nils-herrmann/clean_scirepeval huggingface dataset:
Integration and Virtualization of Relational SQL and NoSQL Systems Including MySQL and MongoDB

NoSQL databases are growing in popularity for Big Data applications in web analytics and supporting large web sites due to their high availability and scalability. Since each NoSQL system has its own API and does not typically support standards such as SQL and JDBC, integrating these systems with other enterprise and reporting software requires extra effort. In this work, we present a generic standards-based architecture that allows NoSQL systems, with specific focus on MongoDB, to be queried using SQL and seamlessly interact with any software supporting JDBC. A virtualization system is built on top of the NoSQL sources that translates SQL queries into the source-specific APIs. The virtualization architecture allows users to query and join data from both NoSQL and relational SQL systems in a single SQL query. Experimental results demonstrate that the virtualization layer adds minimal overhead in translating SQL to NoSQL APIs, and the virtualization system can efficiently perform joins across sources.
A corpus for mining drug-related knowledge from Twitter chatter: Language models and their utilities

In this data article, we present to the data science, natural language processing and public heath communities an unlabeled corpus and a set of language models. We collected the data from Twitter using drug names as keywords, including their common misspelled forms. Using this data, which is rich in drug-related chatter, we developed language models to aid the development of data mining tools and methods in this domain. We generated several models that capture (i) distributed word representations and (ii) probabilities of n-gram sequences. The data set we are releasing consists of 267,215 Twitter posts made during the four-month period-November, 2014 to February, 2015. The posts mention over 250 drug-related keywords. The language models encapsulate semantic and sequential properties of the texts.
Texture Synthesis Using Shallow Convolutional Networks with Random Filters

Here we demonstrate that the feature space of random shallow convolutional neural networks (CNNs) can serve as a surprisingly good model of natural textures. Patches from the same texture are consistently classified as being more similar then patches from different textures. Samples synthesized from the model capture spatial correlations on scales much larger then the receptive field size, and sometimes even rival or surpass the perceptual quality of state of the art texture models (but show less variability). The current state of the art in parametric texture synthesis relies on the multi-layer feature space of deep CNNs that were trained on natural images . Our finding suggests that such optimized multi-layer feature spaces are not imperative for texture modeling. Instead, much simpler shallow and convolutional networks can serve as the basis for novel texture synthesis algorithms.
Skip-Thought Vectors

We describe an approach for unsupervised learning of a generic, distributed sentence encoder. Using the continuity of text from books, we train an encoderdecoder model that tries to reconstruct the surrounding sentences of an encoded passage. Sentences that share semantic and syntactic properties are thus mapped to similar vector representations. We next introduce a simple vocabulary expansion method to encode words that were not seen as part of training, allowing us to expand our vocabulary to a million words. After training our model, we extract and evaluate our vectors with linear models on 8 tasks: semantic relatedness, paraphrase detection, image-sentence ranking, question-type classification and 4 benchmark sentiment and subjectivity datasets. The end result is an off-the-shelf encoder that can produce highly generic sentence representations that are robust and perform well in practice. We will make our encoder publicly available.
Neural Control for a Differential Drive Wheeled Mobile Robot Integrating Stereo Vision Feedback

This paper proposes a tracking control method for a differential drive wheeled mobile robot with nonholonomic constraints with an inverse optimal neural controller. It is based on two techniques: first, an identifier using a discrete-time recurrent high-order neural network (RHONN) trained with an extended Kalman filter (EKF) algorithm is employed; second, an inverse optimal control is used to avoid solving the Hamilton Jacobi Bellman (HJB) equation. The desired trajectory of the robot is computed during the navigation process using a stereo camera sensor.
Dynamic Proximity-Aware Resource Allocation in Vehicle-to-Vehicle (V2V) Communications

In this paper, a novel proximity and load-aware resource allocation for vehicle-to-vehicle (V2V) communication is proposed. The proposed approach exploits the spatio-temporal traffic patterns, in terms of load and vehicles' physical proximity, to minimize the total network cost which captures the tradeoffs between load (i.e., service delay) and successful transmissions while satisfying vehicles' quality-of-service (QoS) requirements. To solve the optimization problem under slowly varying channel information, it is decoupled the problem into two interrelated sub-problems. First, a dynamic clustering mechanism is proposed to group vehicles in zones based on their traffic patterns and proximity information. Second, a matching game is proposed to allocate resources for each V2V pair within each zone. The problem is cast as many-to-one matching game in which V2V pairs and resource blocks (RBs) rank one another in order to minimize their service delay. The proposed game is shown to belong to the class of matching games with externalities. To solve this game, a distributed algorithm is proposed using which V2V pairs and RBs interact to reach a stable matching. Simulation results for a Manhattan model shown that the proposed scheme yields a higher percentage of V2V pairs satisfying QoS as well as significant gain in terms of the signal-to- interference-plus-noise ratio (SINR) as compared to a state-of-art resource allocation baseline.
Recognition for Handwritten English Letters : A Review

