title: Learning to rank search results for time-sensitive queries

abstract: Retrieval effectiveness of temporal queries can be improved by taking into account the time dimension. Existing temporal ranking models follow one of two main approaches: 1) a mixture model linearly combining textual similarity and temporal similarity, and 2) a probabilistic model generating a query from the textual and temporal part of document independently. In this paper, we propose a novel time-aware ranking model based on learning-to-rank techniques. We employ two classes of features for learning a ranking model, entity-based and temporal features, which are derived from annotation data. Entity-based features are aimed at capturing the semantic similarity between a query and a document, whereas temporal features measure the temporal similarity. Through extensive experiments we show that our ranking model significantly improves the retrieval effectiveness over existing time-aware ranking models.


title: All Your IFCException Are Belong to Us

abstract: Existing designs for fine-grained, dynamic information-flow control assume that it is acceptable to terminate the entire system when an incorrect flow is detected-i.e, they give up availability for the sake of confidentiality and integrity. This is an unrealistic limitation for systems such as long-running servers. We identify public labels and delayed exceptions as crucial ingredients for making information-flow errors recoverable in a sound and usable language, and we propose two new error-handling mechanisms that make all errors recoverable. The first mechanism builds directly on these basic ingredients, using not-a-values (NaVs) and data flow to propagate errors. The second mechanism adapts the standard exception model to satisfy the extra constraints arising from information flow control, converting thrown exceptions to delayed ones at certain points. We prove that both mechanisms enjoy the fundamental soundness property of non-interference. Finally, we describe a prototype implementation of a full-scale language with NaVs and report on our experience building robust software components in this setting.


title: SCOPe: Structural Classification of Proteins—extended, integrating SCOP and ASTRAL data and classification of new structures

abstract: Structural Classification of Proteins—extended (SCOPe, http://scop.berkeley.edu) is a database of protein structural relationships that extends the SCOP database. SCOP is a manually curated ordering of domains from the majority of proteins of known structure in a hierarchy according to structural and evolutionary relationships. Development of the SCOP 1.x series concluded with SCOP 1.75. The ASTRAL compendium provides several databases and tools to aid in the analysis of the protein structures classified in SCOP, particularly through the use of their sequences. SCOPe extends version 1.75 of the SCOP database, using automated curation methods to classify many structures released since SCOP 1.75. We have rigorously benchmarked our automated methods to ensure that they are as accurate as manual curation, though there are many proteins to which our methods cannot be applied. SCOPe is also partially manually curated to correct some errors in SCOP. SCOPe aims to be backward compatible with SCOP, providing the same parseable files and a history of changes between all stable SCOP and SCOPe releases. SCOPe also incorporates and updates the ASTRAL database. The latest release of SCOPe, 2.03, contains 59 514 Protein Data Bank (PDB) entries, increasing the number of structures classified in SCOP by 55% and including more than 65% of the protein structures in the PDB.


title: Improved Detection and Tracking of Lane Marking Using Hough Transform

abstract: Autonomous driver assistance system is a core component for the robust detection and tracking of lane markings on roads and pedestrian detection. The lane detection method is a major component of intelligent transport system. Lane detection helps to estimate the road geometry ahead as well as lateral position of vehicle. A fast lane detection algorithm is proposed by Hough transform which is faster as compared with training and testing model based on pixel hierarchy descriptor. The right and left lane markings are marked by different lines. When the vehicle departs from one lane to another, the color of the lane detection system will change accordingly. The proposed algorithm partitions the camera captured image into two parts, lower and higher part and tries to detect the lane separately. The pedestrian detection is done by Histograms of oriented gradients and this system exhibits high accuracy.


title: Designing concurrent, distributed, and real-time applications with UML

abstract: Object-oriented concepts are crucial in software design because they address fundamental issues of adaptation and evolution. With the proliferation of object-oriented notations and methods, the Unified Modeling Language (UML) has emerged to provide a standardized notation for describing object-oriented models. However, for the UML notation to be effectively applied, it needs to be used with an object-oriented analysis and design method. This tutorial describes the COMET method for designing real-time and distributed applications, which integrates object-oriented and concurrency concepts and uses the UML notation.


title: Towards Interactive Construction of Topical Hierarchy: A Recursive Tensor Decomposition Approach

abstract: Automatic construction of user-desired topical hierarchies over large volumes of text data is a highly desirable but challenging task. This study proposes to give users freedom to construct topical hierarchies via interactive operations such as expanding a branch and merging several branches. Existing hierarchical topic modeling techniques are inadequate for this purpose because (1) they cannot consistently preserve the topics when the hierarchy structure is modified; and (2) the slow inference prevents swift response to user requests. In this study, we propose a novel method, called STROD, that allows efficient and consistent modification of topic hierarchies, based on a recursive generative model and a scalable tensor decomposition inference algorithm with theoretical performance guarantee. Empirical evaluation shows that STROD reduces the runtime of construction by several orders of magnitude, while generating consistent and quality hierarchies.


Real Time 3 D Face Alignment with Random Forests-based Active Appearance Models


title: Learning Features for Offline Handwritten Signature Verification using Deep Convolutional Neural Networks

abstract: We propose formulations for learning features for Offline Signature Verification.A novel method that uses knowledge of forgeries from a subset of users is proposed.Learned features are used to train classifiers for other users (without forgeries).Experiments on GPDS-960 show a large improvement in state-of-the-art.Results in other 3 datasets show that the features generalize without fine-tuning. Verifying the identity of a person using handwritten signatures is challenging in the presence of skilled forgeries, where a forger has access to a persons signature and deliberately attempt to imitate it. In offline (static) signature verification, the dynamic information of the signature writing process is lost, and it is difficult to design good feature extractors that can distinguish genuine signatures and skilled forgeries. This reflects in a relatively poor performance, with verification errors around 7% in the best systems in the literature. To address both the difficulty of obtaining good features, as well as improve system performance, we propose learning the representations from signature images, in a Writer-Independent format, using Convolutional Neural Networks. In particular, we propose a novel formulation of the problem that includes knowledge of skilled forgeries from a subset of users in the feature learning process, that aims to capture visual cues that distinguish genuine signatures and forgeries regardless of the user. Extensive experiments were conducted on four datasets: GPDS, MCYT, CEDAR and Brazilian PUC-PR datasets. On GPDS-160, we obtained a large improvement in state-of-the-art performance, achieving 1.72% Equal Error Rate, compared to 6.97% in the literature. We also verified that the features generalize beyond the GPDS dataset, surpassing the state-of-the-art performance in the other datasets, without requiring the representation to be fine-tuned to each particular dataset.


title: Dynamic Background Learning through Deep Auto-encoder Networks

abstract: Background learning is a pre-processing of motion detection which is a basis step of video analysis. For the static background, many previous works have already achieved good performance. However, the results on learning dynamic background are still much to be improved. To address this challenge, in this paper, a novel and practical method is proposed based on deep auto-encoder networks. Firstly, dynamic background images are extracted through a deep auto-encoder network (called Background Extraction Network) from video frames containing motion objects. Then, a dynamic background model is learned by another deep auto-encoder network (called Background Learning Network) using the extracted background images as the input. To be more flexible, our background model can be updated on-line to absorb more training samples. Our main contributions are 1) a cascade of two deep auto-encoder networks which can deal with the separation of dynamic background and foregrounds very efficiently; 2) a method of online learning is adopted to accelerate the training of Background Extraction Network. Compared with previous algorithms, our approach obtains the best performance over six benchmark data sets. Especially, the experiments show that our algorithm can handle large variation background very well.


