Reviewerly/exHarmony huggingface dataset:
FEATURES OF THE DISTANCE LEARNING ORGANIZATION AT UNIVERSITIES IN THE CONDITIONS OF THE CORONAVIRUS PANDEMIC: PROBLEMS AND PROSPECTS

Distance education is becoming in demand in many universities in the country. At the beginning of 2020, an epidemiological situation arose in Russia and the world, which showed that distance learning is the safest way to get an education. But not all Russian universities were ready to quickly switch to distance learning. This led to some difficulties: the load increased on all actors of the educational process, the technical problems of the university and students became actualized, there was a need for additional time for adaptation of students, teachers, and university administration. As a result, the level of satisfaction with the learning process has changed. The purpose of this publication is to characterize the features of the organization of distance learning and identify its future prospects (using the example of Perm State National Research University). The empirical base of the study is materials of a survey of university students about their satisfaction with online learning. The authors assess three areas: 1) the level of material and technical equipment of students; 2) changes that have occurred in the organization of training (workload, time for completing assignments, mastering educational material, academic performance); 3) prospects of distance learning (limitations and reasons for their occurrence, merits). In practical terms, the study can clarify the need to expand distance learning in all universities in the country. Distance learning can become an effective alternative to classical education if state authorities and local governments finance distance learning, if universities provide trainings on organizing and conducting classes in an online format.
A system for supply chain management of covid-19 vaccines using blockchain and bluetooth beacons network based traceability and verification

Ever since a novel Coronavirus disease (COVID-19) emerged in Wuhan(China), things have changed to a large extent. It has shattered the entire world in all the possible sectors by its continuous spread. Scientists all around the world have put in in-numerous amount of efforts as a result of which the world has finally witnessed its some of the first potential vaccines which have got approvals for emergency use in various countries. Now the next crucial step that comes in this process of fighting this virus is the supply of vaccines to all the different parts of the world. But there are certain challenges to this process as vaccines need to be constantly monitored all the time for their temperatures, humidity and other environmental factors which vary a lot depending on the geographical conditions. Also, maintaining drug safety is the most important challenge as counterfeiting of vaccines and drugs has always been a concern for the pharmaceutical industry. Manual traceability for all the ingredients used during manufacturing is a very difficult process and doesn’t even give a full guarantee for the same. This can lead to serious health issues in the consumers and can even result in death. The power of Blockchain and it’s principle of proof of concept can deal with this problem by maintaining a record for all the events taking place in this process right from manufacturing to vaccinations in hospitals. BLE Beacons can also come very handy here as they are small, inexpensive and easy to use devices that can provide real time updates by tracking various parameters of the vaccine. This paper aims at suggesting an entire system of supply chain of vaccines using Blockchain based record, QR(Quick Response) Codes for vaccine safety and BLE Beacons network for real time tracking and news updates of the vaccines.
Metacognitive Awareness of Reading and Reading Strategy Use by Nonproficient College Readers

This research investigated what reading strategies Thai college EFL students who were identified as nonproficient readers are aware of and what reading strategies they use. Data were gathered from a questionnaire, think-aloud and interviews. The analysis identified 3 broad categories of 16 reading strategies employed by the nonproficient readers: text-based strategies, knowledge-based strategies and interactive strategies. Results also showed that the nonproficient readers viewed reading as a decoding process and that they were aware of different reading strategies. However, they could not regulate the strategies to construct meanings from texts effectively and solve reading problems. Their unsuccessful reading was due to the interplay of factors such as a lack of word recognition skills and necessary syntactic knowledge, and ineffective regulation of reading strategies.
Evaluation of Academic Performance of Science and Social Science students in Turkish Universities during COVID-19 Crisis

COVID-19 has cast a shadow over the education sector, as it has pushed schools, universities, and educational institutions to close its doors to reduce the chances of its spread. In light of this crisis that the world is experiencing, universities have turned towards e-learning as a more appropriate alternative to ensure the continuity of the educational process. Most Turkish universities today use what are called LearningManagement Systems (LMS). In this study, the impact of e-learning on the academic performance of Turkish universities students was evaluated. A questionnaire is administered to evaluate the academic performance of science students in comparison with that of social science students. Another questionnaire has been created andtargeted the teachers of the two disciplines. The obtained results showed that several factors inversely affected the motivation of both students and teachers such as anxiety (60.3%), social problems (41.8%) and internet connection (43.2%). However, both students (65.0%) and teachers (48.8%) are satisfied with the used e-learning platform. We recommend a comprehensive evaluation of e-learning education during the crisis that has been applied worldwide in order to develop a smart solution more applicable in future crises.
The use of biological markers in organic geochemical investigations of the origin and geological history of crude oils (I) and in the assessment of oil pollution of rivers and river sediments of Serbia (II)

