query: WIR: Warp Instruction Reuse to Minimize Repeated Computations in GPUs

positive:
CAWA: Coordinated warp scheduling and Cache Prioritization for critical warp acceleration of GPGPU workloads The ubiquity of graphics processing unit (GPU) architectures has made them efficient alternatives to chip-multiprocessors for parallel workloads. GPUs achieve superior performance by making use of massive multi-threading and fast context-switching to hide pipeline stalls and memory access latency. However, recent characterization results have shown that general purpose GPU (GPGPU) applications commonly encounter long stall latencies that cannot be easily hidden with the large number of concurrent threads/warps. This results in varying execution time disparity between different parallel warps, hurting the overall performance of GPUs -- the warp criticality problem.
 To tackle the warp criticality problem, we propose a coordinated solution, criticality-aware warp acceleration (CAWA), that efficiently manages compute and memory resources to accelerate the critical warp execution. Specifically, we design (1) an instruction-based and stall-based criticality predictor to identify the critical warp in a thread-block, (2) a criticality-aware warp scheduler that preferentially allocates more time resources to the critical warp, and (3) a criticality-aware cache reuse predictor that assists critical warp acceleration by retaining latency-critical and useful cache blocks in the L1 data cache. CAWA targets to remove the significant execution time disparity in order to improve resource utilization for GPGPU workloads. Our evaluation results show that, under the proposed coordinated scheduler and cache prioritization management scheme, the performance of the GPGPU workloads can be improved by 23% while other state-of-the-art schedulers, GTO and 2-level schedulers, improve performance by 16% and -2% respectively.

positive: Value Locality and Load Value Prediction Since the introduction of virtual memory demand-paging and cache memories, computer systems have been exploiting spatial and temporal locality to reduce the average latency of a memory reference. In this paper, we introduce the notion of value locality, a third facet of locality that is frequently present in real-world programs, and describe how to effectively capture and exploit it in order to perform load value prediction. Temporal and spatial locality are attributes of storage locations, and describe the future likelihood of references to those locations or their close neighbors. In a similar vein, value locality describes the likelihood of the recurrence of a previously-seen value within a storage location. Modern processors already exploit value locality in a very restricted sense through the use of control speculation (i.e. branch prediction), which seeks to predict the future value of a single condition bit based on previously-seen values. Our work extends this to predict entire 32- and 64-bit register values based on previously-seen values. We find that, just as condition bits are fairly predictable on a per-static-branch basis, full register values being loaded from memory are frequently predictable as well. Furthermore, we show that simple microarchitectural enhancements to two modern microprocessor implementations (based on the PowerPC 620 and Alpha 21164) that enable load value prediction can effectively exploit value locality to collapse true dependencies, reduce average memory latency and bandwidth requirements, and provide measurable performance gains.

positive: Neither more nor less: Optimizing thread-level parallelism for GPGPUs General-purpose graphics processing units (GPGPUs) are at their best in accelerating computation by exploiting abundant thread-level parallelism (TLP) offered by many classes of HPC applications. To facilitate such high TLP, emerging programming models like CUDA and OpenCL allow programmers to create work abstractions in terms of smaller work units, called cooperative thread arrays (CTAs). CTAs are groups of threads and can be executed in any order, thereby providing ample opportunities for TLP. The state-of-the-art GPGPU schedulers allocate maximum possible CTAs per-core (limited by available on-chip resources) to enhance performance by exploiting TLP. However, we demonstrate in this paper that executing the maximum possible number of CTAs on a core is not always the optimal choice from the performance perspective. High number of concurrently executing threads might cause more memory requests to be issued, and create contention in the caches, network and memory, leading to long stalls at the cores. To reduce resource contention, we propose a dynamic CTA scheduling mechanism, called DYNCTA, which modulates the TLP by allocating optimal number of CTAs, based on application characteristics. To minimize resource contention, DYNCTA allocates fewer CTAs for applications suffering from high contention in the memory sub-system, compared to applications demonstrating high throughput. Simulation results on a 30-core GPGPU platform with 31 applications show that the proposed CTA scheduler provides 28% average improvement in performance compared to the existing CTA scheduler.

positive: GPUs and the Future of Parallel Computing This article discusses the capabilities of state-of-the art GPU-based high-throughput computing systems and considers the challenges to scaling single-chip parallel-computing systems, highlighting high-impact areas that the computing research community can address. Nvidia Research is investigating an architecture for a heterogeneous high-performance computing system that seeks to address these challenges.

positive: NVIDIA Tesla: A Unified Graphics and Computing Architecture To enable flexible, programmable graphics and high-performance computing, NVIDIA has developed the Tesla scalable unified graphics and parallel computing architecture. Its scalable parallel array of processors is massively multithreaded and programmable in C or via graphics APIs.

negative: Mechatronic model of a novel slotless permanent magnet DC-motor with air gap winding design This paper presents a mechatronic model of a novel slotless permanent magnet DC-motor with air gap winding. Besides technical advantages of this type of motor like high power density, high torque, very low weight and high efficiency, the motor design allows a very precise and efficient modelling with limited effort. A nonlinear model of magnetic field density can be extracted from a detailed nonlinear FE-model build in ANSYS/Maxwell, approximated by Fourier series and then used to model driving torque and back EMF, representing the coupling between electrical and mechanical subsystems. Analytically founded numerical models for driving torque and back EMF will be given. Real geometry of the phase winding is taken into account to improve model accuracy. The electrical subsystem will be described as coupled three phase system, whose parameters can also be extracted from the nonlinear FE-model with high accuracy. Together with a mechanical model of the rotor a MATLAB/Simulink model is build and extended by models of the hall sensors to detect rotor position and commutation logic to control the HEX-Bridge during operation. Finally, results of a complex simulation model, based on the parameters of the prototype of a wheel-hub motor, implementing the new motor design, are getting shown. Simulation results compare very well to measured data. Simulation time is very short due to the efficient approximation of magnetic flux density.

negative: Reverse Engineering of Embedded Software Using Syntactic Pattern Recognition When a secure component executes sensitive operations, the information carried by the power consumption can be used to recover secret information. Many different techniques have been developped to recover this secret, but only few of them focus on the recovering of the executed code itself. Indeed, the code knowledge acquired through this step of Simple Power Analysis (SPA) can help to identify implementation weaknesses and to improve further kinds of attacks. In this paper we present a new approach improving the SPA based on a pattern recognition methodology, that can be used to automatically identify the processed instructions that leak through power consumption. We firstly process a geometrical classification with chosen instructions to enable the automatic identification of any sequence of instructions. Such an analysis is used to reverse general purpose code executions of a recent secure component.

negative: Communicating Algorithmic Process in Online Behavioral Advertising Advertisers develop algorithms to select the most relevant advertisements for users. However, the opacity of these algorithms, along with their potential for violating user privacy, has decreased user trust and preference in behavioral advertising. To mitigate this, advertisers have started to communicate algorithmic processes in behavioral advertising. However, how revealing parts of the algorithmic process affects users' perceptions towards ads and platforms is still an open question. To investigate this, we exposed 32 users to why an ad is shown to them, what advertising algorithms infer about them, and how advertisers use this information. Users preferred interpretable, non-creepy explanations about why an ad is presented, along with a recognizable link to their identity. We further found that exposing users to their algorithmically-derived attributes led to algorithm disillusionment---users found that advertising algorithms they thought were perfect were far from it. We propose design implications to effectively communicate information about advertising algorithms.

negative: Effective Heart Sound Segmentation and Murmur Classification Using Empirical Wavelet Transform and Instantaneous Phase for Electronic Stethoscope Accurate measurement of heart sound and murmur parameters is of great importance in the automated analysis of phonocardiogram (PCG) signals. In this paper, we propose a novel unified PCG signal delineation and murmur classification method without the use of reference signal for automatic detection and classification of heart sounds and murmurs. The major components of the proposed method are the empirical wavelet transform-based PCG signal decomposition for discriminating heart sounds from heart murmurs and suppressing background noises, the Shannon entropy envelope extraction, the instantaneous phase-based boundary determination, heart sound and murmur parameter extraction, the systole/diastole discrimination and the decision rules-based murmur classification. The accuracy and robustness of the proposed method is evaluated using a wide variety of normal and abnormal PCG signals taken from the standard PCG databases, including PASCAL heart sounds challenge database, PhysioNet/CinC challenge heart sound database, and real-time PCG signals. Evaluation results show that the proposed method achieves an average sensitivity (Se) of 94.38%, positive predictivity (Pp) of 97.25%, and overall accuracy (OA) of 91.92% for heart sound segmentation and Se of 97.58%, Pp of 96.46%, and OA of 94.21% in detecting the presence of heart murmurs for SNR of 10 dB. The method yields an average classification accuracy of 95.5% for the PCG signals with SNR of 20 dB. Results show that the proposed method outperforms other existing heart sound segmentation and murmur classification methods.

