Reviewerly/exHarmony huggingface dataset:
VendorLink: An NLP approach for Identifying &amp; Linking Vendor Migrants &amp; Potential Aliases on Darknet Markets

The anonymity on the Darknet allows vendors to stay undetected by using multiple vendor aliases or frequently migrating between markets. Consequently, illegal markets and their connections are challenging to uncover on the Darknet. To identify relationships between illegal markets and their vendors, we propose VendorLink, an NLP-based approach that examines writing patterns to verify, identify, and link unique vendor accounts across text advertisements (ads) on seven public Darknet markets. In contrast to existing literature, VendorLink utilizes the strength of supervised pre-training to perform closed-set vendor verification, open-set vendor identification, and low-resource market adaption tasks. Through VendorLink, we uncover (i) 15 migrants and 71 potential aliases in the Alphabay-Dreams-Silk dataset, (ii) 17 migrants and 3 potential aliases in the Valhalla-Berlusconi dataset, and (iii) 75 migrants and 10 potential aliases in the Traderoute-Agora dataset. Altogether, our approach can help Law Enforcement Agencies (LEA) make more informed decisions by verifying and identifying migrating vendors and their potential aliases on existing and Low-Resource (LR) emerging Darknet markets.
Trainability barriers and opportunities in quantum generative modeling

Quantum generative models, in providing inherently efficient sampling strategies, show promise for achieving a near-term advantage on quantum hardware. Nonetheless, important questions remain regarding their scalability. In this work, we investigate the barriers to the trainability of quantum generative models posed by barren plateaus and exponential loss concentration. We explore the interplay between explicit and implicit models and losses, and show that using implicit generative models (such as quantum circuit-based models) with explicit losses (such as the KL divergence) leads to a new flavour of barren plateau. In contrast, the Maximum Mean Discrepancy (MMD), which is a popular example of an implicit loss, can be viewed as the expectation value of an observable that is either low-bodied and trainable, or global and untrainable depending on the choice of kernel. However, in parallel, we highlight that the low-bodied losses required for trainability cannot in general distinguish high-order correlations, leading to a fundamental tension between exponential concentration and the emergence of spurious minima. We further propose a new local quantum fidelity-type loss which, by leveraging quantum circuits to estimate the quality of the encoded distribution, is both faithful and enjoys trainability guarantees. Finally, we compare the performance of different loss functions for modelling real-world data from the High-Energy-Physics domain and confirm the trends predicted by our theoretical results.
Zoning System Policy Model in Accepting New Students in Indonesia

This study aims to see and describe the implementation of the Van Meter and Van Horn policy models. The results of this study are: 1) Implementation and communication activities between policy implementing organizations have been implemented, but the results have not been optimal, 2) The characters of Public Senior High School 2 Ciamis as implementers have understood the policy context well, are consistent with the goals and objectives and Policy objectives, implement prospective students firmly and strictly on policy rules, and supervise Zoning System policies following student recruitment technical guidelines and SOP, but the results are optimal, 3) The disposition as policy implementers has not seen what to do, can do these policies and have the commitment to implement student recruitment policies for optimal system zoning systems, 4) Social, economic and political conditions have influenced policy policies, 5) The zoning of system policy implementation has a positive impact on service policies and the quality of education services. This research concludes that Public Senior High School 2 Ciamis has implemented a zoning system policy, but it has yet to be optimal.
Art Education in the Conditions of Globalisation and Cultural Transformations of Modern Times

The purpose of the article is to consider the challenges and opportunities facing art education in the context of globalisation and cultural transformations of modern times. The research methodology is based on the application of an analytical, synthetic, and cultural method, which allow a comprehensive consideration of the given problem and formulating well-founded conclusions. The scientific novelty consists in the systematisation and generalisation of the main trends and problems of the development of art education, mediated by the processes of globalisation and cultural transformations of our time. Conclusions. Based on the study of trends in the development of art education in the conditions of globalisation, their compliance with the requirements of the modern information space, the prospects for the development of art education are outlined, which consist in the deepening of interdisciplinary connections, innovation, creativity, the practical application of information technologies, which allow combining theoretical (scientific) and creative components in the learning process. The article argues that art education must be adapted to the challenges of globalisation and cultural transformations in order to meet the demands of the modern world.&#x0D; Key words: art education, cultural transformations, creativity, creative works, globalisation, educational institutions.
Application of the Average Based Fuzzy Time Series Model in Predictions Seeing the Use of Travo Substations

