Reviewerly/exHarmony huggingface dataset:
Effective Face Recognition using Adaptive Multi-scale Transformer-based Resnet with Optimal Pattern Extraction

Human face is the major characteristic for identifying a person and it helps to differentiate each person.Face recognition methods are mainly useful for determining a person's identity with the help of biometric techniques.Face recognition methods are used in many practical applications like criminal identification, the phone unlocks systems and home security systems.It does not need any key and card, and it only requires facial images to provide high security over several applications.The interdependencies of the encryption methods are highly reduced in the deep learning-enabled face recognition models.Conventional methods did not satisfy the present demand due to poor recognition accuracy.Therefore, an advanced deep learning-based face recognition framework is implemented to authenticate the identity of individuals with high accuracy by using facial images.The required facial images are taken from the standard databases.The collected images are preprocessed using median filtering.The preprocessed facial images are subjected to spatial feature extraction, where the Local Binary patterns (LBP) and Local Vector Patterns (LVP) are utilized to extract the relevant optimal patterns from the facial images.Here, optimal pattern extraction is done with the Improved Rat S warm Optimization Algorithm (IRS O).Then, the facial recognition is done over the extracted optimal features with the usage of the implemented Adapti ve Multi -scale transformerbased Resnet (AMT-ResNet), where the parameters in the recognition network are optimized by using the IRSO.The efficiency of the developed deep learning adopted face recognition model is validated through different heuristics algorithms, and baseline face recognition approaches.
The Rambutan Orchard: Ecocritical Reading of the Kelantanese Malay Identity in Fiction

The paper presents an ecocritical exploration of the representation of the Kelantanese Malay identity and connectivity to the land in The Rambutan Orchard, an anthology of short stories by Che Husna Azhari.Although ecocriticism is a well-established theory, its use to study the female Kelantanese Malay storyteller's portrayal of the environment in the Malaysian state of Kelantan is under-explored.Applying the pastoral approach in analysing the stories, the researchers uncover the representations of rural life, the characters' interactions with their surroundings and the consequences thereof with regards to the construction of identity.The ecocritical reading of the orchard in The Rambutan Orchard illustrates Che Husna's authorialdefined social reality as she narrates the multiple significations of the orchard in the lives of this fictional Kelantanese village.Subsequently, the anthology is framed within the quintessential Kelantanese Malay psyche and connectivity to their land of birth.The study finds that the orchard provides distinctiveness in the narrator's identity, symbolises her yearning for her place of origin and teaches her lessons on social hierarchy and personal worth.Finally, the orchard nurtures her self-efficacy by becoming a place of solace in her memories, consolidating her identity as a Kelantanese Malay in the world at large.
Practicing Local Curriculum in Basic Level: As an Educational Innovation and Creativity

This study aims to investigate the practicing local curriculum at basic level schools through the educational innovation and creativity in Nepal under the teachers’ view. I applied quantitative research design, and positivist paradigm. In Kathmandu district's 25 public lower secondary schools, 125 respondents in the basic level teachers, were randomly selected for the sample (Five teachers from each school). Data collection was carried out using a self-created questionnaires that had 50 statements and five Likert-type response alternatives. The self-developed questionnaire's validity was verified by the opinions of experts, and reliability was established Cronbach's alpha, 0 .895. I prepared five factors according to loading components. According to research, practicing a local curriculum empowers individuals by acknowledging their own knowledge, skills, and expertise. It motivates students to participate actively in their education and community development. Practices can shape their own learning experiences by engaging with the local curriculum and choosing choices that match their interests and objectives.
An Integrated Framework for Relevance Classification of Trending Topics in Arabic Tweets

Social media platforms such as Twitter are a valuable source of information about current events and trends.Trending topics aim to promote public events such as political events, market changes, and other types of breaking news.However, with so much data being generated, it would be difficult to identify relevant tweets that are related to a particular trending topic.Therefore, in this paper, an integrated framework is proposed for the detection of the degree of relevance between Arabic tweets and trending topics.This framework integrates natural language processing, data augmentation, and machine learning techniques to identify text that is likely to be relevant to a given trending topic.The proposed framework was evaluated using a real-life dataset of Arabic tweets that was collected and labeled.The results of the evaluation showed that the proposed framework achieved the highest macro F1 score of 82% in binary classification (relevant/irrelevant) and 77% in categorical classification (degree of relevance), which outperforms the current state of the art.
The Effectiveness of mobile learning media using Smart Apps Creator on Economic subjects

