Reviewerly/exHarmony huggingface dataset:
Recovery funding

Chemistry & IndustryVolume 86, Issue 5 p. 4-4 Leader Recovery funding First published: 07 May 2022 https://doi.org/10.1002/cind.865_2.xAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat No abstract is available for this article. Volume86, Issue5May 2022Pages 4-4 RelatedInformation
A Study of the Pedagogy Students’ Perceptions toward Online Courses’ Quality during the COVID-19 Pandemic

The article presents the results of a study of students' perceptions of the online courses' quality in response to the changes accompanying the COVID-19 pandemic and the need to make the transition from face-to-face to distance learning in higher education. This article aims to survey the pedagogy students’ perceptions – these future pre-school and primary school teachers towards the quality of online courses “Didactics of human – society and human – nature”, “Inclusive education” and “Ecological education” during the COVID-19 pandemic. The survey was conducted with 107 future teachers at Veliko Tarnovo University “St. St. Cyril and Methodius” – Vratsa Branch in June after the completion of the online courses in the summer semester during the academic year 2020/2021. The data collection tool is a structured questionnaire developed by A. Young and C. Norgard with closedended items used in this survey in an adapted version and provided to respondents via an access link. In general, the results of the survey show that students have positive perceptions about the quality of online courses. However, they need to be encouraged to be active participants in each of the online courses, including by engaging in online discussions, other proactive interactions as well as performing a variety of assessment tasks, recognizing the importance of goals. Regardless of the outcome of the crisis caused by the COVID-19 pandemic, efforts towards the quality of online courses and the corresponding perceptions of students will therefore continue to be the focus, as changes in the learning environment become more sustainable and the future of higher education should adapt to these changes.
Planning for parking space navigation in multi-story car parking lot

Currently, the number of motor vehicles in the city continues to increase, and large parking lots have become the main space for parking. One of the challenges of parking space navigation planning is to efficiently and conveniently park the vehicle in a specific parking space in a short time. In addition, the factors that need to be considered in the process of parking space navigation have increased, such as multiple exits, entrances, and multiple floors. In this paper, based on AI planning technology, we model the Parking Space Navigation in Multi-Story Car Parking Lot as a standard planning task and use PDDL to construct the domain modelling. Then a state-of-the-art planner is employed to solve the generated planning task. The plan solution of the parking task can be used to guide the car drivers to park their vehicles to suitable or specified locations. Experiment shows that the method in this paper can precisely describe the parking space navigation problem and give high-quality instruction to help drivers park their vehicles.
Graph Signal Representations

A summary is not available for this content so a preview has been provided. Please use the Get access link above for information on how to access this content.
Grey correlation model based study on influencing factors of urban entrepreneurial activity degree

In the road video shooting of unmanned aerial vehicle (UAV), the UAV is in an unconstrained state, and the video picture is jitter and unstable. When tracking multiple vehicles in uav video, identity switching of tracking targets is easy to occur. Based on this, an improved DeepSort vehicle target tracking algorithm based on Yolov5s is proposed. When Yolov5s trains the vehicle detection model, DIOU-NMS is used instead of NMS to remove the redundant target position prediction box. Aiming at the fact that DeepSort pre-trained appearance extraction model did not contain the appearance model of the vehicle, the vehicle appearance model was obtained by using the lightweight ShuffleNet V2 network for vehicle rerecognition training on VeRi data. The improved algorithm is used to evaluate the MOT of UAVDT data sets. The experimental results show that compared with the original algorithm, the tracking identity switching (IDSW) of this experimental method is reduced by 49.1%, MOTA is improved by 3.2% and MOTP is improved by 0.3%.
A Literature Review and Meta-Analysis of the Effects of Regular Exercise on Health-Related Physical Fitness Factors in Individuals with Intellectual Disabilities in Korea

