nils-herrmann/clean_scirepeval huggingface dataset:
Quality-of-experience driven adaptive HTTP media delivery

This paper presents a Quality of Experience (QoE) driven approach for multi-user resource optimization in Dynamic Adaptive Streaming over HTTP (DASH) over next generation wireless networks. Our objective is to enhance the user experience in adaptive HTTP streaming by jointly considering the characteristics of the media content and the available wireless resources in the operator network. Specifically, we propose a proactive QoE-based approach for rewriting the client HTTP requests at a proxy in the mobile network. The advantage of the proposed approach is its applicability for over-the-top (OTT) streaming as it requires no adaptation of the media content. We compare our proposed scheme to both reactive QoE-optimized and to standard-DASH HTTP streaming. Our contributions are: 1) We first show that standard OTT DASH leads to unsatisfactory performance since the content agnostic resource allocation by the LTE scheduler is far from optimal, and we can achieve a clear QoE improvement when considering the content characteristics. 2) We additionally show that proactively rewriting the client requests gives control of the video content adaptation to the network operator which has better information than the client on the load and radio conditions in the cell. This results in additional gains in user perceived video quality. 3) A standard unmodified DASH client remains unaware of the proposed rewriting of the HTTP requests and can decode and play the redirected media segments.
Stroop effect in Spanish-English bilinguals.

The aim of this study was to analyze the performance of Spanish-English bilinguals on the Golden Stroop Test. The effects of bilingualism, participants' age, age of acquisition of the second language, and proficiency in each language were analyzed. Participants consisted of 71 Spanish-English bilinguals, 40 English monolinguals, and 11 Spanish monolinguals from South Florida. Proficiency in Spanish and English was established using a self-report questionnaire and the English and Spanish versions of the Boston Naming Test. In bilinguals, the Golden Stroop Test was administered in English and in Spanish. Overall, performance was slower in bilinguals than in monolinguals. No significant differences were observed in color reading but bilinguals performed worse in the naming color condition. Even though bilinguals were 5% to 10% slower in the color-word condition, one-way ANOVAs revealed no statistically significant differences between groups. Within the bilingual group, the Stroop Test scores were similar in both English and Spanish. Age of acquisition of the second language did not predict the Stroop Test performance. Repeated measures ANOVA demonstrated a significant interaction between Language Proficiency x Language (in which the test was administered) in some of the ST conditions. In balanced bilinguals, the language used in the ST did not matter, but in unbalanced subjects, the best-spoken language showed better results. In addition, our results support the presence of both between- and within-language interference in Spanish-English bilinguals. Different conceptualization models of the structure of bilingual memory are disclosed.
PATTY: A Taxonomy of Relational Patterns with Semantic Types

This paper presents PATTY: a large resource for textual patterns that denote binary relations between entities. The patterns are semantically typed and organized into a subsumption taxonomy. The PATTY system is based on efficient algorithms for frequent itemset mining and can process Web-scale corpora. It harnesses the rich type system and entity population of large knowledge bases. The PATTY taxonomy comprises 350,569 pattern synsets. Random-sampling-based evaluation shows a pattern accuracy of 84.7%. PATTY has 8,162 subsumptions, with a random-sampling-based precision of 75%. The PATTY resource is freely available for interactive access and download.
Coprime sampling and the music algorithm

A new approach to super resolution line spectrum estimation in both temporal and spatial domain using a coprime pair of samplers is proposed. Two uniform samplers with sample spacings MT and NT are used where M and N are coprime and T has the dimension of space or time. By considering the difference set of this pair of sample spacings (which arise naturally in computation of second order moments), sample locations which are O(MN) consecutive multiples of T can be generated using only O(M + N) physical samples. In order to efficiently use these O(MN) virtual samples for super resolution spectral estimation, a novel algorithm based on the idea of spatial smoothing is proposed, which can be used for estimating frequencies of sinusoids buried in noise as well as for estimating Directions-of-Arrival (DOA) of impinging signals on a sensor array. This technique allows us to construct a suitable positive semidefinite matrix on which subspace based algorithms like MUSIC can be applied to detect O(MN) spectral lines using only O(M + N) physical samples.
Control of Upper-Limb Power-Assist Exoskeleton Using a Human-Robot Interface Based on Motion Intention Recognition

