nils-herrmann/clean_scirepeval huggingface dataset:
Diff-Index: Differentiated Index in Distributed Log-Structured Data Stores

Log-Structured-Merge (LSM) Tree gains much attention recently because of its superior performance in write-intensive workloads. LSM Tree uses an append-only structure in memory to achieve low write latency; at memory capacity, in-memory data are flushed to other storage media (e.g. disk). Consequently, read access is slower comparing to write. These specific features of LSM, including no in-place update and asymmetric read/write performance raise unique challenges in index maintenance for LSM. The structural difference between LSM and B-Tree also prevents mature B-Tree based approaches from being directly applied. To address the issues of index maintenance for LSM, we propose Diff-Index to support a spectrum of index maintenance schemes to suit different objectives in index consistency and performance. The schemes consist of sync-full, sync-insert, async-simple and async-session. Experiments on our HBase implementation quantitatively demonstrate that Diff-Index offers various performance/consistency balance and satisfactory scalability while avoiding global coordination. Syncinsert and async-simple can reduce 60%-80% of the overall index update latency when compared to the baseline syncfull ; async-simple can achieve superior index update performance with an acceptable inconsistency. Diff-Index exploits LSM features such as versioning and the flush-compact process to achieve goals of concurrency control and failure ∗Work done while author was at IBM Almaden Research Center. †Work done while author was an intern at IBM T.
A Graph Query Language for EMF Models

While model queries are important components in modeldriven tool chains, they are still frequently implemented using traditional programming languages, despite the availability of model query languages due to performance and expressiveness issues. In the current paper, we propose EMF-IncQuery as a novel, graph-based query language for EMF models by adapting the query language of the Viatra2 model transformation framework to inherit its concise, declarative nature, but to properly tailor the new query language to the modeling specificities of EMF. The EMF-IncQuery language includes (i) structural restrictions for queries imposed by EMF models, (ii) syntactic sugar and notational shorthand in queries, (iii) true semantic extensions which introduce new query features, and (iv) a constraint-based static type checking method to detect violations of EMF-specific type inference rules.
Predicting disease risks from highly imbalanced data using random forest

We present a method utilizing Healthcare Cost and Utilization Project (HCUP) dataset for predicting disease risk of individuals based on their medical diagnosis history. The presented methodology may be incorporated in a variety of applications such as risk management, tailored health communication and decision support systems in healthcare.



We employed the National Inpatient Sample (NIS) data, which is publicly available through Healthcare Cost and Utilization Project (HCUP), to train random forest classifiers for disease prediction. Since the HCUP data is highly imbalanced, we employed an ensemble learning approach based on repeated random sub-sampling. This technique divides the training data into multiple sub-samples, while ensuring that each sub-sample is fully balanced. We compared the performance of support vector machine (SVM), bagging, boosting and RF to predict the risk of eight chronic diseases.



We predicted eight disease categories. Overall, the RF ensemble learning method outperformed SVM, bagging and boosting in terms of the area under the receiver operating characteristic (ROC) curve (AUC). In addition, RF has the advantage of computing the importance of each variable in the classification process.



In combining repeated random sub-sampling with RF, we were able to overcome the class imbalance problem and achieve promising results. Using the national HCUP data set, we predicted eight disease categories with an average AUC of 88.79%.
An Efficient k-Means Clustering Algorithm: Analysis and Implementation

ÐIn k-means clustering, we are given a set of n data points in d-dimensional space R and an integer k and the problem is to determine a set of k points in R, called centers, so as to minimize the mean squared distance from each data point to its nearest center. A popular heuristic for k-means clustering is Lloyd's algorithm. In this paper, we present a simple and efficient implementation of Lloyd's k-means clustering algorithm, which we call the filtering algorithm. This algorithm is easy to implement, requiring a kd-tree as the only major data structure. We establish the practical efficiency of the filtering algorithm in two ways. First, we present a data-sensitive analysis of the algorithm's running time, which shows that the algorithm runs faster as the separation between clusters increases. Second, we present a number of empirical studies both on synthetically generated data and on real data sets from applications in color quantization, data compression, and image segmentation. Index TermsÐPattern recognition, machine learning, data mining, k-means clustering, nearest-neighbor searching, k-d tree, computational geometry, knowledge discovery.
Potential field methods and their inherent limitations for mobile robot navigation

