Reviewerly/exHarmony huggingface dataset:
Presenting a proposed framework for evaluating university websites

Purpose This study aims to design a proposed framework for evaluating university websites. Design/methodology/approach This study is an exploratory mixed research. It was an applied research in terms of objective and used the Delphi technique and systematic review and meta-analysis approaches. Data collection tools were done through library studies, Delphi checklist and observation. The statistical population of the research comprised 17 experts who are designers of university websites and 20 Iranian university websites selected from the Webometrics website. The statistical data were analyzed using fuzzy methods, descriptive and inferential statistical methods and the SWARA weighting method. Also, the statistical analysis software SPSS 20 and Excel 2016, TOPSIS engineering software and MAXQDA were used. Findings Findings indicated that the dimensions of the designed proposed framework in order of their weights are credibility (0.130), reliability (0.125), usability (0.120), website design (0.110), functionality (0.104), content (0.100), page design (0.0922), efficiency (0.082), Webometrics (0.070) and systematic evaluation (0.067). Mebrate’s (2010) framework had the highest overlap (mean = 74.65), and Webometrics (mean = 18.5) had the least overlap and dependency (mean = 19) with the proposed framework. In the evaluation of the 20 university websites of Iran selected from the Webometrics site, the University of Tehran was ranked first with a score of 82.7 and Shiraz University was ranked last with a score of 75. Originality/value This study provides a comprehensive proposed framework for evaluating university websites that eliminates the shortcomings of all models, frameworks and methods of university website evaluation that focused only on one or more dimensions of university websites.
14086 A new multimodal age prediction image analysis method from hands images of different age groups by neural network model

Several papers on age prediction using face images have been published while very limited studies on hands aging have been conducted. Hands aging has a great impact on perceived age and is becoming increasingly important to physicians and their patients. To fill this gap, a novel age prediction method based on the hand images is developed. Age prediction models based on face images are well investigated mostly because of the persistence of massive datasets like IMDB-WIKI containing more than 500,000 face images. We captured face and hands images of 994 Indian women with age ranging from 20 to 60 years with different skin tones by VISIA CR. We developed two deep neural network architectures for the face and back of the hands, that can be used separately or in conjunction for age prediction. A novel neural network architecture works on hand images and predicts age based on the seven specified areas of the hand image such as central and bottom part of the backhand, knuckles areas of fingers. Predictions for all these seven areas are aggregated for the final age determination. This method based only on hands images showed a great association between actual age and predicted age with mean average error (MAE) of 5.89 years. We got MAE of 6.8 years for face-based age predictor. Our research can help to measure the complex effects of anti-aging products or any dermatologic procedures after treating aging hands objectively and as part of a multimodal age predictor.
Confirmation of Standards of Proof through Bayes Theorem

Legal reasoning on the requirements and application of law has been studied for centuries, but in this subject area the legal profession maintains predominantly the same stance it did in the time of the Ancient Greeks. There is a gap between the standards of proof, one which has been always demonstrated by percentages and in terms of the evaluation of these standards by percentages by mathematical or statistical methods. One method to fill the gap is Bayes theorem that describes an event’s probability based on conditions that might (or might not) be related to an event. Bayes theorem can help to establish or confirm a relation between facts and rules if there is sufficient other evidence (frequency of characteristics) that connect a party in a procedure with a considered legal action.
Image Classification using EXIF Metadata

Semantic classification of unrestricted images is still anopen problem regardless of all efforts done.Present methods have only mainly focused on features extracted from the image content (e.g.colour, texture, shape).Conversely, EXIF metadata recorded by the camera can be exploited to aid the classification process.Demonstrating scenery-object classification as an example, analysis of results has revealed different combinations of metadata features that contribute variedly in predicting scenery-object image classification when using different classifiers.The evaluation was done by using machine learning to which a dataset of 500 digital images, consisting of 250 random scenery images and 250 random object based images were trained and tested.For classification, Support Vector Machine (SVM) and K-Nearest Neighbour (KNN) classifiers were used.The research project achieved a result as high as 75.15% for the classification of scenery-object image using KNN.
Phrasal Complexity Measures as Predictors of EFL University Students’ English Academic Writing Proficiency