PT PLN expects the delivery of information quickly in predicting the capacity of transformer substations in each region in view of population growth in industrial areas. Unbalanced and overloaded electricity that is not suitable for the capacity of the transformer substation so that the results of this study are more optimal in predicting the usage load at the transformer substation using the methodaverage based fuzzy time series. The application of this method can provide fast and accurate information in accordance with consumer expectations in predicting each need in each area and the number of managed substations. The capacity of the substation can be seen in 1 phase and 3 phase with the percentage of loading quickly and precisely with the current transformer card system. The purpose of the transformer distribution in this study is to look at reducing high voltage to low voltage, so that the voltage used is in accordance with the customer's electrical equipment rating or the load rating used by all consumers in each region. The research methodology is to determine the placement of distribution transformer locations that are not suitable which can affect the end voltage drop on consumers or the drop/drop in consumer line end voltage and view complete data from the specifications of the distribution transformers along with the locations of distribution transformers that can be managed through Development of a mobile device-based Distribution Transformer Recording System at PT PLN. The results of this study in transformer power 100 consumption 99.43, unbalanced 28%, fuzzification A8, FLRG G8, forecasting results 10.69 with a Mape forecast value of 0.57%. Furthermore, power consumption of transformer 50 is 36.70, unbalanced 78%, fuzzification A6, FLRG G6, forecasting results 23.91 with Mape 1.11%. Results with the smallest mape with each travo travo 50 in each usage area 28.43, unbalanced 26%, fuzzification A5, FLRG G5, forecasting results 23.91 with Mape 0.28%. The results of this study can determine the location of the transformer along with unbalance, overload and the estimated amount of power consumption load for the use of transformer substations in an area, especially the ULP PT.PLN area for each region in PT.PLN (Persero) Krueng Geukuh. Then the results of this study can be used as a reference for monitoring population growth with excessive transformer power loads (overload) so that later you can install new transformer substations with a capacity according to the number of customers
Implementation User-Centered Design in Agile Software Development: Systematic Literature Review

Agile is one of the commonly adopted methodologies in software development to achieve continuous development and meet customer satisfaction. The popularity of Agile has led to some research on the challenges faced when implemented in software development. The current era of user satisfaction is one of the success factors in product development using Agile. In this study, further research is carried out regarding the challenges in using Agile Software Development (ASD) and mapping solutions by combining it with the User-Centered Design (UCD) method. This research is presented using a systematic literature review method by reviewing 61 literatures. The results found that the combination of these two methods solved challenges related to the process implementation (16%) and in terms of product (18%), especially scope and requirement. These results provide practitioners and academicians with an understanding of ASD challenges and recommendations to deal with the challenges by combining with the UCD method.
Precise measurement for line structure light vision sensor with large range

Abstract High precision and large measurement range are the target of any one three-dimensional scanner. For a line structure light vision sensor, measurement precision depends on its calibration results, i.e., determining mathematical expression of the light plane in camera coordinate system. However, as calibration results are locally optimal solutions, high precise measurement in a large range is difficult. In this paper, we give a precise measurement method and the corresponding calibration procedure for a line structure light vision sensor with a large measurement range. A motorized linear translation stages with a travel range of 150 mm and a planar target which is a surface plate with a machining precision of 0.05 mm are utilized. With the help of the linear translation stage and the planar target, functions which gives the relationship between center point of the laser stripe and the perpendicular/ horizontal distance are obtained. Once image of light stripe is captured, we can get a precise measurement result from the normalized feature points. Compared with a traditional measurement method, distortion compensation is not necessary and precision of measurement is improved significantly. Experiments show that root mean square error of measurement results according to our proposed method is reduced by 64.67% related to the traditional method.
Single-branch Network for Multimodal Training

With the rapid growth of social media platforms, users are sharing billions of multimedia posts containing audio, images, and text. Researchers have focused on building autonomous systems capable of processing such multimedia data to solve challenging multimodal tasks including cross-modal retrieval, matching, and verification. Existing works use separate networks to extract embeddings of each modality to bridge the gap between them. The modular structure of their branched networks is fundamental in creating numerous multimodal applications and has become a defacto standard to handle multiple modalities. In contrast, we propose a novel single-branch network capable of learning discriminative representation of unimodal as well as multimodal tasks without changing the network. An important feature of our single-branch network is that it can be trained either using single or multiple modalities without sacrificing performance. We evaluated our proposed single-branch network on the challenging multimodal problem (face-voice association) for cross-modal verification and matching tasks with various loss formulations. Experimental results demonstrate the superiority of our proposed single-branch network over the existing methods in a wide range of experiments. Code: https://github.com/msaadsaeed/SBNet
Teachers’ perceptions of machine translation as a pedagogical tool

