title: Hardware acceleration of database operations

abstract: As the amount of memory in database systems grows, entire database tables, or even databases, are able to fit in the system's memory, making in-memory database operations more prevalent. This shift from disk-based to in-memory database systems has contributed to a move from row-wise to columnar data storage. Furthermore, common database workloads have grown beyond online transaction processing (OLTP) to include online analytical processing and data mining. These workloads analyze huge datasets that are often irregular and not indexed, making traditional database operations like joins much more expensive. In this paper we explore using dedicated hardware to accelerate in-memory database operations. We present hardware to accelerate the selection process of compacting a single column into a linear column of selected data, joining two sorted columns via merging, and sorting a column. Finally, we put these primitives together to accelerate an entire join operation. We implement a prototype of this system using FPGAs and show substantial improvements in both absolute throughput and utilization of memory bandwidth. Using the prototype as a guide, we explore how the hardware resources required by our design change with the desired throughput.


title: Mining intrusion detection alarms for actionable knowledge

abstract: In response to attacks against enterprise networks, administrators increasingly deploy intrusion detection systems. These systems monitor hosts, networks, and other resources for signs of security violations. The use of intrusion detection has given rise to another difficult problem, namely the handling of a generally large number of alarms. In this paper, we mine historical alarms to learn how future alarms can be handled more efficiently. First, we investigate episode rules with respect to their suitability in this approach. We report the difficulties encountered and the unexpected insights gained. In addition, we introduce a new conceptual clustering technique, and use it in extensive experiments with real-world data to show that intrusion detection alarms can be handled efficiently by using previously mined knowledge.


title: AMI threats, intrusion detection requirements and deployment recommendations

abstract: Advanced Metering Infrastructures (AMI) facilitate bidirectional communication between smart meters and utilities, allowing information about consumption, outages, and electricity rates to be shared reliably and efficiently. However, the numerous smart meters being connected through mesh networks open new opportunities for attackers to interfere with communications and compromise utilities' assets or steal customers' private information. The goal of this paper is to survey the various threats facing AMIs and the common attack techniques used to realize them in order to identify and understand the requirements for a comprehensive intrusion detection solution. The threat analysis leads to an extensive “attack tree” that captures the attackers' key objectives (e.g., energy theft) and the individual attack steps (e.g., eavesdropping on the network) that would be involved in achieving them. With reference to the attack tree, we show the type of information that would be required to effectively detect attacks. We also suggest that the widest coverage in monitoring the attacks can be provided by a hybrid sensing infrastructure that uses both a centralized intrusion detection system and embedded meter sensors.


title: Japanese quail (Coturnix japonica) as a laboratory animal model

abstract: For the past 50 years, the Japanese quail (Coturnix japonica) has been a popular ::: animal model in numerous fields of research. The quail’s 16-d developmental period ::: and its easily accessible embryo make C. japonica a convenient model for studies of ::: developmental biology. Because its lifespan is relatively short and its physiology is ::: comparable to that of humans, the adult quail is useful for studies of aging and disease. ::: The authors describe the Japanese quail as an animal model and, drawing on their ::: experience raising a quail colony at the California Institute of Technology, present ::: detailed guidelines for the husbandry of the species.


Dimensionality Reduced Reinforcement Learning for Assistive Robots.


title: A 0–55-GHz Coplanar Waveguide to Coplanar Strip Transition

abstract: A broadband coplanar waveguide (CPW) to coplanar strip (CPS) transmission line transition directly integrated with an RF microelectromechanical systems reconfigurable multiband antenna is presented in this paper. This transition design exhibits very good performance up to 55 GHz, and uses a minimum number of dissimilar transmission line sections and wire bonds, achieving a low-loss and low-cost balancing solution to feed planar antenna designs. The transition design methodology that was followed is described and measurement results are presented.


title: Generating High-Quality Crowd Density Maps Using Contextual Pyramid CNNs

abstract: We present a novel method called Contextual Pyramid CNN (CP-CNN) for generating high-quality crowd density and count estimation by explicitly incorporating global and local contextual information of crowd images. The proposed CP-CNN consists of four modules: Global Context Estimator (GCE), Local Context Estimator (LCE), Density Map Estimator (DME) and a Fusion-CNN (F-CNN). GCE is a VGG-16 based CNN that encodes global context and it is trained to classify input images into different density classes, whereas LCE is another CNN that encodes local context information and it is trained to perform patch-wise classification of input images into different density classes. DME is a multi-column architecture-based CNN that aims to generate high-dimensional feature maps from the input image which are fused with the contextual information estimated by GCE and LCE using F-CNN. To generate high resolution and high-quality density maps, F-CNN uses a set of convolutional and fractionally-strided convolutional layers and it is trained along with the DME in an end-to-end fashion using a combination of adversarial loss and pixel-level Euclidean loss. Extensive experiments on highly challenging datasets show that the proposed method achieves significant improvements over the state-of-the-art methods.


title: Cooperative communications for wireless networks: techniques and applications in LTE-advanced systems

abstract: Cooperative communications enable efficient utilization of communication resources, by allowing nodes or terminals in a communication network to collaborate with each other in information transmission. It is a promising technique for future communication systems. In this article, we first survey cooperative communication schemes and discuss their advantages in improving system capacity and diversity. Following that, we examine the applications of cooperative relaying schemes in LTE-advanced systems. Specifically, we investigate two intra-cell coordinated multi-point schemes in LTE-advanced systems, and evaluate the performance of the schemes. It is shown that cooperative relaying leads to both network coverage extension and capacity expansion in LTE-advanced systems. Cooperative communications can significantly improve the system spectrum efficiency and performance.


title: Environmental factors modulating antibiotic and siderophore biosynthesis by Pseudomonas fluorescens biocontrol strains

abstract: Understanding the environmental factors that regulate the biosynthesis of antimicrobial compounds by disease-suppressive strains of Pseudomonas fluorescens is an essential step toward improving the level and reliability of their biocontrol activity. We used liquid culture assays to identify several minerals and carbon sources which had a differential influence on the production of the antibiotics 2,4-diacetylphloroglucinol (PHL), pyoluteorin (PLT), and pyrrolnitrin and the siderophores salicylic acid and pyochelin by the model strain CHA0, which was isolated from a natural disease-suppressive soil in Switzerland. Production of PHL was stimulated by Zn2+, NH4Mo2+, and glucose; the precursor compound mono-acetylphloroglucinol was stimulated by the same factors as PHL. Production of PLT was stimulated by Zn2+, Co2+, and glycerol but was repressed by glucose. Pyrrolnitrin production was increased by fructose, mannitol, and a mixture of Zn2+ and NH4Mo2+. Pyochelin production was increased by Co2+, fructose, mannitol, and glucose. Interestingly, production of its precursor salicylic acid was increased by different factors, i.e., NH4Mo2+, glycerol, and glucose. The mixture of Zn2+ and NH4Mo2+ with fructose, mannitol, or glycerol further enhanced the production of PHL and PLT compared with either the minerals or the carbon sources used alone, but it did not improve siderophore production. Extending fermentation time from 2 to 5 days increased the accumulation of PLT, pyrrolnitrin, and pyochelin but not of PHL. When findings with CHA0 were extended to an ecologically and genetically diverse collection of 41 P. fluorescens biocontrol strains, the effect of certain factors was strain dependent, while others had a general effect. Stimulation of PHL by Zn2+ and glucose was strain dependent, whereas PLT production by all strains that can produce this compound was stimulated by Zn2+ and transiently repressed by glucose. Inorganic phosphate reduced PHL production by CHA0 and seven other strains tested but to various degrees. Production of PLT but not pyrrolnitrin by CHA0 was also reduced by 100 mM phosphate. The use of 1/10-strength nutrient broth-yeast extract, compared with standard nutrient broth-yeast extract, amended with glucose and/or glycerol resulted in dramatically increased accumulations of PHL (but not PLT), pyochelin, and salicylic acid, indicating that the ratio of carbon source to nutrient concentration played a key role in the metabolic flow. The results of this study (i) provide insight into the biosynthetic regulation of antimicrobial compounds, (ii) limit the number of factors for intensive study in situ, and (iii) indicate factors that can be manipulated to improve bacterial inoculants.


title: Synthesizing realistic test models

abstract: Tools and methods in the context of Model Driven Engineering (MDE) have to be evaluated and tested using appropriate models as test cases. Unfortunately, adequate test models are scarcely available in many application domains and have to be artificially created. In this regard, model generators have been proposed recently. Principally, they generate test models by modifying a base model using a specified set of edit operations. The modification process should be done in a way that the resulting test models are as "realistic" as possible, i.e. the applied changes should resemble the real evolution that one observes in real software systems at the abstraction level of models. Therefore, we have to (1) properly capture the evolution of real software models, (2) statistically model the evolution (changes) and (3) finally properly reproduce it in the generated test models. To this end, we reversed engineered all revisions of nine typical Java systems into their class diagrams (totally 6,559 distinct models). We compared the subsequent models using a state-of-the-art model differencing tool and we computed the changes between them in terms of applied edit operations. We investigated the fitness of 60 promising distributions on the observed frequencies of edit operations in order to statistically model the changes. Four of our candidate distributions were successful to statistically model the changes with very good success rates. Since it was not known how to implement them, i.e. produce their random variates, we developed a practical implementation. The implemented distributions are then used to reproduce the real evolution of software systems in order to synthesize more realistic test models for MDE tools.


