Reviewerly/exHarmony huggingface dataset:
Light mixed‐supervised segmentation for 3D medical image data

Abstract Background Accurate 3D semantic segmentation models are essential for many clinical applications. To train a model for 3D segmentation, voxel‐level annotation is necessary, which is expensive to obtain due to laborious work and privacy protection. To accurately annotate 3D medical data, such as MRI, a common practice is to annotate the volumetric data in a slice‐by‐slice contouring way along principal axes. Purpose In order to reduce the annotation effort in slices, weakly supervised learning with a bounding box (Bbox) was proposed to leverage the discriminating information via a tightness prior assumption. Nevertheless, this method requests accurate and tight Bboxes, which will significantly drop the performance when tightness is not held, that is when a relaxed Bbox is applied. Therefore, there is a need to train a stable model based on relaxed Bbox annotation. Methods This paper presents a mixed‐supervised training strategy to reduce the annotation effort for 3D segmentation tasks. In the proposed approach, a fully annotated contour is only required for a single slice of the volume. In contrast, the rest of the slices with targets are annotated with relaxed Bboxes. This mixed‐supervised method adopts fully supervised learning, relaxed Bbox prior, and contrastive learning during the training, which ensures the network exploits the discriminative information of the training volumes properly. The proposed method was evaluated on two public 3D medical imaging datasets (MRI prostate dataset and Vestibular Schwannoma [VS] dataset). Results The proposed method obtained a high segmentation Dice score of 85.3% on an MRI prostate dataset and 83.3% on a VS dataset with relaxed Bbox annotation, which are close to a fully supervised model. Moreover, with the same relaxed Bbox annotations, the proposed method outperforms the state‐of‐the‐art methods. More importantly, the model performance is stable when the accuracy of Bbox annotation varies. Conclusions The presented study proposes a method based on a mixed‐supervised learning method in 3D medical imaging. The benefit will be stable segmentation of the target in 3D images with low accurate annotation requirement, which leads to easier model training on large‐scale datasets.
A blockchain framework for digitizing securities issuance: the case of green bonds

Bond issuance is a highly technical and complicated process, including disparate regulatory frameworks, limited traceability and auditability, settlement failures, and mutually untrusted stakeholders. Green bond issuance presents additional challenges, as the qualification of a bond as ‘green’, third-party verification is needed to guarantee that the proceeds fund environmentally beneficial projects. This implies additional administrative and compliance costs. Blockchain technology can address some of the issues mentioned above, to establish trust in impact reporting green bond processes. To this end, we propose a blockchain-enabled green bond issuance architecture that safeguards investors' confidence in the bond's green credentials while keeping the issuer from being accused of greenwashing. To adjust the process of bond issuance to a blockchain-enabled model, we tokenize the bonds. The digital token is created through a smart contract with a specific standard (in our case, ERC-20). Within the smart contracts developed, we use various functions to handle the prerequisites of validators and regulators' approval based on the documentation presented and the parameters of rate and maturity requested by the issuer. We use a separate smart contract to offer forensic-by-design services. The overall system also considers various regulatory compliance instruments and enhances the access of regulatory bodies to issuance records.
Applications and Implication of Generative AI in Non-STEM Disciplines in Higher Education

There has been considerable research on the use of generative artificial intelligence techniques to support teaching and learning in science, technology, engineering, and mathematics (STEM) subjects in higher education. However, few studies have explored the role of such technologies in non-STEM subjects in higher education. This paper reviews the relevant literature on the application of generative AI in higher education and proposes the application and implications of using generative AI tools to support student and instructors work in non-STEM higher education disciplines. An assessment of the role of AI in complex student tasks in non-STEM subjects is provided. Several considerations for the effective use of generative AI in non-STEM higher education are suggested. Faculty and students should focus on: 1) ensuring that ethical and moral implications are addressed; 2) using AI to augment rather than replace human intelligence; 3) using AI as an instructional tool rather than a fully automated system; 4) using AI to improve academic assessment and self-assessment methods; 5) critically reviewing the results of generative AI systems.
Artificial Intelligence for Text Generation: An Intellectual Property Perspective

Purpose: This paper aims at proposing relevant improvement measures for the intellectual property regulation on AI-generated text. Methods: This paper adopts qualitative research methods, collects and analyzes opinions from various sources to illustrate the technical logic of AI-generated text and explore existing intellectual property legal regulations and dilemmas. Results: AI-generated content, especially AI-generated text, follows the process of data collection and processing, models creation and content generation. Regarding intellectual property regulations on the AI-generated text, international law reflects hysteresis while major states’ domestic law varies in terms of intellectual property attribution. Conclusion: Based on the research on current situations, this paper proposes corresponding improvement measures, namely self-censorship, self-improvement, international cooperation and strengthening legislation.
Research on Rural Feature Classification Based on Improved U-Net for UAV Remote Sensing Images

