nils-herrmann/clean_scirepeval huggingface dataset:
Making working memory work: a meta-analysis of executive-control and working memory training in older adults.

This meta-analysis examined the effects of process-based executive-function and working memory training (49 articles, 61 independent samples) in older adults (> 60 years). The interventions resulted in significant effects on performance on the trained task and near-transfer tasks; significant results were obtained for the net pretest-to-posttest gain relative to active and passive control groups and for the net effect at posttest relative to active and passive control groups. Far-transfer effects were smaller than near-transfer effects but were significant for the net pretest-to-posttest gain relative to passive control groups and for the net gain at posttest relative to both active and passive control groups. We detected marginally significant differences in training-induced improvements between working memory and executive-function training, but no differences between the training-induced improvements observed in older adults and younger adults, between the benefits associated with adaptive and nonadaptive training, or between the effects in active and passive control conditions. Gains did not vary with total training time.
Finding academic experts on a multisensor approach using Shannon's entropy

Expert finding is an information retrieval task concerned wi th the search for the most knowledgeable people, in some topic, with basis on document s describing peoples activities. The task involves taking a user query as input and retur ning a list of people sorted by their level of expertise regarding the user query. This pape r introduces a novel approach for combining multiple estimators of expertise based on a mu ltisensor data fusion framework together with the Dempster-Shafer theory of evidence a nd Shannon’s entropy. More specifically, we defined three sensors which detect heteroge neous information derived from the textual contents, from the graph structure of the citati on patterns for the community of experts, and from profile information about the academic exp erts. Given the evidences collected, each sensor may define different candidates as ex perts and consequently do not agree in a final ranking decision. To deal with these conflicts , we applied the DempsterShafer theory of evidence combined with Shannon’s Entropy f rmula to fuse this information and come up with a more accurate and reliable final rankin g list. Experiments made over two datasets of academic publications from the Compute r Science domain attest for the adequacy of the proposed approach over the traditional s tate of the art approaches. We also made experiments against representative supervised s tat of the art algorithms. Results revealed that the proposed method achieved a similar p erformance when compared to these supervised techniques, confirming the capabilities o f the proposed framework. This work was supported by national funds through FCT Funda ção para a Ciência e a Tecnologia, under project PEst-OE/EEI/LA0021/2011 and supported by nationa l funds through FCT Fundação para a Ciência e a Tecnologia, under project PTDC/EIA-CCO/119722/2010
Measurement of Quality of Life III. From the IQOL Theory to the Global, Generic SEQOL Questionnaire

The Danish Quality of Life Survey is based on the philosophy of life known as the integrative quality-of-life (IQOL) theory. It consists of eight different quality-of-life concepts, ranging from the superficially subjective via the deeply existential to the superficially objective (well being, satisfaction with life, happiness, meaning in life, biological order, realizing life potential, fulfillment of needs, and objective factors [ability of functioning and fulfilling societal norms]). This paper presents the work underlying the formulation of the theories of a good life and how these theories came to be expressed in a comprehensive, multidimensional, generic questionnaire for the evaluation of the global quality of life--SEQOL (self-evaluation of quality of life)--presented in full length in this paper. The instruments and theories on which the Quality of Life Survey was based are constantly being updated. It is an on-going process due to aspects such as human development, language, and culture. We arrived at eight rating scales for the quality of life that, guided by the IQOL theory, were combined into a global and generic quality-of-life rating scale. This was simplified to the validated QOL5 with only five questions, made for use in clinical databases. Unfortunately, the depth of human existence is to some extent lost in QOL5. We continue to aim towards greater simplicity, precision, and depth in the questions in order to explore the depths of human existence. We have not yet found a final form that enables us to fully rate the quality of life in practice. We hope that the several hundred questions we found necessary to adequately implement the theories of the Quality of Life Survey can be replaced by far fewer; ideally, only eight questions representing the eight component theories. These eight ideal questions have not yet been evaluated, and therefore they should not form the basis of a survey. However, the perspective is clear. If eight simple questions can accurately rate the quality of life as well as its depth, we have found an instrument of immense practical scope.
Jumping NLP Curves: A Review of Natural Language Processing Research [Review Article]

Natural language processing (NLP) is a theory-motivated range of computational techniques for the automatic analysis and representation of human language. NLP research has evolved from the era of punch cards and batch processing (in which the analysis of a sentence could take up to 7 minutes) to the era of Google and the likes of it (in which millions of webpages can be processed in less than a second). This review paper draws on recent developments in NLP research to look at the past, present, and future of NLP technology in a new light. Borrowing the paradigm of `jumping curves' from the field of business management and marketing prediction, this survey article reinterprets the evolution of NLP research as the intersection of three overlapping curves-namely Syntactics, Semantics, and Pragmatics Curveswhich will eventually lead NLP research to evolve into natural language understanding.
mSwitch: a highly-scalable, modular software switch

