nils-herrmann/clean_scirepeval huggingface dataset:
Modeling Temporal Dynamics and Spatial Configurations of Actions Using Two-Stream Recurrent Neural Networks

Recently, skeleton based action recognition gains more popularity due to cost-effective depth sensors coupled with real-time skeleton estimation algorithms. Traditional approaches based on handcrafted features are limited to represent the complexity of motion patterns. Recent methods that use Recurrent Neural Networks (RNN) to handle raw skeletons only focus on the contextual dependency in the temporal domain and neglect the spatial configurations of articulated skeletons. In this paper, we propose a novel two-stream RNN architecture to model both temporal dynamics and spatial configurations for skeleton based action recognition. We explore two different structures for the temporal stream: stacked RNN and hierarchical RNN. Hierarchical RNN is designed according to human body kinematics. We also propose two effective methods to model the spatial structure by converting the spatial graph into a sequence of joints. To improve generalization of our model, we further exploit 3D transformation based data augmentation techniques including rotation and scaling transformation to transform the 3D coordinates of skeletons during training. Experiments on 3D action recognition benchmark datasets show that our method brings a considerable improvement for a variety of actions, i.e., generic actions, interaction activities and gestures.
High speed locomotion for a quadrupedal microrobot

Research over the past several decades has elucidated some of the mechanisms behind high speed, highly efficient and robust locomotion in insects such as cockroaches. Roboticists have used this information to create biologically-inspired machines capable of running, jumping, and climbing robustly over a variety of terrains. To date, little work has been done to develop an at-scale insect-inspired robot capable of similar feats due to challenges in fabrication, actuation, and electronics integration for a centimeter-scale device. This paper addresses these challenges through the design, fabrication, and control of a 1.27g walking robot, the Harvard Ambulatory MicroRobot (HAMR). The current design is manufactured using a method inspired by pop-up books that enables fast and repeatable assembly of the miniature walking robot. Methods to drive HAMR at low and high speeds are presented, resulting in speeds up to 0.44m/s (10.1 body lengths per second) and the ability to maneuver and control the robot along desired trajectories.
Understanding Virtual Experience and Telepresence - a Review and Synthesis of literature

To enable buyers to be better informed before purchasing, products and services can be virtually experienced on the internet. Research into virtual experience (VE) and the related construct of telepresence (TP) as means of online marketing has made great progress in recent years. However, there is still disagreement in the literature concerning the exact understanding of these terms. In this study, the two terms are analyzed by means of a systematically executed literature review, differentiated from one another, and their understandings explained. This study is to our knowledge the first to compare the concepts of VE and TP in a systematic way. The analysis shows that TP is regarded as the feeling of presence conveyed by a communication medium. VE, on the other hand, is to be defined as an active state of a consumer through the use of computer-based presentation formats, and constituting a subtype of TP. These findings are intended to help VE and TP become more uniformly understood and make it easier to compare the results of future studies. Finally, from the literature review, it is possible to derive focal points for research in future studies.
Sparse and Redundant Representations - From Theory to Applications in Signal and Image Processing

Thank you very much for downloading sparse and redundant representations from theory to applications in signal and image processing 1 ed. Maybe you have knowledge that, people have search hundreds times for their favorite readings like this sparse and redundant representations from theory to applications in signal and image processing 1 ed, but end up in infectious downloads. Rather than reading a good book with a cup of coffee in the afternoon, instead they are facing with some infectious virus inside their desktop computer.
Flash memory cells-an overview

The aim of this paper is to give a thorough overview of Flash memory cells. Basic operations and charge-injection mechanisms that are most commonly used in actual Flash memory cells are reviewed to provide an understanding of the underlying physics and principles in order to appreciate the large number of device structures, processing technologies, and circuit designs presented in the literature. New cell structures and architectural solutions have been surveyed to highlight the evolution of the Flash memory technology, oriented to both reducing cell size and upgrading product functions. The subject is of extreme interest: new concepts involving new materials, structures, principles, or applications are being continuously introduced. The worldwide semiconductor memory market seems ready to accept many new applications in fields that are not specific to traditional nonvolatile memories.
A survey of trust computation models for service management in internet of things systems

