nils-herrmann/clean_scirepeval huggingface dataset:
Acquiring state from control dynamics to learn grasping policies for robot hands

A prominent emerging theory of sensorimotor development in biological systems proposes that control knowledge is encoded in the dynamics of physical interaction with the world. From this perspective, the musculoskeletal system is coupled through sensor feedback and neurological structure to a non-stationary world. Control is derived by reinforcing and learning to predict constructive patterns of interaction. We have adopted the traditions of dynamic pattern theory in which behavior is an artifact of coupled dynamical systems with a number of controllable degrees of freedom. For grasping and manipulation, we propose a closed-loop control process that is parametric in the number and identity of contact resources. We have shown previously that this controller generates a necessary condition for force closure grasps. In this paper, we will show how control decisions can be made by estimating patterns of membership in a family of prototypical dynamic models. A grasp controller can thus be tuned on-line to optimize performance over a variety of object geometries. This same process can be used to match the dynamics to several previously acquired haptic categories. We will illustrate how a grasping policy can be acquired that is incrementally optimal for several objects using our Salisbury hand with tactile sensor feedback.
Evolving Gomoku solver by genetic algorithm

Gomoku, also known as Gobang or five-in-a-row, is a popular two-player strategical board game. Given a squared 15×15 board, two players compete to first obtain an unbroken row of five pieces horizontally, vertically or diagonally. Classic methods for solving such games are based on game-tree theory, for example the minimax tree. These methods have a clear disadvantage: the depth of search becomes a bottleneck all the time. In this paper we propose a genetic algorithm for solving the Gomoku game. We investigated the general framework for applying genetic algorithm to strategical games and designed the fitness function from various game-related aspects. Empirical experimental results showed that the proposed genetic solver can search in greater depth than traditional game-tree-based solvers, resulting in better and more enjoyable solutions, and does so more efficiently.
Automatic Mapping Clinical Notes to Medical Terminologies

Automatic mapping of key concepts from clinical notes to a terminology is an important task to achieve for extraction of the clinical information locked in clinical notes and patient reports. The present paper describes a system that automatically maps free text into a medical reference terminology. The algorithm utilises Natural Language Processing (NLP) techniques to enhance a lexical token matcher. In addition, this algorithm is able to identify negative concepts as well as performing term qualification. The algorithm has been implemented as a web based service running at a hospital to process real-time data and demonstrated that it worked within acceptable time limits and accuracy limits for them. However broader acceptability of the algorithm will require comprehensive evaluations.
Driving to Safety How Many Miles of Driving Would It Take to Demonstrate Autonomous Vehicle Reliability ?

• In parallel to creating new testing methods, it is imperative to develop adaptive regulations that are designed from the outset to evolve with the technology so that society can better harness the benefits and manage the risks of these rapidly evolving and potentially transformative technologies.  In the United States, roughly 32,000 people are killed and more than two million injured in crashes every year (Bureau of Transportation Statistics, 2015). U.S. motor vehicle crashes as a whole can pose economic and social costs of more than $800 billion in a single year (Blincoe et al., 2015). And, more than 90 percent of crashes are caused by human errors (National Highway Traffic Safety Administration, 2015)—such as driving too fast and misjudging other drivers’ behaviors, as well as alcohol impairment, distraction, and fatigue. Autonomous vehicles have the potential to significantly mitigate this public health crisis by eliminating many of the mistakes that human drivers routinely make (Anderson et al., 2014; Fagnant and Kockelman, 2015). To begin with, autonomous vehicles are never drunk, distracted, or tired; these factors are involved in 41 percent, 10 percent, and 2.5 percent of all fatal crashes, respectively (National Highway Traffic Safety Administration, 2011; Bureau of Transportation Statistics, 2014b; U.S. Department of Transportation, 2015).1 Their performance may also be better than human drivers because of better perception (e.g., no blind spots), better decisionmaking (e.g., more-accurate planning of complex driving maneuvers like parallel parking), and better execution (e.g., faster and more-precise control of steering, brakes, and acceleration). However, autonomous vehicles might not eliminate all crashes. For instance, inclement weather and complex driving environments pose challenges for autonomous vehicles, as well as for human drivers, and autonomous vehicles might perform worse than human drivers in some cases (Gomes, 2014). There is also the potential for autonomous vehicles to pose new and
M-learning in Nigerian higher education: an experimental study with Edmodo

