Reviewerly/exHarmony huggingface dataset:
Weakly supervised human skin segmentation using guidance attention mechanisms

Abstract Human skin segmentation is a crucial task in computer vision and biometric systems, yet it poses several challenges such as variability in skin colour, pose, and illumination. This paper presents a robust data-driven skin segmentation method for a single image that addresses these challenges through the integration of contextual information and efficient network design. In addition to robustness and accuracy, the integration into real-time systems requires a careful balance between computational power, speed, and performance. The proposed method incorporates two attention modules, Body Attention and Skin Attention, that utilize contextual information to improve segmentation results. These modules draw attention to the desired areas, focusing on the body boundaries and skin pixels, respectively. Additionally, an efficient network architecture is employed in the encoder part to minimize computational power while retaining high performance. To handle the issue of noisy labels in skin datasets, the proposed method uses a weakly supervised training strategy, relying on the Skin Attention module. The results of this study demonstrate that the proposed method is comparable to, or outperforms, state-of-the-art methods on benchmark datasets.
Desirable or Distasteful? Exploring Uncertainty in Human-Chatbot Relationships

AbstractPresent-day power users of AI-powered social chatbots encounter various uncertainties and concerns when forming relationships with these virtual agents. To provide a systematic analysis of users’ concerns and to complement the current West-dominated approach to chatbot studies, we conducted a thorough observation of the experienced uncertainties users reported in a Chinese online community on social chatbots. The results revealed four typical uncertainties: technical uncertainty, relational uncertainty, ontological uncertainty, and sexual uncertainty. We further conducted visibility and sentiment analysis to capture users’ response patterns toward various uncertainties. We discovered that users’ identification of social chatbots is dynamic and contextual. Our study contributes to expanding, summarizing, and elucidating users’ experienced uncertainties and concerns as they form intimate relationships with AI agents.Keywords: AIsocial chatbotReplikahuman-chatbot relationshipuncertainty Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationNotes on contributorsShuyi PanShuyi Pan is a Ph.D. student in the School of Media and Communication at Shanghai Jiao Tong University and a visiting researcher at Utrecht University. Her research interests include social chatbot, human-AI relationship, and gender.Jie CuiJie Cui is a Ph.D. student in journalism and communication at the School of Media & Communication (SMC), Shanghai Jiao Tong University. Her research interests include political communication and popular culture.Yi MouYi Mou is an associate professor in the School of Media and Communication at Shanghai Jiao Tong University. Her research interests include new media study and human–machine communication.
Methodology of Modern Theatrical Art in the Formation of an Actor’s Emotional Nature and Peculiarities of His or Her Stage Perception

In the majority of Russian cities a theater, a cultural space, which has taken the close attention of the wide audience in recent years, is located not far from the central square or directly on it. Theater can be attractive for people’s leisure activities, take central or peripheral position in the cultural life of a city, give successful and not quite successful performances, but it will definitely remain the place whose life is rather little known about. In this article the author tries to scientifically analyze and critically conceptualize the problematics of the methodology of modern theatrical art in the formation of an actor’s emotional nature and peculiarities of his or her stage perception. The aim of this research is to reveal and analyze the essence of an actor’s emotional nature conjugated into the modern epoch with the performativepractices influencing the condition of traditional theater and personal status of an actor. The methodology of the research includes a systematic approach and a method of comparative analysis applied to the actor's nature of the artist, which developed bythe XX century, which has now covered other professional spheres of a person, thus changing the value orientations of the actor's stage performance.
Formation of Project Culture in Future Teachers

The purpose of the article is to present the methodology of forming the project culture of futureteachers. The methodological basis of the research is: modern ideas of the dialectic of the general, special and individual in the formation of project culture; philosophy of a subjective-humanistic approach to education; theoretical provisions about the social, active and creative essence of the individual, the mechanisms and conditions of its self-expression in culture; the theory of complex personality development and the formation of knowledge, abilities and skills; fundamental studies of the development of the general and professional culture of the teacher. Today, students have to cope with a large volume of tasks, have time to live and study. At the same time, today most students are not ready for the challenges of modern society. The process of forming a project culture should be aimed at creating a project-innovative environment in an educational institution, that is, creating conditions for the emergence, development and implementation of projects. Project-based learning targets future teachers foracademic, personal, and career success, and prepares them for the challenges they will face in their future careers.
Implementation of Steam Technologies for Building Digital Competence of Future Music Art Teachers

