Reviewerly/exHarmony huggingface dataset:
Optimization of Polyurethane Membrane Physical Characteristics of Red Seaweed Biomass Using a Box-Behnken Design

The polyurethane membrane is used as a separator either by filtration or adsorption, and this process is significantly affected by its strength and physical condition. We synthesized polyurethane membranes using red seaweed with Gracilaria sp as a hydroxyl source. The Box-Behnken Design of the Response Surface Methodology (RSM) using Software Design Expert Version 10.0.3.0 with three factors (TRL, TDI, and Glycerin). The F-value of 0.42 suggests that the membrane is less fit, while the P-value of 75.10% indicates that the quadratic design model is suitable for data analysis of physical characteristics. The optimal physical characteristics were obtained at a composition of 0.233 g TRL, 2.675 g TDI, and 0.254 g glycerin with a physical point of 6.5 (strong and elastic). Optimal polyurethane membrane has good thermal and mechanical properties at temperatures of Tg 58 °C, Tm 322 °C, and Td 534 °C, as well as stress and nominal strain values of 69.3 MPa and 5.74%. Polyurethane membrane synthesized from red seaweed has good physical properties. The result of this research is the basis for the development of polyurethane membrane applications from red seaweed.
Spectrally resolved four-photon interference of time-frequency-entangled photons

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Improved Object Matching in Multi-Objects Tracking Based On Zernike Moments and Combination of Multiple Similarity Metrics

In video surveillance, multiple objects tracking (MOT) is a challenging task due to object matching problem in consecutive frames. The present paper aims to propose an improved object matching approach in MOT based on Zernike Moments and combination of multiple similarity distance metrics. In this work, the object is primarily detected using background subtraction method while the Gaussian Mixture Model (GMM) is applied for object extraction in the next frames. Subsequently, the color histogram and the magnitude of Zernike moments of the objects are calculated. In the next step, the objects are matched in the current and the previous frames based on the Hausdorff distance between objects, Earth Mover's (EMD) distance between their color histograms, and Chi-square distance between their Zernike moments. Then, a voting mechanism is designed to find the best consensus object matching from the aforementioned metrics. Eventually, the location of each object is predicted by the Kalman filter to continue tracking in subsequent frames. The results show that the object tracking and matching performance is improved using the proposed method in the video sequences of the multi-camera pedestrian "EPFL" video dataset. Specifically, errors caused by the merging of targets are reduced in the proposed tracking process.
Federated Deep Learning Architecture for Personalized Healthcare

Using deep learning to advance personalized healthcare requires data about patients to be collected and aggregated from disparate sources that often span institutions and geographies. Researchers regularly come face-to-face with legitimate security and privacy policies that constrain access to these data. In this work, we present a vision for privacy-preserving federated neural network architectures that permit data to remain at a custodian’s institution while enabling the data to be discovered and used in neural network modeling. Using a diabetes dataset, we demonstrate that accuracy and processing efficiencies using federated deep learning architectures are equivalent to the models built on centralized datasets.
Security Analysis of Medical Images using ECC over RSA

The security of medical images is very important to maintain the confidentiality and privacy of patient information. Medical practitioners are required to adopt policies for the security of the access of patient’s electronic information. This paper provides the security analysis of the Medical Images based on Elliptic curve cryptography. Elliptic curve cryptography (ECC) is a complex method and involves intense computation which makes it robust for the intruder attack. In this paper, ECC based Medical Image Encryption algorithm is performed on varieties of medical images like MRI, CT scan and X-ray. Security analysis is presented based on Histogram, Entropy, NPCR and UACI measures and compared with traditional method RSA. Histogram after encryption using ECC has uniform distribution which reflects that the information of original medical image is hidden properly. Entropy and NPCR achieved are 7.86 – 7.99 and 99.62 – 99.64 % respectively which are very close to their ideal values. Results reveal that ECC is more powerful and useful for medical image authentication and key distribution. However, it can be used for exchange of secret key rather than encryption.
Formation of supra-professional competencies sought in special economic development zones in graduates of technical universities

