nils-herrmann/clean_scirepeval huggingface dataset:
Efficient Resource Allocation in Device-to-Device Communication Using Cognitive Radio Technology

Device-to-device (D2D) communication is developed as a new paradigm to enhance network performance according to LTE and WiMAX advanced standards. The D2D communication may have dedicated spectrum (overlay) or shared spectrum (underlay). However, the allocated dedicated spectrum may not be effectively used in the overlay mode, while interference between the D2D users and cellular users cause impairments in the underlay mode. Can the resource allocation of a D2D system be optimized using the cognitive approach where the D2D users opportunistically access the underutilized radio spectrum? That is the focus of this paper. In this paper, the transmission rate of the D2D users is optimized while simultaneously satisfying five sets of constraints related to power, interference, and data rate, modeling D2D users as cognitive secondary users. Furthermore, a two-stage approach is considered to allocate the radio resources efficiently. A new adaptive subcarrier allocation scheme is designed first, and then, a novel power allocation scheme is developed utilizing geometric water-filling approach that provides optimal solution with low computation complexity for this nonlinear problem. Numerical results show that the proposed approach achieved significant performance enhancement than the existing schemes.
A Crowdsourcing Worker Quality Evaluation Algorithm on MapReduce for Big Data Applications

Crowdsourcing is a new emerging distributed computing and business model on the backdrop of Internet blossoming. With the development of crowdsourcing systems, the data size of crowdsourcers, contractors and tasks grows rapidly. The worker quality evaluation based on big data analysis technology has become a critical challenge. This paper first proposes a general worker quality evaluation algorithm that is applied to any critical tasks such as tagging, matching, filtering, categorization and many other emerging applications, without wasting resources. Second, we realize the evaluation algorithm in the Hadoop platform using the MapReduce parallel programming model. Finally, to effectively verify the accuracy and the effectiveness of the algorithm in a wide variety of big data scenarios, we conduct a series of experiments. The experimental results demonstrate that the proposed algorithm is accurate and effective. It has high computing performance and horizontal scalability. And it is suitable for large-scale worker quality evaluations in a big data environment.
Design Network Intrusion Detection System using hybrid Fuzzy-Neural Network

As networks grow both in importance and size, there is an increasing need for effective security monitors such as Network Intrusion Detection System to prevent such illicit accesses. Intrusion Detection Systems technology is an effective approach in dealing with the problems of network security. In this paper, we present an intrusion detection model based on hybrid fuzzy logic and neural network. The key idea is to take advantage of different classification abilities of fuzzy logic and neural network for intrusion detection system. The new model has ability to recognize an attack, to differentiate one attack from another i.e. classifying attack, and the most important, to detect new attacks with high detection rate and low false negative. Training and testing data were obtained from the Defense Advanced Research Projects Agency (DARPA) intrusion detection evaluation data set.
Application of Data Mining in Customer Relationship Management

With economic globalization and the rapid development of e-commerce, customer relationship management (CRM) has become the core of growth of the company. Data mining, as a powerful data analysis tool, extracts critical information supporting the company to make better decisions by processing a large number of data in commercial databases. This paper introduced the basic concepts of data mining and CRM, and described the process how to use data mining for CRM. At last, the paper described the applications of several main data mining methods in CRM, such as clustering, classification and association rule.
The Prosogram: Semi-Automatic Transcription of Prosody Based on a Tonal Perception Model

This paper describes a system for semi-automatic tr nscription of prosody based on a stylization of the fundamenta l frequency data (contour) for vocalic (or syllabic) nuclei. Th e stylization is a simulation of tonal perception of human listen ers. The system requires a time-aligned phonetic annotation. The transcription has been applied to several speech co rpora.
Principles of survey research part 2: designing a survey

This second article of our series looks at the process of designing a survey. The design process begins with reviewing the objectives, examining the target population identified by the objectives, and deciding how best to obtain the information needed to address those objectives. However, we also need to consider factors such as determining the appropriate sample size and ensuring the largest possible response rate.To illustrate our ideas, we use the three surveys described in Part 1 of this series to suggest good and bad practice in software engineering survey research.
Information-centric networking: The evolution from circuits to packets to content

