontology to facilitate story extraction in SEP. The experimental results show the effectiveness of our system in security informatics.


title: A one-phase algorithm to detect distributed deadlocks in replicated databases

abstract: Replicated databases that use quorum-consensus algorithms to perform majority voting are prone to deadlocks. Due to the P-out-of-Q nature of quorum requests, deadlocks that arise are generalized deadlocks and are hard to detect. We present an efficient distributed algorithm to detect generalized deadlocks in replicated databases. The algorithm performs reduction of a distributed wait-for-graph (WFG) to determine the existence of a deadlock. If sufficient information to decide the reducibility of a node is not available at that node, the algorithm attempts reduction later in a lazy manner. We prove the correctness of the algorithm. The algorithm has a message complexity of 2e messages and a worst-case time complexity of 2d+2 hops, where e is the number of edges and d is the diameter of the WFG. The algorithm is shown to perform significantly better in both time and message complexity than the best known existing algorithms. We conjecture that this is an optimal algorithm, in time and message complexity, to detect generalized deadlocks if no transaction has complete knowledge of the topology of the WFG or the system and the deadlock detection is to be carried out in a distributed manner.


Cross Sentence Inference for Process Knowledge


Towards Modernizing the Future of American Voting


A Multi-lingual Multi-task Architecture for Low-resource Sequence Labeling


title: A naïve, salience-based method for speaker identification in fiction books

abstract: This paper presents a salience-based technique for the annotation of directly quoted speech from fiction text. In particular, this paper determines to what extent a naive (without the use of complex machine learning or knowledge-based techniques) scoring technique can be used for the identification of the speaker of speech quotes. The presented technique makes use of a scoring technique, similar to that commonly found in knowledge-poor anaphora resolution research, as well as a set of hand-coded rules for the final identification of the speaker of each quote in the text. Speaker identification is shown to be achieved using three tasks: the identification of a speech-verb associated with a quote with a recall of 94.41%; the identification of the actor associated with a quote with a recall of 88.22%; and the selection of a speaker with an accuracy of 79.40%.


title: Vehicle Simulation System: Controls and Virtual-reality-based Dynamics Simulation

abstract: This paper introduces a cable-suspended robot, the vehicle simulation system, designed for pilot and driver training. The main idea of this work is to make a system that will provide realistic simulations of vehicle motion using virtual reality and a motion platform. A method to produce inertial forces acting on the vehicle user is proposed. A detailed description of the robot’s dynamics constraints is provided, accompanied with a numerical solution. Using the developed mathematical model, the robot workspace is analyzed, as well as the influence of the end-effector orientation angle ranges on the available workspace. Adequate robot platform motion is provided through four control systems in a form of a cascade control with a fuzzy logic controller in outer loops. The cable robot is non-linear and difficult from a classical control point of view, since it requires a local linearization of the dynamics equations. A fuzzy logic control concept has been applied in order to control the end-effector position, velocity and acceleration and it turns out to be convenient in simulation, allied with inner-loop classical control. As the system is under-constrained, with nine unknown cable tensions and six dynamics equations, an optimization method is used. The control complexity is increased by the requirement, specific to cable robots, of maintaining positive cable tensions. The system simulations are done using a virtual reality environment and results are presented accompanied with the discussion.


title: Behavioral overlays for non-verbal communication expression on a humanoid robot, Auton

abstract: This research details the application of non-verbal communication display behaviors to an autonomous humanoid robot, including the use of proxemics, which to date has been seldom explored in the field of human-robot interaction. In order to allow the robot to communicate information non-verbally while simultaneously fulfilling its existing instrumental behavior, a "behavioral overlay" model that encodes this data onto the robot's pre-existing motor expression is developed and presented. The state of the robot's system of internal emotions and motivational drives is used as the principal data source for non-verbal expression, but in order for the robot to display this information in a natural and nuanced fashion, an additional para-emotional framework has been developed to support the individuality of the robot's interpersonal relationships with humans and of the robot itself. An implementation on the Sony QRIO is described which overlays QRIO's existing EGO architecture and situated schema-based behaviors with a mechanism for communicating this framework through modalities that encompass posture, gesture and the management of interpersonal distance.


title: High-$Q$ Tunable Dielectric Resonator Filters Using MEMS Technology

abstract: This paper presents the design and implementation of a new class of high-Q tunable dielectric resonator (DR) filters based on microelectromechanical systems (MEMS) technology. The use of MEMS tuning elements results in the compact implementation of the proposed filters with high-Q and near to zero dc power consumption. The proposed filters consist of disk-shaped dielectric resonators with circular holes created in the center of each resonator. Three different filters are designed and measured based on different tuning elements. The first filter operates in TME mode at a center frequency of 4.72 GHz with a bandwidth of 21 MHz. MEMS contact-type switches are used as tuning elements for this filter. Measurement results demonstrate a tuning range of 160 MHz while the quality factor is above 510 (1200-510 over the tuning range). The other two implementations employ GaAs and MEMS varactors for tuning. The tunable filter with GaAs varactor has a continuous tuning range from 4.97 to 4.87 GHz with 65-MHz bandwidth and a Q value from 660 to 170. The MEMS varactor-tuned filter has a better tuning performance from 5.20 to 5.02 GHz with higher Q value from 800 to 550 over the tuning range. The proposed tuning approach is applicable to other modes at other frequencies of DR filters.


title: Voice control of home appliances using Android

abstract: This paper presents the development of home appliances based on voice command using Android. This system has been designed to assist and provide the support to elderly and disabled people at home. Google application has been used as voice recognition and process the voice input from the smart phone. In this paper, the voice input has been captured by the android and will be sent to the Arduino Uno. Bluetooth module in Arduino Uno received the signal and processed the input signal to control the light and fan. The proposed system intended to control electrical appliances with relatively user-friendly interface and ease of installation. We have demonstrated up to 20 meter of range to control the home appliances via Bluetooth.


