Reviewerly/exHarmony huggingface dataset:
Semi-supervised semantic segmentation with multi-reliability and multi-level feature augmentation

Semi-supervised semantic segmentation aims to classify the pixels with both labeled and unlabeled images. How to utilize unlabeled images is a key part of semi-supervised learning. The existing methods tend to exploit the reliable pixels which can be classified to a specific category with high reliability, ignoring the unreliable pixels which cannot be identified into a special category with low reliability, resulting in information loss. Moreover, uneven number of pixels per category often leads to misclassification of small-scale category pixels. In this paper, we propose a multi-reliability and multi-level feature augmentation framework. Multi-reliability feature augmentation dedicates to extract unreliable/reliable pixel features to take full advantage of pixel information. Multi-level feature augmentation dedicates to extract category/image level semantic features to avoid the sparse semantic information provided by category with a small number of pixels, which can lead to misclassification. Furthermore, a variety of data perturbations are employed to enhance the robustness of our model. Experimental results on the PASCAL VOC 2012, Cityscapes datasets verify the effectiveness of our method. Our code is available at https://github.com/JianJianYin/MMFA.
Archipelagic Human-Machine Communication: Building Bridges amidst Cultivated Ambiguity

In this commentary, I call for maintaining the archipelagic character of human-machine communication (HMC). Utilizing the metaphor of the archipelago or a chain of connected islands indicates that HMC entails a variety of islands differing in shape, size, location, and proximity to one another. Rather than aiming for conceptual unity and definitional homogeneity, I call for embracing a cultivated ambiguity related to HMC key concepts. Ambiguity in the sense of allowing these concepts to be flexible enough to be explored in different contexts. Cultivated in the sense of demanding resonance across individual studies and theoretical lineages to allow for cumulative and collaborative theorizing. My hope is that HMC scholars can continue to build bridges that traverse the paradigmatic, methodological, theoretical, and technological archipelago of HMC.
Triggered by Socialbots: Communicative Anthropomorphization of Bots in Online Conversations

This article examines communicative anthropomorphization, that is, assigning of humanlike features, of socialbots in communication between humans and bots. Situated in the field of human-machine communication, the article asks how socialbots are devised as anthropomorphized communication companions and explores the ways in which human users anthropomorphize bots through communication. Through an analysis of two datasets of bots interacting with humans on social media, we find that bots are communicatively anthropomorphized by directly addressing them, assigning agency to them, drawing parallels between humans and bots, and assigning emotions and opinions to bots. We suggest that socialbots inherently have anthropomorphized characteristics and affordances, but their anthropomorphization is completed and actualized by humans through communication. We conceptualize this process as communicative anthropomorphization.
Defining Dialogues: Tracing the Evolution of Human-Machine Communication

This introduction to the volume discusses the evolving field of Human-Machine Communication (HMC), drawing on insights from the philosophy of science. We explore critical debates in the field, underscoring the importance of challenging assumptions, embracing interfield work, and fostering dialogue in shaping our understanding of HMC. Moreover, we celebrate the vibrant collaboration between disciplines that drives progress in HMC. This piece serves as an invitation to join the exploration of this collection and contribute to shaping the future of HMC.
THE DRILLING METHOD TO ENHANCE STUDENTS’ PUBLIC SPEAKING AT TAHFIDZ TAKHASSUS DAARUL QUR’AN ISLAMIC BOARDING SCHOOL PALEMBANG