In recent years software network switches have regained eminence as a result of a number of growing trends, including the prominence of software-defined networks, as well as their use as back-ends to virtualization technologies, to name a few. Consequently, a number of high performance switches have been recently proposed in the literature, though none of these simultaneously provide (1) high packet rates, (2) high throughput, (3) low CPU usage, (4) high port density and (5) a flexible data plane. This is not by chance: these features conflict, and while achieving one or a few of them is (now) a solved problem, addressing the combination requires significant new design effort.
 In this paper we fill the gap by presenting mSwitch. To prove the flexibility and performance of our approach, we use mSwitch to build four distinct modules: a learning bridge consisting of 45 lines of code that outperforms FreeBSD's bridge by up to 8 times; an accelerated Open vSwitch module requiring small changes to the code and boosting performance by 2.6--3 times; a protocol demultiplexer for userspace protocol stacks; and a filtering module that can direct packets to virtualized middleboxes.
A Multibeam Cylindrically Conformal Slot Array Antenna Based on a Modified Rotman Lens

A dual-layer multibeam conformal slot array antenna operating at a center frequency of 10 GHz is proposed in this paper. The antenna consists of a microstrip Rotman lens and a substrate-integrated waveguide (SIW) array of <inline-formula> <tex-math notation="LaTeX">$10{\times} 10$ </tex-math></inline-formula> radiating slots, which is axially mounted onto a cylindrical surface with a radius of 90 mm, i.e., <inline-formula> <tex-math notation="LaTeX">$3\lambda _{0}$ </tex-math></inline-formula>. The Rotman lens feeds the slot array through the coupling slots in the broad sides of the SIWs. An improved design process is introduced for realizing a conformal Rotman lens for cylindrically conformal arrays. There are unique requirements for the phase distribution and phase compensation in cylindrically conformal applications. The Rotman lens and slot antenna are built and optimized in full-wave simulation software. To validate the proposed concepts, a conformal antenna prototype is implemented and measured. The experimental results demonstrate a scanning range of ±46°, with a −3 dB crossover level between adjacent radiation beams in the E-plane and a −20 dB sidelobe level in the H-plane. The measured and simulated radiation patterns of all the scanned beams are in good agreement. This type of multibeam conformal slot array has been theoretically and experimentally studied, and it demonstrated the potential for applications in aircraft, missiles, and high-speed vehicles.
Photonic ADC: overcoming the bottleneck of electronic jitter.

Accurate conversion of wideband multi-GHz analog signals into the digital domain has long been a target of analog-to-digital converter (ADC) developers, driven by applications in radar systems, software radio, medical imaging, and communication systems. Aperture jitter has been a major bottleneck on the way towards higher speeds and better accuracy. Photonic ADCs, which perform sampling using ultra-stable optical pulse trains generated by mode-locked lasers, have been investigated for many years as a promising approach to overcome the jitter problem and bring ADC performance to new levels. This work demonstrates that the photonic approach can deliver on its promise by digitizing a 41 GHz signal with 7.0 effective bits using a photonic ADC built from discrete components. This accuracy corresponds to a timing jitter of 15 fs - a 4-5 times improvement over the performance of the best electronic ADCs which exist today. On the way towards an integrated photonic ADC, a silicon photonic chip with core photonic components was fabricated and used to digitize a 10 GHz signal with 3.5 effective bits. In these experiments, two wavelength channels were implemented, providing the overall sampling rate of 2.1 GSa/s. To show that photonic ADCs with larger channel counts are possible, a dual 20-channel silicon filter bank has been demonstrated.
Semantic Sentiment Analysis of Twitter

Sentiment analysis over Twitter offer organisations a fast and effective way to monitor the publics’ feelings towards their brand, business, directors, etc. A wide range of features and methods for training sentiment classifiers for Twitter datasets have been researched in recent years with varying results. In this paper, we introduce a novel approach of adding semantics as additional features into the training set for sentiment analysis. For each extracted entity (e.g. iPhone) from tweets, we add its semantic concept (e.g. “Apple product”) as an additional feature, and measure the correlation of the representative concept with negative/positive sentiment. We apply this approach to predict sentiment for three different Twitter datasets. Our results show an average increase of F harmonic accuracy score for identifying both negative and positive sentiment of around 6.5% and 4.8% over the baselines of unigrams and part-of-speech features respectively. We also compare against an approach based on sentiment-bearing topic analysis, and find that semantic features produce better Recall and F score when classifying negative sentiment, and better Precision with lower Recall and F score in positive sentiment classification.
An Implementation and Performance Monitoring of Virtual Machines using Ganglia in Eucalyptus Private Cloud