The study aims to investigate phrasal complexity measures that can predict EFL students’ academic writing proficiency. Academic English written test responses were derived from written responses from the Khon Kaen University Academic English Language Test (KKU-­‐AELT). Five hundred and thirty written responses were separated into groups based on their writing scores. Sixty-­‐six phrasal complexity measures (Kyle, 2016) were analyzed for this study. The Tool for the Automatic Analysis of Syntactic Sophistication and Complexity (TAASSC), a computational tool for phrasal complexity analysis, was used to calculate the average numbers of occurring measures in written responses. Phrasal complexity measures occurring in written responses were analyzed with the independent t-­‐test. Then, 11 significant phrasal complexity measures, derived from the independent t-­‐test, were entered into Binary logistic regression in order to examine potential phrasal complexity measures that can predict proficiency levels. The results revealed three phrasal complexity measures that can predict academic writing for higher proficiency level students.
Teachers’ Beliefs in Integrating Digital Literacy in EFL Classroom: Decomposed Theory of Planned Behavior Perspectives

The need for integrating digital technology into learning instructions has been acknowledge as a most prominent aspect in 21st Century learning. With emerging technology rising in prevalence, a digital consciousness to select and manage digital resources safely, appropriately, and efficiently for digital learning instruction is crucial. This present study is intended to explore teachers’ beliefs in the integration of digital literacy in EFL classroom using Decomposed Theory of Planned Behavior (DTPB) perspectives. The study was a qualitative study by means of case study research design to obtain a reliable understanding of the phenomenon. The data were collected using semi-structured interview, open-ended survey, and documentation. The data were retrieved from the purposively chosen informants based on the familiarity with digital technology. The results indicate relatively high levels of integration and consistent beliefs about the advantages, support from the environment, and the technology availability to integrate digital literacy into teaching learning activities. Recommendations are suggested to advance schools equipment to mediate the better support in constructing technology-enhanced classroom for teachers.
Consistency of Extended Kalman Filtering and Particle Filtering Techniques for the State Estimation of Brushless DC Motor

The paper deals with different adaptive filtering algorithms (AFAs) and is implemented to the non-linear systems. As Brushless DC (BLDC) motors are non-linear in nature under the presence of noise signals like gaussian white signals, these AFAs are implemented to BLDC motor by the estimation of state and output measurements. The performance characteristics (both steady state and dynamic state characteristics) of BLDC Motor are improved by using filtering techniques like Extended Kalman Filtering (EKF) and Particle Filtering (PF). The consistency of EKF and PF methods is determined by using consistency tests, and the obtained simulation results proves that the consistency of PF technique is much better than the EKF technique. PF technique shows the better stability, strong robustness with better convergency and reliability and low sensitivity on the estimation of state and output measurements of Sensorless BLDC Motor control with non-linearities.
An Empirical Investigation into Industrial Use of Software Metrics Programs

Practitioners adopt software metrics programs to support their software development from the perspective of either overall quality, performance, or both. Current literature details and justifies the role of a metrics program in a software organization’s software development, but empirical evidence to demonstrate its actual use and concomitant benefits remains scarce. In the context of an EU H2020 Project, we conducted a multiple case study to investigate how two software-intensive Agile companies utilized a metrics program in their software development. We invited practitioners from the two case companies to report on the actual use of the metrics program, the underlying rationale, and any benefits they may have witnessed. We also collected and analyzed metrics data from multiple use cases to explain the reported use of the metrics. The analysis revealed improvements like better code review practices and formalization of quality requirements management, either as a direct consequence or as a byproduct of the use of the metrics. The contrasting contexts like company size, project characteristics, and general perspective towards metrics programs could explain why one company viewed the metrics as a trigger for their reported improvements, while the other company saw metrics as the main driver for their improvements. Empirical evidence from our study should help practitioners adopt a more favorable view towards metrics programs, who were otherwise reluctant due to lack of evidence of their utility and benefits in industrial context.
Integrated resource management pipeline for dynamic resource-effective cloud data center