Recognition of the wearer's motion intention plays an important role in the study of power-assist robots. In this paper, an intention-guided control strategy is proposed and applied to an upper-limb power-assist exoskeleton. Meanwhile, a human-robot interface comprised of force-sensing resistors (FSRs) is designed to estimate the motion intention of the wearer's upper limb in real time. Moreover, a new concept called the “intentional reaching direction (IRD)” is proposed to quantitatively describe this intention. Both the state model and the observation model of IRD are obtained by studying the upper limb behavior modes and analyzing the relationship between the measured force signals and the motion intention. Based on these two models, the IRD can be inferred online using an adapted filtering technique. Guided by the inferred IRD, an admittance control strategy is deployed to control the motions of three DC motors placed at the corresponding joints of the robotic arm. The effectiveness of the proposed approaches is finally confirmed by experiments on a 3 degree-of-freedom (DOF) upper-limb robotic exoskeleton.
Intrusion detection in wireless ad-hoc networks

This book contains information obtained from authentic and highly regarded sources. Reasonable efforts have been made to publish reliable data and information, but the author and publisher cannot assume responsibility for the validity of all materials or the consequences of their use. The authors and publishers have attempted to trace the copyright holders of all material reproduced in this publication and apologize to copyright holders if permission to publish in this form has not been obtained. If any copyright material has not been acknowledged please write and let us know so we may rectify in any future reprint. Except as permitted under U.S. Copyright Law, no part of this book may be reprinted, reproduced, transmitted , or utilized in any form by any electronic, mechanical, or other means, now known or hereafter invented, including photocopying, microfilming, and recording, or in any information storage or retrieval system, without written permission from the publishers. CCC is a not-for-profit organization that provides licenses and registration for a variety of users. For organizations that have been granted a photocopy license by the CCC, a separate system of payment has been arranged.  Product or corporate names may be trademarks or registered trademarks, and are used only for identification and explanation without intent to infringe. Intrusion detection in wireless ad-hoc networks / editors, Nabendu Chaki and Rituparna Chaki. pages cm Includes bibliographical references and index. Contents Preface ix a b o u t t h e e d i t o r s xi c o n t r i b u t o r s xiii chaP t e r 1 intro d u c t i o n 1 Nova ru N De b , M a N a l i CH a k r a bor T y, a N D N a beN Du CH a k i chaP t e r 2 a r c h i t e c t u r e a n d o r g a n i z at i o n is s u e s 43 M a N a l i CH a k r a bor T y, Nova ru N De b , De bDu T Ta ba r M a N roy, a N D r i T u pa r N a CH a k i chaP t e r 3 routin g f o r …
Accelerated PSO Swarm Search Feature Selection for Data Stream Mining Big Data

Big Data though it is a hype up-springing many technical challenges that confront both academic research communities and commercial IT deployment, the root sources of Big Data are founded on data streams and the curse of dimensionality. It is generally known that data which are sourced from data streams accumulate continuously making traditional batch-based model induction algorithms infeasible for real-time data mining. Feature selection has been popularly used to lighten the processing load in inducing a data mining model. However, when it comes to mining over high dimensional data the search space from which an optimal feature subset is derived grows exponentially in size, leading to an intractable demand in computation. In order to tackle this problem which is mainly based on the high-dimensionality and streaming format of data feeds in Big Data, a novel lightweight feature selection is proposed. The feature selection is designed particularly for mining streaming data on the fly, by using accelerated particle swarm optimization (APSO) type of swarm search that achieves enhanced analytical accuracy within reasonable processing time. In this paper, a collection of Big Data with exceptionally large degree of dimensionality are put under test of our new feature selection algorithm for performance evaluation.
Real-time user-guided image colorization with learned deep priors

