Reviewerly/exHarmony huggingface dataset:
Earth Science Education

Earth science is struggling. Despite the economic and social impacts of natural hazards, the growing need for minerals, oil, gas, and other resources extracted from the planet, the loss of clean water and soil resources resulting from human activity, and the looming climate crisis - earth science remains undertaught and undervalued. A direct result of this undervaluing is a severe shortage in scientists trained to work in environmental, energy, and earth-related fields. This chapter reflects on the status of earth science in schools and universities across the globe, as well as recent developments in earth science education and education research. We suggest that earth science education researchers should adopt a more holistic research agenda and to turn their attention to the lack of earth science education in schools and universities as well as the limited focus of earth science curriculum. We suggest that increased attention to earth systems education, environmental insight, and social well-being in the context of earth science is needed to prepare students for effective decision-making around earth phenomena. Rethinking the earth science curriculum to build greater links between science and society would have the added benefit of encouraging more people from many backgrounds to participate in earth science education and careers. This curriculum will also include new avenues of research focused on changing the attitudes of geoscientists toward their role in society and the adoption of geoethical values.
Unity ML-Agents

Now that we have a working installation of ML-Agents, we can take a more hands-on look at the system.
Chenggang Wu Speaks Out on his ACM SIGMOD Jim Gray Dissertation Award, Rejection, Believing in Your Work, and More

Welcome to this installment of ACM SIGMOD Record's series of interviews with distinguished members of the database community. I'm Marianne Winslett, and today we are on Zoom with Chenggang Wu, co-founder and CTO of Aqueduct. Chenggang received the 2022 ACM SIGMOD Jim Gray Dissertation Award for his thesis entitled The Design of Any-scale Serverless Infrastructure with Rich Consistency Guarantees. His PhD is from UC Berkeley. So, Chenggang, welcome!
ANALYSIS OF THE APPLICATION OF DIGITAL HOLOGRAPHY METHODS FOR INFORMATION PROTECTION

The paper provides an overview of digital holography as a method of recording information, provides general information about the concept of “hologram” of different types, draws a parallel between physical (optical, analogous) and digital holography, considers differences and similarities, and points out the reasons for the predominance of digital holography for modern purposes. The principles and some methods of synthesizing digital holograms are briefly considered, their advantages and disadvantages are given. The article also considers a brief historical background on the creation of the concept itself is given, current achievements in the field of research both in Russia and abroad. Besides it as well reviewed various areas for the practical application of digital holograms, in particular, the issue of information security – as a promising area for the use of digital holograms including in combination with various methods of hiding information. The advantages and disadvantages of such a method of information protection are highlighted. The distinction of cryptography from steganography is studied, and some steganographic methods of hiding information are briefly outlined, their disadvantages and advantages are indicated, as well as the basic principle used by those methods to hide information. Conclusions are drawn on the basis of the specified information.
Assessment of the achievement of the goals and priorities of cultural development of the National project "Culture"

The article presents an analysis of the achievement of the goals and implementation of priorities for the development of culture of the Russian Federation, provided by the National Project "Culture" as part of the Federal projects "Cultural Environment", "Creative people", "Digitalization of culture", containing an as-sessment of indicators (indicators) of cultural development.
Analysis Method of Security Critical Components of Industrial Cyber Physical System based on SysML

To solve the problem of an excessive number of component vulnerabilities and limited defense resources in industrial cyber physical systems, a method for analyzing security critical components of system is proposed. Firstly, the components and vulnerability information in the system are modeled based on SysML block definition diagram. Secondly, as SysML block definition diagram is challenging to support direct analysis, a block security dependency graph model is proposed. On this basis, the transformation rules from SysML block definition graph to block security dependency graph are established according to the structure of block definition graph and its vulnerability information. Then, the calculation method of component security importance is proposed, and a security critical component analysis tool is designed and implemented. Finally, an example of a Drone system is given to illustrate the effectiveness of the proposed method. The application of this method can provide theoretical and technical support for selecting key defense components in the industrial cyber physical system.
Weighted Fuzzy Clustering Algorithm Based on Max-Min Distance Criterion and Jeffrey Divergence