Biological markers (BMs) are organic compounds in oils in which a precursor is known, and during the transformation of organic matter these compounds undergo certain structural and stereochemical changes. Based on the established precursors of BMs, the origin of the examined oils can be estimated, and based on the intensity and the type of changes and also geological history. It includes defining the deposition medium, the degree of maturation, the length of the oil migration path, the degree of biodegradation. The most studied and applied BMs are normal alkanes, isoprenoid aliphatic alkanes pristane and phytane, and polycyclic alkanes of the sterane and terapane type. On the other hand, in the environmental chemistry, these compounds can significantly contribute to the identification of petroleum pollutants, as well as to the assessment of the migration mechanism and the intensity of biodegradation. This review paper first presents the results related to the application of BMs in the organic geochemical correlations of oil in the south-eastern part of the Pannonian Basin (I). The second part provides an overview of those researches in which the same BMs were used in the identification of oil pollutants and in monitoring their changes during the migration and the biodegradation in rivers and river sediments of Serbia (II).
Effects of situation-background-assessment-recommendation handover combined with detail nursing intervention on patients with infections.

OBJECTIVE To analyze the nursing effects of situation-background-assessment-recommendation (SBAR) handover combined with detailed nursing intervention on patients with contagious diseases. METHODS A total of 106 patients with infectious diseases who were hospitalized from April 2018 to April 2020 were selected and randomly divided into a control group (n=53, SBAR handover model) and an experimental group (n=53, SBAR handover model, combined with detailed nursing intervention) based on a random number table method. The nursing quality, professional recognition of nursing staff, patients' psychological status, and the quality of life of patients between the two groups were compared. The visual analogue scale (VAS) scores, hospital stay, gastrointestinal recovery time, and complication rate of the two groups were recorded and compared at 24 h and 72 h after intervention. RESULTS Scores for comprehensive nursing quality of doctors, nursing managers, nursing staff, and patients in the experimental group witnessed a greater rise than before (P 0.05), with lower scores in the experimental group in contrast to the control group (P<0.05). Patients in the experimental group enjoyed a better quality of life than those in the control group (P<0.05). More patients were satisfied with the nursing in the experimental group (94.34%) in comparison with the control group (81.13%) (P<0.05). The experimental group experienced better outcomes in terms of 24 h, 48 h, and 72 h VAS scores, hospital stay, and gastrointestinal recovery time, and the incidence of complications than the control group (P<0.05). CONCLUSIONS SBAR handover model combined with detailed nursing intervention can ease negative emotions of patients with infections, optimize their life qualities and nursing satisfaction, and enhance comprehensive nursing performance and professional recognition.
An Exploration of npm Package Co-Usage Examples from Stack Overflow: A Case Study

Third-party package usage has become a common practice in contemporary software development. Developers often face different challenges, including choosing the right libraries, installing errors, discrepancies, setting up the environment, and building failures during software development. The risks of maintaining a third-party package are well known, but it is unclear how information from Stack Overflow (SO) can be useful. This paper performed an empirical study to explore npm package co-usage examples from SO. From over 30,000 SO question posts, we extracted 2,100 posts with package usage information and matched them against the 217,934 npm library package. We find that, popular and highly used libraries are not discussed as often in SO. However, we can see that the accepted answers may prove useful, as we believe that the usage examples and executable commands could be reused for tool support.
Multimode metrology via scattershot sampling

The authors study the application of scattershot single-photon sources to multimode quantum metrology by investigating three different kinds of interferometers. They show that all three of them give the same average amount of information, but two of the three need to be in specific input configurations while the third one always beats the classical limit regardless of the input configuration.
Ovarian cancer diagnosis using pretrained mask CNN-based segmentation with VGG-19 architecture

Abstract Objectives This paper proposed the neural network-based segmentation model using Pre-trained Mask Convolutional Neural Network (CNN) with VGG-19 architecture. Since ovarian is very tiny tissue, it needs to be segmented with higher accuracy from the annotated image of ovary images collected in dataset. This model is proposed to predict and suppress the illness early and to correctly diagnose it, helping the doctor save the patient's life. Methods The paper uses the neural network based segmentation using Pre-trained Mask CNN integrated with VGG-19 NN architecture for CNN to enhance the ovarian cancer prediction and diagnosis. Results Proposed segmentation using hybrid neural network of CNN will provide higher accuracy when compared with logistic regression, Gaussian naïve Bayes, and random Forest and Support Vector Machine (SVM) classifiers.
Software Development Life Cycle Performance Analysis