The problem and goal. Modern economy needs engineers having necessary competencies, able to impart a positive impetus to the development of further competitiveness of enterprises located in Priority Social and Economic Development Areas (PSEDA) and throughout entire Russia. The technical higher educational institutions face the mission to secure not only full-value professional training of engineering personnel, but also retraining of the existing specialists within a short time. The purpose of the article is to substantiate the efficiency of the adaptive organisational/pedagogical system of supplementary professional education aimed at developing trainees’ supra-professional competencies within the system of supplementary professional education. Materials and methods. The study involved 146 various-level top managers of PSEDA resident enterprises and 840 persons who were trained under 19 supplementary professional programmes at 43 advanced training courses structured in accordance with the educational services plan of the Centre for supplementary professional education (SPE). The development of trainees’ supra-professional competencies was monitored through observation, questioning and interviewing. The Likert satisfaction scale was used to analyse the collected data. Results. It was established that employers highly appreciate supra-professional competencies as a necessary component of specialist’s qualification characteristics. Following the survey results, it was revealed that 83 % of specialists out of 1000 interviewed persons had not attended any courses of supplementary professional education over the previous 5 years. The efficiency of personnel retraining was determined by analysing the changes in the company’s performance, based on the results of interviewing the employers; the growth of quantitative and qualitative indicators was 62% in 2018, 74% in 2019 and 93% in 2020. Discussion and conclusion. The components of the adaptive organisational/pedagogical system proposed by the authors made it possible to increase the efficiency of educational activities in the system of supplementary professional education, which was confirmed by a pedagogical experiment. The efficiency was determined through numerical measurement tools allowing accurate diagnostics of the experiment participants’ success.
Multi-Task Curriculum Learning For Semi-Supervised Medical Image Segmentation

The lack of annotated data is a common problem in medical image segmentation tasks. In this paper, we present a novel multi-task semi-supervised segmentation algorithm with a curriculum-style learning strategy. The proposed method includes a segmentation task and an auxiliary regression task. Concretely, the auxiliary regression task aims to learn image-level properties such as the size and centroid position of target region to regularize the segmentation network, enforcing the pixel-level segmentation result match the distributions of these regressions. In addition, these regressions are treated as pseudo labels for the learning of unlabeled data. For the purpose of decreasing noise from the deviation of inferred labels, we adopt the inequality constraint for the learning of unlabeled data, which would generate a tolerance interval where the prediction within it would not be published to reduce the impact of prediction deviation of regression network. Experimental results on both 2017 ACDC dataset and PROMISE12 dataset demonstrate the effectiveness of our method.
Classification of Community Complaints Against Public Services on Twitter

Abstract— Information on public services is an important part of increasing community satisfaction with government policies. Complaints and Complaints of the community become mediators to improve public services according to community needs.Twitter is one of the most widely used social media in the community to post activities, experiences, and complaints about public services through the internet easily and realtime.The amount of information on Twitter is mixed between satisfaction and extensibility of public services, making it difficult for the government to make decisions in public policy. The role of Big Data can be a solution to classifying data to predict satisfaction or extensibility of public services with parameters: markets, transportation and hospitals.Data sources taken from Twitter are 700 data texts. The twitter classification of public service complaints is built using the Naïve Bayes Algorithm Method, because the algorithm can classify based on probability values. Text processing is done by filtering text and selecting text to be ordered.The results of this study indicate that the Naïve Bayes Method is able to properly classify public service complaints based on 3 parameters, transportation, markets and hospitals. System testing using 700 data obtained the best results accuracy value: 86%, and precision: 72%, recall 81% and f-measure: 83%.
Implementing a mobile application Quizlet to help Senior High School students learn vocabulary

This study analyzed how Quizlet, a digital vocabulary learning platform, was implemented in helping students learn vocabulary and how students perceive the application. There were 13 high school students of a private school in Yogyakarta who took part in this study. The vocabulary studied were taken from the students' course book which was on the level B2 of CEFR. This research employed CAR (Classroom Action Research) as its methodology. The findings of this research suggested that the students were engaged when using Quizlet during the class activity. The mean score of Perceived Usefulness is very high (M= 4.03, SD= 0.86) and Perceived Ease of Use is also very high (M=4.38, SD= 0.75). This indicated that students had favorable views towards the application. The data acquired from the interview also indicated that Quizlet was a practical and easy-to-use learning platform. The application was a one-stop vocabulary learning platform. Moreover, it utilized cutting-edge technology in learning and the application was practical and easy to use. Based on the results mentioned, the researchers strongly recommend that teachers start using Quizlet to help their students learn vocabulary.
Application of the K-Nearest Neighbors (K-NN) Algorithm for Classification of Heart Failure

