Reviewerly/exHarmony huggingface dataset:
Shaming behavior in online communities: exploring a new configuration of digital conversations

Purpose This research aims to conceptualize online shaming discourse in virtual communities and to serve as a practical guide for online community managers and members. Design/methodology/approach This conceptual study explores the construction of meanings in human interaction in online communities by presenting a conceptual model, “The Triple-Responsibility Model in Online Communities,” that is based on Kantian ethics. Findings The model includes characterizing the roles of core participants in online communities: writer, reader and group manager; and delineating four ethical principles – truth, necessity, proportionality and caution – that can help society find the golden mean between social change and respecting human dignity and concern for an individual’s public image and provide a theoretical contribution and practical guidelines. Research limitations/implications It addresses shaming in virtual communities by suggesting a balance of several key principles, including truth, necessity, proportionality and caution. This is a new conceptualization of online shaming relevant to today's digital arena. Practical implications The guidelines can contribute to the ongoing political debate over what constitutes appropriate and justified regulation. Moreover, Facebook community leaders are responsible for formatting the group’s identity, the technical facets of group management and for setting group boundaries and determining the rules of participation. The posited rules may affect social media group managers, as they are called upon to leverage their privileged position and channel their media power into influencing online discourse. Social implications The current study provides insights into how shaming can be used as a legitimate tool in society by implementing an ethical approach, resulting in guidelines that restrict online discourse for participants in virtual communities and affect the work of social media group managers and policymakers. Originality/value By presenting a new conceptual model, the authors suggest that ethics are a helpful tool and offer insights into how online communities' participants and managers should use their voice and balance between shaming and maintaining the dignity of the individual.
Hebrew offensive language taxonomy and dataset

Abstract This paper introduces a streamlined taxonomy for categorizing offensive language in Hebrew, addressing a gap in the literature that has, until now, largely focused on Indo-European languages. Our taxonomy divides offensive language into seven levels (six explicit and one implicit level). We based our work on the simplified offensive language (SOL) taxonomy introduced in (Lewandowska-Tomaszczyk et al. 2021a) hoping that our adjustment of SOL to the Hebrew language will be capable of reflecting the unique linguistic and cultural nuances of Hebrew. The study involves both linguistic and cultural analysis beyond Natural Language Processing (NLP). We employed manual linguistic analysis to understand the nuances of offensive language in Hebrew. An accompanying dataset, gathered on Twitter and manually curated by human annotators, is described in detail. This dataset was constructed to both validate the taxonomy and serve as a foundation for future research on offensive language detection and analysis in Hebrew. Preliminary analysis of the dataset reveals intriguing patterns and distributions, underscoring the complexity and specificity of offensive expressions in the Hebrew language. The aim of our work is to capture the complexity and specificity of offensive expressions in Hebrew beyond what automated NLP methods alone can provide. Our findings highlight the significance of considering linguistic and cultural variations when researching and correcting abusive language online. We believe that our streamlined taxonomy and associated dataset will be crucial in improving research in Hebrew language sociocultural studies, natural language processing, and offensive language detection. Our study also makes a substantial contribution to the study of low-resource languages and can be used as a model for future research on other languages.
A Systematic Review of Recent Deep Learning Approaches for 3D Human Pose Estimation

Three-dimensional human pose estimation has made significant advancements through the integration of deep learning techniques. This survey provides a comprehensive review of recent 3D human pose estimation methods, with a focus on monocular images, videos, and multi-view cameras. Our approach stands out through a systematic literature review methodology, ensuring an up-to-date and meticulous overview. Unlike many existing surveys that categorize approaches based on learning paradigms, our survey offers a fresh perspective, delving deeper into the subject. For image-based approaches, we not only follow existing categorizations but also introduce and compare significant 2D models. Additionally, we provide a comparative analysis of these methods, enhancing the understanding of image-based pose estimation techniques. In the realm of video-based approaches, we categorize them based on the types of models used to capture inter-frame information. Furthermore, in the context of multi-person pose estimation, our survey uniquely differentiates between approaches focusing on relative poses and those addressing absolute poses. Our survey aims to serve as a pivotal resource for researchers, highlighting state-of-the-art deep learning strategies and identifying promising directions for future exploration in 3D human pose estimation.
From the philosophy of «harmony of the world» towards the post-non-classical philosophy of music in the context of the phenomenon of transdisciplinarit

The article examines the relationship between the philosophical harmony of the world and the substantial musical being, which brings musical theory and practice into the human life world, which is based on physical and mathematical laws and the principle of self-organization of the musical system in accordance with a single process of peacemaking with an increase in energy tension due to the evolution of the expanding Universe. Intuition and a developed musical consciousness open up expanded sound possibilities.
Education and upbringing as phenomena of European culture: classic, post-classical, and post-post-classical contexts