Today’s information-centric networks (ICNs) represent a 100-year evolution of communication networks from circuit-switched networks to packet-switched networks to ICNs, sharing common features with both of these earlier network architectures, but having many unique characteristics of its own. We describe and survey ongoing research and identify challenges in the modeling, design and analysis of information-centric networks and protocols. We discuss performance modeling frameworks and challenges for ICNs, with a particular focus on content flowing through a network of caches, drawing analogies and distinctions from past research in both circuit-switched and packet-switched networks. We also survey the challenges and recent research results associated with finding content in a network of caches and managing the content in those caches. The challenges posed by mobility (of both the end users accessing content as well as content itself) are also
Model learning for robot control: a survey

Models are among the most essential tools in robotics, such as kinematics and dynamics models of the robot’s own body and controllable external objects. It is widely believed that intelligent mammals also rely on internal models in order to generate their actions. However, while classical robotics relies on manually generated models that are based on human insights into physics, future autonomous, cognitive robots need to be able to automatically generate models that are based on information which is extracted from the data streams accessible to the robot. In this paper, we survey the progress in model learning with a strong focus on robot control on a kinematic as well as dynamical level. Here, a model describes essential information about the behavior of the environment and the influence of an agent on this environment. In the context of model-based learning control, we view the model from three different perspectives. First, we need to study the different possible model learning architectures for robotics. Second, we discuss what kind of problems these architecture and the domain of robotics imply for the applicable learning methods. From this discussion, we deduce future directions of real-time learning algorithms. Third, we show where these scenarios have been used successfully in several case studies.
ABC and 3D: opportunities and obstacles to 3D printing in special education environments

Consumer-grade digital fabrication such as 3D printing is on the rise, and we believe it can be leveraged to great benefit in the arena of special education. Although 3D printing is beginning to infiltrate mainstream education, little to no research has explored 3D printing in the context of students with special support needs. We present a formative study exploring the use of 3D printing at three locations serving populations with varying ability, including individuals with cognitive, motor, and visual impairments. We found that 3D design and printing performs three functions in special education: developing 3D design and printing skills encourages STEM engagement; 3D printing can support the creation of educational aids for providing accessible curriculum content; and 3D printing can be used to create custom adaptive devices. In addition to providing opportunities to students, faculty, and caregivers in their efforts to integrate 3D printing in special education settings, our investigation also revealed several concerns and challenges. We present our investigation at three diverse sites as a case study of 3D printing in the realm of special education, discuss obstacles to efficient 3D printing in this context, and offer suggestions for designers and technologists.
Text Generation for Abstractive Summarization

We have begun work on a framework for abstractive summarization and decided to focus on a module for text generation. For TAC 2010, we thus move away from sentence extraction. Each sentence in the summary we generate is based on a document sentence but it usually contains a smaller amount of information and uses fewer words. The system uses the output of a syntactic parser for a sentence and then regenerates part of the sentence using a Natural Language Generation engine. The sentences of the summary are selected among regenerated sentences based on the document frequency of contained words, while avoiding redundancy. Date and location were handled and generated especially for cluster categories 1 and 2. Even though our initial scores were not outstanding, we intend to continue work on this approach in the coming years.
Influence of interhemispheric interactions on motor function in chronic stroke.

In patients with chronic stroke, the primary motor cortex of the intact hemisphere (M1(intact hemisphere)) may influence functional recovery, possibly through transcallosal effects exerted over M1 in the lesioned hemisphere (M1(lesioned hemisphere)). Here, we studied interhemispheric inhibition (IHI) between M1(intact hemisphere) and M1(lesioned hemisphere) in the process of generation of a voluntary movement by the paretic hand in patients with chronic subcortical stroke and in healthy volunteers. IHI was evaluated in both hands preceding the onset of unilateral voluntary index finger movements (paretic hand in patients, right hand in controls) in a simple reaction time paradigm. IHI at rest and shortly after the Go signal were comparable in patients and controls. Closer to movement onset, IHI targeting the moving index finger turned into facilitation in controls but remained deep in patients, a finding that correlated with poor motor performance. These results document an abnormally high interhemispheric inhibitory drive from M1(intact hemisphere) to M1(lesioned hemisphere) in the process of generation of a voluntary movement by the paretic hand. It is conceivable that this abnormality could adversely influence motor recovery in some patients with subcortical stroke, an interpretation consistent with models of interhemispheric competition in motor and sensory systems.
Stock trading rule discovery with an evolutionary trend following model