negative: Color categories: Evidence for the cultural relativity hypothesis The question of whether language affects our categorization of perceptual continua is of particular interest for the domain of color where constraints on categorization have been proposed both within the visual system and in the visual environment. Recent research (Roberson, Davies, & Davidoff, 2000; Roberson et al., in press) found substantial evidence of cognitive color differences between different language communities, but concerns remained as to how representative might be a tiny, extremely remote community. The present study replicates and extends previous findings using additional paradigms among a larger community in a different visual environment. Adult semi-nomadic tribesmen in Southern Africa carried out similarity judgments, short-term memory and long-term learning tasks. They showed different cognitive organization of color to both English and another language with the five color terms. Moreover, Categorical Perception effects were found to differ even between languages with broadly similar color categories. The results provide further evidence of the tight relationship between language and cognition.

negative: The Airport Gate Assignment Problem: Mathematical Model and a Tabu Search Algorithm In this paper, we consider an Airport Gate Assignment Problem that dynamically assigns airport gates to scheduled ights based on passengers' daily origin and destination ow data. The objective of the problem is to minimize the overall connection times that passengers walk to catch their connection ights. We formulate this problem as a mixed 0-1 quadratic integer programming problem and then reformulate it as a mixed 0-1 integer problem with a linear objective function and constraints. We design a simple tabu search meta-heuristic to solve the problem. The algorithm exploits the special properties of di erent types of neighborhood moves, and create highly e ective candidate list strategies. We also address issues of tabu short term memory, dynamic tabu tenure, aspiration rule, and various intensi cation and diversi cation strategies. Preliminary computational experiments are conducted and the results are presented and analyzed.

negative: A Continuous Beam Steering Slotted Waveguide Antenna Using Rotating Dielectric Slabs The design, simulation and measurement of a beam steerable slotted waveguide antenna operating in X band are presented. The proposed beam steerable antenna consists of a standard rectangular waveguide (RWG) section with longitudinal slots in the broad wall. The beam steering in this configuration is achieved by rotating two dielectric slabs inside the waveguide and consequently changing the phase of the slots excitations. In order to confirm the usefulness of this concept, a non-resonant 20-slot waveguide array antenna with an element spacing of d = 0.58λ0 has been designed, built and measured. A 14o beam scanning from near broadside (θ = 4o) toward end-fire (θ = 18o) direction is observed. The gain varies from 18.33 dB to 19.11 dB which corresponds to the radiation efficiencies between 95% and 79%. The side-lobe level is -14 dB at the design frequency of 9.35 GHz. The simulated co-polarized realized gain closely matches the fabricated prototype patterns.

negative: End-to-End Offline Goal-Oriented Dialog Policy Learning via Policy Gradient Learning a goal-oriented dialog policy is generally performed offline with supervised learning algorithms or online with reinforcement learning (RL). Additionally, as companies accumulate massive quantities of dialog transcripts between customers and trained human agents, encoder-decoder methods have gained popularity as agent utterances can be directly treated as supervision without the need for utterance-level annotations. However, one potential drawback of such approaches is that they myopically generate the next agent utterance without regard for dialog-level considerations. To resolve this concern, this paper describes an offline RL method for learning from unannotated corpora that can optimize a goal-oriented policy at both the utterance and dialog level. We introduce a novel reward function and use both on-policy and off-policy policy gradient to learn a policy offline without requiring online user interaction or an explicit state space definition.

negative: Post-Stroke Rehabilitation with the Rutgers Ankle System: A Case Study The Rutgers Ankle is a Stewart platform-type haptic interface designed for use in rehabilitation. The system supplies six-degree-of-freedom (DOF) resistive forces on the patient's foot, in response to virtual reality-based exercises. The Rutgers Ankle controller contains an embedded Pentium board, pneumatic solenoid valves, valve controllers, and associated signal conditioning electronics. The rehabilitation exercise used in our case study consists of piloting a virtual airplane through loops. The exercise difficulty can be selected based on the number and placement of loops, the airplane speed in the virtual environment, and the degree of resistance provided by the haptic interface. Exercise data is stored transparently, in real time, in an Oracle database. These data consist of ankle position, forces, and mechanical work during an exercise, and over subsequent rehabilitation sessions. The number of loops completed and the time it took to do that are also stored online. A case study is presented of a patient nine months post-stroke using this system. Results showed that, over six rehabilitation sessions, the patient improved on clinical measures of strength and endurance, which corresponded well with torque and power output increases measured by the Rutgers Ankle. There were also substantial improvements in task accuracy and coordination during the simulation and the patient's walking and stair-climbing ability.

negative: Signal Processing Methods for the Automatic Transcription of Music Signal processing methods for the automatic transcription of music are developed in this thesis. Music transcription is here understood as the process of analyzing a music signal so as to write down the parameters of the sounds that occur in it. The applied notation can be the traditional musical notation or any symbolic representation which gives sufficient information for performing the piece using the available musical instruments. Recovering the musical notation automatically for a given acoustic signal allows musicians to reproduce and modify the original performance. Another principal application is structured audio coding: a MIDI-like representation is extremely compact yet retains the identifiability and characteristics of a piece of music to an important degree. The scope of this thesis is in the automatic transcription of the harmonic and melodic parts of real-world music signals. Detecting or labeling the sounds of percussive instruments (drums) is not attempted, although the presence of these is allowed in the target signals. Algorithms are proposed that address two distinct subproblems of music transcription. The main part of the thesis is dedicated to multiple fundamental frequency (F0) estimation, that is, estimation of the F0s of several concurrent musical sounds. The other subproblem addressed is musical meter estimation. This has to do with rhythmic aspects of music and refers to the estimation of the regular pattern of strong and weak beats in a piece of music. For multiple-F0 estimation, two different algorithms are proposed. Both methods are based on an iterative approach, where the F0 of the most prominent sound is estimated, the sound is cancelled from the mixture, and the process is repeated for the residual. The first method is derived in a pragmatic manner and is based on the acoustic properties of musical sound mixtures. For the estimation stage, an algorithm is proposed which utilizes the frequency relationships of simultaneous spectral components, without assuming ideal harmonicity. For the cancelling stage, a new processing principle, spectral smoothness, is proposed as an efficient new mechanism for separating the detected sounds from the mixture signal. The other method is derived from known properties of the human auditory system. More specifically, it is assumed that the peripheral parts of hearing can be modelled by a bank of bandpass filters, followed by half-wave rectification and compression of the subband signals. It is shown that this basic structure allows the combined use of time-domain periodicity and frequency-domain periodicity for F0 extraction. In the derived algorithm, the higher-order (unresolved) harmonic partials of a sound are processed collectively, without the need to detect or estimate individual partials. This has the consequence that the method works reasonably accurately for short analysis frames. Computational efficiency of the method is based on calculating a frequency-domain approximation of the summary autocorrelation function, a physiologically-motivated representation of sound. Both of the proposed multiple-F0 estimation methods operate within a single time frame and arrive at approximately the same error rates. However, the auditorily-motivated method is superior in short analysis frames. On the other hand, the pragmatically-oriented method is “complete” in the sense that it includes mechanisms for suppressing additive noise (drums) and for estimating the number of concurrent sounds in the analyzed signal. In musical interval and chord identification tasks, both algorithms outperformed the average of ten trained musicians.

negative: The Development and Validation of a Measure of Student Attitudes Toward Science , Technology , Engineering , and Math ( S-STEM ) Using an iterative design along with multiple methodological approaches and a large representative sample, this study presents reliability, validity, and fairness evidence for two surveys measuring student attitudes toward science, technology, engineering, and math (S-STEM) and interest in STEM careers for (a) 4ththrough 5th-grade students (Upper Elementary S-STEM) and (b) 6ththrough 12th-grade students (Middle/High S-STEM). Findings from exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) suggested the use of a four-factor structure to measure student attitudes toward science, math, engineering/technology, and 21st century skills. Subject matter experts and literature reviews provided evidence of content validity. Reliability levels were high for both versions. Furthermore, both the Upper Elementary S-STEM and Middle/High S-STEM Surveys demonstrated evidence of configural, metric, and scalar invariance across grade levels, races/ethnicities, and genders. The findings support the validity of interpretations and inferences made from scores on the instruments’ items and subscales.

negative: VCDB: A Large-Scale Database for Partial Copy Detection in Videos The task of partial copy detection in videos aims at finding if one or more segments of a query video have (transformed) copies in a large dataset. Since collecting and annotating large datasets of real partial copies are extremely time-consuming, previous video copy detection research used either small-scale datasets or large datasets with simulated partial copies by imposing several pre-defined transformations (e.g., photometric or geometric changes). While the simulated datasets were useful for research, it is unknown how well the techniques developed on such data work on real copies, which are often too complex to be simulated. In this paper, we introduce a large-scale video copy database (VCDB) with over 100,000 Web videos, containing more than 9,000 copied segment pairs found through careful manual annotation. We further benchmark a baseline system on VCDB, which has demonstrated state-of-the-art results in recent copy detection research. Our evaluation suggests that existing techniques—which have shown near-perfect results on the simulated benchmarks—are far from satisfactory in detecting complex real copies. We believe that the release of VCDB will largely advance the research around this challenging problem.

