nils-herrmann/clean_scirepeval huggingface dataset:
unFriendly: Multi-party Privacy Risks in Social Networks

As the popularity of social networks expands, the information users expose to the public has potentially dangerous implications for individual privacy. While social networks allow users to restrict access to their personal data, there is currently no mechanism to enforce privacy concerns over content uploaded by other users. As group photos and stories are shared by friends and family, personal privacy goes beyond the discretion of what a user uploads about himself and becomes an issue of what every network participant reveals. In this paper, we examine how the lack of joint privacy controls over content can inadvertently reveal sensitive information about a user including preferences, relationships, conversations, and photos. Specifically, we analyze Facebook to identify scenarios where conflicting privacy settings between friends will reveal information that at least one user intended remain private. By aggregating the information exposed in this manner, we demonstrate how a user’s private attributes can be inferred from simply being listed as a friend or mentioned in a story. To mitigate this threat, we show how Facebook’s privacy model can be adapted to enforce multi-party privacy. We present a proof of concept application built into Facebook that automatically ensures mutually acceptable privacy restrictions are enforced
ATHENA: Automatic Text Height Extraction for the Analysis of Text Lines in Old Handwritten Manuscripts

Massive digital acquisition and preservation of deteriorating historical and artistic documents is of particular importance due to their value and fragile condition. The study and browsing of such digital libraries is invaluable for scholars in the Cultural Heritage field but requires automatic tools for analyzing and indexing these datasets. We present two completely automatic methods requiring no human intervention: text height estimation and text line extraction. Our proposed methods have been evaluated on a huge heterogeneous corpus of illuminated medieval manuscripts of different writing styles and with various problematic attributes, such as holes, spots, ink bleed-through, ornamentation, background noise, and overlapping text lines. Our experimental results demonstrate that these two new methods are efficient and reliable, even when applied to very noisy and damaged old handwritten manuscripts.
Business Analytics for Supply Chain: a Dynamic-Capabilities Framework

Supply chain management has become more important as an academic topic due to trends in globalization leading to massive reallocation of production related advantages. Because of the massive amount of data that is generated in the global economy, new tools need to be developed in order to manage and analyze the data, as well as to monitor organizational performance worldwide. This paper proposes a framework of business analytics for supply chain analytics (SCA) as IT-enabled, analytical dynamic capabilities composed of data management capability, analytical supply chain process capability, and supply chain performance management capability. This paper also presents a dynamic-capabilities view of SCA and extensively describes a set of its three capabilities: data management capability, analytical supply chain process capability, and supply chain performance management capability. Next, using the SCM best practice, sales & operations planning (S&OP), the paper demonstrates opportunities to apply SCA in an integrated way. In discussing the implications of the proposed framework, ̄nally, the paper examines several propositions predicting the positive impact of SCA and its individual capability on SCM performance.
Fast beamforming of electronically steerable parasitic array radiator antennas: theory and experiment

A low-power consumption, small-size smart antenna, named electronically steerable parasitic array radiator (ESPAR), has been designed. Beamforming is achieved by tuning the load reactances at parasitic elements surrounding the active central element. A fast beamforming algorithm based on simultaneous perturbation stochastic approximation with a maximum cross correlation coefficient criterion is proposed. The simulation and experimental results validate the algorithm. In an environment where the signal-to-interference-ratio is 0 dB, the algorithm converges within 50 iterations and achieves an output signal-to-interference-plus-noise-ratio of 10 dB. With the fast beamforming ability and its low-power consumption attribute, the ESPAR antenna makes the mass deployment of smart antenna technologies practical.
Rule-based Emotion Detection on Social Media: Putting Tweets on Plutchik's Wheel

We study sentiment analysis beyond the typical granularity of polarity and instead use Plutchik’s wheel of emotions model. We introduce RBEM-Emo as an extension to the Rule-Based Emission Model algorithm to deduce such emotions from human-written messages. We evaluate our approach on two different datasets and compare its performance with the current state-of-the-art techniques for emotion detection, including a recursive autoencoder. The results of the experimental study suggest that RBEM-Emo is a promising approach advancing the current state-of-the-art in emotion detection.
The impact of social media on software engineering practices and tools

Today's generation of software developers frequently make use of social media, either as an adjunct or integrated into a wide range of tools ranging from code editors and issue trackers, to IDEs and web-based portals. The role of social media usage in software engineering is not well understood, and yet the use of these mechanisms influences software development practices. In this position paper, we advocate for research that strives to understand the benefits, risks and limitations of using social media in software development at the team, project and community levels. Guided by the implications of current tools and social media features, we propose a set of pertinent research questions around community involvement, project coordination and management, as well as individual software development activities. Answers to these questions will guide future software engineering tool innovations and software development team practices.
Spatial working memory and arithmetic deficits in children with nonverbal learning difficulties.