Cultural modifications of the phenomenon of education, its priority role in classical culture, deformations of education and upbringing in postmodern culture and new intentions of post-post-non-classical culture in their interpretation are considered.
Learning to read ancient Greek at school: where ancient language learning meets contemporary behaviourism

In this paper, an existing behavioral intervention designed to facilitate the acquisition of, among others, reading skills, known as Technology of Teaching (Berens, 2020) is introduced and applied to the acquisition of phonemic decoding of ancient Greek graphemes in the context of the early phases of ancient Greek learning during the first year of Liceo classico (the Italian high school were both Greek and Latin are studied). The Technology of Teaching is mainly based on Precision Teaching and, as such, on the use of the Standard Celeration Chart, whose application to Greek learning will be detailed in these pages.
Domestic waste management planning at PLN (PERSERO) UPK Lombok ULPLTD Paokmotong: 3R progrrm approach

Effective and sustainable domestic waste management is essential for PT PLN (Persero) UPK Lombok ULPLTD Paokmotong. In facing environmental challenges and increasingly stringent regulatory demands, this company needs to carry out domestic waste management planning based on waste generation and composition. This study aimed to determine waste management planning with the 3R program at ULPLTD Paokmotong. The research method in this study is descriptive qualitative with data collection techniques: interview methods, surveys, and measurements of domestic waste generation and composition at PT PLN (Persero) UPK Lombok ULPLTD Paokmotong. The results of this study indicate that TPS 3R at ULPLTD Paokmotong serves 61 employees. Based on the type of waste, the proposed waste management strategy, namely implementing the 3R program at PT PLN (Persero) UPK Lombok ULPLTD Paokmotong, has proven effective in reducing waste generation and optimizing resource use. Through waste reduction efforts at the source, reuse of recyclable materials, and efficient recycling processes, companies can reduce environmental impacts and generate sustainable economic benefits. Based on the planning of waste containers that must be provided by ULPLTD Paokmotong of 3.14 liters, placed in each building and distinguished by the type of waste, namely organic, inorganic, and B3. With an organic waste volume of 18 L / day with a compostable waste capacity of 14.4 L / day. Meanwhile, in implementing the 3R program based on the calculation results, the total volume of composting produced is 540 L / month. The total number of composter drums that must be prepared is seven pieces. As for residual waste, it has a volume of 4 L / day with a container capacity of 28 L and an inorganic waste storage area of 2 m3 with a length of 2 m, width of 1 m, and height of 1 m. The suggestion in this study is that it is hoped that the next researcher will add the Draft Budget for the construction of TPS 3R and the DED of the TPS 3R building.
Deep Classification of Mammographic Breast Density: DCBARNet

Breast density plays a key part in the early prediction of breast cancer development. Experienced radiologists evaluate breast density from the mammographic images for a particular individual based on the Breast Imaging Reporting and Data System (BI-RADS) categories. While super-vised learning algorithms are increasingly finding application for this task, satisfying accuracy and requirement of human intervention remain two of the most challenging roadblocks. In this study, we introduce a novel attention-driven residual learning based method which adaptively pays attention to the selective features in both spatial and channel dimensions. The other feature of our approach is the use of generalized Global Average Pooling layers (utilizing the 2D Discrete Cosine Transform) to further enhance the classifier performance. The proposed method was validated on two publicly available datasets namely CBIS-DDSM and INBreast. Classification accuracies of 87.05% and 79.00% were obtained respectively for the two datasets with five-fold cross-validation. Our approach is fully automatic and the classification performance obtained is an improvement on the state-of-the-art algorithms. We also designed a dual-view network architecture explicitly for the purpose of leveraging the complementary information present in the two different views (CC and MLO) of a single breast. It was observed that the dual-view approach produces slightly better result than the single-view approach.
Implementasi Metode SAW (Simple Additive Weighting) pada Sistem Informasi Pendukung Keputusan Pembelian Rumah Layak Huni

A very important personal and financial decision for a person is buying a house. This decision can be influenced by many factors, such as location, physical condition of the property, price, and other factors that describe the needs and preferences of prospective buyers. the use of the SAW method in the context of information systems intended to help prospective home buyers make smarter decisions. the SAW (Simple Additive Weighting) method involves a number of stages, namely Determination of Criteria and Weights, Normalization of data, and Alternative Assessment, Ranking Alternatives. decision support system (SPK) based on SAW (Simple Additive Weighting) method using Criteria and weights consisting of criteria for Equipment, Location, Purchase Cost, and Model. From the housing data collected, it was found that Citra Indah Garden housing was at the top with a total value of 0.875, followed by Bumi Asri housing with a total value of 0.825, and ended with Taman Seri Indah housing with a total value of 0.75. Overall, Citra Indah Garden data is a livable housing choice according to predetermined criteria optimally. suggestions for the future of this research are to build a system using a more complex algorithm to get more optimal results and use more specific criteria.
Implementasi Metode Backward Chaining Dalam Mendiagnosis Penyakit Periodontitis Terhadap Perokok Aktif Berbasis Android