title: Spatially aggregating spectral descriptors for nonrigid 3D shape retrieval: a comparative survey

abstract: This paper presents a comprehensive review and analysis of recent spectral shape descriptors for nonrigid 3D shape retrieval. More specifically, we compare the latest spectral descriptors based on the Laplace---Beltrami (LB) operator, including ShapeDNA, heat kernel signature, scale invariant heat kernel signature, heat mean signature, wave kernel signature, and global point signature. We also include the eigenvalue descriptor (EVD), which is a geodesic distance-based shape signature. The global descriptors ShapeDNA and EVD are compared via the chi-squared distance, while all local descriptors are compared using the codebook model. Moreover, we investigate the ambiguity modeling of codebook for the densely distributed low-level shape descriptors. Inspired by the ability of spatial cues to improve discrimination between shapes, we also propose to adopt the isocontours of the second eigenfunction of the LB operator to perform surface partition, which can significantly ameliorate the retrieval performance of the time-scaled local descriptors. In addition, we introduce an intrinsic spatial pyramid matching approach in a bid to further enhance the retrieval accuracy. Extensive experiments are carried out on two 3D shape benchmarks to assess the performance of the spectral descriptors. Our proposed approach is shown to provide the best performance.


title: Semisupervised ECG Ventricular Beat Classification With Novelty Detection Based on Switching Kalman Filters

abstract: Automatic processing and accurate diagnosis of pathological electrocardiogram (ECG) signals remains a challenge. As long-term ECG recordings continue to increase in prevalence, driven partly by the ease of remote monitoring technology usage, the need to automate ECG analysis continues to grow. In previous studies, a model-based ECG filtering approach to ECG data from healthy subjects has been applied to facilitate accurate online filtering and analysis of physiological signals. We propose an extension of this approach, which models not only normal and ventricular heartbeats, but also morphologies not previously encountered. A switching Kalman filter approach is introduced to enable the automatic selection of the most likely mode (beat type), while simultaneously filtering the signal using appropriate prior knowledge. Novelty detection is also made possible by incorporating a third mode for the detection of unknown (not previously observed) morphologies, and denoted as X-factor. This new approach is compared to state-of-the-art techniques for the ventricular heartbeat classification in the MIT-BIH arrhythmia and Incart databases. $F_1$ scores of $\mathbf {98.3\%}$ and $\mathbf {99.5\%}$ were found on each database, respectively, which are superior to other published algorithms’ results reported on the same databases. Only $\mathbf {3\%}$ of all the beats were discarded as X-factor, and the majority of these beats contained high levels of noise. The proposed technique demonstrates accurate beat classification in the presence of previously unseen (and unlearned) morphologies and noise, and provides an automated method for morphological analysis of arbitrary (unknown) ECG leads.


title: 2.4/5.2/5.8 GHz WLAN antenna for the ultrabook computer with metal housing

abstract: A promising small-size WLAN antenna suitable to be mounted along the hinge of the ultrabook computer with metal housing is presented. The antenna is formed by a two-branch structure of a monopole strip and a loop strip. The antenna has attractive features of low profile and wideband operation. In addition, by using the closed resonant path provided by the loop strip, possible coupling of the proposed antenna with nearby conducting elements for frequencies over the 2.4 GHz band can be expected to be decreased. Also, when the antenna is operated in the 5.2/5.8 GHz band, the loop strip is not at resonance and can serve as a shielding element for the monopole strip. This can also lead to decreased possible coupling of the proposed antenna with nearby conducting elements over the 5.2/5.8 GHz bands.


title: A Fast and Robust License Plate Detection Algorithm Based on Two-stage Cascade AdaBoost

abstract: License plate detection (LPD) is one of the most important aspects of an automatic license plate recognition system. Although there have been some successful license plate recognition (LPR) methods in past decades, it is still a challenging problem because of the diversity of plate formats and outdoor illumination conditions in image acquisition. Because the accurate detection of license plates under different conditions directly affects overall recognition system accuracy, different methods have been developed for LPD systems. In this paper, we propose a license plate detection method that is rapid and robust against variation, especially variations in illumination conditions. Taking the aspects of accuracy and speed into consideration, the proposed system consists of two stages. For each stage, Haar-like features are used to compute and select features from license plate images and a cascade classifier based on the concatenation of classifiers where each classifier is trained by an AdaBoost algorithm is used to classify parts of an image within a search window as either license plate or non-license plate. And it is followed by connected component analysis (CCA) for eliminating false positives. The two stages use different image preprocessing blocks: image preprocessing without adaptive thresholding for the first stage and image preprocessing with adaptive thresholding for the second stage. The method is faster and more accurate than most existing methods used in LPD. Experimental results demonstrate that the LPD rate is 98.38% and the average computational time is 54.64 ms.


title: A Network-based End-to-End Trainable Task-oriented Dialogue System

abstract: Teaching machines to accomplish tasks by conversing naturally with humans is challenging. Currently, developing task-oriented dialogue systems requires creating multiple components and typically this involves either a large amount of handcrafting, or acquiring costly labelled datasets to solve a statistical learning problem for each component. In this work we introduce a neural network-based text-in, text-out end-to-end trainable goal-oriented dialogue system along with a new way of collecting dialogue data based on a novel pipe-lined Wizard-of-Oz framework. This approach allows us to develop dialogue systems easily and without making too many assumptions about the task at hand. The results show that the model can converse with human subjects naturally whilst helping them to accomplish tasks in a restaurant search domain.


title: SOBER: statistical model-based bug localization

abstract: Automated localization of software bugs is one of the essential issues in debugging aids. Previous studies indicated that the evaluation history of program predicates may disclose important clues about underlying bugs. In this paper, we propose a new statistical model-based approach, called SOBER, which localizes software bugs without any prior knowledge of program semantics. Unlike existing statistical debugging approaches that select predicates correlated with program failures, SOBER models evaluation patterns of predicates in both correct and incorrect runs respectively and regards a predicate as bug-relevant if its evaluation pattern in incorrect runs differs significantly from that in correct ones. SOBER features a principled quantification of the pattern difference that measures the bug-relevance of program predicates.We systematically evaluated our approach under the same setting as previous studies. The result demonstrated the power of our approach in bug localization: SOBER can help programmers locate 68 out of 130 bugs in the Siemens suite when programmers are expected to examine no more than 10% of the code, whereas the best previously reported is 52 out of 130. Moreover, with the assistance of SOBER, we found two bugs in bc 1.06 (an arbitrary precision calculator on UNIX/Linux), one of which has never been reported before.


title: Cross cultural analysis of the use and perceptions of web Based learning systems

abstract: The main objective of this paper is to examine cultural differences and technology acceptances from students of two universities, one is from a European country: Spain, and the other is in Latin America: Chile. Both of them provide their students with e-learning platforms. The technology acceptance model (TAM) and Hofstede's cultural dimensions are the tools used to measure the acceptance and use of web-based learning platforms and cultural diversity of respondents, respectively. In summary, we can affirm that the sample of tertiary Spanish and Chilean students are culturally different with regard to some of Hofstede's dimensions, but their behavior of acceptance of e-learning technology globally matches according to the TAM model. This study provides relevant implications for on-line courses managers' who have tertiary students from different nationalities.


