Reviewerly/exHarmony huggingface dataset:
CLRN: A reasoning network for multi-relation question answering over Cross-lingual Knowledge Graphs

Cross-lingual Knowledge Graphs-based Question Answering (CLKGQA) requires the question answering (QA) system to combine the knowledge graphs (KGs) in different languages to obtain answers to input questions. In previous works, the common idea is to merge Cross-lingual Knowledge Graphs (CLKGs) into a single KG through aligned entity pairs and then treat it as a traditional KG-based QA. However, as demonstrated by Tan et al. (2023), existing Entity Alignment (EA) models cannot generate highly accurate aligned entity pairs for CLKGs. Therefore, two issues need to be addressed in the CLKGQA task: (1) Remove the dependency of the QA model on the fused KG; (2) Improve the performance of the EA model in obtaining aligned entity pairs from locally isomorphic CLKGs. To solve the above two issues, this paper presents Cross-lingual Reasoning Network (CLRN), a novel multi-hop QA model that allows switching knowledge graphs at any stage of the multi-hop reasoning. Furthermore, we establish an iterative framework that combines CLRN and EA model, in which CLRN is used for extracting potential alignment triple pairs from CLKGs during the QA process. The extracted triple pairs provide pseudo-aligned entities, and the additional aligned entity pairs are used to mine missing relations between entities in CLKGs. These pseudo-aligned entity pairs and relations improve the performance of the EA model, resulting in higher accuracy in QA. Extensive experiments demonstrate the effectiveness of the proposed model, which outperforms the baseline approaches. Through iterative enhancement, the performance of the EA model has also been improved by > 1.0 % in Hit@1 and Hit@10, and the improvement is statistically significant in the confidence interval of p<0.01. Moreover, our work discusses the correlation between QA and EA from the side of QA, which has reference value for the follow-up exploration of related communities. We have open-sourced our dataset and code, which is available at the URL https://github.com/tan92hl/Cross-lingual-Reasoning-Network-for-CLKGQA.
The Effect of AI Chatbot-Based Tourism English Instruction on Intercultural Communicative Competence

The present study aimed at investigating AI chatbot-based Tourism English instruction for college students to develop their intercultural communicative competence. For the study, two distinctive models of AI based educational approach were proposed; learning with AI (LWAI) and learning about AI (LAAI). A total of 50 Tourism English 3 learners with the model of LWAI were assigned to conduct a conversation with the chatbot during the semester, while 20 Tourism English 6 learners with the model of LAAI were asked to build a hotel English chatbot. In LWAI, the collected chatbot conversation reports were analyzed in terms of task completion rates. It was found that the chatbot conversations mostly involved small talk rather than problem-solving tasks. In LAAI, Tourism English learners collected English for hotel reservations and coded data into the hotel English chatbot with the help of computer engineering students. As a result of a team project, the hotel English chatbot building was successful. Lastly, there was an increase in participants’ intercultural communicative competence in terms of respect for cultural differences and interaction attentiveness. In short, the present study showed that the AI chatbot-based Tourism English instruction had a positive effect on the development of intercultural communicative competence.
Shadowing Practice Using TED Talks on the Metaverse Platform, Gather: Affective Factors and Oral Proficiency Development

This study aimed to investigate the effectiveness of employing the metaverse platform Gather for shadowing practice using TED Talks, focusing on the development of oral proficiency and affective attitudes among Korean EFL learners. A total of 49 college students participated, divided into two experimental groups. Group 1 (&lt;i&gt;n&lt;/i&gt; = 24) engaged in shadowing practice on Gather, while group 2 (&lt;i&gt;n&lt;/i&gt; = 25) partook in the same shadowing activities in a traditional classroom. Data collection involved pre- and post-speaking tasks and pre- and post-questionnaires assessing affective attitudes. The results revealed significant improvements in speaking skills for both groups, with group 1 demonstrating a higher degree of effectiveness. Furthermore, participants in both groups exhibited increased interest, motivation, and confidence levels. However, a notable difference emerged in anxiety levels, as the students in group one displayed a significant decrease in speaking anxiety, while those in group two showed no improvement in speaking anxiety after participating in the study. The findings indicate that integrating Gather for shadowing practice can effectively enhance Korean EFL learners’ oral proficiency and positively influence their affective attitudes toward language learning. The study contributes to the growing body of research on the potential of metaverse platforms as alternative learning environments for language instruction.
Revitalizing reference architectures through modularity