Randomly selecting the initial clustering centers and setting the number of clusters has some influence on the results of the Fuzzy C-means (FCM) clustering algorithm. To address this problem, a weighted Fuzzy C-means clustering algorithm based on max-min distance, called the MJ-WFCM algorithm, is proposed. First, the algorithm assigns corresponding weights to the eigenvalues of the data, selects the clustering centers heuristically by the max-min distance criterion to avoid the clustering results from falling into the local optimum, and finally uses the ratio of intra-cluster distance and inter-cluster distance to determine the number of clusters without setting the number of clusters based on experience. Considering that the similarity measure of Euclidean distance only considers the local consistency between data points, a similarity measure based on Jeffery divergence is proposed to improve the clustering accuracy of non-convex complex datasets. Experimental results on artificial datasets and UCI datasets show that the MJ-WFCM algorithm not only exhibits better clustering performance on non-convex datasets, but also improves the efficiency of high-dimensional data clustering.
Mamdani fuzzy parameter estimation algorithm for large-scale interconnected nonlinear systems

For the category of large-scale nonlinear processes that are composed of a set of linked monovariable systems and represented by discrete input-output models with unknown time-varying coefficients, the current study proposes a recursive algorithm of maximum likelihood estimation based on the fuzzy inference technique. This recursive estimator employs a prediction error strategy and a maximum likelihood estimation algorithm to formulate the issue of estimating the parameters of the process under consideration. The established parameter estimation approach is enhanced by the addition of the Mamdani fuzzy inference system. A numerical simulation exemplar is used to verify the efficacy of the generated theoretical results..
From Silos to Solutions

As higher education institutions in the United States grapple with changing demographics resulting in fewer traditional-aged students and the related budgetary implications for tuition-dependent schools seeing reduced enrollment, one method for reducing spend and improving technology-adoption outcomes is to minimize silos and adopt a collaborative approach to acquiring technology solutions.
Automated cervical cancer classification using deep neural network classifier

Cervical cancer (CC) has become one of the serious and deadly diseases in women around the world. Predicting a CC is at risk because testing is not possible at the early stage. Doctors predict the types of cancer by collecting cervical cells and by intervening in person. This approach affects the level of prediction due to human negligence, high cost, and is time consuming. In this paper, we propose the automatic cancer classification using deep neural network to overcome this problem. The proposed work has four stages namely, pre-processing, outlier elimination, dimensionality reduction, and classification. Initially, the missing test data should be removed. Second, the inconsistent data are eliminated based on identical values. Third, principal component analysis (PCA) is used to overcome the limitations caused by high-volume data. Finally, enter the reduced size database in the proposed neural network (DNN) classifier and classify the input data as normal or abnormal. We implement the suggested method using Python and we inspect the performance in terms of different metrics.
Semi-Supervised Domain Adaptation for Cross-Survey Galaxy Morphology Classification and Anomaly Detection

the first time, we demonstrate the successful use of domain adaptation on two very different observational datasets (from SDSS and DECaLS). We show that our method is capable of bridging the gap between two astronomical surveys, and also performs well for anomaly detection and clustering of unknown data in the unlabeled dataset. We apply our model to two examples of galaxy morphology classification tasks with anomaly detection: 1) classifying spiral and elliptical galaxies with detection of merging galaxies (three classes including one unknown anomaly class); 2) a more granular problem where the classes describe more detailed morphological properties of galaxies, with the detection of gravitational lenses (ten classes including one unknown anomaly class).
Research on Evolutionary Game of Supply Chain Manufacturing Information Sharing Based on Blockchain Technology

In view of the problems of unclear production status caused by information asymmetry in supply chain manufacturing, this paper uses the characteristics of decentralization, traceability, and non-tampering of information of blockchain technology to build a supplier and manufacturing system based on blockchain technology. Manufacturers create an evolutionary game model of information sharing, and analyze the influencing factors of each participant's adoption of information sharing strategies in the blockchain system. The results show that the degree of information sharing, sharing favorable information, collaboration index and incentive index have a positive impact on information sharing strategies, while information sharing risks, information sharing costs, and opportunity benefits have a negative impact. Finally, it provides countermeasures to promote information sharing for supply chain participants to improve the overall efficiency of the supply chain.
Contracting Space for Opposing Speech in South East Asia and Restrictions on the Online Freedom of Expression

The right to freedom of expression has been subjected to limitations in South East Asia with restrictions continuing in Thailand, Vietnam, Indonesia, Singapore, and Malaysia, to name a few countries. Speech that is critical of governments has been prevented with many instances of journalists, bloggers, activists, and opposition groups being detained and convicted, resulting in contracting space for the press and civil society. Going further, Malaysia, Thailand, and Cambodia have sought to restrict online activities through various means. This State practice report outlines examples of laws enacted in Malaysia and Cambodia and practices in Thailand that impact the online freedom of expression and examines the literature on content moderation and human rights frameworks. The report expounds the risks posed by States who seek to restrict the use of the internet for the freedom of expression and highlights the need for future research to examine the legal frameworks being used by states to limit the online freedom of expression.
Ghana