Cloud computing is the emerging technology that offers hosted services based on the demand of the user (i.e. Pay as Use model) with scale up and down of the resources such as RAM, CPU and Storage etc. With the help of virtualization technology, a virtual IT infrastructure can be built to provide a private cloud for creating virtual machine instances. This paper discusses how the resources have been allocated and deploy an application into the cloud instances. Furthermore the Eucalyptus is integrated with open source Ganglia tool to analyze the performance of a virtual machine.
Fire flame detection in video sequences using multi-stage pattern recognition techniques

In this paper, we propose an effective technique that is used to automatically detect fire in video images. The proposed algorithm is composed of four stages: (1) an adaptive Gaussian mixture model to detect moving regions, (2) a fuzzy c-means (FCM) algorithm to segment the candidate fire regions from these moving regions based on the color of fire, (3) special parameters extracted based on the tempospatial characteristics of fire regions, and (4) a support vector machine (SVM) algorithm using these special parameters to distinguish between fire and non-fire. Experimental results indicate that the proposed method outperforms other fire detection algorithms, providing high reliability and a low false alarm rate. & 2012 Elsevier Ltd. All rights reserved.
A Compact, Broadband Spiral Antenna With Unidirectional Circularly Polarized Radiation Patterns

A low-profile, compact spiral antenna with a broadband circularly polarized (CP) response is presented. The antenna is backed by a ground plane and has unidirectional radiation patterns over its entire frequency band of operation. This antenna is a multilayer structure composed of a center-fed modified Archimedean spiral that exploits a novel loading structure, a ring-shaped absorber, and a feed network, which includes a 180° power splitter. The loading structure possesses both inductive and capacitive characteristics, which increase the equivalent electrical length of the antenna while maintaining its maximum dimensions. The Archimedean spiral is integrated into the multilayer dielectric structure along with its differential feed network. An optimized ring-shaped absorber is used on the periphery of the antenna to reduce the ground effects on the antenna performance. The proposed antenna occupies a volume that is 89% smaller than that occupied by a conventional ground-plane-backed Archimedean spiral antenna. At its lowest frequency of operation, the antenna has electrical dimensions of 0.21λmin × 0.21λmin × 0.09λmin, where λmin is the free-space wavelength at the lowest frequency of operation (0.5 GHz). Over the frequency range from 0.5 to 1.4 GHz (2.8:1), the antenna has a VSWR of 2.4:1, and it has a CP radiation pattern with an axial ratio better than 1.2 dB. Within this frequency range, the antenna has minimum and maximum realized gain values of -5.0 dBiC and 3.1 dBiC, respectively.
Are We Ready for Driver-less Vehicles? Security vs. Privacy- A Social Perspective

At this moment Automous cars are probably the biggest and most talked about technology in the Robotics Reserach Community. In spite of great technolgical advances over past few years a full fledged autonomous car is still far from reality. This article talks about the existing system and discusses the possibility of a Computer Vision enabled driving being superior than the LiDar based system. A detailed overview of privacy violations that might arise from autonomous driving has been discussed in detail both from a technical as well as legal perspective. It has been proved through evidence and arguments that efficient and accurate estimation and efficient solution of the contraint satisfaction problem adressed in the case of autonomous cars are negatively correlated with the preserving the privacy of the user. It is a very difficult trade-off since both are very important aspects and has to be taken into account. The fact that one cannot compromise with the safety issues of the car makes it inevidable to run into serious privacy concerns that might have adverse social and political effects.
SketchInsight: Natural data exploration on interactive whiteboards leveraging pen and touch interaction

In this work, we advance research efforts in combining the casual sketching approach of whiteboards with the machine's computing power. We present SketchInsight, a system that applies the familiar and collaborative features of a whiteboard interface to the accurate data exploration capabilities of interactive visualizations. SketchInsight enables data analysis with more fluid interaction, allowing people to visually explore their data by drawing simple charts and directly manipulating them. In addition, we report results from a qualitative study conducted to evaluate user experience in exploring data with SketchInsight, expanding our understanding on how people use a pen- and touch-enabled digital whiteboard for data exploration. We also discuss the challenges in building a working system that supports data analytic capabilities with pen and touch interaction and freeform annotation.
Activity recognition using a single accelerometer placed at the wrist or ankle.