Character recognition is one of the most interesting and challenging research areas in the field of Image processing. English character recognition has been extensively studied in the last half century. Nowadays different methodologies are in widespread use for character recognition. Document verification, digital library, reading bank deposit slips, reading postal addresses, extracting information from cheques, data entry, applications for credit cards, health insurance, loans, tax forms etc. are application areas of digital document processing. This paper gives an overview of research work carried out for recognition of hand written English letters. In Hand written text there is no constraint on the writing style. Hand written letters are difficult to recognize due to diverse human handwriting style, variation in angle, size and shape of letters. Various approaches of hand written character recognition are discussed here along with their performance.
Predicting risk of software changes

Reducing the number of software failures is one of the most challenging problems of software production. We assume that software development proceeds as a series of changes and model the probability that a change to software will cause a failure. We use predictors based on the properties of a change itself. Such predictors include size in lines of code added, deleted, and unmodified; diffusion of the change and its component subchanges, as reflected in the number of files, modules, and subsystems touched, or changed; several measures of developer experience; and the type of change and its subchanges (fault fixes or new code). The model is built on historic information and is used to predict the risk of new changes. In this paper we apply the model to 5ESS® software updates and find that change diffusion and developer experience are essential to predicting failures. The predictive model is implemented as a Web-based tool to allow timely prediction of change quality. The ability to predict the quality of change enables us to make appropriate decisions regarding inspection, testing, and delivery. Historic information on software changes is recorded in many commercial software projects, suggesting that our results can be easily and widely applied in practice.
Beam-Scanning Continuous Transverse Stub Antenna Fed by a Ridged Waveguide Slot Array

A continuous transverse stub (CTS) array antenna is presented for beam-scanning applications. Rotatable ridged waveguide slot array (RWSA) is used as a line source to feed a CTS array for the first time. The proposed RWSA has easy design, compact structure, light weight, and easy sidelobe control. The CTS array and RWSA can be designed and optimized separately. A good beam-scanning capability of ±30° is achieved by rotating the RWSA at almost the same angles, thus obtaining a fine beam-scanning resolution. The measured aperture efficiency exceeds 60% in the entire beam-scanning range. Therefore, the proposed antenna can be a good alternative for radar systems and satellite communication terminals, which require low cost and high gain.
OWL 2 modeling and reasoning with complex human activities

In recent years, there has been a growing interest in the adoption of ontologies and ontological reasoning to automatically recognize complex context data such as human activities. In particular, the Web Ontology Language (OWL) emerged as the language of choice, being a standard for the Semantic Web, and supported by a number of tools for knowledge engineering and reasoning. However, the limitations of OWL 1 in terms of expressiveness have been recognized in various fields, and important research e↵orts have been made to extend the language while preserving decidability of its OWL 1 DL fragment. The result of such work is OWL 2. In this paper we investigate the use of OWL 2 for modeling complex activities and reasoning with them. We show that the new language constructors of OWL 2 overcome the main limitations of OWL 1 for the representation of activities; OWL 2 axioms can be used to represent certain rules and rule-based reasoning previously demanded to hybrid approaches, with the advantage of having a unique semantics, avoiding potential inconsistencies. Then, we propose a system architecture showing the integration of a novel OWL 2 activity ontology and reasoning modules with distributed modules for sensor data aggregation and reasoning. The feasibility of our solution is shown by an extensive experimental evaluation with simulations of di↵erent intelligent environments.
Comparing Clusterings - An Overview

As the amount of data we nowadays have to deal with becomes larger and larger, the methods that help us to detect structures in the data and to identify interesting subsets in the data become more and more important. One of these methods is clustering, i.e. segmenting a set of elements into subsets such that the elements in each subset are somehow ”similiar” to each other and elements of different subsets are ”unsimilar”. In the literature we can find a large variety of clustering algorithms, each having certain advantages but also certain drawbacks. Typical questions that arise in this context comprise:
Chapter 8. Sociality through Social Network Sites

