nils-herrmann/clean_scirepeval huggingface dataset:
Optimal Data Fusion in Multiple Sensor Detection Systems

There is an increasing interest in employing multiple sensors for surveillance and communications. Some of the motivating factors are reliability, survivability, increase in the number of targets under consideration, and increase in required coverage. Tenney and Sandell have recently treated the Bayesian detection problem with distributed sensors. They did not consider the design of data fusion algorithms. We present an optimum data fusion structure given the detectors. Individual decisions are weighted according to the reliability of the detector and then a threshold comparison is performed to obtain the global decision.
Massive MIMO Performance Evaluation Based on Measured Propagation Data

Massive MIMO, also known as very-large MIMO or large-scale antenna systems, is a new technique that potentially can offer large network capacities in multi-user scenarios. With a massive MIMO system, we consider the case where a base station equipped with a large number of antenna elements simultaneously serves multiple single-antenna users in the same time-frequency resource. So far, investigations are mostly based on theoretical channels with independent and identically distributed (i.i.d.) complex Gaussian coefficients, i.e., i.i.d. Rayleigh channels. Here, we investigate how massive MIMO performs in channels measured in real propagation environments. Channel measurements were performed at 2.6 GHz using a virtual uniform linear array (ULA), which has a physically large aperture, and a practical uniform cylindrical array (UCA), which is more compact in size, both having 128 antenna ports. Based on measurement data, we illustrate channel behavior of massive MIMO in three representative propagation conditions, and evaluate the corresponding performance. The investigation shows that the measured channels, for both array types, allow us to achieve performance close to that in i.i.d. Rayleigh channels. It is concluded that in real propagation environments we have characteristics that can allow for efficient use of massive MIMO, i.e., the theoretical advantages of this new technology can also be harvested in real channels.
An Active-Rectifier-Based Maximum Efficiency Tracking Method Using an Additional Measurement Coil for Wireless Power Transfer

The efficiency of wireless power transfer (WPT) systems is highly dependent on the load, which may change in a wide range in field applications. Besides, the detuning of WPT systems caused by the component tolerance and aging of inductors and capacitors can also decrease the system efficiency. In order to track the maximum system efficiency under varied loads and detuning conditions in real time, an active single-phase rectifier (ASPR) with an auxiliary measurement coil (AMC) and its corresponding control method are proposed in this paper. Both the equivalent load impedance and the output voltage can be regulated by the ASPR and the inverter, separately. First, the fundamental harmonic analysis model is established to analyze the influence of the load and the detuning on the system efficiency. Second, the soft-switching conditions and the equivalent input impedance of ASPR with different phase shifts and pulse widths are investigated in detail. Then, the analysis of the AMC and the maximum efficiency control strategy are provided in detail. Finally, an 800-W prototype is set up to validate the performance of the proposed method. The experimental results show that with 10% tolerance of the resonant capacitor in the receiver side, the system efficiency with the proposed approach reaches 91.7% at rated 800-W load and 91.1% at 300-W light load, which has an improvement by 2% and 10% separately compared with the traditional diode rectifier.
The Dinu-Model - a Process Model for the Design of Nudges

The sociotechnical paradigm legitimates our discipline and serves as core identity of IS. In this study, we want to focus on IS-induced human behavior by introducing a process model for nudging in IS. In behavioral economics, the concept of nudging has been proposed, which makes use of human cognitive processes and can direct people to an intended behavior. In computer science, the concept of persuasion has evolved with similar goals. Both concepts, nudging and persuasion, can contribute to IS research and may help to explain and steer user behavior in information systems. We aim for an integration of both concepts into one digital nudging process model, making it usable and accessible. We analyzed literature on nudging and persuasion and derived different steps, requirements, and nudging elements. The developed process model aims at enabling researchers and practitioners to design nudges in e.g. software systems but may also contribute to other areas like IT governance. Though the evaluation part of our study has not yet been completed, we present the current state of the process model enabling more research in this area.
Connected Components for a Fast and Robust 2D Lidar Data Segmentation

The paper presents a novel segmentation approach applied to a two-dimensional point-cloud extracted by a LIDAR device. The most common approaches perform well in outdoor environments where usually furniture and other objects are rather big and are composed of smooth surfaces. However, these methods fail to segment uneven, rough surfaces. In this paper we propose a novel range data segmentation algorithm that is based on the popular one-pass version of the Connected Components algorithm. Our algorithm outperforms most commonly used approaches, while keeping the low computational complexity. The algorithm is used as a part of control and perception system in our unmanned ground vehicle where real-time response time is required. We presented experimental results obtained indoors and outdoors. The latter experiment was conducted in the real test field while the vehicle was autonomously driven.
Attention-GAN for Object Transfiguration in Wild Images