title: Max-Margin Markov Networks

abstract: In typical classification tasks, we seek a function which assigns a label to a single object. Kernel-based approaches, such as support vector machines (SVMs), which maximize the margin of confidence of the classifier, are the method of choice for many such tasks. Their popularity stems both from the ability to use high-dimensional feature spaces, and from their strong theoretical guarantees. However, many real-world tasks involve sequential, spatial, or structured data, where multiple labels must be assigned. Existing kernel-based methods ignore structure in the problem, assigning labels independently to each object, losing much useful information. Conversely, probabilistic graphical models, such as Markov networks, can represent correlations between labels, by exploiting problem structure, but cannot handle high-dimensional feature spaces, and lack strong theoretical generalization guarantees. In this paper, we present a new framework that combines the advantages of both approaches: Maximum margin Markov (M3) networks incorporate both kernels, which efficiently deal with high-dimensional features, and the ability to capture correlations in structured data. We present an efficient algorithm for learning M3 networks based on a compact quadratic program formulation. We provide a new theoretical bound for generalization in structured domains. Experiments on the task of handwritten character recognition and collective hypertext classification demonstrate very significant gains over previous approaches.


title: Credit card fraud detection using machine learning techniques: A comparative analysis

abstract: Financial fraud is an ever growing menace with far consequences in the financial industry. Data mining had played an imperative role in the detection of credit card fraud in online transactions. Credit card fraud detection, which is a data mining problem, becomes challenging due to two major reasons — first, the profiles of normal and fraudulent behaviours change constantly and secondly, credit card fraud data sets are highly skewed. The performance of fraud detection in credit card transactions is greatly affected by the sampling approach on dataset, selection of variables and detection technique(s) used. This paper investigates the performance of naive bayes, k-nearest neighbor and logistic regression on highly skewed credit card fraud data. Dataset of credit card transactions is sourced from European cardholders containing 284,807 transactions. A hybrid technique of under-sampling and oversampling is carried out on the skewed data. The three techniques are applied on the raw and preprocessed data. The work is implemented in Python. The performance of the techniques is evaluated based on accuracy, sensitivity, specificity, precision, Matthews correlation coefficient and balanced classification rate. The results shows of optimal accuracy for naive bayes, k-nearest neighbor and logistic regression classifiers are 97.92%, 97.69% and 54.86% respectively. The comparative results show that k-nearest neighbour performs better than naive bayes and logistic regression techniques.


title: Achromatic Waveplates for Liquid Crystal Displays

abstract: We report the results of our investigation on the viewing angle characteristics of achromatic waveplates. In our investigation, both Pancharatnam achromatic waveplate and Beckers achromatic waveplate are examined theoretically. We find that only Beckers configuration is capable of functioning as the achromatic waveplate over wide viewing angles and over the visible spectrum. Two types of extremely wide-viewing-angle achromatic crossed polarizers compensated by Beckers waveplates are obtained. The leakage of light is less than 10-4 over a 50° viewing cone for the visible spectrum. The results are presented and discussed.


title: Zero-shot User Intent Detection via Capsule Neural Networks

abstract: User intent detection plays a critical role in question-answering and dialog systems. Most previous works treat intent detection as a classification problem where utterances are labeled with predefined intents. However, it is labor-intensive and time-consuming to label users' utterances as intents are diversely expressed and novel intents will continually be involved. Instead, we study the zero-shot intent detection problem, which aims to detect emerging user intents where no labeled utterances are currently available. We propose two capsule-based architectures: INTENT-CAPSNET that extracts semantic features from utterances and aggregates them to discriminate existing intents, and INTENTCAPSNET-ZSL which gives INTENTCAPSNET the zero-shot learning ability to discriminate emerging intents via knowledge transfer from existing intents. Experiments on two real-world datasets show that our model not only can better discriminate diversely expressed existing intents, but is also able to discriminate emerging intents when no labeled utterances are available.


title: Empowering group leaders encourages knowledge sharing: integrating the social exchange theory and positive organizational behavior perspective

abstract: Knowledge sharing usually happens in a work group context, but it is rarely know how group leaders influence their members’ knowledge-sharing performance. Based on social exchange theory (SET) and the perspective of positive organizational behavior (POB), this study aims to argue that a group leader’s positive leadership (e.g. empowering leadership) can help group members develop positive psychological capital which can increase their knowledge sharing.,The authors conduct a multilevel analysis to explore the interrelationship among empowering leadership, psychological capital and knowledge sharing. The sample includes 64 work groups consisting of 537 group members, and empirical testing is carried out by hierarchical linear modeling.,The results show that empowering leadership in a work group has a direct cross-level impact on members’ knowledge sharing and that psychological capital partially mediates the relationship between empowering leadership and knowledge sharing. As a result, this study shows that group leaders with positive leadership can help their members develop better positive psychological resources, which should lead to better knowledge sharing.,Based on the multilevel perspective and SET, this is the first study to explore how group leaders’ empowering leadership influences members’knowledge sharing. Depending on integrating the POB perspective into SET, this study is also the first one that connects two emerging and important research issues – POB and knowledge sharing.


title: Augmenting Children's Creative Self-Efficacy and Performance through Enactment-Based Animated Storytelling

abstract: At around age nine when social awareness and self-evaluation heighten, children experience a precipitous slump in creative engagement. We propose an enactment-based approach grounded in embodied cognition theories to support children's creative self-efficacy and creative thinking in storytelling during the period of this slump. Our investigation of the approach with 20 children indicated that enactment-based animated authoring improves children's sense of self-efficacy in creating stories, particularly for children with low to medium extraversion, and enables children to produce richer stories, especially for children who scored low on the baseline creativity test.


title: CpG islands and the regulation of transcription

abstract: Vertebrate CpG islands (CGIs) are short interspersed DNA sequences that deviate significantly from the average genomic pattern by being GC-rich, CpG-rich, and predominantly nonmethylated. Most, perhaps all, CGIs are sites of transcription initiation, including thousands that are remote from currently annotated promoters. Shared DNA sequence features adapt CGIs for promoter function by destabilizing nucleosomes and attracting proteins that create a transcriptionally permissive chromatin state. Silencing of CGI promoters is achieved through dense CpG methylation or polycomb recruitment, again using their distinctive DNA sequence composition. CGIs are therefore generically equipped to influence local chromatin structure and simplify regulation of gene activity.


