nils-herrmann/clean_scirepeval huggingface dataset:
Determining Critical Success Factors of Mobile Banking Adoption in Malaysia

This study is to examine critical success factors that influence the adoption of mobile banking in Malaysia using extended Technology Acceptance Model (TAM). The proposed model was empirically evaluated by using survey data collected from 300 banking users concerning their perceptions of mobile banking. The findings indicate that this model can predict consumer intention to use mobile banking. Specifically, perceived usefulness, perceived credibility and awareness about mobile banking have significant effect on user’s attitude thus influence the intention toward mobile banking. The results may provide further insights into mobile banking strategies.
A Field Relevance Model for Structured Document Retrieval

Many search applications involve documents with structure or fields. Since query terms often are related to specific structural components, mapping queries to fields and assigning weights to those fields is critical for retrieval effectiveness. Although several field-based retrieval models have been developed, there has not been a formal justification of field weighting. In this work, we aim to improve the field weighting for structured document retrieval. We first introduce the notion of field relevance as the generalization of field weights, and discuss how it can be estimated using relevant documents, which effectively implements relevance feedback for field weighting. We then propose a framework for estimating field relevance based on the combination of several sources. Evaluation on several structured document collections show that field weighting based on the suggested framework improves retrieval effectiveness significantly.
Application of generalized Bagley-polygon four-port power dividers to designing microwave dual-band bandpass planar filters

A new type of microwave dual-band bandpass planar filter based on signal-interference techniques is reported. The described filter approach consists of transversal filtering sections made up of generalized Bagley-polygon four-port power dividers. This transversal section, by exploiting feedforward signal-interaction concepts, enables dual-band bandpass filtering transfer functions with several transmission zeros to be obtained. A set of closed formulas and guidelines for the analytical synthesis of the dual-passband transversal filtering section are derived. Moreover, its practical usefulness is proven with the development and testing of a 2.75/3.25-GHz dual-band microstrip prototype.
Efficient SMQT features for snow-based classification on face detection and character recognition tasks

Face detection using local successive mean quantization transform (SMQT) features and the sparse network of winnows (SNoW) classifier has received interest in the computer vision community due to its success under varying illumination conditions. Recent work has also demonstrated the effectiveness of this classification technique for character recognition tasks. However, heavy storage requirements of the SNoW classifier necessitate the development of efficient techniques to reduce storage and computational requirements. This study shows that the SNoW classifier built with only a limited number of distinguishing SMQT features provides comparable performance to the original dense snow classifier. Initial results using the well-known CMU-MIT facial image database and a private character database are used to demonstrate the effectiveness of the proposed method.
Scholarly Use of the Web: What Are the Key Inducers of Links to Journal Web Sites

Web links have been studied by information scientists for at least six years but it is only in the past two that clear evidence has emerged to show that counts of links to scholarly Web spaces (universities and departments) can correlate significantly with research measures, giving some credence to their use for the investigation of scholarly communication. This paper reports on a study to investigate the factors that influence the creation of links to journal Web sites. An empirical approach is used: collecting data and testing for significant patterns. The specific questions addressed are whether site age and site content are inducers of links to a journal’s Web site as measured by the ratio of link counts to Journal Impact Factors, two variables previously discovered to be related. A new methodology for data collection is also introduced that uses the Internet Archive to obtain an earliest known creation date for Web sites. The results show that both site age and site content are significant factors for the disciplines studied: library and information science, and law. Comparisons between the two fields also show disciplinary differences in Web site characteristics. Scholars and publishers should be particularly aware that richer content on a journal’s Web site tends to generate links and thus the traffic to the site.
Interlocking obfuscation for anti-tamper hardware

Tampering and Reverse Engineering of a chip to extract the hardware Intellectual Property (IP) core or to inject malicious alterations is a major concern. Digital systems susceptible to tampering are of immense concern to defense organizations. First, offshore chip manufacturing allows the design secrets of the IP cores to be transparent to the foundry and other entities along the production chain. Second, small malicious modifications to the design may not be detectable after fabrication without anti-tamper mechanisms. Some techniques have been developed in the past to improve the defense against such attacks but they tend to fall prey to the increasing power of the attacker. We present a new way to protect against tampering by a clever obfuscation of the design, which can be unlocked with a specific, dynamic path traversal. Hence, the functional mode of the controller is hidden with the help of obfuscated states, and the functional mode is made operational only on the formation of a specific interlocked Code-Word during state transition. No comparator is needed as the obfuscation is embedded within the transition function of the state machine itself. A side benefit is that any small alteration will be magnified via the obfuscated design. In other words, an alteration to the design will manifest itself as a large difference in the circuit's functionality. Experimental results on an Advanced Encryption Standard (AES) circuit from the open-source IP-cores suite suggest that the proposed method provides better active defense mechanisms against attacks with nominal (7.8%) area overhead.
On the dynamics of SCARA robot