This paper studies the object transfiguration problem in wild images. The generative network in classical GANs for object transfiguration often undertakes a dual responsibility: to detect the objects of interests and to convert the object from source domain to target domain. In contrast, we decompose the generative network into two separat networks, each of which is only dedicated to one particular sub-task. The attention network predicts spatial attention maps of images, and the transformation network focuses on translating objects. Attention maps produced by attention network are encouraged to be sparse, so that major attention can be paid to objects of interests. No matter before or after object transfiguration, attention maps should remain constant. In addition, learning attention network can receive more instructions, given the available segmentation annotations of images. Experimental results demonstrate the necessity of investigating attention in object transfiguration, and that the proposed algorithm can learn accurate attention to improve quality of generated images.
A Field Guide to Genetic Programming

The latest book on Genetic Programming, Poli, Langdon and McPhee’s (with contributions from John R. Koza) A Field Guide to Genetic Programming represents an exciting landmark with the authors choosing to make their work freely available by publishing using a form of the Creative Commons License[1]. In so doing they have created a must-read resource which is, to use their words, ’aimed at both newcomers and old-timers’. The book is freely available from the authors companion website  and Lulu.com  in both pdf and html form. For those who desire the more traditional page turning exercise, inexpensive printed copies can be ordered from Lulu.com. The Field Guides companion website also provides a link to the TinyGP code printed over eight pages of Appendix B, and a Discussion Group centered around the book. The book is divided into four parts with fourteen chapters and two appendices. Part I introduces the basics of Genetic Programming, Part II overviews more advanced topics, Part III highlights some of the real world applications and discusses issues facing the GP researcher or practitioner, while Part IV contains two appendices, the first introducing some key resources and the second appendix describes the TinyGP code. The pdf and html forms of the book have an especially useful feature, providing links to the articles available on-line at the time of publication, and to bibtex entries of the GP Bibliography. Following an overview of the book in chapter 1, chapter 2 introduces the basic concepts of GP focusing on the tree representation, initialisation, selection, and the search operators. Chapter 3 is centered around the preparatory steps in applying GP to a problem, which is followed by an outline of a sample run of GP on a simple instance of symbolic regression in Chapter 4. Overall these chapters provide a compact and useful introduction to GP. The first of the Advanced GP chapters in Part II looks at alternative strategies for initialisation and the search operators for tree-based GP. An overview of Modular, Grammatical and Developmental GP is provided in Chapter 6. While the chapter title
Cotton leaf disease identification using pattern recognition techniques

Leaf diseases on cotton plant must be identified early and accurately as it can prove detrimental to the yield. The proposed work presents a pattern recognition system for identification and classification of three cotton leaf diseases i.e. Bacterial Blight, Myrothecium and Alternaria. The images required for this work are captured from the fields at Central Institute of Cotton Research Nagpur, and the cotton fields in Buldana and Wardha district. Active contour model is used for image segmentation and Hu's moments are extracted as features for the training of adaptive neuro-fuzzy inference system. The classification accuracy is found to be 85 percent.
Workload Modeling for Computer Systems Performance Evaluation

Reliable performance evaluations require the use of representative workloads. This is no easy task because modern computer systems and their workloads are complex, with many interrelated attributes and complicated structures. Experts often use sophisticated mathematics to analyze and describe workload models, making these models difficult for practitioners to grasp. This book aims to close this gap by emphasizing the intuition and the reasoning behind the definitions and derivations related to the workload models. It provides numerous examples from real production systems, with hundreds of graphs. Using this book, readers will be able to analyze collected workload data and clean it if necessary, derive statistical models that include skewed marginal distributions and correlations, and consider the need for generative models and feedback from the system. The descriptive statistics techniques covered are also useful for other domains.
Parameter-Free Auto-Weighted Multiple Graph Learning: A Framework for Multiview Clustering and Semi-Supervised Classification

Graph-based approaches have been successful in unsupervised and semi-supervised learning. In this paper, we focus on the real-world applications where the same instance can be represented by multiple heterogeneous features. The key point of utilizing the graph-based knowledge to deal with this kind of data is to reasonably integrate the different representations and obtain the most consistent manifold with the real data distributions. In this paper, we propose a novel framework via the reformulation of the standard spectral learning model, which can be used for multiview clustering and semisupervised tasks. Unlike other methods in the literature, the proposed methods can learn an optimal weight for each graph automatically without introducing an additive parameter as previous methods do. Furthermore, our objective under semisupervised learning is convex and the global optimal result will be obtained. Extensive empirical results on different real-world data sets demonstrate that the proposed methods achieve comparable performance with the state-of-the-art approaches and can be used more practically.
Second Language Teaching and Learning in the Net Generation.