The aim of the work is to determine the effectiveness of the implementation of STEAM technologies for building the digital competence of future music art teachers. This aim was achieved through a sociological survey, effectiveness calculations, variation, statistical error. It was established that most students' digital competence level was low (47%) before the study, which affected their level of knowledge. Only 12% of the students had a high level of digital competence. The obtained data contributed to developing approaches to training future music art teachers using STEAM technologies and digital opportunities. It was found that the use of the Vocal Range Vocaberry application has a greater impact on students’ digital competence, as the results are related to the detailed study of approaches to voice development. This approach to learning contributed to the acquisition of high-level knowledge by a greater proportion of students (71%). The practical significance of the work is related to the possibility of improving the system of training music art teachers with the use of digital technologies. Further research may focus on establishing the effectiveness of various vocal training programmes for future teachers.
The Interaction of Pedagogy and the Media in Ecuadorian Education: Challenges and Opportunities

Certainly, the relationship between pedagogy and the media today can take various forms, such as the production of educational content by the media or the collaboration between educational institutions and the media to bring education to remote communities. This article proposes a critical analysis of this interaction between pedagogical praxis and the media, based on a conceptual and historical retrospective that shows, in Ecuador, positive experiences such as television programs with pedagogical purposes and websites that offer resources and free educational programs to communities with limited access to education. However, this relationship also faces challenges, such as the lack of technology training for educators and the digital divide in some areas of the country. It is necessary to address these challenges to ensure that the collaboration between pedagogy and the media, rather than reactive, is proactive from both spaces. In any case, through effective collaboration, it is possible to improve the quality and accessibility of education, providing more people in the country with the necessary tools to succeed in their academic and professional lives.
A Preliminary Analysis of Blockchain Impact on Sustainable Supply Chains: COVID-19 Perspective

Transforming a more adaptable and resilient supply chains in the pandemic (COVID-19) environment and understanding how similar future events can be managed has been a great challenge faced by the sustainable supply chain (SSC) processes. With this aim, we have identified studies in sustainable supply chain (SSC) literature to tackle this specific challenge. Hence this research intends to determine and enhance the Blockchain technology research and practise of sustainable supply chain (SSC), making it less vulnerable to environmental risks, such as the pandemic. The preliminary review of the papers has indicated four themes presented across the literature which are as follows: benefits of utilising Blockchain in optimising sustainable supply chains; the role of digitalisation in sustainable supply chains; challenges in supply chains and the impact of Blockchain; and encouraging sustainable supply chains development. Positioned in the literature review we established a framework conceptually to evaluate the context of a hybrid blockchain in the context of SSC arena.
A holistic multi-source transfer learning approach using wearable sensors for personalized daily activity recognition

Abstract Human activity recognition (HAR) aims to collect time series through wearable devices to precisely identify specific actions. However, the traditional HAR method ignores the activity variances among individuals, which will cause low generalization when applied to a new individual and indirectly enhance the difficulties of personalized HAR service. In this paper, we fully consider activity divergence among individuals to develop an end-to-end model, the multi-source unsupervised co-transfer network (MUCT), to provide personalized activity recognition for new individuals. We denote the collected data of different individuals as multiple domains and implement deep domain adaptation to align each pair of source and target domains. In addition, we propose a consistent filter that utilizes two heterogeneous classifiers to automatically select high-confidence instances from the target domain to jointly enhance the performance on the target task. The effectiveness and performance of our model are evaluated through comprehensive experiments on two activity recognition benchmarks and a private activity recognition data set (collected by our signal sensors), where our model outperforms traditional transfer learning methods at HAR.
Multi-Branch Dilation Convolution CenterNet for Object Detection of Underwater Vehicles

Object detection occupies a very important position in the fishing operation and autonomous navigation of underwater vehicles. At present, most deep-learning object detection approaches, such as R-CNN, SPPNet, R-FCN, etc., have two stages and are based on anchors. However, the previous methods generally have the problems of weak generalization ability and not high enough computational efficiency due to the generation of anchors. As a well-known one-stage anchor-free method, CenterNet can accelerate the inference speed by omitting the step of generating anchors, whereas it is difficult to extract sufficient global information because of the residual structure at the bottom layer, which leads to low detection precision for the overlapping targets. Dilation convolution makes the kernel obtain a larger reception field and access more information. Multi-branch structure can not only preserve the whole area information, but also efficiently separate foreground and background. By combining the dilation convolution and multi-branch structure, multi-branch dilation convolution is proposed and applied to the Hourglass backbone network in CenterNet, then an improved CenterNet named multi-branch dilation convolution CenterNet (MDC-CenterNet) is built, which has a stronger ability of object detection. The proposed method is successfully utilized for detection of underwater organisms including holothurian, scallop, echinus and starfish, and the comparison result shows that it outperforms the original CenterNet and the classical object detection network. Moreover, with the MS-COCO and PASCAL VOC datasets, a number of comparative experiments are performed for showing the advancement of our method compared to other best methods.
A Novel Memristor-Based Multi-Vortex Hyper-Chaotic Circuit Design and its Application in Image Encryption