negative: Applying Winnow to Context-Sensitive Spelling Correction Multiplicative weight-updating algorithms such as Winnow have been studied extensively in the COLT literature, but only recently have people started to use them in applications. In this paper, we apply a Winnow-based algorithm to a task in natural language: contextsensitive spelling correction. This is the task of xing spelling errors that happen to result in valid words, such as substituting to for too, casual for causal, and so on. Previous approaches to this problem have been statistics-based; we compare Winnow to one of the more successful such approaches, which uses Bayesian classi ers. We nd that: (1) When the standard (heavily-pruned) set of features is used to describe problem instances, Winnow performs comparably to the Bayesian method; (2) When the full (unpruned) set of features is used, Winnow is able to exploit the new features and convincingly outperform Bayes; and (3) When a test set is encountered that is dissimilar to the training set, Winnow is better than Bayes at adapting to the unfamiliar test set, using a strategy we will present for combining learning on the training set with unsupervised learning on the (noisy) test set. In Machine Learning: Proceedings of the 13th International Conference, Lorenza Saitta, ed., Morgan Kaufmann, San Francisco, CA, 1996, pages 182{190. This work may not be copied or reproduced in whole or in part for any commercial purpose. Permission to copy in whole or in part without payment of fee is granted for nonpro t educational and research purposes provided that all such whole or partial copies include the following: a notice that such copying is by permission of Mitsubishi Electric Information Technology Center America; an acknowledgment of the authors and individual contributions to the work; and all applicable portions of the copyright notice. Copying, reproduction, or republishing for any other purpose shall require a license with payment of fee to Mitsubishi Electric Information Technology Center America. All rights reserved. Copyright c Mitsubishi Electric Information Technology Center America, 1996 201 Broadway, Cambridge, Massachusetts 02139 Dept. of Appl. Math. & CS, Weizmann Institute of Science, Rehovot 76100, Israel. 1. First printing, TR96-07, April 1996

negative: Illuminating cell signalling with optogenetic tools The light-based control of ion channels has been transformative for the neurosciences, but the optogenetic toolkit does not stop there. An expanding number of proteins and cellular functions have been shown to be controlled by light, and the practical considerations in deciding between reversible optogenetic systems (such as systems that use light-oxygen-voltage domains, phytochrome proteins, cryptochrome proteins and the fluorescent protein Dronpa) are well defined. The field is moving beyond proof of concept to answering real biological questions, such as how cell signalling is regulated in space and time, that were difficult or impossible to address with previous tools.

negative: Anytime parallel density-based clustering The density-based clustering algorithm DBSCAN is a state-of-the-art data clustering technique with numerous applications in many fields. However, DBSCAN requires neighborhood queries for all objects and propagation of labels from object to object. This scheme is time consuming and thus limits its applicability for large datasets. In this paper, we propose a novel anytime approach to cope with this problem by reducing both the range query and the label propagation time of DBSCAN. Our algorithm, called AnyDBC, compresses the data into smaller density-connected subsets called primitive clusters and labels objects based on connected components of these primitive clusters to reduce the label propagation time. Moreover, instead of passively performing range queries for all objects as in existing techniques, AnyDBC iteratively and actively learns the current cluster structure of the data and selects a few most promising objects for refining clusters at each iteration. Thus, in the end, it performs substantially fewer range queries compared to DBSCAN while still satisfying the cluster definition of DBSCAN. Moreover, by processing queries in block and merging the results into the current cluster structure, AnyDBC can be efficiently parallelized on shared memory architectures to further accelerate the performance, uniquely making it a parallel and anytime technique at the same time. Experiments show speedup factors of orders of magnitude compared to DBSCAN and its fastest variants as well as a high parallel scalability on multicore processors for very large real and synthetic complex datasets.

negative: The Interaction of Architecture and Operating System Design Today’s high-performance RISC microprocessors have been highly tuned for integer and floating point application performance. These architectures have paid less attention to operating system requirements. At the same time, new operating system designs often have overlooked modern architectural trends which may unavoidably change the relative cost of certain primitive operations. The result is that operating system performance is well below application code performance on contemporary RISCS. This paper examines recent directions in computer architecture and operating systems, and the implications of changes in each domain for the other. The requirements of three components of operating system design are discussed in detail: interprocess communication, virt ual memory, and thread management. For each component, we relate operating system functional and performance needs to the mechanisms available on commercial RISC architectures such as the MIPS R2000 and R3000, Sun SPARC, IBM RS6000, Motorola 88000, and Intel i860. Our analysis reveals a number of specific reasons why the performance of operating system primitives on RISCS has not scaled with integer performance. In addition, we identify areas in which architectures could better (and cost-effectively) accommodate operating system needs, and areas in which operating system design could accommodate certain necessary characteristics of cost-effective highperformance microprocessors. This work was supported in part by the National Science Foundation under Grants No. CCR-8703049, CCR-8619663, and CCR-8907666, by the Washington Technology Center, by the Digital Equipment Corporation Systems Research Center and External Research Program, and by IBM and AT&T Fellowships. Bershad is now with the School of Computer Science, Carnegie Mellon University. Permission to copy without fee all or part of this material is granted provided that the copies are not made or distributed for direct commercial advantage, the ACM copyright notice and the title of the publication and its date appear, and notice is given that copying is by permission of the Association for Computing Machinery. To copy otherwise, or to republish, requires a fee and/or specific permission. 01991 ACM 0-89791 -380 -9/91 /0003 -0108 . ..$1 .50

negative: Frappé: Functional Reactive Programming in Java Functional Reactive Programming (FRP) is a declarative programming model for constructing interactive applications based on a continuous model of time. FRP programs are described in terms of behaviors (continuous, timevarying, reactive values), and events (conditions that occur at discrete points in time). This paper presents Frappé, an implementation of FRP in the Java progamming language. The primary contribution of Frappé is its integration of the FRP event/behavior model with the Java Beans event/property model. At the interface level, any Java Beans component may be used as a source or sink for the FRP event and behavior combinators. This provides a mechanism for extending Frappé with new kinds of I/O connections and allows FRP to be used as a high-level declarative model for composing applications from Java Beans components. At the implementation level, the Java Beans event model is used internally by Frappé to propagate FRP events and changes to FRP behaviors. This allows Frappé applications to be packaged as Java Beans components for use in other applications, and yields an implementation of FRP well-suited to the requirements of event-driven applications (such as graphical user interfaces).

negative: Colour spaces: perceptual, historical and applicational background Absnacr-In this paper we present an overview of colour spaces used In electrical engineering and image processing. We Streis the imparfance of the perceptual, historical and sppllcananal~hackgraund that led to a colour spare. The colour spaces presented are : RGB;opponentsolours rpsces, phenomenal colaur spscer, CMY, CMYK, TV colour s~aces (UW and YIQ), PhotoYCC, CIE XYZ, Lab and Luv colour spaces. Keywordr-colaur spaces, RGB, HSV, CIE

negative: Autonomous indoor object tracking with the Parrot AR.Drone This article presents an image-based visual servoing system for indoor visual tracking of 3D moving objects by an Unmanned Aerial Vehicle. This system autonomously follows a 3D moving target object, maintaining it with a fixed distance and centered on its image plane. The initial setup is tested in a detailed simulation environment. The system is then validated on flights in indoor scenarios using the Parrot AR.Drone and the CMT tracker, demonstrating the robustness of the system to differences in object features, environmental clutter, and target trajectory. The obtained results indicate that the proposed system is suitable for complex controls task, such object surveillance and pursuit.

negative: Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey Deep learning is at the heart of the current rise of artificial intelligence. In the field of computer vision, it has become the workhorse for applications ranging from self-driving cars to surveillance and security. Whereas, deep neural networks have demonstrated phenomenal success (often beyond human capabilities) in solving complex problems, recent studies show that they are vulnerable to adversarial attacks in the form of subtle perturbations to inputs that lead a model to predict incorrect outputs. For images, such perturbations are often too small to be perceptible, yet they completely fool the deep learning models. Adversarial attacks pose a serious threat to the success of deep learning in practice. This fact has recently led to a large influx of contributions in this direction. This paper presents the first comprehensive survey on adversarial attacks on deep learning in computer vision. We review the works that design adversarial attacks, analyze the existence of such attacks and propose defenses against them. To emphasize that adversarial attacks are possible in practical conditions, we separately review the contributions that evaluate adversarial attacks in the real-world scenarios. Finally, drawing on the reviewed literature, we provide a broader outlook of this research direction.

negative: Learning Category-Specific Deformable 3D Models for Object Reconstruction We address the problem of fully automatic object localization and reconstruction from a single image. This is both a very challenging and very important problem which has, until recently, received limited attention due to difficulties in segmenting objects and predicting their poses. Here we leverage recent advances in learning convolutional networks for object detection and segmentation and introduce a complementary network for the task of camera viewpoint prediction. These predictors are very powerful, but still not perfect given the stringent requirements of shape reconstruction. Our main contribution is a new class of deformable 3D models that can be robustly fitted to images based on noisy pose and silhouette estimates computed upstream and that can be learned directly from 2D annotations available in object detection datasets. Our models capture top-down information about the main global modes of shape variation within a class providing a “low-frequency” shape. In order to capture fine instance-specific shape details, we fuse it with a high-frequency component recovered from shading cues. A comprehensive quantitative analysis and ablation study on the PASCAL 3D+ dataset validates the approach as we show fully automatic reconstructions on PASCAL VOC as well as large improvements on the task of viewpoint prediction.