title: Application of Spreading Activation Techniques in Information Retrieval

abstract: This paper surveys the use of Spreading Activation techniques onSemantic Networks in Associative Information Retrieval. The majorSpreading Activation models are presented and their applications toIR is surveyed. A number of works in this area are criticallyanalyzed in order to study the relevance of Spreading Activation forassociative IR.


title: Machine learning to parse breast pathology reports in Chinese

abstract: Large structured databases of pathology findings are valuable in deriving new clinical insights. However, they are labor intensive to create and generally require manual annotation. There has been some work in the bioinformatics community to support automating this work via machine learning in English. Our contribution is to provide an automated approach to construct such structured databases in Chinese, and to set the stage for extraction from other languages. We collected 2104 de-identified Chinese benign and malignant breast pathology reports from Hunan Cancer Hospital. Physicians with native Chinese proficiency reviewed the reports and annotated a variety of binary and numerical pathologic entities. After excluding 78 cases with a bilateral lesion in the same report, 1216 cases were used as a training set for the algorithm, which was then refined by 405 development cases. The Natural language processing algorithm was tested by using the remaining 405 cases to evaluate the machine learning outcome. The model was used to extract 13 binary entities and 8 numerical entities. When compared to physicians with native Chinese proficiency, the model showed a per-entity accuracy from 91 to 100% for all common diagnoses on the test set. The overall accuracy of binary entities was 98% and of numerical entities was 95%. In a per-report evaluation for binary entities with more than 100 training cases, 85% of all the testing reports were completely correct and 11% had an error in 1 out of 22 entities. We have demonstrated that Chinese breast pathology reports can be automatically parsed into structured data using standard machine learning approaches. The results of our study demonstrate that techniques effective in parsing English reports can be scaled to other languages.


title: PiCode: A New Picture-Embedding 2D Barcode

abstract: Nowadays, 2D barcodes have been widely used as an interface to connect potential customers and advertisement contents. However, the appearance of a conventional 2D barcode pattern is often too obtrusive for integrating into an aesthetically designed advertisement. Besides, no human readable information is provided before the barcode is successfully decoded. This paper proposes a new picture-embedding 2D barcode, called PiCode, which mitigates these two limitations by equipping a scannable 2D barcode with a picturesque appearance. PiCode is designed with careful considerations on both the perceptual quality of the embedded image and the decoding robustness of the encoded message. Comparisons with the existing beautified 2D barcodes show that PiCode achieves one of the best perceptual qualities for the embedded image, and maintains a better tradeoff between image quality and decoding robustness in various application conditions. PiCode has been implemented in the MATLAB on a PC and some key building blocks have also been ported to Android and iOS platforms. Its practicality for real-world applications has been successfully demonstrated.


title: Modeling and simulation of a jumping frog robot

abstract: In nature, several animals and insects use jumping locomotion in rough terrains. Jumping locomotion is useful for ground robots as jumping provide high speed movement with enhanced energy efficiency. It also helps in overcoming obstacles relatively larger than their body sizes. Jumping needs coordination and large instantaneous forces within a very short duration. In this paper, a method of jumping by using a four-bar spring/linkage mechanism integrated with spool winding and ratchet release mechanism has been presented. Inspired by the frogs, we have modeled and simulated a simple and efficient four legged jumping frog robot.


title: Setting the stage for computing curricula 2013: computer science -- report from the ACM/IEEE-CS joint task force

abstract: Following a roughly 10 year cycle, the Computing Curricula volumes have helped to set international curricular guidelines for undergraduate programs in computing. In the summer of 2010, planning for the next volume in the series, Computer Science 2013, began. This panel seeks to update and engage the SIGCSE community on the Computer Science 2013 effort. The development of curricular guidelines in Computer Science is particularly challenging given the rapid evolution and expansion of the field. Moreover, the growing diversity of topics in Computer Science and the integration of computing with other disciplines create additional challenges and opportunities in defining computing curricula. As a result, it is particularly important to engage the broader computer science education community in a dialog to better understand new opportunities, local needs, and novel successful models of computing curriculum. The last complete Computer Science curricular volume was released in 2001 [3] and followed by a review effort that concluded in 2008 [2]. While the review helped to update some of the knowledge units in the 2001 volume, it was not aimed at producing an entirely new curricular volume and deferred some of the more significant questions that arose at the time. The Computer Science 2013 effort seeks to provide a new volume reflecting the current state of the field and highlighting promising future directions through revisiting and redefining the knowledge units in CS, rethinking the essentials necessary for a CS curriculum, and identifying working exemplars of courses and curricula along these lines.


title: Flexible design of a wearable lower limb exoskeleton robot

abstract: Wearable exoskeleton robot is a kind of humanoid service robot to help the elderly and the patients with walking dysfunction, it is also an effective medical rehabilitation method to help patients who have walking disorders due to central neural system damage. This paper focuses on the flexible mechanism design of wearable lower limb exoskeleton robot. A wearable exoskeleton robot prototype was developed which can assist human walking. This paper analyzes the role of the major joints of walking human by experimental studies based on bionic design methods from human anatomy and bone surgery. We first analyze parameters of joint movements in a gait cycle, then we design a preliminary bionic model, finally we proposed a control strategy including a pair of electric crutches for the exoskeleton robot.


title: Predicting purchasing intent: Automatic Feature Learning using Recurrent Neural Networks

abstract: We present a neural network for predicting purchasing intent in an Ecommerce setting. Our main contribution is to address the significant investment in feature engineering that is usually associated with state-of-the-art methods such as Gradient Boosted Machines. We use trainable vector spaces to model varied, semi-structured input data comprising categoricals, quantities and unique instances. Multi-layer recurrent neural networks capture both session-local and dataset-global event dependencies and relationships for user sessions of any length. An exploration of model design decisions including parameter sharing and skip connections further increase model accuracy. Results on benchmark datasets deliver classification accuracy within 98% of state-of-the-art on one and exceed state-of-the-art on the second without the need for any domain / dataset-specific feature engineering on both short and long event sequences.


title: Detecting Targeted Attacks By Multilayer Deception

abstract: Over the past few years, enterprises are facing a growing number of highly customized and targeted attacks that use sophisticated techniques and seek after important company assets, such as customer data and intellectual property. Unlike conventional attacks, targeted attacks are operated by experts who use multiple steps to gain access to sensitive assets, and most of time, leave very few network traces behind for detection. In this paper, we propose a multi-layer deception system that provides an in depth defense against such sophisticated targeted attacks. Specifically, based on previous knowledge and patterns of such attacks, we model the attacker as trying to compromising an enterprise network via multiple stages of penetration and propose defenses at each of these layers using deception based detection. Due to multiple layers of deception, the probability of detecting such an attack will be greatly enhanced. We present a proof of concept implementation of one of the key deception methods proposed. Due to various financial constraints of an enterprise, we also model the design of the deception system as an optimization problem in order to minimize the total expected loss due to system deployment and asset compromise. We find that there is an optimal solution to deploy deception entities, and even over spending budget on more entities will only increase the total expected loss to the enterprise. Such a system


title: GraphMeta: A Graph-Based Engine for Managing Large-Scale HPC Rich Metadata

abstract: High-performance computing (HPC) systems face increasingly critical metadata management challenges, especially in the approaching exascale era. These challenges arise not only from exploding metadata volumes but also from increasingly diverse metadata, which contains data provenance and user-defined attributes in addition to traditional POSIX metadata. This "rich" metadata is critical to support many advanced data management functionality such as data auditing and validation. In our prior work, we presented a graph-based model that could be a promising solution to uniformly manage such rich metadata because of its flexibility and generality. At the same time, however, graph-based rich metadata management introduces significant challenges. In this study, we first identify the challenges presented by the underlying infrastructure in supporting scalable, high-performance rich metadata management. To tackle these challenges, we then present GraphMeta, a graph-based engine designed for managing large-scale rich metadata. We also utilize a series of optimizations designed for rich metadata graphs. We evaluate GraphMeta with both synthetic and real HPC metadata workloads and compare it with other approaches. The results show that its advantages in terms of rich metadata management in HPC systems, including better performance and scalability compared with existing solutions.


title: Edge Detection and Feature Line Tracing in 3D-Point Clouds by Analyzing Geometric Properties of Neighborhoods

abstract: This paper presents an automated and effective method for detecting 3D edges and tracing feature lines from 3D-point clouds. This method is named Analysis of Geometric Properties of Neighborhoods (AGPN), and it includes two main steps: edge detection and feature line tracing. In the edge detection step, AGPN analyzes geometric properties of each query point’s neighborhood, and then combines RANdom SAmple Consensus (RANSAC) and angular gap metric to detect edges. In the feature line tracing step, feature lines are traced by a hybrid method based on region growing and model fitting in the detected edges. Our approach is experimentally validated on complex man-made objects and large-scale urban scenes with millions of points. Comparative studies with state-of-the-art methods demonstrate that our method obtains a promising, reliable, and high performance in detecting edges and tracing feature lines in 3D-point clouds. Moreover, AGPN is insensitive to the point density of the input data.


title: Accurate and Robust 3D Facial Capture Using a Single RGBD Camera

abstract: This paper presents an automatic and robust approach that accurately captures high-quality 3D facial performances using a single RGBD camera. The key of our approach is to combine the power of automatic facial feature detection and image-based 3D nonrigid registration techniques for 3D facial reconstruction. In particular, we develop a robust and accurate image-based nonrigid registration algorithm that incrementally deforms a 3D template mesh model to best match observed depth image data and important facial features detected from single RGBD images. The whole process is fully automatic and robust because it is based on single frame facial registration framework. The system is flexible because it does not require any strong 3D facial priors such as blend shape models. We demonstrate the power of our approach by capturing a wide range of 3D facial expressions using a single RGBD camera and achieve state-of-the-art accuracy by comparing against alternative methods.


