nils-herrmann/clean_scirepeval huggingface dataset:
A Multiband Slot Antenna for GPS/WiMAX/WLAN Systems

The design of a four-band slot antenna for the global positioning system (GPS), worldwide interoperability for microwave access (WiMAX), and wireless area network (WLAN) is presented. The antenna consists of a rectangular slot with an area of 0.37λg × 0.14λg = 48 × 18 mm2 (where λg is the guide wavelength), a T-shaped feed patch, an inverted T-shaped stub, and two E-shaped stubs to generate four frequency bands. The radiating portion and total size of the antenna are less than those of the tri-band antennas studied in literature. Parametric study on the parameters for setting the four frequency bands is presented and hence the methodology of using the design for other frequency bands is proposed. The multiband slot antenna is studied and designed using computer simulation. For verification of simulation results, the antenna is fabricated and measured. The simulated and measured return losses, radiation patterns, realized peak gains, and efficiencies of the antenna are presented. Measured results show that the antenna can be designed to cover the frequency bands from 1.575 to 1.665 GHz for the GPS system, 2.4-2.545 GHz for the IEEE 802.11b&g WLAN systems, 3.27- 3.97 GHz for the WiMAX system, and 5.17-5.93 GHz for the IEEE 802.11a WLAN system. The effects of the feeding cable used in measurement and of the cover are also investigated.
Synthetic Data Approach for Classification and Regression

The goal of this paper is to automatically generate synthetic data to enable data analyzers to cope with the problem of insufficient data. Taking the most typical machine learning tasks, classification and regression, as an example, limited and insufficient samples cause low generalization of machine learning models, which cannot provide reasonable predictions. Data are insufficient either because of sample rarity or because data are impeded to be accessed for privacy concerns or confidential protection. To overcome this, we present a Synthetic Data Approach for Classification and Regression, adopting probability distribution and k-nearest neighbor model to generate synthetic data. We first estimate the probability distribution of each feature and construct a k-nearest neighbor model for all original data samples. Then we generate random samples based on probability distributions, adopt the k-nearest neighbor model to validate these random samples, and output the synthetic samples. We use proposed synthetic approaches to generate synthetic data of five publicly available datasets for classification and regression, respectively, and evaluate the performance of machine learning models to evaluate the resemblance between synthetic data and original data. The experimental results show that the synthetic data can resemble the original data, which indicates it is an effective approach for data analyzer to overcome the problem of insufficient data.
Functional imaging of ‘theory of mind’

Our ability to explain and predict other people's behaviour by attributing to them independent mental states, such as beliefs and desires, is known as having a 'theory of mind'. Interest in this very human ability has engendered a growing body of evidence concerning its evolution and development and the biological basis of the mechanisms underpinning it. Functional imaging has played a key role in seeking to isolate brain regions specific to this ability. Three areas are consistently activated in association with theory of mind. These are the anterior paracingulate cortex, the superior temporal sulci and the temporal poles bilaterally. This review discusses the functional significance of each of these areas within a social cognitive network.
Lattice Boltzmann method for moving boundaries

We propose a lattice Boltzmann method to treat moving boundary problems for solid objects moving in a fluid. The method is based on the simple bounce-back boundary scheme and interpolations. The proposed method is tested in two flows past an impulsively started cylinder moving in a channel in two dimensions: (a) the flow past an impulsively started cylinder moving in a transient Couette flow; and (b) the flow past an impulsively started cylinder moving in a channel flow at rest. We obtain satisfactory results and also verify the Galilean invariance of the lattice Boltzmann method.
FACTORS INFLUENCING CONSUMER PURCHASE INTENTION OF DIETARY SUPPLEMENT PRODUCTS IN PENANG ISLAND OOI SAY KEAT

Purchase of dietary supplement products is increasing despite the lack of clinical evidence to support health needs for consumption. The purpose of this crosssectional study is to examine the factors influencing consumer purchase intention of dietary supplement products in Penang based on Theory of Planned Behaviour (TPB). 367 consumers were recruited from chain pharmacies and hypermarkets in Penang. From statistical analysis, the role of attitude differs from the original TPB model; attitude played a new role as the mediator in this dietary supplement products context. Findings concluded that subjective norms, importance of price and health consciousness affected dietary supplement products purchase intention indirectly through attitude formation, with 71.5% of the variance explained. Besides, significant differences were observed between dietary supplement products users and non-users in all variables. Dietary supplement product users have stronger intention to purchase dietary supplement products, more positive attitude, with stronger perceived social pressures to purchase, perceived more availability, place more importance of price and have higher level of health consciousness compared to nonusers. Therefore, in order to promote healthy living through natural ways, consumers’ attitude formation towards dietary supplement products should be the main focus. Policy maker, healthcare providers, educators, researchers and dietary supplement industry must be responsible and continue to work diligently to provide consumers with accurate dietary supplement products and healthy living information.
Convolutional neural networks for mesh-based parcellation of the cerebral cortex