This paper proposes a new four-dimensional hyper-chaotic system capable of generating multi-wing chaotic attractors by introducing active magnetron memristors, multi-segmented square functions and trigonometric functions. The dynamical properties of this new hyper-chaotic system, such as equilibrium point, dissipation, Lyapunov exponential spectrum, bifurcation diagram and Poincaré cross-section and attraction basin, are analyzed theoretically and simulated numerically, and the complexity of this system with different parameters is analyzed. It is observed that this hyper-chaotic system has periodic, chaotic and hyper-chaotic variations with an infinite number of equilibria and coexisting attractors under different parameter conditions. The circuit simulation was performed using Multisim and the results obtained were consistent with the numerical analysis of the dynamics, and the chaotic circuit system is designed by FPGA to verify the realizability of the system. Finally, an image encryption algorithm is designed in conjunction with the DNA algorithm to enable a new system chaotic sequence for image encryption. The results show that the hyper-chaotic system has rich dynamical behavior and has high-security performance when applied to image encryption with strong chaotic key and plaintext sensitivity and large key space in image encryption.
Automatic Genre Identification for Robust Enrichment of Massive Text Collections: Investigation of Classification Methods in the Era of Large Language Models

Massive text collections are the backbone of large language models, the main ingredient of the current significant progress in artificial intelligence. However, as these collections are mostly collected using automatic methods, researchers have few insights into what types of texts they consist of. Automatic genre identification is a text classification task that enriches texts with genre labels, such as promotional and legal, providing meaningful insights into the composition of these large text collections. In this paper, we evaluate machine learning approaches for the genre identification task based on their generalizability across different datasets to assess which model is the most suitable for the downstream task of enriching large web corpora with genre information. We train and test multiple fine-tuned BERT-like Transformer-based models and show that merging different genre-annotated datasets yields superior results. Moreover, we explore the zero-shot capabilities of large GPT Transformer models in this task and discuss the advantages and disadvantages of the zero-shot approach. We also publish the best-performing fine-tuned model that enables automatic genre annotation in multiple languages. In addition, to promote further research in this area, we plan to share, upon request, a new benchmark for automatic genre annotation, ensuring the non-exposure of the latest large language models.
LITHUANIAN HATE SPEECH CLASSIFICATION USING DEEP LEARNING METHODS

The ever-increasing amount of online content and the opportunity for everyone to express their opinions online leads to frequent encounters with social problems: bullying, insults, and hate speech. Some online portals are taking steps to stop this, such as no longer allowing user-generated comments to be made anonymously, removing the possibility to comment under the articles, and some portals employ moderators who identify and eliminate hate speech. However, given the large number of comments, an appropriately large number of people are required to do this work. The rapid development of artificial intelligence in the language technology area may be the solution to this problem. Automated hate speech detection would allow to manage the ever-increasing amount of online content, therefore we report hate speech classification for Lithuanian language by application of deep learning.
Investigating Outlier Detection Techniques Based on Kernel Rough Clustering

Background: Data quality is crucial to the success of big data analytics. However, the presence of outliers affects data quality and data analysis. Employing effective outlier detection techniques to eliminate dirty data can improve data quality and garner more accurate analytical insights. Data uncertainty presents a significant challenge for outlier detection methods and warrants further refinement in the era of big data. Objective: The unsupervised outlier detection based on the integration of clustering and outlier scoring scheme is the current research hotspot. However, hard clustering fails when dealing with abnormal patterns with uncertain and unexpected behavior. Rough boundaries help identify more accurate cluster structures. Therefore, this article uses uncertainty soft clustering based on rough set theory to extend the clustering technology and designs appropriate scoring schemes to capture abnormal instances. This solves the problem of outlier detection in uncertain and nonlinear complex data. Methods: This paper proposes the flow of an outlier detection algorithm based on Kernel Rough Clustering and then compares the detection accuracy with five existing popular methods using synthetic and real-world datasets. The results show that the proposed method has higher detection accuracy. Results: The detection precision and recall of the proposed method were improved. For the detection accuracy, it is superior to popular methods, indicating that the proposed method has a good detection effect in identifying outlier. Conclusion: Compared with popular methods, the proposed method has a slight advantage in detection accuracy and is one of the effective algorithms that can be selected for outlier detection.
An investigation of online and offline learning models for online Just-in-Time Software Defect Prediction