title: Lightweight On-demand Ad hoc Distance-vector Routing - Next Generation (LOADng): Protocol, extension, and applicability

abstract: Abstract This paper studies the routing protocol “Lightweight On-demand Ad hoc Distance-vector Routing Protocol – Next Generation (LOADng)”, designed to enable efficient, scalable and secure routing in low power and lossy networks. As a reactive protocol, it does not maintain a routing table for all destinations in the network, but initiates a route discovery to a destination only when there is data to be sent to that destination to reduce routing overhead and memory consumption. Designed with a modular approach, LOADng can be extended with additional components for adapting the protocol to different topologies, traffic, and data-link layer characteristics. This paper studies several such additional components for extending LOADng: support for smart route requests and expanding ring search, an extension permitting maintaining collection trees, a fast rerouting extension. All those extensions are examined from the aspects of specification, interoperability with other mechanisms, security vulnerabilities, performance and applicability. A general framework is also proposed to secure the routing protocol.


title: Artistic simulation of curly hair

abstract: Artistic simulation of hair presents many challenges - ranging from incorporating artistic control to dealing with extreme motions of characters. Additionally, in a production environment, the simulation needs to be fast and results need to be usable "out of the box" (without extensive parameter modifications) in order to produce content efficiently. These challenges are only increased when simulating curly, stylized hair. We present a method for stably simulating stylized curly hair that addresses these artistic needs and performance demands. To satisfy the artistic requirement of maintaining the curl's helical shape during motion, we propose a hair model based upon an extensible elastic rod. We introduce a method for stably computing a frame along the hair curve, essential for stable simulation of curly hair. Our hair model uses a spring for controlling the bending of the curl and another for maintaining the helical shape during extension. We also address performance concerns often associated with handling hair-hair contact interactions by efficiently parallelizing the simulation. To do so, we present a technique for pruning both hair-hair contact pairs and hair particles. Our method has been used on two full length feature films and has proven to be robust and stable over a wide range of animated motion and on a variety of hair styles, from straight to wavy to curly. It has proven invaluable in providing controllable, stable and efficient simulation allowing our artists to achieve their desired performance even when facing strict scheduling demands.


title: Temporal Convolution Networks for Real-Time Abdominal Fetal Aorta Analysis with Ultrasound

abstract: The automatic analysis of ultrasound sequences can substantially improve the efficiency of clinical diagnosis. In this work we present our attempt to automate the challenging task of measuring the vascular diameter of the fetal abdominal aorta from ultrasound images. We propose a neural network architecture consisting of three blocks: a convolutional layer for the extraction of imaging features, a Convolution Gated Recurrent Unit (C-GRU) for enforcing the temporal coherence across video frames and exploiting the temporal redundancy of a signal, and a regularized loss function, called \textit{CyclicLoss}, to impose our prior knowledge about the periodicity of the observed signal. We present experimental evidence suggesting that the proposed architecture can reach an accuracy substantially superior to previously proposed methods, providing an average reduction of the mean squared error from $0.31 mm^2$ (state-of-art) to $0.09 mm^2$, and a relative error reduction from $8.1\%$ to $5.3\%$. The mean execution speed of the proposed approach of 289 frames per second makes it suitable for real time clinical use.


title: Semi-supervised learning using label mean

abstract: Semi-Supervised Support Vector Machines (S3VMs) typically directly estimate the label assignments for the unlabeled instances. This is often inefficient even with recent advances in the efficient training of the (supervised) SVM. In this paper, we show that S3VMs, with knowledge of the means of the class labels of the unlabeled data, is closely related to the supervised SVM with known labels on all the unlabeled data. This motivates us to first estimate the label means of the unlabeled data. Two versions of the meanS3VM, which work by maximizing the margin between the label means, are proposed. The first one is based on multiple kernel learning, while the second one is based on alternating optimization. Experiments show that both of the proposed algorithms achieve highly competitive and sometimes even the best performance as compared to the state-of-the-art semi-supervised learners. Moreover, they are more efficient than existing S3VMs.


Design of Maximum Power Point Tracker (MPPT) and Phase Locked Loop (PLL) in a PV-Inverter


title: CLORIFI: software vulnerability discovery using code clone verification

abstract: Software vulnerability has long been considered an important threat to the system safety. A vulnerability is often reproduced because of the frequent code reuse by programmers. Security patches are usually not propagated to all code clones; however, they could be leveraged to discover unknown vulnerabilities. Static code auditing approaches are frequently proposed to scan source codes for security flaws; unfortunately, these approaches generate too many false positives. While dynamic execution analysis methods can precisely report vulnerabilities, they are ineffective in path exploration, which limits them to scale to large programs. With the purpose of detecting vulnerability in a scalable way with more preciseness, in this paper, we propose a novel mechanism, called software vulnerability discovery using Code Clone Verification CLORIFI, that scalably discovers vulnerabilities in real world programs using code clone verification. In the beginning, we use a fast and scalable syntax-based way to find code clones in program source codes based on released security patches. Subsequently, code clones are being verified using concolic testing to dramatically decrease the false positives. In addition, we mitigate the path explosion problem by backward sensitive data tracing in concolic execution. Experiments have been conducted with real-world open-source projects recent Linux OS distributions and program packages. As a result, we found 7 real vulnerabilities out of 63 code clones from Ubuntu 14.04 LTS Canonical, London, UK and 10 vulnerabilities out of 40 code clones from CentOS 7.0 The CentOS Projectcommunity contributed. Furthermore, we confirmed more code clone vulnerabilities in various versions of programs including Rsyslog Open SourceOriginal author: Rainer Gerhards, Apache Apache Software Foundation, Forest Hill, Maryland, USA and Firefox Mozilla Corporation, Mountain View, California, USA. In order to evaluate the effectiveness of vulnerability verification in a systematic way, we also utilized Juliet Test Suite as measurement objects. The results show that CLORIFI achieves 98% accuracy with 0 false positives. Copyright © 2015 John Wiley & Sons, Ltd.


title: Box2Pix: Single-Shot Instance Segmentation by Assigning Pixels to Object Boxes

abstract: The task of semantic instance segmentation has gained a large interest within academia as well as industry, especially in the context of autonomous driving. While several published approaches achieve very strong results, only few of them achieve frame rates that are sufficient for the automotive domain. We present an approach that achieves competitive results on the Cityscapes [1] and KITTI [2] datasets, while being twice as fast as any other existing approach. Our method relies on a single fully-convolutional network (FCN [3]) predicting object bounding boxes, as well as pixel-wise semantic object classes and an offset vector pointing to corresponding object centers. Using those outputs, we present an efficient and simple post-processing that assigns each object pixel to its best matching object detection, resulting in an instance segmentation obtained at real-time speeds.


title: All file systems are not created equal: on the complexity of crafting crash-consistent applications

abstract: We present the first comprehensive study of application-level crash-consistency protocols built atop modern file systems. We find that applications use complex update protocols to persist state, and that the correctness of these protocols is highly dependent on subtle behaviors of the underlying file system, which we term persistence properties. We develop a tool named BOB that empirically tests persistence properties, and use it to demonstrate that these properties vary widely among six popular Linux file systems. We build a framework named ALICE that analyzes application update protocols and finds crash vulnerabilities, i.e., update protocol code that requires specific persistence properties to hold for correctness. Using ALICE, we analyze eleven widely-used systems (including databases, key-value stores, version control systems, distributed systems, and virtualization software) and find a total of 60 vulnerabilities, many of which lead to severe consequences. We also show that ALICE can be used to evaluate the effect of new file-system designs on application-level consistency.


title: Why do they still use paper?: understanding data collection and use in Autism education

abstract: Autism education programs for children collect and use large amounts of behavioral data on each student. Staff use paper almost exclusively to collect these data, despite significant problems they face in tracking student data in situ, filling out data sheets and graphs on a daily basis, and using the sheets in collaborative decision making. We conducted fieldwork to understand data collection and use in the domain of autism education to explain why current technology had not met staff needs. We found that data needs are complex and unstandardized, immediate demands of the job interfere with staff ability to collect in situ data, and existing technology for data collection is inadequate. We also identified opportunities for technology to improve sharing and use of data. We found that data sheets are idiosyncratic and not useful without human mediation; improved communication with parents could benefit children's development; and staff are willing, and even eager, to incorporate technology. These factors explain the continued dependence on paper for data collection in this environment, and reveal opportunities for technology to support data collection and improve use of collected data.


title: Multiomic definition of generalizable endotypes in human acute pancreatitis

abstract: Acute pancreatitis (AP) is sudden onset pancreas inflammation that causes multiple organ dysfunction syndrome (MODS) and death in certain individuals who develop AP yet minimal systemic inflammation in others. Here, we show that this observed diversity in systemic response and outcome is accompagnied by diversity in molecular subtypes that can be identified using computational analysis of clinical and multiomic data. We integrated co-incident whole blood transcriptomic, plasma proteomic, and serum metabolomic data at serial time points from a cohort of patients presenting with AP and systematically evaluated four different metrics for patient similarity, using unbiased mathematical, biological and clinical measures of internal and external validity. Our results identify four distinct and stable AP endotypes that are characterized by pathway and biomarker combination stereotypes into hypermetabolic, hepatopancreaticobiliary, catabolic and innate immune endotypes. The catabolic endotype in AP strikingly recapitulates a disease endotype previously reported in acute respiratory distress syndrome, a recognized complication of AP. Our findings demonstrate that clinically-relevant and generalizable endotypes exist in AP.