Review by Ulugbek Nurmukhamedov, University of Illinois at Urbana-Champaign In the field of Computer-Assisted Language Learning (CALL), the term “Net Generation” has been clearly defined by Prensky (2001), who states that Net Generation members are “digital natives” since they “[have] spent their entire lives surrounded by and using computers, videogames, digital music players, video cams, cell phones, and all the other toys and tools of the digital age” (p. 1), all of which make them different from the baby boomer generation. For those who are eager to learn more about the language learning needs and necessities of the Net Generation, the edited book by Oxford and Oxford (2009), entitled Second Language Teaching and Learning in the Net Generation is a must-read resource. Since most of the contributors to the book are second and foreign language instructors as well as researchers, almost every chapter of the book describes empirical studies involving different innovative technologies and state-of-the-art tools, offering pedagogical ideas, effective strategies, and useful suggestions on how these technologies could be applied to enhance language teaching and learning.
Theory and Tools for the Conversion of Analog to Spiking Convolutional Neural Networks

Deep convolutional neural networks (CNNs) have shown great potential for numerous real-world machine learning applications, but performing inference in large CNNs in real-time remains a challenge. We have previously demonstrated that traditional CNNs can be converted into deep spiking neural networks (SNNs), which exhibit similar accuracy while reducing both latency and computational load as a consequence of their data-driven, event-based style of computing. Here we provide a novel theory that explains why this conversion is successful, and derive from it several new tools to convert a larger and more powerful class of deep networks into SNNs. We identify the main sources of approximation errors in previous conversion methods, and propose simple mechanisms to fix these issues. Furthermore, we develop spiking implementations of common CNN operations such as max-pooling, softmax, and batch-normalization, which allow almost loss-less conversion of arbitrary CNN architectures into the spiking domain. Empirical evaluation of different network architectures on the MNIST and CIFAR10 benchmarks leads to the best SNN results reported to date.
Are Mobile Payment and Banking the Killer Apps for Mobile Commerce?

Mobile commerce had been expected to become a major force of e-commerce in the 21st century. However, the rhetoric has far exceeded the reality so far. While academics and practitioners have presented many views about the lack of rapid growth of mobile commerce, we submit that the anticipated mobile commerce take-off hinges on the emergence of a few killer apps. After reviewing the recent history of technologies that have dramatically changed our way of life and work, we propose a set of criteria for identifying and evaluating killer apps. From this vantage point, we argue that mobile payment and banking are the most likely candidates for the killer apps that could bring the expectation of a world of ubiquitous mobile commerce to fruition. Challenges and opportunities associated with this argument are discussed.
Oscillations in the alpha band (9-12 Hz) increase with memory load during retention in a short-term memory task.

To study the role of brain oscillations in working memory, we recorded the scalp electroencephalogram (EEG) during the retention interval of a modified Sternberg task. A power spectral analysis of the EEG during the retention interval revealed a clear peak at 9-12 Hz, a frequency in the alpha band (8-13 Hz). In apparent conflict with previous ideas according to which alpha band oscillations represent brain "idling", we found that the alpha peak systematically increased with the number of items held in working memory. The enhancement was prominent over the posterior and bilateral central regions. The enhancement over posterior regions is most likely explained by the well known alpha rhythm produced close to the parietal-occipital fissure, whereas the lateral enhancement could be explained by sources in somato-motor cortex. A time-frequency analysis revealed that the enhancement was present throughout the last 2.5 s of the 2.8 s retention interval and that alpha power rapidly diminished following the probe. The load dependence and the tight temporal regulation of alpha provide strong evidence that the alpha generating system is directly or indirectly linked to the circuits responsible for working memory. Although a clear peak in the theta band (5-8 Hz) was only detectable in one subject, other lines of evidence indicate that theta occurs and also has a role in working memory. Hypotheses concerning the role of alpha band activity in working memory are discussed.
The role of ontologies in biological and biomedical research: a functional perspective