Potential field methods are rapidly gaining popularity in obstacle avoidance applications for mobile robots and manipulators. While the potential field principle is particularly attractive because of its elegance and simplicity, substantial shortcomings have been identified as problems that are inherent to this principle. Based upon mathematical analysis, this paper presents a systematic criticism of the inherent problems. The heart of this analysis is a differential equation that combines the robot and the environment into a unified system. The identified problems are discussed in qualitative and theoretical terms and documented with experimental results from actual mobile robot runs.
Structured H 2 Optimization of Vehicle Suspensions Based on Multi-Wheel Models

Various control techniques, especially LQG optimal control, have been applied to the design of active and semi-active vehicle suspensions over the past several decades. However passive suspensions remain dominant in the automotive marketplace because they are simple, reliable, and inexpensive. The force generated by a passive suspension at a given wheel can depend only on the relative displacement and velocity at that wheel, and the suspension parameters for the left and right wheels are usually required to be equal. Therefore, a passive vehicle suspension can be viewed as a decentralized feedback controller with constraints to guarantee suspension symmetry. In this paper, we cast the optimization of passive vehicle suspensions as structure-constrained LQG=H2 optimal control problems. Correlated road random excitations are taken as the disturbance inputs; ride comfort, road handling, suspension travel, and vehicle-body attitude are included in the cost outputs. We derive a set of necessary conditions for optimality and then develop a gradient-based method to efficiently solve the structure-constrained H2 optimization problem. An eight-DOF four-wheel-vehicle model is studied as an example to illustrate application of the procedure, which is useful for design of both passive suspensions and active suspensions with controller-structure constraints.
Adaptive Intrusion Detection of Malicious Unmanned Air Vehicles Using Behavior Rule Specifications

In this paper, we propose an adaptive specification-based intrusion detection system (IDS) for detecting malicious unmanned air vehicles (UAVs) in an airborne system in which continuity of operation is of the utmost importance. An IDS audits UAVs in a distributed system to determine if the UAVs are functioning normally or are operating under malicious attacks. We investigate the impact of reckless, random, and opportunistic attacker behaviors (modes which many historical cyber attacks have used) on the effectiveness of our behavior rule-based UAV IDS (BRUIDS) which bases its audit on behavior rules to quickly assess the survivability of the UAV facing malicious attacks. Through a comparative analysis with the multiagent system/ant-colony clustering model, we demonstrate a high detection accuracy of BRUIDS for compliant performance. By adjusting the detection strength, BRUIDS can effectively trade higher false positives for lower false negatives to cope with more sophisticated random and opportunistic attackers to support ultrasafe and secure UAV applications.
Tag, You're It Radar Cross Section of Chipless RFID Tags

Mass deployment of RF identification (RFID) is hindered by its cost per tag. The main cost comes from the application-specific integrated circuit (ASIC) chip set in a tag. A chipless tag costs less than a cent, and these have the potential for mass deployment for low-cost, item-level tagging as the replacement technology for optical barcodes. Chipless RFID tags can be directly printed on paper or plastic packets just like barcodes. They are highly useful for automatic identification and authentication, supply-chain automation, and medical applications. Among their potential industrial applications are authenticating of polymer bank notes; scanning of credit cards, library cards, and the like; tracking of inventory in retail settings; and identification of pathology and other medical test samples.
An Optimal Graph Theoretic Approach to Data Clustering: Theory and Its Application to Image Segmentation

(57) Data collected from devices and human condition may be used to forewarn of critical events such as machine/structural failure or events from brain/heart wave data stroke. By moni toring the data, and determining what values are indicative of a failure forewarning, one can provide adequate notice of the impending failure in order to take preventive measures. This disclosure teaches a computer-based method to convert dynamical numeric data representing physical objects (un structured data) into discrete-phase-space states, and hence into a graph (Structured data) for extraction of condition change.
Shape Reconstruction from Raw Point Clouds using Depth Carving

Shape reconstruction from raw point sets is a hot research topic. Point sets are increasingly available as primary input source, since low-cost acquisition methods are largely accessible nowadays, and these sets are more noisy than used to be. Standard reconstruction methods rely on normals or signed distance functions, and thus many methods aim at estimating these features. Human vision can however easily discern between the inside and the outside of a dense cloud even without the support of fancy measures. We propose, here, a perceptual method for estimating an indicator function for the shape, inspired from image-based methods. The resulting function nicely approximates the shape, is robust to noise, and can be used for direct isosurface extraction or as an input for other accurate reconstruction methods.
3D printed tactile picture books for children with visual impairments: a design probe