title: Software defined Network-on-Chip for scalable CMPs

abstract: Moving from Petascale to Exascale computing necessitates optimizing the micro-architectural to increase the performance/power ratio of multicores (e.g., FLOPS/W). Future manycore processors will contain thousands of low-powered processing elements (kilo-core Chip Multi-Processors — CMPs) to support the execution of a large number of concurrent threads. While data-driven Program eXecution Models (PXMs) are gaining popularity due to the support they provide for thread communication, frequent data exchange among many concurrent threads puts stress on the underlying interconnect subsystem. This results in hotspots and high latency for data packet delivering. As a solution, we propose a scalable Software Defined Network-on-Chip (SDNoC) architecture for future manycore processors. Our design tries to merge the benefits of ring-based NoCs (i.e., performance, energy efficiency) with those brought by dynamic reconfiguration (i.e., adaptation, fault tolerance) while keeping the hard-wired topology (2D-mesh) fixed. To potentially accommodate different application and communication requirements, our interconnect allows mapping different types of topologies (virtual topologies). To allow the software layer to control and monitor the NoC subsystem, few customized instructions supporting a data-driven PXM are added to the core ISA. In experiments, we compared our lightweight reconfigurable architecture to a conventional 2D-mesh interconnection subsystem. Results show that our model allows savings of 39.4% of the chip area and up to 72.4% of the consumed power.


title: Adaptive sliding-mode control of automotive electronic throttle in the presence of input saturation constraint

abstract: In modern vehicles, electronic throttle ( ET ) has been widely utilized to control the airflow into gasoline engine. To solve the control difficulties with an ET, such as strong nonlinearity, unknown model parameters and input saturation constraints, an adaptive sliding-mode tracking control strategy for an ET is presented. Compared with the existing control strategies for an ET, input saturation constraints and parameter uncertainties are adequately considered in the proposed control strategy. At first, the nonlinear dynamic model for control of an ET is described. According to the dynamical model, the nonlinear adaptive sliding-mode tracking control method is presented, where parameter adaptive laws and auxiliary design system are employed. Parameter adaptive law is given to estimate the unknown parameter with an ET. An auxiliary system is designed, and its state is utilized in the tracking control method to handle the input saturation. Stability proof and analysis of the adaptive sliding-mode control method is performed by using Lyapunov stability theory. Finally, the reliability and feasibility of the proposed control strategy are evaluated by computer simulation. Simulation research shows that the proposed sliding-mode control strategy can provide good control performance for an ET.


title: Graph networks as learnable physics engines for inference and control

abstract: Understanding and interacting with everyday physical scenes requires rich knowledge about the structure of the world, represented either implicitly in a value or policy function, or explicitly in a transition model. Here we introduce a new class of learnable models--based on graph networks--which implement an inductive bias for object- and relation-centric representations of complex, dynamical systems. Our results show that as a forward model, our approach supports accurate predictions from real and simulated data, and surprisingly strong and efficient generalization, across eight distinct physical systems which we varied parametrically and structurally. We also found that our inference model can perform system identification. Our models are also differentiable, and support online planning via gradient-based trajectory optimization, as well as offline policy optimization. Our framework offers new opportunities for harnessing and exploiting rich knowledge about the world, and takes a key step toward building machines with more human-like representations of the world.


title: Parallel Implementation of Apriori Algorithm Based on MapReduce

abstract: Searching frequent patterns in transactional databases is considered as one of the most important data mining problems and Apriori is one of the typical algorithms for this task. Developing fast and efficient algorithms that can handle large volumes of data becomes a challenging task due to the large databases. In this paper, we implement a parallel Apriori algorithm based on MapReduce, which is a framework for processing huge datasets on certain kinds of distributable problems using a large number of computers (nodes). The experimental results demonstrate that the proposed algorithm can scale well and efficiently process large datasets on commodity hardware.


title: On the Metric Properties of Discrete Space-Filling Curves

abstract: A space-filling curve is a linear traversal of a discrete finite multidimensional space. In order for this traversal to be useful in many applications, the curve should preserve "locality". We quantify "locality" and bound the locality of multidimensional space-filling curves. Classic Hilbert space-filling curves come close to achieving optimal locality.


title: Detection and Visualization of Android Malware Behavior

abstract: Malware analysts still need to manually inspect malware samples that are considered suspicious by heuristic rules. They dissect software pieces and look for malware evidence in the code. The increasing number of malicious applications targeting Android devices raises the demand for analyzing them to find where the malcode is triggered when user interacts with them. In this paper a framework to monitor and visualize Android applications’ anomalous function calls is described. Our approach includes platform-independent application instrumentation, introducing hooks in order to trace restricted API functions used at runtime of the application. These function calls are collected at a central server where the application behavior filtering and a visualization take place. This can help Android malware analysts in visually inspecting what the application under study does, easily identifying such malicious functions.


title: Real Time Collision Detection and Identification for Robotic Manipulators

abstract: The majority of everyday tasks involve interacting with unstructured environments. This implies that, in order for robots to be truly useful they must be able to handle contacts. This paper explores how a particle filter can be used to localize a contact point and estimate the external force. We demonstrate the capability of the particle filter on a simulated 4DoF planar robotic arm, and compare it to a well-established analytical approach.


title: Isolating Sources of Disentanglement in Variational Autoencoders

abstract: We decompose the evidence lower bound to show the existence of a term measuring the total correlation between latent variables. We use this to motivate our $\beta$-TCVAE (Total Correlation Variational Autoencoder), a refinement of the state-of-the-art $\beta$-VAE objective for learning disentangled representations, requiring no additional hyperparameters during training. We further propose a principled classifier-free measure of disentanglement called the mutual information gap (MIG). We perform extensive quantitative and qualitative experiments, in both restricted and non-restricted settings, and show a strong relation between total correlation and disentanglement, when the latent variables model is trained using our framework.


title: Estimating the State of Charge for Ni-MH Battery in HEV by RBF Neural Network

abstract: Forecasting of the state of charge (SOC) of Ni-MH battery is the most important task for battery management system of hybrid electric vehicle (HEV). On the basis of analyzing charging and discharging characteristics of Ni-MH battery and using the advantages of radial basis function (RBF) neural network, model for estimating the state of charge for Ni-MH battery was established with the piecewise modeling idea. The model was tested with data which was from battery experiments. Results show that the operation speed and estimation accuracy of forecasting model can meet the demands in practice and the model has certain value of application.


title: A Secure and Dynamic Multi-Keyword Ranked Search Scheme over Encrypted Cloud Data

abstract: Due to the increasing popularity of cloud computing, more and more data owners are motivated to outsource their data to cloud servers for great convenience and reduced cost in data management. However, sensitive data should be encrypted before outsourcing for privacy requirements, which obsoletes data utilization like keyword-based document retrieval. In this paper, we present a secure multi-keyword ranked search scheme over encrypted cloud data, which simultaneously supports dynamic update operations like deletion and insertion of documents. Specifically, the vector space model and the widely-used TF $\;\times\;$ IDF model are combined in the index construction and query generation. We construct a special tree-based index structure and propose a “Greedy Depth-first Search” algorithm to provide efficient multi-keyword ranked search. The secure kNN algorithm is utilized to encrypt the index and query vectors, and meanwhile ensure accurate relevance score calculation between encrypted index and query vectors. In order to resist statistical attacks, phantom terms are added to the index vector for blinding search results. Due to the use of our special tree-based index structure, the proposed scheme can achieve sub-linear search time and deal with the deletion and insertion of documents flexibly. Extensive experiments are conducted to demonstrate the efficiency of the proposed scheme.


title: Deep Reinforcement Fuzzing

abstract: Fuzzing is the process of finding security vulnerabilities in input-processing code by repeatedly testing the code with modified inputs. In this paper, we formalize fuzzing as a reinforcement learning problem using the concept of Markov decision processes. This in turn allows us to apply state-of-the-art deep Q-learning algorithms that optimize rewards, which we define from runtime properties of the program under test. By observing the rewards caused by mutating with a specific set of actions performed on an initial program input, the fuzzing agent learns a policy that can next generate new higher-reward inputs. We have implemented this new approach, and preliminary empirical evidence shows that reinforcement fuzzing can outperform baseline random fuzzing.


title: The affective consequences of social comparison: Either direction has its ups and downs

abstract: Research on social comparison processes has assumed that a comparison in a given direction (upward or downward) will lead to a particular affective reaction. In contrast, the present two studies proposed and found that a comparison can produce either positive or negative feelings about oneself, independent of its direction. Several factors moderated the tendency to derive positive or negative alfect from upward and downward comparisons. In Study l, cancer patients low in self-esteem and with low perceived control over their symptoms and illness were more likely to see downward comparisons as having negative implications for themselves. Those low in self-esteem were also more likely to perceive upward comparisons as negative. In Study 2, individuals with high marital dissatisfaction and those who felt uncertain about their marital relationship were morc likely to experience negative affect from upward and downward comparisons. The implications of these findings for social comparison theory and for the coping and adaptation literature arc discussed.