title: Depth Control of a Bioinspired Robotic Dolphin Based on Sliding-Mode Fuzzy Control Method

abstract: This paper presents a hybrid controller that combines sliding-mode control (SMC) with a fuzzy strategy to regulate the vertical displacement of a bioinspired robotic dolphin. The structure of the robot and a simplified mathematical model for depth control are described. An SMC based on the line-of-sight guidance law and the sliding-mode observer is developed to overcome systematic uncertainties and environmental disturbances. The Lyapunov stability theory is utilized to analyze the stability and convergence properties of the closed-loop system. Depth control in the physical robot is realized by utilizing a fuzzy logic controller, which is represented as the mapping of the input propulsion forces/moments and output control parameters. Numerical and experimental results show that the proposed control strategy successfully steers the robot toward and along the desired depth.


title: Deep Learning for Plant Identification in Natural Environment

abstract: Plant image identification has become an interdisciplinary focus in both botanical taxonomy and computer vision. The first plant image dataset collected by mobile phone in natural scene is presented, which contains 10,000 images of 100 ornamental plant species in Beijing Forestry University campus. A 26-layer deep learning model consisting of 8 residual building blocks is designed for large-scale plant classification in natural environment. The proposed model achieves a recognition rate of 91.78% on the BJFU100 dataset, demonstrating that deep learning is a promising technology for smart forestry.


title: Computing the discrete-time 'analytic' signal via FFT

abstract: Starting with a real-valued N-point discrete-time signal, frequency-domain algorithms are provided for computing (1) the complex-valued standard N-point discrete-time "analytic" signal of the same sample rate; (2) the complex-valued decimated N/2-point discrete-time "analytic" signal of half the original sample rate; and (3) the complex-valued interpolated NM-point discrete-time "analytic" signal of M times the original sample rate. Special adjustment of the transform end points are shown to be necessary in order to generate proper discrete-time "analytic" signals.


title: A Symmetrical Four-Port Microstrip Coupler for Crossover Application

abstract: A symmetrical four-port microstrip coupler has been proposed for crossover application. Models have also been developed to provide insight into the performance of such a coupler, which is based on the double-ring design. Laboratory tests have confirmed that the measured isolation and return loss of the prototype coupler exceed 20 dB over a bandwidth of 20% (from 0.92 to 1.12 GHz).


title: Who uses Facebook? An investigation into the relationship between the Big Five, shyness, narcissism, loneliness, and Facebook usage

abstract: The unprecedented popularity of the social networking site Facebook raises a number of important questions regarding the impact it has on sociality. However, as Facebook is a very recent social phenomenon, there is a distinct lack of psychological theory relating to its use. While research has begun to identify the types of people who use Facebook, this line of investigation has been limited to student populations. The current study aimed to investigate how personality influences usage or non-usage of Facebook. The sample consisted of 1324 self-selected Australian Internet users (1158 Facebook users and 166 Facebook nonusers), between the ages of 18 and 44. Participants were required to complete an online questionnaire package comprising the Big Five Inventory (BFI), the Narcissistic Personality Inventory - 29-item version (NPI-29), the Revised Cheek and Buss Shyness Scale (RCBS), and the Social and Emotional Loneliness Scale for Adults - Short version (SELSA-S). Facebook users also completed a Facebook usage questionnaire. The results showed that Facebook users tend to be more extraverted and narcissistic, but less conscientious and socially lonely, than nonusers. Furthermore, frequency of Facebook use and preferences for specific features were also shown to vary as a result of certain characteristics, such as neuroticism, loneliness, shyness and narcissism. It is hoped that research in this area continues, and leads to the development of theory regarding the implications and gratifications of Facebook use.


title: Controversies Surrounding Nonspeech Oral Motor Exercises for Childhood Speech Disorders

abstract: The use of nonspeech oral motor exercises (NSOMEs) for influencing children’s speech sound productions is a common therapeutic practice used by speech-language pathologists (SLPs) in the United States, 1 Canada, 2 and the United Kingdom. 3 Reports from these countries have documented that between 71.5% and 85% of practicing clinicians use some type of NSOMEs in therapy to change children’s speech productions. NSOMEs can be defined as any therapy technique that does not require the child to produce a speech sound but is used to influence the development of speaking abilities. 1 The National Center for Evidence-Based Practice in Communication Disorders (NCEP) 4 of the American Speech-Language-Hearing Association (ASHA) developed this definition: ‘‘Oral-motor exercises are activities that involve sensory stimulation to or actions of the lips, jaw, tongue, soft palate, larynx, and respiratory muscles which are intended to influence the physiologic underpinnings of the oropharyngeal mechanism and thus improve its functions; oral-motor exercises may include active muscle exercise, muscle stretching, passive exercise, and sensory stimulation.’’ The term ‘‘oral motor,’’ which relates to movements and placements of the oral musculature, is established in the field of SLP. Although the existence and importance of the oral-motor aspects of speech production is well understood, the use and effectiveness of nonspeech oral-motor activities is disputed because of the lack of theoretical and empirical support. To understand more about the use of NSOMEs for changing disordered productions of speech, a colleague and I 1 conducted a nationwide survey of 537 practicing clinicians from 48 states. We found that SLPs frequently used the exercises of blowing, tongue wagging and pushups, cheek puffing, the alternating movement of pucker-smile, ‘‘big smile,’’ and tongue-to-nose-to-chin. The clinicians believed these NSOMEs would help their clients obtain tongue elevation, protrusion, and lateralization; increase their tongue and lip strength; become more aware of their articulators; stabilize the jaw; control drooling; and increase velopharyngeal and sucking abilities. The respondents to the survey reported using these techniques for children with a wide variety of different speech disorders stemming from a wide variety of etiologies: dysarthria, childhood apraxia of speech (CAS), Down syndrome, late talkers, phonological impairment, functional misarticulations, and hearing impairment. It makes one curious why clinicians would select these nonspeech therapeutic techniques because they lack adequate theoretical underpinnings for their use. Additionally, the research studies that have evaluated treatment efficacy using the NSOME techniques, although admittedly scant and not at the highest levels of scientific rigor, do not show therapeutic effectiveness. Not only are these nonspeech tasks lacking in theoretical and data support for their use, their application to improve speech intelligibility also often defies logical reasoning. So why do clinicians use these techniques? As I have previously pointed out, 5 SLPs have several possible reasons for using NSOMEs. Some of these reasons may be that