This study aims to produce mobile learning media using the Smart Apps Creator and to determine the effectiveness of these media in increasing student interest in learning. The 4D model (Define, Design, Develop, and Disseminate) is used in the research and development process for this project. This research is limited to the development phase. The participants in this research were XI IPS SMA PGRI Pekanbaru students. Data was obtained based on interviews and questionnaires. Based on the substantiation of media and content specialists, the research findings indicate that the use of mobile learning media created by smart apps creator is feasible, with 87% of the categories being very appropriate according to media experts and 83% of the categories being very appropriate based on subject matter experts. Paired sample t-test obtained differences and positive increases in student learning interest before and after using smart apps creator-based mobile learning media, so the media is said to be effective. The use of media received a good response from students; 64.5% consider mobile learning media using smart app creators to be very practical and helpful in learning, while the remaining 35.5% consider it practical. This research is limited to APBN and APBD material and is only carried out until the development stage.
Synergistic Integration of Blockchain and Machine Learning: A Path to a Decentralized Intelligent Future

The integration of blockchain and machine learning has emerged as a promising paradigm that can revolutionize various industries and applications. Blockchain’s decentralized and immutable nature, coupled with the analytical capabilities of machine learning, presents new opportunities for secure and transparent data sharing, collaborative model training, and intelligent decision-making. This research paper explores the concept of synergistic integration of blockchain and machine learning, providing an overview of the underlying technologies, related work, and existing frameworks. It proposes a novel Decentralized Intelligent Learning Network (DILN) framework that combines the strengths of both technologies to create a decentralized and efficient ecosystem for collaborative machine learning applications. The paper presents case studies in healthcare, finance, supply chain management, IoT, and academic research to showcase the potential impact of this integration. Furthermore, it discusses technical approaches, challenges, and ethical considerations to address in the deployment of decentralized intelligent systems. The research paper concludes by encouraging further research and development in the field to unlock the full potential of this transformative technology.
Impossible Differential Cryptanalysis of Lightweight Block Cipher WARP

Abstract WARP is a lightweight block cipher with 128-bit key size and block size, which can be used as a lightweight replacement of AES-128 without changing the mode of operation. In this paper, we proposed an automatic search algorithm for impossible differentials of WARP with SAT method and searched for impossible differentials of WARP up to 21 rounds. Using one impossible differential of 21-round WARP, we present an impossible differential cryptanalysis of 33-round WARP by appending six rounds before and after the distinguisher, which covers the same round as the best zero-correlation linear attack on WARP in the single-key setting.
Beyond Human Boundaries: Exploring the Proficiency of AI Technology and its Potential in Psychometric Test Construction

The substantial advancements in artificial intelligence, notably the Generative Pretrained Transformers exemplified by GPT4, have instigated comprehensive discussions on the potential and challenges of AI. Following on from research on automatic evaluation of open-ended responses using latent semantic analysis (LSA) undertaken in our team two decades ago, we take an updated look at the possibilities today. In this brief report, we re-evaluate our findings in assessing the linguistic capabilities of computational linguistics and review the performance of current approaches using language subtests of intelligence tests of the Wechsler series and tests of reading comprehension. The results show that current language models perform at the upper end of the spectrum of human abilities and that they therefore offer tremendous opportunities in test development, as well as in the automatic evaluation of open-ended responses.
Contextual novelty detection and novelty-related memory enhancement in amnestic Mild Cognitive Impairment

Introduction: Though novelty processing plays a critical role in memory function, little is known about how it influences learning in memory-impaired populations, such as amnestic Mild Cognitive Impairment (aMCI).Methods: 21 aMCI patients and 22 age- and education-matched healthy older participants performed two tasks— (i) an oddball paradigm where fractals that were often repeated (60% of the stimuli), less frequently repeated (20%), or novel (presented once each) were shown to assess novelty preference (longer viewing time for novel than familiar stimuli), and (ii) a Von Restorff paradigm assessing novelty-related effects on memory. Participants studied 22 lists of 10 words. Among these lists, 18 contained an isolated word different from the others by its distinctive aspect, here the font size (90-point, 120-point or 150-point against 60-point for non-isolated words). The remaining four were control lists without isolated words. After studying each list, participants freely recalled the maximum words possible.Results: For the oddball task, a group-by-stimulus type ANOVA on median viewing times revealed a significant effect of stimulus type, but not of group. Both groups spent more time on novel stimuli. For the Von Restorff task, both aMCI and healthy controls recalled the isolated words (presented in 120-point or 150-point, but not 90-point) better than others (excluding primacy and recency effects). Novelty-related memory benefit—gain factor—was computed as the difference between the recall scores for isolated and other words. A group-by-font size ANOVA on gain factors revealed no group effect, nor interaction, suggesting that aMCI patients benefited from novelty, alike controls.Conclusion: Novelty preference and the boosting effect of isolation-related novelty on subsequent recall seem preserved despite impaired episodic memory in aMCI patients. This is discussed in the light of contemporary divergent theories regarding the relationship between novelty and memory, as either being independent or parts of a continuum.
Learning from the ground up: lessons from civil society engagement in addressing the human rights implications of cybercrime legislation