Heart failure is a type of disease that has the largest number of patients in the world. Based on information from the data center, there were 229,696 people with heart failure in 2013. Lack of public knowledge about what indications of a person having heart failure make the main cause not handled properly by heart failure patients. In this study, data classification was carried out using KNN algorithm because it has a simple calculation and has a fast time. This study only uses 12 attributes, while the previous study compared 6 algorithms with 13 attributes from 299 data. The highest algorithm with 94.31% accuracy by Random Forest while KNN had an accuracy rate of 86.95% with the same data. In this study, the accuracy of the sample data was compared between 20 data and 299 total data. Both of them have different accuracy. 20 sample data has an accuracy rate of 89.29% while 299 data has an accuracy rate of 96.66%.
Usability Evaluation of Encyclopedia Websites

Usability is an important aspect that every website should focus more. It tells us how well and success website will function with real users. Many people often think usability tests are expensive and time-consuming. It can be a cost-effective and time saver with usability testing instead of spending more time fixing an unusable website. This study evaluates the usability of encyclopedia websites by using automated usability testing tools and questionnaire methods. The questionnaire was developed based on a standard form called Website Analysis and Measurement Inventory (WAMMI) that identified 20 common usability questions divided into five categories. Each category deals with one aspect of usability. Simultaneously, the automated usability testing tools used in this study were Pingdom and GT Metrix to calculate and analyse the website performance of selected encyclopedia websites based on website components including page load time, media size and overall web performance grades. This study could help web designer, developer, and practitioners design better and more user-friendly encyclopedia websites.
Utilizing graph neural networks to improving dialogue-based relation extraction

Relation extraction has been an active research interest in the field of Natural Language Processing (NLP). The past works primarily focused on a corpus of formal text which is inherently non-dialogic. Recently, the dialogue-based relation extraction task, which detects relations among speaker-aware entities scattering in dialogues, has been gradually arousing people's attention. Some sequence-based neural methods have been carried out to obtain the relevant information. However, identifying cross-sentence relations remains unsolved, especially in the context of a specific-domain dialogue system. In this paper, we propose a Relational Attention Enhanced Graph Convolutional Network (RAEGCN), which constructs the whole dialogue as a semantic interactive graph by emphasizing the speaker-related information and leveraging various inter-sentence dependencies. A dense connectivity mechanism is also introduced to empower the multi-hop relational reasoning across sentences, which can capture both local and non-local features simultaneously. Experiments show the significant superiority and robustness of our model on a real-world dataset DialogRE, as compared with previous approaches.
Study of Human Motion Recognition Algorithm Based on Multichannel 3D Convolutional Neural Network

Aiming at the problem that it is difficult to balance the speed and accuracy of human behaviour recognition, this paper proposes a method of motion recognition based on random projection. Firstly, the optical flow picture and Red, Green, Blue (RGB) picture obtained by the Lucas-Kanade algorithm are used. Secondly, the data of optical flow pictures and RGB pictures are compressed based on a random projection matrix of compressed sensing, which effectively reduces power consumption. At the same time, based on random projection compression data, it can effectively find the optimal linear representation to reconstruct training samples and test samples. Thirdly, a multichannel 3D convolutional neural network is proposed, and the multiple information extracted by the network is fused to form an output recognizer. Experimental results show that the algorithm in this paper significantly improves the recognition rate of multicategory actions and effectively reduces the computational complexity and running time of the recognition algorithm.
Students’ Attitude towards the Sustainable Use of Mobile Technologies in Higher Education