Young children with visual impairments greatly benefit from tactile graphics (illustrations, images, puzzles, objects) during their learning processes. In this paper we present insight about using a 3D printed tactile picture book as a design probe. This has allowed us to identify and engage stakeholders in our research on improving the technical and human processes required for creating 3D printed tactile pictures, and cultivate a community of practice around these processes. We also contribute insight about how our inperson and digital methods of interacting with teachers, parents, and other professionals dedicated to supporting children with visual impairments contributes to research practices.
Celebrity's self-disclosure on Twitter and parasocial relationships: A mediating role of social presence

This study investigated how celebrities' self-disclosure on personal social media accounts, particularly Twitter, affects fans' perceptions. An online survey was utilized among a sample of 429 celebrity followers on Twitter. Results demonstrated that celebrities' professional self-disclosure (e.g., sharing their work-related life), personal self-disclosure (e.g., sharing their personal life such as friends and family), and fans' retweeting behavior, enhanced fans’ feeling of social presence, thereby positively affecting parasocial interaction with celebrities. Further, the study found that the effects of self-disclosure and retweeting on parasocial interaction were mediated by social presence. Implications and future research directions are provided.
Finding interesting posts in Twitter based on retweet graph analysis

Millions of posts are being generated in real-time by users in social networking services, such as Twitter. However, a considerable number of those posts are mundane posts that are of interest to the authors and possibly their friends only. This paper investigates the problem of automatically discovering valuable posts that may be of potential interest to a wider audience. Specifically, we model the structure of Twitter as a graph consisting of users and posts as nodes and retweet relations between the nodes as edges. We propose a variant of the HITS algorithm for producing a static ranking of posts. Experimental results on real world data demonstrate that our method can achieve better performance than several baseline methods.
Dynamical evolutionary psychology: individual decision rules and emergent social norms.

A new theory integrating evolutionary and dynamical approaches is proposed. Following evolutionary models, psychological mechanisms are conceived as conditional decision rules designed to address fundamental problems confronted by human ancestors, with qualitatively different decision rules serving different problem domains and individual differences in decision rules as a function of adaptive and random variation. Following dynamical models, decision mechanisms within individuals are assumed to unfold in dynamic interplay with decision mechanisms of others in social networks. Decision mechanisms in different domains have different dynamic outcomes and lead to different sociospatial geometries. Three series of simulations examining trade-offs in cooperation and mating decisions illustratehow individual decision mechanisms and group dynamics mutually constrain one another, and offer insights about gene-culture interactions.
Mobile app recommendations with security and privacy awareness

With the rapid prevalence of smart mobile devices, the number of mobile Apps available has exploded over the past few years. To facilitate the choice of mobile Apps, existing mobile App recommender systems typically recommend popular mobile Apps to mobile users. However, mobile Apps are highly varied and often poorly understood, particularly for their activities and functions related to privacy and security. Therefore, more and more mobile users are reluctant to adopt mobile Apps due to the risk of privacy invasion and other security concerns. To fill this crucial void, in this paper, we propose to develop a mobile App recommender system with privacy and security awareness. The design goal is to equip the recommender system with the functionality which allows to automatically detect and evaluate the security risk of mobile Apps. Then, the recommender system can provide App recommendations by considering both the Apps' popularity and the users' security preferences. Specifically, a mobile App can lead to security risk because insecure data access permissions have been implemented in this App. Therefore, we first develop the techniques to automatically detect the potential security risk for each mobile App by exploiting the requested permissions. Then, we propose a flexible approach based on modern portfolio theory for recommending Apps by striking a balance between the Apps' popularity and the users' security concerns, and build an App hash tree to efficiently recommend Apps. Finally, we evaluate our approach with extensive experiments on a large-scale data set collected from Google Play. The experimental results clearly validate the effectiveness of our approach.
Omnidirectional Circularly Polarized Dielectric Resonator Antenna With Top-Loaded Alford Loop for Pattern Diversity Design