We introduce a new method of building digital reference architectures (RA) using modular, reusable elements within the RA itself. Modular RAs using this method define the domain of interest via reusable elements that are used as building blocks for downstream system developments. This method eliminates the reliance on static documentation and enables the building of tailored systems aligned to current RA objectives and content. An advantage of this method is that users interact with the RA without the need for software licenses or architecture expertise. This paper describes the underlying modular RA design and tools developed for user interaction.
NFT Appraisal Using Machine Learning

Non-Fungible Tokens (NFTs) are digital assets based on a blockchain and those are characterized as unique cryptographic tokens and non-interchangeable. To date, research into the NFT marketplace has been relatively limited. As it is an emerging platform with many unique elements, The NFT market has been impacted due to recent fluctuations in crypto-asset markets more broadly. This current bear market cycle has shed light on concerns around the value of NFTs, profit-based motivation, and environmental sustainability. However, periods of volatility and cyclicality are to be expected with any nascent technology as it develops a product-market fit. consequently, the appraisal of real-price for NFT collections is essential for individual financial security and investment making. In this study, we evaluate the machine learning algorithms to appraise their real-price based on NFT item's characteristics, market event information, and their rarity score data acquired by retrieved from the biggest marketplace OpenSea. Furthermore, the procedures were applied to meet the objectives of this study we built prediction models based on various machine-learning algorithms ranging from Random Forest, XGBoost, SVM, Lasso, ElasticNet, Ridge, Linear Polynomial Regression, TabNet, CatBoost, and LightGBM models. From the results, LightGBM regression model outperformed the other by RMSE around 0.905. The best R2 is only found in this model, which has a value of 0.917.
CircuitGIue

We present CircuitGlue, an electronic converter board that allows heterogeneous electronic components to be readily interconnected. Electronic components are plugged into an eight-pin programmable header on the board, and the assignment of each pin in the header is configured in software. CircuitGlue supports a variety of connections, including power, ground, analog signals, and various digital protocols at different voltages. As such, off-the-shelf electronic components and modules are instantly compatible no matter what voltage levels, interface types, communication protocols, and pinouts they use. In this paper, we demonstrate the use of CircuitGlue to ease and expedite prototyping with electronics and we explore new opportunities enabled by CircuitGlue. Finally, we reflect on the results of a preliminary user study evaluating the usability of CircuitGlue for people new to electronics.
Supporting Solar Energy Coordination among Communities

The transition to renewable energy is likely to require the creation of growing numbers of energy communities: collectives organized around shared, local renewable resources. Unlike individual households however, the requirements for such communities to share a resource and demand-shift their consumption are still unexplored. By deploying a custom sensor energy monitoring kit and data physicalization workshops with 17 households, we examine the factors that impact their coordination around the shared resource. We found that collective demand-shifting has an extended set of considerations including trade-offs related to privacy, flexibility and social cohesion which are core for navigating already delicate neighborly relations. We use these factors to propose design considerations for a digital system that can act as a mediator among households. Such a system should enable multiple levels of immediacy to account for people's routines, should have adjustable levels of privacy to balance policing and fairness and should be able to offload some of the mundane decision-making. This study moves beyond individual energy consumption behavior to help identify energy as a collective issue that demands collective action. Accordingly, our findings contribute to the development of a next generation of Ubicomp technologies that can support collective action for environmental sustainability.
PrintShear

Most touch-based input devices, such as touchscreens and touchpads, capture low-resolution capacitive images when a finger touches the device's surface. These devices only output the two-dimensional (2D) positions of contacting points, which are insufficient for complex control tasks, such as the manipulation of 3D objects. To expand the modalities of touch inputs, researchers have proposed a variety of techniques, including finger poses, chording gestures, touch pressure, etc. With the rapid development of fingerprint sensing technology, especially under-screen fingerprint sensors, it has become possible to generate input commands to control multiple degrees of freedom (DOF) at a time using fingerprint images. In this paper, we propose PrintShear, a shear input technique based on fingerprint deformation. Lateral, longitudinal and rotational deformations are extracted from fingerprint images and mapped to 3DOF control commands. Further DOF expansion can be achieved through recognition of the contact region of the touching finger. We conducted a 12-person user study to evaluate the performance of PrintShear on 3D docking tasks. Comparisons with other input methods demonstrated the superiority of our approach. Specifically, a 19.79% reduction in completion time was achieved compared with conventional touch input in a full 6DOF 3D object manipulation task.
The effect of intrinsic decoherence on the dynamics of an Ξ-type qutrit system interacting with a coherent field

