Reviewerly/exHarmony huggingface dataset:
Document Resource Management of University Library Based on Data Analysis

University library plays a special role in the study and scientific research of colleges and universities. However, due to various reasons, most university libraries only play the role of collection, and the actual utilization rate is not high. Therefore, this paper puts forward the research of literature resource management in University Library Based on data analysis method. This paper makes an in-depth investigation and Research on the management mode and the utilization rate of literature resources of University Libraries in China. According to the survey results, data analysis technology is seldom used to analyze the utilization of literature resources in University Libraries in China at present, and the lack of data guidance makes it difficult to play the real role of the library. In view of these problems, this paper puts forward the improvement measures to improve the utilization level of literature resources in university libraries. The measures emphasize the importance of data analysis, and take data management and sharing as the main work of improvement. The new management mode of library literature resources makes up for the shortcomings of the traditional mode and improves the utilization rate of library literature resources. This paper also analyzes the development of the application of data analysis in the management of library literature resources, and points out that data analysis technology will play a key role in the reform of university library.
CREATING A VIRTUAL STUDY ABROAD EXPERIENCE TO RUSSIA

COVID-19 has drastically altered our world. Though travel is halted, global education does not have to stop. We used this time to reconceive the notion of study abroad and designed a study abroad program that could be facilitated virtually and enhanced with face-to-face classroom interaction. We were inspired to embark on this journey for several reasons. First, the realities of the pandemic create risks associated with international travel. Second, international experiences need to be more accessible–more students should be able to participate in global education, even if they do not have the means or ability to do so. We present our design considerations in building and implementing this virtual study abroad program. As part of the design, we partnered with VEXA (Virtual Experiences Abroad), a Moscow-based company that built the online interface and facilitated the interactions between our students and Russian citizens, including visits to a Russian Orthodox Church, the Bolshoi Ballet theater, and elementary and middle schools. We also brought elements of Russian culture to life through face-to-face experiences including a live cooking session with a Russian chef, discussions with a Russian Orthodox priest, and a ballet lesson with a company member of the Bolshoi theatre. These types of experiences facilitated group discussions and social interaction opportunities, crucial for establishing relationships. Overall, our main goal was to reconceive the traditional notion of study abroad while garnering results that would match the transformational gains that global education provides.
Peer review declaration

All papers published in this volume of Journal of Physics: Conference Series have been peer reviewed through processes administered by the Editors. Reviews were conducted by expert referees to the professional and scientific standards expected of a proceedings journal published by IOP Publishing. • Type of peer review: Single-blind / Double-blind • Conference submission management system: We developed the submission of full paper and revised version management by our self. The review process used a form which is consisted of the 9 elements of assessments and comments. • Number of submissions received: 188 Manuscripts • Number of submissions sent for review: 188 Manuscripts • Number of submissions accepted: 146 Manuscripts • Acceptance Rate (Number of Submissions Accepted / Number of Submissions Received X 100): 0.77 • Average number of reviews per paper: 1.2 • Total number of reviewers involved: 16 Reviewers • Any additional info on review process: - • Contact person for queries: Name : Prof. Nur Islami, Ph.D Affiliation: Universitas Riau Email : nurislami@lecturer.unri.ac.id
Latent Attribute Control for Story Generation

Neural open-domain story generation aims to generate long and fluent text as human writing. Recent work attempts to generate stories in fine-grained controls such as plot-like structure and ending valence. Although those outputs comply with the rules of grammar, they generally have logical conflicts and a lack of long-range cohesion because of explicit controlling. In this study, we propose to capture challenging story representation using latent variable modeling for the storytelling model, and we align the encoder output with story latent embeddings. Our approach and baselines are all built on the pre-trained BART language model. Experimental results demonstrated that our model largely improved compared to strong baselines on human evaluation. Human evaluators favored our generated stories, and the results were more relevant to the prompt and more coherent than the baselines.
The Sixth World Professional Forum “The Book. Culture. Education. Innovations” – “Crimea–2021”. The review