Ghana discusses domestic politics, foreign policy, and socioeconomic change in 2021. Ghana’s 2020 election results were contested in court, but the petition was ultimately dismissed. The country’s first hung parliament led to heightened deliberations, but particularly regarding a proposed electronic transactions levy. The disruption of a paralegal training on violations against lgbtq people, the closure of the first lgbt+ centre, the introduction of an anti-lgbtq bill in parliament, and security forces’ attacks on protesters and reporters thrust Ghana into global dialogues on human rights. A real gdp growth rate of 5.4% was achieved, but inflation and debt rose throughout 2021.
INFLUENCE OF CORPORATE SOCIO-PSYCHOLOGICAL CLIMATE ON EMPLOYEES’ ACHIEVEMENT MOTIVATION

The article considers the influence of corporate socio-psychological climate on employees’ achievement motivation. Socio-psychological climate is regarded as an integral characteristic of the system of interpersonal relations within a group and an attitude toward professional activity and a component of corporate culture. Achievement motivation is associated with an individual striving to perform better and reach high outcomes which can be recognized as successful. Achievement motivation is reckoned as a combination of two motives – a motive to achieve success and a motive to avoid failure. Based on the findings of empirical research, the authors analyzed the correlation between the levels of socio-psychological climate (assessed by organization employees) and the levels of achievement motivation. It was found that surveyed company employees demonstrated an average level of psychological climate, which is characterized by higher indices of responsibility, unity, and good organization. Such indices as awareness and sociability predominated in participants with a low level of corporate SPC. It was established that respondents had medium high and average levels of motivation to achieve success. Thus, a higher level of psychological climate is followed by higher work satisfaction than at a lower level of SPC. An analysis of correlations between levels of socio-psychological climate and achievement motivation proves that respondents with average SPC have a medium high or somewhat high level of motivation to achieve success and average and somewhat low levels of motivation to avoid failure. Average and somewhat low levels of motivation to achieve success and somewhat high and average levels of motivation to avoid failure is peculiar to respondents with low SPC. A correlation between indicators of unity, responsibility, organized nature, and collectivism and motivation to achieve success was determined. As a result, one can believe that in companies with a more favorable psychological climate, employees have somewhat higher indices of motivation to achieve success.
The Effect of Mobile Application Types on Learner’s Vocabulary Ability and Affective Domain

This study aims to examine the effect of mobile application types on learners' vocabulary ability and affective domain.Using two different types of mobile applications, ClassCard and Kahoot!, this study investigates how flashcard-type and game-type mobile applications affect students' vocabulary retention and factors related to affective domain.One hundred forty-one participants in S public girl's high school in South Korea participated in this study, and they were divided into ClassCard and Kahoot! group based on their pre-test vocabulary scores.Results collected from pre-, post-, and delayed-post vocabulary tests and pre-and post-surveys were analyzed using SPSS program.Independent sample t-test was used to compare and contrast the results between the ClassCard and Kahoot group, and paired sample t-test and repeated measures ANOVA were conducted to analyze in detail the effect of each mobile application.The results have shown that there are no significant differences between the two experimental groups in terms of vocabulary retention and affective factors.However, in-depth analysis of each application showed significant improvement in all areas.
Alla ricerca del canto perduto: tra Dante e Topolino

This paper is intended to illustrate the possibilities offered by comics in linguistic and literary education. In particular, the subject of analysis concerns a publication by Disney, Zio Paperone e il centounesimo canto, made as a tribute to Dante and the Divina Commedia. The proposal is introduced by a reference to the mystery that surrounds various discoveries of songs of the Divina Commedia supposedly lost; it continues with a description of the comic as a medium in linguistic and literary education; the idea of language varieties and the coexistence of different languages is then presented, linking the proposal to the requirements contained in ministerial school documents. To conclude, an analysis of the language present in the balloons of the comic is carried out, with an examination of the choices made by the authors, also through references in diachrony.
Threats on the Critical Infrastructure Elements

The paper threats on the critical infrastructure elements provides an up-to-date view of relevant threats that currently affect critical infrastructure and critical entities. Research in the field of resilience and protection of critical infrastructure brings daily results. The company gradually moved from stating that the activated threat caused the following damage to the opposite procedure. Today, thanks to prepared scenarios, we can anticipate possible impacts even before a specific threat is activated. In the future, all significant threats will be evaluated and procedures will be prepared in the information systems to prepare for individual threats in the phase of ordinary life. How to reduce possible impacts at the time of activation of the threat and how to bring the infrastructure or entity to the state before the emergency as effectively as possible during the recovery phase.
An Exploratory Research on the Expression of Knowledge and Its Generation Process Based on the Concept of “Dark-Matter”

