Reviewerly/exHarmony huggingface dataset:
Responsible Recommendation Services with Blockchain Empowered Asynchronous Federated Learning

Privacy and trust are highly demanding in practical recommendation engines. Although Federated Learning (FL) has significantly addressed privacy concerns, commercial operators are still worried about several technical challenges while bringing FL into production. Additionally, classical FL has several intrinsic operational limitations such as single-point failure, data and model tampering, and heterogenic clients participating in the FL process. To address these challenges in practical recommenders, we propose a responsible recommendation generation framework based on blockchain-empowered asynchronous FL that can be adopted for any model-based recommender system. In standard FL settings, we build an additional aggregation layer in which multiple trusted nodes guided by a mediator component perform gradient aggregation to achieve an optimal model locally in a parallel fashion. The mediator partitions users into K clusters, and each cluster is represented by a cluster head. Once a cluster gets semi-global convergence, the cluster head transmits model gradients to the FL server for global aggregation. Additionally, the trusted cluster heads are responsible to submit the converged semi-global model to a blockchain to ensure tamper resilience. In our settings, an additional mediator component works like an independent observer that monitors the performance of each cluster head, updates a reward score, and records it into a digital ledger. Finally, evaluation results on three diversified benchmarks illustrate that the recommendation performance on selected measures is considerably comparable with the standard and federated version of a well-known neural collaborative filtering recommender.
Analysis of Movie Recommendation Systems

This research paper presents a comprehensive literature review of various movie recommendation systems, including techniques such as sentiment analysis of comments, movie trailer data, facial expressions, browsing history, and view percentage. The paper also compares various recommenders based on deep learning and swarm algorithms. Furthermore, this paper provides a novel approach for movie recommender system that uses a hybrid model combining content-based and collaborative filtering approaches. Specifically, the proposed approach involves creating a matrix factorization-based model and providing certain custom-designed features, such as averages of all movies and users, top similar movies, and top similar users, to produce the final model. The proposed approach is evaluated on the MovieLens dataset and achieves a mean absolute percentage error (MAPE) of 19.868316 and a root mean squared error (RMSE) of 0.672788. These outcomes illustrate the potency of the suggested strategy and its potential to enhance movie recommendation systems.
Detecting Contradiction and Entailment in Multilingual Text

With many applications, including text categorization, question-and-answer systems, and sentiment mapping, entailment and contradiction detection in multilingual content is a crucial task in the discipline of natural language processing. This study examines the capabilities of two advanced models, the BERT-based multilingual case model and the XLM-RoBERTa large model, in detecting entailment and contradiction in a multilingual dataset containing 15 diverse languages. While the initial performance of BERT and XLM-RoBERTa models was 30% and 40% respectively, we have successfully enhanced the XLM-RoBERTa model’s accuracy to an impressive 70% using data processing techniques. These results imply that, by carefully selecting models and employing the relevant data processing techniques, it is possible to detect contradiction and entailment in multilingual text with high accuracy. The study’s outcomes are relevant to various areas of natural language processing, including the analysis of multilingual text. These results lay the groundwork for future studies in language processing and can guide the development of more sophisticated models.
A Study on Similarity and Difficulty Evaluation of Elementary School Mathematics Application Problems Based on Cosine Similarity and AHP

With the rapid development of online education, how to design a model for evaluating the similarity and difficulty of elementary math application problems has become an important research direction in the field of education. In order to measure the similarity and difficulty of elementary school math application problems, in this paper, after transforming the problems into feature vectors, the similarity of the two problems can be calculated by using measures such as cosine similarity and Euclidean distance. Then, six indicators, namely, the difficulty of the knowledge point of the problem, the difficulty of solving the problem, the logical difficulty of the problem, the linguistic difficulty of the problem, the difficulty of the picture of the problem, and the practicality of the problem, are selected, and the weights of each indicator are assigned by using the method of hierarchical analysis to determine the degree of importance of each indicator, which is used to determine the difficulty of the mathematical application problems at last.
Research on Teaching Reform of Mechanical Drawing Course for Applied Undergraduate Robot Engineering Major

Under the background of national manufacturing transformation and upgrading, the teaching reform of "mechanical drawing" course of robot engineering major is carried out. Based on the training objectives of robot engineering major and the positioning of school, the training direction of this major is optimized. The reform of "mechanical drawing" course is proposed to keep up with the frontier of the discipline and rely on discipline competition and teacher scientific research. On this basis, the curriculum is optimized to reduce the obsolete content of descriptive geometry, enrich the engineering drawing part, rationally arrange the teaching purpose, teaching content and teaching methods of the course according to the requirements of engineering certification, and constantly improve the course scoring standards and the evaluation system of course evaluation standards. The process evaluation gauge, curriculum objective and graduation requirement index points make the curriculum reform and construction achieves good results. The improvement of the teaching quality of mechanical drawing will be promoted through the enhancement of Students' engineering application and innovation ability.
Large Language Models for Recommendation: Progresses and Future Directions