title: A support vector approach for cross-modal search of images and texts

abstract: We propose a novel and generic approach for cross-modal search based on Structural SVM.Our approach provides max-margin guarantees and better generalization than competing methods.We analyze and compare different aspects of our approach such as training and testing time, and performance across datasets.Extensive experiments demonstrate the efficacy of our approach. Building bilateral semantic associations between images and texts is among the fundamental problems in computer vision. In this paper, we study two complementary cross-modal prediction tasks: (i) predicting text(s) given a query image ("Im2Text"), and (ii) predicting image(s) given a piece of text ("Text2Im"). We make no assumption on the specific form of text; i.e., it could be either a set of labels, phrases, or even captions. We pose both these tasks in a retrieval framework. For Im2Text, given a query image, our goal is to retrieve a ranked list of semantically relevant texts from an independent text-corpus (i.e., texts with no corresponding images). Similarly, for Text2Im, given a query text, we aim to retrieve a ranked list of semantically relevant images from a collection of unannotated images (i.e., images without any associated textual meta-data).We propose a novel Structural SVM based unified framework for these two tasks, and show how it can be efficiently trained and tested. Using a variety of loss functions, extensive experiments are conducted on three popular datasets (two medium-scale datasets containing few thousands of samples, and one web-scale dataset containing one million samples). Experiments demonstrate that our framework gives promising results compared to competing baseline cross-modal search techniques, thus confirming its efficacy.


title: Integral Design and Analysis of Passive Magnetic Bearing and Active Radial Magnetic Bearing for Agile Satellite Application

abstract: In this paper, the design and development of a magnetic bearing system which consists of passive magnetic axial/tilting bearing and active two-axial radial magnetic bearing (RMB) used in magnetically suspended wheel (MSW) or magnetically suspended control moment gyroscope (MSCMG) for agile satellite application is presented. The passive axial/tilting magnetic bearing supplies an axial position stiffness to stabilize the rotor in the axial direction and a tilting stiffness to restrain the rotor when subjected to gyroscopic torque. The active two-axis radial magnetic bearing with bias permanent magnet stabilizes the rotor on the axes perpendicular to the rotation axis. Considering the complex distribution of flux density the system performance cannot be accurately analyzed by the conventional magnetic circuit method because of the high coupling between force and moment produced by the axial magnetic axial/tilting bearing and RMB. To analyze the coupling problem of the force, moment, position stiffness, and tilting stiffness between the passive magnetic axial/tilting bearing and RMB, an integral design and analysis method based on three-dimensional finite-element method is presented. An example is given and analysis results prove that the high coupling occurs on the radial direction and the light occurs on the axial and tilting directions orthogonal to the spin axis. Then the linearized model is given in this paper.


title: SentiCircles for contextual and conceptual semantic sentiment analysis of Twitter

abstract: Lexicon-based approaches to Twitter sentiment analysis are gaining much popularity due to their simplicity, domain independence, and relatively good performance. These approaches rely on sentiment lexicons, where a collection of words are marked with fixed sentiment polarities. However, words’ sentiment orientation (positive, neural, negative) and/or sentiment strengths could change depending on context and targeted entities. In this paper we present SentiCircle; a novel lexicon-based approach that takes into account the contextual and conceptual semantics of words when calculating their sentiment orientation and strength in Twitter. We evaluate our approach on three Twitter datasets using three different sentiment lexicons. Results show that our approach significantly outperforms two lexicon baselines. Results are competitive but inconclusive when comparing to state-of-art SentiStrength, and vary from one dataset to another. SentiCircle outperforms SentiStrength in accuracy on average, but falls marginally behind in F-measure.


title: Privacy challenges in proximity based social networking: Techniques & solutions

abstract: The development of the Proximity based Mobile Social Networking (PMSN) has been growing exponentially with the adoption of smartphones and introduction of Wi-Fi hotspots in public and remote areas. Users present in the vicinity can interact with each other using the embedded technologies in their mobile devices such as GPS, Wi-Fi and Bluetooth. Due to its growing momentum, this new social networking has also aroused the interest of business people and advertisers. However, due to a lack of security in these networks, several privacy concerns were reported. Users are more reluctant to share their locations and to address this issue, some initial solutions to preserve location privacy were implemented. The aim of this paper is to present a clear categorization of the different privacy threats in PMSN. Moreover, the location privacy enforcement policies and techniques used to ensure privacy are outlined and some solutions employed in existent systems are presented and discussed. To the best of our knowledge, this is the first study done outlining several categories of PMSN privacy challenges and their solutions in this new type of social networking services. Finally, some privacy research challenges and future perspectives are proposed.


title: Knowledge management, social media and employee creativity

abstract: The study investigates the relation between social media use and employee creativity by adopting a knowledge management (KM) approach in order to consider the influence of social networks and interactions on individuals’ creativity. The literature review debates how the use of social media empowers people to engage in conversational and collaborative KM, which in turn enriches people's cognitive and creative processes. Data collected from Greek tourism professionals confirmed that employees’ creativity is positively related to their participation in social networks and their use of social media for: searching, storing and reading information (internal cognitive processes); and sharing, discussing and co-creating information (external cognitive processes). The study contributes to the field by providing a theoretical underpinning and practical evidence showing how social networks (i.e. a meso level) can influence employees’ creativity. Hence, the findings highlight the need to shift focus from identifying and managing creative individuals (micro level) and/or organisational contexts (macro level) to creating and managing creative social networks (meso level).


title: Triaging Content Severity in Online Mental Health Forums

abstract: In recent years, social media has become a significant resource for improving healthcare and mental health. Mental health forums are online communities where people express their issues, and seek help from moderators and other users. In such forums, there are often posts with severe content indicating that the user is in acute distress and there is a risk of attempted self-harm. Moderators need to respond to these severe posts in a timely manner to prevent potential self-harm. However, the large volume of daily posted content makes it difficult for the moderators to locate and respond to these critical posts. We propose an approach for triaging user content into four severity categories that are defined based on an indication of self-harm ideation. Our models are based on a feature-rich classification framework, which includes lexical, psycholinguistic, contextual, and topic modeling features. Our approaches improve over the state of the art in triaging the content severity in mental health forums by large margins (up to 17% improvement over the F-1 scores). Furthermore, using our proposed model, we analyze the mental state of users and we show that overall, long-term users of the forum demonstrate decreased severity of risk over time. Our analysis on the interaction of the moderators with the users further indicates that without an automatic way to identify critical content, it is indeed challenging for the moderators to provide timely response to the users in need.


title: Neural networks letter Machine learning approach to color constancy

abstract: A number of machine learning (ML) techniques have recently been proposed to solve color constancy problem in computer vision. Neural networks (NNs) and support vector regression (SVR) in particular, have been shown to outperform many traditional color constancy algorithms. However, neither neural networks nor SVR were compared to simpler regression tools in those studies. In this article, we present results obtained with a linear technique known as ridge regression (RR) and show that it performs better than NNs, SVR, and gray world (GW) algorithm on the same dataset. We also perform uncertainty analysis for NNs, SVR, and RR using bootstrapping and show that ridge regression and SVR are more consistent than neural networks. The shorter training time and single parameter optimization of the proposed approach provides a potential scope for real time video tracking application.


title: Underwater acoustic network with random access: a physical layer perspective

abstract: In this paper, we take a physical layer approach and investigate the outage probability in frequency selective underwater channels with uncoordinated multiple access and bursty transmissions. We first derive the statistical distribution of the interference as seen by a typical receiver, considering both burstiness of interference transmissions and spatial distributions of the interferers. We then derive expressions for the probability density functions of the frequency-dependent signal to noise and interference ratio. Expressions for the outage probability of a typical link between two nodes are obtained in the frequency selective scenarios and the flat fading special cases. The outage probabilities depend on the bursty transmission probability and the number of interfering transmitters. Simulation results are presented that validate the theoretical results and illustrate typical features of the interference in an underwater network.


title: Dexfenfluramine treatment of obesity: a double blind trial with post trial follow up.

abstract: OBJECTIVE ::: As successful weight management demands a long term approach, a better understanding of weight changes during and for significant periods after cessation of dexfenfluramine therapy is essential to the evaluation of the drug's effectiveness in clinical practice. This study aimed to investigate additional benefits to weight loss during 6 months treatment with dexfenfluramine in 60 patients enrolled in a weight loss programme. ::: ::: ::: DESIGN ::: Sixty obese subjects (21 males; 39 females) were randomised to dexfenfluramine (15 mg twice daily) or placebo for six months. Fifty one (27 dexfenfluramine; 24 placebo) subjects completed the double blind, randomised, placebo controlled clinical trial. ::: ::: ::: RESULTS ::: After a one month 'run in' phase and six months treatment, weight loss in the dexfenfluramine group was significantly greater than placebo, 9.7 +/- 1.1 kg vs 4.9 +/- 0.9 kg (mean +/- s.e.m.); P = 0.002. Reduction in body fat, 5.0 +/- 0.7 kg vs 1.0 +/- 0.9 kg; P = 0.002 and waist circumference, 10.5 +/- 1.9 cm vs 5.7 +/- 1.1 cm; P = 0.04 was also greater in the dexfenfluramine group. Despite significant weight loss, waist to hip ratio (WHR) did not change in either group. The dexfenfluramine group reported a significantly greater incidence of nausea, dry mouth and dizziness which tended to decrease as treatment progressed. No subjects withdrew due to drug induced side effects. Reduction in serum triglyceride levels and an increase in HDL cholesterol (in female subjects) in conjunction with a reduction in fasting insulin, collectively support an improved cardiovascular risk profile in the dexfenfluramine group. Despite significant weight loss, these risk factor measurements worsened in the placebo group. After cessation of dexfenfluramine therapy, there was a significantly greater weight regain indicating a loss of treatment effect. By 5 months after cessation of dexfenfluramine, the treatment effect was negated with weight loss in the dexfenfluramine (6.0 +/- 1.6 kg) and placebo (6.2 +/- 1.3 kg) group, similar. ::: ::: ::: CONCLUSION ::: The results of this study support the longer term use of dexfenfluramine therapy for patients with chronic obesity.