negative: A Prism-Free Method for Silhouette Rendering in Inverse Displacement Mapping Silhouette is a key feature that distinguishes displacement mapping from normal mapping. However the silhouette rendering in the GPU implementation of displacement mapping (which is often called inversed displacement mapping) is tricky. Previous approaches rely mostly on construction of additional extruding prism-like geometry, which slows down the rendering significantly. In this paper, we proposed a method for solving the silhouette rendering problem in inverse displace mapping without using any extruding prism-like geometry. At each step of intersection finding, we continuously bends the viewing ray according to the current local tangent space associated with the surface. Thus, it allows mapping a displacement map onto an arbitrary curved surface with more accurate silhouette. While our method is simple, it offers surprisingly good results over Curved Relief Map (CRM) [OP05] in many difficult or degenerated cases.

negative: Chemical Process Control Education and Practice Chemical process control textbooks and courses differ significantly from their electrical or mechanical-oriented brethren. It is our experience that colleagues in electrical engineering (EE) and mechanical engineering (ME) assume that we teach the same theory in our courses and merely have different application examples. The primary goals of this article are to i) emphasize the distinctly challenging characteristics of chemical processes, ii) present a typical process control curriculum, and iii) discuss how chemical process control courses can be revised to better meet the needs of a typical B.S.-level chemical engineer. In addition to a review of material covered in a standard process control course, we discuss innovative approaches in process control education, including the use of case studies, distributed control systems in laboratories, identification and control simulation packages, and studio-based approaches combining lecture, simulation, and experiments in the same room. We also provide perspectives on needed developments in process control education.

negative:
Solving the Search for Source Code Programmers frequently search for source code to reuse using keyword searches. The search effectiveness in facilitating reuse, however, depends on the programmer's ability to specify a query that captures how the desired code may have been implemented. Further, the results often include many irrelevant matches that must be filtered manually. More semantic search approaches could address these limitations, yet existing approaches are either not flexible enough to find approximate matches or require the programmer to define complex specifications as queries.
 We propose a novel approach to semantic code search that addresses several of these limitations and is designed for queries that can be described using a concrete input/output example. In this approach, programmers write lightweight specifications as inputs and expected output examples. Unlike existing approaches to semantic search, we use an SMT solver to identify programs or program fragments in a repository, which have been automatically transformed into constraints using symbolic analysis, that match the programmer-provided specification.
 We instantiated and evaluated this approach in subsets of three languages, the Java String library, Yahoo! Pipes mashup language, and SQL select statements, exploring its generality, utility, and trade-offs. The results indicate that this approach is effective at finding relevant code, can be used on its own or to filter results from keyword searches to increase search precision, and is adaptable to find approximate matches and then guide modifications to match the user specifications when exact matches do not already exist. These gains in precision and flexibility come at the cost of performance, for which underlying factors and mitigation strategies are identified.

negative: Android Malware Detection Using Feature Fusion and Artificial Data For the Android malware detection / classification anti-malware community has relied on traditional malware detection methods as a countermeasure. However, traditional detection methods are developed for detecting the computer malware, which is different from Android malware in structure and characteristics. Thus, they may not be useful for Android malware detection. Moreover, majority of suggested detection approaches may not be generalized and are incapable of detecting zero-day malware due to different reasons such as available data set with specific set of examples. Thus, their detection accuracy may be questionable. To address this problem, this paper presents a malware classification approach with a reliable detection accuracy and evaluate the approach using artificially generated examples. The suggested approach generates the signature profiles and behavior profiles of each application in the data set, which are further used as input for the classification task. For improving the detection accuracy, feature fusion of features from filter methods and wrapper method and algorithm fusion is investigated. Without affecting the detection accuracy, the optimal balance between real world examples and synthetic examples is also investigated. The experimental results suggest that both AUC and F1 can be obtained up to 0.94 for both known and unknown malware using original examples and synthetic examples.

negative: Repeatable texture sampling with interchangeable patches Rendering textures in real-time environments is a key task in computer graphics. This paper presents a new parallel patch-based method which allows repeatable sampling without cache, and does not create visual repetitions. Interchangeable patches of arbitrary shape are prepared in a preprocessing step, such that patches may lie over the boundary of other patches in a repeating tile. This compresses the example texture into an infinite texture map with small memory requirements, suitable for GPU and ray-tracing applications. The quality of textures rendered with this method can be tuned in the offline preprocessing step, and they can then be rendered in times comparable to Wang tiles. Experimental results demonstrate combined benefits in speed, memory requirements, and quality of randomisation when compared to previous methods.

negative: Anatomy of GPU Memory System for Multi-Application Execution As GPUs make headway in the computing landscape spanning mobile platforms, supercomputers, cloud and virtual desktop platforms, supporting concurrent execution of multiple applications in GPUs becomes essential for unlocking their full potential. However, unlike CPUs, multi-application execution in GPUs is little explored. In this paper, we study the memory system of GPUs in a concurrently executing multi-application environment. We first present an analytical performance model for many-threaded architectures and show that the common use of misses-per-kilo-instruction (MPKI) as a proxy for performance is not accurate without considering the bandwidth usage of applications. We characterize the memory interference of applications and discuss the limitations of existing memory schedulers in mitigating this interference. We extend the analytical model to multiple applications and identify the key metrics to control various performance metrics. We conduct extensive simulations using an enhanced version of GPGPU-Sim targeted for concurrently executing multiple applications, and show that memory scheduling decisions based on MPKI and bandwidth information are more effective in enhancing throughput compared to the traditional FR-FCFS and the recently proposed RR FR-FCFS policies.

negative: Fuzzy memoization for floating-point multimedia applications Instruction memoization is a promising technique to reduce the power consumption and increase the performance of future low-end/mobile multimedia systems. Power and performance efficiency can be improved by reusing instances of an already executed operation. Unfortunately, this technique may not always be worth the effort due to the power consumption and area impact of the tables required to leverage an adequate level of reuse. In this paper, we introduce and evaluate a novel way of understanding multimedia floating-point operations based on the fuzzy computation paradigm: performance and power consumption can be improved at the cost of small precision losses in computation. By exploiting this implicit characteristic of multimedia applications, we propose a new technique called tolerant memoization. This technique expands the capabilities of classic memoization by associating entries with similar inputs to the same output. We evaluate this new technique by measuring the effect of tolerant memoization for floating-point operations in a low-power multimedia processor and discuss the trade-offs between performance and quality of the media outputs. We report energy improvements of 12 percent for a set of key multimedia applications with small LUT of 6 Kbytes, compared to 3 percent obtained using previously proposed techniques.

negative: Groute: An Asynchronous Multi-GPU Programming Model for Irregular Computations Nodes with multiple GPUs are becoming the platform of choice for high-performance computing. However, most applications are written using bulk-synchronous programming models, which may not be optimal for irregular algorithms that benefit from low-latency, asynchronous communication. This paper proposes constructs for asynchronous multi-GPU programming, and describes their implementation in a thin runtime environment called Groute. Groute also implements common collective operations and distributed work-lists, enabling the development of irregular applications without substantial programming effort. We demonstrate that this approach achieves state-of-the-art performance and exhibits strong scaling for a suite of irregular applications on 8-GPU and heterogeneous systems, yielding over 7x speedup for some algorithms.

negative:
Effective resource management for enhancing performance of 2D and 3D stencils on GPUs GPUs are an attractive target for data parallel stencil computations prevalent in scientific computing and image processing applications. Many tiling schemes, such as overlapped tiling and split tiling, have been proposed in past to improve the performance of stencil computations. While effective for 2D stencils, these techniques do not achieve the desired improvements for 3D stencils due to the hardware constraints of GPU.
 A major challenge in optimizing stencil computations is to effectively utilize all resources available on the GPU. In this paper we develop a tiling strategy that makes better use of resources like shared memory and register file available on the hardware. We present a systematic methodology to reason about which strategy should be employed for a given stencil and also discuss implementation choices that have a significant effect on the achieved performance. Applying these techniques to various 2D and 3D stencils gives a performance improvement of 200-400% over existing tools that target such computations.

negative: Fast and Accurate Template Matching Using Pixel Rearrangement on the GPU A GPU (Graphics Processing Unit) is a specialized processor for graphics processing. GPUs have the ability to perform high-speed parallel processing using its many processing cores. To utilize the powerful computing ability, GPUs are widely used for general purpose processing. The main contribution of this paper is to show a new template matching algorithm using pixel rearrangement. Template Matching is a technique for finding small parts of an image which match a template image. The feature of our proposed algorithm is that using pixel rearrangement, multiple low-resolution images are generated and template matching for the low-resolution images is performed to reduce the computing time. Also, we implemented our algorithm on a GPU system. The experimental results show that, for an input image with size of 4096 $\times$ 4096 and a template image with size of 256 $\times$ 256, our implementation can achieve a speedup factor of approximately 78 times over the conventional sequential implementation.

