Reviewerly/exHarmony huggingface dataset:
Preliminary Findings of the Trends and Patterns of Darknet-Related Criminals in the Last Decade

Approaching and using the darknet has become an inevitable trend in the last decade. The identities and locations of darknet accounts stay anonymous to make more curious with its users. It is also difficult to track with several layered encryption systems that create more opportunities to commit illicit goods and services in the cryptomarket. When the Silk Road has dominated the most dynamic and diverse forum among cryptomarkets since the 2010s. After its closure, many studies started focusing on the trend and patterns of darknet-related crimes. Among 1,150 publications on the Web of Science, this article collected 49 papers in criminology and penology to review and analyse. This first study combined a systematic literature review and biblometrix analysis in the field, which could not avoid limitations. However, it also contributes to some valuable expectations to identify the researched evolutions in darknet-related crimes in the last decade. The findings point out 1) leading articles identified based on the global and local citation score; 2) almost all leading authors with their most influential papers came from the Global South with predominant contributions from the U.S., the U.K, Australia, Canada, and European countries; 3) unbalancing publications between regional scholars and their institutions and countries (Western and Asian areas) although the darknet-related criminals occurred and operated without border; 4) after dismantling ten years ago, Silk Road with its first and later version has still been received many considerations to analyse the structure and modus operandi of cryptomarkets; and 5) some specialised themes in the cybercrime and darknet have identified to call further extensive research such as policing interventions in the darknet and flows of the cryptocurrency in crypto markets. Some implications are considered, and share its six relevant recommendations in future directions about darknet-related criminals.
INTEREST: INteractive Tool for Exploring REsults from Simulation sTudies

Simulation studies allow us to explore the properties of statistical methods. They provide a powerful tool with a multiplicity of aims; among others: evaluating and comparing new or existing statistical methods, assessing violations of modelling assumptions, helping with the understanding of statistical concepts, and supporting the design of clinical trials. The increased availability of powerful computational tools and usable software has contributed to the rise of simulation studies in the current literature. However, simulation studies involve increasingly complex designs, making it difficult to provide all relevant results clearly. Dissemination of results plays a focal role in simulation studies: it can drive applied analysts to use methods that have been shown to perform well in their settings, guide researchers to develop new methods in a promising direction, and provide insights into less established methods. It is crucial that we can digest relevant results of simulation studies. Therefore, we developed INTEREST: an INteractive Tool for Exploring REsults from Simulation sTudies. The tool has been developed using the Shiny framework in R and is available as a web app or as a standalone package. It requires uploading a tidy format dataset with the results of a simulation study in R, Stata, SAS, SPSS, or comma-separated format. A variety of performance measures are estimated automatically along with Monte Carlo standard errors; results and performance summaries are displayed both in tabular and graphical fashion, with a wide variety of available plots. Consequently, the reader can focus on simulation parameters and estimands of most interest. In conclusion, INTEREST can facilitate the investigation of results from simulation studies and supplement the reporting of results, allowing researchers to share detailed results from their simulations and readers to explore them freely.
A Data Visualization of Rubber Planting in Thailand

The rubber tree is considered one of the most important economic crops in Thailand generating a large amount of income for the country. The problem of rubber prices that have been continuously declining since 2012 is a critical issue that requires an appropriate solution. Consequently, a study to understand the demand and supply model of rubber along with data collection and visualization of rubber planting in Thailand are highly essential. A finding from this paper presents that there is an excessive supply of rubber over its demands. Accordingly, one of the proposed solutions to solve the rubber price problems is that the Thai government should have strict control over rubber planting areas in Thailand as the rules specified in agro-economic zones. Another proposed solution is that the Thai government may include some provinces in the northeast of Thailand in agro-economic zones as these areas also generate a high volume of rubber. Keywords—rubber price, data visualization, web scraping, system dynamics, causal loop diagram, agro-economic zone.
Analysis of facial ultrasonography images based on deep learning

Abstract Transfer learning using a pre-trained model with the ImageNet database is frequently used when obtaining large datasets in the medical imaging field is challenging. We tried to estimate the value of deep learning for facial US images by assessing the classification performance for facial US images through transfer learning using current representative deep learning models and analyzing the classification criteria. For this clinical study, we recruited 86 individuals from whom we acquired ultrasound images of nine facial regions. To classify these facial regions, 15 deep learning models were trained using augmented or non-augmented datasets and their performance was evaluated. The F-measure scores average of all models was about 93% regardless of augmentation in the dataset, and the best performing model was the classic model VGGs. The models regarded the contours of skin and bones, rather than muscles and blood vessels, as distinct features for distinguishing regions in the facial US images. The results of this study can be used as reference data for future deep learning research on facial US images and content development.
Evaluation Method of Street Green Landscape Viewing Degree Based on Machine Learning

