Reviewerly/exHarmony huggingface dataset:
Harm, Offense, and Hate Speech

According to the doctrine of "hate speech," any message qualifying as such will imply an abuse of the fundamental right to freedom of speech. This doctrine, elaborated in different stages by the European Court of Human Rights over the last decades, has been followed by Spanish jurisprudence as an interpretive guideline for the so-called "apologetic crimes." In keeping with this interpretative line, the ratio legis of this group of crimes would consist of the prohibition of hate speech. The vagueness of this idea has led a doctrinal sector to defend the distinction between damage (serious violation of individual interests) and offense (slight annoyance for collective interests). In this paper, the assumptions and effects of the doctrine of "hate speech" will be exposed and critically assessed and the practical efficacy of the "harm-offense" binomial will be relativized, to reach a common conclusion: both conceptions are tautological.
Bullying Tweets Detection using CNN- Attention

The prevalence of cyberbullying toward minorities has been a global concern in the last decade. This concern reached a crescendo during the COVID-19 pandemic as many online users became more active on Twitter, using the social media to harass and threaten vulnerable groups. The after effects of COVID-19. Aside from the increase in technology use, there are other factors at play that are causing an increase in cyberbullying. For instance, when there is a major crisis like the one that COVID-19 brings, this puts everyone on edge, and kids are no exception. As a result, hostility toward others tends to increase along with self-preserving and self-defensive behaviours. In this work, we proposed a novel framework to detect cyberbullying on Twitter. This framework combined the attention layer and the convolutional pooling layer to extract cyberbullying-related keywords from users’ tweets efficiently. We probed the effectiveness of the proposed model using 47000 labelled tweets, which were categorized into cyberbullying classes such as age, ethnicity, gender, religion, type of cyberbullying, and non-cyberbullying. In this research we used two sets of combinations. In the first part we used the combination of CNN and ML models. In this structure we used convolutional layers as feature extractor and then we used ML models such as RF and LR , CNN-XGB, CNN-LSTM, CNN attention for classification.
Neural Coreference Resolution for Turkish

Coreference resolution deals with resolving mentions of the same underlying entity in a given text. This challenging task is an indispensable aspect of text understanding and has important applications in various language processing systems such as question answering and machine translation. Although a significant amount of studies is devoted to coreference resolution, the research on Turkish is scarce and mostly limited to pronoun resolution. To our best knowledge, this article presents the first neural Turkish coreference resolution study where two learning-based models are explored. Both models follow the mention-ranking approach while forming clusters of mentions. The first model uses a set of hand-crafted features whereas the second coreference model relies on embeddings learned from large-scale pre-trained language models for capturing similarities between a mention and its candidate antecedents. Several language models trained specifically for Turkish are used to obtain mention representations and their effectiveness is compared in conducted experiments using automatic metrics. We argue that the results of this study shed light on the possible contributions of neural architectures to Turkish coreference resolution.
Determinants and Impact of Youth Involvement in Agricultural Sector: Evidence from Young Farmers Beneficiaries of PEA-Youth and PCP AFOP Public Programs Living in Central and Littoral Regions of Cameroon

Purpose: Agriculture continue to be an important sector for African countries’ development and for Cameroon development in particular. This study aimed at investigating determinants of youths’ involvement in agriculture and their impact on agriculture income for different entry mode.&#x0D; Methodology: The study used both qualitative and quantitative data from young farmers’ beneficiaries of PEA-Youth and PCP AFOP public programs living in Central and Coastal Regions in Cameroon.&#x0D; Findings: The study found out that age, number of dependent children, matrimonial status, presence of parents in agriculture, education, distance between farm and market, migrate from urban to rural before and the presence of public program for integration in agriculture are keys determinants of the different involvement ways in agriculture in Cameroon. Besides, qualitative analysis shows that farmers declare more pull or positive factors that have led them to entry into agriculture and amongst these factors the reasons such as profitability and autonomy of food security for the family are the most common.&#x0D; Conclusion: It is more profitable for farmers to adopt agriculture by integration as first work or by mobility, rather than by diversification.&#x0D; Recommendation: Public programs should promote agriculture by integration as first work or by mobility and target that kind of profile during the selection process of youth.
CPSO: Chaotic Particle Swarm Optimization for Cluster Analysis