In order to understand the organization of the cerebral cortex, it is necessary to create a map or parcellation of cortical areas. Reconstructions of the cortical surface created from structural MRI scans, are frequently used in neuroimaging as a common coordinate space for representing multimodal neuroimaging data. These meshes are used to investigate healthy brain organization as well as abnormalities in neurological and psychiatric conditions. We frame cerebral cortex parcellation as a mesh segmentation task, and address it by taking advantage of recent advances in generalizing convolutions to the graph domain. In particular, we propose to assess graph convolutional networks and graph attention networks, which, in contrast to previous mesh parcellation models, exploit the underlying structure of the data to make predictions. We show experimentally on the Human Connectome Project dataset that the proposed graph convolutional models outperform current state-ofthe-art and baselines, highlighting the potential and applicability of these methods to tackle neuroimaging challenges, paving the road towards a better characterization of brain diseases.
Top-k Hierarchical Classification

This paper studies a top-k hierarchical classification problem. In top-k classification, one is allowed to make k predictions and no penalty is incurred if at least one of k predictions is correct. In hierarchical classification, classes form a structured hierarchy, and misclassification costs depend on the relation between the correct class and the incorrect class in the hierarchy. Despite that the fact that both top-k classification and hierarchical classification have gained increasing interests, the two problems have always been studied separately. In this paper, we define a top-k hierarchical loss function using a real world application. We provide the Bayes-optimal solution that minimizes the expected top-k hierarchical misclassification cost. Via numerical experiments, we show that our solution outperforms two baseline methods that address only one of the two issues.
E-payment Gateway Model

The extraordinary growth of interconnected computer network & pervasive trends of using this network as new field for conducting Business process are stimulating the demand for new payment methods. These new methods attain high level of security, speed, privacy, decentralization & internationalization. This seminar surveys the state of art in payment technology & we proposed model for electronic payment gateway, On the basis of requirement of electronic payment gateway. E-payment is now one of most central research areas in E-commerce. E-payment system the automated process of exchanging monetary values among parties in business transactions. In this paper a brief overview of electronic payment gateway is provided. This, addresses the requirements for an electronic payment gateway from both the customers and the merchants' point of view. Most of the population doesn’t trust on the local existing online payment gateway because it is not very secure. Mostly people want to adopt electronic payment system as it has lots of advantages. They need such a gateway that fulfil their all requirements and provide security, privacy etc. On the basis of these requirements and the local infrastructure, we propose an electronic payment gateway for local environment..  E-payment, Gateway
LEXenstein: A Framework for Lexical Simplification

Lexical Simplification consists in replacing complex words in a text with simpler alternatives. We introduce LEXenstein, the first open source framework for Lexical Simplification. It covers all major stages of the process and allows for easy benchmarking of various approaches. We test the tool’s performance and report comparisons on different datasets against the state of the art approaches. The results show that combining the novel Substitution Selection and Substitution Ranking approaches introduced in LEXenstein is the most effective approach to Lexical Simplification.
Perception of dialect variation in noise: intelligibility and classification.

Listeners can explicitly categorize unfamiliar talkers by regional dialect with above-chance performance under ideal listening conditions. However, the extent to which this important source of variation affects speech processing is largely unknown. In a series of four experiments, we examined the effects of dialect variation on speech intelligibility in noise and the effects of noise on perceptual dialect classification. Results revealed that, on the one hand, dialect-specific differences in speech intelligibility were more pronounced at harder signal-to-noise ratios, but were attenuated under more favorable listening conditions. Listener dialect did not interact with talker dialect; for all listeners, at a range of noise levels, the General American talkers were the most intelligible and the Mid-Atlantic talkers were the least intelligible. Dialect classification performance, on the other hand, was poor even with only moderate amounts of noise. These findings suggest that at moderate noise levels, listeners are able to adapt to dialect variation in the acoustic signal such that some cross-dialect intelligibility differences are neutralized, despite relatively poor explicit dialect classification performance. However, at more difficult noise levels, participants cannot effectively adapt to dialect variation in the acoustic signal and cross-dialect differences in intelligibility emerge for all listeners, regardless of their dialect.
Energy Tradeoff in Ground-to-UAV Communication via Trajectory Design