The work of the Sixth World Professional Forum “The Book. Culture. Education. Innovations” (June 5–13, 2021, Sudak, Republic of Crimea, Russian Federation) is reviewed; its themes and topics are discussed: library mission in the digitization era; pandemic impact on libraries; prospects for library and information community; national information systems; libraries collaboration with research and educational organizations; modern competences of library specialists; partnerships of national libraries, etc. The focus is made on the following events: The Twenty Seventh International Conference «Libraries and information resources in the modern world of science, culture, education and business», The Third Scientific and Educational Symposium “Building and developing the modern digital environment for education and science”; The Fifth Industry Conference “Book publishing and libraries: Vectors of cooperation”. The authors also give overview of discussions and presentations at the open press conference, the work and conclusions of the central discussion site, Day of Crimean Libraries, Day of Rospatent, The Third Scientific Conference “Scientometrics, bibliometrics, open data and publications in science”, The Third International Conference on the global ecological problems, the research and practice events held within the framework of the Forum, as well as the opening and closing ceremonies, and Forum plenary session.
Challenges in Translation of English Phraseological Units With Onomastic Constituents Into Vietnamese Equivalents

Translation of phraseological units with proper names (PUPs) is topical for the contemporary translation studies nowadays. It is noted that PUPs reflect the culture and national mentality of a definite nation. Quite a few studies have prospectively examined English PUPs and their translation into other languages, but it is hard to find such an in-depth study in the case the target language is Vietnamese. By employing the qualitative approach, this paper sets out the findings of the study where 241 English PUPs in our compiled database were classified into four groups according to their translations into Vietnamese. The group of non-idiomatic and descriptive translation equivalents accounts for a majority of more than 57% of all the PUPs, proving that PUPs in both languages are highly culture-specific. Although the other three groups share a minority of approximately 43% of all the PUPs, they hold interesting implications and multiple levels of similar or different metaphors. Based on the findings, the paper discusses the challenges translators encounter during the translation process of English PUPs into their Vietnamese equivalents. It is evident that among various translation obstacles, the proper name factor is clearly one of the most challenging issues. The paper then proposes some translation solutions to cope with these special expressions. In addition to recommending to flexibly apply translation strategies, the author's conclusion emphasizes that only when translators manage to decode and grasp how PUPs work cross-linguistically in both languages and cultures can they achieve an appropriate translation of English PUPs.
Mitigation of traffic disaster by analyzing motorist’s movement behavior based on an image captured by a drone

Uncontrolled motorists’ movement behavior could trigger a disaster for road users. A deep understanding of the motorist’s movement behavior might improve our mitigation planning. This paper presents an observation of the movement of motorists from a minor road crossing a major road at an unsignalised intersection. The study’s objectives are to estimate the gap and lag of motorcyclists when crossing the intersection, observe the forced gap, and predict the effect of traffic volume on the variation of the gap and lag. The case study was located at the intersection between Ahmad Yani Road as a major road and Bandar Purus Road as a minor road in Padang, Indonesia. A drone was used and placed vertically above the center of the intersection to record the movement. The study found that the average lag is 2.4 seconds at off-peak, and the average gap is 4.3 seconds; at the peak hour are 2.0 seconds and 3.1 seconds, respectively. At peak hours, more than 80% of motorcyclists are committed to the force gap/lag.
Refusal Speech

This article aims to analyze the constitution of the refusal speech in Shona, English and Portuguese. For this purpose, data from 30 University students who speak the three languages as mother tongue, second and foreign language is analyzed. Based on the theories of speech acts and discourse analysis, the study adopts qualitative and quantitative approaches. The study reveals that, in order to lessen the negative impact of the refusal act, irrespective of the language and its social status, the constitution of the refusal speech, whether direct or indirect, consists of a combination of illocutionary refusal acts (main illocutionary act) with assertive and expressive illocutionary acts. These acts serve to minimizing the impact of the refusal act. Apologies, expressions of feelings and justificative phrases are some of the expressions mostly used to soften the refusal.
Ad-hoc Stigmeric Load Balancing

This paper offers an improvement over previous load balancing research. Previous research proposed a novel algorithm, allowing a network of nodes that track and report entities to rapidly distribute that responsibility with no dedicated command and control messaging or thrashing of responsibility. The assumption and limitation of the previous research is the algorithm assumed a predefined balance point for the network. The predefined balance point could be an equal load by all participants or some other distribution defined as pre-mission data. New research provides a method for a network to balance around the immediate needs of the network, constrained only by the capacity that each participant is available to help at that time while maintaining the favorable convergence and scaling properties of load balancing. This value can be limited by the available sensor timelines or constrained by the node operator. This approach is particularly of interest to networks with a symmetric information exchange scheme where the signal to noise ratio (and hence bandwidth) can change between participants due to range or jamming conditions.
Early Information on Active Cases in Zero Rejection Efforts for COVID-19 Patients in West Java Province 2021 Using the Feedforwards Multilayer Perceptron Neural Network

