nils-herrmann/clean_scirepeval huggingface dataset:
User Oriented Resource Management With Virtualization: A Hierarchical Game Approach

The explosive advancements in mobile Internet and Internet of Things challenge the network capacity and architecture. The ossification of wireless networks hinders the further evolution toward the fifth generation of mobile communication systems. Ultra-dense small cell networks are considered as a feasible way to meet high-capacity demands. Meanwhile, ultra-dense small cell network virtualization also exploits an insightful perspective for the evolution because of its superiority, such as diversity, flexibility, low cost, and scalability. In this paper, we specify the necessity of resource management in virtualized ultra-dense small cell networks through a mapping and management architecture and consider the problem of user-oriented virtual resource management. Then, we model the virtual resource management problem as a hierarchical game and obtain the closed-form solutions for spectrum, power, and price, respectively. Furthermore, we propose a customer-first (CF) algorithm that characterizes the user-oriented service of virtualization, and analyze its convergence. Simulation results present the effectiveness of the proposed CF algorithm.
Automated Essay Scoring 1 Automated Essay Scoring: Writing Assessment and Instruction

Automated Essay Scoring 2  This chapter documents the advent and rise of automated essay scoring (AES) as a means of both assessment and instruction. The first section discusses what AES is, how it works, and who the major purveyors of the technology are. The second section describes outgrowths of the technology as it applies to ongoing projects in measurement and education. In 1973, the late Ellis Page and colleagues at the University of Connecticut programmed the first successful automated essay scoring engine, " Project Essay Grade (PEG) " (1973). The technology was foretold some six years earlier in a landmark Phi Delta Kappan article entitled, " The Imminence of Grading Essays by Computer " (Page, 1966). At the time the article was provocative and a bit outrageous, though in hindsight, it can only be deemed prophetic. As a former high school English teacher , Page was convinced that students would benefit greatly by having access to technology that would provide quick feedback on their writing. He also realized that the greatest hindrance to having secondary students write more was the requirement that, ultimately, a teacher had to review stacks of papers. While PEG produced impressive results, the technology of the time was too primitive to make it a practical application. Text had to be typed on IBM 80-column punched cards and read into a mainframe computer before it could be evaluated. As a consequence, the technology sat dormant until the early 1990s and was revitalized with the confluence of two technological developments: microcomputers and the Internet. Microcomputers permitted the generation of electronic text from a regular keyboard and the internet provided a universal platform to submit text for review Automated Essay Scoring 3 Automated essay scoring is a measurement technology in which computers evaluate written work (Shermis & Burstein, 2003). Most of the initial applications have 2002). Computers do not " understand " the written text being evaluated. So, for example, the computer would not " get " the following joke. Q-When is a door not a door? A-When it is ajar. Unlike humans, a computer cannot interpret the play on words, and infer that the predicate in the answer (i.e., " ajar ") is being cleverly used as a noun (i.e., " a jar "). What the computer does in an AES context is to analyze the written text into its observable components. Different AES systems evaluate different …
Wideband millimeter-wave SIW cavity backed patch antenna fed by substrate integrated coaxial line

In this paper, a wideband substrate integrated waveguide (SIW) cavity backed patch antenna element is proposed for high-gain arrays at millimeter-wave bands. By compensating the inductance of the feeding metallic via with a capacitive ring slot on the ground plate, an additional resonance is introduced to the patch mode to expand the bandwidth. To suppress the backward radiation from the slot and enhance antenna gain, a back SIW cavity is designed. Moreover, the low-loss and compact substrate integrated coaxial line (SICL) is employed as the feeding line. The simulated -10/-15 dB impedance bandwidth of the element is 14.9%/11.6% (both at 43 GHz). The introducing of the back cavity and SICL makes the element suitable for large wideband high-gain millimeter-wave arrays with excellent front-to-back ratios (FBRs) and stable radiation patterns on printed circuit boards (PCBs). As an illustrative example, a 2×2 corporately-fed sub-array is developed and tested. The -10 dB impedance bandwidth is 11.7%, and the peak gain is 11.4 dBi with a simulated radiation efficiency of 88% and measured FBR of above 20 dB.
An empirical study of community and sub-community detection in social networks applying Newman-Girvan algorithm

A social network can be represented by a set of human beings in which one member is connected to one or more members from the same set. We can obtain visual and mathematical models of human relationship by analysing a social network. There are several inherent properties of social networks such as power law distribution, centrality, small world network, modularity etc. Community structure is another important property of social network and it has gained tremendous popularity in terms of current research trends. With the increasing popularity, community structure is also getting equally complex within online social network services like Facebook, Google+, MySpace and Twitter. Newman-Girvan algorithm is the widely used community detection algorithm in social networks. This paper reflects the structure of communities as well as sub-communities occurring in a social network by applying Newman-Girvan algorithm. We have implemented this community detection algorithm on real world networks. We have given a new concept to detect sub-communities in real world networks in this paper. This paper is mainly focused on an empirical study of the Newman-Girvan algorithm.
Image recognition of plant diseases based on principal component analysis and neural networks

