Reviewerly/exHarmony huggingface dataset:
Decentralized Storage for Educational Resources

The growth of blockchain over the last ten years has been astounding: from bitcoin to over 2,000 altcoins, and from decentralized electronic payments to programmable transactions, by complicated tokens and smart contracts controlled by autonomous entities. The technological aspects of blockchain are developing at the same time as the new generation of blockchain applications are also developing. This paper considers one such sphere, to develop a decentralized solution for accessing Educational Resources which will alleviate the issues due to the single point of failure of centralized systems, democratize access to educational content and provide a facile experience for users who are not exposed to web3 technologies. This platform was developed by implementing Smart Contracts using Solidity and deploying them on the Ethereum blockchain to store educational resource metadata and files on the Inter Planetary File System (IPFS). Interfacing of blockchain and the frontend is done using the web3.js. The platform is more tenacious and fault-tolerant than systems using a centralized architecture and provides a better user experience by incorporating a search mechanism for the files uploaded to the platform. This paper proposes a decentralized platform that allows users to upload educational resources that can be accessed by others and implemented a file meta-data-based search feature to remove the need for users to remember the IPFS hash of a file.
Significance and Impact of Artificial Intelligence and Immersive Technologies in the field of Education

Teachers are active ingredients of educational system. Pandemic made us aware of online learning. Teachers improvising the learners to add values to educational behavior and attitude. Education never ages, the importance of its lifelong learning everywhere at every time. Artificial intelligence (AI) is a broad context, helps to develop educational strategies for present day scenario. It allows educators to benchmark and develop critical analysis to redesign educational policies for the implementation of innovative learning and teaching strategies in educational institutions. AI helps in educational transformation and accelerating basic educational skills by introducing software robots implemented in classrooms and other devices to make reminders for essential educational activities and assignments. In European countries they are utilizing teaching assistants as robots paired with Augmented Reality (AR) and Virtual Reality (VR) system and now they are providing MR (Mixed reality) altogether called Immersive Technologies promotes lifelong learning capabilities which makes learners engaged in creation, active learning collaboration, problem solving and makes learning as a real life experience in all educational perspectives.
Product Review Classification using Machine Learning and Statistical Data Analysis

The aim of the paper is to implement and analyze the machine learning models for product review dataset. The project focuses on binary classification, multi-class classification, and clustering approaches to analyze and categorize product reviews. The performance of the models over each of the five classification tasks is measured by the 5-fold cross-validation scores over the training data.
Named Entity Recognition (NER) and Relation Extraction in Scientific Publications

Scientific publications are essential sources of information for researchers across various fields. However, the increasing number of publications has made it challenging for researchers to keep up with the latest advancements. The task of extracting key phrases and relationships from scientific papers is of utmost importance in the field of natural language processing. This task plays a crucial role in helping researchers efficiently identify relevant articles and extract valuable insights from them. This research focuses on the problem of key phrase extraction, classification, and relationship identification in scientific publications. The problem is divided into two sub-problems: key phrase extraction and classification into PROCESS, TASK, and MATERIAL categories, and relationship identification. To address these sub-problems, advanced technologies such as SciBERT, MiniLM Sentence Transformer, and SVM are utilized. These techniques enable efficient processing and analysis of scientific text, facilitating key phrase extraction, and classification, and relationship identification. By effectively tackling these challenges, researchers can navigate the vast amount of scientific literature more efficiently, identifying relevant articles, and uncovering valuable connections and insights within the text.
Axial Gap Motor Shape Optimization Using Multi-Objective Based Genetic Programming Technique

The goal of this study is to investigate the application of optimization algorithms and their employability in manufacturing. Genetic programming, which is performed in this study, can simulate the evolutionary process of organisms in nature on a computer using a population of individuals with genetic information to search for the optimal solution. In this study, an axial-gap motor with a low profile and high output is aimed, and genetic programming is used to accomplish multi-objective optimal design, utilizing the following four objective functions: average torque, torque ripple, cogging torque, and iron loss. The design aim is the shape of a permanent magnet and teeth, and the analysis is performed using the three-dimensional finite element method. As a result, a model was developed, in which each attribute was improved with a maximum gain of 5.01 % average torque. Furthermore, the significance of this method was demonstrated by visualizing the high-dimensional optimization result data via dimensional compression.
Traveling salesman problem solution using magnonic combinatorial device