With the rapid development of economy and society, the planning and utilization of agricultural land is also crucial. Aiming at the problems of the current lack of Unmanned Aerial Vehicle Remote Sensing (UAV Remote Sensing) Image Dataset of rural surface features and the low efficiency and low automation of traditional surface feature classification methods, a rural surface feature detection segmentation method based on Improved U-net (U-net+) is proposed based on the semantic segmentation task in Deep Learning. This method integrates the Swin Transformer framework into the U-net infrastructure, which can not only effectively improve the operation efficiency of the model, but also easily extract feature information to improve the accuracy and generalization of the model. At the same time, the UAV Remote Sensing image is used as the data source to produce a general UAV Remote Sensing Image Dataset of rural features: Air Dataset. In order to verify the advantages of U-net+, this study uses two UAV Datasets, the self-made Air Dataset and the famous Semantic Drone Dataset, to operate in U-net+. The experimental results show that the U-net+ proposed in this paper achieves 60.3% of mean Intersection-over-Union (mIoU) in the Air Dataset, which is better than U-net in PA, detection efficiency and model generalization ability; The Semantic Drone Dataset mIoU achieves 39.7%, which is also better than U-net According to the comparative analysis of the two Datasets, the segmentation accuracy of the two models for house buildings and grassland vegetation is relatively high, and the U-net+ is better. Therefore, in actual production and life, the U-net+ is more suitable for the rural terrain scene segmentation task mainly based on “Fine Operation”.
CanFuUI: A Canvas-Centric Web User Interface for Iterative Image Generation with Diffusion Models and ControlNet

Today, various AI generation tools are emerging in succession. And the majority of existing tools are predominantly model-centric in design, resulting in steep learning curves and high usability thresholds for users. Moreover, current user interfaces lack built-in image editing capabilities, forcing users to rely on external software even for basic image editing tasks. Considering that most image generation is an iterative process, this limitation significantly hampers user experience and creative potential. Instead, this paper proposes a novel canvas-centric design that seamlessly integrates editing functionalities into the UI called CanFuUI, streamlining secondary image processing. Users can crop, modify, and annotation of specific regions of generated images within the same canvas in CanFuUI. Furthermore, canvas content is utilized as preprocessed images, directly integrated into the ControlNet preprocessing procedure, reinforcing the customization capabilities of AI-generated outputs.
A Survey of Translation Learners’ Uses and Perceptions of Neural Machine Translation

This paper reports a questionnaire-based survey that was designed to investigate how a group of Chinese translation learners used and perceived Neural Machine Translation (NMT), with a view to providing pedagogical implications for translation instruction which is being confronted with the AI-powered translation technology. 326 second- and third-year college students, who were translation learners as well as English majors from the same university in China participated in the survey. They reported high frequency of NMT use in their translation learning as well as English major learning. Instead of directly borrowing the NMT output, most of them post-edited it and/or used it as inspiration to accomplish their translation tasks. Although they evaluated NMT in a positive way and held an optimistic view toward the future of translation career, they expressed varying degrees of worry and anxiety toward their future employment and toward the use of NMT in the process of the translation learning. They clearly articulated the needs for NMT instruction in translation courses. Based on these findings, this paper proposes several ways to help reduce the learners’ worry and anxiety in translation instruction.
A Completion Rate Conundrum: Reducing bias in the Single Usability Metric

In the Single Usability Metric benchmarking method, the calculation of completion rates creates a bias for completion rates by ignoring the z-score transform that is conducted for satisfaction and time-on-task measures. This artificially inflates all ‘good’ scores and marks some ‘poor’ scores as ‘good’. This paper discusses two methods to augment the SUM so that it will accurately calculate completion rates into the final SUM score.
Critical Discourse Analysis and Political Facebook Posts in Bangsamoro Tapatan Lane