Plant disease identification based on image processing could quickly and accurately provide useful information for the prediction and control of plant diseases. In this study, 21 color features, 4 shape features and 25 texture features were extracted from the images of two kinds wheat diseases (wheat stripe rust and wheat leaf rust) and two kinds of grape diseases (grape downy mildew and grape powdery mildew), principal component analysis (PCA) was performed for reducing dimensions in feature data processing, and then neural networks including backpropagation (BP) networks, radial basis function (RBF) neural networks, generalized regression networks (GRNNs) and probabilistic neural networks (PNNs) were used as the classifiers to identify wheat diseases and grape diseases, respectively. The results showed that these neural networks could be used for image recognition of these diseases based on reducing dimensions using PCA and acceptable fitting accuracies and prediction accuracies could be obtained. For the two kinds of wheat diseases, the optimal recognition result was obtained when image recognition was conducted based on PCA and BP networks, and the fitting accuracy and the prediction accuracy were both 100%. For the two kinds of grape diseases, the optimal recognition results were obtained when GRNNs and PNNs were used as the classifiers after reducing the dimensions of feature data with PCA, and the prediction accuracies were 94.29% with the fitting accuracies equal to 100%.
Deep Multi-User Reinforcement Learning for Distributed Dynamic Spectrum Access

We consider the problem of dynamic spectrum access for network utility maximization in multichannel wireless networks. The shared bandwidth is divided into  $K$  orthogonal channels. In the beginning of each time slot, each user selects a channel and transmits a packet with a certain transmission probability. After each time slot, each user that has transmitted a packet receives a local observation indicating whether its packet was successfully delivered or not (i.e., ACK signal). The objective is a multi-user strategy for accessing the spectrum that maximizes a certain network utility in a distributed manner without online coordination or message exchanges between users. Obtaining an optimal solution for the spectrum access problem is computationally expensive, in general, due to the large-state space and partial observability of the states. To tackle this problem, we develop a novel distributed dynamic spectrum access algorithm based on deep multi-user reinforcement leaning. Specifically, at each time slot, each user maps its current state to the spectrum access actions based on a trained deep-Q network used to maximize the objective function. Game theoretic analysis of the system dynamics is developed for establishing design principles for the implementation of the algorithm. The experimental results demonstrate the strong performance of the algorithm.
Flow Theory, Evolution & Creativity: or, 'Fun & Games'

In this paper videogames and transmedia are examined from the perspectives of both creation (game design) and audience reception (gameplay experience), in light of the theories of the DPFi (Domain, Person Field interaction) systems model of creativity (Csikszentmihalyi 1988, 1996, 2006, 2014); its herein contended theoretical equivalent, evolutionary epistemology (Popper 1963, DT Campbell 1974, Simonton 2010) and the inherent biocultural evolutionary creative algorithm of selection, variation and transmission-with-heredity; 'flow' theory in creativity (Csikszentmihalyi 1975, 1990, 1996) as a determinant of 'fun factor' in games; 'narrative transportation' theory in fiction (Gerrig 1993, Green & Brock 2000, Van Laer et al 2014) as an additional (necessary but not sufficient) determinant of 'fun-factor' in 'story' videogames; and Boyd's (2009) general theory of creativity in the arts as 'cognitive play with pattern' - ultimately arguing that game play of any kind may potentially enhance animal intelligence, and therefore that videogames as an art form may potentially enhance human intelligence.
Velocity estimation for slow motion devices equipped with encoder

This paper addresses the problem of velocity estimation for slow motion elements equipped with encoders. The slow movement causes rare pulses generated from the encoder, rarer than the sampling frequency. In such conditions, the velocity calculation by numeric derivative or Luenbergera observer fails. For that, it is proposed a new velocity estimation algorithm that uses information specific for encoder operating mode. Simulation and real-time experiments have shown a significant reduction in velocity error estimation of the proposed algorithm in comparison to conventional algorithms.
A 5-MHz 91% Peak-Power-Efficiency Buck Regulator With Auto-Selectable Peak- and Valley-Current Control