(Background) To solve the cluster analysis better, we propose a new method based on the chaotic particle swarm optimization (CPSO) algorithm.&#x0D; (Methods) In order to enhance the performance in clustering, we propose a novel method based on CPSO. We first evaluate the clustering performance of this model using the Variance Ratio Criterion (VRC) as the evaluation metric. The effectiveness of the CPSO algorithm is compared with that of the traditional Particle Swarm Optimization (PSO) algorithm. The CPSO aims to improve the VRC value while avoiding local optimal solutions. The simulated dataset is set at three levels of overlapping: non-overlapping, partial overlapping, and severe overlapping. Finally, we compare CPSO with two other methods.&#x0D; (Results) By observing the comparative results, our proposed CPSO method performs outstandingly. In the conditions of non-overlapping, partial overlapping, and severe overlapping, our method has the best variance ratio criterion values of 1683.2, 620.5, and 275.6, respectively. The mean VRC values in these three cases are 1683.2, 617.8, and 222.6.&#x0D; (Conclusion) The CPSO performed better than other SOTA methods for cluster analysis problems. CPSO is effective for cluster analysis.
PROMPT: Learning dynamic resource allocation policies for network applications

A growing number of service providers are exploring methods to improve server utilization and reduce power consumption by co-scheduling high-priority latency-critical workloads with best-effort workloads. This practice requires strict resource allocation between workloads to reduce contention and maintain Quality-of-Service (QoS) guarantees. Prior work demonstrated promising opportunities to dynamically allocate resources based on workload demand, but may fail to meet QoS objectives in more stringent operating environments due to the presence of resource allocation cliffs, transient fluctuations in workload performance, and rapidly changing resource demand. We therefore propose PROMPT, a novel resource allocation framework using proactive QoS prediction to guide a reinforcement learning controller. PROMPT enables more precise resource optimization, more consistent handling of transient behaviors, and more robust generalization when co-scheduling new best-effort workloads not encountered during policy training. Evaluation shows that the proposed method incurs 4.2x fewer QoS violations, reduces severity of QoS violations by 12.7x, improves best-effort workload performance, and improves overall power efficiency over prior work.
The off-Record Politeness Strategy and Cultural Values of the Belis Negotiation Speech Event: A Sociopragmatic Study

This study aims to find out the language politeness strategies applied by the tongka (spokesperson) and other parties involved in the belis negotiation speech event in the Manggarai community, the Pasat-Ruis dialect, Indonesia. This study used a phenomenological approach. Data were collected through in-depth interviews with five key informants with experience as spokespersons (tongka) and adequate knowledge of the Manggarai language and culture. Data were analyzed using the interactive model proposed by Miles and Haberman (1992). The study found that the off-record politeness strategy was applied in two patterns; (1) The use of metaphorical phrases to refine the speech. Knowledge and the ability to interpret the meaning of speech through metaphorical phrases is an important aspect that must be possessed by a tongka (spokesperson) by connecting the phrase with the context of its use so that it can be understood and fulfills the elements of Politeness. (2) The use of sopi while conveying speech. The patterns show Politeness and the distinctive and authentic characteristics of Manggarai culture. Meanwhile, the cultural values of the utterances of belis negotiations include kinship, brotherhood, responsibility, appreciation, and respect. Thus, the findings indicate that the spokespersons selected in the belis negotiation process need to consider the knowledge of Manggarai culture and the ability to use metaphorical phrases in cultural speech so that the belisnegotiation process runs smoothly according to the expectations of the two extended families.
Reconstructing seen and unseen attractors from data via autonomous-mode reservoir computing

Reservoir computing is a powerful tool for creating digital twins of a target systems. They can both predict future values of a chaotic timeseries to a high accuracy and also reconstruct the general properties of a chaotic attractor. In this. We show that their ability to learn the dynamics of a complex system can be extended to systems with multiple co-existing attractors, here a four-dimensional extension of the well-known Lorenz chaotic system. Even parts of the phase space that were not in the training set can be explored with the help of a properly-trained reservoir computer. This includes entirely separate attractors, which we call "unseen". Training on a single noisy trajectory is sufficient. Because Reservoir Computers are substrate-agnostic, this allows the creation of conjugate autonomous reservoir computers for any target dynamical systems.
ImageCLEF 2023 Highlight: Multimedia Retrieval in Medical, Social Media and Content Recommendation Applications

In this paper, we provide an overview of the upcoming ImageCLEF campaign. ImageCLEF is part of the CLEF Conference and Labs of the Evaluation Forum since 2003. ImageCLEF, the Multimedia Retrieval task in CLEF, is an ongoing evaluation initiative that promotes the evaluation of technologies for annotation, indexing, and retrieval of multimodal data with the aim of providing information access to large collections of data in various usage scenarios and domains. In its 21st edition, ImageCLEF 2023 will have four main tasks: (i) a Medical task addressing automatic image captioning, synthetic medical images created with GANs, Visual Question Answering for colonoscopy images, and medical dialogue summarization; (ii) an Aware task addressing the prediction of real-life consequences of online photo sharing; (iii) a Fusion task addressing late fusion techniques based on the expertise of a pool of classifiers; and (iv) a Recommending task addressing cultural heritage content-recommendation. In 2022, ImageCLEF received the participation of over 25 groups submitting more than 258 runs. These numbers show the impact of the campaign. With the COVID-19 pandemic now over, we expect that the interest in participating, especially at the physical CLEF sessions, will increase significantly in 2023.
Pedagogical Principles in Learning and Its Impact on Enhancing Motivation of Students