Ontologies are widely used in biological and biomedical research. Their success lies in their combination of four main features present in almost all ontologies: provision of standard identifiers for classes and relations that represent the phenomena within a domain; provision of a vocabulary for a domain; provision of metadata that describes the intended meaning of the classes and relations in ontologies; and the provision of machine-readable axioms and definitions that enable computational access to some aspects of the meaning of classes and relations. While each of these features enables applications that facilitate data integration, data access and analysis, a great potential lies in the possibility of combining these four features to support integrative analysis and interpretation of multimodal data. Here, we provide a functional perspective on ontologies in biology and biomedicine, focusing on what ontologies can do and describing how they can be used in support of integrative research. We also outline perspectives for using ontologies in data-driven science, in particular their application in structured data mining and machine learning applications.
A New Sub-Pixel Detector for X-Corners in Camera Calibration Targets

X-corner patterns are most widely used in camera calibration. In this paper, we propose a new sub-pixel detector for X-corners, which is much simpler than the traditional sub-pixel detection algorithm. In this new algorithm, the pixel position of X-corner is firstly detected by a new operator. Then a second order Taylor polynomial describing the local intensity profile around the corner is educed. The sub-pixel position of X-corner can be determined directly by calculating the saddle point of this intensity profile. Neither preliminary intensity interpolation nor quadratic fitting of the intensity surface is necessary, which greatly reduces the computation load of the detection process. Furthermore, computer simulation results indicate that our new algorithm is slightly more accurate than the traditional algorithm.
TWITOBI: A Recommendation System for Twitter Using Probabilistic Modeling

Twitter provides search services to help people find new users to follow by recommending popular users or their friends' friends. However, these services do not offer the most relevant users to follow for a user. Furthermore, Twitter does not provide yet the search services to find the most interesting tweet messages for a user either. In this paper, we propose TWITOBI, a recommendation system for Twitter using probabilistic modeling for collaborative filtering which can recommend top-K users to follow and top-K tweets to read for a user. Our novel probabilistic model utilizes not only tweet messages but also the relationships between users. We develop an estimation algorithm for learning our model parameters and present its parallelized algorithm using MapReduce to handle large data. Our performance study with real-life data sets confirms the effectiveness and scalability of our algorithms.
The Spatial, Temporal, and Configurational Characteristics of Geographic Dispersion in Teams

Geographically dispersed teams are rarely 100% dispersed. However, by focusing on teams that are either fully dispersed or fully co-located, team research to date has lived on the ends of a spectrum at which relatively few teams may actually work. In this paper, we develop a more robust view of geographic dispersion in teams. Specifically, we focus on the spatialtemporal distances among team members and the configuration of team members across sites (independent of the spatial and temporal distances separating those sites). To better understand the nature of dispersion, we develop a series of five new measures and explore their relationships with communication frequency data from a sample of 182 teams (of varying degrees of dispersion) from a Fortune 500 telecommunications firm. We conclude with recommendations regarding the use of different measures and important questions that they could help address. Geographic Dispersion in Teams 1
PerKApp: A context aware motivational system for healthier lifestyles

In this paper we present PerKApp, a context-aware system for inducing the user to adopt healthier lifestyles, based on a novel combination of persuasion technologies, natural language generation techniques, and deep knowledge representation tools. In our view, personalized and tailored messages generated according to the characteristic of user, user preferences and the context are extremely useful to increase the effectiveness of persuasion efforts in terms of user acceptance of the proposed behaviors. The architecture of PerKApp is designed with the goal of ease scalability and extendibility to other domains by redefinition of the knowledge and linguistic content.
Trajectory-based ball detection and tracking with applications to semantic analysis of broadcast soccer video

This paper first presents an improved trajectory-based algorithm for automatically detecting and tracking the ball in broadcast soccer video. Unlike the object-based algorithms, our algorithm does not evaluate whether a sole object is a ball. Instead, it evaluates whether a candidate trajectory, which is generated from the candidate feature image by a candidate verification procedure based on Kalman filter,, which is generated from the candidate feature image by a candidate verification procedure based on Kalman filter, is a ball trajectory. Secondly, a new approach for automatically analyzing broadcast soccer video is proposed, which is based on the ball trajectory. The algorithms in this approach not only improve play-break analysis and high-level semantic event detection, but also detect the basic actions and analyze team ball possession, which may not be analyzed based only on the low-level feature. Moreover, experimental results show that our ball detection and tracking algorithm can achieve above 96% accuracy for the video segments with the soccer field. Compared with the existing methods, a higher accuracy is achieved on goal detection and play-break segmentation. To the best of our knowledge, we present the first solution in detecting the basic actions such as touching and passing, and analyzing the team ball possession in broadcast soccer video.