Abstract Just-in-Time Software Defect Prediction (JIT-SDP) operates in an online scenario where additional training data is received over time. Existing online JIT-SDP studies used online Oza ensemble learning methods with Hoeffding Trees as base learners to learn and update JIT-SDP models over time in this scenario. However, it is unknown how these approaches compare against offline learning approaches adapted to operate in online scenarios, and how the use of any other online or offline base learners would affect online JIT-SDP in terms of predictive performance and computational cost. We therefore propose a new approach called Batch Oversampling Rate Boosting (BORB) that is able to use offline base learners in an online JIT-SDP scenario. Based on 10 open source projects, we provide a comprehensive evaluation of BORB with 5 different base learners and the existing online approach Oversampling Rate Boosting with 4 different base learners, both in within-project and cross-project online JIT-SDP scenarios. The results show that offline learning can lead to better predictive performance than the top performing online learning approaches considered in our study, at a higher computational cost. Cross-project data was helpful to improve predictive performance both for offline and online learning, but especially for online learning.
Pegmatite zonation and the use of muscovite as a geochemical indicator for tin-tantalum-tungsten mineralization: Case studies from the Kalehe and Idjwi areas, Democratic Republic of Congo

The central part of the Karagwe-Ankole Belt (KAB) in the Great Lakes area of Central Africa is rich in various rare and strategic elements. The area north of Kalehe and Idjwi island within the Sud-Kivu Province of eastern Democratic Republic of the Congo (DRC) contains Nb, Ta, Sn and W mineralization hosted in pegmatites and quartz veins that are related to leucogranite intrusions. In this work, two granite-pegmatite fields were studied: the Bugarula field in the northern part of Idjwi island and the Kalehe field, along the west coast of Lake Kivu, with a view to determine the co-genetic link between different pegmatites and leucogranites in these regions. Based on mineralogical assemblages, five pegmatite zones were identified, occurring with some spatial overlap, but with an overall increasing distance from the inferred parental granite: 1) albite pegmatites, 2) two-mica pegmatites, 3) muscovite pegmatites, 4) beryl-bearing Nb–Ta–Sn mineralized pegmatites and 5) tourmaline-bearing Nb–Ta–Sn mineralized pegmatites. Muscovite compositions were analyzed from each zone and used to calculate pegmatite fractionation trends by using Rayleigh fractionation modelling. The model uses published partition coefficients (Kd) and measured contents of K, Rb and Cs in muscovite. Muscovites from the consecutive pegmatite zones in both the Bugarula and Kalehe pegmatite fields record a continuous fractionation trend, indicating a co-genetic magmatic evolution. A parental leucogranitic composition of 3.6 wt% K2O, 8 ppm Cs and 300 ppm Rb was iteratively determined to yield a best fit fractionation trend with the data. This composition is within the range of exposed leucogranites sampled in this study, which average at 4.2 wt% K2O, 7 ppm Cs and 400 ppm Rb (n = 12). It should be noted that individually, none of the sampled leucogranites gave satisfactory fractionation trajectories that would suggest a direct co-genetic link between them and the pegmatites. In the Idjwi granite-pegmatite field, the degree of fractionation with respect to the inferred parental leucogranite ranges from 20% to 99% for the least fractionated and most evolved Nb–Ta–Sn mineralized pegmatites, respectively. In the Kalehe area, fractionation values also range from 22% up to 99% for the least fractionated and most evolved (mineralized) pegmatites, respectively. Muscovites from mineralized pegmatites of zones 4 and 5 show high concentrations of Nb, Ta and Sn. As such, we show that muscovite compositions in these pegmatites provide a powerful tool to track Nb–Ta, Sn (and W) enrichment and potential mineralization. These high degrees of fractionation associated with the enrichment in economically interesting elements are similar to reported values for pegmatite fields elsewhere in the Karagwe-Ankole Belt.
A Study of Creative Teaching Behaviors : Focusing on Empathy and Psychological Empowerment

This study investigated the mediating effect of childcare center teachers’ psychological empowerment on the relationship between empathy and creative teaching behaviors. Study subjects were 231 teachers employed in childcare centers in Daegu-Gyeongbuk Provinces, Korea. Questionnaires, which required self-reporting by teachers, were used to investigate empathy, psychological empowerment, and creative teaching behaviors. Collected data were analyzed by means of Hayes’ PROCESS Macro Model 4.1 using SPSS Win 21.0. Results are as follows. BR First, childcare teachers