The powerful large language models (LLMs) have played a pivotal role in advancing recommender systems. Recently, in both academia and industry, there has been a surge of interest in developing LLMs for recommendation, referred to as LLM4Rec. This includes endeavors like leveraging LLMs for generative item retrieval and ranking, as well as the exciting possibility of building universal LLMs for diverse open-ended recommendation tasks. These developments hold the potential to reshape the traditional recommender paradigm, paving the way for the next-generation recommender systems.
Chuweb21D: A Deduped English Document Collection for Web Search Tasks

As a traditional information retrieval task, ad hoc web search has long been an important part of IR research and evaluation tracks (e.g. TREC, NTCIR and CLEF). A crawled, large-scale web document collection is a central component to offline web search evaluation. Although there already exist several English document collections, such as the ClueWeb series, GOV2 and c4, a collection that satisfies properties of both strong timeliness and raw HTML formatting is still relatively scarce.
Multi-Behavior Job Recommendation with Dynamic Availability

In recent years, we can see a lot of job postings on the Internet, providing us with more diverse job opportunities. As a result, it is getting more and more difficult for job seekers to find job postings relevant to their preferences. Consequently, job recommendations play an important role to reduce the burden of job searching. Generally, job postings have a publication period, for example, 30 days. Then they have been expired since the positions were occupied. As a result, job seekers may be frustrated when they experience such situations as they cannot apply for the positions. This indicates that job seekers may have strong preferences for job postings even if their application behaviors cannot be observed. This kind of gap has not been investigated in the line of Multi-Behavior Recommendation. Therefore, in this work, we propose a new job recommendation model, called Multi-Behavior Job Recommendation with Dynamic Availability (MBJ-DA), which takes into account: (1) auxiliary behaviors other than an application behavior and (2) the influence of dynamic availability of job postings. MBJ-DA enables a more accurate estimation of each user’s actual preferences by explicitly distinguishing the noise potentially inherent in auxiliary behaviors. Furthermore, by explicitly considering the influence of the dynamic availability of job postings, MBJ-DA can mitigate biases resulting from the influence and estimate each user’s actual preferences more accurately. Experimental results on our dataset constructed from an actual job search website show that MBJ-DA outperforms several state-of-the-arts in terms of MRR and nDCG.
Investigation of Future Teachers’ Digital Literacy and Technology Use Skills

It is essential for pre-service teachers to have digital literacy and technology use skills in order to integrate technology into education and training processes both effectively and efficiently. In this study, the digital literacy and technology use skills of pre-service teachers from different universities in Kazakhstan were examined comparatively in terms of some variables. The study was conducted with the participation of 209 pre-service teachers according to the survey and relational survey model. 'Digital Literacy' and 'Technology Use Skills' scales were used to collect the research data. According to the findings of the study, in general, pre-service teachers' technology use skills and digital literacy were found to be at a high level. The digital literacy and technology use levels of the participant pre-service teachers differed according to gender and grade level. The technology use skills of male participants were found to be higher than their female peers. It was also discovered that pre-service teachers studying in upper grades had high digital literacy and technology use skills. Finally, pre-service teachers' digital literacy significantly predicts their technology use skills.
Analysis of Peeling Chain Model in Bitcoin Mixing Service

In Bitcoin, users often use mixing services to conceal the true information of transactions and protect their privacy. In these services, the peeling chain is a common mixing method that distributes funds to users through multiple consecutive transactions connected by change addresses. This paper investigates the peeling chain model in Bitcoin mixing services, categorizing it into three parts: user input transactions, merge transactions, and distribution transactions. Additionally, a peeling chain search algorithm is proposed to detect distribution transactions in the chain, and its validity is demonstrated through experiments. The findings of this study have implications for other Bitcoin mixing services.
Dataset of user interactions across four large pilots on the use of augmented reality in learning experiences