Our study aims to identify students’ attitudes towards the use of mobile technologies (MT) during learning activities in higher education. Data were collected using the Mobile Technologies Questionnaire/MTQ, a ten-item brief questionnaire that was designed to determine attitudes towards the use of mobile technologies in the learning process among university students and academic staff. The MTQ was completed by 575 students from a state university in the northeastern region of Romania. Exploratory and confirmatory factor analyses revealed two latent factors: MT facilities for study resources and communication and MT facilities for learning. Along with general analysis of the statistical indicators regarding the attitude towards the use of MT, the relationships between the use of MT and five socio-demographic variables (gender, age, place of residence, year of study, academic status and study program) were analyzed. Comparative data showed some statistically significant differences but with small or modest effect sizes, depending on age, year of study, place of residence, academic status and the study program in which the students were enrolled. This study provides additional support for the construct validity of a brief tool that was designed to measure students’ attitudes towards the use of MT during learning activities carried out in higher education.
THE MEDIATING EFFECT OF DYNAMIC CAPABILITY ON THE HR INFORMATION SYSTEMS AND THE ATTAINMENT OF COMPETITIVE ADVANTAGE IN JORDANIAN HIGHER EDUCATION INSTITUTIONS

This paper aims to investigate the relationship between the use of a Human Resource Information System (HRIS) and the attainment of Competitive Advantage (CA) through the mediating effect of Dynamic Capability (DC) atHigher Education Institutions (JHEIs) in Jordan.Data were collected from 416 full-time academic staff at 13 universities in Jordan.The research adopted a quantitative statistical approach and used Partial Least Squares Structural Equation Modeling (PLS-SEM) to analyze the obtained data.The findings showed that the HRIS has both a direct and indirect significant impact on CA in JHEIs.The HRIS applications and benefits showed a direct and positive influence on CA.As for the indirect effect, all the HRIS applications were positively related to DC, which, in turn, was positively related to CA.Thus DC was shown to play a mediating role between HRIS and CA.In addition, the paper highlights that the education sector in Jordan consists of many institutions which are attempting to implement new, innovative working mechanisms such as the HRIS and utilize it to make a valuable contribution to CA.Moreover, the effect on DC of applying HRIS supports JHEIs in bringing about innovations to attain CA.
Face recognition for presence system by using residual networks-50 architecture

Presence system is a system for recording the individual attendance in the company, school or institution. There are several types presence system, including the manually presence system using signatures, presence system using fingerprints and presence system using face recognition technology. Presence system using face recognition technology is one of presence system that implements biometric system in the process of recording attendance. In this research we used one of the convolutional neural network (CNN) architectures that won the imagenet large scale visual recognition competition (ILSVRC) in 2015, namely the Residual Networks-50 architecture (ResNet-50) for face recognition. Our contribution in this research is to determine effectiveness ResNet architecture with different configuration of hyperparameters. This hyperparameters includes the number of hidden layers, the number of units in the hidden layer, batch size, and learning rate. Because hyperparameter are selected based on how the experiments performed and the value of each hyperparameter affects the final result accuracy, so we try 22 configurations (experiments) to get the best accuracy. We conducted experiments to get the best model with an accuracy of 99%.
Digital Archaeological Data in the Wild West: the challenge of practising responsible digital data archiving and access in the United States

Archaeology in the United States is conducted by a number of different sorts of entities under a variety of legal mandates that lack uniform standards for data archiving. The difficulty of accessing data from projects in which one was not directly involved indicates an apparent reluctance to archive raw data and supplemental information with digital repositories to be reused in the future. There is hope that additional legislation, guidelines from professional organisations, and educational efforts will change these practices.
Learning subtree pattern importance for Weisfeiler-Lehman based graph kernels

Graph is an usual representation of relational data, which are ubiquitous in many domains such as molecules, biological and social networks. A popular approach to learning with graph structured data is to make use of graph kernels, which measure the similarity between graphs and are plugged into a kernel machine such as a support vector machine. Weisfeiler-Lehman (WL) based graph kernels, which employ WL labeling scheme to extract subtree patterns and perform node embedding, are demonstrated to achieve great performance while being efficiently computable. However, one of the main drawbacks of a general kernel is the decoupling of kernel construction and learning process. For molecular graphs, usual kernels such as WL subtree, based on substructures of the molecules, consider all available substructures having the same importance, which might not be suitable in practice. In this paper, we propose a method to learn the weights of subtree patterns in the framework of WWL kernels, the state of the art method for graph classification task (Togninalli et al., in: Advances in Neural Information Processing Systems, pp. 6439–6449, 2019). To overcome the computational issue on large scale data sets, we present an efficient learning algorithm and also derive a generalization gap bound to show its convergence. Finally, through experiments on synthetic and real-world data sets, we demonstrate the effectiveness of our proposed method for learning the weights of subtree patterns.