title: A Compact Koch Fractal UWB MIMO Antenna With WLAN Band-Rejection

abstract: In this letter, a compact octagonal shaped fractal ultrawideband multiple-input–multiple-output antenna is presented, and its characteristics are investigated. In order to achieve the desired miniaturization and wideband phenomena, self-similar and space filling properties of Koch fractal geometry are used in the antenna design. These fractal monopoles are placed orthogonal to each other for good isolation. Moreover, grounded stubs are used in the geometry to provide further improvement in the isolation. The band rejection phenomenon in wireless local area network band is achieved by etching a C-shaped slot from the monopole of the antenna. The proposed antenna has compact dimensions of $45~\hbox{mm} \times 45~\hbox{mm}$ and exhibits quasi-omnidirectional radiation pattern. In addition, it shows an impedance bandwidth ( ${{\rm S}_{11}}\! ) from 2 to 10.6 GHz with isolation better than 17 dB over the entire ultra-wideband range. Diversity performance is also evaluated in terms of envelope correlation coefficient and capacity loss. The measured results show good agreement with the simulated ones.


title: REAL TIME HAND GESTURE RECOGNITION SYSTEM FOR DYNAMIC APPLICATIONS

abstract: Virtual environments have always been considered as a means for more visceral and efficient human computer interaction by a diversified range of applications. The spectrum of applications includes analysis of complex scientific data, medical training, military simulation, phobia therapy and virtual prototyping. Evolution of ubiquitous computing, current user interaction approaches with keyboard, mouse and pen are not sufficient for the still widening spectrum of Human computer interaction. Gloves and sensor based trackers are unwieldy, constraining and uncomfortable to use. Due to the limitation of these devices the useable command set based diligences is also limited. Direct use of hands as an input device is an innovative method for providing natural Human Computer Interaction which has its inheritance from textbased interfaces through 2D graphical-based interfaces, multimedia-supported interfaces, to full-fledged multi-participant Virtual Environment (VE) systems. Conceiving a future era of human-computer interaction with the implementations of 3D application where the user may be able to move and rotate objects simply by moving and rotating his hand - all without help of any input device. The research effort centralizes on the efforts of implementing an application that employs computer vision algorithms and gesture recognition techniques which in turn results in developing a low cost interface device for interacting with objects in virtual environment using hand gestures. The prototype architecture of the application comprises of a central computational module that applies the camshift technique for tracking of hands and its gestures. Haar like technique has been utilized as a classifier that is creditworthy for locating hand position and classifying gesture. The patterning of gestures has been done for recognition by mapping the number of defects that is formed in the hand with the assigned gestures. The virtual objects are produced using Open GL library. This hand gesture recognition technique aims to substitute the use of mouse for interaction with the virtual objects. This will be useful to promote controlling applications like virtual games, browsing images etc in virtual environment using hand gestures.


title: Two-Step Joint Attention Network for Visual Question Answering

abstract: Visual Question Answering(VQA) system is a task that answers natural language questions automatically according to the content of a reference image. Common method for VQA is to extract image feature and question feature by using deep neural network, and then combine the two features with attention mechanism to predict answer. Most of the attention methods for VQA merely concern about where the local regions of image are relevant to answer and ignore the question words have different weights to answer. Hence, we propose two-step joint attention that use the combining representation of the image feature and question feature to guide visual attention and question attention. Two-step joint attention is able to focus the given image and question from coarse-drained parts to fine-grained parts gradually to predict answer. For purpose of extracting image feature precisely, we also propose a BiSRU and use RNN based on BiSRU to allow the adjacent local region vectors of the image to maintain information each other. We demonstrate and analyze the effectiveness on the VQA dataset, and use visualization to show the results intuitively.


title: Longitudinal Effects of Violent Video Games on Aggression in Japan and the United States

abstract: CONTEXT ::: Youth worldwide play violent video games many hours per week. Previous research suggests that such exposure can increase physical aggression. ::: ::: ::: OBJECTIVE ::: We tested whether high exposure to violent video games increases physical aggression over time in both high- (United States) and low- (Japan) violence cultures. We hypothesized that the amount of exposure to violent video games early in a school year would predict changes in physical aggressiveness assessed later in the school year, even after statistically controlling for gender and previous physical aggressiveness. ::: ::: ::: DESIGN ::: In 3 independent samples, participants' video game habits and physically aggressive behavior tendencies were assessed at 2 points in time, separated by 3 to 6 months. ::: ::: ::: PARTICIPANTS ::: One sample consisted of 181 Japanese junior high students ranging in age from 12 to 15 years. A second Japanese sample consisted of 1050 students ranging in age from 13 to 18 years. The third sample consisted of 364 United States 3rd-, 4th-, and 5th-graders ranging in age from 9 to 12 years. RESULTS. Habitual violent video game play early in the school year predicted later aggression, even after controlling for gender and previous aggressiveness in each sample. Those who played a lot of violent video games became relatively more physically aggressive. Multisample structure equation modeling revealed that this longitudinal effect was of a similar magnitude in the United States and Japan for similar-aged youth and was smaller (but still significant) in the sample that included older youth. ::: ::: ::: CONCLUSIONS ::: These longitudinal results confirm earlier experimental and cross-sectional studies that had suggested that playing violent video games is a significant risk factor for later physically aggressive behavior and that this violent video game effect on youth generalizes across very different cultures. As a whole, the research strongly suggests reducing the exposure of youth to this risk factor.


title: From Theory to Practice: Plug and Play with Succinct Data Structures

abstract: Engineering efficient implementations of compact and succinct structures is time-consuming and challenging, since there is no standard library of easy-to-use, highly optimized, and composable components. One consequence is that measuring the practical impact of new theoretical proposals is difficult, since older baseline implementations may not rely on the same basic components, and reimplementing from scratch can be time-consuming. In this paper we present a framework for experimentation with succinct data structures, providing a large set of configurable components, together with tests, benchmarks, and tools to analyze resource requirements. We demonstrate the functionality of the framework by recomposing two succinct solutions for top-k document retrieval which can operate on both character and integer alphabets.


title: A fuzzy ontology for semantic modelling and recognition of human behaviour

abstract: We propose a fuzzy ontology for human activity representation, which allows us to model and reason about vague, incomplete, and uncertain knowledge. Some relevant subdomains found to be missing in previous proposed ontologies for this domain were modelled as well. The resulting fuzzy OWL 2 ontology is able to model uncertain knowledge and represent temporal relationships between activities using an underlying fuzzy state machine representation. We provide a proof of concept of the approach in work scenarios such as the office domain, and also make experiments to emphasize the benefits of our approach with respect to crisp ontologies. As a result, we demonstrate that the inclusion of fuzzy concepts and relations in the ontology provide benefits during the recognition process with respect to crisp approaches.


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title: Forensic Malware Analysis: The Value of Fuzzy Hashing Algorithms in Identifying Similarities

abstract: This research aims to examine the effectiveness and efficiency of fuzzing hashing algorithm in the identification of similarities in Malware Analysis. More precisely, it will present the benefit of using fuzzy hashing algorithms, such as ssdeep, sdhash, mvHash and mrsh – v2, in identifying similarities in Malware domain. The obtained results will be compared with the traditional and most common Cryptographic Hashes, such as the MD5, SHA-1 and SHA-256. Furthermore, it will highlight the pros and cons of fuzzy and cryptographic hashing, as well as their adoption in real world applications.


title: Towards an Ontology of Economic Value: a Preliminary Analysis

abstract: The notion of value and of value creation has raised interest over the last 30 years for both researchers and practitioners. Although several studies have been conducted in marketing, value remains and elusive and often ill-dened concept. A clear understanding of value and value determinants can increase the awareness in strategic decisions and pricing choices. Objective of this paper is to preliminary discuss the main kinds of entity that an ontology of economic value should deal with.


title: Drawing and Labeling High-Quality Metro Maps by Mixed-Integer Programming

abstract: Metro maps are schematic diagrams of public transport networks that serve as visual aids for route planning and navigation tasks. It is a challenging problem in network visualization to automatically draw appealing metro maps. There are two aspects to this problem that depend on each other: the layout problem of finding station and link coordinates and the labeling problem of placing nonoverlapping station labels. In this paper, we present a new integral approach that solves the combined layout and labeling problem (each of which, independently, is known to be NP-hard) using mixed-integer programming (MIP). We identify seven design rules used in most real-world metro maps. We split these rules into hard and soft constraints and translate them into an MIP model. Our MIP formulation finds a metro map that satisfies all hard constraints (if such a drawing exists) and minimizes a weighted sum of costs that correspond to the soft constraints. We have implemented the MIP model and present a case study and the results of an expert assessment to evaluate the performance of our approach in comparison to both manually designed official maps and results of previous layout methods.