Research work on machine learning techniques has been going on since the invention of computers. With the development of machine learning techniques, researches on how knowledge is expressed in computers and the learning process of knowledge have become more important. Unlike other machine learning models such as artificial neural networks, in the previous work of this paper, a machine learning model based on the concept of “dark-matter” is presented. In this model, matrixes are used to represent temporal and non-temporal data. The term “matter” is used to denote non-temporal data. The term “dark-matter”, on the other hand, is used to represent temporal data. In this paper, an exploratory research on the expression of knowledge and its generation process based on the concept “dark-matter” is presented. A case study is used to illustrate how knowledge is generated and expressed. The contribution of this paper is that new methods of knowledge generation and expression are proposed based on the concept of “dark-matter”. In the paper, first, the concept of “dark-matter” is briefly reviewed. After that, the methods of knowledge generation and knowledge expression are illustrated with examples. The process of knowledge generation is also illustrated with examples. Finally, the relationship between knowledge and “dark-matter” is revealed.
FAGR: Fairness-aware Group Recommendation in Event-based Social Networks

An event-based social network (EBSN) is a new type of social network that combines online and offline n etworks. In recent years, an important task in EBSN recommendation systems has been to design better and more reasonable recommendation algorithms to improve the accuracy of recommendation and enhance user satisfaction. However, the current research seldom considers how to coordinate fairness among individual users and reduce the impact of individual unreasonable feedback in group event recommendation. In addition, while considering the fairness of individuals, the accuracy of recommendation is less improved by fully combining the context key information. To solve these problems, we propose a prefiltering algorithm to filter t he c andidate e vent s et, a m ultidimensional context recommendation to provide personalized event recommendations for each user in the group, and a group consensus function fusion strategy to fuse the recommendation results of members in the group. To improve overall satisfaction with the recommendations, we propose a ranking adjustment strategy of the key context. Finally, we verify the effectiveness of our proposed algorithm in real data sets and find t hat F AGR i s s uperior t o t he latest algorithms in terms of global satisfaction, distance satisfaction and user fairness.
Software-Based Supplementary Materials in MAPEH for Enhanced Learning Resource Management and Delivery System

The study aimed to determine the effectiveness of a software-based supplementary material in MAPEH in enhancing the learning resource management of the said subject and ultimately, enriching learners’ performance in MAPEH. The respondents were 160 students from JHS in Pagbilao National High School, Division of Quezon, stratified sampling was used due to the existing classification per grade level. The study used mixed-method research and utilized experimental, descriptive-evaluative, and phenomenological research designs to come up with the findings and analyses of data. A set of assessment tools as well as a survey questionnaire were used to observe the effectiveness of software-based supplementary material in MAPEH. Data were analyzed using frequency, percentage, t-test, Pearson correlation coefficient, and phenomenological. The salient findings of the study are summarized as follows: Student’s pre-test results were low while; the overall mean of post-test was high. The Test of correlation between the effectiveness of software-based supplementary materials and the perception of the respondents was found to be Very effective. There were significant differences between the pre and post-assessment tests after the utilization of the software-based supplementary materials in MAPEH. The study recommends that the DepEd officials may help the teachers who intend to replicate the creation of software-based supplementary material to seek assistance from software creators. Keywords: software-based supplementary materials, competency, learning resource management, delivery system
Validation of Individual Identification Through Decision Tree Packet Header Profiling

The drastic rise in the cybercrime rate associated with the surge of users' dependence on the Internet has elevated the concern of digital forensic examiners toward the footprints of perpetrators left in a virtual environment. However, suspect identification is a big challenge in network forensics due to the anonymous nature of data transmission across the network. This study utilises the decision tree classification approach to characterise users from their behavioural web navigation pattern using the meta-data of captured network packets (Destination IP, Protocol, Port Source, and Port Destination). A total of 95,795,379 network packet headers from 96 subjects were successfully collected. Their meta-data header packets were statistically profiled to generate digital fingerprints that try to link their action on the network to their identity accurately. Hence, CHAID decision tree modelling using Destination IP, Unique protocols, and a combination of the two, including Port source and Port destination, resulted in an accuracy of 4.07%, 6.34%, and 6.36%, respectively. However, the modelling could not create a reliable decision tree for the Port source and destination. The validation study on all the combined variables had a similar accuracy of 6.36%, indicating model created had reproducibility capability. Despite the outcome, the proposed method is not yet sufficiently strong for suspect identification. Further enhancement to improve its accuracy is required.