Visuospatial working memory and its involvement in arithmetic were examined in two groups of 7- to 11-year-olds: one comprising children described by teachers as displaying symptoms of nonverbal learning difficulties (N = 21), the other a control group without learning disabilities (N = 21). The two groups were matched for verbal abilities, age, gender, and sociocultural level. The children were presented with a visuospatial working memory battery of recognition tests involving visual, spatial-sequential and spatial-simultaneous processes, and two arithmetic tasks (number ordering and written calculations). The two groups were found to differ on some spatial tasks but not in the visual working memory tasks. On the arithmetic tasks, the children with nonverbal learning difficulties made more errors than controls in calculation and were slower in number ordering. A discriminant function analysis confirmed the crucial role of spatial-sequential working memory in distinguishing between the two groups. Results are discussed with reference to spatial working memory and arithmetic difficulties in nonverbal learning disabilities. Implications for the relationship between visuospatial working memory and arithmetic are also considered.
Development of a nursing-care assistant robot RIBA that can lift a human in its arms

In aging societies, there is a strong demand for robotics to tackle problems caused by the aging population. Patient transfer, such as lifting and moving a bedridden patient from a bed to a wheelchair and back, is one of the most physically challenging tasks in nursing care, the burden of which should be reduced by the introduction of robot technologies. We have developed a new prototype robot named RIBA with human-type arms that is designed to perform heavy physical tasks requiring human contact, and we succeeded in transferring a human from a bed to a wheelchair and back. To use RIBA in changeable and realistic environments, cooperation between the caregiver and the robot is required. The caregiver takes responsibility for monitoring the environment and determining suitable actions, while the robot undertakes hard physical tasks. The instructions can be intuitively given by the caregiver to RIBA through tactile sensors using a newly proposed method named tactile guidance. In the present paper, we describe RIBA's design concept, its basic specifications, and the tactile guidance method. Experiments including the transfer of humans are also reported.
Worry-free encryption: functional encryption with public keys

In this work, we put forward the notion of Worry-Free Encryption. This allows Alice to encrypt confidential information under Bob's public key and send it to him, without having to worry about whether Bob has the authority to actually access this information. This is done by encrypting the message under a hidden access policy that only allows Bob to decrypt if his credentials satisfy the policy. Our notion can be seen as a functional encryption scheme but in a public-key setting. As such, we are able to insist that even if the credential authority is corrupted, it should not be able to compromise the security of any honest user.
 We put forward the notion of Worry-Free Encryption and show how to achieve it for any polynomial-time computable policy, under only the assumption that IND-CPA public-key encryption schemes exist. Furthermore, we construct CCA-secure Worry-Free Encryption, efficiently in the random oracle model, and generally (but inefficiently) using simulation-sound non-interactive zero-knowledge proofs.
Learning A Physical Long-term Predictor

Evolution has resulted in highly developed abilities in many natural intelligences to quickly and accurately predict mechanical phenomena. Humans have successfully developed laws of physics to abstract and model such mechanical phenomena. In the context of artificial intelligence, a recent line of work has focused on estimating physical parameters based on sensory data and use them in physical simulators to make long-term predictions. In contrast, we investigate the effectiveness of a single neural network for end-to-end long-term prediction of mechanical phenomena. Based on extensive evaluation, we demonstrate that such networks can outperform alternate approaches having even access to ground-truth physical simulators, especially when some physical parameters are unobserved or not known a-priori. Further, our network outputs a distribution of outcomes to capture the inherent uncertainty in the data. Our approach demonstrates for the first time the possibility of making actionable long-term predictions from sensor data without requiring to explicitly model the underlying physical laws.
Fast Registration Based on Noisy Planes With Unknown Correspondences for 3-D Mapping