title: Performance Issues of Heterogeneous Hadoop Clusters in Cloud Computing

abstract: A bstract- N owadays most of the cloud applications process large amount of data to provide the desired results. Data volumes to be processed by cloud applications are growing much faster than computing power. This growth demands new strategies for processing and analyzing information. Dealing with large data volumes requires two things: 1) Inexpensive, reliable storagee 2) New tools for analyzing unstructured and structured data. Hadoop is a powerful open source software platform that addresses both of these problems. The current Hadoop implementation assumes that computing nodes in a cluster are homogeneous in nature. Hadoop lacks performance in heterogeneous clusters where the nodes have different computing capacity. In this paper we address the issues that affect the performance of hadoop in eterogeneous clusters and also provided some guidelines on how to overcome these bottlenecks. K eywords: Cloud Computing, Hadoop, HDFS, Mapreduce


title: Systems AI: A Declarative Learning Based Programming Perspective

abstract: Data-driven approaches are becoming dominant problem-solving techniques in many areas of research and industry. Unfortunately, current technologies do not make such techniques easy to use for application experts who are not fluent in machine learning nor for machine learning experts who aim at testing ideas on real-world data and need to evaluate those as a part of an end-to-end system. We review key efforts made by various AI communities to provide languages for high-level abstractions over learning and reasoning techniques needed for designing complex AI systems. We classify the existing frameworks based on the type of techniques as well as the data and knowledge representations they use, provide a comparative study of the way they address the challenges of programming real-world applications, and highlight some shortcomings and future directions.


title: Electricity Theft Detection Techniques for Distribution System in GUVNL

abstract: Electricity consumer dishonesty is a problem faced by all power utilities. Finding efficient measurements for detecting fraudulent electricity consumption has been an active research area in recent years. This paper presents a new approach towards Distribution Power Loss analysis for electric utilities using a novel intelligence- based techniques like Extreme Learning Machine (ELM), OS-ELM & Support Vector Machine (SVM). The main motivation of this study is to assist Gujarat Urjha Vikas Nigam LTD (GUVNL) to reduce its Distribution Power Loss due to electricity theft. The proposed model preselects suspected customers to be inspected onsite for fraud based on irregularities and abnormal consumption behaviour. This approach provides a method of data mining and involves feature extraction from historical customer consumption data. The approach uses customer load profile information to expose abnormal behaviour that is known to be highly correlated with Power Loss activities. The result yields classification classes that are used to shortlist potential fraud suspects for onsite inspection, based on significant behaviour that emerges due to irregularities in consumption. Simulation results prove the proposed method is more effective compared to the current actions taken by GUVNL in order to reduce Power Loss activities.


title: Data Compression Using Adaptive Coding and Partial String Matching

abstract: The recently developed technique of arithmetic coding, in conjunction with a Markov model of the source, is a powerful method of data compression in situations where a linear treatment is inappropriate. Adaptive coding allows the model to be constructed dynamically by both encoder and decoder during the course of the transmission, and has been shown to incur a smaller coding overhead than explicit transmission of the model's statistics. But there is a basic conflict between the desire to use high-order Markov models and the need to have them formed quickly as the initial part of the message is sent. This paper describes how the conflict can be resolved with partial string matching, and reports experimental results which show that mixed-case English text can be coded in as little as 2.2 bits/ character with no prior knowledge of the source.


title: Composed sketch framework for quantiles and cardinality queries over big data streams

abstract: Quantiles and Cardinality queries are important tools to analyze statistical information from big data streams. Due to the features of the streams, such as huge volume and high velocity, it is a challenging problem to quickly provide responses for the two types of queries using constrained space over big data streams. In this paper, we propose a composed sketch framework, which can support both quantiles queries and cardinality queries over the data streams. We introduce cardinality estimators into a baseline q-digest structure and propose unified sketch merging and query processing operations. Our approach can support these two types of queries simultaneously. We conduct detailed theoretical and experimental analysis in terms of query accuracy and query response time. The analytical and experimental results show that our approach can obtain accurate estimates quicker than traditional method and system in big data streams environments, and it just produces less than 0.8‰ storage overhead in TB-scale real-world data sets.


title: Semantic colorization with internet images

abstract: Colorization of a grayscale photograph often requires considerable effort from the user, either by placing numerous color scribbles over the image to initialize a color propagation algorithm, or by looking for a suitable reference image from which color information can be transferred. Even with this user supplied data, colorized images may appear unnatural as a result of limited user skill or inaccurate transfer of colors. To address these problems, we propose a colorization system that leverages the rich image content on the internet. As input, the user needs only to provide a semantic text label and segmentation cues for major foreground objects in the scene. With this information, images are downloaded from photo sharing websites and filtered to obtain suitable reference images that are reliable for color transfer to the given grayscale photo. Different image colorizations are generated from the various reference images, and a graphical user interface is provided to easily select the desired result. Our experiments and user study demonstrate the greater effectiveness of this system in comparison to previous techniques.


title: How to Sell SaaS: A Model for Main Factors of Marketing and Selling Software-as-a-Service

abstract: Software-as-a-Service providers have been growing fast while the contemporary research literature has neglected analysis of their business-critical marketing and sales processes. In this paper we collect the key factors characterizing how to market and sell SaaS to business customers into an eight dimensional model. We also use an explorative multi-case study to observe six SaaS providers and validate the model. The interviewed providers emphasized use of the Internet for marketing communication while personal direct sale was the dominating sales approach. Customer acquisition cost was the key performance indicator for marketing and sales while customer lifetime value and churn were the KPIs in customer relationship management.


title: Compressive imaging of color images

abstract: In this paper, we propose a novel compressive imaging framework for color images. We first introduce an imaging architecture based on combining the existing single-pixel Compressive Sensing (CS) camera with a Bayer color filter, thereby enabling acquisition of compressive color measurements. Then we propose a novel CS reconstruction algorithm that employs joint sparsity models in simultaneously recovering the R, G, B channels from the compressive measurements. Experiments simulating the imaging and reconstruction procedures demonstrate the feasibility of the proposed idea and the superior quality in reconstruction.


title: Resonant Response of High-Temperature Superconducting Split-Ring Resonators

abstract: A study of the resonant response of high-temperature superconducting YBCO split-ring resonators (SRRs) deposited on a single crystal MgO substrate is presented. Measurement of an array of seven SRRs inside a WR-90 waveguide at temperatures below the transition temperature of YBCO (Tc ≈ 90 K) shows a stop band centered at around 8 GHz with a 1.6-GHz bandwidth, which is also seen in the full wave simulation. An individual SRR element is measured to investigate the temperature dependence of the resonance frequency and quality factor. The single SRR measurements show a small gradual shift in the transmission resonance frequency as the temperature is lowered from 90.5 to 40 K, which can be attributed to the presence of kinetic inductance. The behavior of the measured resonance frequency versus temperature is fitted to a model to study the kinetic inductance and penetration depth. The quality factor is then calculated from the measurements in the same temperature range. Finally, the permittivity and permeability are extracted from the measured scattering parameters.


title: Intelligent PV module for grid-connected PV systems

abstract: Most issues carried out about building integrated PV systems (BIPV) performance show average losses about 20-25% in electricity production. The causes are varied: mismatching losses, partial shadows, variations in I-V characteristics of PV modules due to manufacturing processes, differences in the orientations and inclinations of solar surfaces, temperature effects, etc. Most losses can be decreased by means of a suitable electronics. This paper shows the Intelligent PV Module concept, a low cost and high efficiency DC-DC converter with MPPT functions, control and power line communications (PLC). Several DC-DC configurations have been considered as well as MPPT methods and communication options. Matlab simulations describe how this Intelligent PV Module can make a significant improvement in the energy delivered, above all in facades and roofs, in which the number of modules and the external conditions make this device most suitable.


title: Test data generation and feasible path analysis

abstract: This paper describes techniques used by Test Specification and Determination Tool (TSDT), an experimental prototype for analysis and testing of critical applications written in Ada. Two problems dominate structural testing of programs: exponential explosion in the number of execution paths and feasible path determination, A path is feasible if there exists some input that will cause the path to be traversed during execution. We present techniques based on new representations combined with automated theorem proving to deal with these problems, The paper describes how these techniques can be used to determine the feasibility of expressions containing references to Ada arrays. Finally, we present algorithms specific to generating test data under the modified condition decision coverage (MCDC) criterion. While we realize that many of the problems we are attempting to solve are, in general, undecidable, we are encouraged by preliminary results obtained from running TSDT against vendor supplied code. Based on our results, we feel these techniques can be applied to a broad enough section of Ada code to make them cost effective.


abstract: Due to the prevalence of unlabeled data, semi-supervised learning has drawn significant attention and has been found applicable in many real-world applications. In this paper, we present the so-called Budgeted Semi-supervised Support Vector Machine (BS3VM), a method that leverages the excellent generalization capacity of kernel-based method with the adjacent and distributive information carried in a spectral graph for semi-supervised learning purpose. The fact that the optimization problem of BS3VM can be solved directly in the primal form makes it fast and efficient in memory usage. We validate the proposed method on several benchmark datasets to demonstrate its accuracy and efficiency. The experimental results show that BS3VM can scale up efficiently to the large-scale datasets where it yields a comparable classification accuracy while simultaneously achieving a significant computational speed-up compared with the baselines.