Evolutionary learning is one of the most popular techniques for designing quantitative investment (QI) products. Trend following (TF) strategies, owing to their briefness and efficiency, are widely accepted by investors. Surprisingly, to the best of our knowledge, no related research has investigated TF investment strategies within an evolutionary learning model. This paper proposes a hybrid long-term and short-term evolutionary trend following algorithm (eTrend) that combines TF investment strategies with the eXtended Classifier Systems (XCS). The proposed eTrend algorithm has two advantages: (1) the combination of stock investment strategies (i.e., TF) and evolutionary learning (i.e., XCS) can significantly improve computation effectiveness and model practicability, and (2) XCS can automatically adapt to market directions and uncover reasonable and understandable trading rules for further analysis, which can help avoid the irrational trading behaviors of common investors. To evaluate eTrend, experiments are carried out using the daily trading data stream of three famous indexes in the Shanghai Stock Exchange. Experimental results indicate that eTrend outperforms the buy-and-hold strategy with high Sortino ratio after the transaction cost. Its performance is also superior to the decision tree and artificial neural network trading models. Furthermore, as the concept drift phenomenon is common in the stock market, an exploratory concept drift analysis is conducted on the trading rules discovered in bear and bull market phases. The analysis revealed interesting and rational results. In conclusion, this paper presents convincing evidence that the proposed hybrid trend following model can indeed generate effective trading guid-
An Overview of the SWI-Prolog Programming Environment

The Prolog programmer’s needs have always been the focus for guiding the development of the SWI-Prolog system. This article accompanies an invited talk about how the SWI-Prolog environment helps the Prolog programmer solve common problems. It describes the central parts of the graphical development environment as well as the command line tools which we see as vital to the success of the system. We hope this comprehensive overview of particularly useful features will both inspire other Prolog developers, and help SWI-Prolog users to make more productive use of the system.
Writing narrative literature reviews for peer-reviewed journals: secrets of the trade.

To describe and discuss the process used to write a narrative review of the literature for publication in a peer-reviewed journal. Publication of narrative overviews of the literature should be standardized to increase their objectivity.



In the past decade numerous changes in research methodology pertaining to reviews of the literature have occurred. These changes necessitate authors of review articles to be familiar with current standards in the publication process.



Narrative overview of the literature synthesizing the findings of literature retrieved from searches of computerized databases, hand searches, and authoritative texts.



An overview of the use of three types of reviews of the literature is presented. Step by step instructions for how to conduct and write a narrative overview utilizing a 'best-evidence synthesis' approach are discussed, starting with appropriate preparatory work and ending with how to create proper illustrations. Several resources for creating reviews of the literature are presented and a narrative overview critical appraisal worksheet is included. A bibliography of other useful reading is presented in an appendix.



Narrative overviews can be a valuable contribution to the literature if prepared properly. New and experienced authors wishing to write a narrative overview should find this article useful in constructing such a paper and carrying out the research process. It is hoped that this article will stimulate scholarly dialog amongst colleagues about this research design and other complex literature review methods.
Ontology-Based Text Classification into Dynamically Defined Topics

We present a method for the automatic classification of text documents into a dynamically defined set of topics of interest. The proposed approach requires only a domain ontology and a set of user-defined classification topics, specified as contexts in the ontology. Our method is based on measuring the semantic similarity of the thematic graph created from a text document and the ontology sub-graphs resulting from the projection of the defined contexts. The domain ontology effectively becomes the classifier, where classification topics are expressed using the defined ontological contexts. In contrast to the traditional supervised categorization methods, the proposed method does not require a training set of documents. More importantly, our approach allows dynamically changing the classification topics without retraining of the classifier. In our experiments, we used the English language Wikipedia converted to an RDF ontology to categorize a corpus of current Web news documents into selection of topics of interest. The high accuracy achieved in our tests demonstrates the effectiveness of the proposed method, as well as the applicability of Wikipedia for semantic text categorization purposes.
STOCK PRICE PREDICTION USING REINFORCEMENT LEARNING

Recently, numerous investigations for stock price prediction and portfolio management using machine learning have been trying to develop efficient mechanical trading systems. But these systems have a limitation in that they are mainly based on the supervised leaming which is not so adequate for leaming problems with long-term goals and delayed rewards. This paper proposes a method of applying reinforcement leaming, suitable for modeling and leaming various kinds of interactions in real situations, to the problem of stock price prediction. The stock price prediction problem is considered as Markov process which can be optimized by reinforcement learning based algorithm. TD(O), a reinforcement learning algorithm which leams only from experiences, is adopted and function approximation by artificial neural network is performed to leam the values of states each of which corresponds to a stock price trend at a given time. An experimental result based on the Korean stock market is presented to evaluate the performance of the proposed method.
Self-Regulation, Individual Characteristics and Cyberloafing