West Java noted, as of August 14, 2021, 653,741 people were confirmed positive for COVID-19. On the same date, the number of active COVID-19 cases in West Java was 65,000. There is a significant increase in active cases of COVID-19 in 2021 in West Java. In the period 5 June – 17 July 2021, there was an increase in the number of active cases by 95,532. In that period, active cases increased by 484%, and the Bed Occupancy Ratio (BOR) in West Java had jumped in June 2021 with the highest number of 91.6%, this figure far exceeded the WHO recommendation of 60% before finally continuing to decline and finally in August was at 30.69%. This has an impact on the incidence of patient rejection at the COVID-19 referral hospital. Active cases talk about COVID-19 patients who need medical treatment and new cases talk about the rate of spread of COVID-19 in West Java, so these two things are very strategic to study. In this study, active cases and new case were predicted using Multilayer Perceptron (MLP). The data used in this study were sourced from the COVID-19 Task Force. The data is the number of positive cases, recovered and died of COVID-19 sufferers in 34 provinces in Indonesia in the period 2 March 2020 - 14 August 2021. The results of the study found, from the results of the evaluation using data testing the number of active cases in the last 19 weeks, namely April 10 – August 14, 2021, MLP is accurate in predicting the number of active cases for the first coming week 17 times, and the next two weeks for the second week 12 times with an absolute percentage error (APE) &lt; 20%. As for weekly new cases, MLP has been accurate 10 times for the next one week and 9 times for the next two weeks. It is hoped that the results of this study can be useful for the government as a reference in conditioning the hospital bed capacity to deal with active cases of COVID-19 in West Java in the next two weeks so that no COVID-19 patients are rejected by the hospital because the hospital is full.
An Efficient Deep Neural Network-Based Framework for Building an Automatic Attendance System

In the journey toward automation, tasks that are dull, monotonous and time-consuming are being replaced with automated systems which complete the task at a much faster rate and save quality time. In efforts to contribute to this journey, the manual attendance system of students inside lecture halls in universities and institutions is targeted. To replace the conventional approach where teachers manually note down the students who are present with a system that uses the power of deep learning and computer vision to automatically detect student’s faces, recognize the name and roll no. of each face and mark them present or absent in the database in a matter of seconds. This paper proposes a combination of state-of-the-art (SOTA) frameworks for object detection and face recognition for developing an automated attendance system that detects and recognizes the students attending a lecture with an accuracy or rather mean average precision (mAP) of 81.4 proving to be a very efficient framework. Using a camera sensor and linking with the university’s server, the attendance can be updated in the database in real time. The time saved by this system can be used by educators on more fruitful interactions. The limitations of this system are that it was done under a time constraint with limited computational resources, training on a relatively small dataset compared to other standard datasets. While this model is the current state of the art, it still requires a high-end GPU or the Nvidia’s more powerful Jetson development boards to work optimally. Also, the system cannot differentiate between facially very similar faces which are difficult even for the human eye.
Overriding

A member in a subclass can redefine a member in its superclass. This is most often done to give instance methods new implementations.
Cloudlet Based Medical Data Sharing with Privacy Protection

This modern society expects sophisticated modern health care services to enhance the healthy living conditions. The world’s ageing population and prevalence of chronic diseases has lead to high demand for medical data monitoring. The modern technology is supporting in all the way to realize the sophisticated health services. Specialized doctors are generally very rare and they may be geographically far away from needy patient. Without the support of technology, they cannot provide health services from remote places. In most of the cases, patients are immovable to doctor’s place due to their critical health conditions. But, the technology is a coming to help in such situations in a great way. The modern technology is helping in providing health services remotely with same quality as a local doctor can provide. The key concept behind this technology is collecting health data with highest possible accuracy and sharing it with remotely present health specialists. The wearable devices can collect body data most accurately and can send data to cloud for sharing it with remotely present health specialists. But, we know, sharing medical information is very critical and challenging issue because medical information contains patient’s important and highly confidential information. On the other hand, medical ethics says that doctors cannot share patient’s health records with others without the consent of patient. Such data will be shared between only authentic persons in cloud. The chain of processing mainly includes data collection, data storage, and data sharing. In this work a protected cloudlet based medical data sharing is proposed. In this paper, three major issues pertaining to medical data sharing are addressed. Firstly, a method for secured medical data sharing using cloudlet is proposed. During data collection, Number Theory Research Unit (NTRU) method used to encrypt user’s body data collected by wearable devices, then transmitted to nearby cloudlet. Secondly, a new trust model to help users to select trustable partners for sharing stored data in the cloudlet is presented. This model is to help similar patients to communicate with each other. Thirdly, a novel collaborative intrusion detection system (IDS) method based on cloudlet mesh is proposed, which can effectively prevent the remote healthcare big data cloud from attacks. Finally, an analysis of security to the information is presented and the results are quite encouraging.
Using the Split Bregman Algorithm to Solve the Self-repelling Snakes Model