[Objective] The purpose of this study is to evaluate the effects of regular exercise on health-related physical fitness factors in individuals with intellectual disabilities. [Methods] Literatures were searched for using RISS, KISS, and the national assembly library to derive conclusions. Cochrane ROB was used for the qualitative evaluation of selected researches. Mean difference, standardized mean difference(SMD), and 95% CI were selected and a random-effect model were used for effect size. Moreover, heterogeneity of included researches was assessed using Cochran’s Q statistics and I2. [Results] Regular exercise improved muscular endurance(ES=1.44, 95% CI .53-2.35, p=.002), flexibility(ES=.63, 95% CI .08-1.17, p=.02), and agility(.62, 95% CI .08-1.16, p=.03) in individuals with intellectual disabilities. The effect size of muscular strength(ES=1.23, 95%CI .39-2.85, p=.14) and cardiorespiratory endurance(ES=.49, 95% CI-.50-1.49, p=.33) was comparatively high but was not statistically significant. [Conclusions] It is confirmed that regular exercise is an effective method to improve health-related physical fitness factors in individuals with intellectual disabilities. Moreover, it is found that physical fitness factors which can be improved are different according to an exercise intervention program. The results will be applied as basic data to design more precise exercise programs to improve the health-related physical fitness factors of individuals with intellectual disabilities.
Sentiment analysis on consumers’ opinions – evaluating online retailers through analyzing sentiment for face masks during COVID-19 pandemic

Customer opinions on a product or retailer can be assessed through analyzing their textual reviews submitted to online communities. While the attitudinal information of a text post can be gauged by studying the sentiment words used, a method has to be sought for more effectively evaluating the sentiment strengths, because different writers may have varied interpretations on a word’s sentiment. The objective of this paper is to develop a new integrated approach by adopting Sentiment Analysis and Evidential Reasoning for the online retailers’ timely evaluation of the public sentiments toward different product alternatives. The finding of the study has established the benefit of efficiently evaluating the customers’ feedback toward products and monitoring the changes of customer preferences on product attributes over time. The results obtained from the proposed approach are superiority to other decision-making tools.
문장 정보량 기반 문서 추출 요약의 효과성 제고

문서 추출 요약 연구에서는 문장 간 관계를 기반으로 중요한 문장을 선택하는 다양한 방법들이 제안되었다. 문장의 도합유사도를 이용한 한국어 문서 요약에서는 문장의 도합유사도를 문장 정보량으로 보고, 이를 기준으로 중요한 문장을 선택하여 요약문을 추출하였다. 그러나 이는 각 문장이 전체 문서에 기여하는 다양한 중요도를 고려하지 못한다는 문제가 있다. 이에 본 연구에서는 문장의 정량적 정보량과 의미적 정보량을 기반으로 중요한 문장을 선택하여 요약문을 제공하는 문서 추출 요약 방법을 제안한다. 실험 결과, 추출 문장 일치도는 58.56%, ROUGE 점수가 34로 비교 연구보다 우수한 성능을 보였으며, 딥러닝 기반 방법과 비교해 추출 방법은 가볍지만 성능은 유사하였다. 이를 통해 문장 간 의미적 유사성을 기반으로 정보를 압축해 나가는 방식이 문서 추출 요약에서 중요한 접근 방법임을 확인하였다. 또한 빠르게 추출된 요약문을 기반으로 문서 생성요약 단계를 효과적으로 수행할 수 있으리라 기대한다.In the document extraction summary study, various methods for selecting important sentences based on the relationship between sentences were proposed. In the Korean document summary using the summation similarity of sentences, the summation similarity of the sentences was regarded as the amount of sentence information, and the summary sentences were extracted by selecting important sentences based on this. However, the problem is that it does not take into account the various importance that each sentence contributes to the entire document. Therefore, in this study, we propose a document extraction summary method that provides a summary by selecting important sentences based on the amount of quantitative and semantic information in the sentence. As a result, the extracted sentence agreement was 58.56% and the ROUGE-L score was 34, which was superior to the method using only the combined similarity. Compared to the deep learning-based method, the extraction method is lighter, but the performance is similar. Through this, it was confirmed that the method of compressing information based on semantic similarity between sentences is an important approach in document extraction summary. In addition, based on the quickly extracted summary, the document generation summary step can be effectively performed.
A privacy-preserving data aggregation scheme with fault tolerance for smart grid based on blockchain