Padhy, S.K., On the dynamics of SCARA robot, Robotics and Autonomous Systems, 10 (1992) 71-78. Assembly automation using robots has proven to be very successful. It has been shown that use of robots improves the accuracy of assembly, and saves assembly time and cost as well. SCARA (Selective Compliance Assembly Robot Arm) type robots have been widely used in industry for assembly operations in both the mechanical and electronics domains of manufacturing. One of the important issues in designing an optimal manipulator is the dynamic behavior of the manipulator which is highly non-linear and strongly coupled due to the interaction of inertial, centripetal, coriolis and gravitational forces. Although extensive study involving optimal time study, dynamic study using the Lagrange method, path planning etc. have been made, no closed solution for the dynamics of this important robot has been reported. This paper presents the study of kinematics and dynamics of the SCARA assembly robot. Closed solutions for the dynamics of this robot are derived. To study the variations of the torques of the links a computer code is developed. It is found that the torque is independent of the angular position, which makes the robot highly compliant.
GameFlow in Different Game Genres and Platforms

The GameFlow model strives to be a general model of player enjoyment, applicable to all game genres and platforms. Derived from a general set of heuristics for creating enjoyable player experiences, the GameFlow model has been widely used in evaluating many types of games, as well as non-game applications. Initial applications of the GameFlow model were limited to real-time strategy games. However, in order to be considered a general model of player enjoyment in games, the GameFlow model needs to be applied to a more varied set of play experiences. In this article, we revisit the design of the GameFlow model, review the various applications and derivative models, and discuss on-going analysis of the model. Subsequently, we describe a study that aims to extend the initial validation of the GameFlow model to incorporate additional game types. We report the results of expert reviews conducted using the GameFlow criteria to evaluate first person shooter games on Sony PlayStation 3 and adventure games on Apple iPhone. Our findings provide insight into the manifestation of the GameFlow elements in these types of games and also highlight some of the genre-specific considerations in the application of the GameFlow model. Key issues are raised and discussed in relation to immersion, social interaction, and multiplayer games.
Study of induction heating power supply based on fuzzy controller

In order to satisfy the higher control performance requirement of the Induction Heating Supply, a fuzzy logic control technology for induction heating power supply power control system is researched. This study presents the composition and design of the induction heating control system based on the fuzzy logic controller. In this paper, a complete simulation model of induction heating systems is obtained by using the Matlab /Simulink software, Simulation results show the effectiveness and superiority of the control system.
A Survey of Vehicular Ad hoc Networks Routing Protocols

In recent years, the aspect of vehicular ad hoc network (VANET) is becoming an interesting research area; VANET is a mobile ad hoc network considered as a special case of mobile ad hoc network (MANET). Similar to MANET, VANET is characterized as autonomous and self-configured wireless network. However, VANET has very dynamic topology, large and variable network size, and constrained mobility; these characteristics led to the need for efficient routing and resource saving VANET protocols, to fit with different VANET environments. These differences render traditional MANET’s protocols unsuitable for VANET. The aim of this work is to give a survey of the VANETs routing mechanisms, this paper gives an overview of Vehicular ad hoc networks (VANETs) and the existing VANET routing protocols; mainly it focused on vehicle to vehicle (V2V) communication and protocols. The paper also represents the general outlines and goals of VANETs, investigates different routing schemes that have been developed for VANETs, as well as providing classifications of VANET routing protocols (focusing on two classification forms), and gives summarized comparisons between different classes in the context of their methodologies used, strengths, and limitations of each class scheme compared to other classes. Finally, it extracts the current trends and the challenges for efficient routing mechanisms in VANETs.
Variational Inference for Beta-Bernoulli Dirichlet Process Mixture Models

A commonly used paradigm in diverse application areas is to assume that an observed set of individual binary features is generated from a Bernoulli distribution with probabilities varying according to a Beta distribution. In this paper, we present our nonparametric variational inference algorithm for the Beta-Bernoulli observation model. Our primary focus is clustering discrete binary data using the Dirichlet process (DP) mixture model.
Modeling Source Syntax for Neural Machine Translation