We present a robot-pose-registration algorithm, which is entirely based on large planar-surface patches extracted from point clouds sampled from a three-dimensional (3-D) sensor. This approach offers an alternative to the traditional point-to-point iterative-closest-point (ICP) algorithm, its point-to-plane variant, as well as newer grid-based algorithms, such as the 3-D normal distribution transform (NDT). The simpler case of known plane correspondences is tackled first by deriving expressions for least-squares pose estimation considering plane-parameter uncertainty computed during plane extraction. Closed-form expressions for covariances are also derived. To round-off the solution, we present a new algorithm, which is called minimally uncertain maximal consensus (MUMC), to determine the unknown plane correspondences by maximizing geometric consistency by minimizing the uncertainty volume in configuration space. Experimental results from three 3-D sensors, viz., Swiss-Ranger, University of South Florida Odetics Laser Detection and Ranging, and an actuated SICK S300, are given. The first two have low fields of view (FOV) and moderate ranges, while the third has a much bigger FOV and range. Experimental results show that this approach is not only more robust than point- or grid-based approaches in plane-rich environments, but it is also faster, requires significantly less memory, and offers a less-cluttered planar-patches-based visualization.
SIW (substrate integrated waveguide) quasi-elliptic filter based on LTCC for 60-GHz application

This paper presents substrate integrated waveguide (SIW) cavity resonator and SIW quasi-elliptic filter with low insertion loss and high selectivity based on LTCC for 60-GHz application. The SIW cavity resonators with high Q factor are suggested and applied for the filter having low insertion loss and high selectivity, the resulting Q value and the resonance frequency are 224.5 and 60.371 GHz, respectively. In the filter design, the quasi-elliptic filter using positive and negative couplings is applied to get high selectivity, the positive and negative couplings are evaluated by the holes between resonators and the slots on ground planes, respectively. The proposed SIW quasi-elliptic filter exhibits an insertion loss 1.77 dB at 60.8 GHz and the return loss is better than 10.2 dB with a 3-dB fractional bandwidth of 11 % (6.92 GHz). The measured insertion loss is lower and the selectivity is higher than those in previous works due to the effects of the high Q resonators and quasi-elliptic structure adapting the negative coupling. The overall size of the fabricated filter is 4.6 mm ×2.8 mm× 0.2 mm.
RIEMANN’S ZETA FUNCTION AND BEYOND

In recent years L-functions and their analytic properties have assumed a central role in number theory and automorphic forms. In this expository article, we describe the two major methods for proving the analytic continuation and functional equations of L-functions: the method of integral representations, and the method of Fourier expansions of Eisenstein series. Special attention is paid to technical properties, such as boundedness in vertical strips; these are essential in applying the converse theorem, a powerful tool that uses analytic properties of L-functions to establish cases of Langlands functoriality conjectures. We conclude by describing striking recent results which rest upon the analytic properties of L-functions.
Diffusion Kernels on Statistical Manifolds

A family of kernels for statistical learning is introduced that exploits the geometric structure of statistical models. The kernels are based on the heat equation on the Riemannian manifold defined by the Fisher information metric associated with a statistical family, and generalize the Gaussian kernel of Euclidean space. As an important special case, kernels based on the geometry of multinomial families are derived, leading to kernel-based learning algorithms that apply naturally to discrete data. Bounds on covering numbers and Rademacher averages for the kernels are proved using bounds on the eigenvalues of the Laplacian on Riemannian manifolds. Experimental results are presented for document classification, for which the use of multinomial geometry is natural and well motivated, and improvements are obtained over the standard use of Gaussian or linear kernels, which have been the standard for text classification. This research was partially supported by the Advanced Research and Development Activity in Information Technology (ARDA), contract number MDA904-00-C-2106, and by the National Science Foundation (NSF), grants CCR-0122581 and IIS-0312814. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of ARDA, NSF, or the U.S. government.
The Ontop Framework for Ontology Based Data Access

Ontology Based Data Access (OBDA)  is a paradigm of accessing data trough a conceptual layer. Usually, the conceptual layer is expressed in the form of an RDF(S)  or OWL  ontology, and the data is stored in relational databases. The terms in the conceptual layer are mapped to the data layer using mappings which associate to each element of the conceptual layer a (possibly complex SQL) query over the data sources. The mappings have been formalized in the recent R2RML W3C standard . This virtual graph can then be queried using an RDF query language such as SPARQL [7]. Formally, an OBDA system is a triple O = 〈T ,S,M〉, where:
Link Prediction in Social Networks Using Computationally Efficient Topological Features