title: Cold-Start Item and User Recommendation with Decoupled Completion and Transduction

abstract: A major challenge in collaborative filtering based recommender systems is how to provide recommendations when rating data is sparse or entirely missing for a subset of users or items, commonly known as the cold-start problem. In recent years, there has been considerable interest in developing new solutions that address the cold-start problem. These solutions are mainly based on the idea of exploiting other sources of information to compensate for the lack of rating data. In this paper, we propose a novel algorithmic framework based on matrix factorization that simultaneously exploits the similarity information among users and items to alleviate the cold-start problem. In contrast to existing methods, the proposed algorithm decouples the following two aspects of the cold-start problem: (a) the completion of a rating sub-matrix, which is generated by excluding cold-start users and items from the original rating matrix; and (b) the transduction of knowledge from existing ratings to cold-start items/users using side information. This crucial difference significantly boosts the performance when appropriate side information is incorporated. We provide theoretical guarantees on the estimation error of the proposed two-stage algorithm based on the richness of similarity information in capturing the rating data. To the best of our knowledge, this is the first algorithm that addresses the cold-start problem with provable guarantees. We also conduct thorough experiments on synthetic and real datasets that demonstrate the effectiveness of the proposed algorithm and highlights the usefulness of auxiliary information in dealing with both cold-start users and items.


title: DESIGN CONSIDERATIONS FOR MEMS-SCALE PIEZOELECTRIC MECHANICAL VIBRATION ENERGY HARVESTERS

abstract: ABSTRACT Design considerations for piezoelectric-based energy harvesters for MEMS-scale sensors are presented, including a review of past work. Harvested ambient vibration energy can satisfy power needs of advanced MEMS-scale autonomous sensors for numerous applications, e.g., structural health monitoring. Coupled 1-D and modal (beam structure) electromechanical models are presented to predict performance, especially power, from measured low-level ambient vibration sources. Models are validated by comparison to prior published results and tests of a MEMS-scale device. A non-optimized prototype low-level ambient MEMS harvester producing 30 μW/cm3 is designed and modeled. A MEMS fabrication process for the prototype device is presented based on past work.


title: Self-Adaptive and Online QoS Modeling for Cloud-Based Software Services

abstract: In the presence of scale, dynamism, uncertainty and elasticity, cloud software engineers faces several challenges when modeling Quality of Service (QoS) for cloud-based software services. These challenges can be best managed through self-adaptivity because engineers’ intervention is difficult, if not impossible, given the dynamic and uncertain QoS sensitivity to the environment and control knobs in the cloud. This is especially true for the shared infrastructure of cloud, where unexpected interference can be caused by co-located software services running on the same virtual machine; and co-hosted virtual machines within the same physical machine. In this paper, we describe the related challenges and present a fully dynamic, self-adaptive and online QoS modeling approach, which grounds on sound information theory and machine learning algorithms, to create QoS model that is capable to predict the QoS value as output over time by using the information on environmental conditions, control knobs and interference as inputs. In particular, we report on in-depth analysis on the correlations of selected inputs to the accuracy of QoS model in cloud. To dynamically selects inputs to the models at runtime and tune accuracy, we design self-adaptive hybrid dual-learners that partition the possible inputs space into two sub-spaces, each of which applies different symmetric uncertainty based selection techniques; the results of sub-spaces are then combined. Subsequently, we propose the use of adaptive multi-learners for building the model. These learners simultaneously allow several learning algorithms to model the QoS function, permitting the capability for dynamically selecting the best model for prediction on the fly. We experimentally evaluate our models in the cloud environment using RUBiS benchmark and realistic FIFA 98 workload. The results show that our approach is more accurate and effective than state-of-the-art modelings.


title: Comparison of maturity levels in CMMI-DEV and ISO/IEC 15504

abstract: Although a classic staged maturity framework with 5 levels was introduced by W. S. Humphrey back in 1988, its basic ideas remain essentially unchanged and they are still employed in CMMI and other maturity models. ISO/IEC 15504, formerly known as SPICE, has promoted a continuous model for process capability assessment. Not long ago SPICE community recognized the benefits of a staged representation. As a result, the organizational maturity framework has been introduced in ISO/IEC 15504-7:2008. It should be noted that this new framework defines 6 maturity levels. This paper investigates relationship between CMMI-DEV and ISO/IEC 15504 maturity levels. It presents the mapping approach and correspondence of CMMI-DEV and ISO/IEC 15504 maturity levels.


A Next-Generation Smart Contract and Decentralized Application Platform


A Grid-basedClusteringAlgorithm usingAdaptiveMeshRefinement


title: Anatomical Measures

abstract: A probabilistic framework is presented that enables image registration, tissue classification, and bias correction to be combined within the same generative model. A derivation of a log-likelihood objective function for the unified model is provided. The model is based on a mixture of Gaussians and is extended to incorporate a smooth intensity variation and nonlinear registration with tissue probability maps. A strategy for optimising the model parameters is described, along with the requisite partial derivatives of the objective function.


title: Antisense suppression of type 1 diacylglycerol acyltransferase adversely affects plant development in Brassica napus.

abstract: Diacylglycerol acyltransferase (DGAT) catalyzes the acyl-coenzyme A (CoA) dependent acylation of sn-1,2-diacylglycerol to form triacylglycerol in the terminal step of seed oil formation. Previous work has suggested that the level of DGAT activity may have a substantial effect on the flow of carbon into triacylglycerol, implying that the enzyme may represent a promising target for seed oil modification through biotechnological approaches. In the current study, Brassica napus DH12075 was transformed with an antisense type 1 DGAT construct, resulting in a reduction in DGAT1 gene expression, total DGAT activity and seed oil content. In addition, reduced seed yield and germination rates were observed along with severe developmental abnormalities. These data suggest that in addition to its critical role in seed oil formation, DGAT1 enzyme may also be important for normal seed development in B. napus, although the underlying mechanism(s) remain to be determined.


title: An Information-Theoretic Approach to Neural Computing

abstract: From the Publisher: ::: Neural networks provide a powerful new technology to model and control nonlinear and complex systems. In this book, the authors present a detailed formulation of neural networks from the information-theoretic viewpoint. They show how this perspective provides new insights into the design theory of neural networks. In particular, they show how these methods may be applied to the topics of supervised and unsupervised learning, including feature extraction, linear and nonlinear independent component analysis, and Boltzmann machines. Readers are assumed to have a basic understanding of neural networks, but all of the relevant concepts from information theory are carefully introduced and explained. Consequently, readers from several different scientific disciplines - notably, cognitive scientists, engineers, physicists, statisticians, and computer scientists - will find this book to be a very valuable contribution to this topic.