Abstract This paper solves milburn’s intrinsic noise (IN) model for a 3-level atom of an Ξ-type interacting with a coherent cavity field via multiphoton transitions. Therefore, the effects of the intrinsic noise and the multi-photon interactions are investigated for some quantum phenomena, such as total correlation, entanglement, and atomic inversion. In general, we found that the collapse-revival phenomenon occurs during the oscillatory behaviour of atomic population dynamics. In addition, the birth-death phenomenon is observed in the negativity dynamics. Entropy, negativity, and mutual information have various dynamics. It is found that, as the entropy increases, the negativity and mutual information diminish to stationary levels. When intrinsic noise is considered, all the phenomena of atomic inversion, entropies, negativity and mutual information exhibit high sensitivity to high intrinsic noise values, except the mutual information dynamics, which is more resistant than that of the other quantifiers.
A Case Study on Pronunciation Self-Assessment for Korean Learners Using Speech Recognition Technology, ‘Naver CLOVA Note’

The purpose of this study is to verify the effectiveness of semester-long pronunciation education using CLOVA Note voice recognition program. 38 students in Korean class were asked to describe what they experienced from the recording assignments using CLOVA Note, teacher feedback, and self-pronunciation assessment. Through this process, students identified the frequent errors they made, analyzed the errors and found the way to improve the accuracy of their pronunciation. The significance of this study is that the self-assessment of learners’ pronunciation using speech recognition technology was effective in pronunciation education.(Hankuk University of Foreign Studies)
Application Practice of Intelligent Steel Bar Centralized Processing Control Based on RMES Cloud Platform–Taking Chongqing East Station Project as an Example

In centralized management of steel bar processing in construction industry, the following problems are present, including unclear plan, no saving of raw materials, uncontrolled progress, difficulty in responsibility tracking, low management efficiency and untimely warning. The project of Chongqing East Station adopts the intelligent operating system of "MAX RMES Steel Bar Management Cloud Platform", which integrates advanced technologies such as Internet of Things, cloud computing, big data, modern logistics and distribution to achieve digitalization of the whole process from raw material arrival, intelligent steel bar retrofitting, automatic processing of platform distribution equipment and informatized delivery. A set of experience is summarized for intelligent centralized steel bar construction, management and operation, which carries important practical and guiding significance for the intelligent application of centralized steel bar processing, generating enormous economic and social benefits, and demonstrating strong promotion and application value.
A study on the corpus expansion method of neural machine translation based on reverse transcription grammar

The current conventional corpus expansion methods mainly obtain the core words by mining the similarity of utterances to achieve the expansion of the corpus, which is better than the lack of decoding and encoding processing of the corpus data, resulting in the poor expansion effect. In this regard, a neural translation corpus expansion method based on reverse transcription grammar is proposed. The CYK decoding algorithm is combined with the move-in-reduction decoding algorithm to encode and decode the corpus data, and the difference and product of the corpus elements are quantified to realize the matching of utterance information, and finally the three-level filtering mechanism is used to obtain the core words under different classes to realize the corpus expansion. In the experiment, the proposed method is validated for the expansion effect. The analysis of the experimental results shows that the proposed method expands the corpus with high bilingual evaluation substitution values and has a more desirable expansion effect.
Food Safety Traceability and Application Based on Blockchain

This paper aims at the current food safety problems, because the traditional food traceability system can not guarantee that the food safety problems can accurately trace and trace. With the improvement of science and technology levels and the development of society, blockchain technology has gradually entered people's vision. Using blockchain technology can not be tampered with, information transparency and other characteristics can make food safety traceability can be realized. This article analyzes the traditional food traceability system and the food safety traceability system based on blockchain technology, compares the differences between the two, and lists the advantages of the food safety traceability system under blockchain technology.
Adaptive Channel Scheduling for Acceleration and Fine Control of RNN-Based Image Compression

The existing target-dependent scalable image compression network can control the target of the compressed images between the human visual system and the deep learning based classification task. However, in its RNN based structure controls the bit-rate through the number of iterations, where each iteration generates a fixed size of the bit stream. Therefore, a large number of iterations are required at the high BPP, and fine-grained image quality control is not supported at the low BPP. In this paper, we propose a novel RNN-based image compression model that can schedule the channel size per iteration, to reduce the number of iterations at the high BPP and fine-grained bit-rate control at the low BPP. To further enhance the efficiency, multiple network models for various channel sizes are combined into a single model using the slimmable network architecture. The experimental results show that the proposed method achieves comparable performance to the existing method with finer BPP adjustment, increases parameters by only 0.15% and reduces the average amount of computation by 40.4%.
Characterizing and Identifying Socially Shared Self-Descriptions in Product Reviews