Indonesia is preparing to enter the era of Society 5.0 where all aspects of life are interconnected without space and time limitations. Students in Islamic boarding schools must also have the competence to communicate, to think critically, and to collaborate on projects. It is important for the younger generation to improve their public speaking competence so that they can express their critical and creative thoughts in front of others. This study is a descriptive qualitative study and the data are collected by observations. This study aims to describing how to train students’ public speaking skill through drilling method. The samples as well as subjects of this study were 28 students. Public speaking training was conducted for a month using the drilling method at the Tahfidz Takhasus Daarul Qur'an Islamic Boarding School Palembang. The drill method was carried out in seven steps namely association, objectives and core material delivery, motivation, practices, demonstration, evaluation, and program follow-up. The training was carried out by introducing the material through lectures and discussions, and was continued with the drilling method to train the students' rhetorical or speech competence in public. Students demonstrated public speaking in English, Arabic and Indonesian. The advice given to the training participants is to maximize training in public speaking, especially making speeches in various languages so that they can be used when engaging in society.Indonesia is preparing to enter the era of Society 5.0 where all aspects of life are interconnected without space and time limitations. Students in Islamic boarding schools must also have the competence to communicate, to think critically, and to collaborate on projects. It is important for the younger generation to improve their public speaking competence so that they can express their critical and creative thoughts in front of others. This study is a descriptive qualitative study and the data are collected by observations. This study aims to describing how to train students’ public speaking skill through drilling method. The samples as well as subjects of this study were 28 students. Public speaking training was conducted for a month using the drilling method at the Tahfidz Takhasus Daarul Qur'an Islamic Boarding School Palembang. The drill method was carried out in seven steps namely association, objectives and core material delivery, motivation, practices, demonstration, evaluation, and program follow-up. The training was carried out by introducing the material through lectures and discussions, and was continued with the drilling method to train the students' rhetorical or speech competence in public. Students demonstrated public speaking in English, Arabic and Indonesian. The advice given to the training participants is to maximize training in public speaking, especially making speeches in various languages so that they can be used when engaging in society.
BOOSTING READING COMPREHENSION ACHIEVEMENT AND VOCABULARY MASTERY: A QUASI-EXPERIMENTAL DESIGN WITH RAP

The aim of this research is to ascertain the impact of the RAP reading strategy on the academic progress of pupils in vocabulary and reading comprehension. The sample for this study comprised 45 pupils (22 females and 23 males) from Sriwijaya State Polytechnic who were in their second semester during the 2022-2023 academic year. This research was conducted using a quasi-experimental approach. The study employed a vocabulary test, a reading comprehension test, and the RAP reading strategy. The data analysis used mean, standard deviation, standard error mean, and t-tests for both the vocabulary test and the reading comprehension achievement test. The statistical significance level was set at .05. The study demonstrated that there were significant differences in reading comprehension achievement and vocabulary mastery between the experimental group and the control group. However, the RAP reading strategy was found to be more effective in enhancing reading comprehension achievement and vocabulary mastery than the traditional reading strategy. Overall, the RAP reading strategy can be a valuable tool for improving students' vocabulary and reading comprehension.
The Golden Age of the Big Data Audit: Agile Practices and Innovations for E-Commerce, Post-Quantum Cryptography, Psychosocial Hazards, Artificial Intelligence Algorithm Audits, and Deepfakes

Since the audit of the Parthenon in the 5th century, the audit has remained unchanged, focusing backward on financial statements. Global regulatory pressures are mounting for audit professionals and public accounting firms to make structural changes. In the 21st century, a new type of audit, the EPSAC value audit, is emerging, focusing on non-financial information, including operational data from company business functions and processes, and big external data, including social media. In addition, EPSAC focuses on the critical engagement areas of e-commerce, post-quantum cryptography, psychosocial hazards, artificial intelligence and machine learning, and tackling deepfakes. While a talent shortage of subject matter experts exists, auditors can use artificial intelligence knowledge bases to deliver the value audit. Such knowledge bases extend to the key EPSAC domains of cybersecurity and ESG, creating proxies for virtual team members. For example, industry 5.0, combining A.I. and IoT innovations, requires auditors to augment their skill sets with knowledge bases beyond traditional financial expertise to effectively navigate the complexities of the changing business environment, enhance the quality of audits, and provide valuable insights and assurance to stakeholders.
Cyber Security Threats to Educational Institutes: A Growing Concern for the New Era of Cybersecurity

The outbreak of the Covid-19 pandemic has significantly affected the operations of higher education institutions.Due to the limited use of video conferencing and cloud computing in these facilities, distance learning became the only option available to them.The study focused on identifying the most common types of attacks that can affect e-learning assets.There was a lack of clear cybersecurity policies for educational institutes and universities in 2020, according to a report by Microsoft Security Intelligence.The report showed that the education industry was the most targeted sector for malware attacks in the last 30 days.The recommendations for improving the security of e-learning systems.Some of these include implementing policies that restrict access to the resources and applications, updating security patches, and using cryptographic protocols.
The Same Size Distribution of Data Based on Unsupervised Clustering Algorithms