Cyberloafinga form of counter-productive work behaviour has been found to be affected by several personality and contextual factors. An important relationship that has not been adequately explored in the past is how cyberloafing behaviours could be conceptualized as a failure to self-regulate. In this paper, we examine the impact of this lack self-regulation on cyberloafing. We also examine the effects of individual characteristics (self-efficacy, conscientiousness and achievement orientation) in moderating the relationship between self-regulation and cyberloafing. Results showed that all the individual characteristics are significant moderators. Implications are discussed.
3D bipedal walking with knees and feet: A hybrid geometric approach

Motivated by the goal of obtaining more-anthropomorphic walking in bipedal robots, this paper considers a hybrid model of a 3D hipped biped with feet and locking knees. The main observation of this paper is that functional Routhian Reduction can be used to extend two-dimensional walking to three dimensions—even in the presence of periods of underactuation—by decoupling the sagittal and coronal dynamics of the 3D biped. Specifically, we assume the existence of a control law that yields stable walking for the 2D sagittal component of the 3D biped. The main result of the paper is that utilizing this controller together with “reduction control laws” yields walking in three dimensions. This result is supported through simulation.
Robot Learning from Human Teachers

Intelligent systems often depend on data provided by information agents, for example, sensor data or crowdsourced human computation. Providing accurate and relevant data requires costly effort that agents may not always be willing to provide. Thus, it becomes important not only to verify the correctness of data, but also to provide incentives so that agents that provide highquality data are rewarded while those that do not are discouraged by low rewards. We cover different settings and the assumptions they admit, including sensing, human computation, peer grading, reviews, and predictions. We survey different incentive mechanisms, including proper scoring rules, prediction markets and peer prediction, Bayesian Truth Serum, Peer Truth Serum, Correlated Agreement, and the settings where each of them would be suitable. As an alternative, we also consider reputation mechanisms. We complement the gametheoretic analysis with practical examples of applications in prediction platforms, community sensing, and peer grading.
Rapid and accurate spoken term detection

We present a state-of-the-art system for performing spoken term detection on continuous telephone speech in multiple languages. The system compiles a search index from deep word lattices generated by a large-vocabulary HMM speech recognizer. It estimates word posteriors from the lattices and uses them to compute a detection threshold that minimizes the expected value of a user-specified cost function. The system accommodates search terms outside the vocabulary of the speechto-text engine by using approximate string matching on induced phonetic transcripts. Its search index occupies less than 1Mb per hour of processed speech and it supports sub-second search times for a corpus of hundreds of hours of audio. This system had the highest reported accuracy on the telephone speech portion of the 2006 NIST Spoken Term Detection evaluation, achieving 83% of the maximum possible accuracy score in English.
A Modified Particle Swarm Optimizer for the Coordination of Directional Overcurrent Relays

The coordination of directional overcurrent relays (DOCR) is treated in this paper using particle swarm optimization (PSO), a recently proposed optimizer that utilizes the swarm behavior in searching for an optimum. PSO gained a lot of interest for its simplicity, robustness, and easy implementation. The problem of setting DOCR is a highly constrained optimization problem that has been stated and solved as a linear programming (LP) problem. To deal with such constraints a modification to the standard PSO algorithm is introduced. Three case studies are presented, and the results are compared to those of LP technique to demonstrate the effectiveness of the proposed methodology.
A universal VAD based on jointly trained deep neural networks

In this paper, we propose a joint training approach to voice activity detection (VAD) to address the issue of performance degradation due to unseen noise conditions. Two key techniques are integrated into this deep neural network (DNN) based VAD framework. First, a regression DNN is trained to map the noisy to clean speech features similar to DNN-based speech enhancement. Second, the VAD part to discriminate speech against noise backgrounds is also a DNN trained with a large amount of diversified noisy data synthesized by a wide range of additive noise types. By stacking the classification DNN on top of the enhancement DNN, this integrated DNN can be jointly trained to perform VAD. The feature mapping DNN serves as a noise normalization module aiming at explicitly generating the “clean” features which are easier to be correctly recognized by the following classification DNN. Our experiment results demonstrate the proposed noise-universal DNNbased VAD algorithm achieves a good generalization capacity to unseen noises, and the jointly trained DNNs consistently and significantly outperform the conventional classification-based DNN for all the noise types and signal-to-noise levels tested.