Reviewerly/exHarmony huggingface dataset:
Automatic textual description of interactions between two objects in surveillance videos

Abstract The purpose of our work is to automatically generate textual video description schemas from surveillance video scenes compatible with police incidents reports. Our proposed approach is based on a generic and flexible context-free ontology. The general schema is of the form [actuator] [action] [over/with] [actuated object] [+ descriptors: distance, speed, etc.]. We focus on scenes containing exactly two objects. Through elaborated steps, we generate a formatted textual description. We try to identify the existence of an interaction between the two objects, including remote interaction which does not involve physical contact and we point out when aggressivity took place in these cases. We use supervised deep learning to classify scenes into interaction or no-interaction classes and then into subclasses. The chosen descriptors used to represent subclasses are keys in surveillance systems that help generate live alerts and facilitate offline investigation.
Motivational Components of a Blended Learning System for Teachers and Students

The relevance of the study of the problem of blended learning and the motivational component is conditioned by its increasing publicity among modern scientists and teachers. The purpose of this study is to reflect the scenario of modern educational trends, namely the gradual transition to a hybrid learning format, which is inevitable for modern society. Theoretical, empirical, and comparative research methods were used to study the problem of motivation of students and teachers in the application of blended learning. These methods helped to deeply explore the subject matter, to identify and analyse the individual elements of the problem and to understand the main idea of the object under study. Blended learning has been found to be a new educational approach that combines the advantages of the conventional system of knowledge acquisition and e-learning. It is demonstrated that such training is considered an effective approach and is popular among educational institutions due to its positive impact on student motivation. It is substantiated that the hybrid form helps teachers to involve students in learning and promotes the development of social and practical skills. It is confirmed that with the development of blended learning, students feel more confident and motivated, and teachers develop professionally and become good professionals. Technology has simplified young people's lives and brought teachers closer to students. It is proved that with the development of blended learning it is possible to obtain materials with relevant information, practice of the so-called hard skills is carried out using modern technology, new versions of educational software. Thus, the study reproduced the problem of blended learning as such, and argued the need for a detailed consideration of any scientific problem, considering all its primary factors and further consequences
Internationalization of Financial Markets: World Financial Centres

The chapter analyses the processes of financial markets and global financial centers development. It is revealed that internationalization has provided a subjective division of financial markets, aggravated the growing confrontation between traditional financial institutions and entities with lower costs when selling financial products and financial services. This makes it necessary to reformat the business models of financial organizations and to measure financial markets’ digitalization. The deformation of the traditional structure of financial markets ought to take place alongside the expansion of alternative financial mechanisms under the influence of the internationalization of tool-financial technologies and the weak flexibility of financial institutions. It is proved that technological innovations have predetermined changes in the financial market in developing its infrastructure, subject composition, products, and the regulator’s role.
CREATING AN ADAPTIVE DISTANCE LEARNING ENVIRONMENT FOR FAMILY EDUCATION

Now no one doubts the need to build a distance learning support system in an educational organization. The article points out the problems of using distance educational technologies in the implementation of family education, associated with the technical equipment of families, methodological support for learning, as well as the availability of educational resources, such as teachers, curators, psychologists. The experience of the stage-by-stage construction of a system of interaction between specialists involved in the educational process, as well as interaction between specialists of pedagogical university, school personnel and a family with children of different ages is considered. Based on the conditions listed above, mandatory adaptation and optimization of distance learning in conditions of family isolation is required, taking into account each presented age of children, as well as the level of training of parents.
Image Retargeting Quality Assessment Based on Aspect Ratio Similarity of Salient Region

The shape distortions of the important regions in retargeted image attracts human perception, which are seldom considered in the existing image retargeting quality assessment (IRQA) algorithms. This would lower the performance of evaluation algorithms obviously. To address this issue, we propose an IRQA method which measures the aspect ratio similarity of the salient regions between the original and the retargeted images. To evaluate the distortions of the visual important contents, we first use a novel algorithm to extract the salient regions of retargeted images accurately, which makes full use of the original image to perform backward registration and index matching of salient region. Then the aspect ratio similarity of salient regions is proposed to measure the geometric distortion of silent regions in retargeted images. Experimental results show that the performance of the proposed IRQA method is superior to the other existing algorithms.
CRITERIA FOR SELECTING CRYPTOCURRENCY FOR EFFECTIVE OPERATIONAL MANAGEMENT