This paper presents a multi-MHz buck regulator for portable applications using an auto-selectable peak- and valley-current control (ASPVCC) scheme. The proposed ASPVCC scheme can enable the current-mode buck regulator to reduce the settling-time requirement of the current sensing by two times. In addition, the dynamically biased shunt feedback technique is employed to improve the sensing speed and the sensing accuracy of both the peak and valley current sensors. With both ASPVCC scheme and advanced current sensors, the buck regulator can thus operate at high switching frequencies with a wide range of duty ratios for reducing the required off-chip inductance. The proposed current-mode buck regulator was fabricated in a standard 0.35-μm CMOS process and occupies a small chip area of 0.54 mm2. Measurement results show that the buck regulator can deliver a maximum output current of 500 mA, operate at 5 MHz with a wide duty-ratio range of about 0.6, use a small-value off-chip inductor of 1 μH, and achieve the peak power efficiency of 91%.
Problem Solving and Computational Thinking in a Learning Environment

Computational thinking is a new problem solving method named for its extensive use of computer science techniques. It synthesizes critical thinking and existing knowledge and applies them to solve complex technological problems. The term was coined by J. Wing , but the relationship between computational and critical thinking, the two modes of thinking in solving problems, has not been yet clearly established. This paper aims in shedding some light into this relationship. We also present two classroom experiments performed recently at the Graduate Technological Educational Institute (TEI) of Patras, Greece. The result of these experiment give a strong indication that the use of computers as a tool for problem solving enhances the students‟ abilities in solving real world problems involving mathematical modelling. This is crossed by earlier findings of other researchers for the problem solving process in general (not only for mathematical problems).
A New Feature for Automatic Speaker Verification Anti-Spoofing: Constant Q Cepstral Coefficients

Efforts to develop new countermeasures in order to protect automatic speaker verification from spoofing have intensified over recent years. The ASVspoof 2015 initiative showed that there is great potential to detect spoofing attacks, but also that the detection of previously unforeseen spoofing attacks remains challenging. This paper argues that there is more to be gained from the study of features rather than classifiers and introduces a new feature for spoofing detection based on the constant Q transform, a perceptually-inspired time-frequency analysis tool popular in the study of music. Experimental results obtained using the standard ASVspoof 2015 database show that, when coupled with a standard Gaussian mixture model-based classifier, the proposed constant Q cepstral coefficients (CQCCs) outperform all previously reported results by a significant margin. In particular, those for a subset of unknown spoofing attacks (for which no matched training data was used) is 0.46%, a relative improvement of 72% over the best, previously reported results.
Text Document Clustering based on Phrase Similarity using Affinity Propagation

Affinity propagation (AP) was recently introduced as an un-supervised learning algorithm for exemplar based clustering. In this paper novel text document clustering algorithm has been developed based on vector space model, phrases and affinity propagation clustering algorithm. Proposed algorithm can be called Phrase affinity clustering (PAC). PAC first finds the phrase by ukkonen suffix tree construction algorithm, second finds the vector space model using tf-idf weighting scheme of phrase. Third calculate the similarity matrix form VSD using cosine similarity . In Last affinity propagation algorithm generate the clusters . F-Measure ,Purity and Entropy of Proposed algorithm is better than GAHC ,ST-GAHC and ST-KNN on OHSUMED ,RCV1 and News group data sets.
Effect of Manipulated Amplitude and Frequency of Human Voice on Dominance and Persuasiveness in Audio Conferences

We propose to artificially manipulate participants' vocal cues, amplitude and frequency, in real time to adjust their dominance and persuasiveness during audio conferences. We implemented a prototype system and conducted two experiments. The first experiment investigated the effect of vocal cue manipulation on the perception of dominance. The results showed that participants perceived higher dominance while listening to a voice with a high amplitude and low frequency. The second experiment investigated the effect of vocal cue manipulation on persuasiveness. The results indicated that a person with a low amplitude and low frequency voice had greater persuasiveness in conversations with biased dominance, while there was no statistically significant difference in persuasiveness in conversations with unbiased dominance.
A FRAMEWORK FOR DESIGNING MOBILE QURANIC MEMORIZATION TOOL USING MULTIMEDIA INTERACTIVE LEARNING METHOD FOR CHILDREN

Quran is the fundamental holy book of Islam. Among the most important concerns is to learn it by heart. However, current method in Quranic schools is becoming less effective towards young generation. Thus, there is a need to find alternative solutions to memorize Quran for young Muslims. Mobile learning is an alternative to conventional learning that will support the existing method. Mobile devices have made an immediate impact on teaching and learning practices. However, for mobile learning to be effective for children in memorizing Quran, it is necessary to find specific design guidelines and pedagogy to this learning method. This paper aims at providing a unifying framework for developing Quran memorizer application using multimedia interactive method and learning theories for mobile learning.
Predicting and Improving Memory Retention : Psychological Theory Matters in the Big Data Era