The role of Machine Translation (MT) within the L2 classroom has proven controversial. While many students value MT to support various learning objectives, teachers fear its potential to promote passive learning, and undermine academic integrity. Nonetheless, improved reliability and accessibility of MT, combined with student attitudes, makes it increasingly hard to “Google-proof” the classroom. Alternatively, MT might be incorporated into the classroom, while highlighting to students its limitations. A key factor in this dynamic is the attitude of classroom teachers. The current study details the development of a pilot translation app, Transpose, which then acted as discussion prompt to explore teachers’ attitudes towards MT in focus groups. Participants were four UK language teachers working with various age groups and in various contexts. The Transpose app exploited possible advantages of MT by visually comparing the syntactic structure of source and target languages. A thematic analysis found that, overall, teachers viewed translation as an important pedagogical tool. Regarding MT, views were mixed. They enthusiastically suggested novel uses, such as supporting children with English as an Additional Language, yet also felt that such technology was best employed to support independent study in advanced learners. Technological and institutional factors still prevent the seamless integration of IT-related methods within classroom contexts. Overall, while teachers expressed positive attitudes towards translation, and a cautious acceptance of the place the Transpose app could take within the classroom, there remain attitudinal barriers to widespread adoption.
Uncer2Natural: Uncertainty-Aware Unsupervised Image Denoising

Recently, unsupervised image denoising methods learning from paired noisy samples have received increasing attention. These methods build on the idea that the mean of multiple noisy images of the same scene is the ideal clean image. However, these methods ignore the effect of Aleatoric uncertainty in the noisy image (e.g., pixels deviating from the expected distribution). The presence of Aleatoric uncertainty causes degradation of the reconstructed target pixels, resulting in high uncertainty for these pixels (i.e., low confidence), which in turn leads to sub-optimal denoising results. To address this problem, we propose a novel uncertainty-aware unsupervised image denoising method named Uncer2Natural (U2N). It dynamically predicts the Aleatoric uncertainty for each noisy sample and produces satisfactory denoising results by reducing the effect of Aleatoric uncertainty. Extensive experimental results show that U2N outperforms state-of-the- art unsupervised image denoising methods in terms of both quantitative metrics and qualitative visual quality.
AI-Powered Personalized Mobile Education for New Zealand Students

This research aims to develop and evaluate a personalized mobile education system for New Zealand students, utilizing AI and UCD principles. The system will address the limited personalization in existing mobile education solutions, by providing tailored learning content and recommendations based on individual preferences, catering to the diverse needs of students. The study will employ a mixed-methods approach, including user research, persona development, user journey mapping, design, development, and evaluation. Participants, including New Zealand students, parents, and teachers, will be involved in several phases of research to ensure that user-centered design principles are effectively implemented. By demonstrating the potential of AI-powered personalization to improve the learning experience for students, this study contributes to the increasing use of AI algorithms and systems in education.
Perkembangan Teknologi Dibidang Pendidikan

The rapid development of technology today has led to the development of the world of education and one of them is the development of learning media. Learning media is the most important factor that supports success in learning. Learning technology is an application or media that has been designed in a modern way and utilized as theory and practice in learning, as a learning resource. Currently, technology is widely used in the world of education is information technology. But also the development of technology has negative and positive impacts, which are included in the development of technology from a positive point of view, namely adding information, making the delivery of material more interesting, increasing interest in learning. With that, there is also an impact in negative terms, namely causing students to be lazy to learn, immoral violations, the emergence of deviant behavior, misuse of technology to commit criminal acts. The purpose of this research is an effort to find answers to the above problems, the development of technology in the field of education which is a very rapid and advanced development at this time especially in the learning media in the field of education. From the results of this study, the authors can conclude that the development of technology, especially in the world of education, is also stated to be very good so that students do not bore learning, especially nowadays that is increasingly sophisticated using information technology. With this, the answer to all the factors there are benefits from technological developments, namely the existence of new learning methods, facilitating access to learning can be from a distance or close distance, can also search for information using the web, improve learning abilities. Communication as an educational medium is carried out using communication media such as telephones, computers, the internet, e-mail and so on. Interaction between teachers and students is not only done through face-to-face relationships but also by using these media.
Pengembangan Media Buku Cerita BergambarUntuk Meningkatkan Keterampilan BahasaAnak Usia Dini di DKI Jakarta