negative:
Binary stirring: self-randomizing instruction addresses of legacy x86 binary code Unlike library code, whose instruction addresses can be randomized by address space layout randomization (ASLR), application binary code often has static instruction addresses. Attackers can exploit this limitation to craft robust shell codes for such applications, as demonstrated by a recent attack that reuses instruction gadgets from the static binary code of victim applications.
 This paper introduces binary stirring, a new technique that imbues x86 native code with the ability to self-randomize its instruction addresses each time it is launched. The input to STIR is only the application binary code without any source code, debug symbols, or relocation information. The output is a new binary whose basic block addresses are dynamically determined at load-time. Therefore, even if an attacker can find code gadgets in one instance of the binary, the instruction addresses in other instances are unpredictable. An array of binary transformation techniques enable STIR to transparently protect large, realistic applications that cannot be perfectly disassembled due to computed jumps, code-data interleaving, OS callbacks, dynamic linking and a variety of other difficult binary features. Evaluation of STIR for both Windows and Linux platforms shows that stirring introduces about 1.6% overhead on average to application runtimes.


query: Low Latency Live Video Streaming over HTTP 2.0

positive: Dynamic Adaptive Streaming over HTTP/2.0 MPEG Dynamic Adaptive Streaming over HTTP (DASH) is a new streaming standard that has been recently ratified as an international standard (IS). In comparison to other streaming systems, e.g., HTTP progressive download, DASH is able to handle varying bandwidth conditions providing smooth streaming. Furthermore, it enables NAT and Firewall traversal, flexible and scalable deployment as well as reduced infrastructure costs due to the reuse of existing Internet infrastructure components, e.g., proxies, caches, and Content Distribution Networks (CDN). Recently, the Hypertext Transfer Protocol Bis (httpbis) working group of the IETF has officially started the development of HTTP 2.0. Initially three major proposals have been submitted to the IETF i.e., Googles' SPDY, Microsofts' HTTP Speed+Mobility and Network-Friendly HTTP Upgrade, but SPDY has been chosen as working draft for HTTP 2.0. In this paper we implemented MPEG-DASH over HTTP 2.0 (i.e., SPDY), demonstrating its potential benefits and drawbacks. Moreover, several experimental evaluations have been performed that compare HTTP 2.0 with HTTP 1.1 and HTTP 1.0 in the context of DASH. In particular, the protocol overhead, the performance for different round trip times, and DASH with HTTP 2.0 in a lab test scenario has been evaluated in detail.

positive: An experimental evaluation of rate-adaptation algorithms in adaptive streaming over HTTP Adaptive (video) streaming over HTTP is gradually being adopted, as it offers significant advantages in terms of both user-perceived quality and resource utilization for content and network service providers. In this paper, we focus on the rate-adaptation mechanisms of adaptive streaming and experimentally evaluate two major commercial players (Smooth Streaming, Netflix) and one open source player (OSMF). Our experiments cover three important operating conditions. First, how does an adaptive video player react to either persistent or short-term changes in the underlying network available bandwidth. Can the player quickly converge to the maximum sustainable bitrate? Second, what happens when two adaptive video players compete for available bandwidth in the bottleneck link? Can they share the resources in a stable and fair manner? And third, how does adaptive streaming perform with live content? Is the player able to sustain a short playback delay? We identify major differences between the three players, and significant inefficiencies in each of them.

positive: Dynamic adaptive streaming over HTTP dataset The delivery of audio-visual content over the Hypertext Transfer Protocol (HTTP) got lot of attention in recent years and with dynamic adaptive streaming over HTTP (DASH) a standard is now available. Many papers cover this topic and present their research results, but unfortunately all of them use their own private dataset which -- in most cases -- is not publicly available. Hence, it is difficult to compare, e.g., adaptation algorithms in an objective way due to the lack of a common dataset which shall be used as basis for such experiments. In this paper, we present our DASH dataset including our DASHEncoder, an open source DASH content generation tool. We also provide basic evaluations of the different segment lengths, the influence of HTTP server settings, and, in this context, we show some of the advantages as well as problems of shorter segment lengths.

positive: A VLC media player plugin enabling dynamic adaptive streaming over HTTP This paper describes the implementation of a VLC media player plugin enabling Dynamic Adaptive Streaming over HTTP (DASH). DASH is an emerging ISO/IEC MPEG and 3GPP standard for HTTP streaming. It aims to standardize formats enabling segmented progressive download by exploiting existing Internet infrastructure as such. Our implementation of these formats as described in this paper is based on the well-known VLC. Hence, it is fully integrated into the VLC structure and has been also submitted to the VLC development team for consideration in future releases of VLC. Therefore, it is licensed under the GNU Lesser General Public License (LGPL). The plugin provides a very flexible structure that could be easily extended with respect to different adaptation logics or profiles of the DASH standard. As a consequence, the plugin enables the integration of a variety of adaptation logics and comparison thereof, making it attractive for the research community.

negative: Microcontroller Based Smart Helmet Using GSM & GPRS The thought of developing this proposed model comes from social responsibility towards the society. Lack of immediate First Aid and Emergency medical services during accident are prime cause of death in majority of cases of accidents. The one of the main reasons for this may be late arrival of ambulance, no person at place of accident to give information to the ambulance. This thought of taking responsibility of society resulted in our proposed model “Microcontroller based smart helmet using GSM & GPRS”. The aim of this proposed model design is to inform the responsible persons at the earliest about the accident so that they can take required actions to save the life of the injured person. Our proposed system consists of Arduino as Microcontroller, GSM for calling purpose, GPRS for tracking purpose and mainly Sensors to detect the accident. Our proposed system detects the accident and sends text message along with a voice message within a minute to the registered number.

negative: Optimal Beaconing Control for Epidemic Routing in Delay-Tolerant Networks Owing to the uncertainty of transmission opportunities between mobile nodes, the routing in delay-tolerant networks (DTNs) exploits the mechanism of opportunistic forwarding. Energy-efficient algorithms and policies for DTN are crucial to maximizing the message delivery probability while reducing the delivery cost. In this contribution, we investigate the problem of energy-efficient optimal beaconing control in a DTN. We model the message dissemination under variable beaconing rate with a continuous-time Markov model. Based on this model, we then formulate the optimization problem of the optimal beaconing control for epidemic routing and obtain the optimal threshold policy from the solution of this optimization problem. Furthermore, through extensive numerical results, we demonstrate that the proposed optimal threshold policy significantly outperforms the static policy with constant beaconing rate in terms of system energy consumption savings.

negative: RY-ON WITH CLOTHING REGION We propose a virtual try-on method based on generative adversarial networks (GANs). By considering clothing regions, this method enables us to reflect the pattern of clothes better than Conditional Analogy GAN (CAGAN), an existing virtual try-on method based on GANs. Our method first obtains the clothing region on a person by using a human parsing model learned with a large-scale dataset. Next, using the acquired region, the clothing part is removed from a human image. A desired clothing image is added to the blank area. The network learns how to apply new clothing to the area of people’s clothing. Results demonstrate the possibility of reflecting a clothing pattern. Furthermore, an image of the clothes that the person is originally wearing becomes unnecessary during testing. In experiments, we generate images using images gathered from Zaland (a fashion E-commerce site).

negative: Linking Transformational Leadership and Knowledge Sharing: The Mediating Roles of Perceived Team Goal Commitment and Perceived Team Identification It is widely assumed that transformational leadership can effectively facilitate the sharing of knowledge among followers, but most investigations of the underlying mechanisms were based on the social exchange perspective. Based on a sensegiving theory perspective, this article attempts to uncover the mechanisms by which transformational leadership has its impact on employee knowledge sharing behavior by proposing two team-directed mediating mechanisms: perceived team goal commitment and perceived team identification. Results of multi-source and time-lagged data from 186 leader-follower pairs supported the proposed mediating effects. Implications and limitations are discussed.

negative: Flattening Gamma: Radiometric Terrain Correction for SAR Imagery Enabling intercomparison of synthetic aperture radar (SAR) imagery acquired from different sensors or acquisition modes requires accurate modeling of not only the geometry of each scene, but also of systematic influences on the radiometry of individual scenes. Terrain variations affect not only the position of a given point on the Earth's surface but also the brightness of the radar return as expressed in radar geometry. Without treatment, the hill-slope modulations of the radiometry threaten to overwhelm weaker thematic land cover induced backscatter differences, and comparison of backscatter from multiple satellites, modes, or tracks loses meaning. The ASAR & PALSAR sensors provide state vectors and timing with higher absolute accuracy than was previously available, allowing them to directly support accurate tie-point-free geolocation and radiometric normalization of their imagery. Given accurate knowledge of the acquisition geometry of a SAR image together with a digital height model (DHM) of the area imaged, radiometric image simulation is applied to estimate the local illuminated area for each point in the image. Ellipsoid-based or sigma naught (σ0) based incident angle approximations that fail to reproduce the effect of topographic variation in their sensor model are contrasted with a new method that integrates terrain variations with the concept of gamma naught (γ0) backscatter, converting directly from beta naught (β0) to a newly introduced terrain-flattened γ0 normalization convention. The interpretability of imagery treated in this manner is improved in comparison to processing based on conventional ellipsoid or local incident angle based σ0 normalization.

negative:
Low Data Drug Discovery with One-Shot Learning Recent advances in machine learning have made significant contributions to drug discovery. Deep neural networks in particular have been demonstrated to provide significant boosts in predictive power when inferring the properties and activities of small-molecule compounds (Ma, J. et al. J. Chem. Inf.