Traveling salesman problem (TSP) is a decision-making problem that is essential for a number of practical applications. Today, this problem is solved on digital computers exploiting Boolean-type architecture by checking one by one a number of possible routes. In this work, we describe a special type of hardware for the TSP solution. It is a magnonic combinatorial device comprising magnetic and electric parts connected in the active ring circuit. There is a number of possible propagation routes in the magnetic mesh made of phase shifters, frequency filters, and attenuators. The phase shifters mimic cities in TSP while the distance between the cities is encoded in the signal attenuation. The set of frequency filters makes the waves on different frequencies propagate through the different routes. The principle of operation is based on the classical wave superposition. There is a number of waves coming in all possible routes in parallel accumulating different phase shifts and amplitude damping. However, only the wave(s) that accumulates the certain phase shift will be amplified by the electric part. The amplification comes first to the waves that possess the minimum propagation losses. It makes this type of device suitable for TSP solution, where waves are similar to the salesmen traveling in all possible routes at a time. We present the results of numerical modeling illustrating the TSP solutions for four and six cities. Also, we present experimental data for the TSP solution with four cities. The prototype device is built of commercially available components including magnetic phase shifters/filters, coaxial cables, splitters, attenuators, and a broadband amplifier. There are three examples of finding the shortest route between the cities for three different sets of city-to-city distances. The proposed approach is scalable to TSP with a larger number of cities. Physical limits and challenges are also discussed.
The Translational Data Catalog - discoverable biomedical datasets

The discoverability of datasets resulting from the diverse range of translational and biomedical projects remains sporadic. It is especially difficult for datasets emerging from pre-competitive projects, often due to the legal constraints of data-sharing agreements, and the different priorities of the private and public sectors. The Translational Data Catalog is a single discovery point for the projects and datasets produced by a number of major research programmes funded by the European Commission. Funded by and rooted in a number of these European private-public partnership projects, the Data Catalog is built on FAIR-enabling community standards, and its mission is to ensure that datasets are findable and accessible by machines. Here we present its creation, content, value and adoption, as well as the next steps for sustainability within the ELIXIR ecosystem.
Code-switching functions in online advertisements on Snapchat

Code-switching is a well-researched phenomenon. However, little research has been conducted on code-switching in orally communicated advertisements on social media despite social media platforms witnessing the birth of many linguistic trends and having an impact on the linguistic behaviour of their users. To bridge this gap, the current study investigates the functions of Arabic-English code-switching in advertisements made by Saudi influencers on Snapchat. The data comprise 4000 words produced by 40 advertisers (20 male and 20 female, 100 words each). The advertisers, who belong to the same age group, advertised for a range of products and targeted mostly monolingual Saudi users of Snapchat. The sample produced 102 instances of switching from Arabic to English. The most common function in the data consisted of switching for availability (n = 78), but other functions were observed (quotation n = 9, specifying addressee n = 5, interjection n = 7, reiteration n = 1, and message qualification n = 2). The potential reasons for the higher frequency of switching for availability are discussed, and venues for future research are suggested.
Comparing different search methods for the open access journal recommendation tool B!SON

Abstract Finding a suitable open access journal to publish academic work is a complex task: Researchers have to navigate a constantly growing number of journals, institutional agreements with publishers, funders’ conditions and the risk of predatory publishers. To help with these challenges, we introduce a web-based journal recommendation system called B!SON. A systematic requirements analysis was conducted in the form of a survey. The developed tool suggests open access journals based on title, abstract and references provided by the user. The recommendations are built on open data, publisher-independent and work across domains and languages. Transparency is provided by its open source nature, an open application programming interface (API) and by specifying which matches the shown recommendations are based on. The recommendation quality has been evaluated using two different evaluation techniques, including several new recommendation methods. We were able to improve the results from our previous paper with a pre-trained transformer model. The beta version of the tool received positive feedback from the community and in several test sessions. We developed a recommendation system for open access journals to help researchers find a suitable journal. The open tool has been extensively tested, and we found possible improvements for our current recommendation technique. Development by two German academic libraries ensures the longevity and sustainability of the system.
Learning to disentangle latent physical factors of deformable faces

We proposed a monocular image disentanglement framework based on a compositional model. Our model disentangles the input image into its constituent components of albedo, depth, deformation, pose, and illumination. Instead of relying on any handcrafted priors, we trained our deep neural network to understand the physical meaning of each element by mimicking real-world operations, allowing it to reconstruct images in a self-supervised manner. Our model, trained on multi-frame images of each subject, demonstrates a better understanding of the objects without requiring any supervision or strong model assumptions. We utilized a deformation-free canonical space to align multi-frame images in the same space. This approach enables the understanding of information from multi-frame images in the same space. Our experiments showed that our approach accurately disentangled the physical elements of deformable faces from images with wide variations found in the wild.
<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e1174" altimg="si11.svg"><mml:mi mathvariant="sans-serif">TraceNet</mml:mi></mml:math>: Tracing and locating the key elements in sentiment analysis