Data aggregation is one of the most commonly used privacy protection schemes for smart grids, and smart meters will inevitably fail. Therefore, fault tolerance helps to improve the robustness of data aggregation schemes. Existing solutions often require the trusted third parties. Therefore, a fault tolerance privacy-preserving data aggregation scheme (FPDA) based on blockchain for smart grid is proposed. An improved Practical Byzantine Fault Tolerance (PBFT) is used to select a data collection unit (DCU) from all DCUs as a mining node (MN) to construct piece. The scheme protects users' private data while achieving fault tolerance without relying on any trusted third party. Analysis of security and performance shows that our scheme can achieve security and fault tolerance while ensuring the cost of lightweight computing and communication.
Blockchain of Things: Benefits and Research Challenges

Internet of things (IoT) provides an infrastructure where ubiquitous things are connected to each other in a decentralized fashion. Technologies such as cloud computing, data analytics and ML are an integral part of IoT and plays a major role in information exchange, modeling, and analytics. Blockchain is the recent disruptive technology, which is based on tamper resistant distributed ledger spreading over distributed systems ensuring privacy, scalability, and reliability. Blockchain and IoT both can complement each other to provide benefits of both the technologies while suppressing each other’s limitations. Blockchain is the disruptive technology providing a seamless and autonomous environment for sharing and maintaining business ledgers in digitized form among various parties involved in the application. It is defined as “Blockchain is a decentralized computation and information sharingplatform which enables multiple authoritative domains who do not trust each other to cooperate, coordinate, and collaborate in a rational decision-making process”.
An Overview of IoT in Financial Sectors

Internet of things (IoT) enables humans to transfer the data over a network using connected devices without human-to-human or human to computers interaction. With the growing demand for technology in vast areas of everyday life, IoT is widely been used in several fields, including home automation, health care and fitness, automated machines, agriculture, smart cities, etc. Apart from this, there are wide applications of IoT in financial sectors. A lot of technological advancements were seen in financial sectors in the last decade. The advancements in technology lead to several new dimensions to conventional banking. More and more financial organizations are getting adopted to new technologies which in turn increases their operational efficiency and user-friendliness. IoT in financial sectors can be explored much to minimize the risks involved owing to the rapid increase in digitalization and mobilization of those sectors. Use of IoTs in banks can help them to improve the decision-making process and to gather information about customers and provide personalized services. It has been estimated that 40% of the financial sectors are now experimenting with IoT and big data. This chapter gives an overview of IoT in financial sectors and the various risks involved in it. Proper use of IoT in financial sectors can lead to various growth opportunities.
Analysis on the Efficiency of China’s Leading Logistics Companies

중국의 35개 상장물류기업들은 2015∼2020년의 5년간 효율성이 매우 빠르게 증가하였다. 중국 정부의 물류산업 중장기발전계획을 비롯한 다양한 지원정책들이 효과를 나타내고 있음을 시사한다. 하지만 중국 물류기업들의 효율성 증가는 일부 기업들의 매우 빠른 성장에 기인하고 기업별로 편차가 크다. 기업들의 흥망성쇠도 비교적 빈번할 뿐만 아니라 규모에 대한 효율성도 최근까지크게 개선되지 않고 있다. 총요소생산성지수(TFPI)로 산정한 생산성 역시 5년간 매우 빠르게 상승하였는데, 기술변화지수(TCI)와변화 양상이 매우 유사하였다. 반면 기술적효율성변화지수(TEI)는 같은 기간에 거의 변동이 없었다. 결국, 중국 상장물류기업의 총요소생산성은 내부적인 생산요소의 효율적 결합에 의한 효율화보다는 외부환경 요인으로서 기술 변화가 주도한 것으로 판단된다.This paper analyzes the efficiency and productivity of the leading 35 logistics companies in China from 2015 to 2020 based on the DEA Model and Malmquist Index Method. We use assets, capital, and operating cost of the 35 logistics companies as input variables, while operating revenue, net profit, earning per share, and inventory turnover rate as output ones. In this analysis, we find that China’s leading logistics companies have increased operational efficiency measured by DEA methodology very fast, while the increase was led by a few companies. Furthermore the disparity of increasing rate between them was very big, and the companies have often risen and fallen. The increase in total factor productivity index (TFPI) of the companies during the same period has also been very fast and parallel with that of the Technical Change Index (TCI) whereas the technical Efficiency Change Index (TEI) has little changed, which implies that the productivity increase has been driven not by the endeavor of the companies to combine production factors more efficiently but by the technology change external to the companies.
REVIEW OF HAEMATOPATHOLOGY IN THE AGE OF ARTIFICIAL INTELLIGENCE-MACHINE LEARNING BETWEEN 2011 AND 2021