In 1960s SDLC (Software development life cycle) was initiated to develop high quality systems in large scale. Software development life cycle is one of the most liked techniques nowadays to develop a powerful and high quality software product. Here various types of methods present in software development life cycle like waterfall, iterative, prototype, v-shaped, spiral and etc. each method has drawback and benefit. According to any one of these models, the software will be developed. This method helps a lot to developer’s team to develop a product in easy and faster way. This technique is referred to the decrement of problems and increment of value of the software project. The SDLC methodology is very hard without a sequence order process to develop a good quality product (Manzoor Ahmad Rather: “A Comparative Study of Software Development Life Cycle Models”, IJACSA (2015).).
Retrieve, Caption, Generate: Visual Grounding for Enhancing Commonsense in Text Generation Models

We investigate the use of multimodal information contained in images as an effective method for enhancing the commonsense of Transformer models for text generation. We perform experiments using BART and T5 on concept-to-text generation, specifically the task of generative commonsense reasoning, or CommonGen. We call our approach VisCTG: Visually Grounded Concept-to-Text Generation. VisCTG involves captioning images representing appropriate everyday scenarios, and using these captions to enrich and steer the generation process. Comprehensive evaluation and analysis demonstrate that VisCTG noticeably improves model performance while successfully addressing several issues of the baseline generations, including poor commonsense, fluency, and specificity.
Privacy-Preserving Gradient Descent for Distributed Genome-Wide Analysis

Genome-wide analysis, which provides perceptive insights into complex diseases, plays an important role in biomedical data analytics. It usually involves large-scale human genomic data, and thus may disclose sensitive information about individuals. While existing studies have been conducted against data exfiltration by external malicious actors, this work focuses on the emerging identity tracing attack that occurs when a dishonest insider attempts to re-identify obtained DNA samples. We propose a framework named \(\upsilon \textsc {Frag}\) to facilitate privacy-preserving data sharing and computation in genome-wide analysis. \(\upsilon \textsc {Frag}\) mitigates privacy risks by using vertical fragmentations to disrupt the genetic architecture on which the adversary relies for re-identification. The fragmentation significantly reduces the overall amount of information the adversary can obtain. Notably, it introduces no sacrifice to the capability of genome-wide analysis—we prove that it preserves the correctness of gradient descent, the most popular optimization approach for training machine learning models. We also explore the efficiency performance of \(\upsilon \textsc {Frag}\) through experiments on a large-scale, real-world dataset. Our experiments demonstrate that \(\upsilon \textsc {Frag}\) outperforms not only secure multiparty computation (MPC) and homomorphic encryption (HE) protocols with a speedup of more than 221x for training neural networks, but also noise-based differential privacy (DP) solutions and traditional non-private algorithms in most settings.
A novel consumer preference mining method based on improved weclat algorithm

Purpose Conventional frequent itemsets mining ignores the fact that the relative benefits or significance of “transactions” belonging to different customers are different in most of the relevant applied studies, which leads to failure to obtain some association rules with lower support but from higher-value consumers. Because not all customers are financially attractive to firms, it is necessary that their values be determined and that transactions be weighted. The purpose of this study is to propose a novel consumer preference mining method based on conventional frequent itemsets mining, which can discover more rules from the high-value consumers. Design/methodology/approach In this study, the authors extend the conventional association rule problem by associating the “annual purchase amount” – “price preference” (AP) weight with a consumer to reflect the consumer’s contribution to a market. Furthermore, a novel consumer preference mining method, the AP-weclat algorithm, is proposed by introducing the AP weight into the weclat algorithm for discovering frequent itemsets with higher values. Findings The experimental results from the survey data revealed that compared with the weclat algorithm, the AP-weclat algorithm can make some association rules with low support but a large contribution to a market pass the screening by assigning different weights to consumers in the process of frequent itemsets generation. In addition, some valuable preference combinations can be provided for related practitioners to refer to. Originality/value This study is the first to introduce the AP-weclat algorithm for discovering frequent itemsets from transactions through considering AP weight. Moreover, the AP-weclat algorithm can be considered for application in other markets.
Non-Markovian dynamics control of spin-1/2 system interacting with magnets

The unavoidable interaction of an open quantum system with its surrounding environment may follow non-Markovian dynamics behavior, which causes its memory effects to play a key role in many quantum technologies. In this work, we first consider the dynamics control of a spin-1/2 system that simultaneously interacts with two magnets. Subsequently, we study in detail the evolution of a classically driven spin-1/2 system that is coupled with a magnet. Two dynamical crossovers of the spin system, namely, from Markovian dynamics to non-Markovian dynamics and from no-speedup evolution to speedup evolution, can be controlled in these two models. Slightly different from the previous quantum speedup scenarios with controllable non-Markovianity, the stronger non-Markovianity does not necessarily lead to the quantum speedup dynamics process of the spin system in our spin-magnet schemes.
Duterte’s Polemic Against the Catholic Church as Hate Speech