In this paper we survey trust computation models for Internet of things (IoT) systems for the purpose of service management, i.e., whether or not to select an IoT device as a service provider. Future IoT systems will connect the physical world into cyberspace everywhere and everything via billions of smart objects, and are expected to have a high economic impact. To date there is little work on trust computation in IoT environments for service management, especially for dealing with misbehaving owners of IoT devices that provide services to other IoT devices in the system. Our approach is to classify existing trust computation models for service management in IoT systems based on five essential design dimensions for a trust computation model: trust composition, trust propagation, trust aggregation, trust update, and trust formation. We summarize pros and cons of each dimension’s options, and highlight the effectiveness of defense mechanisms against malicious attacks. We also summarize the most, least, and little visited trust computation techniques in the literature and provide insight on the effectiveness of trust computation techniques as applying to IoT systems. Finally, we identify gaps in IoT trust computation research and suggest future research directions.
Overconfidence and Trading Volume

Theoretical models predict that overconfident investors will trade more than rational investors. We directly test this hypothesis by correlating individual overconfidence scores with several measures of trading volume of individual investors. Approximately 3,000 online broker investors were asked to answer an internet questionnaire which was designed to measure various facets of overconfidence (miscalibration, volatility estimates, better than average effect). The measures of trading volume were calculated by the trades of 215 individual investors who answered the questionnaire. We find that investors who think that they are above average in terms of investment skills or past performance (but who did not have above average performance in the past) trade more. Measures of miscalibration are, contrary to theory, unrelated to measures of trading volume. This result is striking as theoretical models that incorporate overconfident investors mainly motivate this assumption by the calibration literature and model overconfidence as underestimation of the variance of signals. In connection with other recent findings, we conclude that the usual way of motivating and modeling overconfidence which is mainly based on the calibration literature has to be treated with caution. Moreover, our way of empirically evaluating behavioral finance models the correlation of economic and psychological variables and the combination of psychometric measures of judgment biases (such as overconfidence scores) and field data seems to be a promising way to better understand which psychological phenomena actually drive economic behavior.
The mirror neuron system and the consequences of its dysfunction

The discovery of premotor and parietal cells known as mirror neurons in the macaque brain that fire not only when the animal is in action, but also when it observes others carrying out the same actions provides a plausible neurophysiological mechanism for a variety of important social behaviours, from imitation to empathy. Recent data also show that dysfunction of the mirror neuron system in humans might be a core deficit in autism, a socially isolating condition. Here, we review the neurophysiology of the mirror neuron system and its role in social cognition and discuss the clinical implications of mirror neuron dysfunction.
A Software Product Line Approach for Configuring Cloud Robotics Applications

The computational requirements of the increasingly sophisticated algorithms used in today's robotics software applications have outpaced the onboard processors of the average robot. Furthermore, the development and configuration of these applications are difficult tasks that require expertise in diverse domains, including software engineering, control engineering, and computer vision. As a solution to these problems, this paper extends and integrates our previous works, which are based on two promising techniques: Cloud Robotics and Software Product Lines. Cloud Robotics provides a powerful and scalable environment to offload the computationally expensive algorithms resulting in low-cost processors and light-weight robots. Software Product Lines allow the end user to deploy and configure complex robotics applications without dealing with low-level problems such as configuring algorithms and designing architectures. This paper discusses the proposed method in depth, and demonstrates its advantages with a case study.
Supervised Distance Preserving Projections

In this work, we consider dimensionality reduction in supervised settings and, specifically, we focus on regression problems. A novel algorithm, the supervised distance preserving projection (SDPP), is proposed. The SDPP minimizes the difference between pairwise distances among projected input covariates and distances among responses locally. This minimization of distance differences leads to the effect that the local geometrical structure of the low-dimensional subspace retrieved by the SDPP mimics that of the response space. This, not only facilitates an efficient regressor design but it also uncovers useful information for visualization. The SDPP achieves this goal by learning a linear parametric mapping and, thus, it can easily handle out-of-sample data points. For nonlinear data, a kernelized version of the SDPP is also derived. In addition, an intuitive extension of the SDPP is proposed to deal with classification problems. The experimental evaluation on both synthetic and real-world data sets demonstrates the effectiveness of the SDPP, showing that it performs comparably or superiorly to state-of-the-art approaches.
Edge Detection in Digital Images Using Fuzzy Logic Technique

The fuzzy technique is an operator introduced in order to simulate at a mathematical level the compensatory behavior in process of decision making or subjective evaluation. The following paper introduces such operators on hand of computer vision application. In this paper a novel method based on fuzzy logic reasoning strategy is proposed for edge detection in digital images without determining the threshold value. The proposed approach begins by segmenting the images into regions using floating 3x3 binary matrix. The edge pixels are mapped to a range of values distinct from each other. The robustness of the proposed method results for different captured images are compared to those obtained with the linear Sobel operator. It is gave a permanent effect in the lines smoothness and straightness for the straight lines and good roundness for the curved lines. In the same time the corners get sharper and can be defined easily. Fuzzy logic, Edge detection, Image processing, computer vision, Mechanical parts, Measurement.
Automatically Categorizing Written Texts by Author Gender