Online social networking sites have become increasingly popular over the last few years. As a result, new interdisciplinary research directions have emerged in which social network analysis methods are applied to networks containing hundreds millions of users. Unfortunately, links between individuals may be missing due to imperfect acquirement processes or because they are not yet reflected in the online network (i.e., friends in real world did not form a virtual connection.) Existing link prediction techniques lack the scalability required for full application on a continuously growing social network which may be adding everyday users with thousands of connections. The primary bottleneck in link prediction techniques is extracting structural features required for classifying links. In this paper we propose a set of simple, easy-to-compute structural features that can be analyzed to identify missing links. We show that a machine learning classifier trained using the proposed simple structural features can successfully identify missing links even when applied to a hard problem of classifying links between individuals who have at least one common friend. A new friends measure that we developed is shown to be a good predictor for missing links and an evaluation experiment was performed on five large social networks datasets: Face book, Flickr, You Tube, Academia and The Marker. Our methods can provide social network site operators with the capability of helping users to find known, offline contacts and to discover new friends online. They may also be used for exposing hidden links in an online social network.
Wearable Sensors for Remote Health Monitoring

Life expectancy in most countries has been increasing continually over the several few decades thanks to significant improvements in medicine, public health, as well as personal and environmental hygiene. However, increased life expectancy combined with falling birth rates are expected to engender a large aging demographic in the near future that would impose significant  burdens on the socio-economic structure of these countries. Therefore, it is essential to develop cost-effective, easy-to-use systems for the sake of elderly healthcare and well-being. Remote health monitoring, based on non-invasive and wearable sensors, actuators and modern communication and information technologies offers an efficient and cost-effective solution that allows the elderly to continue to live in their comfortable home environment instead of expensive healthcare facilities. These systems will also allow healthcare personnel to monitor important physiological signs of their patients in real time, assess health conditions and provide feedback from distant facilities. In this paper, we have presented and compared several low-cost and non-invasive health and activity monitoring systems that were reported in recent years. A survey on textile-based sensors that can potentially be used in wearable systems is also presented. Finally, compatibility of several communication technologies as well as future perspectives and research challenges in remote monitoring systems will be discussed.
Identifying Justifications in Written Dialogs by Classifying Text as Argumentative

In written dialog, discourse participants need to justify claims they make, to convince the reader the claim is true and/or relevant to the discourse. This paper presents a new task (with an associated corpus), namely detecting such justifications. We investigate the nature of such justifications, and observe that the justifications themselves often contain discourse structure. We therefore develop a method to detect the existence of certain types of discourse relations, which helps us classify whether a segment is a justification or not. Our task is novel, and our work is novel in that it uses a large set of connectives (which we call indicators), and in that it uses a large set of discourse relations, without choosing among them.
LSHTC: A Benchmark for Large-Scale Text Classification

LSHTC is a series of challenges which aims to assess the performance of classification systems in large-scale classification in a a large number of classes (up to hundreds of thousands). This paper describes the dataset that have been released along the LSHTC series. The paper details the construction of the datsets and the design of the tracks as well as the evaluation measures that we implemented and a quick overview of the results. All of these datasets are available online and runs may still be submitted on the online server of the challenges.
Decision Tree and Naïve Bayes Algorithm for Classification and Generation of Actionable Knowledge for Direct Marketing

Many companies like credit card, insurance, bank, retail industry require direct marketing. Data mining can help those institutes to set marketing goal. Data mining techniques have good prospects in their target audiences and improve the likelihood of response. In this work we have investigated two data mining techniques: the Naïve Bayes and the C4.5 decision tree algorithms. The goal of this work is to predict whether a client will subscribe a term deposit. We also made comparative study of performance of those two algorithms. Publicly available UCI data is used to train and test the performance of the algorithms. Besides, we extract actionable knowledge from decision tree that focuses to take interesting and important decision in business area.
Hierarchical Attention Network for Action Recognition in Videos

Understanding human actions in wild videos is an important task with a broad range of applications. In this paper we propose a novel approach named Hierarchical Attention Network (HAN), which enables to incorporate static spatial information, short-term motion information and long-term video temporal structures for complex human action understanding. Compared to recent convolutional neural network based approaches, HAN has following advantages – (1) HAN can efficiently capture video temporal structures in a longer range; (2) HAN is able to reveal temporal transitions between frame chunks with different time steps, i.e. it explicitly models the temporal transitions between frames as well as video segments and (3) with a multiple step spatial temporal attention mechanism, HAN automatically learns important regions in video frames and temporal segments in the video. The proposed model is trained and evaluated on the standard video action benchmarks, i.e., UCF-101 and HMDB-51, and it significantly outperforms the state-of-the arts.