Even though a linguistics-free sequence to sequence model in neural machine translation (NMT) has certain capability of implicitly learning syntactic information of source sentences, this paper shows that source syntax can be explicitly incorporated into NMT effectively to provide further improvements. Specifically, we linearize parse trees of source sentences to obtain structural label sequences. On the basis, we propose three different sorts of encoders to incorporate source syntax into NMT: 1) Parallel RNN encoder that learns word and label annotation vectors parallelly; 2) Hierarchical RNN encoder that learns word and label annotation vectors in a two-level hierarchy; and 3) Mixed RNN encoder that stitchingly learns word and label annotation vectors over sequences where words and labels are mixed. Experimentation on Chinese-to-English translation demonstrates that all the three proposed syntactic encoders are able to improve translation accuracy. It is interesting to note that the simplest RNN encoder, i.e., Mixed RNN encoder yields the best performance with an significant improvement of 1.4 BLEU points. Moreover, an in-depth analysis from several perspectives is provided to reveal how source syntax benefits NMT.
Signal analysis, design methodolgy, and modular development of a TR module for phased array radars

Phased array radars have received considerable importance both in commercial and defense communications. Lot of research has been carried out regarding development of its necessary components such as the antenna array, transmit/receive module (TRM) and processor unit. The cost, efficiency, wide bandwidth, and fabrication technology for the TRM have been pursued as key research areas. Cost reduction techniques till date have focused on the use of high tech MMIC fabrication techniques. Literature lacks in guiding a novice design engineer working on TRM in a cost effective manner. This paper presents mathematical analysis of signals processed in a TRM to enable a design engineer understand the constraints involved in the system design. The behavioral simulation, at 2.0 GHz, of TR module has been performed and, simulated results of phase shifter and TR switch sub-modules are also presented. Detailed system simulation is performed using lumped components for cost effective implementation in a lab environment. The results depict a close realization of the mathematical formulation.
A Compact Wideband Circularly Polarized Antenna Array for $C$ -band Applications

In this letter, we present a compact single-layer microstrip antenna array for applications in the C-band. The device is designed and fabricated with an overall volume of 45 mm × 45 mm × 1.6 mm, (0.86 λ0 × 0.86 λ0 × 0.03 λ0 at a frequency of 5.75 GHz), a suitable dimension for compact handhelds. The proposed array is based on a modified sequential phase network, composed of nonuniform curved transmission lines, feeding a set of 2 × 2 disc based patch elements. The proposed network has space-filling properties, and is arranged in the unoccupied area between the elements, minimizing the overall dimensions without sacrificing the operating bandwidth. Adequate agreements between the simulated and measured results validate the proposed design: a 3-dB axial ratio band of 900 MHz, meaning 15.5%, a peak gain of 8.25 dB maintained within 3 dB for 1.2 GHz, and a very wide 10-dB return loss bandwidth of 29%. This performance is unmatched for the given dimensional constraint with a single-layer via-less technology .
GNU Radio 802 . 15 . 4 En-and Decoding

The IEEE wireless standard 802.15.4 gets widespread attention because of its adoption in sensor networks, home automation, and other networked systems. The goal of the project is to implement an enand decoding block for the IEEE 802.15.4 protocol in GNU Radio, an open source solution for software defined radios. This report will give an insight into the working of GNU Radio and some of its hardware components. Additionally, it gives details about the implementation of the enand decoding blocks. At the end, we will verify the implementation by sending and receiving messages to and from an actual IEEE 802.15.4 radio chip, the CC2420 from ChipCon, and give a small bandwidth comparison of the two solutions.
Automatic optimization of data perturbation distributions for multi-style training in speech recognition

Speech recognition performance using deep neural network based acoustic models is known to degrade when the acoustic environment and the speaker population in the target utterances are significantly different from the conditions represented in the training data. To address these mismatched scenarios, multi-style training (MTR) has been used to perturb utterances in an existing uncorrupted and potentially mismatched training speech corpus to better match target domain utterances. This paper addresses the problem of determining the distribution of perturbation levels for a given set of perturbation types that best matches the target speech utterances. An approach is presented that, given a small set of utterances from a target domain, automatically identifies an empirical distribution of perturbation levels that can be applied to utterances in an existing training set. Distributions are estimated for perturbation types that include acoustic background environments, reverberant room configurations, and speaker related variation like frequency and temporal warping. The end goal is for the resulting perturbed training set to characterize the variability in the target domain and thereby optimize ASR performance. An experimental study is performed to evaluate the impact of this approach on ASR performance when the target utterances are taken from a simulated far-field acoustic environment.
Real-time detection of distributed denial-of-service attacks using RBF networks and statistical features