We propose a deep learning approach for user-guided image colorization. The system directly maps a grayscale image, along with sparse, local user "hints" to an output colorization with a Convolutional Neural Network (CNN). Rather than using hand-defined rules, the network propagates user edits by fusing low-level cues along with high-level semantic information, learned from large-scale data. We train on a million images, with simulated user inputs. To guide the user towards efficient input selection, the system recommends likely colors based on the input image and current user inputs. The colorization is performed in a single feed-forward pass, enabling real-time use. Even with randomly simulated user inputs, we show that the proposed system helps novice users quickly create realistic colorizations, and offers large improvements in colorization quality with just a minute of use. In addition, we demonstrate that the framework can incorporate other user "hints" to the desired colorization, showing an application to color histogram transfer.
Improving Accelerometer-Based Activity Recognition by Using Ensemble of Classifiers

In line with the increasing use of sensors and health application, there are huge efforts on processing of collected data to extract valuable information such as accelerometer data. This study will propose activity recognition model aim to detect the activities by employing ensemble of classifiers techniques using the Wireless Sensor Data Mining (WISDM). The model will recognize six activities namely walking, jogging, upstairs, downstairs, sitting, and standing. Many experiments are conducted to determine the best classifier combination for activity recognition. An improvement is observed in the performance when the classifiers are combined than when used individually. An ensemble model is built using AdaBoost in combination with decision tree algorithm C4.5. The model effectively enhances the performance with an accuracy level of 94.04 %. Activity Recognition; Sensors; Smart phones; accelerometer data; Data mining; Ensemble
Multisensor Data Fusion for Next Generation Distributed Intrusion Detection Systems

1 Multisensor Data Fusion for Next Generation Distributed Intrusion Detection Systems Tim Bass ERIM International & Silk Road Ann Arbor, MI 48113 Abstract| Next generation cyberspace intrusion detection systems will fuse data from heterogeneous distributed network sensors to create cyberspace situational awareness. This paper provides a few rst steps toward developing the engineering requirements using the art and science of multisensor data fusion as the underlying model. Current generation internet-based intrusion detection systems and basic multisensor data fusion constructs are summarized. The TCP/IP model is used to develop framework sensor and database models. The SNMP ASN.1 MIB construct is recommended for the representation of context-dependent threat & vulnerabilities databases.
Wireless Home Automation System Using Zigbee

Home Automation industry is growing rapidly; this is fuelled by provide supporting systems for the elderly and the disabled, especially those who live alone. Coupled with this, the world population is confirmed to be getting older. Home automation systems must comply with the household standards and convenience of usage. This paper details the overall design of a wireless home automation system (WHAS) which has been built and implemented. The automation centers on recognition of voice commands and uses low-power RF ZigBee wireless communication modules which are relatively cheap. The home automation system is intended to control all lights and electrical appliances in a home or office using voice commands. The system has been tested and verified. The verification tests included voice recognition response test, indoor ZigBee communication test. The tests involved a mix of 10 male and female subjects with different Indian languages. 7 different voice commands were sent by each person. Thus the test involved sending a total of 70 commands and 80.05% of these commands were recognized correctly.  Home automation, ZigBee transceivers, voice streaming, HM 2007, voice recognition. ——————————  ——————————
Concept Development and Design of a Spherical Wheel Motor (SWM)