This study examined political posts in the Facebook Group, Bangsamoro Tapatan Lane. More specifically, it investigated (1) the ways political posts in the Bangsamoro Tapatan Lane constructed social inequalities; (2) the ways Facebook contributed to represent public opinion; (3) the ways the Facebook posts impacted beliefs and value systems; and (4) the possible actions to mitigate the issue. To determine these, the paper employed Norman Fairclough’s Critical Discourse Analysis, which was a qualitative analytical approach to critically describe, interpret, and explain the ways in which discourses construct, maintain, and legitimize social inequalities. It asserted that discourses could be used to resist power, criticize and assert power, and express knowledge and identity. The findings revealed that political posts on the Facebook Group, Bangsamoro Tapatan Lane, constructed social inequities through the effective use of language. The language in the posts was purposeful, conscious, measured, manipulative, and persuasive. Important findings were: (1) derogative adjectives were used in the posts to call for reform in the government and expose erroneous actions done by the government. The choice of adjectives communicated that something was wrong with the administration, which required the public to correct or fix the government system. They were used as a political ploy to rule the public. That is, they were used to subtly influence the audience to take the side of the writer; (2) passive voice was used in the posts to hide agents or to hide lack of information; (3) intertextuality was used to strengthen the argument of the writer and/or for the writer to emphasize a point; (4) modals were used as a mode of persuasion; (5) pronouns were used to show inclusivity; (6) Facebook was used as a platform to influence the public through the effective use of language and the media. It had become a platform to let people know of one’s knowledge, thoughts, and opinions in just in a few clicks of a button; (7) Facebook became a platform where people could reach a large number of audience and freely talk about their political ideologies and convince other people to adapt their way of thinking; (8) the posts were made to try and turn the audience against their government by feeding their sense of insecurity; (9) political beliefs and ideologies could be hidden underneath the simple structure of language choices in text, thereby changing the audiences’ perspective, attitude, and even beliefs; and (10) raising awareness through webinars and seminar-workshops among the youth and all social media users about critical reading and critical discourse analysis is important as indeed, with the use of simple personal pronouns and adjectives, people could be persuaded to accept and even embrace another person’s political ideologies.
Using the Shadowing Technique in Teaching Russian as a Foreign Language

At present, there is an active search for ways to improve and modernize the process of teaching Russian as a foreign language. Teachers need to study and implement in their practice the most efficient methods and modern innovative technology, combine traditional and innovative teaching methods, and creatively develop the accumulated theoretical and practical experience in education. The choice of teaching methods is a vital and urgent task in the contemporary methodology of teaching Russian as a foreign language. The article reports research findings on the efficiency of the main prerequisites for the introduction of the shadowing technique in teaching Russian as a foreign language. In addition, the study involves a qualitative analysis of the efficiency of teaching by means of shadowing compared to traditional teaching methodology. The paper discusses various shadowing techniques, describes the basics of the technique, and offers practical recommendations on how it could be utilized in the classroom. The study proposes and experimentally tests a shadowing technique in teaching students from Kyrgyzstan. The findings reveal a positive dynamic in performance in listening among students taught Russian as a foreign language with shadowing.
Strong-AI Autoepistemic Robots Build on Intensional First Order Logic

Neuro-symbolic AI attempts to integrate neural and symbolic architectures in a manner that addresses strengths and weaknesses of each, in a complementary fashion, in order to support robust strong AI capable of reasoning, learning, and cognitive modeling. In this paper we consider the intensional First Order Logic (IFOL) [1] as a symbolic architecture of modern robots, able to use natural languages to communicate with humans and to reason about their own knowledge with self-reference and abstraction language property. We intend to obtain the grounding of robot’s language by experience of how it uses its neuronal architectures and hence by associating this experience with the mining (sense) of non-defined language concepts (particulars/individuals and universals) in PRP (Properties/Relations/Propositions) theory of IFOL. We consider the robot’s four-levels knowledge structure: The syntax level of particular natural language (Italian, French, etc.), two universal language levels: its semantic logic structure (based on virtual predicates of FOL and logic connectives), and its corresponding conceptual PRP structure level which universally represents the composite mining of FOL formulae grounded on the last robot’s neuro-system level. Finally, we provide the general method how to implement in IFOL (by using the abstracted terms) different kinds of modal logic operators and their deductive axioms: we present a particular example of robots autoepistemic deduction capabilities by introduction of the special temporal Konow predicate and deductive axioms for it: reflexive, positive introspection and distributive axiom.
Investigating the Long-Run Sectoral Growth Linkages in Ethiopia: Is Agriculture or Service an Engine of Growth?