Social media technologies have recently gained remarkable popularity in the education sector. Recent research indicated that students rely on social media for educational purposes like social networking, chatting, and knowledge sharing. With social media aiding learning experiences, m-learning is anticipated to improve the application of social media. In the paper, we investigated the preference of social media tools and mobile devices for learning, their benefits and effectiveness, and how they can possibly improve learning process in Nigeria. Furthermore, we evaluated learning experiences of students using Edmodo social media-based learning environment in a Nigerian university. We used a mixed method research approach, where the data was triangulated from two questionnaires and students’ interviews. Data gathered after the course shows that students learning response was improved. Students were eager to use mobile devices for accessing social media sites, thus providing experimental evidence of the place of social media in m-learning.
Arabic Diacritization with Gated Recurrent Unit

Arabic and similar languages require the use of diacritics in order to determine the necessary parameters to pronounce and identify every part of the speech correctly. Therefore, when it comes to perform Natural Language Processing (NLP) over Arabic, diacritization is a crucial step. In this paper we use a gated recurrent unit network as a language-independent framework for Arabic diacritization. The end-to-end approach allows to use exclusively vocalized text to train the system without using external resources. Evaluation is performed versus the state-of-the-art literature results. We demonstrate that we achieve state-of-the-art results and enhance the learning process by scoring better performance in the training and testing timing.
Vulnerable Narcissism Is (Mostly) a Disorder of Neuroticism.

Increasing attention has been paid to the distinction between the dimensions of narcissistic grandiosity and vulnerability. We examine the degree to which basic traits underlie vulnerable narcissism, with a particular emphasis on the importance of Neuroticism and Agreeableness.



Across four samples (undergraduate, online community, clinical-community), we conduct dominance analyses to partition the variance predicted in vulnerable narcissism by the Five-Factor Model personality domains, as well as compare the empirical profiles generated by vulnerable narcissism and Neuroticism.



These analyses demonstrate that the lion's share of variance is explained by Neuroticism (65%) and Agreeableness (19%). Similarity analyses were also conducted in which the extent to which vulnerable narcissism and Neuroticism share similar empirical networks was tested using an array of criteria, including self-, informant, and thin slice ratings of personality; interview-based ratings of personality disorder and pathological traits; and self-ratings of adverse events and functional outcomes. The empirical correlates of vulnerable narcissism and Neuroticism were nearly identical (MrICC  = .94). Partial analyses demonstrated that the variance in vulnerable narcissism not shared with Neuroticism is largely specific to disagreeableness-related traits such as distrustfulness and grandiosity.



These findings demonstrate the parsimony of using basic personality to study personality pathology and have implications for how vulnerable narcissism might be approached clinically.
Distributed job scheduling based on Swarm Intelligence: A survey

Scientists and engineers need computational power to satisfy the increasing resource intensive nature of their simulations. For example, running Parameter Sweep Experiments (PSE) involve processing many independent jobs, given by multiple initial configurations (input parameter values) against the same program code. Hence, paradigms like Grid Computing and Cloud Computing are employed for gaining scalability. However, job scheduling in Grid and Cloud environments represents a difficult issue since it is basically NP-complete. Thus, many variants based on approximation techniques, specially those from Swarm Intelligence (SI), have been proposed. These techniques have the ability of searching for problem solutions in a very efficient way. This paper surveys SI-based job scheduling algorithms for bag-of-tasks applications (such as PSEs) on distributed computing environments, and uniformly compares them based on a derived comparison framework. We also discuss open problems and future research in the area.
Impression Fraud in On-line Advertising via Pay-Per-View Networks

Advertising is one of the primary means for revenue generation for millions of websites and mobile apps. While the majority of online advertising revenues are based on payper-click, alternative forms such as impression-based display and video advertising have been growing rapidly over the past several years. In this paper, we investigate the problem of invalid traffic generation that aims to inflate advertising impressions on websites. Our study begins with an analysis of purchased traffic for a set of honeypot websites. Data collected from these sites provides a window into the basic mechanisms used for impression fraud and in particular enables us to identify pay-per-view (PPV) networks. PPV networks are comprised of legitimate websites that use JavaScript provided by PPV network service providers to render unwanted web pages "underneath" requested content on a real user’s browser so that additional advertising impressions are registered. We describe the characteristics of the PPV network ecosystem and the typical methods for delivering fraudulent impressions. We also provide a case study of scope of PPV networks in the Internet. Our results show that these networks deliver hundreds of millions of fraudulent impressions per day, resulting in hundreds of millions of lost advertising dollars annually. Characteristics unique to traffic delivered via PPV networks are also discussed. We conclude with recommendations for countermeasures that can reduce the scope and impact of PPV networks.
Eavesdropping Attacks on High-Frequency RFID Tokens

