nils-herrmann/clean_scirepeval huggingface dataset:
A wireless body area network of intelligent motion sensors for computer assisted physical rehabilitation

Recent technological advances in integrated circuits, wireless communications, and physiological sensing allow miniature, lightweight, ultra-low power, intelligent monitoring devices. A number of these devices can be integrated into a Wireless Body Area Network (WBAN), a new enabling technology for health monitoring. Using off-the-shelf wireless sensors we designed a prototype WBAN which features a standard ZigBee compliant radio and a common set of physiological, kinetic, and environmental sensors. We introduce a multi-tier telemedicine system and describe how we optimized our prototype WBAN implementation for computer-assisted physical rehabilitation applications and ambulatory monitoring. The system performs real-time analysis of sensors' data, provides guidance and feedback to the user, and can generate warnings based on the user's state, level of activity, and environmental conditions. In addition, all recorded information can be transferred to medical servers via the Internet and seamlessly integrated into the user's electronic medical record and research databases. WBANs promise inexpensive, unobtrusive, and unsupervised ambulatory monitoring during normal daily activities for prolonged periods of time. To make this technology ubiquitous and affordable, a number of challenging issues should be resolved, such as system design, configuration and customization, seamless integration, standardization, further utilization of common off-the-shelf components, security and privacy, and social issues.
YADING: Fast Clustering of Large-Scale Time Series Data

Fast and scalable analysis techniques are becoming increasingly important in the era of big data, because they are the enabling techniques to create real-time and interactive experiences in data analysis. Time series are widely available in diverse application areas. Due to the large number of time series instances (e.g., millions) and the high dimensionality of each time series instance (e.g., thousands), it is challenging to conduct clustering on largescale time series, and it is even more challenging to do so in realtime to support interactive exploration. In this paper, we propose a novel end-to-end time series clustering algorithm, YADING, which automatically clusters large-scale time series with fast performance and quality results. Specifically, YADING consists of three steps: sampling the input dataset, conducting clustering on the sampled dataset, and assigning the rest of the input data to the clusters generated on the sampled dataset. In particular, we provide theoretical proof on the lower and upper bounds of the sample size, which not only guarantees YADING’s high performance, but also ensures the distribution consistency between the input dataset and the sampled dataset. We also select L1 norm as similarity measure and the multi-density approach as the clustering method. With theoretical bound, this selection ensures YADING’s robustness to time series variations due to phase perturbation and random noise. Evaluation results have demonstrated that on typical-scale (100,000 time series each with 1,000 dimensions) datasets, YADING is about 40 times faster than the state-of-the-art, sampling-based clustering algorithm DENCLUE 2.0, and about 1,000 times faster than DBSCAN and CLARANS. YADING has also been used by product teams at Microsoft to analyze service performance. Two of such use cases are shared in this paper.
Comparing Children's Crosshair and Finger Interactions in Handheld Augmented Reality: Relationships Between Usability and Child Development

Augmented reality technology is a unique medium that can be helpful for young children's entertainment and education, but in order to achieve the benefits of this technology, augmented reality experiences need to be appropriately designed for young children's developing physical and cognitive skills. In the present study we investigated how 5-10 year-old children react to typical handheld augmented reality interaction techniques such as crosshair selection and finger selection, in AR environments that require them to change perspective or not. Our analysis shows significant impacts of age upon AR performance, with young children having slower selection times, more tracking losses, and taking longer to recover tracking. Significant differences were also found between AR interaction technique conditions, with finger selection being faster than crosshair selection, and interactions which required changes in perspective taking longer, generating more tracking losses, and more errors in selection. Furthermore, by analyzing children's performance in relation to metrics of physical and cognitive development, we identified correlations between AR interaction techniques performance and developmental tests of spatial relations, block construction and visuomotor precision. Gender differences were analyzed but no significant effects were detected.
Further improvement in reducing superficial contamination in NIRS using double short separation measurements