This study examines thirteen speeches of President Rodrigo Duterte that contain lines, paragraphs, and expletives directed against the Roman Catholic Church, to determine if they are forms of hate speech. These speeches were delivered from August 2016, two months after he assumed office, up to May 2017. These rhetorical resources were directed toward the Church and its clergy, which criticized Duterte’s war on drugs, and they have been analyzed using a modified version of the dangerous speech framework of the U.S.-based Dangerous Speech Project (Benesch, 2013), which has five rhetorical elements: speaker, content and its context, audience, medium, and response. This study defines hate speech as a speech that attacks personal dignity, dehumanizes groups, incites discrimination, advocates hostility, creates a social wedge, and imputes a crime.
An approach to expose dithering-based JPEG anti-forensics

A forensic analyst needs to exploit various kinds of artefacts for developing a powerful forensic technique. Among those, the JPEG compression artefacts such as blocking artefacts & DCT histogram artefacts are the most widely used. JPEG anti-forensic techniques aim to disguise forensic detectors by removing these artefacts. Hence, a technique for the detection of dithering-based JPEG anti-forensic methods is the need of an hour. An approach proposed in this paper exploits the spread of discrete cosine transform (DCT) coefficients around integer multiple of quantization step size with repeated application of dithering-based JPEG anti-forensics. The paper first briefly explains dithering-based state-of-the-art JPEG anti-forensic techniques. Then the proposed method is illustrated thoroughly, and finally, experimental results are reported to demonstrate the accuracy of the proposed method.
On Some Problems with Truth and Satisfaction

We compare two approaches to semantics: one of them takes the notion of satisfaction as basic, treating the concept of truth as defined in terms of satisfaction; the second approach treats the notion of truth as primary. We search for a minimally adequate semantic theory, guaranteeing a mutual definability of truth and satisfaction.
COVID-19 Challenged Me to Re-Create My Teaching Entirely": Adaptation Challenges of Four Novice EFL Teachers of Moving from 'Face-to-Face' To 'Face-to-Screen' Teaching"

Language teaching is noted to be a stressful profession at the best of times, but in 2020 it became even more difficult for all teachers because of the spread of COVID-19 pandemic worldwide. Teachers were required to switch suddenly to deliver their lessons on online platforms, with many having little or no prior training. This has certainly been the case for language teachers, language students and language schools because most language courses, initially designed for face-to-face instruction, were suddenly ‘forced’ to move to online platforms. This sudden move meant that language schools, language teachers and their students needed to adapt fast to a new virtual world that for many was an unknown teaching world. For language teachers the main challenge was how to adapt their courses and lessons to make them suitable for this new online delivery mode. This paper reports on the reflections of the adaptation challenges of four English as a foreign language (EFL) teachers at a prominent English language institution in Costa Rica, Central America, as they suddenly had to shift to online lesson delivery due to the COVID-19 pandemic.
Iterative Oblivious Pseudo-Random Functions and Applications

We consider the problem of a client querying an encrypted binary tree structure, outsourced to an untrusted server. While the server must not learn the contents of the binary tree, we also prevent the client from maliciously crafting a query that traverses the tree out-of-order. That is, the client should not be able to retrieve nodes outside one contiguous path from the root to a leaf. Finally, the server should not learn which path the client accesses, but is guaranteed that the access corresponds to one valid path in the tree. This is an extension of protocols such as structured encryption, where it is only guaranteed that the tree's encrypted data remains hidden from the server.
SDTP: Accelerating Wide-Area Data Analytics With Simultaneous Data Transfer and Processing

For the efficient analysis of geo-distributed datasets, cloud providers implement data-parallel jobs across geo-distributed sites (e.g., datacenters and edge clusters), which are generally interconnected by wide-area network links. However, current state-of-the-art geo-distributed data analytic methods fail to make full use of the available network and computing resources. The main reason is that such geo-distributed methods must wait for bottleneck sites to complete the corresponding transmission and computation in each phase. Furthermore, such geo-distributed methods may be impractical to the network bandwidth dynamicity and diverse job parallelism. To this end, we propose a Simultaneous Data Transfer and Processing (SDTP) mechanism to accelerate wide-area data analytics, with the joint consideration of network bandwidth dynamics and job parallelism. In the SDTP, a site can execute the computation, provided that it obtains the required input data. As a result, the input data loading, map, shuffle, and reduce phases at each site need not wait for the completion of the previous phases of other sites. We further improve the SDTP method by offering more accurate time estimation and generalizing the mechanism to dynamic situations. The trace-driven results demonstrate that SDTP can improve the wide-area analytic job response time by 19% to 72% compared to other methods.