It is known that dividing data into groups based on certain rules not only helps to separate meaning from the data but also increases the efficiency of the large-volume data processing process. The same size distribution of data is especially effective in approaches such as distributed computing or parallel processing. The main reason for this is that dividing the data into as equal clusters as possible allows achieving the highest efficiency results in these approaches. But the distribution of data in the same size based on the human factor is a complicated process due to the impossibility of pre-planning the data contained in the data flow and the size of the data. In such a situation, unsupervised clustering algorithms are one of the main solutions to the problem of uniform data distribution. In this research work, hierarchical, K-means, Bisecting K-means, and DBSCAN unsupervised clustering algorithms are analyzed in order to solve the given problem, and the results of research on equal clustering of different groups of data are presented using them. Moreover, a new unsupervised equal-size clustering algorithm for equal clustering of data is proposed. During the research, the efficiency indicator of this algorithm for dividing data into equal groups is compared with the indicators of existing algorithms, and the conclusions of the experimental results are presented.
Enriched Image Embeddings as a Combined Outputs from Different Layers of CNN for Various Image Similarity Problems More Precise Solution

The number of images is growing daily. Thus, the task of image similarity detection (also known as the de-duplication problem) becomes more and more strong and important for a wide range of applications. The complexity of the two images comparison is too high because of the structure of the data and the general complication of similarity understanding. There are a lot of approaches mentioned in this work have been developed to solve this class of tasks, including SIFT, SURF, ORB, key points, ResNet50-based and many others. But the quality of these methods measured by F1 score is still non-satisfactory. In this work, we propose a solution for specified tasks, which exceeds the known state-of-the-art models in quality for image pairs similarity detection. The new image embedding, namely the descriptive image vector built as a combination of outputs at different layers of the proposed pre-trained and fine-tuned Convolutional Neural Network, based on ResNet50, is presented here. The model based on a Neural Network was trained over the introduced image embedding vectors over the pairs of similar images from prepared datasets to detect input images similarity. The measurements show that the proposed approach outperforms the state-of-the-art results by F1 score for image similarity or images near-duplicate detection tasks, described in this work. The real-world applications for three specified sub-tasks from the real estate domain are considered in this work. The proposed model has significantly improved the existing solution for the considered tasks. It is also promising to help solve more general and connected tasks with higher result quality.
Exploring the Perceptions of Technical Teachers Towards Introducing Blockchain Technology in Teaching and Learning

This paper presents a study that assesses technical teachers’ perceptions of learning blockchain technology and its applications to plan efficient faculty development programs for industry, academia, and research. With universities and technical councils introducing courses to bridge the gap between academic learning and industry demands, the demand for blockchain developers has been on the rise. The study's findings indicate that technical teachers generally hold positive attitudes towards learning blockchain technology, recognizing its potential applications in various fields such as finance, healthcare, and energy. Quantitative and qualitative study conducted on 79 engineering teachers and professionals who participated in an online faculty development program on Blockchain Technology and its Applications at JIIT University, India by providing survey questionnaire. The study highlights the importance of continuous adaptation and learning of new technologies for teachers to equip students with the skills they need to succeed in the industry. The study also provides insights into technical teachers’ perceptions towards integrating blockchain technology in teaching and learning and offers implications for planning efficient faculty development programs. Query ID="Q1" Text="This is to inform you that corresponding author has been identified as per the information available in the Copyright form." ‘
College Foreign Language Teacher Learning in the Context of Artificial Intelligence

This study adopts a combination of quantitative and qualitative methods, uses the “teacher learning questionnaire” and interviews to explore and analyze the composition, characteristics and impact of AI on teacher learning of 148 college foreign language teachers in the context of AI language learning. The results are as follows: college foreign language teachers’ learning includes three dimensions: intrinsic motivation, organizational support, and feedback and evaluation, and the participants’ intrinsic motivation and feedback and evaluation reach the high-frequency experience level; the characteristics of college foreign language teachers’ learning are ecological orientation, practical orientation and individual orientation; artificial intelligence technology guarantees the autonomy of teachers’ learning through learner characteristics, supports the normalization of teachers’ learning through effective supply of learning resources, and improves the collaboration of teachers’ learning through virtual teaching and research rooms. Artificial intelligence integrates information technology and education, which helps to deepen and expand the research on teacher learning.
Boundary Refinement via Zoom-In Algorithm for Keyshot Video Summarization of Long Sequences