Cognitive psychology has long had the aim of understanding mechanisms of human memory, with the expectation that such an understanding will yield practical techniques that support learning and retention. Although research insights have given rise to qualitative advice for students and educators, we present a complementary approach that offers quantitative, individualized guidance. Our approach synthesizes theory-driven and data-driven methodologies. Psychological theory characterizes basic mechanisms of human memory shared among members of a population, whereas machine-learning techniques use observations from a population to make inferences about individuals. We argue that despite the power of big data, psychological theory provides essential constraints on models. We present models of forgetting and spaced practice that predict the dynamic time-varying knowledge state of an individual student for specific material. We incorporate these models into retrieval-practice software to assist students in reviewing previously mastered material. In an ambitious year-long intervention in a middle-school foreign language course, we demonstrate the value of systematic review on long-term educational outcomes, but more specifically, the value of adaptive review that leverages data from a population of learners to personalize recommendations based on an individual’s study history and past performance.
Design and Implementation of T-junction Triangular Microstrip Patch Antenna Array for Wireless Applications

This paper describes 2 x 2 triangular microstrip patch antenna using T-junction with quarter wave transformer. By regulating the distance in patch antenna and adjusting feed position, bandwidth can be obtained and by using an array, directivity is enhanced. The requirement of large bandwidth, high directivity, and minimum size leads to the design of 2 x 2 triangular microstrip patch antenna array feeding with T-junction network operate at 5.5 GHz. An antenna designed on an FR4 substrate that had a dielectric constant (r) 4.4, a loss tangent 0.02 and thickness of 1.6 mm. Simulated results showed that designed antenna has directivity 12.91 dB and bandwidth 173 MHz with VSWR 1.07 using T-junction feeding network. The proposed 2 x 2 triangular array has the benefit of light weight, simplicity of fabrication, single layer structure, and high directivity. Keyword Bandwidth, Corporate feeding, Return Loss, T-junction, VSWR.
Automatic procedural model generation for 3D object variation

3D objects are used for numerous applications. In many cases not only single objects but also variations of objects are needed. Procedural models can be represented in many different forms, but generally excel in content generation. Therefore this representation is well suited for variation generation of 3D objects. However, the creation of a procedural model can be time-consuming on its own. We propose an automatic generation of a procedural model from a single exemplary 3D object. The procedural model consists of a sequence of parameterizable procedures and represents the object construction process. Changing the parameters of the procedures changes the surface of the 3D object. By linking the surface of the procedural model to the original object surface, we can transfer the changes and enable the possibility of generating variations of the original 3D object. The user can adapt the derived procedural model to easily and intuitively generate variations of the original object. We allow the user to define variation parameters within the procedures to guide a process of generating random variations. We evaluate our approach by computing procedural models for various object types, and we generate variations of all objects using the automatically generated procedural model.
Multi-view point clouds registration and stitching based on SIFT feature

In order to solve multi-view point clouds registration in large non-feature marked scenes, a new registration and stitching method was proposed based on 2D SIFT(Scale-invariant feature transform) features. Firstly, we used texture mapping method to generate 2D effective texture image, and then extracted and match SIFT features, obtained accurate key points and registration relationship between the effective texture images. Secondly, we reflected the SIFT key points and registration relationship to the 3D point clouds data, obtained key points and registration relationship of multi-view point clouds, we can achieve multi-view point clouds stitching. Our algorithm used texture mapping method to generate 2D effective texture image, it can eliminate interference of the holes and ineffective points, and can eliminate unnecessary mistake matching. Our algorithm used correct extracted matching point pairs to stitch, avoiding stepwise iterated of ICP algorithm, so our algorithm is simple to calculate, and it's matching precision and matching efficiency are improved to some extent. We carried experiments on two-view point clouds in two large indoor; the experiment results verified the validity of our algorithm.
Psychological Needs as a Predictor of Cyber bullying : a Preliminary Report on College Students

Recent surveys show that cyber bullying is a pervasive problem in North America. Many news stories have reported cyber bullying incidents around the world. Reports on the prevalence of cyber bullying and victimization as a result of cyber bullying increase yearly. Although we know what cyber bullying is it is important that we learn more about the psychological eff ects of it. Th erefore, the aim of the current study is to investigate the relationship between psychological needs and cyber bullying. Participants of the study included 666 undergraduate students (231 males and 435 females) from 15 programs in the Faculty of Education at Selcuk University, Turkey. Questions about demographics, engagement in and exposure to cyber bullying, and the Adjective Check List were administered. 22.5% of the students reported engaging in cyber bullying at least one time, and 55.3% of the students reported being victims of cyber bullying at least once in their lifetime. Males reported more cyber bullying behavior than females. Results indicate that aggression and succorance positively predict cyber bullying wheras intraception negatively predict it. In addition, endurance and aff iliation negatively predict cyber victimization. Only the need for change was found as a positive, but weak predictor of cyber victimization. In light of these findings, aggression and intraception should be investigated further in future research on cyber bullying.