Background and Objective: Articial intelligence has transformed pathology diagnostics over the past decade between January 2011 to December 2021, with new emerging technologies and software promising to transform and enhance haematopathology diagnostics further. More rapid and procient AI systems appears to be threatening the role of Haematopathologist in the diagnostic process. This systemic review aims to explore the success of articial intelligence applications in the eld of haematopathology and assess whether the role of haematopathologist will indeed prove redundant in the future. Methods:We performed an extensive search of Pubmed, Medline and National Center for Biotechnology Information (NCBI) at the U.S. National Library of Medicine (NLM) and google scholar databases for articial intelligence in Haematopathology between January 2011 and December 2021.Reference lists of articles were thereafter reviewed for additional reviews. The results are grouped and discussed according to the world health organization grouping of haematopathology disease. Studies where the AI algorithms were compared to that of specialist pathologist were included as this was the main focus and aim of the review. Key content and ndings: Articial intelligent applications on peripheral smears, bone marrow aspirate smears, immunohistochemical stains are documented sequentially in the manuscript from the introduction of whole slide imaging applied to peripheral and bone marrow smears for identication of white blood cells to the application of more complex convoluted neural networks for discrimination of lymphoma and leukaemia subtypes and lymphoma grading. All the studies documented in this review have shown favourable outcome for articial intelligence applications to haematopathology disease. Conclusion: The above studies have demonstrated that articial intelligence can be successfully integrated into haematopathology diagnostics. Although all studies were shown to be comparable to the pathologist, there is a requirement for further standardisation and validation studies for optimization of deep learning algorithms. The notion that AI will replace the pathologist is also incorrect. The microscope will not be replaced. Rather, AI integration into pathology is meant enhance the accuracy and speed of diagnostic workows enabling the pathologist to focus on more complex laboratory problems. AI and human pathologists should co- operate, rather than compete.
First Letter Dominance in Word Recognition

Current neural network models of word recognition claim that all letters in a word are used in parallel to build up activation. This disagrees with the dictionary model of word recognition, which claims that people use letters serially from left to right.
Testing a Model of Role Assignment

ACT-R is a general theory of cognition (Anderson, 1993) which is capable of learning the relative usefulness of alternative rules. In previous work (Matessa and Anderson, 1997), a model utilizing this implicit procedural learning mechanism explained results from a concept formation task created by McDonald and MacWhinney (1991). The same model explains results from a role assignment task for artificial languages created by Blackwell (1995). By focusing learning on one cue at a time, the model predicted a blocking phenomenon where certain cues came to dominate and block learning of other cues. This prediction was supported by the data from both of the experiments. This abstract describes results from a role assignment experiment designed to further test the blocking prediction of the model.
Bridging the Physical and Digital Worlds in Data-Driven Systems

Summary form only given, as follows. The complete presentation was not made available for publication as part of the conference proceedings. Mixed-signal interfaces are the essential bridges between the physical world and the digital information processing backbone. In recent years, innovation in such interfaces has been increasingly fueled by application-level insight and the data-driven nature of modern systems. As a result, the traditional building block boundaries are blurring, and the extraction of information occurs through symbiotic interplay between analog and digital signal processing. In this talk, I will illustrate this trend using examples of small-scale data analysis and machine learning inference systems that operate near the physical interface. Specific examples include data-compressive interfaces for high-dimensional sensor inputs and mixed-signal feature extraction and compute fabrics for machine learning.
Predictive accuracy of time series model using COVID-19 cases in Eastern Visayas