The global system of networked computers, servers and routers known as the Internet has transformed many aspects of modern society and social interaction. The online distribution of goods and services, for instance, has influenced almost every industry and has radically transformed many. Alongside commerce-oriented technological development has been a rise in what has been termed " social media. " One of the most significant developments connected to social media is the rise of social network sites (SNSs), such as Facebook, LinkedIn, MySpace, Cyworld, and Google Plus. Although sites of this nature first emerged around 1997, they rose to cultural significance as a phenomenon in 2003, when Friendster first attracted mass media attention. Less than a decade later, millions of people of all ages across the globe have joined In the early stages of this phenomenon, terminology varied widely with the interchangeable use of " social networking sites, " " online social networks " or even simply, " social networks " to refer to a diffuse—and sometimes improbable—range of sites and services. In 2007we attempted to stabilize the discussion by offering a definition of social network sites: web-based services that allow individuals to (1) construct a public or semi-public profile within a bounded system, (2) articulate a list of other users with whom they share a connection, and (3) view and traverse their list of connections and those made by others within the system. (boyd and Ellison, 2007) This definition served a need, but the social and technical landscape of these sites has changed dramatically since then. In this chapter, we document some of the ways SNSs have evolved since 2007, place their evolution within a context of Web 2.0 and scholarship on computer-mediated communication, and discuss some of the opportunities and challenges embedded in the study of social media and social network sites.
Blockchain Enabled Privacy Audit Logs

Privacy audit logs are used to capture the actions of participants in a data sharing environment in order for auditors to check compliance with privacy policies. However, collusion may occur between the auditors and participants to obfuscate actions that should be recorded in the audit logs. In this paper, we propose a Linked Data based method of utilizing blockchain technology to create tamper-proof audit logs that provide proof of log manipulation and non-repudiation. We also provide experimental validation of the scalability of our solution using an existing Linked Data privacy audit log model.
Exploiting Data-Usage Statistics for Website Fingerprinting Attacks on Android

The browsing behavior of a user allows to infer personal details, such as health status, political interests, sexual orientation, etc. In order to protect this sensitive information and to cope with possible privacy threats, defense mechanisms like SSH tunnels and anonymity networks (e.g., Tor) have been established. A known shortcoming of these defenses is that website fingerprinting attacks allow to infer a user's browsing behavior based on traffic analysis techniques. However, website fingerprinting typically assumes access to the client's network or to a router near the client, which restricts the applicability of these attacks.
 In this work, we show that this rather strong assumption is not required for website fingerprinting attacks. Our client-side attack overcomes several limitations and assumptions of network-based fingerprinting attacks, e.g., network conditions and traffic noise, disabled browser caches, expensive training phases, etc. Thereby, we eliminate assumptions used for academic purposes and present a practical attack that can be implemented easily and deployed on a large scale. Eventually, we show that an unprivileged application can infer the browsing behavior by exploiting the unprotected access to the Android data-usage statistics. More specifically, we are able to infer 97% of 2,500 page visits out of a set of 500 monitored pages correctly. Even if the traffic is routed through Tor by using the Orbot proxy in combination with the Orweb browser, we can infer 95% of 500 page visits out of a set of 100 monitored pages correctly. Thus, the READ_HISTORY_BOOKMARKS permission, which is supposed to protect the browsing behavior, does not provide protection.
From the wisdom of crowds to my own judgment in microfinance through online peer-to-peer lending platforms

Information asymmetry is one of the fundamental problems that online peer-to-peer (P2P) lending platforms face. This problem becomes more acute when platforms are used for microfinance, where the targeted customers are mostly economically under-privileged people. Most of the prior empirical studies have been based on data from Prosper.com or similar sites that compete in traditional consumer loan markets. Our study examines P2P lending in microfinance for which borrowers are unbankable so that signals on creditworthiness of new borrowers are very limited. In addition, microfinance customers have more incentive to repeatedly seek loans from the market. Under this microfinance setting, we examine how lenders change their decisions as creditworthiness inference becomes increasingly possible through the accumulation of transaction history. Our findings confirm that lenders seek the wisdom of crowds when information on creditworthiness is extremely limited but switch to their own judgment when more signals are transmitted through the market. Different information sets are utilized according to the structures of decisions. Due to the possibility of a repeated game, it is also shown that borrowers try to maintain a good reputation, and direct communication with lenders may adjust incorrect inference from hard data when their creditworthiness is questioned.
ITALICA at PAN 2013: An Ensemble Learning Approach to Author Profiling Notebook for PAN at CLEF 2013

This notebook discusses the approach to the Author Profiling task developed by the Italica group for PAN 2013. This system implements two different sets of classifiers which are combined later in order to build a final classifier that takes into account the decisions of the previous ones. The initial classifiers are focused on vector space representations of the documents as a bag of words and n-grams of POS tags and also on a set of stylistic features of the texts. The final classifier consists of a stacking schema that combines the other ones. This approach has obtained better results for the Spanish dataset than for the English dataset, probably due to the use of more detailed POS tagset in the former.
A similarity-based prognostics approach for Remaining Useful Life estimation of engineered systems