Near-Infrared Spectroscopy (NIRS) allows the recovery of the evoked hemodynamic response to brain activation. In adult human populations, the NIRS signal is strongly contaminated by systemic interference occurring in the superficial layers of the head. An approach to overcome this difficulty is to use additional NIRS measurements with short optode separations to measure the systemic hemodynamic fluctuations occurring in the superficial layers. These measurements can then be used as regressors in the post-experiment analysis to remove the systemic contamination and isolate the brain signal. In our previous work, we showed that the systemic interference measured in NIRS is heterogeneous across the surface of the scalp. As a consequence, the short separation measurement used in the regression procedure must be located close to the standard NIRS channel from which the evoked hemodynamic response of the brain is to be recovered. Here, we demonstrate that using two short separation measurements, one at the source optode and one at the detector optode, further increases the performance of the short separation regression method compared to using a single short separation measurement. While a single short separation channel produces an average reduction in noise of 33% for HbO, using a short separation channel at both source and detector reduces noise by 59% compared to the standard method using a general linear model (GLM) without short separation. For HbR, noise reduction of 3% is achieved using a single short separation and this number goes to 47% when two short separations are used. Our work emphasizes the importance of integrating short separation measurements both at the source and at the detector optode of the standard channels from which the hemodynamic response is to be recovered. While the implementation of short separation sources presents some difficulties experimentally, the improvement in noise reduction is significant enough to justify the practical challenges.
Universal semantic communication I

Is it possible for two intelligent beings to communicate meaningfully, without any common language or background? This question has interest on its own, but is especially relevant in the context of modern computational infrastructures where an increase in the diversity of computers is making the task of inter-computer interaction increasingly burdensome. Computers spend a substantial amount of time updating their software to increase their knowledge of other computing devices. In turn, for any pair of communicating devices, one has to design software that enables the two to talk to each other. Is it possible instead to let the two computing entities use their intelligence (universality as computers) to learn each others' behavior and attain a common understanding? What is 'common understanding?' We explore this question in this paper.
 To formalize this problem, we suggest that one should study the 'goal of communication:' why are the two entities interacting with each other, and what do they hope to gain by it? We propose that by considering this question explicitly, one can make progress on the question of universal communication.
 We start by considering a computational setting for the problem where the goal of one of the interacting players is to gain some computational wisdom from the other player. We show that if the second player is "sufficiently" helpful and powerful, then the first player can gain significant computational power (deciding PSPACE complete languages).
 Our work highlights some of the definitional issues underlying the task of formalizing universal communication, but also suggests some interesting phenomena and highlights potential tools that may be used for such communication.
Liquid Biopsy in Liquid Tumors

The availability of a minimally invasive patient simple, capable of providing tumor information, represents a valuable clinical tool. The liquid biopsy has the potential to achieve this need. Circulating cell free DNA (ccfDNA), other circulating nucleic acids such as microRNA and circulating tumor cells (CTCs), can be obtained from a peripheral blood sample. Liquid biopsy has been particularly studied in solid tumors, specially of the epitelial origin, such as a pancreatic carcinoma and advanced breast cancer. It has been considerably less applied to the study of non-solid tumors. It represents an important source for diagnosis, prognosis and predictive information. Also it is suitable to evaluate response to therapy and drugs pharmacokinetics. It provides a unique opportunity to evaluate the disease evolution in serial blood samples collection, otherwise difficult to obtain. Liquid biopsy can be rehearsed using different circulating biological fluids such as whole blood, serum, plasma and lymph, as well as, non-circulating fluids such as urine, feces, saliva, bile and accumulated pathological fluids such as ascites. This review summarizes the current status of circulating material analysis in non-solid tunors. It is specially focused on Hodgkin Lymphoma and among Non-Hodgkin Lymphoma, it refers particularly to Diffuse Large B cell Lymphoma, the most common aggressive Non-Hodgkin Lymphoma derived from germinal center B-cells in adults. It further discusses the benefit of liquid biopsy in oncohemtaological diseases and potential clinical applications.
An Energy-Balanced Routing Method Based on Forward-Aware Factor for Wireless Sensor Networks

As an important part of industrial application (IA), the wireless sensor network (WSN) has been an active research area over the past few years. Due to the limited energy and communication ability of sensor nodes, it seems especially important to design a routing protocol for WSNs so that sensing data can be transmitted to the receiver effectively. An energy-balanced routing method based on forward-aware factor (FAF-EBRM) is proposed in this paper. In FAF-EBRM, the next-hop node is selected according to the awareness of link weight and forward energy density. Furthermore, a spontaneous reconstruction mechanism for local topology is designed additionally. In the experiments, FAF-EBRM is compared with LEACH and EEUC, experimental results show that FAF-EBRM outperforms LEACH and EEUC, which balances the energy consumption, prolongs the function lifetime and guarantees high QoS of WSN.
Natural Language Processing for Information Retrieval

The paper summarizes the essential properties of document retrieval and reviews both conventional practice and research findings, the latter suggesting that simple statistical techniques can be effective. It then considers the new opportunities and challenges presented by the user’s ability to search full text directly (rather than e.g. titles and abstracts), and suggests appropriate approaches to doing this, with a focus on the potential role of natural language processing. The paper also comments on possible connections with data and knowledge retrieval, and concludes by emphasizing the importance of rigorous performance testing.
Random Survival Forests Models for SME Credit Risk Measurement