SARS-Cov-2 is a novel coronavirus strain that has not previously been associated with human infection. COVID-19 is the name given to the disease caused by SARS-Cov-2. The World Health Organization declared it a Public Health Emergency of International Concern on January 30, 2020, and was later declared a pandemic on March 11, 2020. In this paper, we used MAD, MAPE, and RMSE to measure the predictive accuracy of the forecasting models for the COVID-19 cases of the provinces/cities in the region, namely, Biliran, Eastern Samar, Northern Samar, Samar, Leyte, Southern Leyte, Tacloban City, and Ormoc City. The data for this study comes from an official COVID-19 cases dataset provided by the Regional Epidemiology & Surveillance Unit of Department of Health (DOH) – Eastern Visayas Center for Health and Development from March 1, 2020 to October 30, 2021. The dataset contains confirmed cases on a weekly basis for 87 weeks (79 weeks were used for forecasting and 8 weeks were used to determine the predictive accuracy). From the results, it has been found out that ARIMA models can be highly reliable in forecasting the cases in Eastern Visayas.
Current trends in the formation of the digital infrastructure of Russian education

The article analyzes the problems of forming a digital infrastructure for education in the process of the widespread and active use of new digital technologies in educational activities. The relationship of their use with traditional teaching methods and with educational processes is considered, attempts are made to find possible ways to combine new digital learning formats with traditional.
Two- and three-dimensional deep human detection by generating orthographic top view image from dense point cloud

Human detection still suffers from occlusion, complex backgrounds, and scale-variant problems. Projecting three-dimensional (3D) points onto the ground to generate an orthographic top view (OTV) image for detection can effectively alleviate these problems. However, depth sensors may be placed arbitrarily, making it difficult to create OTV images by the dense point cloud converted from a depth image. We focus on the generation of OTV images and human detection via the constructed OTV image. First, we propose a ground plane extraction method that is well suitable for various camera positions and orientations in complex scenes. Next, points are converted to a uniform coordinate system by ground parameters and encoded to generate a three-channel OTV image. Then, the mainstream two-dimensional (2D) network is employed to detect the human directly on OTV images and further obtain the 3D bounding box by computing the mapping from the OTV image. Besides, we propose a semiautomated annotation method to solve the problem of few OTV image annotations. The proposed method is evaluated on the EPFL dataset, including two subsets, and achieves state-of-the-art performance compared with the existing approaches. Moreover, our 2D and 3D human detection method can run more than 26FPS on the CPU.
Computer Models for Playing Billiards Using Frontal Shot

Computer models with pool table oriented to the school teaching of mathematics are presented. The pool table in them has a board with six pockets and twelve “diamonds”, due to the expectation when working with these resources to form and apply not only mathematical knowledge, but also to master elements of the game of billiards. These computer models use a frontal impact, i.e. the cue ball hits the object ball in its center. The force of the shot, special shots, friction, etc. are not taken into account. The computer models are developed with dynamic GeoGebra software. They are provided with free access in the Virtual Mathematics Laboratory, developed at the Institute of Mathematics and Informatics of the Bulgarian Academy of Sciences.
Visual duplicates search of fracture zones of seismic databasesbased on the method of solving the ICP variational problemin closed form and inverted index

In this paper fast methods are proposed for search the fracture zones in seismic databases on two types of data: seismic section (two-dimensional data) and seismic cube (three-dimensional data). These methods are an integral part of the mapping technology for filtering channels and large volumes of seismic data and useful for automating interpretation of heterogeneous seismic data. The proposed methods for searching the similarity of fracture zones were investigated using the Open Seismic Repository reference dataset, which contains information about geological rocks in the area of the North Sea and compared with other known methods for solving this problem, the results were discussed in the article.