title: Static detection of malicious code in executable programs

abstract: In this paper, we propose a new approach for the static detection of malicious code in executable programs. Our approach rests on a semantic analysis based on behaviour that even makes possible the detection of unknown malicious code. This analysis is carried out directly on binary code. Static analysis offers techniques for predicting properties of the behaviour of programs without running them. The static analysis of a given binary executable is achieved in three major steps: construction of an intermediate representation, flow-based analysis that catches securityoriented program behaviour, and static verification of critical behaviours against security policies (model checking). 1. Motivation and Background With the advent and the rising popularity of networks, Internet, intranets and distributed systems, security is becoming one of the focal points of research. As a matter of fact, more and more people are concerned with malicious code that could exist in software products. A malicious code is a piece of code that can affect the secrecy, the integrity, the data and control flow, and the functionality of a system. Therefore, ∗This research is jointly funded by a research grant from the Natural Sciences and Engineering Research Council, NSERC, Canada and also by a research contract from the Defence Research Establishment, Valcartier (DREV), 2459, Pie XI Nord, Val-Belair, QC, Canada, G3J 1X5 their detection is a major concern within the computer science community as well as within the user community. As malicious code can affect the data and control flow of a program, static flow analysis may naturally be helpful as part of the detection process. In this paper, we address the problem of static detection of malicious code in binary executables. The primary objective of this research initiative is to elaborate practical methods and tools with robust theoretical foundations for the static detection of malicious code. The rest of the paper is organized in the following way. Section 2 is devoted to a comparison of static and dynamic approaches. Section 3 presents our approach to the detection of malices in binary executable code. Section 4 discusses the implementation of our approach. Finally, a few remarks and a discussion of future research are ultimately sketched as a conclusion in Section 5. 2. Static vs dynamic analysis There are two main approaches for the detection of malices : static analysis and dynamic analysis. Static analysis consists in examining the code of programs to determine properties of the dynamic execution of these programs without running them. This technique has been used extensively in the past by compiler developers to carry out various analyses and transformations aiming at optimizing the code [10]. Static analysis is also used in reverse engineering of software systems and for program understanding [3, 4]. Its use for the detection of malicious code is fairly recent. Dynamic analysis mainly consists in monitoring the execution of a program to detect malicious behaviour. Static analysis has the following advantages over dynamic analysis: • Static analysis techniques permit to make exhaustive analysis. They are not bound to a specific execution of a program and can give guarantees that apply to all executions of the program. In contrast, dynamic analysis techniques only allow examination of behaviours that correspond to selected test cases. • A verdict can be given before execution, where it may be difficult to determine the proper action to take in the presence of malices. • There is no run-time overhead. However, it may be impossible to certify statically that certain properties hold (e.g., due to undecidability). In this case, dynamic monitoring may be the only solution. Thus, static analysis and dynamic analysis are complementary. Static analysis can be used first, and properties that cannot be asserted statically can be monitored dynamically. As mentioned in the introduction, in this paper, we are concerned with static analysis techniques. Not much has been published about their use for the detection of malicious code. In [8], the authors propose a method for statically detecting malicious code in C programs. Their method is based on so-called tell-tale signs, which are program properties that allow one to distinguish between malicious and benign programs. The authors combine the tell-tale sign approach with program slicing in order to produce small fragments of large programs that can be easily analyzed. 3. Description of the Approach Static analysis techniques are generally used to operate on source code. However, as we explained in the introduction, we need to apply them to binary code, and thus, we had to adapt and evolve these techniques. Our approach is structured in three major steps: Firstly, the binary code is translated into an internal intermediate form (see Section 3.1) ; secondly, this intermediate form is abstracted through flowbased analysis as various relevant graphs (controlflow graph, data-flow graph, call graph, critical-API 1 graph, etc.) (Section 3.2); the third step is the static verification and consists in checking these graphs against security policies (Section 3.3). 3.1 Intermediate Representation A binary executable is the machine code version of a high-level or assembly program that has been compiled (or assembled) and linked for a particular platform and operating system. The general format of binary executables varies widely among operating systems. For example, the Portable Executable format (PE) is used by the Windows NT/98/95 operating system. The PE format includes comprehensive information about the different sections of the program that form the main part of the file, including the following segments: • .text, which contains the code and the entry point of the application, • .data, which contains various type of data, • .idata and .edata, which contain respectively the list of imported and exported APIs for an application or a Dynamic-Linking Library (DLL). The code segment (.text) constitutes the main part of the file; in fact, this section contains all the code that is to be analyzed. In order to translate an executable program into an equivalent high-level-language program, we use the disassembly tool IDA32 Pro [7], which can disassemble various types of executable files (ELF, EXE, PE, etc.) for several processors and operating systems (Windows 98, Windows NT, etc.). Also, IDA32 automatically recognizes calls to the standard libraries (i.e., API calls) for a long list of compilers. Statically analysing a program requires the construction of the syntax tree of this program, also called intermediate representation. The various techniques of static analysis are based on this abstract representation. The goal of the first step is to disassemble the binary code and then to parse the assembly code thus generated to produce the syntax tree (Figure 1). API: Application Program Interface.


title: An End-To-End Machine Learning Pipeline That Ensures Fairness Policies

abstract: In consequential real-world applications, machine learning (ML) based systems are expected to provide fair and non-discriminatory decisions on candidates from groups defined by protected attributes such as gender and race. These expectations are set via policies or regulations governing data usage and decision criteria (sometimes explicitly calling out decisions by automated systems). Often, the data creator, the feature engineer, the author of the algorithm and the user of the results are different entities, making the task of ensuring fairness in an end-to-end ML pipeline challenging. Manually understanding the policies and ensuring fairness in opaque ML systems is time-consuming and error-prone, thus necessitating an end-to-end system that can: 1) understand policies written in natural language, 2) alert users to policy violations during data usage, and 3) log each activity performed using the data in an immutable storage so that policy compliance or violation can be proven later. We propose such a system to ensure that data owners and users are always in compliance with fairness policies.


title: Use of Principal Component Analysis (PCA) of Remote Sensing Images in Wetland Change Detection on the Kafue Flats, Zambia

abstract: Abstract The paper describes the use of Principal Component Analysis (PCA) of remote sensing images as a method of change detection for the Kafue Flats, an inland wetland system in southern Zambia. The wetland is under human and natural pressures but is also an important wildlife habitat. A combination of Landsat MSS and TM images were used. The images used were from 24 September 1984 (MSS), 3 September 1988 (MSS), 12 September 1991 (TM) and 20 September 1994 (TM). They were geometrically co‐registered and, in the process, the 80m resolution MSS images were resampled to 30m using nearest neighbour resampling. Preliminary PCA revealed that for the MSS images most of the data variance was in near infrared reflectance while for the TM images it was in mid and thermal infrared bands. Holding sensor type constant, separate inter‐band correlation analysis for each image could indicate whether the wetland was drier or wetter on one date versus another. The 1994 image was made the reference image and equivalent g...


title: A Metric for Performance Evaluation of Multi-Target Tracking Algorithms

abstract: Performance evaluation of multi-target tracking algorithms is of great practical importance in the design, parameter optimization and comparison of tracking systems. The goal of performance evaluation is to measure the distance between two sets of tracks: the ground truth tracks and the set of estimated tracks. This paper proposes a mathematically rigorous metric for this purpose. The basis of the proposed distance measure is the recently formulated consistent metric for performance evaluation of multi-target filters, referred to as the OSPA metric. Multi-target filters sequentially estimate the number of targets and their position in the state space. The OSPA metric is therefore defined on the space of finite sets of vectors. The distinction between filtering and tracking is that tracking algorithms output tracks and a track represents a labeled temporal sequence of state estimates, associated with the same target. The metric proposed in this paper is therefore defined on the space of finite sets of tracks and incorporates the labeling error. Numerical examples demonstrate that the proposed metric behaves in a manner consistent with our expectations.


The Human Nervous System—An Anatomical Viewpoint


title: MAPS: multiresolution adaptive parameterization of surfaces

abstract: An irregular connectivity mesh representative of a surface having an arbitrary topology is processed to generate a parameterization which maps points in a coarse base domain to points in the mesh. An illustrative embodiment uses a multi-level mesh simplification process in conjunction with conformal mapping to efficiently construct a parameterization of a mesh comprising a large number of triangles over a base domain comprising a smaller number of triangles. The parameterization in this embodiment corresponds to the inverse of function mapping each point in the original mesh to one of the triangles of the base domain, such that the original mesh can be reconstructed from the base domain and the parameterization. The mapping function is generated as a combination of a number of sub-functions, each of which relates data points in a mesh of one level in a simplification hierarchy to data points in a mesh of the next coarser level of the simplification hierarchy. The parameterization can also be used to construct, from the original irregular connectivity mesh, an adaptive remesh having a regular connectivity which is substantially easier to process than the original mesh.


title: Deep Attention Recurrent Q-Network

abstract: A deep learning approach to reinforcement learning led to a general learner able to train on visual input to play a variety of arcade games at the human and superhuman levels. Its creators at the Google DeepMind's team called the approach: Deep Q-Network (DQN). We present an extension of DQN by"soft"and"hard"attention mechanisms. Tests of the proposed Deep Attention Recurrent Q-Network (DARQN) algorithm on multiple Atari 2600 games show level of performance superior to that of DQN. Moreover, built-in attention mechanisms allow a direct online monitoring of the training process by highlighting the regions of the game screen the agent is focusing on when making decisions.


title: The Performance Cost of Shadow Stacks and Stack Canaries

abstract: Control flow defenses against ROP either use strict, expensive, but strong protection against redirected RET instructions with shadow stacks, or much faster but weaker protections without. In this work we study the inherent overheads of shadow stack schemes. We find that the overhead is roughly 10% for a traditional shadow stack. We then design a new scheme, the parallel shadow stack, and show that its performance cost is significantly less: 3.5%. Our measurements suggest it will not be easy to improve performance on current x86 processors further, due to inherent costs associated with RET and memory load/store instructions. We conclude with a discussion of the design decisions in our shadow stack instrumentation, and possible lighter-weight alternatives.


title: Flexible and Fine-Grained Mandatory Access Control on Android for Diverse Security and Privacy Policies

abstract: In this paper we tackle the challenge of providing a generic security architecture for the Android OS that can serve as a flexible and effective ecosystem to instantiate different security solutions. In contrast to prior work our security architecture, termed FlaskDroid, provides mandatory access control simultaneously on both Android's middleware and kernel layers. The alignment of policy enforcement on these two layers is non-trivial due to their completely different semantics. We present an efficient policy language (inspired by SELinux) tailored to the specifics of Android's middleware semantics. We show the flexibility of our architecture by policy-driven instantiations of selected security models such as the existing work Saint as well as a new privacy-protecting, user-defined and fine-grained per-app access control model. Other possible instantiations include phone booth mode, or dual persona phone. Finally we evaluate our implementation on SE Android 4.0.4 illustrating its efficiency and effectiveness.