title: Reproducible Research in Computational Science

abstract: Computational science has led to exciting new developments, but the nature of the work has exposed limitations in our ability to evaluate published findings. Reproducibility has the potential to serve as a minimum standard for judging scientific claims when full independent replication of a study is not possible.


title: Obfuscation in Bitcoin: Techniques and Politics

abstract: In the cryptographic currency Bitcoin, all transactions are recorded in the blockchain - a public, global, and immutable ledger. Because transactions are public, Bitcoin and its users employ obfuscation to maintain a degree of financial privacy. Critically, and in contrast to typical uses of obfuscation, in Bitcoin obfuscation is not aimed against the system designer but is instead enabled by design. We map sixteen proposed privacy-preserving techniques for Bitcoin on an obfuscation-vs.-cryptography axis, and find that those that are used in practice tend toward obfuscation. We argue that this has led to a balance between privacy and regulatory acceptance.


title: Extending AMCW lidar depth-of-field using a coded aperture

abstract: By augmenting a high resolution full-field Amplitude Modulated Continuous Wave lidar system with a coded aperture, we show that depth-of-field can be extended using explicit, albeit blurred, range data to determine PSF scale. Because complex domain range-images contain explicit range information, the aperture design is unconstrained by the necessity for range determination by depth-from-defocus. The coded aperture design is shown to improve restoration quality over a circular aperture. A proof-of-concept algorithm using dynamic PSF determination and spatially variant Landweber iterations is developed and using an empirically sampled point spread function is shown to work in cases without serious multipath interference or high phase complexity.


title: Design and Simulation of Legged Walking Robots in MATLAB® Environment

abstract: It is well known that legged locomotion is more efficient,speedy, and versatile than the one by track and wheeled vehicles when it operates in a rough terrain or in unconstructed environment. The potential advantages of legged locomotion can be indicated such as better mobility, obstacles overcoming ability, active suspension, energy efficiency, and achievable speed (Song & Waldron, 1989). Legged walking robots have found wide application areas such as in military tasks, inspection of nuclear power plants, surveillance, planetary explorations, and in forestry and agricultural tasks (Carbone & Ceccarelli, 2005; Gonzalez et al., 2006; Kajita & Espiau, 2008). In the past decades, an extensive research has been focused on legged walking robots. A lot of prototypes such as biped robots, quadrupeds, hexapods, and multi-legged walking robots have been built in academic laboratories and companies (Kajita & Espiau, 2008). Significant examples can be indicated as ASIMO (Sakagami et al., 2002), Bigdog (Raibert, 2008), Rhex (Buehler, 2002), and ATHLETE (Wilcox et al., 2007). However, it is still far away to anticipate that legged walking robots can work in a complex environment and accomplish different tasks successfully. Mechanical design, dynamical walking control, walking pattern generation, and motion planning are still challenge problems for developing a reliable legged walking robot, which can operate in different terrains and environments with speedy, efficient, and versatility features. Mechanism design, analysis, and optimization, as well as kinematic and dynamic simulation of legged walking robots are important issues for building an efficient, robust, and reliable legged walking robot. In particular, leg mechanism is a crucial part of a legged walking robot. A leg mechanism will not only determine the DOF (degree of freedom) of a robot, but also actuation system efficiency and its control strategy. Additionally, it is well understood that a torso plays an important role during animal and human movements. Thus, the aforementioned two aspects must be taken into account at the same time for developing legged walking robots. Computer aided design and simulation can be considered useful for developing legged walking robots. Several commercial simulation software packages are available for performing modeling, kinematic, and dynamic simulation of legged walking robots. In particular, Matlab® is a widely used software package. It integrates computation, visualization, and programming in an easy-to-use environment where problems and solutions are expressed in familiar mathematical notation. By using a flexible programming environment, embedded functions, and several useful simulink® toolboxes, it is relative


title: The Use of Epidermal Growth Factor Receptor Monoclonal Antibodies in Squamous Cell Carcinoma of the Head and Neck

abstract: Targeting of the EGF receptor (EGFR) has become a standard of care in several tumor types. In squamous cell carcinoma of the head and neck, monoclonal antibodies directed against EGFR have become a regular component of therapy for curative as well as palliative treatment strategies. These agents have anti-tumor efficacy as a single modality and have demonstrated synergistic tumor killing when combined with radiation and/or chemotherapy. While cetuximab has been the primary anti-EGFR monoclonal antibody used in the US, variant anti-EGFR monoclonal antibodies have been used in several clinical studies and shown benefit with improved toxicity profiles. Next generation anti-EGFR monoclonal antibodies may demonstrate multi-target epitope recognition, enhanced immune cell stimulation, or conjugation with radioisotopes in order to improve clinical outcomes. Identification of the specific patient subset that would optimally benefit from anti-EGFR monoclonal antibodies remains an elusive goal.


title: When Game Becomes Life: The Creators and Spectators of Online Game Replays and Live Streaming

abstract: Online gaming franchises such as World of Tanks, Defense of the Ancients, and StarCraft have attracted hundreds of millions of users who, apart from playing the game, also socialize with each other through gaming and viewing gamecasts. As a form of User Generated Content (UGC), gamecasts play an important role in user entertainment and gamer education. They deserve the attention of both industrial partners and the academic communities, corresponding to the large amount of revenue involved and the interesting research problems associated with UGC sites and social networks. Although previous work has put much effort into analyzing general UGC sites such as YouTube, relatively little is known about the gamecast sharing sites. In this work, we provide the first comprehensive study of gamecast sharing sites, including commercial streaming-based sites such as Amazon’s Twitch.tv and community-maintained replay-based sites such as WoTreplays. We collect and share a novel dataset on WoTreplays that includes more than 380,000 game replays, shared by more than 60,000 creators with more than 1.9 million gamers. Together with an earlier published dataset on Twitch.tv, we investigate basic characteristics of gamecast sharing sites, and we analyze the activities of their creators and spectators. Among our results, we find that (i) WoTreplays and Twitch.tv are both fast-consumed repositories, with millions of gamecasts being uploaded, viewed, and soon forgotten; (ii) both the gamecasts and the creators exhibit highly skewed popularity, with a significant heavy tail phenomenon; and (iii) the upload and download preferences of creators and spectators are different: while the creators emphasize their individual skills, the spectators appreciate team-wise tactics. Our findings provide important knowledge for infrastructure and service improvement, for example, in the design of proper resource allocation mechanisms that consider future gamecasting and in the tuning of incentive policies that further help player retention.