MODEL
2015, 55, 263-274). However, the applicability of these techniques has been limited by the requirement for large amounts of training data. In this work, we demonstrate how one-shot learning can be used to significantly lower the amounts of data required to make meaningful predictions in drug discovery applications. We introduce a new architecture, the iterative refinement long short-term memory, that, when combined with graph convolutional neural networks, significantly improves learning of meaningful distance metrics over small-molecules. We open source all models introduced in this work as part of DeepChem, an open-source framework for deep-learning in drug discovery (Ramsundar, B. deepchem.io. https://github.com/deepchem/deepchem, 2016).

negative: Multi-objective optimisation algorithms for GIS-based multi-criteria decision analysis : an application for evacuation planning Geographic Information Systems have gradually acquired greater relevance as tools to support decision-making processes, and during the last decades they have been used in conjunction with Multi-Criteria Decision Analysis techniques (GIS-MCDA) to solve real-world spatial problems. GIS-MCDA can be generally divided in two main approaches: Multi-Attribute and MultiObjective techniques. Until now most of the applications of GIS-MCDA have been focused only on using the multi-attribute approach, and less than 10% of the research has been related to a specific type of multi-objective technique: the use of heuristic/meta-heuristic algorithms. The present study explores how different heuristic/meta-heuristic methods perform on solving a spatial multi-objective optimisation problem. To achieve this, four algorithms representing different types of heuristics methods were implemented, and applied to solve the same spatial multi-objective optimisation problem related with an evacuation planning situation. The implemented algorithms were Standard Particle Swarm Optimisation (SPSO), Non-dominated Sorting Genetic Algorithm II (NSGA-II), Archived Multi-Objective Simulated Annealing (AMOSA) and Multi-Objective Grey Wolf Optimiser (MOGWO). The results show that the four algorithms were effective on solving the given problem, although in general AMOSA and MOGWO had a higher performance for the evaluated aspects (number of solutions, effectiveness of the optimisation, diversity, execution time and repeatability). However, the differences in the results obtained from each algorithm were not clear enough to state that one type of heuristic is superior than others. Since AMOSA and MOGWO are the most recent algorithms among the implemented ones, they include several improvements achieved by the latest research, and their superior performance could be connected to these improvements more than to the specific type of algorithms they belong to. Further research is suggested to explore the suitability of these methods for many-objectives spatial problems, to consider the temporal variability and dynamism of real-world situations, to create a standard set of algorithms to be used for benchmarking, and to integrate them with the currently available GIS-MCDA tools. Despite this, from the performed research it is possible to conclude that heuristics methods are reliable techniques for solving spatial problems with multiple and conflictive objectives, and future research and practical implementations in this field can strengthen the capacities of GIS as a multi-criteria decisionmaking support tool.

negative: Bayesian Modeling with Gaussian Processes using the GPstuff Toolbox Gaussian processes (GP) are powerful tools for probabilistic modeling purposes. They can be used to define prior distributions over latent functions in hierarchical Bayesian models. The prior over functions is defined implicitly by the mean and covariance function, which determine the smoothness and variability of the function. The inference can then be conducted directly in the function space by evaluating or approximating the posterior process. Despite their attractive theoretical properties GPs provide practical challenges in their implementation. GPstuff is a versatile collection of computational tools for GP models compatible with Linux and Windows MATLAB and Octave. It includes, among others, various inference methods, sparse approximations and tools for model assessment. In this work, we review these tools and demonstrate the use of GPstuff in several models.

negative: Noncontact Proximity Vital Sign Sensor Based on PLL for Sensitivity Enhancement In this paper, a noncontact proximity vital sign sensor, using a phase locked loop (PLL) incorporated with voltage controlled oscillator (VCO) built-in planar type circular resonator, is proposed to enhance sensitivity in severe environments. The planar type circular resonator acts as a series feedback element of the VCO as well as a near-field receiving antenna. The frequency deviation of the VCO related to the body proximity effect ranges from 0.07 MHz/mm to 1.8 MHz/mm (6.8 mV/mm to 205 mV/mm in sensitivity) up to a distance of 50 mm, while the amount of VCO drift is about 21 MHz in the condition of 60 °C temperature range and discrete component tolerance of ±5%. Total frequency variation occurs in the capture range of the PLL which is 60 MHz. Thus, its loop control voltage converts the amount of frequency deviation into a difference of direct current (DC) voltage, which is utilized to extract vital signs regardless of the ambient temperature. The experimental results reveal that the proposed sensor placed 50 mm away from a subject can reliably detect respiration and heartbeat signals without the ambiguity of harmonic signals caused by respiration signal at an operating frequency of 2.4 GHz.

negative: Principles of micro-RNA production and maturation Micro-RNAs (miRNAs) are a class of approximately 22-nucleotide non-coding RNAs expressed in multicellular organisms. They are first transcribed in a similar manner to pre-mRNAs. The transcripts then go through a series of processing steps, including endonucleolytic cleavage, nuclear export and a strand selection procedure, to yield the single-stranded mature miRNA products. The transcription and processing of miRNAs determines the abundance and the sequence of mature miRNAs and has important implications for the function of miRNAs.

negative: A survey on nature inspired metaheuristic algorithms for partitional clustering The partitional clustering concept started with K-means algorithm which was published in 1957. Since then many classical partitional clustering algorithms have been reported based on gradient descent approach. The 1990 kick started a new era in cluster analysis with the application of nature inspired metaheuristics. After initial formulation nearly two decades have passed and researchers have developed numerous new algorithms in this field. This paper embodies an up-to-date review of all major nature inspired metaheuristic algorithms employed till date for partitional clustering. Further, key issues involved during formulation of various metaheuristics as a clustering problem and major application areas are discussed. & 2014 Published by Elsevier B.V.

negative: Broadband Substrate Integrated Waveguide Cavity-Backed Bow-Tie Slot Antenna A novel design technique for broadband substrate integrated waveguide cavity-backed slot antenna is demonstrated in this letter. Instead of using a conventional narrow rectangular slot, a bow-tie-shaped slot is implemented to get broader bandwidth performance. The modification of the slot shape helps to induce strong loading effect in the cavity and generates two closely spaced hybrid modes that help to get a broadband response. The slot antenna incorporates thin cavity backing (height <;0.03λ0 ) in a single substrate and thus retains low-profile planar configuration while showing unidirectional radiation characteristics with moderate gain. A fabricated prototype is also presented that shows a bandwidth of 1.03 GHz (9.4%), a gain of 3.7 dBi over the bandwidth, 15 dB front-to-back ratio, and cross-polarization level below -18 dB.

negative: Scaling and criticality in a stochastic multi-agent model of a financial market Financial prices have been found to exhibit some universal characteristics that resemble the scaling laws characterizing physical systems in which large numbers of units interact. This raises the question of whether scaling in finance emerges in a similar way — from the interactions of a large ensemble of market participants. However, such an explanation is in contradiction to the prevalent ‘efficient market hypothesis’ in economics, which assumes that the movements of financial prices are an immediate and unbiased reflection of incoming news about future earning prospects. Within this hypothesis, scaling in price changes would simply reflect similar scaling in the ‘input’ signals that influence them. Here we describe a multi-agent model of financial markets which supports the idea that scaling arises from mutual interactions of participants. Although the ‘news arrival process’ in our model lacks both power-law scaling and any temporal dependence in volatility, we find that it generates such behaviour as a result of interactions between agents.

negative: From genetic privacy to open consent Recent advances in high-throughput genomic technologies are showing concrete results in the form of an increasing number of genome-wide association studies and in the publication of comprehensive individual genome–phenome data sets. As a consequence of this flood of information the established concepts of research ethics are stretched to their limits, and issues of privacy, confidentiality and consent for research are being re-examined. Here, we show the feasibility of the co-development of scientific innovation and ethics, using the open-consent framework that was implemented in the Personal Genome Project as an example.

negative: Classification of Heart Disease Dataset using Multilayer Feed forward backpropogation Algorithm In this study, for the classification of heart diseases dataset, multilayer feed forward network with backpropogation algorithm is introduced. Artificial Neural Network (ANN) is widely used data mining method to extract patterns. Data mining is the process of automating information discovery .Its main aim is to find relationships in data and to predict outcomes. Classification is one of the important data mining techniques for classifying given set of input data. Many real world problems in various fields can be solved by using classification approach such as business, science, industry and medicine. For analysis the medical data related to Heart diseases is considered and analyzed using artificial neural network (ANN). To perform classification task of medical data, the neural network is trained using back propagation algorithm with momentum. To increase the efficiency of the classification process parallel processing approach is also applies on each neuron in different layers.