The problem of automatically determining the gender of a document's author would appear to be a more subtle problem than those of categorization by topic or authorship attribution. Nevertheless, it is shown that automated text categorization techniques can exploit combinations of simple lexical and syntactic features to infer the gender of the author of an unseen formal written document with approximately 80% accuracy. The same techniques can be used to determine if a document is fiction or non-fiction with approximately 98% accuracy.
Automatically verifying and reproducing event-based races in Android apps

Concurrency has been a perpetual problem in Android apps, mainly due to event-based races. Several event-based race detectors have been proposed, but they produce false positives, cannot reproduce races, and cannot distinguish be- tween benign and harmful races. To address these issues, we introduce a race verification and reproduction approach named ERVA. Given a race report produced by a race detector, ERVA uses event dependency graphs, event flipping, and replay to verify the race and determine whether it is a false positive, or a true positive; for true positives, ERVA uses state comparison to distinguish benign races from harmful races. ERVA automatically produces an event schedule that can be used to deterministically reproduce the race, so developers can fix it. Experiments on 16 apps indicate that only 3% of the races reported by race detectors are harmful, and that ERVA can verify an app in 20 minutes on average.
Flow Motifs in Soccer : What can passing behavior tell us ?

In soccer, both individual and team performance is crucial to win matches. Passing is the backbone of the game and forms the basis of important decisions made by managers and owners; such as buying players, picking offensive or defensive strategies or even defining a style of play. These decisions can be supported by analyzing how a player performs and how his style affects team performance. The flow of a player or a team can be studied by finding unique passing motifs from the patterns in the subgraphs of a possession-passing network of soccer games. These flow motifs can be used to analyze individual players and teams based on the diversity and frequency of their involvement in different motifs. Building on the flow motif analyses, we introduce an expected goals model to measure the effectiveness of each style of play. We also make use of a novel way to represent motif data that is easy to understand and can be used to compare players, teams and seasons. Further, we exploit the relationship between play style and the pass probability matrix to support our analysis. Our data set has the last 4 seasons of 6 big European leagues with 8219 matches, 3532 unique players and 155 unique teams. We will use flow motifs to analyze different events, such as for example the transfer of Claudio Bravo to Pep Guardiola’s Manchester City, who Jean Seri is and why he must be an elite midfielder and the difference in attacking style between Lionel Messi and Cristiano Ronaldo. Ultimately, an analysis of Post-Fàbregas Arsenal is conducted wherein different techniques are combined to analyze the impact the acquisition of Mesut Özil and Alexis Sánchez had on the strategies implemented at Arsenal.
Human activity recognition using inertial sensors with invariance to sensor orientation

This work deals with the task of human daily activity recognition using miniature inertial sensors. The proposed method reduces sensitivity to the position and orientation of the sensor on the body, which is inherent in traditional methods, by transforming the observed signals to a “virtual” sensor orientation. By means of this computationally low-cost transform, the inputs to the classification algorithm are made invariant to sensor orientation, despite the signals being recorded from arbitrary sensor placements. Classification results show that improved performance, in terms of both precision and recall, is achieved with the transformed signals, relative to classification using raw sensor signals, and the algorithm performs competitively compared to the state-of-the-art. Activity recognition using data from a sensor with completely unknown orientation is shown to perform very well over a long term recording in a real-life setting.
Up next: retrieval methods for large scale related video suggestion

The explosive growth in sharing and consumption of the video content on the web creates a unique opportunity for scientific advances in video retrieval, recommendation and discovery. In this paper, we focus on the task of video suggestion, commonly found in many online applications. The current state-of-the-art video suggestion techniques are based on the collaborative filtering analysis, and suggest videos that are likely to be co-viewed with the watched video. In this paper, we propose augmenting the collaborative filtering analysis with the topical representation of the video content to suggest related videos. We propose two novel methods for topical video representation. The first method uses information retrieval heuristics such as tf-idf, while the second method learns the optimal topical representations based on the implicit user feedback available in the online scenario. We conduct a large scale live experiment on YouTube traffic, and demonstrate that augmenting collaborative filtering with topical representations significantly improves the quality of the related video suggestions in a live setting, especially for categories with fresh and topically-rich video content such as news videos. In addition, we show that employing user feedback for learning the optimal topical video representations can increase the user engagement by more than 80% over the standard information retrieval representation, when compared to the collaborative filtering baseline.
A Conversational Agent Framework using Semantic Analysis