title: Multiple-query optimization

abstract: Some recently proposed extensions to relational database systems, as well as to deductive database systems, require support for multiple-query processing. For example, in a database system enhanced with inference capabilities, a simple query involving a rule with multiple definitions may expand to more than one actual query that has to be run over the database. It is an interesting problem then to come up with algorithms that process these queries together instead of one query at a time. The main motivation for performing such an interquery optimization lies in the fact that queries may share common data. We examine the problem of multiple-query optimization in this paper. The first major contribution of the paper is a systematic look at the problem, along with the presentation and analysis of algorithms that can be used for multiple-query optimization. The second contribution lies in the presentation of experimental results. Our results show that using multiple-query processing algorithms may reduce execution cost considerably.


title: Efficient Batched Oblivious PRF with Applications to Private Set Intersection

abstract: We describe a lightweight protocol for oblivious evaluation of a pseudorandom function (OPRF) in the presence of semihonest adversaries. In an OPRF protocol a receiver has an input r; the sender gets output s and the receiver gets output F(s; r), where F is a pseudorandom function and s is a random seed. Our protocol uses a novel adaptation of 1-out-of-2 OT-extension protocols, and is particularly efficient when used to generate a large batch of OPRF instances. The cost to realize m OPRF instances is roughly the cost to realize 3:5m instances of standard 1-out-of-2 OTs (using state-of-the-art OT extension). We explore in detail our protocol's application to semihonest secure private set intersection (PSI). The fastest state-of- the-art PSI protocol (Pinkas et al., Usenix 2015) is based on efficient OT extension. We observe that our OPRF can be used to remove their PSI protocol's dependence on the bit-length of the parties' items. We implemented both PSI protocol variants and found ours to be 3.1{3.6 faster than Pinkas et al. for PSI of 128-bit strings and sufficiently large sets. Concretely, ours requires only 3.8 seconds to securely compute the intersection of 220-size sets, regardless of the bitlength of the items. For very large sets, our protocol is only 4:3 slower than the insecure naive hashing approach for PSI.


title: A Hardware Design Language for Timing-Sensitive Information-Flow Security

abstract: Information security can be compromised by leakage via low-level hardware features. One recently prominent example is cache probing attacks, which rely on timing channels created by caches. We introduce a hardware design language, SecVerilog, which makes it possible to statically analyze information flow at the hardware level. With SecVerilog, systems can be built with verifiable control of timing channels and other information channels. SecVerilog is Verilog, extended with expressive type annotations that enable precise reasoning about information flow. It also comes with rigorous formal assurance: we prove that SecVerilog enforces timing-sensitive noninterference and thus ensures secure information flow. By building a secure MIPS processor and its caches, we demonstrate that SecVerilog makes it possible to build complex hardware designs with verified security, yet with low overhead in time, space, and HW designer effort.


title: Online vs. Face-to-Face Deliberation: Effects on Civic Engagement

abstract: Deliberation is considered to produce positive effects on public opinion, in that it exposes participants to alternative perspectives and rational arguments. However, whereas benefits of face-to-face deliberation have been supported by many empirical studies, the effects of online deliberation remain unclear to date. This research compares the effects of online and face-to-face deliberation in experimental settings. A theoretical review of computer-mediated communication and deliberative democracy suggests that online deliberation is not necessarily inferior to face-to-face deliberation. An experiment was conducted to compare the relative outcomes of a deliberation performed in face-to-face and computer-mediated settings. The results suggest that both online and face-to-face deliberation can increase participants’ issue knowledge, political efficacy, and willingness to participate in politics.


How do teachers perceive educational robots in formal education ? A study based on the Thymio robot *


Predicting Asymmetric Transitive Relations in Knowledge Bases


title: Transforming Face-to-Face Identity Proofing into Anonymous Digital Identity Using the Bitcoin Blockchain

abstract: The most fundamental purpose of blockchain technology is to enable persistent, consistent, distributed storage of information. Increasingly common are authentication systems that leverage this property to allow users to carry their personal data on a device while a hash of this data is signed by a trusted authority and then put on a blockchain to be compared against. For instance, in 2015, MIT introduced a schema for the publication of their academic certificates based on this principle. In this work, we propose a way for users to obtain assured identities based on face-to-face proofing that can then be validated against a record on a blockchain. Moreover, in order to provide anonymity, instead of storing a hash, we make use of a scheme of Brands to store a commitment against which one can perform zero-knowledge proofs of identity. We also enforce the confidentiality of the underlying data by letting users control a secret of their own.We show how our schema can be implemented on Bitcoin's blockchain and how to save bandwidth by grouping commitments using Merkle trees to minimize the number of Bitcoin transactions that need to be sent. Finally, we describe a system in which users can gain access to services thanks to the identity records of our proposal.


title: Understanding Infographics through Textual and Visual Tag Prediction

abstract: We introduce the problem of visual hashtag discovery for infographics: extracting visual elements from an infographic that are diagnostic of its topic. Given an infographic as input, our computational approach automatically outputs textual and visual elements predicted to be representative of the infographic content. Concretely, from a curated dataset of 29K large infographic images sampled across 26 categories and 391 tags, we present an automated two step approach. First, we extract the text from an infographic and use it to predict text tags indicative of the infographic content. And second, we use these predicted text tags as a supervisory signal to localize the most diagnostic visual elements from within the infographic i.e. visual hashtags. We report performances on a categorization and multi-label tag prediction problem and compare our proposed visual hashtags to human annotations.


title: Advanced Circadian Phase in Mania and Delayed Circadian Phase in Mixed Mania and Depression Returned to Normal after Treatment of Bipolar Disorder

abstract: Abstract Disturbances in circadian rhythms have been suggested as a possible cause of bipolar disorder (BD). Included in this study were 31 mood episodes of 26 BD patients, and 18 controls. Circadian rhythms of BD were evaluated at admission, at 2-week intervals during hospitalization, and at discharge. All participants wore wrist actigraphs during the studies. Saliva and buccal cells were obtained at 8:00, 11:00, 15:00, 19:00, and 23:00 for two consecutive days. Collected saliva and buccal cells were used for analysis of the cortisol and gene circadian rhythm, respectively. Circadian rhythms had different phases during acute mood episodes of BD compared to recovered states. In 23 acute manic episodes, circadian phases were ~ 7 hour advanced (equivalent to ~ 17 hour delayed). Phases of 21 out of these 23 cases returned to normal by ~ 7 hour delay along with treatment, but two out of 23 cases returned to normal by ~ 17 hour advance. In three cases of mixed manic episodes, the phases were ~ 6–7 hour delayed. For five cases of depressive episodes, circadian rhythms phases were ~ 4–5 hour delayed. After treatment, circadian phases resembled those of healthy controls. Circadian misalignment due to circadian rhythm phase shifts might be a pathophysiological mechanism of BD.