negative: Lightweight Authentication Scheme with Dynamic Group Members in IoT Environments In IoT environments, the user may have many devices to connect each other and share the data. Also, the device will not have the powerful computation and storage ability. Many studies have focused on the lightweight authentication between the cloud server and the client in this environment. They can use the cloud server to help sensors or proxies to finish the authentication. But in the client side, how to create the group session key without the cloud capability is the most important issue in IoT environments. The most popular application network of IoT environments is the wireless body area network (WBAN). In WBAN, the proxy usually needs to control and monitor user's health data transmitted from the sensors. In this situation, the group authentication and group session key generation is needed. In this paper, in order to provide an efficient and robust group authentication and group session key generation in the client side of IoT environments, we propose a lightweight authentication scheme with dynamic group members in IoT environments. Our proposed scheme can satisfy the properties including the flexible generation of shared group keys, the dynamic participation, the active revocation, the low communication and computation cost, and no time synchronization problem. Also our scheme can achieve the security requirements including the mutual authentication, the group session key agreement, and prevent all various well-known attacks.

negative: Training a Fully Convolutional Neural Network to Route Integrated Circuits We present a deep, fully convolutional neural network that learns to route a circuit layout ‘net’ with appropriate choice of metal tracks and wire class combinations. Inputs to the network are the encoded layouts containing spatial location of pins to be routed. After 15 fully convolutional stages followed by a score comparator, the network outputs 8 layout layers (corresponding to 4 route layers, 3 via layers and an identity-mapped pin layer) which are then decoded to obtain the routed layouts. We formulate this as a binary segmentation problem on a per-pixel per-layer basis, where the network is trained to correctly classify pixels in each layout layer to be ‘on’ or ‘off’. To demonstrate learnability of layout design rules, we train the network on a dataset of 50,000 train and 10,000 validation samples that we generate based on certain pre-defined layout constraints. Precision, recall and F1 score metrics are used to track the training progress. Our network achieves F1 ≈ 97% on the train set and F1 ≈ 92% on the validation set. We use PyTorch for implementing our model. Code is made publicly available1.

negative: Smart home for elderly living using Wireless Sensor Networks and an Android application A Smart Home (SH) is a house or an apartment equipped with advanced automation technologies to provide the occupants with intelligent monitoring and actionable information that can be situation specific. It allows for improvements in the way we live or work, and for improved energy efficiencies. The smart home is especially relevant for the elderly because the intelligent sensing systems would allow for remote monitoring and possibly control of health and environmental parameters according to their health status and living needs. The burgeoning elderly population and rising costs of elderly healthcare and home needs have led to a rapid evolution of the elderly smart living environment. However, the lack of proper eldercare smart home technologies has inspired us to develop Information and Communication Technologies (ICT) based smart home for the elderly using the Android platform. We have proposed a model for the elderly using Wireless Sensor Networks (WSNs) implemented as an Android application. Our smart home research potentially allows the elderly to continue to live in their own homes while being monitored non-invasively, seamlessly and economically according to their healthcare needs and status.

negative: E-GOVERNMENT EVALUATION FACTORS: CITIZEN’S PERSPECTIVE The e-government field is growing to a considerable size, both in its contents and position with respect to other research fields. The government to citizen segment of egovernment is taking the lead in terms of its importance and size. Like the evaluation of all other information systems initiatives, the evaluation of egovernments in both theory and practice has proved to be important but complex. The complexity of evaluation is mostly due to the multiple perspectives involved, the difficulties of quantifying benefits, and the social and technical context of use. The importance of e-government evaluation is due to the enormous investment of governments on delivering e-government services, and to the considerable pace of growing in the e-government field. However, despite the importance of the evaluation of e-government services, literature shows that e-government evaluation is still an immature area in terms of development and management. This work is part of a research effort that aims to develop a holistic evaluation framework for e-government systems. The main aim of this paper is to investigate the citizen’ perspective in evaluating e-government services, and present a set of evaluating factors that influence citizens’ utilization of e-government services. These evaluation factors can serve as part of an e-government evaluation framework. Moreover, the evaluation factors can also be used as means of providing valuable feedback for the planning of future egovernment initiatives.

negative: Attentive Moment Retrieval in Videos In the past few years, language-based video retrieval has attracted a lot of attention. However, as a natural extension, localizing the specific video moments within a video given a description query is seldom explored. Although these two tasks look similar, the latter is more challenging due to two main reasons: 1) The former task only needs to judge whether the query occurs in a video and returns an entire video, but the latter is expected to judge which moment within a video matches the query and accurately returns the start and end points of the moment. Due to the fact that different moments in a video have varying durations and diverse spatial-temporal characteristics, uncovering the underlying moments is highly challenging. 2) As for the key component of relevance estimation, the former usually embeds a video and the query into a common space to compute the relevance score. However, the later task concerns moment localization where not only the features of a specific moment matter, but the context information of the moment also contributes a lot. For example, the query may contain temporal constraint words, such as "first'', therefore need temporal context to properly comprehend them. To address these issues, we develop an Attentive Cross-Modal Retrieval Network. In particular, we design a memory attention mechanism to emphasize the visual features mentioned in the query and simultaneously incorporate their context. In the light of this, we obtain the augmented moment representation. Meanwhile, a cross-modal fusion sub-network learns both the intra-modality and inter-modality dynamics, which can enhance the learning of moment-query representation. We evaluate our method on two datasets: DiDeMo and TACoS. Extensive experiments show the effectiveness of our model as compared to the state-of-the-art methods.

negative: First-person hyper-lapse videos We present a method for converting first-person videos, for example, captured with a helmet camera during activities such as rock climbing or bicycling, into hyper-lapse videos, i.e., time-lapse videos with a smoothly moving camera. At high speed-up rates, simple frame sub-sampling coupled with existing video stabilization methods does not work, because the erratic camera shake present in first-person videos is amplified by the speed-up. Our algorithm first reconstructs the 3D input camera path as well as dense, per-frame proxy geometries. We then optimize a novel camera path for the output video that passes near the input cameras while ensuring that the virtual camera looks in directions that can be rendered well from the input. Finally, we generate the novel smoothed, time-lapse video by rendering, stitching, and blending appropriately selected source frames for each output frame. We present a number of results for challenging videos that cannot be processed using traditional techniques.

negative: CubeNet: Equivariance to 3D Rotation and Translation 3D Convolutional Neural Networks are sensitive to transformations applied to their input. This is a problem because a voxelized version of a 3D object, and its rotated clone, will look unrelated to each other after passing through to the last layer of a network. Instead, an idealized model would preserve a meaningful representation of the voxelized object, while explaining the pose-difference between the two inputs. An equivariant representation vector has two components: the invariant identity part, and a discernable encoding of the transformation. Models that can’t explain pose-differences risk “diluting” the representation, in pursuit of optimizing a classification or regression loss function. We introduce a Group Convolutional Neural Network with linear equivariance to translations and right angle rotations in three dimensions. We call this network CubeNet, reflecting its cube-like symmetry. By construction, this network helps preserve a 3D shape’s global and local signature, as it is transformed through successive layers. We apply this network to a variety of 3D inference problems, achieving state-of-the-art on the ModelNet10 classification challenge, and comparable performance on the ISBI 2012 Connectome Segmentation Benchmark. To the best of our knowledge, this is the first 3D rotation equivariant CNN for voxel representations.

negative: Like: The Discourse Particle and Semantics Using data from interviews with high school students, I first adduce evidence that lends support to Schourup’s (1985) claim that the United States English adolescent hedge like is a discourse particle signalling a possible slight mismatch between words and meaning. Such a particle would generally be included in a grammar in a post-compositional pragmatic component, but, surprisingly, like also affects basic semantic attributes. These include both truth-conditions and the weak/strong distinction—though only in existential there and sluicing sentences. I argue that the differential behaviour of like in various constructions selecting weak NP’s stems from the restricted free variable it introduces, a variable which only there and sluicing require. This variable is available for binding, quantifier interpretation and other syntactic-semantic processes, yet is pragmatically conditioned. Indeed, I show that, due to its formal properties, like can be interpreted only during the assignment of model-theoretic denotations to expressions, along the lines of Lasersohn’s (1999) pragmatic haloes. These results support the idea that weak/strong is not a unitary distinction and suggest that the various components of grammars must be organized to allow information from pragmatic/discourse elements to affect basic compositional semantics.

negative: A Maturity Model for Green ICT: The Case of the SURF Green ICT Maturity Model This paper discusses the development and evaluation of a maturity model for Green ICT. We describe how the model was developed with the input of a number of Green ICT experts before it was released to the general public. The model consists of three domains with attributes on Green ICT that encompass both Greening of ICT as well as Greening by ICT. The quality of the model and its accuracy to capture the full scope of Green ICT has been evaluated through an online survey. We evaluated the quality of the model on relevancy of attributes, whether the attributes were well defined and whether the domains were complete. Twenty participants contributed meaningfully. Two attributes were considered to be irrelevant and six new attributes were suggested. With these results the quality of the maturity model can be improved. Our next step is to test the usefulness of the model by seeing how it is used in practice. We hope this paper inspires more work on testing the quality and usefulness of models and frameworks on Green ICT.