A new compact omnidirectional circularly polarized (CP) cylindrical dielectric resonator antenna (DRA) with a top-loaded modified Alford loop is investigated. Fed by an axial probe, the DRA is excited in its TM01δ-mode, which radiates like a vertically polarized electric monopole. The modified Alford loop comprises a central circular patch and four curved branches. It is placed on the top of the DRA and provides an equivalent horizontally polarized magnetic dipole mode. Omnidirectional CP fields can be obtained when the two orthogonally polarized fields are equal in amplitude but different in phase by 90°. This CP DRA is applied to the design of a two-port CP diversity DRA which provides omnidirectional and broadside radiation patterns. The broadside radiation pattern is obtained by making use of the broadside HEM12δ+ 1-mode of the DRA, which is excited by a balanced slot serially fed by a microstrip line. For demonstration, both the omnidirectional CP DRA and the diversity CP DRA were designed at ~ 2.4 GHz for WLAN applications. Their S-parameters, axial ratios, radiation patterns, antenna gains, and antenna efficiencies are studied. The envelope correlation is also found for the diversity design. Reasonable agreement between the simulated and measured results is observed.
Gestural communication over video stream: supporting multimodal interaction for remote collaborative physical tasks

We present a system integrating gesture and live video to support collaboration on physical tasks. The architecture combines network IP cameras, desktop PCs, and tablet PCs to allow a remote helper to draw on a video feed of a workspace as he/she provides task instructions. A gesture recognition component enables the system both to normalize freehand drawings to facilitate communication with remote partners and to use pen-based input as a camera control device. Results of a preliminary user study suggest that our gesture over video communication system enhances task performance over traditional video-only systems. Implications for the design of multimodal systems to support collaborative physical tasks are also discussed.
HaTS : Large-scale In-product Measurement of User Attitudes & Experiences with Happiness Tracking Surveys

With the rise of Web-based applications, it is both important and feasible for human-computer interaction practitioners to measure a product’s user experience. While quantifying user attitudes at a small scale has been heavily studied, in this industry case study, we detail best Happiness Tracking Surveys (HaTS) for collecting attitudinal data at a large scale directly in the product and over time. This method was developed at Google to track attitudes and open-ended feedback over time, and to characterize products’ user bases. This case study of HaTS goes beyond the design of the questionnaire to also suggest best practices for appropriate sampling, invitation techniques, and its data analysis. HaTS has been deployed successfully across dozens of Google’s products to measure progress towards product goals and to inform product decisions; its sensitivity to product changes has been demonstrated widely. We are confident that teams in other organizations will be able to embrace HaTS as well, and, if necessary, adapt it for their unique needs.
Visual Touch in Virtual Environments: An Exploratory Study of Presence, Multimodal Interfaces, and Cross-Modal Sensory Illusions

How do users generate an illusion of presence in a rich and consistent virtual environment from an impoverished, incomplete, and often inconsistent set of sensory cues? We conducted an experiment to explore how multimodal perceptual cues are integrated into a coherent experience of virtual objects and spaces. Specifically, we explored whether inter-modal integration contributes to generating the illusion of presence in virtual environments. To discover whether intermodal integration might play a role in presence, we looked for evidence of intermodal integration in the form of cross-modal interactionsperceptual illusions in which users use sensory cues in one modality to fill in the missing components of perceptual experience. One form of cross-modal interaction, a cross-modal transfer, is defined as a form of synesthesia, that is, a perceptual illusion in which stimulation to a sensory modality connected to the interface (such as the visual modality) is accompanied by perceived stimulation to an unconnected sensory modality that receives no apparent stimulation from the virtual environment (such as the haptic modality). Users of our experimental virtual environment who manipulated the visual analog of a physical force, a virtual spring, reported haptic sensations of physical resistance, even though the interface included no haptic displays. A path model of the data suggested that this cross-modal illusion was correlated with and dependent upon the sensation of spatial and sensory presence. We conclude that this is evidence that presence may derive from the process of multi-modal integration and, therefore, may be associated with other illusions, such as cross-modal transfers, that result from the process of creating a coherent mental model of the space. Finally, we suggest that this perceptual phenomenon might be used to improve user experiences with multimodal interfaces, specifically by supporting limited sensory displays (such as haptic displays) with appropriate synesthetic stimulation to other sensory modalities (such as visual and auditory analogs of haptic forces).
Automatically Early Detection of Skin Cancer: Study Based on Nueral Netwok Classification

In this paper, an automatically skin cancer classification system is developed and the relationship of skin cancer image across different type of neural network are studied with different types of preprocessing.. The collected images are feed into the system, and across different image processing procedure to enhance the image properties. Then the normal skin is removed from the skin affected area and the cancer cell is left in the image. Useful information can be extracted from these images and pass to the classification system for training and testing. Recognition accuracy of the 3-layers back-propagation neural network classifier is 89.9% and auto-associative neural network is 80.8% in the image database that include dermoscopy photo and digital photo