Periodontitis is a serious dental and oral health problem, especially for individuals who are involved in smoking habits. This study discusses the implementation of the backward chaining method in diagnosing periodontitis in active smokers using the Android platform. The backward chaining method is used as an approach in expert systems to analyze symptoms and risk factors associated with periodontitis. The system implemented on the Android platform allows users to access it easily via mobile devices. The results of this study are expected to help active smokers receive early information about the possibility of periodontitis and encourage them to seek further treatment from medical professionals. With this application, it is hoped that awareness about dental and oral health and the impact of smoking on periodontitis can increase among active smokers
Sistem Informasi Geospasial Penerima Bantuan Sosial Disabilitas Menggunakan Klasterisasi Fuzzy K-Means

The data collection process for recipients of disability social assistance at Dinas Sosial has been going well, but the existing dataset is still in the form of raw data that has not been analyzed for the importance of the Dinas Sosial or Cirebon Satu data. The purpose of this study is to design a Geospatial Information System that can help to analyze, classify, and visualize spatial data from recipients of disability social assistance based on age. The methods used are fuzzy and k-means methods. Fuzzy is a method that can be used to group the age of recipients of disability social assistance and find out the degree of membership. The use of the k-means method is one method that is suitable for use in age clustering. Age will be grouped into three clusters, namely cluster 1 (Young), cluster 2 (Middle-Aged) and cluster 3 (Old). The results of this research are in the form of dataset analysis using the fuzzy k-means method in Microsoft Excel, map analysis in QGIS and maps in html format. After testing, the clustering results of fuzzy and k-means methods carried out in two iterations are "SAME". The results of map visualization are also good. The conclusion of this study is that the Geospatial Information System runs well and can be used to assist Dinas Sosial and for the importance of Cirebon Satu Data
Investigating The Impact of Sampling Techniques on an Imbalanced Classification Problem

Classification with imbalanced datasets can be quite challenging as it can make classifiers biased towards the majority class. This can cause imbalanced classification, which indicates poor predictive performance, particularly with the minority class. This paper is concerned with a comparative study to investigate the impact of the different sampling techniques to address the imbalanced classification problem of an extremely imbalanced binary dataset with 284,807 instances. In the dataset, 99.83% of the samples belong to one class and the remaining 0.17% belong to the other class. The impact of the imbalanced dataset was studied on three classification algorithms: Logistic Regression, K-Nearest Neighbor, and Random Forest. Moreover, Over Sampling, Under Sampling, and Hybrid Sampling, were applied to tackle the problem of imbalanced classification. A detailed evaluation of the performance of each algorithm, before and after applying the sampling techniques is provided. The performance is evaluated based on six metrics which are: Confusion Matrix, Accuracy, Recall, Precision, F1 Score, and Area Under Precision-Recall Curve. Results showed that Logistic Regression was heavily impacted by the imbalanced dataset. The best classification results were achieved by K-Nearest Neighbor and Random Forest classifiers when applying the Random Over Sampling technique to balance the dataset.
On the Sensitivity of Deep Load Disaggregation to Adversarial Attacks

Non-intrusive Load Monitoring (NILM) algorithms, commonly referred to as load disaggregation algorithms, are fundamental approaches for effective energy management. Despite the success of deep models in load disaggregation, they face various challenges, particularly those pertaining to privacy and security. This paper investigates the sensitivity of prominent deep NILM baselines to adversarial attacks, which have proven to be a significant threat in domains such as computer vision and speech recognition. Adversarial attacks entail the introduction of imperceptible noise into the input data with the aim of misleading the neural network into generating erroneous outputs. We investigate the Fast Gradient Sign Method (FGSM), a well-known adversarial attack, to perturb the input sequences fed into two commonly employed CNN-based NILM baselines: the Sequence-to-Sequence (S2S) and Sequence-to-Point (S2P) models. Our findings provide compelling evidence for the vulnerability of these models, particularly the S2P model which exhibits an average decline of 20% in the F1-score even with small amounts of noise. Such weakness has the potential to generate profound implications for energy management systems in residential and industrial sectors reliant on NILM models.
Emirati Sign Language Recognition Using YOLOv5