RFID systems often use near-field magnetic coupling to implement communication channels. The advertised operational range of these channels is less than 10 cm and therefore several implemented systems assume that the communication channel is location limited and therefore relatively secure. Nevertheless, there have been repeated questions raised about the vulnerability of these near-field systems against eavesdropping and skimming attacks. In this paper I revisit the topic of RFID eavesdropping attacks, surveying previous work and explaining why the feasibility of practical attacks is still a relevant and novel research topic. I present a brief overview of the radio characteristics for popular HF RFID standards and present some practical results for eavesdropping experiments against tokens adhering to the ISO 14443 and ISO 15693 standards. Finally, I discuss how an attacker could construct a low-cost eavesdropping device using easy to obtain parts and reference designs.
Model-Free Dual Heuristic Dynamic Programming

Model-based dual heuristic dynamic programming (MB-DHP) is a popular approach in approximating optimal solutions in control problems. Yet, it usually requires offline training for the model network, and thus resulting in extra computational cost. In this brief, we propose a model-free DHP (MF-DHP) design based on finite-difference technique. In particular, we adopt multilayer perceptron with one hidden layer for both the action and the critic networks design, and use delayed objective functions to train both the action and the critic networks online over time. We test both the MF-DHP and MB-DHP approaches with a discrete time example and a continuous time example under the same parameter settings. Our simulation results demonstrate that the MF-DHP approach can obtain a control performance competitive with that of the traditional MB-DHP approach while requiring less computational resources.
Object volume estimation based on 3D point cloud

An approach to estimating the volume of an object based on 3D point cloud is proposed in this paper. Firstly, 3D point cloud is cut into slices of equal thickness along the z-axis. Bisect each slice along the y-axis and cut each slice into sub-intervals along the x-axis. Using the minimum and maximum coordinates in each sub-interval, two surface curve functions can be fitted accordingly for the bisected slices. Then, the curves are integrated to estimate the area of each slice and further integrated along the z-axis for an estimated volume of the object.
Improving Login Authorization by Providing Graphical Password (Security)

Usable security has unique usability challenges because the need for security often means that standard humancomputer-interaction approaches cannot be directly applied. An important usability goal for authentication systems is to support users in selecting better passwords. Users often create memorable passwords that are easy for attackers to guess, but strong system-assigned passwords are difficult for users to remember. So researchers of modern days have gone for alternative methods where in graphical pictures are used as passwords. Graphical passwords essentially use images or representation of images as passwords. Human brain is good in remembering picture than textual character. There are various graphical password schemes or graphical password software in the market. However, very little research has been done to analyze graphical passwords that are still immature. There for, this project work merges persuasive selective click points and password guessing resistant protocol. The major goal of this work is to reduce the guessing attacks as well as encouraging users to select more random, and difficult passwords to guess. Well known security threats like brute force attacks and dictionary attacks can be successfully abolished using this method.
Analysis of Load Balancing Techniques in Cloud Computing

Cloud Computing is an emerging computing paradigm. It aims to share data, calculations, and service transparently over a scalable network of nodes. Since Cloud computing stores the data and disseminated resources in the open environment. So, the amount of data storage increases quickly. In the cloud storage, load balancing is a key issue. It would consume a lot of cost to maintain load information, since the system is too huge to timely disperse load. Load balancing is one of the main challenges in cloud computing which is required to distribute the dynamic workload across multiple nodes to ensure that no single node is overwhelmed. It helps in optimal utilization of resources and hence in enhancing the performance of the system. A few existing scheduling algorithms can maintain load balancing and provide better strategies through efficient job scheduling and resource allocation techniques as well. In order to gain maximum profits with optimized load balancing algorithms, it is necessary to utilize resources efficiently. This paper discusses some of the existing load balancing algorithms in cloud computing and also their challenges.
A Visual Analytics Approach for Understanding Reasons behind Snowballing and Comeback in MOBA Games