We study sentiment analysis task where the outcomes are mainly contributed by a few key elements of the inputs. Motivated by the two-streams hypothesis, we explore processing input items and their weights separately by developing a neural architecture, named TraceNet, to address this type of task. It not only learns discriminative representations for the target task via its encoders, but also traces key elements at the same time via its locators. In TraceNet, both encoders and locators are organized in a layer-wise manner, and a smoothness regularization is employed between adjacent encoder-locator combinations. Moreover, a sparsity constraint is enforced on locators for tracing purposes and items are proactively masked according to the item weights output by locators. A major advantage of TraceNet is that the outcomes are easier to understand, as it identifies the key components responsible for the outcomes, making them easier to understand. The experimental results demonstrate its effectiveness in sentiment classification. Furthermore, we present case studies to showcase the interpretability of the model and conduct comprehensive analyses to highlight the impacts of each component.
TEACHING STUDENTS' WRITING ABILITY BY USING CLUSTERING TECHNIQUE TO THE EIGHTH GRADE STUDENTS OF STATE JUNIOR HIGH SCHOOL 2 OF MAKARTI JAYA

The main objective of this thesis was to find out the effectiveness of using clustering technique in improving students' writing ability to the eighth grade students of state junior high school 2 of Makarti Jaya. The method of used in this study was the experimental method. In the other words, experiment tries to compare one or more groups which are given the treatment, and other groups that is not given the treatment. The population of this research was students the eighth grade students of state junior high school 2 of Makarti Jaya with 46 students. The writer took two classes that were class 8A and 8B using purposive sampling technique. The data of this research were collected by written test: giving pre-test and post-test to the students sample. The collected data were analyzed using t-test formula. The result of the data analysis showed that the two-tailed-test with 22 (df) had a critical value of 0.000 at the 0,05 significant level. Because the t-value of 1,645 was higher that the t-table, Ho was rejected and Ha was accepted. Students in the experimental group were able to improve their writing skills through descriptive writing as a result of their treatment. It also means that clustering technique was effective to improve the students writing ability to the eighth grade students of state junior high school 2 of Makarti Jaya. _____________________
Prospects of digital scientific publishing on blockchain: The concept of DAP

<ns3:p><ns3:bold>Background:</ns3:bold> Traditional publishing models, open access and major publishers, cannot adequately address the key challenges of academic publishing today: Speed of peer review, recognition of work and incentive mechanisms, transparency and thrust of the system.</ns3:p><ns3:p> <ns3:bold>Methods:</ns3:bold> To address these challenges, the authors propose Decentralised Academic Publishing (DAP), which is based on the novel HashNET DLT platform. The DAP introduces several innovative components: tracking the activities of all participants in the peer review process using blockchain and smart contracts, the introduction of the Scholarly Wallet for holding reputation (non-fungible) and reward (fungible) tokens, the use of the Scholarly Wallet as the main interface to the DAP platform, the Virtual Editor that enables automatic discovery of the research area and invitation of reviewers, and finally the global database of evaluated reviewers, ranked by the quality of their previous work.</ns3:p><ns3:p> <ns3:bold>Results:</ns3:bold> The DAP platform is in the development phase, with the design and functionalities of all modules defined. An exception is the central component of DAP, the Scholarly Wallet module, whose first prototype has already been created, tested and published. The implementation of DAP is planned for the next phase of the HorizonEurope TruBlo project and other research initiatives. The DAP platform will be connected to the publishing ecosystem: 1) as a backend system (distributed blockchain database) for existing publishing platforms and 2) as a standalone publishing platform with its own API interface.</ns3:p><ns3:p> <ns3:bold>Conclusions:</ns3:bold> The authors believe that DAP has the potential to significantly improve academic peer review and knowledge dissemination. It is expected that the use of blockchain technology, the fast HashNET consensus platform and tokens for reward (fungible) and reputation/ranking (non-fungible) will lead to a more efficient and transparent way of rewarding all participants in the peer review process and ultimately advance scientific research.</ns3:p>
Freedom of expression meets deepfakes

Abstract Would suppressing deepfakes violate freedom of expression norms? The question is pressing because the deepfake phenomenon in its more poisonous manifestations appears to call for a response, and automated targeting of some kind looks to be the most practically viable. Two simple answers are rejected: that deepfakes do not deserve protection under freedom of expression legislation because they are fake by definition; and that deepfakes can be targeted if but only if they are misleadingly presented as authentic. To make progress, following a discussion of why freedom of expression deserves protection in a democracy, the question is reframed. At issue is not whether the arrival of deepfakes brings new and potentially serious dangers (it does), nor whether these dangers call for measures that potentially limit freedom of expression (they do), but whether the need for such measures raises any new and unfamiliar freedom-of-expression challenges. The answer to that question, surprisingly, is no. The balancing act needed to cope with the arrival of deepfakes brings plenty of difficulties, certainly, but none of the measures likely to be effective in tackling deepfake harms raises freedom-of-expression concerns that aren’t familiar from consideration of non-deepfake harms. In that respect, at least, the arrival of deepfakes makes no difference.
An efficient and secure dynamic quantum direct two-secrets sharing scheme