This paper presents the design concept, models, and open-loop control of a particular form of a variablereluctance spherical motor (VRSM), referred here as a spherical wheel motor (SWM). Unlike existing spherical motors where design focuses have been on controlling the three degrees of freedom (DOF) angular displacements, the SWM offers a means to control the orientation of a continuously rotating shaft in an open-loop (OL) fashion. We provide a formula for deriving different switching sequences (full step and fractional step) for a specified current magnitude and pole configurations. The concept feasibility of an OL controlled SWM has been experimentally demonstrated on a prototype that has 8 rotor permanent-magnet (PM) pole-pairs and 10 stator electromagnet (EM) pole-pairs.
Efficient multithreaded untransposed, transposed or symmetric sparse matrix-vector multiplication with the Recursive Sparse Blocks format

In earlier work we have introduced the “Recursive Sparse Blocks” (RSB) sparse matrix storage scheme oriented towards cache efficient matrix-vector multiplication (SpMV ) and triangular solution (SpSV ) on cache based shared memory parallel computers. Both the transposed (SpMV T ) and symmetric (SymSpMV ) matrix-vector multiply variants are supported. RSB stands for a meta-format: it recursively partitions a rectangular sparse matrix in quadrants; leaf submatrices are stored in an appropriate traditional format — either Compressed Sparse Rows (CSR) or Coordinate (COO). In this work, we compare the performance of our RSB implementation of SpMV, SpMV T, SymSpMV to that of the state-of-the-art Intel Math Kernel Library (MKL) CSR implementation on the recent Intel’s Sandy Bridge processor. Our results with a few dozens of real world large matrices suggest the efficiency of the approach: in all of the cases, RSB’s SymSpMV (and in most cases, SpMV T as well) took less than half of MKL CSR’s time; SpMV ’s advantage was smaller. Furthermore, RSB’s SpMV T is more scalable than MKL’s CSR, in that it performs almost as well as SpMV. Additionally, we include comparisons to the state-of-the art format Compressed Sparse Blocks (CSB) implementation. We observed RSB to be slightly superior to CSB in SpMV T, slightly inferior in SpMV, and better (in most cases by a factor of two or more) in SymSpMV. Although RSB is a non-traditional storage format and thus needs a special constructor, it can be assembled from CSR or any other similar rowordered representation arrays in the time of a few dozens of matrix-vector multiply executions. Thanks to its significant advantage over MKL’s CSR routines for symmetric or transposed matrix-vector multiplication, in most of the observed cases the assembly cost has been observed to amortize with fewer than fifty iterations.
Parallel Architecture With Resistive Crosspoint Array for Dictionary Learning Acceleration

This paper proposes a parallel architecture with resistive crosspoint array. The design of its two essential operations, read and write, is inspired by the biophysical behavior of a neural system, such as integrate-and-fire and local synapse weight update. The proposed hardware consists of an array with resistive random access memory (RRAM) and CMOS peripheral circuits, which perform matrix-vector multiplication and dictionary update in a fully parallel fashion, at the speed that is independent of the matrix dimension. The read and write circuits are implemented in 65 nm CMOS technology and verified together with an array of RRAM device model built from experimental data. The overall system exploits array-level parallelism and is demonstrated for accelerated dictionary learning tasks. As compared to software implementation running on a 8-core CPU, the proposed hardware achieves more than 3000 × speedup, enabling high-speed feature extraction on a single chip.
Deep Learning for Automatic Detection of Abnormal Findings in Breast Mammography

Automatic identification of abnormalities is a key problem in medical imaging. While the majority of previous work in mammography has focused on classification of abnormalities rather than detection and localization, here we introduce a novel deep learning method for detection of masses and calcifications. The power of this approach comes from generating an ensemble of individual Faster-RCNN models each trained for a specific set of abnormal clinical categories, together with extending a modified two stage Faster-RCNN scheme to a three stage cascade. The third stage being an additional classifier working directly on the image pixels with the handful of sub-windows generated by the first two stages. The performance of the algorithm is evaluated on the INBreast benchmark and on a large internal multi-center dataset. Quantitative results compete well with state of the art in terms of accuracy. Computationally the methods runs significantly faster than current state-of-the art techniques.
Falcon: Scaling Up Hands-Off Crowdsourced Entity Matching to Build Cloud Services