title: Smart Query Execution for Event-Driven Stream Processing

abstract: With the increase of streams, i.e., multimedia surveillance streams, social media streams, sensor data streams, etc., a demand for stream processing has become diverse and complicated. To meet this demand, several stream processing engines have been developed. Event-driven stream processing, which is one of the important requirements, continuously gets the incoming stream data however generates query results only on the occurrence of specified events. In the basic query execution scheme, even when no event is raised, input stream tuples are continuously processed by query operators, though they do not generate any query result. This results in increased system load and wastage of system resources. To cope with this problem, we propose a smart event-driven stream processing scheme, which makes use of smart windows to buffer the incoming stream tuples during the absence of an event. When the event is raised, the buffered tuples are flushed and processed by the downstream operators. If some of the buffered tuples in the smart window expire due to the window size before the occurrence of the event, they are deleted directly from the smart window, resulting in reduced system load and increased system throughput. Extensive experiments are performed to show the effectiveness of the proposed smart scheme. For the sake of experiments, a prototype system has been developed capable of processing JSON data and enables users to register CQL style Jaql queries.


title: A decision feedback channel estimation approach of the satellite mobile communication system

abstract: In this paper we present a decision feedback channel estimation way for the mobile satellite communication system in order to improve the bit error performance. The transmission is disturbed not only by shadowing and multipath effects and additive white Gaussian noise, but also by the weather attenuation. The conventional Least Square(LS) is not well suited to detect the transmitter sequence, whereas the LS-based decision feedback is able to take into account the fading channel and therefore is able to detect the signal much better. The LS-based decision feedback channel algorithm first estimate channel response, then the estimation channel response use decision feedback estimation to get the next frame channel response. Our simulation results show that LS-based decision feedback channel estimation algorithm performs much better than the origin LS channel estimation method in terms of bit error rate(BER) with a gain of probably 1 dB.


title: A randomised comparative trial of yoga and relaxation to reduce stress and anxiety.

abstract: OBJECTIVE ::: To compare yoga and relaxation as treatment modalities at 10 and 16 weeks from study baseline to determine if either of modality reduces subject stress, anxiety, blood pressure and improve quality of life. ::: ::: ::: DESIGN ::: A randomised comparative trial was undertaken comparing yoga with relaxation. ::: ::: ::: PARTICIPANTS ::: One hundred and thirty-one subjects with mild to moderate levels of stress were recruited from the community in South Australia. ::: ::: ::: INTERVENTIONS ::: Ten weekly 1- h sessions of relaxation or hatha yoga. ::: ::: ::: MAIN OUTCOME MEASURES ::: Changes in the State Trait Personality Inventory sub-scale anxiety, General Health Questionnaire and the Short Form-36. ::: ::: ::: RESULTS ::: Following the 10 week intervention stress, anxiety and quality of life scores improved over time. Yoga was found to be as effective as relaxation in reducing stress, anxiety and improving health status on seven domains of the SF-36. Yoga was more effective than relaxation in improving mental health. At the end of the 6 week follow-up period there were no differences between groups in levels of stress, anxiety and on five domains of the SF-36. Vitality, social function and mental health scores on the SF-36 were higher in the relaxation group during the follow-up period. ::: ::: ::: CONCLUSION ::: Yoga appears to provide a comparable improvement in stress, anxiety and health status compared to relaxation.


title: What's in a name: a study of names, gender inference, and gender behavior in facebook

abstract: In this paper, by crawling Facebook public profile pages of a large and diverse user population in New York City, we create a comprehensive and contemporary first name list, in which each name is annotated with a popularity estimate and a gender probability. ::: ::: First, we use the name list as part of a novel and powerful technique for inferring Facebook users' gender. Our name-centric approach to gender prediction partitions the users into two groups, A and B, and is able to accurately predict genders for users belonging to A. Applying our methodology to NYC users in Facebook, we are able to achieve an accuracy of 95.2% for group A consisting of 95.1% of the NYC users. This is a significant improvement over recent results of gender prediction [14], which achieved a maximum accuracy of 77.2% based on users' group affiliations. ::: ::: Second, having inferred the gender of most users in our Facebook dataset, we learn several interesting gender characteristics and analyze how males and females behave in Facebook.We find, for example, that females and males exhibit contrasting behaviors while hiding their attributes, such as gender, age, and sexual preference, and that females are more conscious about their online privacy on Facebook.


title: Leaf recognition algorithm using support vector machine with Hu moments and local binary patterns

abstract: Leaf recognition is convenient for plant classification and it is an important subfield of pattern recognition. Different leaf features such as color, shape and texture are used as well as different classifiers including artificial neural networks, k-nearest neighbor and support vector machines. In this paper we propose an algorithm based on tuned support vector machine as a classifier and Hu moments and uniform local binary pattern histogram parameters as features. Our proposed algorithm was tested on leaf images from standard benchmark database and compared with other approaches from literature where it proved to be more successful (higher recognition percentage).


title: Aligned and Non-Aligned Double JPEG Detection Using Convolutional Neural Networks

abstract: Abstract Due to the wide diffusion of JPEG coding standard, the image forensic community has devoted significant attention to the development of double JPEG (DJPEG) compression detectors through the years. The ability of detecting whether an image has been compressed twice provides paramount information toward image authenticity assessment. Given the trend recently gained by convolutional neural networks (CNN) in many computer vision tasks, in this paper we propose to use CNNs for aligned and non-aligned double JPEG compression detection. In particular, we explore the capability of CNNs to capture DJPEG artifacts directly from images. Results show that the proposed CNN-based detectors achieve good performance even with small size images (i.e., 64 × 64), outperforming state-of-the-art solutions, especially in the non-aligned case. Besides, good results are also achieved in the commonly-recognized challenging case in which the first quality factor is larger than the second one.