This paper investigates the long run relationship between agriculture, industry and service using cointegrated vector autoregressive (CVAR) methodology for the period 1981-2011. Using this approach, the article provides a fresh evidence for Ethiopia that has not been deployed by the previous researches in the country. The paper finds that service is a driving force of the growth of agriculture and itself, an evidence obtained from analysis of the long run impact matrix C. From the fully identified long run structure, growth in service has positive and significant impacts on agriculture and industry. It deeply investigates the effects on agriculture by disaggregating service into other service and distributive service whereas industry comprises manufacturing and non-manufacturing. Concerning the impacts of disaggregated service, other service sub-sector is found to be a pushing force for the growth of agriculture, non-manufacturing and service itself. A variable comprising transportation and communication is vital for the long term growth of agriculture. These results are robust to changes in sample size and sample source. The policy prescription is that government should invest on this small sub-sector as the returns to the whole economy are tremendous.&#x0D; The paper finds positive and strong effects from non-manufacturing to manufacturing reflecting that improved access to electricity, gas and water triggers unprecedented growth in manufacturing productivity. Finally, a shock to agriculture has little impacts on the growth of entire economy as the variable is purely adjusting in most of the models considered in the paper while aggregate service is weakly exogenous.
Interference: Affixation of Mongondow Dialect in Indonesian Learning

The bilingual backgrounds of speakers, the lack of loyalty to the target language, the limited knowledge of the vocabulary of the target language, the need for synonyms, the taking into consideration of prestige and style, and the inadequate command of the target language are just a few of the things that contribute to language interference. One type of language interference that occurs is interference where a native speaker uses a pattern or rule that leads to an error or incorrect form in the target language. The use of two or more languages by a speech community causes deviations in morphology, syntax, phonology, and even semantics. This deviation will result in a linguistic phenomenon known as interference. This research aimed to determine whether the Mongondow dialect can interfere with students' learning of Indonesian. The researcher wanted to investigate the impact of students’ Mongondow dialect particularly when communicating in diverse cultural environments. The researcher described the difficulties students had in forming verbs with affixation in the Mongondow local dialect in their learning of the Indonesian language. The researcher also identified factors that indicate how the local dialect can interfere with students' language skills. A qualitative descriptive method was chosen for the study because it is suitable for collecting data on the students' perceptions through semi-structured interviews. The researcher interviewed Indonesian teachers and students to understand why they ask certain types of questions during the teaching and learning process. The analysis of students' interferences in Indonesian learning revealed that the use of the Mongondow dialect in communication had a positive impact on the oral performance of students. Moreover, it also increased their motivation to learn and helped teachers build their self-confidence. Additionally, utilizing various media and facilities proved to be beneficial in promoting active learning among students.
Uncovering Digital Competence in Language Instruction: A Study of Pre-Service English Teachers in Saudi Arabi

the role of technology as a catalyst for change and development has become irrefutable in various language learning settings. However, not all language teachers are mindful about using technology in their language teaching practices, owing to their lack of adequate digital competencies. The purpose of this research is to promote digital competencies and related components among the study participants. This investigation applies the Framework of Competency Profile for digital teachers. The results were obtained by quantitative analysis, using data collected from a sample of 151 language learners, all enrolled in BA English language programmes across 8 universities in Saudi Arabia. These learners are considered pre-service English language teachers, as they are near graduation. The findings demonstrated participants’ highest and lowest digital competences, leading the researcher to identify practical implications, particularly regarding how to conduct training to compensate for low competencies. The findings also revealed that there is no correlation between levels of study and digital competencies. There was also no influence of gender on digital competencies. From these findings, the researcher concluded that the identification of competencies is essential for instructors, educators, administrators, and policymakers, in order to design more effective and appropriate training programmes for teachers in each context. Finally, the findings provided realistic analysis of the components and sub-components related to digital competencies among the target audience.
Formation of Social Competence in the Process of Learning at the University

This article analyzes the essence of social competence and the features of its formation in the process of obtaining a university education. Conditions are put forward that promote the development of the components of social competence. It is proposed to consider the possibility of organizing problem-based and socio-psychological education.
Convolutional Neural Network (CNN) to Reduce Construction Loss in JPEG Compression Caused by Discrete Fourier Transform (DFT)

In recent decades, digital image processing has gained enormous popularity. Consequently, a number of data compression strategies have been put forth, with the goal of minimizing the amount of information required to represent images. Among them, JPEG compression is one of the most popular methods that has been widely applied in multimedia and digital applications. The periodic nature of DFT makes it impossible to meet the periodic condition of an image’s opposing edges without producing severe artifacts, which lowers the image’s perceptual visual quality. On the other hand, deep learning has recently achieved outstanding results for applications like speech recognition, image reduction, and natural language processing. Convolutional Neural Networks (CNN) have received more attention than most other types of deep neural networks. The use of convolution in feature extraction results in a less redundant feature map and a smaller dataset, both of which are crucial for image compression. In this work, an effective image compression method is purposed using autoencoders. The study’s findings revealed a number of important trends that suggested better reconstruction along with good compression can be achieved using autoencoders.