Investing in cryptoassets can be tricky. At the moment, there are many different cryptocurrencies operating on different blockchains with different ecosystems. The use of cryptoassets requires defining a goal in relation to the type of cryptoassets and the degree to which their properties affect their functioning and development. In this article, the author analyzes two of the most popular and significant cryptocurrencies for the cryptoindustry Bitcoin and Ethereum on two different blockchains. The author specifically took two cryptocurrencies that are completely different in their meaning and purpose. In view of the fact that the author does not see an ideal solution to several problems, he described the need for diversification of assets by purpose of use. The author describes each cryptocurrency as a separate ecosystem with its own properties, which requires playing by the rules. The author cites socio-economic factors due to which he chose these two blockchains for analysis, tries to analyze the dependence of the properties of each cryptocurrency on the level of popularization, and gives empirical data stating the consequences in a historical context.The author also considers technical properties as a factor of scalability and attractiveness of each of the blockchains, how cryptocurrencies interact with each other, influencing the financial mood of users. The author of the article also tries to determine the technical and social factors that led to the adaptation of these cryptocurrencies to the traditional financial sector and how they depend on each other. The author describes Bitcoin as a system for saving and multiplying funds, while Ethereum sees it as an ecosystem, an intermediary protocol between already formed market sectors and decentralized applications within the network. After analyzing the results of the study, the author provides general criteria for the formation of a methodology for choosing a cryptocurrency and blockchain for conducting effective operational activities, as well as the formation of its own mechanism for managing the efficiency of cryptocurrency operations.
Clinically Applicable Deep Learning for Predicting Prognosis of Hepatocellular Carcinoma Directly From Histology Images

Background: Prognosis classifier is necessary for patients with hepatocellular carcinoma (HCC). Here, we developed a clinically applicable automatic system to predict prognosis directly from HCC histology image.Methods: In total, 1480 participants were enrolled (ZJU1: n=202, ZJU2: n=179, ZJU3: n=738, TCGA: n=361). We trained 7 convolutional neural network (CNN) nets to select the top one in learning HCC image. Firstly, lesion detector and lesion decomposer net (classifying lesion into five different classes) were developed in ZJU1 and validated in ZJU2. Second, a prognosis classifier using the classes derived from decomposer net was constructed by LASSO Cox in ZJU3. Lastly, the two nets and the prognosis classifier were validated in external TCGA.Findings: Of the 7 CNNs, VGG19 achieved the top performer for detecting lesion with an accuracy of 96.4%, and classifying lesion with test accuracy of 99% in ZJU2. In ZJU3, two classes derived from the decomposer net were significantly associated with RFS (p<0.001): the neuron status of lymphocytes (HR: 0.69, 95%CI: 0.57-0.84) and stroma (HR: 1.39, 95%CI: 1.14–1.70). Lastly, the detector and decomposer nets were used to automatically detect lesion and extract prognosticators in TCGA. The category-free, event and non-event NRI of the supplementing stage_AJCC with lymphocytes and stroma neuron status for predicting 2-year RFS were 31.2%, 12.8% and 18.4%, respectively. Moreover, the prognostic classifier was confirmed in TCGA cohorts.Interpretation: The two clinically applicable models can precisely detect lesion and extract the prognostic factors from histology images and holds great potential for prognosis prediction and decision-making.Funding: National Major Scientific and Technological Special Project (No.2017ZX10203205), and National Natural Science Foundation of China (81802889).Declaration of Interest: The authors declare no any ethical/legal conflicts involved in the article.Ethical Approval: The study was supported by National Major Scientific and Technological SpecialProject (No.2017ZX10203205), Science Foundation of Zhejaing province (LQ18H160006). The results in this study are in part based upon data from the TCGA.
The analysis of exploratory factors on the development of data, technology, and human literacy assessment instrument

Abstract This study focused on constructing data providers, technology, and human factors based on the interaction of items using factor analysis. The research instruments developed using the 4-D model are defined, design, develop, and disseminate. The research subjects were 115 students of the tenth grade of SMK Texmaco Semarang. The instruments that were tested were instruments form; they are multiple-choice and non-test instruments, they are observation sheets and scales. Data analysis was carried out through a construct validity test using factor analysis. Based on the results of the construct validity, it was found that all indicators in the test and nontest instruments were effectively involved in measuring data, technology, and human literacy. This result is shown from the effective proportion of each literacy indicator which reaches more than 30%. The results of the analysis factors showed that the test instrument contains literacy, technology, and human data according to the provisions of Education 4.0. The results on the nontest instrument found that human literacy skills can be assessed through openness, communication, collaboration and innovation, technological literacy skills through concepts and technology development.
Experimental study of the functioning and quality of the developed artificial neural networks configurations according to the selected accuracy criteria