Pedagogical principles are an important part of the learning and teaching process that is important for students to perform effective and efficient learning activities. The pedagogical principles are important for students to learn the curriculum more effectively and also help them to learn new skills conveniently. Implementing pedagogical principles in learning helps to motivate students towards their academic careers. There are different types of pedagogical principles that help to improve the overall learning process of students and help them to get success in their academic and professional life as well. Pedagogical principles enable students to think in different ways and act beyond the traditional method to get benefited in the academic field. Pedagogical principles are important to enhance productivity and creativity and increase critical thinking among students. This study aims to evaluate the pedagogical principles of learning and their impacts on enhancing motivation among students. This study also emphasises the importance of motivation among the students and also tries to determine different strategies to increase motivation among the students. Secondary qualitative research is followed here in this study to improve the authenticity of the study. Thematic analysis of the topic also gives a clear understanding and that helps to make decisions more relevant. Keyword :Academic Field, Enhance, Learning, Motivation, Pedagogical Principle, Students.
"Lessons learned about converting from Dewey Decimal Classification to Library of Congress Classification"

purchased an 80,000 volume library collection from a closing Bible institution.During 2017-2020, both the original 40,000 volume collection and purchased 80,000 volume collection were merged, relocated to a larger facility, thoroughly weeded into a 60,000 volume collection, and converted from Dewey Decimal Classification into the Library of Congress Classification.This article summarizes several factors to consider on switching classifications and our experience from the conversion process Lessons Learned About Converting from Dewey DecimalClassification to Library of Congress Classification
Design considerations for a hierarchical semantic compositional framework for medical natural language understanding

Medical natural language processing (NLP) systems are a key enabling technology for transforming Big Data from clinical report repositories to information used to support disease models and validate intervention methods. However, current medical NLP systems fall considerably short when faced with the task of logically interpreting clinical text. In this paper, we describe a framework inspired by mechanisms of human cognition in an attempt to jump the NLP performance curve. The design centers on a hierarchical semantic compositional model (HSCM), which provides an internal substrate for guiding the interpretation process. The paper describes insights from four key cognitive aspects: semantic memory, semantic composition, semantic activation, and hierarchical predictive coding. We discuss the design of a generative semantic model and an associated semantic parser used to transform a free-text sentence into a logical representation of its meaning. The paper discusses supportive and antagonistic arguments for the key features of the architecture as a long-term foundational framework.
Spatial frequency and global–local visual processing in capuchin monkeys (Sapajus spp.) and humans (Homo sapiens).

Two experiments employing an identity matching-to-sample procedure were carried out to clarify the factors affecting global-local visual processing of capuchin monkeys (Sapajus spp.) in comparison with humans. In the first experiment, we assessed the relative ability of the two species to discriminate high, medium, or low spatial frequencies (HSFs, MSFs, or LSFs). Then, in a second experiment, we determined if the use of a procedure designed to induce a bias toward attending given spatial frequencies could produce a top-down or selection-history modulation of global-local visual processing in capuchins and humans. In the first experiment, monkeys discriminated better HSFs. By contrast, humans discriminated better MSFs and LSFs. The second experiment showed an effect of SF processing on global-local processing in both species. However, this effect was confined to local trials only and occurred under different conditions in the two species. In monkeys, it occurred following a bias toward attending HSFs, whereas in humans, it occurred following a bias toward attending LSFs. These results provide new information about the relative sensitivity of humans and capuchins to different spatial frequencies in vision. Moreover, they suggest that global-local visual processing can be modulated in both humans and monkeys by processes that are not confined to attending one or the other level of stimulus structure. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
Influence of Training Parameters on Real-Time Similar Object Detection Using YOLOv5s