Early Childhood Education (ECE) is the level of education before the level of basic education which is an effort of guidance that is shown for children aged 0-6 years which is done through providing educational stimuli, to help the growth and development of physical and spiritual so that children have readiness in entering further education. Speaking is closely related to language development which is acquired by children through listening, listening, and storytelling activities. Children can speak well because they have sufficient language vocabulary, and are able to express it. Language development in children is carried out by using learning media as a supporting tool. In line with the previous opinion Hadders-Algra said that, through media, early childhood will be easier to present abstract understanding into a more concrete understanding. That way, learning media is one of the most important parts of the learning process because it can facilitate limited understanding. Therefore, based on the problems and obstacles that exist in the field, the researchers tried to solve the problem by developing picture storybook media. The purpose of this study is to invite teachers/educators to motivate children to speak through storybook reading activities. The goal to be achieved in this study is that teachers/educators can create and express ideas in making a children's story based on existing themes. In this study, the research method of developing picture storybook media, using the ADDIE development model developed by Cennamo, Abell &amp; Chung, that the development procedure starts from the Analysis, Design, Development, Implementation, Evaluate stages. Meanwhile, according to Sezer et al, states that the ADDIE model is the system approach implies an analysis of how the components of all phases. This opinion can be interpreted that the ADDIE model is an approach that emphasizes an analysis of how each component interacts with each other in coordination with the existing phases. This research was conducted in several PAUDs in the DKI Jakarta area.
Simulated Annulling in Convolutional Neural Network

Deep learning is a new branch of machine learning research with the goal of bringing us closer to artificial intelligence. This approach can learn several layers to abstract and represent in order to generate a shared understanding of dataset like text, music, and image. Even though DL is effective in wide range, it is difficult to train. Stochastic Gradient Descent and Conjugate Gradient have been offered as approaches for training DL to make it effective. This paper aims to propose Simulated Annealing (SA) as an alternative way for optimum DL employing a current optimization technique, namely a metaheuristic algorithm, to enhance the effectiveness of Convolution Neural Network (CNN). Two classical CNN models AlexNet and ResNet are used for the experiment. The suggested method is tested by the CIFAR-10 dataset to confirm its correctness and efficiency. In addition, we compare our proposed solution to CNN's original at different standards, such as model accuracy, test error rate and learning efficiency. After the experiment, it can be concluded that despite the longer computation time, the results of the experiments suggest that the proposed approach of this paper can enhance the effectiveness of several models of CNN, such as AlexNet and ResNet34.
The Algorithmic Management of Polarization and Violence on Social Media

Social media platforms are involved in all aspects of social life, including in conflict settings. Incidental choices about how they are designed can have profound effects on people when conflict has the potential to escalate to violence. We review theories of conflict escalation and the practice of professional peacebuilders, and distinguish between constructive conflict, which can be part of important societal changes, and destructive conflict where positions become more identity based and intractable. Platforms have largely responded to conflict through content moderation thus far, yet moderation will never affect more than a small amount of objectively policy-violating content, and expanding those efforts will only lead to more backtracking, biased over-enforcement, and controversy. Instead we draw on recently-published platform experiments, the reports of content creators, international peacebuilding practitioners, and the experiences of those in conflict settings to argue that platforms often incentivize conflict actors toward more divisive and potentially violence-inducing speech, while also facilitating mass harassment and manipulation. We propose that platforms monitor for the conflict relevant side effects of prioritizing distribution based on engagement, such as the incentivization of divisive content, and that they deprecate certain engagement signals (such as comments, shares or time spent) in sensitive contexts. It may also be possible for platforms to support the transformation from destructive to constructive conflict by drawing attention to cross-cutting content, and supporting the on-platform efforts of conflict transformation professionals. To produce widespread legitimacy for these efforts, and overcome the problem of business incentives, we recommend the public creation of clear guidelines for conflict-sensitive platform design, and the creation of new kinds of practical conflict metrics.