Abstract The significant volume, as well as the semantic and lexicological diversity of Internet content, necessitates the creation of new methods for its computer analysis in order to identify potential threats and undesirable content, in particular, using artificial intelligence (AI) methods. This scientific problem can be solved with the use of neural network technologies, including frequency preprocessing of text arrays, justification of the structure and construction of a domain-oriented database of text data bodies, justification and experimental study of the architecture and macroparameters of a hybrid neural network based on the vector representation of pre-formed dictionaries of terms.
Designing and Testing Assessments and Scaffolds for Mathematics Practices in Science Inquiry

This project seeks to operationalize the mathematics practices that are needed for mathematically-integrated science inquiry, as outlined in the NGSS, and examine the effects of scaffolding these on competencies on the mathematics practices and on content acquisition. Using Evidence-Centered Design, I will design and pilot an assessment that can assess and scaffold competency in mathematics practices integral to virtual scientific inquiry. The assessment and scaffolds will be piloted with students and tested as to their efficacy at improving students’ competencies on the practices of interest. After scaffolding on the practices (versus control), students will investigate two new phenomena. Statistical analyses will test if competencies on the practices are predictive of content acquisition in the new topics. The fine-grained operationalization of the practices into sub-practices and the empirical link of scaffolding these on content acquisition have implications for both research as well as science assessment and instruction.
Developing electronic bahasa Indonesia (E-BIPA) 2.0 version media to improve critical literacy of foreigner students

Abstract In this era of industrial revolution 4.0, technology is growing rapidly with the emergence of high-tech electronic goods equipped with internet facilities to further speed up the communication process. The objective of this research was to develop Electronic Bahasa Indonesia (E-BIPA) 2.0 Version to improve critical literacy of foreigners’ student. This study is Research and Development (R&amp;D) using ADDIE model. The data of this study were obtained through observation, interviews, questionnaires, and scale. The feasibility of E-BIPA 2.0 Version media was obtained from assessments by media and material experts. The results of the validators indicate that the E-BIPA 2.0 Version is very feasible to be seen from the media aspects with score is 85 and from material aspects with score is 87. Based on the results, the E-BIPA Version 2.0 media is very feasible for Indonesian lecturer to be used in teaching foreigner students. The E-BIPA 2.0 Version media was expected to support critical literacy of foreigner students in learning Indonesian language.
A Flexible Distributed Stochastic Optimization Framework for Concurrent Tasks in Processing Networks

Distributed stochastic optimization has important applications in the practical implementation of machine learning and signal processing setup by providing means to allow interconnected network of processors to work towards the optimization of a global objective with intermittent communication. Existing works on distributed stochastic optimization predominantly assume all the processors storing related data to perform updates for the optimization task in each iteration. However, such optimization processes are typically executed at shared computing/data centers along with other concurrent tasks. Therefore, it is necessary to develop efficient optimization methods that possess the flexibility to share the computing resources with other ongoing tasks. In this work, we propose a new first-order framework that allows this flexibility through a probabilistic computing resource allocation strategy while guaranteeing the satisfactory performance of distributed stochastic optimization. Our results, both analytical and numerical, show that by controlling a flexibility parameter, our suite of algorithms (designed for various scenarios) can achieve the lower computation and communication costs of distributed stochastic optimization than their inflexible counterparts. This framework also enables the fair sharing of the common resources with other concurrent tasks being processed by the processing network.
Generative Adversarial Network and Reinforcement Learning to Estimate Channel Coefficients

The emergence of massive multiple-input multiple-output (MIMO) systems throughout the world, due to the promise of enhanced data rates, has led to an increasing need to guarantee accuracy. There is little value in large data rates if the channel state information (CSI) is subject to frequent contamination. In the context of massive MIMO systems, error in decoding the signal is introduced mainly due to two key factors: (i) intercell interference (ii) intracell interference. The problem of extracting the information signal from the contaminated signal can be interpreted as a signal separation problem where all the signals involved are Gaussian. A two-step approach is proposed to achieve this. First, a generative adversarial network (GAN) is used, to learn the distribution of three Gaussian sources (the desired signal, interference, and noise) from their mixture. The learnt distributions yield the mean and variances of three Gaussian signals. The variances predicted by the GAN, along with the sum, are used to generate the three original signals. This is interpreted as a reinforcement learning (RL) problem. Such an interpretation provides for life-long learning with decreasing error in the estimated signals. As a result, the desired signal is recovered from a corrupted signal, in a two-step process.
Malware Attacks on Electronic Health Records