Online e-commerce product reviews can be highly influential in a customer's decision-making processes. Reviews often describe personal experiences with a product and provide candid opinions about a product's pros and cons. In some cases, reviewers choose to share information about themselves, just as they might do in social platforms. These descriptions are a valuable source of information about who finds a product most helpful. Customers benefit from key insights about a product from people with their same interests and sellers might use the information to better serve their customers needs. In this work, we present a comprehensive look into voluntary self-descriptive information found in public customer reviews. We analyzed what people share about themselves and how this contributes to their product opinions. We developed a taxonomy of types of self-descriptions, and a machine-learned classification model of reviews according to this taxonomy. We present new quantitative findings, and a thematic study of the perceived purpose descriptions in reviews.
Digital Competencies in Blockchain Technology

Blockchain is a development that draws in a got and circled segment to record trades. Blockchain used as digital competencies based in education fields to enhance the education in era. The advantages are enabled through the use of public-key encryption and peer-to-peer (P2P) architecture, as well as the use of public or private blockchain networks. Today, although blockchain is becoming more popular, some people are still skeptical about its long-term viability, scalability, and security. Distributed ledger technology, or blockchains, have been compared to the early days of the internet, with comments and debates regarding the technology’s potential to disrupt a variety of industries, including healthcare, government, energy, manufacturing, and, most significantly, financial services, where it is expected to be the beating heart of finance and the eventual giver of a new industry fabric.
Blockchain for Network Service Orchestration: Trust and Adoption in Multi-Domain Environments

In the coming years, blockchain technologies will be used in a variety of industries, including telecommunications. In this article, due to strict governance of telecommunication infrastructure, we propose a blockchain supported architecture, based on a permissioned distributed ledger (PDL) scheme, for a network management and orchestration platform. The main goal is to create a trusted environment for multiple-stakeholders, such as Cloud Service Providers (CSPs), a Mobile Network Operator (MNO), Vertical Service Providers (SPs), Legal and Regulation Authorities, and Responsible Ministry so that the life cycle of automated vertical network services (e.g., instantiation, scaling, termination, and migration/reallocation) can be managed securely and transparently in a multi-cloud and multi-domain environment. The proposed approach is also validated with an experimental Industry 4.0 scenario using the Quorum blockchain network (BCN) to measure various performance metrics (e.g., number of transactions and blocks, and time to write) of various service orchestrator (SO)-related instantiation metrics. At the end of the article, we present the main discussions on the evaluation results and existing standardization efforts for the convergence of BCN, Management and Orchestration (MANO), and network services for a given telecommunication infrastructure.
Gesture Controlled Automated Vehicle

Future roads will look very different if the concept of Autonomous vehicles would come in existence. This can be achieved by making fully autonomous vehicles by keeping human drivers out of the loop. However, even having such a facility, people worldwide believe that the link between the car and the driver is of utmost importance to a certain extent. So making manual driving a bit easier and having some advantages of an automated vehicle, we proposed an autonomous model in which the driver is allowed to control the vehicle’s lateral and longitudinal motions. An automated vehicle has 6 levels of automation ranging from level 0 to level 5. Level 0: is entirely handled by a human; Level 2: Assisted driving Automation; Level 3: Partial Automation; Level 4: High Automation and 5 being entirely handled by a machine. In our model for "level 3," the Advanced Driving System (ADS) is capable of handling all aspects of driving under some circumstances, but a human driver is still required to be able to maintain control. Hand-gesture controlled Automated Vehicle, can carry out tasks such as line detection, road-end detection and object detection. The hand- gesture controlling can act as a semi-autonomous mode which can be attained with the help of a switch as per the need of the user. This can be done by allowing the user to control the vehicle using an accelerometer sensor connected via a hand glove, helping with the movement of the vehicle. The hand gesture controlled automated vehicle provides a number of benefits, including the ability to manage the speed of the vehicle, reduce the incidence of accidents, and easily make tight turns.
Sign Language Translator for Speech Impaired People

Sign language is a way of transfer feelings and our thoughts non-verbally. People who are hearing impaired, dumb or speechless use sign language as their primary means of communication. For communication these people apply gestures which are based on hand signals to share their ideas. Sadly, the overwhelming most of the individuals aren't awake to the linguistics of those gestures. In a try to overcome these gaps, we offer real time Sign Language identification system which is based on the American Sign Language (ASL) Dataset. The system we proposed uses the CNN (Convolutional Neural Network) algorithm to recognize and interpret static hand signals of letters relating to American Sign Language into written output. An Android based application is produced for this system and further it can convert the text into Speech. The text on the screen is translated into voice using a text-to-speech feature. Text-to-speech is frequently employed as an accessibility tool to aid individuals who struggle to read text on screens, but it is also practical for those who want to be read to. This feature has proven to be quite popular and helpful for users.