ABSTRACTThe UN began negotiations to develop a possible cybercrime convention in 2022, and the impact on human rights is one of the key areas of discussion and scrutiny. This three-part article is intended to inform the reader about human-rights-respecting and inclusive approaches to the development of the convention, and draws on case studies to provide evidence-based recommendations. In the first part, it sets out how cybercrime legislation impacts human rights, including both the positive and negative implications of measures taken in tackling cybercrime. It provides specific examples of the areas of cybercrime legislation (e.g. relating to the scope of criminalisation and procedural powers) that are part of discussions for the convention and explain how they implicate human rights. In doing so, it explains the role of civil society in supporting the development of frameworks and responses to cybercrime which promote and protect human rights. Second, it provides case studies from different regions, highlighting how civil society expertise can directly support the development of rights-respecting approaches to cybercrime. Finally, it provides recommendations to member states and other stakeholders. These seek to guide stakeholders, including member states, to develop a rights-respecting instrument which effectively addresses cybercrime in a rights-respecting manner.KEYWORDS: Cybercrimecivil societyhuman rights AcknowledgementsThe authors are grateful to Kennedy Kachwanya and James Wamathai (Bloggers Association of Kenya), Juan Carlos de Lara and Michel de Souza (Derechos Digitales), and Jessamine Pacis and Lisa Garcia (Foundation for Media Alternatives) for their indispensable contributions to the case studies.Disclosure statementNo potential conflict of interest was reported by the author(s).
A Multinomial Model-Based Analysis of Feature Binding in Working Memory

We examined how elements are integrated into larger units in working memory (WM). Four contrasting conceptual models exist with regard to this question: (1) a Unitization model, in which there is a single integrated representation which is retrieved in an all-or-none fashion; (2) a Unitization-With-Element-Failure model, in which a single integrated representation is retrieved as a whole, but access to its elements can still fail individually; (3) a Pairwise-Binding model, in which elements of a unit are represented separately and are bound together in pairs; (4) a Hybrid model that includes an integrated representation as well as pairwise bindings between element representations. We developed four multinomial process tree (MPT) models to test these theories. In three experiments, participants memorized multiple units which were random combinations of three elements. They were given one element as a cue and prompted to report the other two elements. The model-comparison analysis revealed that the Hybrid model provides the best quantitative fit to the data. We conclude that multi-element units are represented on two levels, as an integrated unit retrieved in an all-or-none manner, and in addition through pairwise bindings between their elements. Moreover, the assumption that bindings of non-spatial elements are mediated through their shared spatial location – a special case of the Pairwise-Binding model – was not supported by the data. Keywords: working memory, binding, multinomial modeling
Analyzing Students' Skills in Writing Narrative Texts at SDN Cemorokondang 4 Malang

This study aims to determine the ability to write narrative essays of fifth grade elementary school students. In this study, there are 2 types of narrative essays, namely expository narrative essays and suggestive narrative essays. In this study used a descriptive research method, a qualitative approach. The subjects in this study were 20 students of class V at SDN Cemorokandang 4, Malang City. The instrument used is the instrument for students' ability to write narrative essays. The data analysis technique used is the Milles and Huberman model, which consists of data reduction, data presentation, and verification/conclusion. Based on the results of an analysis of the ability to write narrative essays for fifth grade students at Cemorokandang 4 Elementary School, Malang City, the assessment guidelines for writing narrative essays consist of 6 aspects, namely plot, characters, setting, content, style (choice and vocabulary structure), and spelling and punctuation. The results category for writing expository narrative essays showed 15 students had good criteria, 4 students had enough criteria, and 1 student lacked criteria. The category of students' suggestive narrative writing results showed 2 students with very good criteria, 9 students for good criteria, 3 students for sufficient criteria, and 6 students for poor criteria.
Chapter 30. Weakly Supervised Reasoning by Neuro-Symbolic Approaches

Deep learning has largely improved the performance of various natural language processing (NLP) tasks. However, most deep learning models are black-box machinery, and lack explicit interpretation. In this chapter, we will introduce our recent progress on neuro-symbolic approaches to NLP, which combines different schools of AI, namely, symbolism and connectionism. Generally, we will design a neural system with symbolic latent structures for an NLP task, and apply reinforcement learning or its relaxation to perform weakly supervised reasoning in the downstream task. Our framework has been successfully applied to various tasks, including table query reasoning, syntactic structure reasoning, information extraction reasoning, and rule reasoning. For each application, we will introduce the background, our approach, and experimental results.
Analisis Sentimen Penggunaan Teknologi Pada Pendidikan Anak Usia Dini