Abstract Cloud computing is a popular emerging computing technology that has revolutionized information technology through flexible provisioning of computing resources. Therefore, efforts to develop an effective resource management approach have found that implementing efficient resource sharing among multiple customers that considers power saving, service-level agreements, and network traffic simultaneously is difficult. This paper proposes a practical integrated pipeline that can use various algorithms. The performance of each algorithm is evaluated independently to obtain the combination of algorithms that guarantees a resource-effective cloud data center framework. This integrated resource management pipeline approach would optimize performance based on several key performance indicators, such as power saving, network traffic, and service-level agreements, for either the whole system or the end-user. The performance of the proposed resource management framework was evaluated using a real testbed. The results demonstrated that the proactive double exponential smoothing algorithm prevents unnecessary migrations, the MMTMC2 VM selection algorithm improved the quality of service for end-users and reduced overall energy consumption and network traffic, and the medium-fit placement algorithm provided load balancing between active servers and decreased service level agreement violations. The performance comparison illustrated that the combination of these algorithms was considered to be the best solution toward a dynamic resource-effective cloud data center. Our results showed that energy consumption and the total number of migrations decreased by 16.64 % and 49.44 % , respectively.
Machine Learning in EDA: Opportunities and Challenges

Electronic Design Automation software has delivered semiconductor design productivity improvements for decades. The next leap in productivity will come from the addition of machine learning techniques to the toolbox of computational software capabilities employed by EDA developers. Recent research and development into machine learning for EDA point to clear patterns for how it impacts EDA tools, flows, and design challenges. This research has also illustrated some of the challenges that will come with production deployment of machine learning techniques into EDA tools and flows. This talk will detail patterns observed in ML for EDA development, as well as discussing challenges with productization of ML for EDA developments and the opportunities that it presents for researchers.
Balancing Access Control and Privacy for Data Deduplication via Functional Encryption

Data deduplication serves as an effective way to optimize the storage occupation and the bandwidth consumption over clouds. As for the security of deduplication mechanism, users’ privacy and accessibility are of utmost concern since data are outsourced. However, the functionality of redundancy removal and the indistinguishability of deduplication labels are naturally incompatible, which bring about a lot of threats on data security. Besides, the access control of sharing copies may lead to infringement on users’ attributes and cumbersome query overheads. To balance the usability with the confidentiality of deduplication labels and securely realize an elaborate access structure, a novel data deduplication scheme is proposed in this paper. Briefly speaking, we drew support from learning with errors (LWE) to make sure that the deduplication labels are only differentiable during the duplication check process. Instead of authority matching, the proof of ownership (PoW) is then implemented under the paradigm of inner production. Since the deduplication label is light-weighted and the inner production is easy to carry out, our scheme is more efficient in terms of computation and storage. Security analysis also indicated that the deduplication labels are distinguishable only for duplication check, and the probability of falsifying a valid ownership is negligible.
Topological similarity measurement: a methodology for fast full-field deformation estimation

The correlation-based image subset matching methods such as digital image correlation (DIC) are widely used for measuring the deformation fields. The specimen surface must be covered with speckle patterns, or more exactly, random intensity distributions with sufficient contrast, which act as a carrier of deformation information, to acquire reliable and accurate matching in the correlation calculation. The requirement of the artificial speckles on the specimen surface inevitably contaminates the specimen. To maintain the cleanliness of the specimen surface, an in situ deformation estimation method without speckle fabrication is developed and its result is demonstrated. Topological similarity measurement (TSM) is a feature vector-based displacement estimating algorithm for full-field deformations that uses feature vectors that encode the relative positions of neighboring mark spots. Compared with the DIC method, this approach makes up for its deficiency in displacement estimation of speckle-free flat surface by incorporating a topological arrangement-based feature vector, subpixel center location method, and biharmonic spline interpolation method. Experimental results indicate that the proposed TSM method is feasible to become the preferred displacement estimation method for uniform deformation. As a complement to the DIC method, the TSM method is more suitable for uniform deformation distribution, which requires high measurement accuracy but cannot use DIC.
Artificial Intelligence for Software Engineering: An Initial Review on Software Bug Detection and Prediction

The need for speed and quality in delivering all software engineering artifacts has inevitably remained the biggest challenge in today's software development environment.While everyone caters to complex software engineering processes, new releases are expected by the market on almost a daily basis.Thus, several Artificial Intelligence (AI) techniques have been introduced that are intensively used in the modern software engineering industry to fulfill market needs.This paper presents the initial results of our review work on software bug detection and prediction studies using AI techniques.Our focus is to (i) identify factors affecting the effectiveness of current software bug detection and prediction techniques and (ii) identify the effectiveness of AI techniques in improving current software bug detection and prediction techniques.The evidence showed that the software engineering domain has utilized artificial intelligence approaches and techniques to facilitate the complex tasks of software bug detection and bug prediction.It mainly demonstrates the significance of merging artificial intelligence with the software engineering domain in terms of reduced overhead and efficient results to enhance the quality of software products.
A Principle of Least Action for the Training of Neural Networks