This paper presents a similarity-based approach for estimating the Remaining Useful Life (RUL) in prognostics. The approach is especially suitable for situations in which abundant run-to-failure data for an engineered system are available. Data from multiple units of the same system are used to create a library of degradation patterns. When estimating the RUL of a test unit, the data from it will be matched to those patterns in the library and the actual life of those matched units will be used as the basis of estimation. This approach is used to tackle the data challenge problem defined by the 2008 PHM Data Challenge Competition, in which, run-to-failure data of an unspecified engineered system are provided and the RUL of a set of test units will be estimated. Results show that the similarity-based approach is very effective in performing RUL estimation.
Clinical, dermoscopic, and histopathologic features of body hair disorders.

Dermoscopic examination of hair and scalp, also named "trichoscopy," is an essential tool in diagnosis of hair and scalp diseases. Trichoscopy is fast and noninvasive and can be used to evaluate hair disorders in all body areas. Body hair disorders are uncommon, and most publications on their dermoscopic features are limited to case reports or series. In this review we present the available information on the dermoscopic diagnosis of body hair disorders including keratosis pilaris, trichostasis spinulosa, pili multigemini, circle hairs, rolled hairs, eruptive vellus hair cyst, and ingrown hairs.
Reinforcement Learning: A Review from a Machine Learning Perspective

Machine Learning is the study of methods for programming computers to learn. Reinforcement Learning is a type of Machine Learning and refers to learning problem which allows machines and software agents to automatically determine the ideal behaviour within a specific context, in order to maximize its performance. RL is inspired by behaviourist psychology based on the mechanism of learning from rewards and does not require prior knowledge and automatically get optimal policy with the help of knowledge obtained by trial-and-error and continuous interaction with the dynamic environment. This paper provides the overview of Reinforcement Learning from Machine learning perspective. It discusses the fundamental principles and techniques used to solve RL problems. It presents nature of RL problems, with focus on some influential model free RL algorithms, challenges and recent trends in theory and practice of RL. It concludes with the future scope of RL.
Credibility Microscope: Relating Web Page Credibility Evaluations to Their Textual Content

The popularization of the Internet made it a primary information source for many people. Unfortunately quality of information available on the Internet varies. Therefore, evaluation of credibility of web page content, especially while making important decisions like those concerning health care, medical information, and large purchases, is crucial, but users often lack a necessary knowledge. The main goal of the paper is to study to what extent the textual content of a webpage determines its credibility evaluations. This goal is achieved by an experiment in which we ask respondents to rate webpage credibility as well as credibility and importance of each statement from this site. We formulate a number of hypotheses about the nature of the dependence between webpage and statements credibility and we test these hypotheses on the data obtained from experiment. The evaluation of those hypotheses is essential for design of classifier that will aggregate statement credibility into webpage credibility score.
Baselines for Reinforcement Learning in Text Games

The ability to learn optimal control policies in systems where action space is defined by sentences in natural language would allow many interesting real-world applications such as automatic optimisation of dialogue systems. Text-based games with multiple endings and rewards are a promising platform for this task, since their feedback allows us to employ reinforcement learning techniques to jointly learn text representations and control policies. We argue that the key property of AI agents, especially in the text-games context, is their ability to generalise to previously unseen games. We present a minimalistic text-game playing agent, testing its generalisation and transfer learning performance and showing its ability to play multiple games at once. We also present pyfiction, an open-source library for universal access to different text games that could, together with our agent that implements its interface, serve as a baseline for future research.
Community Preserving Network Embedding

Network embedding, aiming to learn the low-dimensional representations of nodes in networks, is of paramount importance in many real applications. One basic requirement of network embedding is to preserve the structure and inherent properties of the networks. While previous network embedding methods primarily preserve the microscopic structure, such as the firstand second-order proximities of nodes, the mesoscopic community structure, which is one of the most prominent feature of networks, is largely ignored. In this paper, we propose a novel Modularized Nonnegative Matrix Factorization (M-NMF) model to incorporate the community structure into network embedding. We exploit the consensus relationship between the representations of nodes and community structure, and then jointly optimize NMF based representation learning model and modularity based community detection model in a unified framework, which enables the learned representations of nodes to preserve both of the microscopic and community structures. We also provide efficient updating rules to infer the parameters of our model, together with the correctness and convergence guarantees. Extensive experimental results on a variety of real-world networks show the superior performance of the proposed method over the state-of-the-arts.