This paper extends the existing literature on empirical research in the field of credit risk default for Small Medium Enterprizes (SMEs). We propose a nonparametric approach based on Random Survival Forests (RSF) and we compare its performance with a standard logit model. To the authors’ knowledge, no studies in the area of credit risk default for SMEs have used a variety of statistical methodologies to test the reliability of their predictions and to compare their performance against one another. As for the in-sample results, we find that our non-parametric model performs much better that the classical logit model. As for the out-of-sample performances, the evidence is just the opposite, and the logit performs better than the RSF model. We explain this evidence by showing how error in the estimates of default probabilities can affect classification error when the estimates are used in a classification rule.
Survey and Challenges of Mining Customer Data Sets to Enhance Customer Relationship Management

Data mining is a useful and powerful tool for any organization especially for marketing people. Data mining is used in managing relationships with customers. New trends in customer relationship management –CRMhave growth. One of them is social customer relationship management –sCRM-. The major problem here is data accessibility. If the research process is not started from inside the organization, the researchers cannot have access to customer&apos;s data. In this research paper, the research creates a survey to find out available customer&apos;s data sets that can be used in academic research. In addition, the research reviews basic concepts of CRM and Data Mining Techniques. Finally, the research focuses on current challenges of mining customer&apos;s data from business perspective and analytical customer relationship management -aCRM-.
LaSTUS/TALN+INCO @ CL-SciSumm 2018 - Using Regression and Convolutions for Cross-document Semantic Linking and Summarization of Scholarly Literature

Task 2 CL-SciSumm 17 Dataset Task 1: ● Neural network based on convolations. ● The input is a combination of word embeddings with a set of features based on a pair of sentences (reference and citance sentences). ● The approach outperformed results reported in last year CL-SciSumm-17 Shared Task A1. ● We believe that one system is not enough to identify which exact sentences have been cited in a reference paper. Text Summarization (Task 2): ● Neural network based on convolutions. ● The input is a combination of word embeddings with sentence relevance and citation features. ● The approach outperformed results reported in last year CL-SciSumm-17 Shared Task 2. Ahmed AbuRa’ed, Àlex Bravo, Luis Chiruzzo, Horacio Saggion
Fast Line Description for Line-based SLAM

Simultaneous localization and mapping (SLAM) is a technique to simultaneously perform mapping of environments and localization of a camera in real-time. Most existing monocular vision based SLAM techniques use point features as landmarks. However, images of artificial environments with little texture often contain many line segments, whereas few point features can be localized in such a scene. We propose here a realtime line-based SLAM system, and a novel method for describing the features of line segments (LEHF:Line-based Eight-directional Histogram Feature) in order to establish correct 2D and 3D line correspondences (2D-3D correspondences). LEHF is a fast and efficient way of describing features of line segments, which are detected by the line segment detector (LSD) method. The line-based orthogonal iteration (LBOI) method and the RANSAC algorithm are applied for the camera pose estimation. We conducted an experiment in order to test our SLAM system in a desktop environment and to perform augmented reality (AR). Moreover our SLAM system was evaluated by synthetic data.
egoSlider: Visual Analysis of Egocentric Network Evolution

Ego-network, which represents relationships between a specific individual, i.e., the ego, and people connected to it, i.e., alters, is a critical target to study in social network analysis. Evolutionary patterns of ego-networks along time provide huge insights to many domains such as sociology, anthropology, and psychology. However, the analysis of dynamic ego-networks remains challenging due to its complicated time-varying graph structures, for example: alters come and leave, ties grow stronger and fade away, and alter communities merge and split. Most of the existing dynamic graph visualization techniques mainly focus on topological changes of the entire network, which is not adequate for egocentric analytical tasks. In this paper, we present egoSlider, a visual analysis system for exploring and comparing dynamic ego-networks. egoSlider provides a holistic picture of the data through multiple interactively coordinated views, revealing ego-network evolutionary patterns at three different layers: a macroscopic level for summarizing the entire ego-network data, a mesoscopic level for overviewing specific individuals' ego-network evolutions, and a microscopic level for displaying detailed temporal information of egos and their alters. We demonstrate the effectiveness of egoSlider with a usage scenario with the DBLP publication records. Also, a controlled user study indicates that in general egoSlider outperforms a baseline visualization of dynamic networks for completing egocentric analytical tasks.
Classification and Detection of Micro-Level Impact of Issue-Focused Documentary Films based on Reviews