negative: Zero-Shot Cross-lingual Classification Using Multilingual Neural Machine Translation Transferring representations from large supervised tasks to downstream tasks has shown promising results in AI fields such as Computer Vision and Natural Language Processing (NLP). In parallel, the recent progress in Machine Translation (MT) has enabled one to train multilingual Neural MT (NMT) systems that can translate between multiple languages and are also capable of performing zero-shot translation. However, little attention has been paid to leveraging representations learned by a multilingual NMT system to enable zeroshot multilinguality in other NLP tasks. In this paper, we demonstrate a simple framework, a multilingual EncoderClassifier, for cross-lingual transfer learning by reusing the encoder from a multilingual NMT system and stitching it with a task-specific classifier component. Our proposed model achieves significant improvements in the English setup on three benchmark tasks Amazon Reviews, SST and SNLI. Further, our system can perform classification in a new language for which no classification data was seen during training, showing that zero-shot classification is possible and remarkably competitive. In order to understand the underlying factors contributing to this finding, we conducted a series of analyses on the effect of the shared vocabulary, the training data type for NMT, classifier complexity, encoder representation power, and model generalization on zero-shot performance. Our results provide strong evidence that the representations learned from multilingual NMT systems are widely applicable across languages and tasks.

negative: 360ProbDASH: Improving QoE of 360 Video Streaming Using Tile-based HTTP Adaptive Streaming Recently, there has been a significant interest towards 360-degree panorama video. However, such videos usually require extremely high bitrate which hinders their widely spread over the Internet. Tile-based viewport adaptive streaming is a promising way to deliver 360-degree video due to its on-request portion downloading. But it is not trivial for it to achieve good Quality of Experience (QoE) because Internet request-reply delay is usually much higher than motion-to-photon latency. In this paper, we leverage a probabilistic approach to pre-fetch tiles countering viewport prediction error, and design a QoE-driven viewport adaptation system, 360ProbDASH. It treats user's head movement as probability events, and constructs a probabilistic model to depict the distribution of viewport prediction error. A QoE-driven optimization framework is proposed to minimize total expected distortion of pre-fetched tiles. Besides, to smooth border effects of mixed-rate tiles, the spatial quality variance is also minimized. With the requirement of short-term viewport prediction under a small buffer, it applies a target-buffer-based rate adaptation algorithm to ensure continuous playback. We implement 360ProbDASH prototype and carry out extensive experiments on a simulation test-bed and real-world Internet with real user's head movement traces. The experimental results demonstrate that 360ProbDASH achieves at almost 39% gains on viewport PSNR, and 46% reduction on spatial quality variance against the existed viewport adaptation methods.

negative: Analysis of YouTube’s traffic adaptation to dynamic environments The popular Internet service, YouTube, has adopted by default the HyperText Markup Language version 5 (HTML5). With this adoption, YouTube has moved to Dynamic Adaptive Streaming over HTTP (DASH) as Adaptive BitRate (ABR) video streaming technology. Furthermore, rate adaptation in DASH is solely receiver-driven. This issue motivates this work to make a deep analysis of YouTube’s particular DASH implementation. Firstly, this article provides a state of the art about DASH and adaptive streaming technology, and also YouTube traffic characterization related work. Secondly, this paper describes a new methodology and test-bed for YouTube’s DASH implementation traffic characterization and performance measurement. This methodology and test-bed do not make use of proxies and, moreover, they are able to cope with YouTube traffic redirections. Finally, a set of experimental results are provided, involving a dataset of 310 YouTube’s videos. The depicted results show a YouTube’s traffic pattern characterization and a discussion about allowed download bandwidth, YouTube’s consumed bitrate and quality of the video. Moreover, the obtained results are cross-validated with the analysis of HTTP requests performed by YouTube’s video player. The outcomes of this article are applicable in the field of Quality of Service (QoS) and Quality of Experience (QoE) management. This is valuable information for Internet Service Providers (ISPs), because QoS management based on assured download bandwidth can be used in order to provide a target end-user’s QoE when YouTube service is being consumed.

negative: A Survey on Quality of Experience of HTTP Adaptive Streaming Changing network conditions pose severe problems to video streaming in the Internet. HTTP adaptive streaming (HAS) is a technology employed by numerous video services that relieves these issues by adapting the video to the current network conditions. It enables service providers to improve resource utilization and Quality of Experience (QoE) by incorporating information from different layers in order to deliver and adapt a video in its best possible quality. Thereby, it allows taking into account end user device capabilities, available video quality levels, current network conditions, and current server load. For end users, the major benefits of HAS compared to classical HTTP video streaming are reduced interruptions of the video playback and higher bandwidth utilization, which both generally result in a higher QoE. Adaptation is possible by changing the frame rate, resolution, or quantization of the video, which can be done with various adaptation strategies and related client- and server-side actions. The technical development of HAS, existing open standardized solutions, but also proprietary solutions are reviewed in this paper as fundamental to derive the QoE influence factors that emerge as a result of adaptation. The main contribution is a comprehensive survey of QoE related works from human computer interaction and networking domains, which are structured according to the QoE impact of video adaptation. To be more precise, subjective studies that cover QoE aspects of adaptation dimensions and strategies are revisited. As a result, QoE influence factors of HAS and corresponding QoE models are identified, but also open issues and conflicting results are discussed. Furthermore, technical influence factors, which are often ignored in the context of HAS, affect perceptual QoE influence factors and are consequently analyzed. This survey gives the reader an overview of the current state of the art and recent developments. At the same time, it targets networking researchers who develop new solutions for HTTP video streaming or assess video streaming from a user centric point of view. Therefore, this paper is a major step toward truly improving HAS.

negative: Collaborative Edge and Cloud Neural Networks for Real-Time Video Processing The efficient processing of video streams is a key component in many emerging Internet of Things (IoT) and edge applications, such as Virtual and Augmented Reality (V/AR) and self-driving cars. These applications require real-time high-throughput video processing. This can be attained via a collaborative processing model between the edge and the cloud—called an Edge-Cloud model. To this end, many approaches were proposed to optimize the latency and bandwidth consumption of Edge-Cloud video processing, especially for Neural Networks (NN)-based methods. In this demonstration. We investigate the efficiency of these NN techniques, how they can be combined, and whether combining them leads to better performance. Our demonstration invites participants to experiment with the various NN techniques, combine them, and observe how the underlying NN changes with different techniques and how these changes affect accuracy, latency and bandwidth consumption. PVLDB Reference Format: Philipp M. Grulich and Faisal Nawab. Collaborative Edge and Cloud Neural Networks for Real-Time Video Processing. PVLDB, 11 (12): 2046-2049, 2018. DOI: https://doi.org/10.14778/3229863.3236256

negative: Live-streaming mobile video: production as civic engagement Live-streaming mobile video is an emerging medium. Few have measured how this new form of production is contributing to civic engagement or broadening the public sphere by circulating visual footage of community interest. Here I explore the overall trends in production of live-streaming mobile video from producers around the world, and focus more narrowly on the motivations and practices surrounding the production of civic content. Informing my study are mobile videos on Qik.com, a popular web service for live-streaming mobile video. I offer a quantitative content analysis of 1,000 videos, summarizing general trends in content production as well as analyzing the motivations behind the production of civic content, based on qualitative interviews with frequent producers. Results indicate that production is higher among those who self-identify as activists, journalists, community leaders or educators, suggesting this new medium can be best appropriated by those who are already civically engaged.

negative: Internet Scale User-Generated Live Video Streaming: The Twitch Case Twitch is a live video streaming platform used for broadcasting video gameplay, ranging from amateur players to eSports tournaments. This platform has gathered a substantial world wide community, reaching more than 1.7 million broadcasters and 100 million visitors every month. Twitch is fundamentally different from “static” content distribution platforms such as YouTube and Netflix, as streams are generated and consumed in real time. In this paper, we explore the Twitch infrastructure to understand how it manages live streaming delivery to an Internet-wide audience. We found Twitch manages a geo-distributed infrastructure, with presence in four continents. Our findings show that Twitch dynamically allocates servers to channels depending on their popularity. Additionally, we explore the redirection strategy of clients to servers depending on their region and the specific channel.

negative: Low-cost traffic analysis of Tor Tor is the second generation onion router supporting the anonymous transport of TCP streams over the Internet. Its low latency makes it very suitable for common tasks, such as Web browsing, but insecure against traffic-analysis attacks by a global passive adversary. We present new traffic-analysis techniques that allow adversaries with only a partial view of the network to infer which nodes are being used to relay the anonymous streams and therefore greatly reduce the anonymity provided by Tor. Furthermore, we show that otherwise unrelated streams can be linked back to the same initiator Our attack is feasible for the adversary anticipated by the Tor designers. Our theoretical attacks are backed up by experiments performed on the deployed, albeit experimental, Tor network. Our techniques should also be applicable to any low latency anonymous network. These attacks highlight the relationship between the field of traffic-analysis and more traditional computer security issues, such as covert channel analysis. Our research also highlights that the inability to directly observe network links does not prevent an attacker from performing traffic-analysis: the adversary can use the anonymising network as an oracle to infer the traffic load on remote nodes in order to perform traffic-analysis.