Abstract Augmented Reality in education can support students in a wide range of cognitive tasks–fostering understanding, remembering, applying, analysing, evaluating, and creating learning-relevant information more easily. It can help keep up engagement, and it can render learning more fun. Within the framework of a multi-year investigation encompassing primary and secondary schools across Europe, the ARETE project developed several Augmented Reality applications, providing tools for user interaction and data collection in the education sector. The project developed innovative AR learning technology and methodology, validating these in four comprehensive pilot studies, in total involving more than 2,900 students and teachers. Each pilot made use of a different Augmented Reality application covering specific subjects (English literacy skills, Mathematics and Geography, Positive Behaviour, plus, additionally, an Augmented Reality authoring tool applied in a wide range of subjects). In this paper, we introduce the datasets collected during the pilots, describe how the data enabled the validation of the technology, and how the approach chosen could enhance existing augmented reality applications in data exploration and modelling.
Marker-based Augmented Reality Application for Car Basic Maintenance

In general, Augmented Reality (AR) is a technology that allows the virtual object to complement the real-world environment. The rapid growth of technology has enabled mostly anyone to use AR technology. Thus, applying AR technology in the learning experience should normalize as it is now easy to access and there is much positive feedback based on the past research regarding AR in education. Unlike watching tutorial videos, AR teaching applications would provide users with different learning experiences. Therefore, the aim of this research is to provide more immersive learning experiences to drivers to learn simple car maintenance. This application will illustrate 3D steps and procedures of simple car maintenance for drivers to learn in helping them to self-service their car. Consequently, there are 4 phases of this project that need to be carried out to achieve the objectives of this research. The first phase is to review the AR application concept and understand the car services techniques. The second phase is to design and develop the AR application that will provide simple car services techniques. The third phase is integrating the AR car services manual and finally evaluating the effectives of this application in providing the car basic maintenance tasks and knowledge to new drivers. The evaluation discovered that the drivers have more interaction during learning experience using the AR application.
Comparative Efficacy of Elsa and English Speaking Practice: A Quasi-Experimental Study on EFL Learning Outcomes

In an era where digital learning tools are reshaping the educational landscape, selecting the right language learning application becomes paramount, especially for institutions aiming to enhance their students' English proficiency. While numerous language applications exist, only a few are accessible without cost. This study zeroes in on two such free applications: ELSA and English Speaking Practice. The choice of these apps aimed to discern the instructional differences and their subsequent impact on learners. Conducted at SMK Negeri 17 Samarinda, the research involved two experimental classes: X Pharmacy 4 (33 students) and X Medical Laboratory Technology 1 (34 students). Using a Quasi-Experimental design, the study employed quantitative methods, including pre-test and post-test evaluations, to gauge the comparative effectiveness of the apps. The results were illuminating: the class utilizing ELSA witnessed a proficiency uptick of 15.21%, whereas the English Speaking Practice class observed a 13% increase. With a p-value of 0.000 &lt;0.05, the data conclusively shows significant disparities in the efficacy of the two applications. This research underscores the importance of judiciously selecting digital tools in EFL contexts, ensuring optimal learning outcomes.
THE SYNTHESIS OF THE ARTS IN THE WORKS OF PAUL FLORENSKY AS THE BASIS OF THE DESCRIPTION OF STAGE SYNTHESIS IN THE CULTURE OF THE 20TH CENTURY

The purpose of the article is to determine the scenic phenomenon of the anthropological boundary in the context of the synthesis of arts as iconography, according to P. Florenskyi. The research methodology is presented with comparative and systematic approaches. Transcendental, phenomenological and dialectical methods are used from philosophical methods, which make it possible to carry out a philosophical and anthropological analysis of the synthesis of arts as a dynamic integrity in the context of possible, existing and post-existing modes of existence of an artistic work. Scientific novelty of the article. It is proved that Pavlo Florenskyi, in the context of the search for the Silver Century, carried out a monumental project of the synthesis of arts, which presents several faces of the human artist as a subject of the culture of the 20th century.
Analysis and correspondence between the entities of Europeana Data Model, IFLA LRM and BIBFRAME conceptual models

Objective: To propose the harmonization between the EDM, BIBFRAME, and IFLA LRM models with the purpose of analyzing the equivalence of entities to enable the interoperability between systems that use these conceptual models as a basis. Method: This research is characterized as exploratory and descriptive with a qualitative approach, in which a bibliographic survey was used to identify the studies already carried out on the topic. In addition, the analysis of entities was based on the methodology substantiated by Pierre et al. (1999), Taniguchi (2018), Arakaki (2019), and Carrasco (2019). Results: From the analysis of the models, six ontological categories of entities were identified: (i) Intellectual Content, (ii) Subject, (iii) Concrete Unit, (iv) Agent, (v) Space-time, and (vi) Reification. Consistent equivalences were observed between the entities, their functionalities, and terminology in most categories, except for the entities of the Intellectual Content category and the structuring of the Concrete Unit category. Conclusions: It was concluded that the most notable difference between the models was expressed in the Intellectual Content category, and that, because they are based on the cataloging tradition, the IFLA LRM and BIBFRAME models have functionally equivalent entities, while the EDM model, focused on the cultural heritage object itself, does not have any entity that may be mapped in this category. Likewise, a structural difference was found in the Concrete Unit category, in which the EDM model makes a distinction between the object itself and its digital representation, whereas such a distinction occurs in the Intellectual Content category for the IFLA LRM and BIBFRAME models.
A GANs-Based Approach for Stock Price Anomaly Detection and Investment Risk Management