This paper focuses on the implementation of a novel semantic-based Conversational Agent (CA) framework. Traditional CAs interpret scripts consisting of structural patterns of sentences. User input is matched against such patterns and an associated response is sent as output. This traditional CA approach, which solely takes into account the structural form of a sentence, requires the scripter to anticipate the inordinate ways that a user may send input. This is a tiresome and timeconsuming process. As such, a semantic-based CA that interprets scripts consisting of natural language sentences alleviates this burden by removing the process of pattern generation. The CA was evaluated by participants using a domain of a specific nature, that is, student debt management, which indicated promising results.
Attribution Strategies and Return on Keyword Investment in Paid Search Advertising

Firms use different attribution strategies such as last-click attribution or first-click attribution to assign conversion credits to search keywords that appear in their consumers’ paths to purchase. These attributed credits impact a firm’s future bidding and budget allocations among keywords and, in turn, determine the overall return-on-investment of search campaigns. In this paper, we model the relationship among the advertiser’s bidding decision for keywords, the search engine’s ranking decision for these keywords, and the consumer’s click-through rate and conversion rate on each keyword, and analyze the impact of the attribution strategy on the overall return-oninvestment of paid search advertising. We estimate our simultaneous equations model using a six-month panel data of several hundred keywords from an online jewelry retailer. The data comprises a quasi-experiment as the firm changed attribution strategy from last-click attribution to first-click attribution half-way through the data window. Our results show that returns for keyword investments vary significantly under the different attribution strategies. For the focal firm, first-click attribution leads to lower returns in terms of revenue and the decrease is more pronounced for the more specific keywords. Our policy simulation exercise shows how the firm can increase its overall returns by better attributing the real contribution of keywords. We discuss how an appropriate attribution strategy can help firms to better target customers and lower acquisition costs in the context of paid search advertising.
Apache Hadoop YARN: yet another resource negotiator

The initial design of Apache Hadoop  was tightly focused on running massive, MapReduce jobs to process a web crawl. For increasingly diverse companies, Hadoop has become the data and computational agorá---the de facto place where data and computational resources are shared and accessed. This broad adoption and ubiquitous usage has stretched the initial design well beyond its intended target, exposing two key shortcomings: 1) tight coupling of a specific programming model with the resource management infrastructure, forcing developers to abuse the MapReduce programming model, and 2) centralized handling of jobs' control flow, which resulted in endless scalability concerns for the scheduler.
 In this paper, we summarize the design, development, and current state of deployment of the next generation of Hadoop's compute platform: YARN. The new architecture we introduced decouples the programming model from the resource management infrastructure, and delegates many scheduling functions (e.g., task fault-tolerance) to per-application components. We provide experimental evidence demonstrating the improvements we made, confirm improved efficiency by reporting the experience of running YARN on production environments (including 100% of Yahoo! grids), and confirm the flexibility claims by discussing the porting of several programming frameworks onto YARN viz. Dryad, Giraph, Hoya, Hadoop MapReduce, REEF, Spark, Storm, Tez.
Open Domain Targeted Sentiment

We propose a novel approach to sentiment analysis for a low resource setting. The intuition behind this work is that sentiment expressed towards an entity, targeted sentiment, may be viewed as a span of sentiment expressed across the entity. This representation allows us to model sentiment detection as a sequence tagging problem, jointly discovering people and organizations along with whether there is sentiment directed towards them. We compare performance in both Spanish and English on microblog data, using only a sentiment lexicon as an external resource. By leveraging linguisticallyinformed features within conditional random fields (CRFs) trained to minimize empirical risk, our best models in Spanish significantly outperform a strong baseline, and reach around 90% accuracy on the combined task of named entity recognition and sentiment prediction. Our models in English, trained on a much smaller dataset, are not yet statistically significant against their baselines.
Teaching Software Architecture to Undergraduate Students: An Experience Report

Software architecture lies at the heart of system thinking skills for software. Teaching software architecture requires contending with the problem of how to make the learning realistic -- most systems which students can learn quickly are too simple for them to express architectural issues. We address here the ten years' history of teaching an undergraduate software architecture course, as a part of a bachelor's program in software engineering. Included are descriptions of what we perceive the realistic goals to be, of teaching software architecture at this level. We go on to analyze the successes and issues of various approaches we have taken over the years. We finish with recommendations for others who teach this same subject, either as a standalone undergraduate course or integrated into a software engineering course.