Urban streetscape is a complex and multifaceted landscape system, which is an important part of urban public space system. With the acceleration of the urbanization process, the connotation of street landscape is becoming more and more abundant. It not only has natural and social attributes but also bears the function of protecting the urban ecological environment. However, in recent years, due to the dramatic increase in the size of the urban population, more and more problems have appeared in urban road landscape. To this end, relevant government departments continue to update the design of urban streets and accelerate the construction of urban street landscapes, but urban streets still have problems in terms of function and environment. The viewing degree of street green landscape is also less and less in line with people’s aesthetic needs, which is difficult to meet people’s life and spiritual needs. In order to change this situation, this paper combined machine learning with the evaluation method of street green landscape viewing degree and conducted experiments on it based on machine learning. The experimental results showed that the evaluation method of street green landscape viewing degree based on machine learning not only made the city more beautiful but also improved the ecological environment of the city. The air quality of the city was improved by 20.96%, which was supported and loved by the general public.
A Review on Vocabulary Learning-Designed MALL Applications in the EFL Context

This review synthesizes the role of applications designed for vocabulary learning in Mobile-Assisted Language Learning (MALL) in previous studies in the EFL (English as a Foreign Language) context from 2010-2022, according to a set of criteria: (quasi) experimental studies, open access peer-reviewed articles and book chapters, types of applications installed on smartphones, vocabulary learning in the EFL context, students, and publication period (2010-2022). Specifically, the paper emphasizes the role that vocabulary learning-tailored applications have played in impacting learners’ EFL vocabulary learning (henceforth EFLVL) process. The papers that have met the criteria (51 out of 146) were examined using a set of categories: context of the study, level of education, types of applications, learning impact, major, and gender. The analysis showed that specifically designed mobile applications play an important role in boosting learners’ EFL vocabulary knowledge, motivation, attitudes, and perceptions. Research gaps and future recommendations are suggested based on the findings.
ANALYSIS OF DATA SECURITY AND CYBER-ATTACK METHODS IN DIGITAL CURRENCY

With the rapid development of technology in recent years, digital data in information technology has become an indispensable area of life. The use of digital data services in our daily lives has become inevitable. Money transactions, purchases, and money transfers are made by banks and users every day. With the transition from paper systems to digital systems, the number of users is increasing day by day, but there are security concerns about these systems. In modern technologies, the possibility of information theft, the risk of cyber-attack, and the fear of breaches are constantly being worried about financial losses. Since such digital currency transactions carry the personal data and privacy of users, everyone needs to complete the correct transactions reliably. Due to the great importance of financial transactions and digital currencies in daily life, this article explains the features of digital currency and how to prevent counterfeiting. It will also analyze what tools are safe to use in a cryptocurrency. The risks that the algorithm mechanisms used in these processes can handle are examined and security problems are explained. In addition, security methods, algorithms, digital currency cyber-attack methods, and security measures of crypto money are examined.
Parallel YOLO-based Model for Real-time Mitosis Counting

It is estimated that breast cancer incidences will increase by more than 50% by 2030 from 2011. Mitosis counting is one of the most commonly used methods of assessing the level of progression, and is a routine task for every patient diagnosed with invasive cancer. Although mitotic count is the strongest prognostic value, it is a tedious and subjective task with poor reproducibility, especially for non-experts. Object detection networks such as Faster RCNN have recently been adapted to medical applications to automatically localize regions of interest better than a CNN alone. However, the speed and accuracy of newer state-of-the-art models such as YOLO are now leaders in object detection, which had yet be applied to mitosis counting. Moreover, combining results of multiple YOLO versions run in parallel and increasing the size of the data in a way that is appropriate for the specific task are some of the other methods can be used to further improve the score overall. Using these techniques the highest F-scores of 0.95 and 0.96 on the MITOS-ATYPIA 2014 challenge and MITOS-ATYPIA 2012 challenge mitosis counting datasets are achieved, respectively.
CURRENT STATE OF SCIENTIFIC VIEWS ON THE PSYCHOLOGICAL WELL-BEING OF PERSONALITY