This paper addresses the challenges of risk management in the financial market through a data-driven approach. In investment management, it is important to detect and avoid market anomalies, defined as significant deviations from typical stock price movement patterns. We propose a method that utilizes Generative Adversarial Networks (GANs) to detect anomalous patterns in stock prices. We devise simple investment strategies based on the GANs-based anomaly detection model and validate them on real-world data to demonstrate if the proposed approach can reduce investment risk. Experimental results show that GANs-based anomaly detection can be successfully incorporated into investment strategies to yield superior returns and Sharpe ratio and much reduced volatility and maximum drawdown compared to the S&P500 benchmark. This study shows the potential of data-driven approaches in detecting and managing investment risk.
Truthmakers and Relevance for FDE, LP, K3, and CL

In this paper, we first develop truthmaker semantics for four relevance logics defined as the non-transitive relevant cores [as introduced in Verdée et al. (Aust J Log 16:10–40, 2019)] of the well-known propositional logics $${\textbf {CL}}$$ (classical logic), $${\textbf {LP}}$$ (the logic of paradox), $${\textbf {K3}}$$ (strong Kleene logic), and $${\textbf {FDE}}$$ (first degree entailment). The semantics is similar to Kit Fine’s truthmaker semantics for classical logic, but we define the notion of exact verification similarly to Fine’s notion of loose verification. Dropping Fine’s principle of Downward Closure of the set of consistent states nevertheless warrants the exactness of our verification notion. The semantics of the four non-transitive relevance logics shows that they are in fact straightforward cut-free substructural logics, despite their definition by means of a filtering criterion. We develop the associated sound and complete sequent calculi for these logics. Finally, we argue that the four presented truthmaker semantics are also interesting alternatives to the standard Kit Fine style truthmaker semantics for the original (irrelevant) consequence relations $${\textbf {FDE}}$$ , $${\textbf {LP}}$$ , $${\textbf {K3}}$$ , and $${\textbf {CL}}$$ themselves. The most interesting difference with Fine’s approach is the way in which tautologies are handled: next to their usual verifiers, they are also made true by the empty state. We provide philosophical arguments for the plausibility of such an account.
Multimedia Application based on Virtual Reality to Introduce College Majors in Universities

The goal of this project is to create virtual reality-based multimedia programs that can introduce users to fresh university experiences and information. Many prospective students are currently selecting the incorrect major, which could have negative effects on those prospective students. The Game Development Life Cycle, often known as the GDLC approach, is the development process method employed in this application. The GDLC process begins with initiation and progresses through pre-production, testing, beta, and release. Several respondents have tested the application. According to the survey's findings, 74.5% of respondents said that this application may encourage students to select the majors they are passionate about. According to the testimonial results, 77.1% of respondents thought this application was simple to understand.
The Role of Customer Satisfaction and Awareness as A Measure of Customer Loyalty- A Study on Select Banks

Purpose: The study wants to examine customer loyalty through customer satisfaction and awareness using developed constructs for select Nifty Banks.&#x0D; &#x0D; Theoretical framework: The theoretical framework for the study is to analyze customer loyalty through customer satisfaction and awareness using constructs of General Services (GS), Product Cognizance (PC), Technology Services (TS), and Social Responsibility (SR).&#x0D; &#x0D; Design/methodology/approach: The cluster sampling method was used to collect data from bank customers (n=360) using the self-administered questionnaire. The research used Partial Least Squares – Structural Equation Modelling (PLS-SEM) to test the hypothesis using SmartPLS 4.&#x0D; &#x0D; Findings: The result showed, that there is low customer satisfaction with Technology Services (TS) and that hypothesis was rejected.&#x0D; &#x0D; Research, Practical &amp; Social implications: The study has provided recommendations for banks were discussed to pay more attention to customer fulfilment and awareness which convert satisfaction to customer delight.&#x0D; &#x0D; Originality/value: Due to market sustainability and dynamics, the Indian banking industry has undergone many major changes such as digital banking units (DBU), digital currencies, and social green initiatives. Bank has incorporated many advancements to overcome its rivals. So, the study wants to know the customers are aware of these changes among their banks and whether they are satisfied with products and services.