To design a successful Multiplayer Online Battle Arena (MOBA) game, the ratio of snowballing and comeback occurrences to all matches played must be maintained at a certain level to ensure its fairness and engagement. Although it is easy to identify these two types of occurrences, game developers often find it difficult to determine their causes and triggers with so many game design choices and game parameters involved. In addition, the huge amounts of MOBA game data are often heterogeneous, multi-dimensional and highly dynamic in terms of space and time, which poses special challenges for analysts. In this paper, we present a visual analytics system to help game designers find key events and game parameters resulting in snowballing or comeback occurrences in MOBA game data. We follow a user-centered design process developing the system with game analysts and testing with real data of a trial version MOBA game from NetEase Inc. We apply novel visualization techniques in conjunction with well-established ones to depict the evolution of players' positions, status and the occurrences of events. Our system can reveal players' strategies and performance throughout a single match and suggest patterns, e.g., specific player' actions and game events, that have led to the final occurrences. We further demonstrate a workflow of leveraging human analyzed patterns to improve the scalability and generality of match data analysis. Finally, we validate the usability of our system by proving the identified patterns are representative in snowballing or comeback matches in a one-month-long MOBA tournament dataset.
Vestibular papillomatosis as a normal vulvar anatomical condition.

At the beginning of the nineteen-eighties, vulvar vestibular papillomatosis (VVP) was thought to be a human papilloma virus (HPV) disease. Based upon these findings many clinicians have been treating this condition with laser ablation or by topical application of podophyllin or trichloroacetic acid. Currently, most authors believe that VVP should be considered an anatomical variant of the vestibular mucosa and not HPV related. We present a case of VVP in which there was no histological or molecular evidence of HPV; unnecessary treatment should be avoided.
Reuse Intention of Third-Party Online Payments : A Focus on the Sustainable Factors of Alipay

An anonymous transaction environment and the advantage of virtual property have resulted in trust playing an important role in the rapid growth of online shopping in China. To satisfy this trust issue, Alibaba (China) Co., Ltd. (Hangzhou, China) invented Alipay, the largest third-party online payment service. Using a structural equation model (SEM), this paper attempts to determine whether Alipay’s service quality factors are truly sustainable. The results indicate that only two of five factors—convenience and security—are significantly mediated by the sustainable performance of customer satisfaction as a mediator. The other three factors—usefulness, responsiveness and economy—were rejected for the role of customer satisfaction, even if they are accepted regarding the direct effect on reuse intention. This result implies that Chinese web companies need to make greater efforts not to ensure initial success, but instead to ensure sustainable performance.
Sparse concept coding for visual analysis

We consider the problem of image representation for visual analysis. When representing images as vectors, the feature space is of very high dimensionality, which makes it difficult for applying statistical techniques for visual analysis. To tackle this problem, matrix factorization techniques, such as Singular Vector Decomposition (SVD) and Non-negative Matrix Factorization (NMF), received an increasing amount of interest in recent years. Matrix factorization is an unsupervised learning technique, which finds a basis set capturing high-level semantics in the data and learns coordinates in terms of the basis set. However, the representations obtained by them are highly dense and can not capture the intrinsic geometric structure in the data. In this paper, we propose a novel method, called Sparse Concept Coding (SCC), for image representation and analysis. Inspired from the recent developments on manifold learning and sparse coding, SCC provides a sparse representation which can capture the intrinsic geometric structure of the image space. Extensive experimental results on image clustering have shown that the proposed approach provides a better representation with respect to the semantic structure.
Overview of the EVS codec architecture

The recently standardized 3GPP codec for Enhanced Voice Services (EVS) offers new features and improvements for low-delay real-time communication systems. Based on a novel, switched low-delay speech/audio codec, the EVS codec contains various tools for better compression efficiency and higher quality for clean/noisy speech, mixed content and music, including support for wideband, super-wideband and full-band content. The EVS codec operates in a broad range of bitrates, is highly robust against packet loss and provides an AMR-WB interoperable mode for compatibility with existing systems. This paper gives an overview of the underlying architecture as well as the novel technologies in the EVS codec and presents listening test results showing the performance of the new codec in terms of compression and speech/audio quality.
Finding trending local topics in search queries for personalization of a recommendation system

In this paper, we present our approach for geographic personalization of a content recommendation system. More specifically, our work focuses on recommending query topics to users. We do this by mining the search query logs to detect trending local topics. For a set of queries we compute their counts and what we call buzz scores, which is a metric for detecting trending behavior. We also compute the entropy of the geographic distribution of the queries as means of detecting their location affinity. We cluster the queries into trending topics and assign the topics to their corresponding location. Human editors then select a subset of these local topics and enter them into a recommendation system. In turn the recommendation system optimizes a pool of trending local and global topics by exploiting user feedback. We present some editorial evaluation of the technique and results of a live experiment. Inclusion of local topics in selected locations into the global pool of topics resulted in more than 6% relative increase in user engagement with the recommendation system compared to using the global topics exclusively.