In this paper we present and evaluate a Radial-basis-function neural network detector for Distributed-Denial-of-Service (DDoS) attacks in public networks based on statistical features estimated in short-time window analysis of the incoming data packets. A small number of statistical descriptors were used to describe the DDoS attacks behaviour, and an accurate classification is achieved using the Radial-basis-function neural networks (RBF-NN). The proposed method is evaluated in a simulated public network and showed detection rate better than 98% of DDoS attacks using only three statistical features estimated from one window of data packets of 6 s length. The same type of experiments were carried out on a real network giving significantly better results: a 100% DDoS detection rate is achieved followed by a 0% of false alarm rate using different statistical descriptors and training conditions for the RBF-NN.
A unified protocol stack solution for LTE and WLAN in future mobile converged networks

The interworking of the LTE system and WLAN technologies has drawn much attention lately, due to the growing demands for various multimedia services and large data traffic in hotspot areas. Existing research studies have mostly investigated the coupling architectures for these two wireless communication standards at the network layer. However, in the current architectures, many important coordination functions and joint optimizations cannot be accomplished efficiently. To tackle this problem, a new CBS solution is proposed, which integrates different RATs at layer 2 in the true sense of convergence. We design a unified protocol stack that includes all the original functions of both LTE and WLAN systems. Then we propose a convergence architecture, the RMC sublayer, for joint management of these two RATs. The proposed CBS solution can support seamless offloading through soft handover, guaranteed QoS, forwarding management by a single IP address, and customized bandwidth aggregation service. Finally, our simulation and initial experiment results demonstrate the feasibility and efficiency of the CBS solution in future mobile converged networks.
A Novel Fuzzy C-Means Clustering Algorithm for Image Thresholding

Image thresholding has played an important role in image segmentation. In this paper, we present a novel spatially weighted fuzzy c-means (SWFCM) clustering algorithm for image thresholding. The algorithm is formulated by incorporating the spatial neighborhood information into the standard FCM clustering algorithm. Two improved implementations of the k-nearest neighbor (k-NN) algorithm are introduced for calculating the weight in the SWFCM algorithm so as to improve the performance of image thresholding. To speed up the FCM algorithm, the iteration is carried out with the gray level histogram of image instead of the conventional whole data of image. The performance of the algorithm is compared with those of an existed fuzzy thresholding algorithm and widely applied between variance and entropy methods. Experimental results with synthetic and real images indicate the proposed approach is effective and efficient. In addition, due to the neighborhood model, the proposed method is more tolerant to noise.
The SWAL-QOL Outcomes Tool for Oropharyngeal Dysphagiain Adults: II. Item Reduction and Preliminary Scaling

The SWAL-QOL outcomes tool was constructed for use in clinical research for patients with oropharyngeal dysphagia. The SWAL-QOL was constructed a priori to enable preliminary psychometric analyses of items and scales before its final validation. This article describes data analysis from a pretest of the SWAL-QOL. We evaluated the different domains of the SWAL-QOL for respondent burden, data quality, item variability, item convergent validity, internal consistency reliability as measured by Cronbach's alpha, and range and skewness of scale scores upon aggregation and floor and ceiling effects. The item reduction techniques outlined reduced the SWAL-QOL from 185 to 93 items. The pretest of the SWAL-QOL afforded us the opportunity to select items for the ongoing validation study which optimally met our a priori psychometric criteria of high data quality, normal item distributions, and robust evidence of item convergent validity.
Dual Sparse Constrained Cascade Regression for Robust Face Alignment

Localizing facial landmarks is a fundamental step in facial image analysis. However, the problem continues to be challenging due to the large variability in expression, illumination, pose, and the existence of occlusions in real-world face images. In this paper, we present a dual sparse constrained cascade regression model for robust face alignment. Instead of using the least-squares method during the training process of regressors, sparse constraint is introduced to select robust features and compress the size of the model. Moreover, sparse shape constraint is incorporated between each cascade regression, and the explicit shape constraints are able to suppress the ambiguity in local features. To improve the model's adaptation to large pose variation, face pose is estimated by five fiducial landmarks located by deep convolutional neuron network, which is used to adaptively design the cascade regression model. To the best of our best knowledge, this is the first attempt to fuse explicit shape constraint (sparse shape constraint) and implicit context information (sparse feature selection) for robust face alignment in the framework of cascade regression. Extensive experiments on nine challenging wild data sets demonstrate the advantages of the proposed method over the state-of-the-art methods.