Many works have applied crowdsourcing to entity matching (EM). While promising, these approaches are limited in that they often require a developer to be in the loop. As such, it is difficult for an organization to deploy multiple crowdsourced EM solutions, because there are simply not enough developers. To address this problem, a recent work has proposed Corleone, a solution that crowdsources the entire EM workflow, requiring no developers. While promising, Corleone is severely limited in that it does not scale to large tables. We propose Falcon, a solution that scales up the hands-off crowdsourced EM approach of Corleone, using RDBMS-style query execution and optimization over a Hadoop cluster. Specifically, we define a set of operators and develop efficient implementations. We translate a hands-off crowdsourced EM workflow into a plan consisting of these operators, optimize, then execute the plan. These plans involve both machine and crowd activities, giving rise to novel optimization techniques such as using crowd time to mask machine time. Extensive experiments show that Falcon can scale up to tables of millions of tuples, thus providing a practical solution for hands-off crowdsourced EM, to build cloud-based EM services.
A robust omnidirectional vision sensor for soccer robots

How to make robot vision work robustly under varying lighting conditions and without the constraint of the current color-coded environment are two of the most challenging issues in the RoboCup community. In this paper, we present a robust omnidirectional vision sensor to deal with these issues for the RoboCup Middle Size League soccer robots, in which two novel algorithms are applied. The first one is a camera parameters auto-adjusting algorithm based on image entropy. The relationship between image entropy and camera parameters is verified by experiments, and camera parameters are optimized by maximizing image entropy to adapt the output of the omnidirectional vision to the varying illumination. The second one is a ball recognition method based on the omnidirectional vision without color classification. The conclusion is derived that the ball on the field can be imaged to be an ellipse approximately in our omnidirectional vision, and the arbitrary FIFA ball can be recognized by detecting the ellipse imaged by the ball. The experimental results show that a robust omnidirectional vision sensor can be realized by using the two algorithms mentioned above.
Query to Knowledge: Unsupervised Entity Extraction from Shopping Queries using Adaptor Grammars

Web search queries provide a surprisingly large amount of information, which can be potentially organized and converted into a knowledgebase. In this paper, we focus on the problem of automatically identifying brand and product entities from a large collection of web queries in online shopping domain. We propose an unsupervised approach based on adaptor grammars that does not require any human annotation efforts nor rely on any external resources. To reduce the noise and normalize the query patterns, we introduce a query standardization step, which groups multiple search patterns and word orderings together into their most frequent ones. We present three different sets of grammar rules used to infer query structures and extract brand and product entities. To give an objective assessment of the performance of our approach, we conduct experiments on a large collection of online shopping queries and intrinsically evaluate the knowledgebase generated by our method qualitatively and quantitatively. In addition, we also evaluate our framework on extrinsic tasks on query tagging and chunking. Our empirical studies show that the knowledgebase discovered by our approach is highly accurate, has good coverage and significantly improves the performance on the external tasks.
Adolescents on the net: Internet use and well-being.

With the growing popularity of Internet communication applications among adolescents, the Internet has become an important social context for their development. This paper examined the relationship between adolescent online activity and well-being. Participants included 156 adolescents between 15 to 18.4 years of age who were surveyed about their access to and use of the Internet. Participants also completed measures of loneliness and perceived social support. An ANOVA suggested that loneliness was not related to the total time spent online, nor to the time spent on e-mail, but was related to participants' gender. Regression analyses suggested that gender and participants' perceptions regarding their online relationships were the only variables that predicted loneliness. Adolescents who felt that their relationship with online partners was one that they could turn to in times of need were more lonely. However, perceived support from significant others was not related to time spent online, time on e-mail, participants' relationships with online partners, and to their perceptions about these relationships. The implications of our results for researchers, parents, and other lay persons are discussed.