Object detection is one of the most popular areas today. The new models of object detection are created continuously and applied in various fields that help to modernize the old solutions in practice. In this manuscript, the focus has been on investigating the influence of training parameters on similar object detection: image resolution, batch size, iteration number, and color of images. The results of the model have been applied in real-time object detection using mobile devices. The new construction detail dataset has been collected and used in experimental investigation. The models have been evaluated by two measures: the accuracy of each prepared model has been measured; results of real-time object detection on testing data, where the recognition ratio has been calculated. The highest influence on the accuracy of the created models has the iteration number chosen in the training process and the resolution of the images. The higher the resolution of the images that have been selected, the lower the accuracy that has been obtained. The small iteration number leads to the model not being well trained and the accuracy of the models being very low. Slightly better results were obtained when the color images were used.
Evolution of Information and Communication Technologies in Education

The ways of teaching and, consequently, the educational processes are constantly evolving and the innovation of technology is an important part of this learning process. The use of new Information and Communication Technologies (ICT) has expanded to different social areas and education represents a relevant aspect in the life of the human being, where technological achievements have been used to the maximum to put them into practice in such a way that, at the same time that the use is potentiated, a permanent update of educational strategies is necessary. In this bibliographic review, a historical recount of the evolution of ICT is made, the influence they have exerted on education from the beginning of personal computers, until reaching the most recent technological innovations, such as artificial intelligence, highlighting how new information and communication technologies are a fundamental element in the evolution of the teaching-learning process.
Anomaly Detection using combination of Autoencoder and Isolation Forest

The process of identifying abnormal objects or patterns that deviate from the typical behavior in a dataset or other observations is known as Anomaly Detection.It is an essential technique in many fields, such as cyber security, finance, transportation, and fraud detection.This paper combines an autoencoder and an isolation forest algorithm to enhance anomaly detection where the individual methods might not perform well due to the specific context and the nature of the dataset.The autoencoder is a neural network trained to reconstruct the input data, while the isolation forest is a tree-based algorithm that can identify outliers in the data.By combining these two methods, the autoencoder can learn a compact representation of the data, and the isolation forest can then be applied to the reconstructed data to identify anomalies.This combination effectively enhances the anomaly detection process in high-dimensional data when compared to utilizing individual algorithms.
Improving Naturalness of Neural-based TTS System Trained with Arabic Limited Data

In this paper, we examined different approaches, including a neural network speech synthesis system and a nonautoregressive text-to-speech (TTS) model.We demonstrated how a baseline system based on Merlin can be used for TTS synthesis to produce a voice that is similar to a human.Typically, this system is only implemented with a front-end text processor and a WORLD vocoder.Here, we adapted Continuous and Ahocoder vocoders, and then we evaluated the effectiveness of each vocoder's techniques in producing highquality speech.In the non-autoregressive TTS model, we implemented the state-of-the-art Fastspeech2 system, which provided high-quality speech synthesis in a timely manner without controllability and robustness issues.We focused on integrating a different language with limited data while maintaining high-quality speech synthesis.Through objective and subjective evaluations, we verified that our method could outperform the baseline system with full data.
CreativeStone: a Creativity Booster for Hearthstone Card Decks

Digital Collectible Card Games (DCCG) rely on human expertise to create competitive card decks. The most effective decks are shared and become popular among players through online communities. Players then keep enhancing those decks by replacing cards and testing those new deck variations in online arenas. This fine-tuning process is time-consuming and most of the time leads to small improvements in the players win rate. This paper presents CreativeStone, a creativity booster for Hearthstone card decks. Our creativity-guided approach uses a genetic algorithm and the Regent-Dependent Creativity (RDC) metric to identify the core cards of an existing deck, and then improves it towards a more valuable and novel deck by adding cards that are synergic to the core cards, but also different from the ones in the original deck. Our experimental results show that CreativeStone can boost even legendary decks, outperforming handcrafted ones by 21% in win rate.
An automatic entropy method to efficiently mask histology whole-slide images

Tissue segmentation of histology whole-slide images (WSI) remains a critical task in automated digital pathology workflows for both accurate disease diagnosis and deep phenotyping for research purposes. This is especially challenging when the tissue structure of biospecimens is relatively porous and heterogeneous, such as for atherosclerotic plaques. In this study, we developed a unique approach called 'EntropyMasker' based on image entropy to tackle the fore- and background segmentation (masking) task in histology WSI. We evaluated our method on 97 high-resolution WSI of human carotid atherosclerotic plaques in the Athero-Express Biobank Study, constituting hematoxylin and eosin and 8 other staining types. Using multiple benchmarking metrics, we compared our method with four widely used segmentation methods: Otsu's method, Adaptive mean, Adaptive Gaussian and slideMask and observed that our method had the highest sensitivity and Jaccard similarity index. We envision EntropyMasker to fill an important gap in WSI preprocessing, machine learning image analysis pipelines, and enable disease phenotyping beyond the field of atherosclerosis.