Psychological well-being is traditionally considered in the contextof the problem of quality of life as its subjective component, thesystemic quality of a person, which it acquires in the process oflife; generalized and relatively stable experience, which is of particularimportance for the individual; a person’s own attitude to hispersonality, life and processes that are important to him in termsof learned normative ideas about the external and internal environment;tolerance, as an integral psychological indicator of humanlife, reflecting the value aspects of his attitude to various spheres ofhis life, through a meta-implicative model of motivation to choose,which includes ideas about the well-being of the individual in relationshipswith the environment; the complex interplay of cultural,social, psychological, physical, economic, and spiritual factors. Sciencehas studied the social and ontogenetic aspects of subjectivewell-being. However, despite the importance of this issue, sciencelacks a theoretical understanding of the subjective well-being offuture psychologists, the links between the internal components ofsubjective well-being and its main psychological determinants arenot identified. Thus, the theoretical and empirical data accumulatedby modern psychological science on the subjective well-being of theindividual can serve as a starting point in the initiated research.The purpose of the article is to give a generalized phenomenonof the current state of views on the psychological well-being ofthe individual on the basis of a theoretical analysis of modernresearch in psychological science.The article is devoted to the analysis of the problem of personalwell-being, which is the object of study of psychology, sociology,philosophy and other sciences that study various aspectsof human life. Well-being is considered in terms of its internalstructure — cognitive, emotional and motivational-behavioralcomponents. Well-being depends on the presence of clear goals,the success of the implementation of action plans and behavior,the availability of resources and conditions to achieve goals.Well-being — a concept that expresses a person’s own attitude tohis personality, life and processes that are important in terms oflearned normative ideas about the external and internal environmentand characterized by a sense of satisfaction.
Teleportation of Hybrid Entangled States with Continuous-Variable Entanglement

Hybrid entanglement between discrete-variable (DV) and continuous-variable (CV) quantum systems is an essential resource for heterogeneous quantum networks. Our previous work showed that in lossy channels the teleportation of DV qubits, via CV-entangled states, can be significantly improved by a new protocol defined by a modified Bell state measurement at the sender. This work explores whether a new, similarly modified, CV-based teleportation protocol can lead to improvement in the transfer of hybrid entangled states. To set the scene, we first determine the performance of such a modified protocol in teleporting CV-only qubits, showing that significant improvement over traditional CV-based teleportation is obtained. We then explore similar modifications in the teleportation of a specific hybrid entangled state showing that significant improvement over traditional CV-based teleportation is again found. For a given channel loss, we find teleporting the DV qubit of the hybrid entangled state can always achieve higher fidelity than teleporting the CV qubit. We then explore the use of various non-Gaussian operations in our modified teleportation protocol, finding that, at a cost of lower success probability, quantum scissors provides the most improvement in the loss tolerance. Our new results emphasize that in lossy conditions, the quantum measurements undertaken at the sender can have a surprising and dramatic impact on CV-based teleportation.
PELATIHAN MENDELEY SEBAGAI REFERENCE TOOL PADA SKRIPSI MAHASISWA TADRIS IPS IAIN PONOROGO

Thesis is a form of scientific work in a particular field of study written by undergraduate students (S-1) according to scientific guidelines at the end of their studies. The IPS IAIN Ponorogo Tadris study program facilitates students to find convenience in writing thesis. This is done so that students can write a thesis properly and correctly, especially how to make the correct citations and references according to the specified guidelines. Based on this, it is necessary to carry out training that can provide knowledge about writing citations and references with application stones. The main objective of this service is to educate students through Mendeley training as a reference tool, thereby improving the quality of student theses, especially in how to write citations and references. The results of Mendeley's training as a reference tool in the Tadris IPS student thesis Iain Ponorogo were successfully implemented with a success rate of 85%. This can be seen from the ability of students to be able to create Mendeley accounts independently, to integrate installation and operation between the Mendeley application and Microsoft Office Word, to make citations or citations with Mendeley. Students are able to write references or bibliography with Mendeley. There are several factors that cause the success rate of this training to be high, including: 1) the ease of features in using the Mendeley application, 2) the existence of a guide sheet, and 3) hands-on practice.
EXAMINATION OF FACTORS INFLUENCING THE INTENTION TO ADOPT CRYPTOCURRENCIES IN TANZANIA