title: ColorBars: increasing data rate of LED-to-camera communication using color shift keying

abstract: LED-to-camera communication allows LEDs deployed for illumination purposes to modulate and transmit data which can be received by camera sensors available in mobile devices like smartphones, wearable smart-glasses etc. Such communication has a unique property that a user can visually identify a transmitter (i.e. LED) and specifically receive information from the transmitter. It can support a variety of novel applications such as augmented reality through mobile devices, navigation using smart signs, fine-grained location specific advertisement etc. However, the achievable data rate in current LED-to-camera communication techniques remains very low (a 12 bytes per second) to support any practical application. In this paper, we present ColorBars, an LED-to-camera communication system that utilizes Color Shift Keying (CSK) to modulate data using different colors transmitted by the LED. It exploits the increasing popularity of Tri-LEDs (RGB) that can emit a wide range of colors. We show that commodity cameras can efficiently and accurately demodulate the color symbols. ColorBars ensures flicker-free and reliable communication even in the presence of inter-frame loss and diversity of rolling shutter cameras. We implement ColorBars on embedded platform and evaluate it with Android and iOS smartphones as receivers. Our evaluation shows that ColorBars can achieve a data rate of 5.2 Kbps on Nexus 5 and 2.5 Kbps on iPhone 5S, which is significantly higher than previous approaches. It is also shown that lower CSK modulations (e.g. 4 and 8 CSK) provide extremely low symbol error rates (


title: A social influence model of consumer participation in network-and small-group-based virtual communities

abstract: We investigate two key group-level determinants of virtual community participation - group norms and social identity - and consider their motivational antecedents and mediators. We also introduce a marketing-relevant typology to conceptualize virtual communities, based on the distinction between network-based, and small-group-based virtual communities. Our survey-based study, which was conducted across a broad range of virtual communities, supports the proposed model, and finds further that virtual community type moderates consumers' reasons for participating, as well as the strengths of their impact on group norms and social identity. We conclude with a consideration of managerial and research implications of the findings.


title: Study about IOT's application in "Digital Agriculture" construction

abstract: Along with acceleration of various countries' digitization and informationization, according to the 12th 5-year development plan, the internet of things (IOT) will become one of Chinese emerging strategic industries. Based on the analysis of IOT's basic concept and key technology, this paper studies IOT's application in "Digital Agriculture" construction. The main contents include: IOT's application in agricultural production, IOT's application in logistics and distribution of agricultural products, and IOT's application in agricultural products safety traceability.


title: Why heuristics work

abstract: The adaptive toolbox is a Darwinian-inspired theory that conceives of the mind as a modular system that is composed of heuristics, their building blocks, and evolved capacities. The study of the adaptive toolbox is descriptive and analyzes the selection and structure of heuristics in social and physical environments. The study of ecological rationality is prescriptive and identifies the structure of environments in which specific heuristics either succeed or fail. Results have been used for designing heuristics and environments to improve professional decision making in the real world.


title: An analytical least-squares solution to the line scan LIDAR-camera extrinsic calibration problem

abstract: In this paper, we present an elegant solution to the 2D LIDAR-camera extrinsic calibration problem. Specifically, we develop a simple method for establishing correspondences between a line-scan (2D) LIDAR and a camera using a small calibration target that only contains a straight line. Moreover, we formulate the nonlinear least-squares problem for finding the unknown 6 degree-of-freedom (dof) transformation between the two sensors, and solve it analytically to determine its global minimum. Additionally, we examine the conditions under which the unknown transformation becomes unobservable, which can be used for avoiding ill-conditioned configurations. Finally, we present extensive simulation and experimental results for assessing the performance of the proposed algorithm as compared to alternative analytical approaches.


title: A fuzzy logic approach for timely adaptive traffic light based on traffic load

abstract: In this research, an adaptive timely traffic light is proposed as solution for congestion in typical area in Indonesia. Makassar City, particularly in the most complex junction (fly over, Pettarani, Reformasi highway and Urip S.) is observed for months using static cameras. The condition is mapped into fuzzy logic to have a better time transition of traffic light as opposed to the current conventional traffic light system. In preliminary result, fuzzy logic shows significant number of potential reduced in congestion. Each traffic line has 20–30% less congestion with future implementation of the proposed system.


title: A solution for the automated detection of clickjacking attacks

abstract: Clickjacking is a web-based attack that has recently received a wide media coverage. In a clickjacking attack, a malicious page is constructed such that it tricks victims into clicking on an element of a different page that is only barely (or not at all) visible. By stealing the victim's clicks, an attacker could force the user to perform an unintended action that is advantageous for the attacker (e.g., initiate an online money transaction). Although clickjacking has been the subject of many discussions and alarming reports, it is currently unclear to what extent clickjacking is being used by attackers in the wild, and how significant the attack is for the security of Internet users. In this paper, we propose a novel solution for the automated and efficient detection of clickjacking attacks. We describe the system that we designed, implemented and deployed to analyze over a million unique web pages. The experiments show that our approach is feasible in practice. Also, the empirical study that we conducted on a large number of popular websites suggests that clickjacking has not yet been largely adopted by attackers on the Internet.


title: Rural distribution system planning using Smart Grid Technologies

abstract: The 2007 Energy Independence and Security Act states that all utilities must consider Smart Grid Technologies when making system investments and improvements. This paper will: • Review the language in the Act that sets this requirement • Review the Values and Characteristics of a Smart Grid as defined by the Department of Energy • Discuss how these Values and Characteristics specifically can be applied to rural utility distribution systems • Describe some of the Smart Grid Technologies available today for use on rural distribution utility systems


Placement of “Guiding Sutures” to Counteract Undesirable Retraction of Tissues in and Around Functionally and Cosmetically Important Structures


title: Design Considerations and Performance Evaluation of 1200-V 100-A SiC MOSFET-Based Two-Level Voltage Source Converter

abstract: Silicon carbide (SiC) MOSFET is capable of achieving better efficiency and better power density of power converters due to its low on-state resistance and lower switching losses compared to silicon (Si) Insulated Gate Bipolar Transistor. Operation of power converters at higher switching frequency using SiC devices allows reduction in filter size and hence improves the power to weight ratio of the converter. This paper presents switching characterization of 1200-V 100-A SiC MOSFET module and compares the efficiency of a two-level voltage source converter (2L-VSC) using SiC MOSFETs and Si IGBTs. Also, various design considerations of the 1200-V 100-A SiC MOSFET-based 2L-VSC including gate drive design, bus bar packaging, and thermal management have been elaborated. The designed and developed 2L-VSC is operated to supply 35 kVA load at 20-kHz switching frequency with dc bus voltage of 800 V and the corresponding experimental results are presented.


title: Context based spelling correction

abstract: Abstract Some mistakes in spelling and typing produce correct words, such as typing “fig” when “fog” was intended. These errors are undetectable by traditional spelling correction techniques. In this paper we present a statistical technique capable of detecting and correcting some of these errors when they occur in sentences. Experimental results show that this technique is capable of detecting 76% of simple spelling errors and correcting 73%.


title: Airbnb in tourist cities: comparing spatial patterns of hotels and peer-to-peer accommodation

abstract: In recent years, what has become known as collaborative consumption has undergone rapid expansion through peer-to-peer (P2P) platforms. In the field of tourism, a particularly notable example is that of Airbnb. This article analyses the spatial patterns of Airbnb in Barcelona and compares them with hotels and sightseeing spots. New sources of data, such as Airbnb listings and geolocated photographs are used. Analysis of bivariate spatial autocorrelation reveals a close spatial relationship between Airbnb and hotels, with a marked centre-periphery pattern, although Airbnb predominates around the city's main hotel axis and hotels predominate in some peripheral areas of the city. Another interesting finding is that Airbnb capitalises more on the advantages of proximity to the city's main tourist attractions than does the hotel sector. Multiple regression analysis shows that the factors explaining location are also different for hotels and Airbnb. Finally, it was possible to detect those parts of the city that have seen the greatest increase in pressure from tourism related to Airbnb's recent expansion.


title: Teaching Memristors to EE Undergraduate Students [Class Notes]

abstract: Generations of electrical engineers have learned that there are three fundamental passive two-terminal circuit elements: resistor, capacitor, and inductor. Nevertheless, this apparently immovable situation changed in 2008 when Nature published an article on the memristor, which was proved to be the fourth fundamental circuit element. Since then, researchers have devoted time and effort to find how this device may possibly change the future of electronics. It is time then to introduce the memristor in EE undergraduate courses, but how? The great majority of works on memristor published so far have been aimed at experienced researchers and not at young students. The goal of this paper is providing an original point of view on this issue and describing a simple approach to memristor which is suitable to be used in EE undergraduate courses.


Advances in Knowledge Discovery and Data Mining


title: CYC: a large-scale investment in knowledge infrastructure

abstract: Since 1984, a person-century of effort has gone into building CYC, a universal schema of roughly 10 5 general concepts spanning human reality. Most of the time has been spent codifying knowledge about these concepts; approximately 10 6 commonsense axioms have been handcrafted for and entered into CYC's knowledge base, and millions more have been inferred and cached by CYC. This article examines the fundamental assumptions of doing such a large-scale project, reviews the technical lessons learned by the developers, and surveys the range of applications that are or soon will be enabled by the technology.


title: Player localization using multiple static cameras for sports visualization

abstract: We present a novel approach for robust localization of multiple people observed using multiple cameras. We use this location information to generate sports visualizations, which include displaying a virtual offside line in soccer games, and showing players' positions and motion patterns. Our main contribution is the modeling and analysis for the problem of fusing corresponding players' positional information as finding minimum weight K-length cycles in complete K-partite graphs. To this end, we use a dynamic programming based approach that varies over a continuum of being maximally to minimally greedy in terms of the number of paths explored at each iteration. We present an end-to-end sports visualization framework that employs our proposed algorithm-class. We demonstrate the robustness of our framework by testing it on 60,000 frames of soccer footage captured over 5 different illumination conditions, play types, and team attire.