Dynamic quantum secret sharing occupies an important position in quantum cryptography. In this paper, an efficient and secure dynamic quantum direct two-secrets sharing scheme is proposed based on the GHZ state. The proposed scheme is a one-time sharing of a determined classic message and quantum states. For recovering the secret messages, the agents only need to have the ability to perform X-basis measurements on the particles without performing any unitary operation. When dynamically deleting agents, our scheme only requires the other agents to announce some information without transmitting any quantum. Moreover, the analysis shows that our scheme is able to resist dishonest revoked agent attack as well as a range of other common attacks. Compared with the existing dynamic quantum direct secret sharing schemes, the proposed scheme is more efficient and more secure.
Non-fungible tokens as a framework for sustainable innovation in pharmaceutical R&amp;D: a smart contract-based platform for data sharing and rightsholder protection

ABSTRACTABSTRACTResearch and development (R&D) in the pharmaceutical sector traditionally operated with in closed, siloed institutional settings, driven by intellectual property rights concerns that viewed data sharing as a threat. However, the evolving scientific landscape demands a more collaborative approach involving external engagement and dynamic partnerships. To address this, a hybrid contractual framework combining smart contracts, non-fungible tokens (NFTs), and traditional licensing schemes is introduced. This framework was developed through an experimental pilot platform that adhered to FAIR data principles, allowing participants to store, find, and reuse data related to drug discovery. The platform utilizes blockchain technology to document real-world assets in an immutable digital ledger. Smart contracts and NFTs offer an open and global collaborative platform for advancing drug research assets, overcoming hurdles related to standardization, interoperability, and disclosure. This framework aims to reconcile the conflict between the demand for greater data sharing and the protection of rightsholder interests in pharmaceutical R&D. By providing mechanisms for resolving practical challenges, it facilitates further cooperation and innovation in the field.KEYWORDS: Non-fungible token (NFT)moleculesmart contract Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThis work was partially supported by the Novo Nordisk Foundation under [grant number NNF17SA0027784] for a scientifically independent Collaborative Research Program in Biomedical Innovation Law, and by the VINNOVA project, ‘Blockchain-powered open sharing platform for collaborative drug discovery’, reference number [grant number 2019-02815].
Ballad of the Bullet: Gangs, Drill Music, and the Power of Online InfamyBallad of the Bullet: Gangs, Drill Music, and the Power of Online Infamy, by Forrest Stuart, Princeton: Princeton University Press, 2020, 288 pp., $27.95 (hardcover), ISBN: 9780691194431.

"Ballad of the Bullet: Gangs, Drill Music, and the Power of Online Infamy." Journal of Criminal Justice Education, 34(3), pp. 467–468
The Development of a Kazakh Speech Recognition Model Using a Convolutional Neural Network with Fixed Character Level Filters

This study is devoted to the transcription of human speech in the Kazakh language in dynamically changing conditions. It discusses key aspects related to the phonetic structure of the Kazakh language, technical considerations in collecting the transcribed audio corpus, and the use of deep neural networks for speech modeling. A high-quality decoded audio corpus was collected, containing 554 h of data, giving an idea of the frequencies of letters and syllables, as well as demographic parameters such as the gender, age, and region of residence of native speakers. The corpus contains a universal vocabulary and serves as a valuable resource for the development of modules related to speech. Machine learning experiments were conducted using the DeepSpeech2 model, which includes a sequence-to-sequence architecture with an encoder, decoder, and attention mechanism. To increase the reliability of the model, filters initialized with symbol-level embeddings were introduced to reduce the dependence on accurate positioning on object maps. The training process included simultaneous preparation of convolutional filters for spectrograms and symbolic objects. The proposed approach, using a combination of supervised and unsupervised learning methods, resulted in a 66.7% reduction in the weight of the model while maintaining relative accuracy. The evaluation on the test sample showed a 7.6% lower character error rate (CER) compared to existing models, demonstrating its most modern characteristics. The proposed architecture provides deployment on platforms with limited resources. Overall, this study presents a high-quality audio corpus, an improved speech recognition model, and promising results applicable to speech-related applications and languages beyond Kazakh.