This paper examined factors influencing the intention to adopt cryptocurrencies in Tanzania. Specific objectives were to assess the roles of cryptocurrencies in Tanzania, to evaluate the risks of adopting cryptocurrencies to the existing financial and banking ecosystem and to examine the challenges of using cryptocurrencies in Tanzania. The study was carried out in Dar es salaam city. Purposive and snowball sampling technique were used to reach 368 respondents. The study employed quantitative method. Data were collected by using structural questionnaires and analyzed by employing regressions and correlationtesting. Technology Acceptance Model (TAM) was used to formulate conceptual framework and hypotheses. The findings indicated that the perceived usefulness and the perceived ease of use showed positive association on the intention to use cryptocurrencies. The ease and usefulness of cryptocurrencies was found in fulfilling various roles including making instant international money transfer, transacting at low charges, using it as an alternative source of currency, making free anonymous transaction and payingfor the online services that accept cryptocurrencies. On contrary, the perceived challenges and risks were insignificance to both the perceived usefulness and the perceived ease of using cryptocurrencies. The study concludes that cryptocurrencies such as Bitcoin can be used in Tanzania to fulfill various roles. Furthermore, the study concludes that, risks and challenges are the key factors that delays cryptocurrencies from being used as a legal tender in many countries including Tanzania. The study recommends to the government of Tanzania to consider establishing a cryptocurrency based in Tanzania. Also, the syllabus on blockchain technology particularly cryptocurrencies should be established in both school and higher education. Finally, the study recommends central bank to review the Tanzania monetary policy in order to accommodate this financial technology
ReuseKNN: Neighborhood Reuse for Differentially-Private KNN-Based Recommendations

User-based KNN recommender systems (UserKNN) utilize the rating data of a target user's k nearest neighbors in the recommendation process. This, however, increases the privacy risk of the neighbors since their rating data might be exposed to other users or malicious parties. To reduce this risk, existing work applies differential privacy by adding randomness to the neighbors' ratings, which reduces the accuracy of UserKNN. In this work, we introduce ReuseKNN, a novel differentially-private KNN-based recommender system. The main idea is to identify small but highly reusable neighborhoods so that (i) only a minimal set of users requires protection with differential privacy, and (ii) most users do not need to be protected with differential privacy, since they are only rarely exploited as neighbors. In our experiments on five diverse datasets, we make two key observations: Firstly, ReuseKNN requires significantly smaller neighborhoods, and thus, fewer neighbors need to be protected with differential privacy compared to traditional UserKNN. Secondly, despite the small neighborhoods, ReuseKNN outperforms UserKNN and a fully differentially private approach in terms of accuracy. Overall, ReuseKNN leads to significantly less privacy risk for users than in the case of UserKNN.
A Bi-level Assessment of Twitter Data for Election Prediction: Delhi Assembly Elections 2020

Elections are the backbone of any democratic country, where voters elect the candidates as their representatives. The emergence of social networking sites has provided a platform for political parties and their candidates to connect with voters in order to spread their political ideas. Our study aims to use Twitter in assessing the outcome of the Delhi Assembly elections held in 2020, using a bi-level approach, i.e., concerning political parties and their candidates. We analyze the correlation of election results with the activities of different candidates and parties on Twitter, and the response of voters on them, especially the mentions and sentiment of voters towards a party over time. The Twitter profiles of the candidates are compared both at the party level as well as the candidate level to evaluate their association with the outcome of the election. We observe that the number of followers and the replies to candidates’ tweets are good indicators for predicting actual election outcomes. However, we observe that the number of tweets mentioning a party and the temporal analysis of voters’ sentiment towards the party shown in tweets are not aligned with the election result. Moreover, the variations in the activeness of candidates and political parties on Twitter with time also not very helpful in identifying the winner. Thus, merely using temporal data from Twitter is not sufficient to make accurate predictions, especially for countries like India.
Testing the Causes of a Levee Failure Using Bayesian Networks

Context: Forensic geotechnical engineering aims to determine the most likely causes leading to geotechnical failures. Standard practice tests a set of credible hypotheses against the collected evidence using backward analysis and complex but deterministic geotechnical models. Geotechnical models involving uncertainty are not usually employed to analyze the causes of failure, even though soil parameters are uncertain, and evidence is often incomplete. Method: This paper introduces a probabilistic model approach based on Bayesian Networks to test hypotheses in light of collected evidence. Bayesian networks simulate patterns of human reasoning under uncertainty through a bidirectional inference process known as “explaining away.” In this study, Bayesian Networks are used to test several credible hypotheses about the causes of levee failures. Probability queries and the K-Most Probable Explanation algorithm (K-MPE) are used to assess the hypotheses. Results: This approach was applied to the analysis of a well-known levee failure in Breitenhagen, Germany, where previous forensic studies found a multiplicity of competing explanations for the causes of failure. The approach allows concluding that the failure was most likely caused by a combination of high phreatic levels, a conductive layer, and weak soils, thus allowing to discard a significant number of competing explanations. Conclusions: The proposed approach is expected to improve the accuracy and transparency of conclusions about the causes of failure in levee structures.