Assessing the Quality of Unstructured Data: An Initial Overview


title: Invited review: Reproductive and genomic technologies to optimize breeding strategies for genetic progress in dairy cattle

abstract: Dairy cattle breeders have exploited technological advances that have emerged in the past in regards to reproduction and genomics. The implementation of such technologies in routine breeding programs has permitted genetic gains in traditional milk production traits as well as, more recently, in low-heritability traits like health and fertility. As demand for dairy products increases, it is important for dairy breeders to optimize the use of available technologies and to consider the many emerging technologies that are currently being investigated in various fields. Here we review a number of technologies that have helped shape dairy breeding programs in the past and present, along with those potentially forthcoming. These tools have materialized in the areas of reproduction, genotyping and sequencing, genetic modification, and epigenetics. Although many of these technologies bring encouraging opportunities for genetic improvement of dairy cattle populations, their applications and benefits need to be weighed with their impacts on economics, genetic diversity, and society.


title: Evaluation of Kinect 3D Sensor for Healthcare Imaging

abstract: Microsoft Kinect is a three-dimensional (3D) sensor originally designed for gaming that has received growing interest as a cost-effective and safe device for healthcare imaging. Recent applications of Kinect in health monitoring, screening, rehabilitation, assistance systems, and intervention support are reviewed here. The suitability of available technologies for healthcare imaging applications is assessed. The performance of Kinect I, based on structured light technology, is compared with that of the more recent Kinect II, which uses time-of-flight measurement, under conditions relevant to healthcare applications. The accuracy, precision, and resolution of 3D images generated with Kinect I and Kinect II are evaluated using flat cardboard models representing different skin colors (pale, medium, and dark) at distances ranging from 0.5 to 1.2 m and measurement angles of up to 75°. Both sensors demonstrated high accuracy (majority of measurements <2 mm) and precision (mean point to plane error <2 mm) at an average resolution of at least 390 points per cm2. Kinect I is capable of imaging at shorter measurement distances, but Kinect II enables structures angled at over 60° to be evaluated. Kinect II showed significantly higher precision and Kinect I showed significantly higher resolution (both p < 0.001). The choice of object color can influence measurement range and precision. Although Kinect is not a medical imaging device, both sensor generations show performance adequate for a range of healthcare imaging applications. Kinect I is more appropriate for short-range imaging and Kinect II is more appropriate for imaging highly curved surfaces such as the face or breast.


title: Deep Keyframe Detection in Human Action Videos

abstract: Detecting representative frames in videos based on human actions is quite challenging because of the combined factors of human pose in action and the background. This paper addresses this problem and formulates the key frame detection as one of finding the video frames that optimally maximally contribute to differentiating the underlying action category from all other categories. To this end, we introduce a deep two-stream ConvNet for key frame detection in videos that learns to directly predict the location of key frames. Our key idea is to automatically generate labeled data for the CNN learning using a supervised linear discriminant method. While the training data is generated taking many different human action videos into account, the trained CNN can predict the importance of frames from a single video. We specify a new ConvNet framework, consisting of a summarizer and discriminator. The summarizer is a two-stream ConvNet aimed at, first, capturing the appearance and motion features of video frames, and then encoding the obtained appearance and motion features for video representation. The discriminator is a fitting function aimed at distinguishing between the key frames and others in the video. We conduct experiments on a challenging human action dataset UCF101 and show that our method can detect key frames with high accuracy.


Final report : Dynamic Asset Allocation Using Reinforcement Learning


title: Aoristic analysis: the spatial interpretation of unspecific temporal events

abstract: Temporal limitations of GIS databases are never more apparent than when the time of a change to any spatial object is unknown. This paper examines an unusual type of spatiotemporal imprecision where an event occurs at a known location but at an unknown time. Aoristic analysis can provide a temporal weight and give an indication of the probability that the event occurred within a defined period. Visualisation of temporal weights can be enhanced by modifications to existing surface generation algorithms and a temporal intensity surface can be created. An example from burglaries in Central Nottingham (UK) shows that aoristic analysis can smooth irregularities arising from poor database interrogation, and provide an alternative conceptualisation of space and time that is both comprehensible and meaningful.


title: Residual Gated Graph ConvNets

abstract: Graph-structured data such as social networks, functional brain networks, gene regulatory networks, communications networks have brought the interest in generalizing deep learning techniques to graph domains. In this paper, we are interested to design neural networks for graphs with variable length in order to solve learning problems such as vertex classification, graph classification, graph regression, and graph generative tasks. Most existing works have focused on recurrent neural networks (RNNs) to learn meaningful representations of graphs, and more recently new convolutional neural networks (ConvNets) have been introduced. In this work, we want to compare rigorously these two fundamental families of architectures to solve graph learning tasks. We review existing graph RNN and ConvNet architectures, and propose natural extension of LSTM and ConvNet to graphs with arbitrary size. Then, we design a set of analytically controlled experiments on two basic graph problems, i.e. subgraph matching and graph clustering, to test the different architectures. Numerical results show that the proposed graph ConvNets are 3-17% more accurate and 1.5-4x faster than graph RNNs. Graph ConvNets are also 36% more accurate than variational (non-learning) techniques. Finally, the most effective graph ConvNet architecture uses gated edges and residuality. Residuality plays an essential role to learn multi-layer architectures as they provide a 10% gain of performance.


title: An Architecture for the Acceleration of a Hybrid Leaky Integrate and Fire SNN on the Convey HC-2ex FPGA-Based Processor

abstract: Neuromorphic computing is expanding by leaps and bounds through custom integrated circuits (digital and analog), and large scale platforms developed by industry or government-funded projects (e.g. TrueNorth and BrainScaleS, respectively). Whereas the trend is for massive parallelism and neuromorphic computation in order to solve problems, such as those that may appear in machine learning and deep learning algorithms, there is substantial work on brain-like highly accurate neuromorphic computing in order to model the human brain. In such a form of computing, spiking neural networks (SNN) such as the Hodgkin and Huxley model are mapped to various technologies, including FPGAs. In this work, we present a highly efficient FPGA-based architecture for the detailed hybrid Leaky Integrate and Fire SNN that can simulate generic characteristics of neurons of the cerebral cortex. This architecture supports arbitrary, sparse O(n2) interconnection of neurons without need to re-compile the design, and plasticity rules, yielding on a four-FPGA Convey 2ex hybrid computer a speedup of 923x for a non-trivial data set on 240 neurons vs. the same model in the software simulator BRAIN on a Intel(R) Xeon(R) CPU E5-2620 v2 @ 2.10GHz, i.e. the reference state-of-the-art software. Although the reference, official software is single core, the speedup demonstrates that the application scales well among multiple FPGAs, whereas this would not be the case in general-purpose computers due to the arbitrary interconnect requirements. The FPGA-based approach leads to highly detailed models of parts of the human brain up to a few hundred neurons vs. a dozen or fewer neurons on the reference system.


title: A Software Approach to Defeating Side Channels in Last-Level Caches

abstract: We present a software approach to mitigate access-driven side-channel attacks that leverage last-level caches (LLCs) shared across cores to leak information between security domains (e.g., tenants in a cloud). Our approach dynamically manages physical memory pages shared between security domains to disable sharing of LLC lines, thus preventing "Flush-Reload" side channels via LLCs. It also manages cacheability of memory pages to thwart cross-tenant "Prime-Probe" attacks in LLCs. We have implemented our approach as a memory management subsystem called CacheBar within the Linux kernel to intervene on such side channels across container boundaries, as containers are a common method for enforcing tenant isolation in Platform-as-a-Service (PaaS) clouds. Through formal verification, principled analysis, and empirical evaluation, we show that CacheBar achieves strong security with small performance overheads for PaaS workloads.


title: Estimating Fingerprint Deformation

abstract: Fingerprint matching is affected by the nonlinear distortion introduced in fingerprint impressions during the image acquisition process. This nonlinear deformation causes fingerprint features such as minutiae points and ridge curves to be distorted in a complex manner. In this paper we develop an average deformation model for a fingerprint impression (baseline impression) by observing its relative distortion with respect to several other impressions of the same finger. The deformation is computed using a Thin Plate Spline (TPS) model that relies on ridge curve correspondences between image pairs. The estimated average deformation is used to distort the minutiae template of the baseline impression prior to matching. An index of deformation has been proposed to select the average deformation model with the least variability corresponding to a finger. Preliminary results indicate that the average deformation model can improve the matching performance of a fingerprint matcher.


title: Efficient Class-E design for inductive powering wireless biotelemetry applications

abstract: This paper introduces a modified an efficient design for different Class-E power amplifiers suitable to be use in biomedical implantable devices and in biomedical wireless telemetry devices which is usually have a small load resistor (several Ohms) due to its inductive powering. The different Class-E amplifiers operated according to the frequency chart for the medical implant communications service (MICS) 27 MH Z , and industrial scientific and medical (ISM) (13.56 MH Z ), and low frequency 125–135 KH Z . The results show that the proposed class-E design could use the same values of C 1 , C 2 , L 2 for small and optimum load resistor with high efficient more than 80% All these three class-E power amplifiers will be presents in this work and will be simulate with the MATLAB/SIMULINK environment, and for further verification all the circuits will be simulated with electronics workbench MULTISIM 9.