Unmanned aerial vehicles (UAVs) have a great potential for improving the performance of wireless communication systems due to their high mobility. In this correspondence, we study a UAV-enabled data collection system, where a UAV is dispatched to collect a given amount of data from a ground terminal (GT) at fixed location. Intuitively, if the UAV flies closer to the GT, the uplink transmission energy of the GT required to send the target data can be more reduced. However, such UAV movement may consume more propulsion energy of the UAV, which needs to be properly controlled to save its limited on-board energy. As a result, the transmission energy reduction of the GT is generally at the cost of higher propulsion energy consumption of the UAV, which leads to a new fundamental energy tradeoff in ground-to-UAV wireless communication. To characterize this tradeoff, we consider two practical UAV trajectories, namely circular flight and straight flight. In each case, we first derive the energy consumption expressions of the UAV and GT and then find the optimal GT transmit power and UAV trajectory that achieve different Pareto optimal tradeoffs between them. Numerical results are provided to corroborate our study.
Loan Fraud Detection And IT-Based Combat Strategies

Loan portfolio problems have historically been the major cause of bank losses because of inherent risk of possible loan losses (credit risk). The study of Bank Loan Fraud Detection and IT-Based Combat Strategies in Nigeria which focused on analyzing the loan assessment system was carried out purposely to overcome the challenges of high incidence of NonPerforming Loan (NPL) that are currently being experienced as a result of lack of good decision making mechanisms in disbursing loans. NPL has led to failures of some banks in the past, contributed to shareholders losing their investment in the banks and inaccessibility of bank loans to the public. Information Technology (IT) is a critical component in creating value in banking industries. It provides decision makers with an efficient means to store, calculate, and report information about risk, profitability, collateral analysis, and precedent conditions for loan. This results in a quicker response for client and efficient JIBC August 2011, Vol. 16, No.2 2 identification of appropriate risk controls to enable the financial institution realize a profit. In this paper we discussed the values of various applications of information technology in mitigating the problems of loan fraud in Nigeria financial Institutions.
Optimization of a furniture factory layout

This paper deals with the problem of optimizing a factory floor layout in a Slovenian furniture factory. First, the current state of the manufacturing system is analyzed by constructing a discrete event simulation (DES) model that reflects the manufacturing processes. The company produces over 10,000 different products, and their manufacturing processes include approximately 30,000 subprocesses. Therefore, manually constructing a model to include every subprocess is not feasible. To overcome this problem, a method for automated model construction was developed to construct a DES model based on a selection of manufacturing orders and relevant subprocesses. The obtained simulation model provided insight into the manufacturing processes and enable easy modification of model parameters for optimizing the manufacturing processes. Finally, the optimization problem was solved: the total distance the products had to traverse between machines was minimized by devising an optimal machine layout. With the introduction of certain simplifications, the problem was best described as a quadratic assignment problem. A novel heuristic method based on force-directed graph drawing algorithms was developed. Optimizing the floor layout resulted in a significant reduction of total travel distance for the products.
A survey and taxonomy of approaches for mining software repositories in the context of software evolution

A comprehensive literature survey on approaches for mining software repositories (MSR) in the context of software evolution is presented. In particular, this survey deals with those investigations that examine multiple versions of software artifacts or other temporal information. A taxonomy is derived from the analysis of this literature and presents the work via four dimensions: the type of software repositories mined (what), the purpose (why), the adopted/invented methodology used (how), and the evaluation method (quality). The taxonomy is demonstrated to be expressive (i.e., capable of representing a wide spectrum of MSR investigations) and effective (i.e., facilitates similarities and comparisons of MSR investigations). Lastly, a number of open research issues in MSR that require further investigation are identified.
Vietnam ’ s Infrastructure Constraints