We present novel research at the intersection of review mining and impact assessment of issue-focused information products, namely documentary films. We develop and evaluate a theoretically grounded classification schema, related codebook, corpus annotation, and prediction model for detecting multiple types of impact that documentaries can have on individuals, such as change versus reaffirmation of behavior, cognition, and emotions, based on user-generated content, i.e., reviews. This work broadens the scope of review mining tasks, which typically comprise the prediction of ratings, helpfulness, and opinions. Our results suggest that documentaries can change or reinforce peoples' conception of an issue. We perform supervised learning to predict impact on the sentence level by using data driven as well as predefined linguistic, lexical, and psychological features; achieving an accuracy rate of 81% (F1) when using a Random Forest classifier, and 73% with a Support Vector Machine.
Improv: A System for Scripting Interactive Actors in Virtual Worlds

Improv is a system for the creation of real−time behavior−based animated actors. There have been seve recent efforts to build network distributed autonomous agent But in general these efforts do not focus on the author’s view To create rich interactive worlds inhabited by believable animated actors, authors need the proper tools. Im prov provides tools to create actors that respond to users and each other in real−time, with personalities and mood consistent with the author’s goals and intentions. Improv consists of two subsystems. The first subsystem is an Animation Engine that uses procedur techniques to enable authors to create layered, continuo non−repetitive motions and smooth transitions between the The second subsystem is a Behavior Engine that enab authors to create sophisticated rules governing how acto communicate, change, and make decisions. The combin system provides an integrated set of tools for authoring th "minds" and "bodies" of interactive actors. The system uses a english−style scripting language so that creative experts wh are not primarily programmers can create powerful interactiv applications.
Learning Deep Face Representation

Face representation is a crucial step of face recognition systems. An optimal face representation should be discriminative, robust, compact, and very easyto-implement. While numerous hand-crafted and learning-based representations have been proposed, considerable room for improvement is still present. In this paper, we present a very easy-to-implement deep learning framework for face representation. Our method bases on a new structure of deep network (called Pyramid CNN). The proposed Pyramid CNN adopts a greedy-filter-and-down-sample operation, which enables the training procedure to be very fast and computationefficient. In addition, the structure of Pyramid CNN can naturally incorporate feature sharing across multi-scale face representations, increasing the discriminative ability of resulting representation. Our basic network is capable of achieving high recognition accuracy (85.8% on LFW benchmark) with only 8 dimension representation. When extended to feature-sharing Pyramid CNN, our system achieves the state-of-the-art performance (97.3%) on LFW benchmark. We also introduce a new benchmark of realistic face images on social network and validate our proposed representation has a good ability of generalization.
Discovery of Online Shopping Patterns Across Websites

• In the online world, customers can easily navigate to different online stores to make purchases • Market basket analysis is often used to discover associations among products for brick-and-mortar stores, but rarely for online shops • Defined to online shopping patterns and developed 2 novel methods to perform market basket analysis across websites • The methods presented in this paper can be only not only to online shopping application, but to other dimensions as well. Uyanga, Do young suk
Differentially private aggregation of distributed time-series with transformation and encryption

We propose the first differentially private aggregation algorithm for distributed time-series data that offers good practical utility without any trusted server. This addresses two important challenges in participatory data-mining applications where (i) individual users collect temporally correlated time-series data (such as location traces, web history, personal health data), and (ii) an untrusted third-party aggregator wishes to run aggregate queries on the data.
 To ensure differential privacy for time-series data despite the presence of temporal correlation, we propose the Fourier Perturbation Algorithm (FPAk). Standard differential privacy techniques perform poorly for time-series data. To answer n queries, such techniques can result in a noise of Θ(n) to each query answer, making the answers practically useless if n is large. Our FPAk algorithm perturbs the Discrete Fourier Transform of the query answers. For answering n queries, FPAk improves the expected error from Θ(n) to roughly Θ(k) where k is the number of Fourier coefficients that can (approximately) reconstruct all the n query answers. Our experiments show that k << n for many real-life data-sets resulting in a huge error-improvement for FPAk.
 To deal with the absence of a trusted central server, we propose the Distributed Laplace Perturbation Algorithm (DLPA) to add noise in a distributed way in order to guarantee differential privacy. To the best of our knowledge, DLPA is the first distributed differentially private algorithm that can scale with a large number of users: DLPA outperforms the only other distributed solution for differential privacy proposed so far, by reducing the computational load per user from O(U) to O(1) where U is the number of users.