Large physical activity surveillance projects such as the UK Biobank and NHANES are using wrist-worn accelerometer-based activity monitors that collect raw data. The goal is to increase wear time by asking subjects to wear the monitors on the wrist instead of the hip, and then to use information in the raw signal to improve activity type and intensity estimation. The purposes of this work was to obtain an algorithm to process wrist and ankle raw data and to classify behavior into four broad activity classes: ambulation, cycling, sedentary, and other activities.



Participants (N = 33) wearing accelerometers on the wrist and ankle performed 26 daily activities. The accelerometer data were collected, cleaned, and preprocessed to extract features that characterize 2-, 4-, and 12.8-s data windows. Feature vectors encoding information about frequency and intensity of motion extracted from analysis of the raw signal were used with a support vector machine classifier to identify a subject's activity. Results were compared with categories classified by a human observer. Algorithms were validated using a leave-one-subject-out strategy. The computational complexity of each processing step was also evaluated.



With 12.8-s windows, the proposed strategy showed high classification accuracies for ankle data (95.0%) that decreased to 84.7% for wrist data. Shorter (4 s) windows only minimally decreased performances of the algorithm on the wrist to 84.2%.



A classification algorithm using 13 features shows good classification into the four classes given the complexity of the activities in the original data set. The algorithm is computationally efficient and could be implemented in real time on mobile devices with only 4-s latency.
Organizational Learning and Communities of Practice ; A social constructivist perspective

In this paper, the relationship between organizational learning (OL) and communities of practice (COP) is addressed. A social constructivist lens is used to analyzing the potential contributions of COP’s in supporting learning by organizations. A social constructivist approach sees organizational learning as an institutionalizing process. The attention is on the process through which individual or local knowledge is transformed into collective knowledge as well as the process through which this socially constructed knowledge influences, and is part of, local knowledge. In order to analyse COP’s contribution to OL, we use the three phases or ‘moments’ described by Berger and Luckman (1966) that can be discerned during institutionalizing knowledge: ‘externalizing, objectifying and internalizing’. Externalizing knowledge refers to the process through which personal knowledge is exchanged with others. Objectifying knowledge refers to the process through which knowledge becomes an objective reality. Internalizing knowledge refers to the process through which objectified knowledge is used by individuals in the course of their socialization. In relation to OL processes, learning can be analyzed as consisting of these three knowledge sharing activities: externalizing individual knowledge resulting in shared knowledge; objectifying shared knowledge resulting in organizational knowledge; internalizing organizational knowledge resulting in individual knowledge. These various processes that in combination make up OL processes, are visualized by the use of a OL cycle. The cycle provides a simplified picture of OL seen as a process of institutionalization. The cycle is subsequently used to analyze the possible contribution of COP’s to support organizational learning. The paper concludes that COP’s are well suited to support processes of internalization and externalization. As a result, COP’s stimulate social learning processes within organizations. However, COP’s do not seem to be the appropriate means to support the process of objectification. This means that COP’s contribution in supporting learning at the organizational level or ‘organizational learning’ is much more complicated.
Color Quantization by Dynamic Programming and Principal Analysis

Color quantization is a process of choosing a set of <italic>K</italic> representative colors to approximate the <italic>N</italic> colors of an image, <italic>K < N</italic>, such that the resulting <italic>K</italic>-color image looks as much like the original <italic>N</italic>-color image as possible. This is an optimization problem known to be NP-complete in <italic>K</italic>. However, this paper shows that by ordering the <italic>N</italic> colors along their principal axis and partitioning the color space with respect to this ordering, the resulting constrained optimization problem can be solved in <italic>O</italic>(<italic>N</italic> + <italic>KM</italic><supscrpt>2</supscrpt>) time by dynamic programming (where <italic>M</italic> is the intensity resolution of the   device).
Traditional color quantization algorithms recursively bipartition the color space. By using the above dynamic-programming algorithm, we can construct a globally optimal <italic>K</italic>-partition, <italic>K</italic>>2, of a color space in the principal direction of the input data. This new partitioning strategy leads to smaller quantization error and hence better image quality. Other algorithmic issues in color quantization such as efficient statistical computations and nearest-neighbor searching are also studied. The interplay between luminance and chromaticity in color quantization with and without color dithering is investigated. Our color quantization method allows the user to choose a balance between the image smoothness and hue accuracy for a given   <italic>K</italic>.