Friction stir welding (FSW) process of copper alloys


title: The Language of Toys: Gendered Language in Toy Advertisements

abstract: Children are socialized to conform to traditional gender role stereotypes, a developmental process affected by elements of popular culture that associate femininity with passivity, nurturance, and emotional expressivity; and masculinity with power, agency, and aggression. Toys, as one element of popular culture, often reflect these stereotypes through manipulations of design features that signify the gender appropriateness of toy play. Prior research on toys has documented the gender-differentiated use of such overt markers as colors, names, and logos in toy marketing. However, subtler gender markers such as the language used to advertise toys has received little research attention. This is surprising given the evidence of distinctive language styles adopted by females and males that are considered to reflect traditional gender role stereotypes. In this study, the narrative language that accompanied action figures marketed for girls, and action figures marketed for boys was analyzed for the presence of gendered language. It was hypothesized that lexical elements associated with traditional gender role stereotypes would differentiate the narratives as a function of gender. Results showed that female action figure narratives contained more intensive adverbs, more social words, and more adjectival references to physical appearance, fantasy, and triviality, supporting stereotypes of females as emotional, social, and uninvolved with real world concerns. Male narratives contained more second-person plural pronouns, more aggression words, and more adjectival references to power, destructive action, and science and technology, supporting stereotypic masculinity associations to power, aggression, action, and involvement with real-life endeavors. Discussion focuses on potential adverse consequences to children’s psychosocial development that is posed by exposure to gender-polarized toys.


Importance of IoT for Smart Agriculture


title: Urban Tracker: Multiple object tracking in urban mixed traffic

abstract: In this paper, we study the problem of detecting and tracking multiple objects of various types in outdoor urban traffic scenes. This problem is especially challenging due to the large variation of road user appearances. To handle that variation, our system uses background subtraction to detect moving objects. In order to build the object tracks, an object model is built and updated through time inside a state machine using feature points and spatial information. When an occlusion occurs between multiple objects, the positions of feature points at previous observations are used to estimate the positions and sizes of the individual occluded objects. Our Urban Tracker algorithm is validated on four outdoor urban videos involving mixed traffic that includes pedestrians, cars, large vehicles, etc. Our method compares favorably to a current state of the art feature-based tracker for urban traffic scenes on pedestrians and mixed traffic.


title: Epidemiology of Parkinson's disease

abstract: The causes of Parkinson's disease (PD), the second most common neurodegenerative disorder, are still largely unknown. Current thinking is that major gene mutations cause only a small proportion of all cases and that in most cases, non-genetic factors play a part, probably in interaction with susceptibility genes. Numerous epidemiological studies have been done to identify such non-genetic risk factors, but most were small and methodologically limited. Larger, well-designed prospective cohort studies have only recently reached a stage at which they have enough incident patients and person-years of follow-up to investigate possible risk factors and their interactions. In this article, we review what is known about the prevalence, incidence, risk factors, and prognosis of PD from epidemiological studies.


title: LightGBM: An Effective miRNA Classification Method in Breast Cancer Patients

abstract: miRNAs are small noncoding RNA molecules, mainly responsible for post-transcriptional control of gene expressions. Machine learning is becoming more and more widely used in breast tumor classification and diagnosis. In this paper, we compared the performance of different machine learning methods, such as Random Forest (RF), eXtreme Gradient Boosting(XGBoost) and Light Gradient Boosting Machine(LightGBM), for miRNAs identification in breast cancer patients. The performance comparison of each algorithm was evaluated based on the accuracy and logistic loss and where LightGBM was found better performing in several aspects. hsa-mir-139 was found as an important target for the breast cancer classification. As a powerful tool, LightGBM can be used to identify and classify miRNA target in breast cancer.


BinaryNet: Training Deep Neural Networks with Weights and Activations Constrained to +1 or -1


title: Distributed prefetch-buffer/cache design for high performance memory systems

abstract: Microprocessor execution speeds are improving at a rate of 50%-80% per year while DRAM access times are improving at a much lower rate of 5%-10% per year. Computer systems are rapidly approaching the point at which overall system performance is determined not by the speed of the CPU but by the memory system speed. We present a high performance memory system architecture that overcomes the growing speed disparity between high performance microprocessors and current generation DRAMs. A novel prediction and prefetching technique is combined with a distributed cache architecture to build a high performance memory system. We use a table based prediction scheme with a prediction cache to prefetch data from the on-chip DRAM array to an on-chip SRAM prefetch buffer. By prefetching data we are able to hide the large latency associated with DRAM access and cycle times. Our experiments show that with a small (32 KB) prediction cache we can get an effective main memory access time that is close to the access time of larger secondary caches.


title: Paleo: A Performance Model for Deep Neural Networks

abstract: Although various scalable deep learning software packages have been proposed, it remains unclear how to best leverage parallel and distributed computing infrastructure to accelerate their training and deployment. Moreover, the effectiveness of existing parallel and distributed systems varies widely based on the neural network architecture and dataset under consideration. In order to efficiently explore the space of scalable deep learning systems and quickly diagnose their effectiveness for a given problem instance, we introduce an analytical performance model called Paleo. Our key observation is that a neural network architecture carries with it a declarative specification of the computational requirements associated with its training and evaluation. By extracting these requirements from a given architecture and mapping them to a specific point within the design space of software, hardware and communication strategies, Paleo can efficiently and accurately model the expected scalability and performance of a putative deep learning system. We show that Paleo is robust to the choice of network architecture, hardware, software, communication schemes, and parallelization strategies. We further demonstrate its ability to accurately model various recently published scalability results for CNNs such as NiN, Inception and AlexNet.


title: 3D reconstruction of soccer sequences using non-calibrated video cameras

abstract: We present a global approach that enables the production of 3D soccer sequences from non-calibrated video cameras. Our system can produce a 3D animated model of the scene from a single non-calibrated moving camera (a TV sequence for example). The results presented here are very encouraging even with a single camera approach and will probably improve with the future introduction of multiple images that will help resolving occlusion issues and integrating into a single model information coming from various locations on the field. The key point of our approach is that it doesn't need any camera calibration and it still works when the camera parameters vary along the process. Details on the registration and tracking processes are given as well as the description of the "Virtual Reality" system used for displaying the resulting animated model.


title: Psychological Aspects of Natural Language Use: Our Words, Our Selves

abstract: The words people use in their daily lives can reveal important aspects of their social and psychological worlds. With advances in computer technology, text analysis allows researchers to reliably and quickly assess features of what people say as well as subtleties in their linguistic styles. Following a brief review of several text analysis programs, we summarize some of the evidence that links natural word use to personality, social and situational fluctuations, and psychological interventions. Of particular interest are findings that point to the psychological value of studying particles—parts of speech that include pronouns, articles, prepositions, conjunctives, and auxiliary verbs. Particles, which serve as the glue that holds nouns and regular verbs together, can serve as markers of emotional state, social identity, and cognitive styles.


title: The Analysis of Emotion Recognition from GSR Based on PSO

abstract: A method for recognizing the emotion states of subjects based on 30 features extracted from their Galvanic Skin Response (GSR) signals was proposed. GSR signals were acquired by means of experiments attended by those subjects. Next the data was normalized with the calm signal of the same subject after being de-noised. Then the normalized data were extracted features before the step of feature selection. Immune Hybrid Particle Swarm Optimization (IH-PSO) was proposed to select the feature subsets of different emotions. Classifier for feature selection was evaluated on the correct recognition as well as number of the selected features. At last, this paper verified the effectiveness of the feature subsets selected with another new data. All performed in this paper illustrate that IH-PSO can achieve much effective results, and further more, demonstrate that there is significant emotion information in GSR signal.


title: Learning to Coordinate with Deep Reinforcement Learning in Doubles Pong Game

abstract: This paper discusses the emergence of cooperative and coordinated behaviors between joint and concurrent learning agents using deep Q-learning. Multi-agent systems (MAS) arise in a variety of domains. The collective effort is one of the main building blocks of many fundamental systems that exist in the world, and thus, sequential decision making under uncertainty for collaborative work is one of the important and challenging issues for intelligent cooperative multiple agents. However, the decisions for cooperation are highly sophisticated and complicated because agents may have a certain shared goal or individual goals to achieve and their behavior is inevitably influenced by each other. Therefore, we attempt to explore whether agents using deep Q-networks (DQN) can learn cooperative behavior. We use doubles pong game as an example and we investigate how they learn to divide their works through iterated game executions. In our approach, agents jointly learn to divide their area of responsibility and each agent uses its own DQN to modify its behavior. We also investigate how learned behavior changes according to environmental characteristics including reward schemes and learning techniques. Our experiments indicate that effective cooperative behaviors with balanced division of workload emerge. These results help us to better understand how agents behave and interact with each other in complex environments and how they coherently choose their individual actions such that the resulting joint actions are optimal.


title: Towards Understanding Human Similarity Perception in the Analysis of Large Sets of Scatter Plots

abstract: We present a study aimed at understanding how human observers judge scatter plot similarity when presented with a large set of iconic scatter plot representations. The work we present involves 18 participants with a scientific background in a similarity perception study. The study asks participants to group a carefully selected set of plots according to their subjective perceptual judgement of similarity, and it integrates the results into a consensus similarity grouping. We then use this consensus grouping to generate insights on similarity perception. The main output of this work is a list of concepts we derive to describe major perceptual features, and a description of how these concepts relate and rank. We also evaluate scagnostics (scatter plot diagnostics), a popular and established set of scatter plot descriptors, and show that they do not reliably reproduce our participants judgements. Finally, we discuss the major implications of this study and how these results can be used for future research.