Abstract Preserving contour topology during image segmentation is useful in many practical scenarios. By keeping the contours isomorphic, it is possible to prevent over-segmentation and under-segmentation, as well as to adhere to given topologies. The Self-repelling Snakes model (SR) is a variational model that preserves contour topology by combining a non-local repulsion term with the geodesic active contour model. The SR is traditionally solved using the additive operator splitting (AOS) scheme. In our paper, we propose an alternative solution to the SR using the Split Bregman method. Our algorithm breaks the problem down into simpler sub-problems to use lower-order evolution equations and a simple projection scheme rather than re-initialization. The sub-problems can be solved via fast Fourier transform or an approximate soft thresholding formula which maintains stability, shortening the convergence time, and reduces the memory requirement. The Split Bregman and AOS algorithms are compared theoretically and experimentally.
Automated Facial Expression Recognition and Age Estimation Using Deep Learning

With the advancement of computer vision techniques in surveillance systems, the need for more proficient, intelligent, and sustainable facial expressions and age recognition is necessary. The main purpose of this study is to develop accurate facial expressions and an age recognition system that is capable of error-free recognition of human expression and age in both indoor and outdoor environments. The proposed system first takes an input image pre-process it and then detects faces in the entire image. After that landmarks localization helps in the formation of synthetic face mask prediction. A novel set of features are extracted and passed to a classifier for the accurate classification of expressions and age group. The proposed system is tested over two benchmark datasets, namely, the Gallagher collection person dataset and the Images of Groups dataset. The system achieved remarkable results over these benchmark datasets about recognition accuracy and computational time. The proposed system would also be applicable in different consumer application domains such as online business negotiations, consumer behavior analysis, E-learning environments, and emotion robotics.
Serviks Kanser Hücrelerinin Ön-Eğitimli Ağ Temelli Otomatik Sınıflandırılması İçin Bir Ara Yüz Tasarımı ve Geliştirilmesi

Cervical cancer is the second most common cancer in women after breast cancer, causing the death of one woman every two minutes in the world.The most important risk factor originating from the cervix is infection with the papilloma virus (HPV).Cervical cancer screening programs are extremely important to reduce the incidence and death rates of this cancer.The primary goal of screening for cervical cancer is the accurate detection and timely treatment of intraepithelial precursor lesions of the cervix, in order to prevent cervical cancer.With the PAP smear test, cells in the cancerous stage are detected in the endocervical canal, and cancer development can be prevented before the cells turn into cancer with cancer treatment at this stage.The PAP test, which is used in early diagnosis, is an easyto-apply, low-cost, harmless, high-sensitivity test that also reduces the burden of treatment.Recent developments in the field of artificial intelligence have achieved serious success in the diagnosis of cervical cancer.In this study, a transfer learning-based cervical cancer detection method and an application developed to easily perform these procedures are presented.Cancerous and non-cancerous cervical cells were classified using pre-trained networks.Five popular pre-trained networks, namely Xception, VGG-16, DenseNet, InceptionV3, and InceptionResNetV2, were used for the problem and the obtained performance results were compared.In addition, an application has been developed so that experts working in this field can easily make such classifications.With this application, users can create their own models by conducting a new training, use the model created in this study, and quickly test which class the newly obtained images belong to.As a result of the study, DenseNet network obtained the highest accuracy with 94.72% accuracy.Experimental results show that the proposed approach can provide an inexpensive and rapid decision support system for cervical cancer detection that anyone can apply.