Technological advancements along with the surge of high-speed Internet has enabled healthcare organizations (HCO) to provide enhanced medical treatment and better patient support. Equivalently, cybercriminals are lured by the sensitive patient data available which urges them to launch attacks to their benefits. Artificial intelligence systems involving machine learning and deep learning techniques are progressively utilized for medical diagnosis and predictions. Attackers try to disrupt the working of the learning techniques to cause misclassification or launch privacy attacks. Attackers daunt the health sector to seek personally identifiable information (PII) causing Health Insurance Portability and Accountability Act (HIPAA) violation. These attacks can have severe effects in a critical clinical environment. This article tries to detail on the possibility of adversarial attacks on medical reports by considering the portable document format (PDF) file format. Electronic health records (EHR) and DICOM reports supporting PDF file format are under constant threat to malware attacks. The results demonstrate the validity of the suggested hypothesis and provides security guidelines for health sector to thwart such attacks in realtime.
Multi-phase Adaptive Competitive Learning Neural Network for Clustering Big Datasets

The Competitive Learning Neural Network (CLNN) algorithm is used for the classification of numerical datasets. The Adaptive Competitive Learning Neural Network (ACLNN) algorithm is a modification of the CLNN algorithm and produces high accuracy results with small and moderate sizes of datasets. However, it has high time complexity with big datasets, and the accuracy of the data clustering is low. To overcome these drawbacks, a Multi-phase Adaptive Competitive Learning Neural Network (MACLNN) is proposed. The proposed algorithm consists of three phases. The first phase in the proposed algorithm splits big datasets into equal partitions called sub-datasets. This phase aims to keep the dataset’s characteristics and speeding up the clustering process. The second phase in the proposed algorithm uses the sub-datasets as input data to the ACLNN algorithm. This phase aims to determine the optimal number of clusters. To speed up this phase, a parallel processing technique is used. The last phase in the proposed algorithm uses the extensive dataset and the optimal number of clusters determined from the second phase as input to the ACLNN algorithm. This phase aims to determine the clustering id for every data object in the input dataset. To assess the effectiveness of the proposed algorithm, twelve experimental datasets are used. The results obtained are compared to those obtained by the ACLNN algorithm. Evaluation of the proposed algorithm on big datasets shows that it outperforms the ACLNN algorithm on both the clustering accuracy and the running time.
Are all the word ranking methods the same?

Text as a complex system is commonly studied by various methods, like complex networks or time series analysis, in order to discover its properties. One of the most important properties of each text is its keywords, which are extracted by word ranking methods. There are various methods to rank words of a text. Each method differently ranks words according to their frequency, spatial distribution or other word properties. Here, we aimed to explore how similar various word ranking methods are. For this purpose, we studied the rank correlation of some important word ranking methods for number of sample texts with different subjects and text sizes. We found that by increasing text size the correlation between ranking methods grows. It means that as the text size increases, the associated word ranks calculated by different ranking methods converge. Also, we found out that the rank correlations of word ranking methods approach their maximum value in the case of large enough texts.
Research on the Key Technologies of Relation Extraction from Quality Text for Industrial Manufacturing

Relation extraction of quality reports in industrial manufacturing industries such as automobiles and machinery is of great significance to the research of quality knowledge graph and quality question answering system of the industry. However, there is no available public dataset for relation extraction of quality reports, and there are few pieces of research on relation extraction of Chinese quality reports. For the above problems, this paper constructs a professional dataset for relation extraction and proposes a Chinese-based Piecewise Convolutional Neural Networks (C-PCNN) for the relation extraction of quality reports. To be more specific, this paper first collects quality reports in the field of industrial manufacturing and makes corresponding professional labels to construct a professional dataset. Then this paper learns a developed PCNN model based on Chinese characteristics by this constructed dataset, and finally this paper applies it to relation extraction in Chinese quality reports. Experimental results on the constructed dataset show that the accuracy, recall, and F1 values of the C-PCNN are 0.8234, 0.8076, 0.8154 respectively, better than PCNN and RNN, indicating the feasibility and effectiveness of the method. This research has practical significance for personnel engaged in the manufacturing industry.