title: Ask, Acquire, and Attack: Data-free UAP Generation using Class Impressions

abstract: Deep learning models are susceptible to input specific noise, called adversarial perturbations. Moreover, there exist input-agnostic noise, called Universal Adversarial Perturbations (UAP) that can affect inference of the models over most input samples. Given a model, there exist broadly two approaches to craft UAPs: (i) data-driven: that require data, and (ii) data-free: that do not require data samples. Data-driven approaches require actual samples from the underlying data distribution and craft UAPs with high success (fooling) rate. However, data-free approaches craft UAPs without utilizing any data samples and therefore result in lesser success rates. In this paper, for data-free scenarios, we propose a novel approach that emulates the effect of data samples with class impressions in order to craft UAPs using data-driven objectives. Class impression for a given pair of category and model is a generic representation (in the input space) of the samples belonging to that category. Further, we present a neural network based generative model that utilizes the acquired class impressions to learn crafting UAPs. Experimental evaluation demonstrates that the learned generative model, (i) readily crafts UAPs via simple feed-forwarding through neural network layers, and (ii) achieves state-of-the-art success rates for data-free scenario and closer to that for data-driven setting without actually utilizing any data samples.


title: Recent Advances of Large-Scale Linear Classification

abstract: Linear classification is a useful tool in machine learning and data mining. For some data in a rich dimensional space, the performance (i.e., testing accuracy) of linear classifiers has shown to be close to that of nonlinear classifiers such as kernel methods, but training and testing speed is much faster. Recently, many research works have developed efficient optimization methods to construct linear classifiers and applied them to some large-scale applications. In this paper, we give a comprehensive survey on the recent development of this active research area.


BPD, Not BPD, or Iatrogenic BPD: Findings of Lung Ultrasound Examinations


title: A micro wireless remotely-operated vehicle

abstract: For decades remotely-operated vehicles (ROVs) have been designed and implemented in various size and shape. Among them, very small-sized ROV which is called micro ROV has the working range with tens of meters and takes the place of divers. In this paper, we develop a micro ROV operating not by umbilical cable which is troublesome for movements but by wireless acoustic communication. The micro wireless ROV is designed to conduct the commands of movement and measurement from a remote user and to report the result. Experiments in water tanks have verified functional operations of the vehicle and shown the feasibility for applications.


title: Engineering three-dimensional bone tissue in vitro using biodegradable scaffolds: Investigating initial cell-seeding density and culture period

abstract: New three-dimensional (3D) scaffolds for bone tissue engineering have been developed throughout which bone cells grow, differentiate, and produce mineralized matrix. In this study, the percentage of cells anchoring to our polymer scaffolds as a function of initial cell seeding density was established; we then investigated bone tissue formation throughout our scaffolds as a function of initial cell seeding density and time in culture. Initial cell seeding densities ranging from 0.5 to 10 × 106 cells/cm3 were seeded onto 3D scaffolds. After 1 h in culture, we determined that 25% of initial seeded cells had adhered to the scaffolds in static culture conditions. The cell-seeded scaffolds remained in culture for 3 and 6 weeks, to investigate the effect of initial cell seeding density on bone tissue formation in vitro. Further cultures using 1 × 106 cells/cm3 were maintained for 1 h and 1, 2, 4, and 6 weeks to study bone tissue formation as a function of culture period. After 3 and 6 weeks in culture, scaffolds seeded with 1 × 106 cells/cm3 showed similar tissue formation as those seeded with higher initial cell seeding densities. When initial cell seeding densities of 1 × 106 cells/cm3 were used, osteocalcin immunolabeling indicative of osteoblast differentiation was seen throughout the scaffolds after only 2 weeks of culture. Von Kossa and tetracycline labeling, indicative of mineralization, occurred after 3 weeks. These results demonstrated that differentiated bone tissue was formed throughout 3D scaffolds after 2 weeks in culture using an optimized initial cell density, whereas mineralization of the tissue only occurred after 3 weeks. Furthermore, after 6 weeks in culture, newly formed bone tissue had replaced degrading polymer. © 2000 John Wiley & Sons, Inc. J Biomed Mater Res, 51, 376–382, 2000.


title: The impact of eye gaze on communication using humanoid avatars

abstract: In this paper we describe an experiment designed to investigate the importance of eye gaze in humanoid avatar's representing people engaged in conversation. We compare responses to dyadic conversations in four mediated conditions: video, audio-only, and two avatar conditions. The avatar conditions differed only in their treatment of eye gaze. In the random-gaze condition the avatars head and eye animations were unrelated to conversational flow. In the informed-gaze condition, they were related to turn-taking during the conversation. The head animations were tracked and the eye animations were inferred from the audio stream. Our comparative analysis of 100 post-experiment questionnaires showed that the random-gaze avatar did not improve on audio-only communication. The informed-gaze avatar significantly outperformed the random-gaze model and also outperformed audio-only on several response measures. We conclude that an avatar whose gaze behaviour is related to the conversation provides a marked improvement on an avatar that merely exhibits liveliness.


Reviews and Descriptions of Tables and Books


Receptive fields of single neurones in the cat's striate cortex


title: Generalized TM Dual-Mode Cavity Filters

abstract: A new cavity arrangement, namely, the generalized TM dual-mode cavity, is presented in this paper. In contrast with the previous contributions on TM dual-mode filters, the generalized TM dual-mode cavity allows the realization of both symmetric and asymmetric filtering functions, simultaneously exploiting the maximum number of finite frequency transmission zeros. The high design flexibility in terms of number and position of transmission zeros is obtained by exciting and exploiting a set of nonresonating modes. Five structure parameters are used to fully control its equivalent transversal topology. The relationship between structure parameters and filtering function realized is extensively discussed. The design of multiple cavity filters is presented along with the experimental results of a sixth-order filter having six asymmetrically located transmission zeros.


title: Design of vital sign monitor based on wireless sensor networks and telemedicine technology

abstract: Heart attack is a term generally used for medical conditions like Acute Myocardial Infarction, Cardiac Arrest, and Atrial Fibrillation. Before the onset of such an abnormal condition, there are symptoms which generally we avoid to care. Even after the attack the patient needs continuous monitoring and care. But all this needs to be at a very affordable cost, thus developing a Quality, Secured and Cost effective health monitoring system becomes the need of the hour. The Proposed Vital Sign Monitor is based on a wireless sensor network and telemedicine technology. This system is designed to be a mini patient monitoring system which measures the vital parameters like ECG, Heart Rate and Respiration Rate. The system uses Bluetooth technology which is embedded with the sensor for faster transmission. The transmitter unit is designed to be an Android based smart phone through an application that connects to wireless networks using the best source of connectivity which is either a 3G network or IEEE 802.11 i.e. Wi-Fi based transmission. The proposed system implements the IEEE 1073 standard for medical device communication. The remote system consists of a cloud server where the centralized monitoring takes place. The expert in the remote center can view all the patient data and at the same time can witness full duplex real time streaming video if required, so that the expert can take a confident call. If required the expert can ask the individual in the spot to take indispensable action and in critical cases can accelerate an ambulance for attending the emergency case. The power utilized by the entire system is low due to the intelligent algorithms that control the system. The principles of green computing and signal processing applied in the system help in the development of a green smart system. The vital sign monitor designed can improve the reach and affordability of healthcare in developing countries affected by various socioeconomic crises.


title: Adaptive Task Assignment for Crowdsourced Classification

abstract: Crowdsourcing markets have gained popularity as a tool for inexpensively collecting data from diverse populations of workers. Classification tasks, in which workers provide labels (such as "offensive" or "not offensive") for instances (such as "websites"), are among the most common tasks posted, but due to human error and the prevalence of spam, the labels collected are often noisy. This problem is typically addressed by collecting labels for each instance from multiple workers and combining them in a clever way, but the question of how to choose which tasks to assign to each worker is often overlooked. We investigate the problem of task assignment and label inference for heterogeneous classification tasks. By applying online primal-dual techniques, we derive a provably near-optimal adaptive assignment algorithm. We show that adaptively assigning workers to tasks can lead to more accurate predictions at a lower cost when the available workers are diverse.


title: Automatic recognition of elephant infrasound calls using formant analysis and Hidden Markov Model

abstract: A method is proposed for automatic recognition of elephant infrasound calls. It uses Linear Predictive Coding (LPC) for extracting the formants of the elephant rumble. A novel feature extraction technique is proposed to capture the unique features of elephant rumbles. The features are then processed using a two state Hidden Markov Model (HMM) to automatically detect the presence of elephants. The method is tested on pure elephant rumbles as well as field recorded signals containing environmental noise. The results show that the proposed method can be used to successfully detect the presence of elephants even under noisy conditions.


title: Access control, confidentiality and privacy for video surveillance databases

abstract: In this paper we have addressed confidentiality and privacy for video surveillance databases. First we discussed our overall approach for suspicious event detection. Next we discussed an access control model and accedes control algorithms for confidentiality. Finally we discuss privacy preserving video surveillance. Our goal is build a comprehensive system that can detect suspicious events, ensure confidentiality as well as privacy.