Neural networks have been achieving high generalization performance on many tasks despite being highly over-parameterized. Since classical statistical learning theory struggles to explain this behavior, much effort has recently been focused on uncovering the mechanisms behind it, in the hope of developing a more adequate theoretical framework and having a better control over the trained models. In this work, we adopt an alternate perspective, viewing the neural network as a dynamical system displacing input particles over time. We conduct a series of experiments and, by analyzing the network's behavior through its displacements, we show the presence of a low kinetic energy displacement bias in the transport map of the network, and link this bias with generalization performance. From this observation, we reformulate the learning problem as follows: finding neural networks which solve the task while transporting the data as efficiently as possible. This offers a novel formulation of the learning problem which allows us to provide regularity results for the solution network, based on Optimal Transport theory. From a practical viewpoint, this allows us to propose a new learning algorithm, which automatically adapts to the complexity of the given task, and leads to networks with a high generalization ability even in low data regimes.
Audio recordings dataset of genuine and replayed speech at both ends of a telecommunication channel

The recordings in this database were collected for the purpose of evaluating the ability of a copy-detection based playback attack detector to safeguard a remote-access speaker-verified and passphrase-protected system from playback attacks. The database includes multiple utterances of the same phrase by the same person in addition to a variety of distorted versions of many of the utterances. Multiple distortions of an utterance were obtained, in part, by simultaneously recording the utterance at both ends of a telecommunication channel - using a digital voice recorder to obtain the user-end (i.e., in-person) recording and a telephony board to obtain the system-end recording. While the former suffers little distortion, the latter suffers the "non-stationary" distortion imposed by the channel. Additional distortions of the same utterance were captured at the system-end of the channel when the in-person recording was replayed at the user-end; these additional recordings simulate playback attacks and suffer the distortion imposed by both the playback device and the channel. The database may be used: to evaluate the vulnerability of a speaker verification system (SVS) to playback attacks; to evaluate the performance of a copy-detection or distortion-detection based playback attack detector (PAD); to evaluate the overall security of a speaker verification system in tandem with a playback attack countermeasure; or to investigate the distortion imposed by various telecommunication channels and/or playback speakers.
RiCaSi: Rigorous Cache Side Channel Mitigation via Selective Circuit Compilation

Cache side channels constitute a persistent threat to crypto implementations. In particular, block ciphers are prone to attacks when implemented with a simple lookup-table approach. Implementing crypto as software evaluations of circuits avoids this threat but is very costly. We propose an approach that combines program analysis and circuit compilation to support the selective hardening of regular C implementations against cache side channels. We implement this approach in our toolchain RiCaSi. RiCaSi avoids unnecessary complexity and overhead if it can derive sufficiently strong security guarantees for the original implementation. If necessary, RiCaSi produces a circuit-based, hardened implementation. For this, it leverages established circuit-compilation technology from the area of secure computation. A final program analysis step ensures that the hardening is, indeed, effective.
Patient Health Monitoring Information System

This article presents the developed information system for patient health monitoring during and after rehabilitation. The information system comprises a mobile app and a web app. The mobile app collects the data from a smartwatch and sends them to a relational database. The web app manages the information in the database.
Urban Space Insights Extraction Using Acoustic Histogram Information

Urban data mining can be identified as a highly potential area that can enhance the smart city services towards better sustainable development especially in the urban residential activity tracking. While existing human activity tracking systems have demonstrated the capability to unveil the hidden aspects of citizens' behavior, they often come with a high implementation cost and require a large communication bandwidth. In this paper, we study the implementation of low-cost analogue sound sensors to detect outdoor activities and estimate the raining period in an urban residential area. The analogue sound sensors are transmitted to the cloud every 5 minutes in histogram format, which consists of sound data sampled every 100ms (10Hz). We then use wavelet transformation (WT) and principal component analysis (PCA) to generate a more robust and consistent feature set from the histogram. After that, we performed unsupervised clustering and attempt to understand the individual characteristics of each cluster to identify outdoor residential activities. In addition, on-site validation has been conducted to show the effectiveness of our approach.