Many hearing-impaired people use sign language to share their thoughts and express their emotions on a daily basis. Those people are part of our community, and their presence is just as valid as any other person. However, their interaction with the wider community remains limited due to the lack of understanding of sign language. Sign language recognition systems provide a solution that can detect, analyze, and recognize the motion of hand gestures. This project is the first of its kind to recognize Emirati sign language, which is the most popular form of communication for sign language users in the United Arab Emirates. The proposed model is trained for 9 expressions that are used in daily communications, including some dynamic expressions involving multiple gestures. It utilizes a real-time Deep Learning-based object detection system using YOLOv5. The system uses a camera to capture the gesture in order to process and classify it. After the system recognizes the word or expression, it will display the text on a screen. The overall testing accuracy reached 99.11%.
Enhanced Image Encryption Using the Hénon Map, the LCG and the Lorenz System

There is an increasing demand for efficient image encryption methods due to the progress in multimedia creation and sharing over insecure networks. At the same time, researchers are increasingly employing chaotic functions to introduce controlled chaos and generate pseudo-random bit streams for encryption keys and substitution boxes. This study suggests the adoption of a 3-stage image encryption scheme. The first stage implements the Hénon Chaotic Map, then an S-box is implemented using a Linear Congruential generator in the second stage, and the Lorenz system is implemented in the third stage. The performance assessment demonstrates excellent entropy values and yields positive results when various performance metrics are computed and compared to existing literature.
Serials Analysis Directions Part 2: Finding Gems, Pulling Weeds, and Reclaiming Space: Case Studies from GreenGlass for Serials

The presenters of the session, "Serials Analysis Directions Part 2: Finding Gems, Pulling Weeds, and Reclaiming Space: Case Studies from GreenGlass for Serials" provided attendees with valuable perspectives on using GreenGlass for Serials to conduct serials collection review projects effectively, efficiently, and with more confidence. The first two presenters, Katy Gabrio and Ann Roll, are librarians who have used GreenGlass for Serials. They shared their reasons for reviewing serials collections, their libraries' goals for the projects, and the results. The third speaker, Andy Breeding, a senior product manager at OCLC, spoke about the history of GreenGlass and GreenGlass for Serials, as well as the time savings gained from using GreenGlass for Serials, and trends he noticed in serials holdings of libraries through the years.
Upending the Consortial Narrative: Our Story of Affecting Change Through Board Volunteerism

The nature and purpose of academic library consortia can be both complex and obscure. Two technical services librarians discuss their involvement with the WALDO (Westchester Academic Library Directorsâ€™ Organization) consortium, its history, its growth, and its future in the current times of market consolidation and contraction. They also share some of the challenges, pitfalls, and joys of learning how to become a board member of an academic library consortium and the benefits of building new relationships across the vendor, library, and consortia spectrums.
Building a Ladder to Access

The University of Tennessee (UT) Libraries and the University of South Carolina (USC) Libraries drafted documents to guide licensing practices during 2020 and 2021. In 2021, the Association of Southeastern Research Libraries (ASERL) developed licensing principles for its members, including UT and USC. With the principles completed, both institutions moved on to implementation in 2022. This paper outlines the steps taken to develop licensing principles at UT, USC, and ASERL, and discusses plans to incorporate these principles into regular negotiations and licensing agreements at UT and USC. Readers will learn important considerations for implementing licensing principles at their own institution and be warned about pitfalls they might encounter.
MemComputing: an Opportunity for Nanotechnology*

MemComputing is a new physics-based approach to computation that employs time non-locality (memory) to process information on the same physical location where the computation result is ultimately stored [1]. After an introduction to this paradigm, I will discuss its application on a variety of combinatorial optimization problems, the challenges in implementing it in hardware and the opportunities it offers for nanotechnology.
The Effect of Using Facebook on Student Learning Outcomes in Integrated Social Studies Subjects

The development of social media makes performance faster, more precise and accurate so that it can improve learning outcomes. The social media that is often used nowadays is Facebook. A person's learning outcomes can be seen from the level of success in studying subject matter which is expressed in the form of grades or report cards for each field of study after experiencing the teaching and learning process. In the national education system, the formulation of educational goals, both curricular goals and instructional goals. The aim of this research is to determine the effect of using Facebook on student learning outcomes in integrated social studies subjects at SMP Negeri 2 Satap Biluhu. This research uses a quantitative approach, using survey research methods. The data used is primary data obtained from distributing questionnaires to students at SMP Negeri 2 Satap Biluhu. WhereasThe population in this study was all 69 students at SMP Negeri 2 Satap Biluhu. The sample for this study was taken from the entire population, namely 69 people.The data analysis technique uses simple linear regression.The results of the research show that there is an influence of using Facebook on student learning outcomes in the eyesIntegrated Social Sciences Lessons at SMP Negeri 2 Satap Biluhu. The magnitude of the influence of Facebook use on student learning outcomes is 31.6%.