title: Robust Pose Estimation from a Planar Target

abstract: In theory, the pose of a calibrated camera can be uniquely determined from a minimum of four coplanar but noncollinear points. In practice, there are many applications of camera pose tracking from planar targets and there is also a number of recent pose estimation algorithms which perform this task in real-time, but all of these algorithms suffer from pose ambiguities. This paper investigates the pose ambiguity for planar targets viewed by a perspective camera. We show that pose ambiguities - two distinct local minima of the according error function - exist even for cases with wide angle lenses and close range targets. We give a comprehensive interpretation of the two minima and derive an analytical solution that locates the second minimum. Based on this solution, we develop a new algorithm for unique and robust pose estimation from a planar target. In the experimental evaluation, this algorithm outperforms four state-of-the-art pose estimation algorithms


title: Developing Intuition: Neural Correlates of Cognitive-Skill Learning in Caudate Nucleus

abstract: The superior capability of cognitive experts largely depends on automatic, quick information processing, which is often referred to as intuition. Intuition develops following extensive long-term training. There are many cognitive models on intuition development, but its neural basis is not known. Here we trained novices for 15 weeks to learn a simple board game and measured their brain activities in early and end phases of the training while they quickly generated the best next-move to a given board pattern. We found that the activation in the head of caudate nucleus developed over the course of training, in parallel to the development of the capability to quickly generate the best next-move, and the magnitude of the caudate activity was correlated with the subject9s performance. In contrast, cortical activations, which already appeared in the early phase of training, did not further change. Thus, neural activation in the caudate head, but not those in cortical areas, tracked the development of capability to quickly generate the best next-move, indicating that circuitries including the caudate head may automate cognitive computations.


title: Hybrid Optimization Algorithm for Large-Scale QoS-Aware Service Composition

abstract: In this paper we present a hybrid approach for automatic composition of web services that generates semantic input-output based compositions with optimal end-to-end QoS, minimizing the number of services of the resulting composition. The proposed approach has four main steps: 1) generation of the composition graph for a request; 2) computation of the optimal composition that minimizes a single objective QoS function; 3) multi-step optimizations to reduce the search space by identifying equivalent and dominated services; and 4) hybrid local-global search to extract the optimal QoS with the minimum number of services. An extensive validation with the datasets of the Web Service Challenge 2009-2010 and randomly generated datasets shows that: 1) the combination of local and global optimization is a general and powerful technique to extract optimal compositions in diverse scenarios; and 2) the hybrid strategy performs better than the state-of-the-art, obtaining solutions with less services and optimal QoS.


Learning Systems for Manufacturing Management Support


title: Efficient Filtering of XML Documents for Selective Dissemination of Information

abstract: Information Dissemination applications are gaining increasing popularity due to dramatic improvements in communications bandwidth and ubiquity. The sheer volume of data available necessitates the use of selective approaches to dissemination in order to avoid overwhelming users with unnecessaryinformation. Existing mechanisms for selective dissemination typically rely on simple keyword matching or “bag of words” information retrieval techniques. The advent of XML as a standard for information exchangeand the development of query languages for XML data enables the development of more sophisticated filtering mechanisms that take structure information into account. We have developed several index organizations and search algorithms for performing efficient filtering of XML documents for large-scale information dissemination systems. In this paper we describe these techniques and examine their performance across a range of document, workload, and scale scenarios.


title: An Optimal Online Method of Selecting Source Policies for Reinforcement Learning

abstract: Transfer learning significantly accelerates the reinforcement learning process by exploiting relevant knowledge from previous experiences. The problem of optimally selecting source policies during the learning process is of great importance yet challenging. There has been little theoretical analysis of this problem. In this paper, we develop an optimal online method to select source policies for reinforcement learning. This method formulates online source policy selection as a multi-armed bandit problem and augments Q-learning with policy reuse. We provide theoretical guarantees of the optimal selection process and convergence to the optimal policy. In addition, we conduct experiments on a grid-based robot navigation domain to demonstrate its efficiency and robustness by comparing to the state-of-the-art transfer learning method.


title: Sentiment Classification on Polarity Reviews: An Empirical Study Using Rating-based Features

abstract: We present a new feature type named rating-based feature and evaluate the contribution of this feature to the task of document-level sentiment analysis. We achieve state-of-the-art results on two publicly available standard polarity movie datasets: on the dataset consisting of 2000 reviews produced by Pang and Lee (2004) we obtain an accuracy of 91.6% while it is 89.87% evaluated on the dataset of 50000 reviews created by Maas et al. (2011). We also get a performance at 93.24% on our own dataset consisting of 233600 movie reviews, and we aim to share this dataset for further research in sentiment polarity analysis task.


title: String Matching and 1d Lattice Gases

abstract: We calculate the probability distribution for the number of occurrences n of a given l letter word x inside a random string of k letters, whose letters have been generated by a known stationary stochastic process. Denoting by p(x) the probability of occurrence of the word, it is well-known that the distribution of occurrences in the asymptotic regime k → ∞ such that kp(x) ≫ 1 is Gaussian, while in the limit k→ ∞, and p(x) → 0 , such that kp(x) is finite, the distribution is Compound Poisson. It is also known that these limiting forms do not work well in the intermediate regime when kp(x) >rsim 1 and k is finite. We show that the problem of calculating the probability of occurrences is equivalent to determining the configurational partition function of a 1d lattice gas of interacting particles, with the probability distribution given by the n-particle terms of the grand-partition function and the number of particles corresponding to the number of occurrences on the string. Utilizing this equivalence, we obtain the probability distribution from the equation of state of the lattice gas. Our result reproduces rather well the behavior of the distribution in the asymptotic as well as the intermediate regimes. Within the lattice gas description, the asymptotic forms of the distribution naturally emerge as certain low density approximations. Thus our approach which is based on statistical mechanics, also provides an alternative to the usual statistics based treatments employing the central limit and Chen–Stein theorems.


What Is a Good Linear Finite Element? - Interpolation, Conditioning, Anisotropy, and Quality Measures


title: Long-term outcomes of Ross and Ross–Konno operations in patients under 15 years of age

abstract: Objective ::: The Ross operation is an excellent surgical option for young children, regardless of aortic pathology type. However, failure rates are concerning during the second postoperative decade. We sought to determine the predictors of long-term outcomes after Ross and Ross–Konno operation performed in childhood.


title: Dict2vec : Learning Word Embeddings using Lexical Dictionaries

abstract: Learning word embeddings on large unla-beled corpus has been shown to be successful in improving many natural language tasks. The most efficient and popular approaches learn or retrofit such representations using additional external data. Resulting embeddings are generally better than their corpus-only counterparts, although such resources cover a fraction of words in the vocabulary. In this paper, we propose a new approach, Dict2vec, based on one of the largest yet refined datasource for describing words – natural language dictionaries. Dict2vec builds new word pairs from dictionary entries so that semantically-related words are moved closer, and negative sampling filters out pairs whose words are unrelated in dictionaries. We evaluate the word representations obtained using Dict2vec on eleven datasets for the word similarity task and on four datasets for a text classification task.


title: Preconditioning with ischemia: a delay of lethal cell injury in ischemic myocardium

abstract: We have previously shown that a brief episode of ischemia slows the rate of ATP depletion during subsequent ischemic episodes. Additionally, intermittent reperfusion may be beneficial to the myocardium by washing out catabolites that have accumulated during ischemia. Thus, we proposed that multiple brief ischemic episodes might actually protect the heart from a subsequent sustained ischemic insult. To test this hypothesis, two sets of experiments were performed. In the first set, one group of dogs (n = 7) was preconditioned with four 5 min circumflex occlusions, each separated by 5 min of reperfusion, followed by a sustained 40 min occlusion. The control group (n = 5) received a single 40 min occlusion. In the second study, an identical preconditioning protocol was followed, and animals (n = 9) then received a sustained 3 hr occlusion. Control animals (n = 7) received a single 3 hr occlusion. Animals were allowed 4 days of reperfusion thereafter. Histologic infarct size then was measured and was related t...


title: Polyglot Neural Language Models: A Case Study in Cross-Lingual Phonetic Representation Learning

abstract: We introduce polyglot language models, recurrent neural network models trained to predict symbol sequences in many different languages using shared representations of symbols and conditioning on typological information about the language to be predicted. We apply these to the problem of modeling phone sequences---a domain in which universal symbol inventories and cross-linguistically shared feature representations are a natural fit. Intrinsic evaluation on held-out perplexity, qualitative analysis of the learned representations, and extrinsic evaluation in two downstream applications that make use of phonetic features show (i) that polyglot models better generalize to held-out data than comparable monolingual models and (ii) that polyglot phonetic feature representations are of higher quality than those learned monolingually.


title: Solving TSP Using Various Meta-Heuristic Algorithms

abstract: Real world problems like Travelling Salesman Problem (TSP) belong to NP-hard optimization problems which are difficult to solve using classical mathematical methods. Therefore, many alternate solutions have been developed to find the optimal solution in shortest possible time. Nature-inspired algorithms are one of the proposed solutions which are successful in finding the solutions that are very near to the optimal. In this paper, Classical TSP (CTSP) along with its variant Random TSP (RTSP) are solved using various meta-heuristic algorithms and their performance is compared on the basis of tour length. Results show that the Nature-inspired algorithms outperform both Traditional and Evolutionary algorithms and obtain optimal solutions.


title: High performance 32nm logic technology featuring 2nd generation high-k + metal gate transistors

abstract: A 32nm logic technology for high performance microprocessors is described. 2nd generation high-k + metal gate transistors provide record drive currents at the tightest gate pitch reported for any 32nm or 28nm logic technology. NMOS drive currents are 1.62mA/um Idsat and 0.231mA/um Idlin at 1.0V and 100nA/um I off . PMOS drive currents are 1.37mA/um Idsat and 0.240mA/um Idlin at 1.0V and 100nA/um I off . The impact of SRAM cell and array size on Vccmin is reported.