Successful countries provide economy and society with infrastructure needed to maintain growth. Development experience suggests that investing 7 percent of GDP in infrastructure is the right order of magnitude for high and sustained growth. Over the last twelve years, the government of Vietnam was able to sustain infrastructure investment at 10 percent of GDP. This remarkably high level of investment has resulted in a rapid expansion of infrastructure stocks and improved access. Despite this achievement, Vietnam is experiencing more and more infrastructure weaknesses that negatively affect its ability to sustain high economic growth in the long term. Transport and electricity – the two most essential infrastructure activities – appear to be the weakest infrastructure sectors in Vietnam with blackouts and traffic jams occurring more and more frequently. In transport, many large-scale railway, seaport and airport projects are being planned in near total disregard of the emergence of fast growing industrial clusters. These wrongheaded projects will need to be terminated in order to make funds available for a few crucial projects in the most rapidly growing regions that currently face severe transport bottlenecks. The private sector participation in transport development will help identify and execute the most viable projects. But its potential will only be realized if the returns to private investors come from the projects’ own cash flow, rather than from government subsidies in the form of land. In electricity, the investment pattern of over-reliance on hydro needs to be changed. If hydro continues to be the single largest production source, then extensive idle time will be inevitable for thermal stations, since the wet/dry season power output ratio is so uneven. Vietnam must determine the appropriate mix of hydro and thermal generating capacity that can reliably supply the country’s demand. Electricity prices have to be raised to levels that enable EVN or a single buyer in the future to contract for new generating capacity through competitive bidding. The roadmap for liberalization in the energy sector contemplated in the 2004 Electricity Law needs to be implemented if Viet Nam is to successfully attract the volume of investment and promote the levels of competition and private sector participation required to meet Viet Nam’s long term energy and, hence, developmental needs.
Learning Hidden Quantum Markov Models

Hidden Quantum Markov Models (HQMMs) can be thought of as quantum probabilistic graphical models that can model sequential data. We extend previous work on HQMMs with three contributions: (1) we show how classical hidden Markov models (HMMs) can be simulated on a quantum circuit, (2) we reformulate HQMMs by relaxing the constraints for modeling HMMs on quantum circuits, and (3) we present a learning algorithm to estimate the parameters of an HQMM from data. While our algorithm requires further optimization to handle larger datasets, we are able to evaluate our algorithm using several synthetic datasets generated by valid HQMMs. We show that our algorithm learns HQMMs with the same number of hidden states and predictive accuracy as the HQMMs that generated the data, while HMMs learned with the BaumWelch algorithm require more states to match the predictive accuracy.
A Survey of Domain Adaptation for Neural Machine Translation

Neural machine translation (NMT) is a deep learning based approach for machine translation, which yields the state-of-the-art translation performance in scenarios where large-scale parallel corpora are available. Although the high-quality and domain-specific translation is crucial in the real world, domain-specific corpora are usually scarce or nonexistent, and thus vanilla NMT performs poorly in such scenarios. Domain adaptation that leverages both out-of-domain parallel corpora as well as monolingual corpora for in-domain translation, is very important for domainspecific translation. In this paper, we give a comprehensive survey of the state-of-the-art domain adaptation techniques for NMT.
Text Document Topical Recursive Clustering and Automatic Labeling of a Hierarchy of Document Clusters

The overwhelming amount of textual documents available nowadays highlights the need for information organization and discovery. Effectively organizing documents into a hierarchy of topics and subtopics makes it easier for users to browse the documents. This paper borrows community mining from social network analysis to generate a hierarchy of topically coherent document clusters. It focuses on giving the document clusters descriptive labels. We propose to use betweenness centrality measure in networks of co-occurring terms to label the clusters. We also incorporate keyphrase extraction and automatic titling in cluster labeling. The results show that the cluster labeling method utilizing KEA to extract keyphrases from the documents generates the best labels overall comparing to other methods and baselines.
Reducing medical claims cost to Ghana’s National Health Insurance scheme: a cross-sectional comparative assessment of the paper- and electronic-based claims reviews

A robust medical claims review system is crucial for addressing fraud and abuse and ensuring financial viability of health insurance organisations. This paper assesses claims adjustment rate of the paper- and electronic-based claims reviews of the National Health Insurance Scheme (NHIS) in Ghana.



The study was a cross-sectional comparative assessment of paper- and electronic-based claims reviews of the NHIS. Medical claims of subscribers for the year, 2014 were requested from the claims directorate and analysed. Proportions of claims adjusted by the paper- and electronic-based claims reviews were determined for each type of healthcare facility. Bivariate analyses were also conducted to test for differences in claims adjustments between healthcare facility types, and between the two claims reviews.



The electronic-based review made overall adjustment of 17.0% from GHS10.09 million (USD2.64 m) claims cost whilst the paper-based review adjusted 4.9% from a total of GHS57.50 million (USD15.09 m) claims cost received, and the difference was significant (p < 0.001). However, there were no significant differences in claims cost adjustment rate between healthcare facility types by the electronic-based (p = 0.0656) and by the paper-based reviews (p = 0.6484).



The electronic-based review adjusted significantly higher claims cost than the paper-based claims review. Scaling up the electronic-based review to cover claims from all accredited care providers could reduce spurious claims cost to the scheme and ensure long term financial sustainability.