This study presents a novel algorithm for keyshot video summarization in the context of real-world videos, such as those found on platforms like YouTube or Facebook. Our proposed boundary refinement algorithm via zoom-in addresses the problem of finding keyshots in long sequences, which is a major challenge for real-world video summarization. Existing state-of-the-art techniques tend to subsample such videos with small frame rate, leading to a coarse prediction of the summarization boundaries, or make several predictions on overlapping frames, which results in significant computation overhead and degraded performance due to loss of the context. In contrast, our approach refines these boundaries by zooming in on selected fragments, resulting in a more accurate and efficient summary. Additionally, we propose a coarse-to-fine score alignment procedure that minimizes the score discrepancy between the different stages of algorithm resulting in improved quality. The developed algorithm was evaluated on two standard datasets, SumMe and TVSum, and achieved state-of-the-art performance with scores of 56.2% (+0.6%) and 63.1% (+0.5%), respectively. Our proposed method demonstrates significant improvements in keyshot video summarization and has the potential to enhance the effectiveness of video summarization in real-world applications.
Heuristic Search for Nonlinear Substitutions for Cryptographic Applications

Heuristic algorithms are used to solve complex computational problems quickly in various computer applications. Such algorithms use heuristic functions that rank the search alternatives instead of a full enumeration of possible variants. The algorithm selects, at each iteration, an alternative with the best value for the heuristics. In this paper, we investigate the complex computational problem of finding highly nonlinear substitutions (S-boxes) in the space of 8-bit permutations. The generation of S-boxes is an important field of research, since nonlinear substitutions are widely used in various cryptographic applications. For instance, S-boxes in symmetric ciphers are responsible for cryptographic strength to linear, differential, algebraic, and other types of cryptanalysis. We propose new heuristics in the form of a cost function, calculated with the Walsh-Hadamard transform. We use the Hill Climbing Algorithm to find highly nonlinear substitutions. Our experiments demonstrate that the new heuristics give good results – it takes about 80,000 iterations of the algorithm to generate 8-bit S-boxes with a nonlinearity of 104. For the optimized parameters, the number of iterations of the algorithm is comparable to the best known results, which confirms the significance and value of the research.
Cross-border Logistics Model Design e-Tower Based on Blockchain Consensus Algorithm

Logistics plays an important role in the development of cross-border e-commerce. Cross border e-commerce enterprises have to choose appropriate logistics channels. This paper discusses the typical application models of blockchain technology in cross-border logistics, and proposes specific implementation paths to improve the performance of cross-border logistics management. This paper first analyzes the structure framework and core technology (common consensus algorithms) of the blockchain. Taking Shenzhen ZK cross-border logistics company as an example, a cross-border logistics mode ZK e-Tower based on cross-border blockchain has been proposed. The key issues in the system design, such as access control and identity management, consensus algorithm, blockchain based transaction process, its application in cross-border customs clearance, security and privacy protection have been analyzed in detail, and a prototype system was built for simulation experiments thus solving the problems of traditional information systems, such as excessive power of central organizations and information tracking difficulties.
Evaluating Usability of E-Learning Applications in Bangladesh: A Semiotic and Heuristic Evaluation Approach

In this age of technology, the popularity, and necessity of mobile applications in the field of education are increasing day by day. Many students in Bangladesh are not able to develop as qualified due to a lack of a suitable environment and skilled teachers. This problem has been solved to a large extent through various E-Learning applications. But no research has yet been done on exactly how efficient, intuitive, and usable these applications are. Therefore, this study's objectives are to assess these E-Learning apps’ usability in the context of Bangladesh and to offer potential architectural recommendations for improving their usability and acceptance. To accomplish the objectives, the usability of five E-Learning applications (Bacbon, Prottoy, Shikho, Classroom, and Muktopaath) developed in the context of Bangladesh was evaluated through Heuristic Evaluation and Semiotic Evaluation process. The study showed that all selected applications have several usability problems. The heuristic evaluation is based on the principle of efficiency, consistency, flexibility, conventions, and error management of each application while the semiotic evaluation upheld the intuitiveness of the interface elements of the selected applications. Finally, to enhance the usability of these applications, some sets of recommendations and implications have been presented for practitioners.
AMGSRAD Optimization Method in Multilayer Neural Networks

The AMSGrad optimization learning method for multilayer neural network (MNN) was tested using the logistic function and the Fourier spectra of the error function. The logistic function describes the process of doubling the number of local minima. It was established that due to retraining of MNN, the learning error function of each neuron is characterized by a set of wave vectors with different periodicities. The average value of the learning error for all neurons can be considered as the average value for all existing periodicities. At the same time, the wave vector of the total oscillation can take both commensurate and incommensurate values. The optimization method of AMSGrad training leads to a change in the frequency spectrum of the existing periodicities of the error function on each neuron. That is, the speed of learning of each neuron is corrected, which removes the degeneracy of this system by preventing relearning processes of each neuron.