Groundwater management in the Urban Area of Hanover, Germany


title: Personalized Security Indicators to Detect Application Phishing Attacks in Mobile Platforms

abstract: Phishing in mobile applications is a relevant threat with successful attacks reported in the wild. In such attacks, malicious mobile applications masquerade as legitimate ones to steal user credentials. In this paper we categorize application phishing attacks in mobile platforms and possible countermeasures. We show that personalized security indicators can help users to detect phishing attacks and have very little deployment cost. Personalized security indicators, however, rely on the user alertness to detect phishing attacks. Previous work in the context of website phishing has shown that users tend to ignore the absence of security indicators and fall victim of the attacker. Consequently, the research community has deemed personalized security indicators as an ineffective phishing detection mechanism. ::: We evaluate personalized security indicators as a phishing detection solution in the context of mobile applications. We conducted a large-scale user study where a significant amount of participants that used personalized security indicators were able to detect phishing. All participants that did not use indicators could not detect the attack and entered their credentials to a phishing application. We found the difference in the attack detection ratio to be statistically significant. Personalized security indicators can, therefore, help phishing detection in mobile applications and their reputation as an anti-phishing mechanism should be reconsidered. ::: We also propose a novel protocol to setup personalized security indicators under a strong adversarial model and provide details on its performance and usability.


title: Analysis of personal information security behavior and awareness

abstract: We analyze the personal information security behavior and awareness of 881 IS users.We propose four scales to measure how risky individuals' behavior is when using IS.The more the respondents perceive threats, their behavior becomes more protective.Students, compared to other groups, are more vulnerable against risks.The education level and information security awareness are positively correlated. Hardware and software security mechanisms are widely used to strengthen information systems (IS) against attacks. However, these systems are still highly vulnerable to threats from users' undesirable behaviors, which are closely related to IS users' information security awareness. This study has been carried out in an effort to investigate the IS users' risky behaviors that may threaten information security. The preventive actions employed by users, the threats they may be exposed to, or whether they had an adverse experience or to what extent they perceive risks have also been investigated. Four scales: Risky Behavior Scale (RBS), Conservative Behavior Scale (CBS), Exposure to Offence Scale (EOS) and Risk Perception Scale (RPS), were developed depending on the data collected with the use of surveys. The scales developed from the content of the survey were applied to students, academics and administrative staff of a university, which also embodies hospitals and educational organizations located in different geographical and socio-economical regions of Turkey. On the base of developed scales, the results of the study show that there are significant differences within samples and according to the habits of Internet usage.


Artificial intelligence - a modern approach: the intelligent agent book


3D-Aided Deep Pose-Invariant Face Recognition


Marshaling social support: The state of the art in research and practice.


title: ENG: End-to-end Neural Geometry for Robust Depth and Pose Estimation using CNNs

abstract: Recovering structure and motion parameters given a image pair or a sequence of images is a well studied problem in computer vision. This is often achieved by employing Structure from Motion (SfM) or Simultaneous Localization and Mapping (SLAM) algorithms based on the real-time requirements. Recently, with the advent of Convolutional Neural Networks (CNNs) researchers have explored the possibility of using machine learning techniques to reconstruct the 3D structure of a scene and jointly predict the camera pose. In this work, we present a framework that achieves state-of-the-art performance on single image depth prediction for both indoor and outdoor scenes. The depth prediction system is then extended to predict optical flow and ultimately the camera pose and trained end-to-end. Our motion estimation framework outperforms the previous motion prediction systems and we also demonstrate that the state-of-the-art metric depths can be further improved using the knowledge of pose.


title: Recolored Image Detection via a Deep Discriminative Model

abstract: Image recoloring is a technique that can transfer image color or theme and result in an imperceptible change in human eyes. Although image recoloring is one of the most important image manipulation techniques, there is no special method designed for detecting this kind of forgery. In this paper, we propose a trainable end-to-end system for distinguishing recolored images from natural images. The proposed network takes the original image and two derived inputs based on illumination consistency and inter-channel correlation of the original input into consideration and outputs the probability that it is recolored. Our algorithm adopts a convolutional neural network (CNN)- based deep architecture, which consists of three feature extraction blocks and a feature fusion module. To train the deep neural network, we synthesize a data set comprised of recolored images and corresponding ground truth using different recoloring methods. Extensive experimental results on the recolored images generated by various methods show that our proposed network is well generalized and very robust.


Exploring the Leading Authors and Journals in Major Topics by Citation Sentences and Topic Modeling


title: A wearable hand system for virtual reality

abstract: In this paper, a wearable hand system for virtual reality is proposed. The proposed system can deliver accurate force to three fingertips (thumb, index, and middle fingers), which play dominant roles in manipulating and interacting with the external environment, and also measure fingertip positions of the three fingers. The proposed system has five degrees of freedom (DOFs) for each finger structure to permit natural finger motion, can be easily worn by clicking the silicon-coated fingertip structures and buckling the wearable structure around the palm. The forces delivered to the fingertips are controlled by the motor current with the robust control algorithm, and the fingertip position is calculated by forward kinematics. The performance of the proposed system was verified experimentally; the calculated fingertip position was compared to a camera-based motion capture system and interactions with objects of different stiffness were tested.


title: BM3D-Net: A Convolutional Neural Network for Transform-Domain Collaborative Filtering

abstract: Denoising is a fundamental task in image processing with wide applications for enhancing image qualities. BM3D is considered as an effective baseline for image denoising. Although learning-based methods have been dominant in this area recently, the traditional methods are still valuable to inspire new ideas by combining with learning-based approaches. In this letter, we propose a new convolutional neural network inspired by the classical BM3D algorithm, dubbed as BM3D-Net. We unroll the computational pipeline of BM3D algorithm into a convolutional neural network structure, with “extraction” and “aggregation” layers to model block matching stage in BM3D. We apply our network to three denoising tasks: gray-scale image denoising, color image denoising, and depth map denoising. Experiments show that BM3D-Net significantly outperforms the basic BM3D method, and achieves competitive results compared with state of the art on these tasks.


title: Products as Agents: Metaphors for Designing the Products of the IoT Age

abstract: Design-based inquiries into the networked products of the Internet of Things (IoT) lack a coherent understanding of the effect of such products on society. This paper proposes a new taxonomy for networked products, which would allow articulation on their current state and future, and provide insights to designers for creating meaningful and aesthetic products of IoT. Central to this framework is the proposition that our current product-scape should be understood as a distribution of material agencies and best analyzed through the metaphor of "agency". We identify three types of agencies, i.e., the Collector, the Actor, and the Creator, and discuss how this approach could create new design methodologies to create more meaningful networked products that would empower people in their everyday lives.


title: Cayley graphs and symmetric interconnection networks

abstract: These lecture notes are on automorphism groups of Cayley graphs and their applications to optimal fault-tolerance of some interconnection networks. We first give an introduction to automorphisms of graphs and an introduction to Cayley graphs. We then discuss automorphism groups of Cayley graphs. We prove that the vertex-connectivity of edge-transitive graphs is maximum possible. We investigate the automorphism group and vertex-connectivity of some families of Cayley graphs that have been considered for interconnection networks; we focus on the hypercubes, folded hypercubes, Cayley graphs generated by transpositions, and Cayley graphs from linear codes. New questions and open problems are also discussed.


The State of Open Data Limits of Current Open Data Platforms


title: Orientation selectivity based visual pattern for reduced-reference image quality assessment

abstract: Image quality assessment (IQA) is in great demand for high quality image selection in the big data era. The challenge of reduced-reference (RR) IQA is how to use limited data to effectively represent the visual content of an image in the context of IQA. Research on neuroscience indicates that the human visual system (HVS) exhibits obvious orientation selectivity (OS) mechanism for visual content extraction. Inspired by this, an OS based visual pattern (OSVP) is proposed to extract visual content for RR IQA in this paper. The OS arises from the arrangement of the excitatory and inhibitory interactions among connected cortical neurons in a local receptive field. According to the OS mechanism, the similarity of preferred orientations between two nearby pixels is first analyzed. Then, the orientation similarities of pixels in a local neighborhood are arranged, and the OSVP is built for visual information representation. With the help of OSVP, the visual content of an image is extracted and mapped into a histogram. By calculating the changes between the two histograms of reference and distorted images, a quality score is produced. Experimental results on five public databases demonstrate that the proposed RR IQA method has performance consistent with the human perception under a small amount of reference data (only 9 values).


title: Facial performance enhancement using dynamic shape space analysis

abstract: The facial performance of an individual is inherently rich in subtle deformation and timing details. Although these subtleties make the performance realistic and compelling, they often elude both motion capture and hand animation. We present a technique for adding fine-scale details and expressiveness to low-resolution art-directed facial performances, such as those created manually using a rig, via marker-based capture, by fitting a morphable model to a video, or through Kinect reconstruction using recent faceshift technology. We employ a high-resolution facial performance capture system to acquire a representative performance of an individual in which he or she explores the full range of facial expressiveness. From the captured data, our system extracts an expressiveness model that encodes subtle spatial and temporal deformation details specific to that particular individual. Once this model has been built, these details can be transferred to low-resolution art-directed performances. We demonstrate results on various forms of input; after our enhancement, the resulting animations exhibit the same nuances and fine spatial details as the captured performance, with optional temporal enhancement to match the dynamics of the actor. Finally, we show that our technique outperforms the current state-of-the-art in example-based facial animation.


title: Reference-Dependent Risk Attitudes

abstract: We use Koszegi and Rabin's (2006) model of reference-dependent utility, and an extension of it that applies to decisions with delayed consequences, to study preferences over monetary risk. Because our theory equates the reference point with recent probabilistic beliefs about outcomes, it predicts specific ways in which the environment influences attitudes toward modest-scale risk. It replicates "classical" prospect theory—including the prediction of distaste for insuring losses—when exposure to risk is a surprise, but implies first-order risk aversion when a risk, and the possibility of insuring it, are anticipated. A prior expectation to take on risk decreases aversion to both the anticipated and additional risk. For large-scale risk, the model allows for standard "consumption utility" to dominate reference-dependent "gain-loss utility," generating nearly identical risk aversion across situations. (JEL D81)


title: Artificial Bee Colony algorithm for optimization of truss structures

abstract: The main goal of the structural optimization is to minimize the weight of structures while satisfying all design requirements imposed by design codes. In this paper, the Artificial Bee Colony algorithm with an adaptive penalty function approach (ABC-AP) is proposed to minimize the weight of truss structures. The ABC algorithm is swarm intelligence based optimization technique inspired by the intelligent foraging behavior of honeybees. Five truss examples with fixed-geometry and up to 200 elements were studied to verify that the ABC algorithm is an effective optimization algorithm in the creation of an optimal design for truss structures. The results of the ABC-AP compared with results of other optimization algorithms from the literature show that this algorithm is a powerful search and optimization technique for structural design.


title: Automated classification of brain tumor type in whole-slide digital pathology images using local representative tiles.

abstract: Computerized analysis of digital pathology images offers the potential of improving clinical care (e.g. automated diagnosis) and catalyzing research (e.g. discovering disease subtypes). There are two key challenges thwarting computerized analysis of digital pathology images: first, whole slide pathology images are massive, making computerized analysis inefficient, and second, diverse tissue regions in whole slide images that are not directly relevant to the disease may mislead computerized diagnosis algorithms. We propose a method to overcome both of these challenges that utilizes a coarse-to-fine analysis of the localized characteristics in pathology images. An initial surveying stage analyzes the diversity of coarse regions in the whole slide image. This includes extraction of spatially localized features of shape, color and texture from tiled regions covering the slide. Dimensionality reduction of the features assesses the image diversity in the tiled regions and clustering creates representative groups. A second stage provides a detailed analysis of a single representative tile from each group. An Elastic Net classifier produces a diagnostic decision value for each representative tile. A weighted voting scheme aggregates the decision values from these tiles to obtain a diagnosis at the whole slide level. We evaluated our method by automatically classifying 302 brain cancer cases into two possible diagnoses (glioblastoma multiforme (N = 182) versus lower grade glioma (N = 120)) with an accuracy of 93.1% (p << 0.001). We also evaluated our method in the dataset provided for the 2014 MICCAI Pathology Classification Challenge, in which our method, trained and tested using 5-fold cross validation, produced a classification accuracy of 100% (p << 0.001). Our method showed high stability and robustness to parameter variation, with accuracy varying between 95.5% and 100% when evaluated for a wide range of parameters. Our approach may be useful to automatically differentiate between the two cancer subtypes.


title: Cloud computing and its impact on economic and environmental performance: A transaction cost economics perspective

abstract: For many organizations, managing both economic and environmental performance has emerged as a key challenge. Further, with expanding globalization organizations are finding it more difficult to maintain adequate supplier relations to balance both economic and environmental performance initiatives. Drawing on transaction cost economics, this study examines how novel information technology like cloud computing can help firms not only maintain adequate supply chain collaboration, but also balance both economic and environmental performance. We analyze survey data from 247 IT and supply chain professionals using structural equation modeling and partial least squares to verify the robustness of our results. Our analyses yield several interesting findings. First, contrary to other studies we find that collaboration does not necessarily affect environmental performance and only partially mediates the relationship between cloud computing and economic performance. Secondly, the results of our survey provide evidence of the direct effect of cloud computing on both economic and environmental performance. Cloud computing is positively associated with collaboration.Cloud computing is positively associated with economic and environmental performance.Collaboration mediates cloud computing and economic performance relationship.


title: A review and synthesis of the first 20years of PET and fMRI studies of heard speech, spoken language and reading

abstract: The anatomy of language has been investigated with PET or fMRI for more than 20 years. Here I attempt to provide an overview of the brain areas associated with heard speech, speech production and reading. The conclusions of many hundreds of studies were considered, grouped according to the type of processing, and reported in the order that they were published. Many findings have been replicated time and time again leading to some consistent and undisputable conclusions. These are summarised in an anatomical model that indicates the location of the language areas and the most consistent functions that have been assigned to them. The implications for cognitive models of language processing are also considered. In particular, a distinction can be made between processes that are localized to specific structures (e.g. sensory and motor processing) and processes where specialisation arises in the distributed pattern of activation over many different areas that each participate in multiple functions. For example, phonological processing of heard speech is supported by the functional integration of auditory processing and articulation; and orthographic processing is supported by the functional integration of visual processing, articulation and semantics. Future studies will undoubtedly be able to improve the spatial precision with which functional regions can be dissociated but the greatest challenge will be to understand how different brain regions interact with one another in their attempts to comprehend and produce language.


Inferring the score of a tennis match from in-play betting exchange markets


title: Adult Tea Green Leafhoppers, Empoasca onukii (Matsuda), Change Behaviors under Varying Light Conditions

abstract: Insect behaviors are often influenced by light conditions including photoperiod, light intensity, and wavelength. Understanding pest insect responses to changing light conditions may help with developing alternative strategies for pest control. Little is known about the behavioral responses of leafhoppers (Hemiptera: Cicadellidae) to light conditions. The behavior of the tea green leafhopper, Empoasca onukii Matsuda, was examined when exposed to different light photoperiods or wavelengths. Observations included the frequency of locomotion and cleaning activities, and the duration of time spent searching. The results suggested that under normal photoperiod both female and male adults were generally more active in darkness (i.e., at night) than in light. In continuous darkness (DD), the locomotion and cleaning events in Period 1 (7:00–19:00) were significantly increased, when compared to the leafhoppers under normal photoperiod (LD). Leafhoppers, especially females, changed their behavioral patterns to a two day cycle under DD. Under continuous illumination (continuous quartz lamp light, yellow light at night, and green light at night), the activities of locomotion, cleaning, and searching were significantly suppressed during the night (19:00–7:00) and locomotion activities of both females and males were significantly increased during the day (7:00–19:00), suggesting a shift in circadian rhythm. Our work suggests that changes in light conditions, including photoperiod and wavelength, can influence behavioral activities of leafhoppers, potentially affecting other life history traits such as reproduction and development, and may serve as a method for leafhopper behavioral control.


title: Sliding-mode control for trajectory-tracking of a Wheeled Mobile Robot in presence of uncertainties

abstract: Wheeled Mobile Robots (WMRs) are the most widely used class of mobile robots. This is due to their fast maneuvering, simple controllers and energy saving characteristics. A dynamics-based sliding mode controller for WMR trajectorytracking is proposed. Robustness to external disturbances and parameter uncertainties is achieved. Closed loop real-time results show good performances in trajectory tracking even if for high upper bound of uncertainties.


Modeling and Design Analysis of a Permanent Magnet Linear Synchronous Generator


title: Recursive Regression with Neural Networks: Approximating the HJI PDE Solution

abstract: Most machine learning applications using neural networks seek to approximate some function g(x) by minimizing some cost criterion. In the simplest case, if one has access to pairs of the form (x, y) where y = g(x), the problem can be framed as a regression problem. Beyond this family of problems, we find many cases where the unavailability of data pairs makes this approach unfeasible. However, similar to what we find in the reinforcement learning literature, if we have some known properties of the function we are seeking to approximate, there is still hope to frame the problem as a regression problem. In this context, we present an algorithm that approximates the solution to a partial differential equation known as the Hamilton-Jacobi-Isaacs PDE and compare it to current state of the art tools. This PDE, which is found in the fields of control theory and robotics, is of particular importance in safety critical systems where guarantees of performance are a must.


title: Agent-Based Service Composition in Cloud Computing

abstract: In a Cloud-computing environment, consumers, brokers, and service providers interact to achieve their individual purposes. In this regard, service providers offer a pool of resources wrapped as web services, which should be composed by broker agents to provide a single virtualized service to Cloud con- sumers. In this study, an agent-based test bed for simulating Cloud-computing environments is developed. Each Cloud participant is represented by an agent, whose behavior is defined by means of colored Petri nets. The relationship be- tween web services and service providers is modeled using object Petri nets. Both Petri net formalisms are combined to support a design methodology for defining concurrent and parallel service choreographies. This results in the crea- tion of a dynamic agent-based service composition algorithm. The simulation results indicate that service composition is achieved with a linear time complex- ity despite dealing with interleaving choreographies and synchronization of heterogeneous services.


Traffic Analysis of the HTTP Protocol over TLS


Enhanced Sentiment Classification of Telugu Text using ML Techniques