The era of disruption has caused many changes in people's lives, both in behavior and in system settings, where it is impossible to return to the previous era. Efforts are needed to face this era with revitalization, one of which is the urgency of early childhood education so that it remains based on Islamic values with technological developments that accompany the growth of the next generation of the Indonesian nation. The application of Islamic values from an early age is very necessary because it is the initial foundation for the generation of a religious nation by adapting itself according to the times that cannot be separated from technology. By realizing the goal that people are able to adapt to existing conditions, of course it is not easy because there are pros and cons that can be seen from social media which discusses several issues regarding the influence of Islamic values that are taught from an early age to the presence of technology. In this study using opinion mining found on social media to determine the positive or negative conditions that occur so that it is known how big the relationship between variables is. The discussion of data on this problem uses four machine learning algorithms including: decision tree, naïve Bayes, k-nn and svm. The results of data processing show that the decision tree algorithm gets the highest accuracy value of 78.20% among the other algorithms. The results of the study are used to determine strategies for improving human resources or the nation's next generation, starting with early improvements.
Sistem Pakar Konsultasi Penyakit Kulit Dengan Metode Forward Chaining Pada Klinik Medika

Expert systems have information gained through a combination of readings, training, and practical experience. As a result, professionals can make faster and more accurate decisions while tackling complicated situations than non-experts. Klinik Medika is a health-care organization that focuses on public health, particularly skin health issues. This clinic's services to patients for consultation and treatment need to be improved in terms of service time efficiency. For this reason, an expert system is required so that patients can consult online rather than visiting the clinic.The goal of this research is to apply Forward Chaining method to create an expert system that can be used to consult on various skin conditions. Based on the results of the tests, the expert system can correctly diagnose the signs of skin disorders with a 90% accuracy. The system can also accept errors caused by differences in knowledge based on computational data and the experience of skin disease experts.
Application of K-Mean in Clustering Mapping of Underprivileged Communities

Jatiyoso is one of 17 sub-districts in Karanganyar Regency, Central Java Province. Through this sub-district office, residents can take care of various forms of permits. There are many other functions and duties of the subdistrict office. The distance from the county seat is 30.0 km to the south. Jatiyoso is divided into 9 village areas, namely, Jatisawit, Jatiyoso, Karangsari, Petung, Tlobo, Wonokeling, Wonorejo, and Wukirsawit. With the total population of Jatiyoso District is 40,709 people. With a large population, and also a large amount of poverty. To help reduce poverty in Jatiyoso District, Jatiyoso District, an information system is needed that can explain the mapping of poverty areas in Jatiyoso Village. This information system is expected to assist the government in distributing aid to the poor. K-Means method was applied in this study to overcome the problem of grouping poor families by category. According to the findings of study, there were 23 groups of persons with low assets (C1) and four groups of people with substantial assets (C2). The findings of the feasibility testing process with the User Acceptance Test (UAT) respondents agreed (average 95%) that the application of mapping the clustering of poor people using the k-means method can help officers in mapping the poor and facilitate the distribution of aid to the poor in Jatiyoso village
Role Playing Learning Models in Short Story Materials in SMA 1 Sidrap

The study's goals are to 1) determine how guessing games help second-class students improve their English-speaking abilities. 2. To understand the challenges second-class students are having with improving their English-speaking abilities. To illustrate the evolution and challenges encountered in using guessing games, the researcher used descriptive methods and purposive sampling. The results showed that 6 (20%) of the 30 pupils were classed as having very good grades, 12 (40%) as having good grades, 9 (30%) as having average grades, 3 (10%) as having low grades, and none as having very poor grades. It demonstrated that they were very interested in integrating the teaching and learning of English language using a guessing game technique
The Use Of Speed Reading To Boost Reading Comprehension For First Grade Students At SMA Muhammadiyah Rappang

In this study, first grade students at SMA Muhammadiyah Rappang used speed reading to improve their reading comprehension. A writer and an observer participated in this research together with 40 other students in class X-1. A CAR (classroom action research) was used to carry out the study. The results suggested that improving the pupils' reading abilities might be accomplished by using speed reading techniques. The use of speed reading techniques could be successful if they were backed up by the teacher's capacity to arrange learning in the classroom by incorporating exercises proportionately in the teaching process. By providing adequate infrastructure and study facilities, a conducive learning environment was maintained. The pupils' enthusiastic responses and active engagement in each learning activity demonstrated the effectiveness of the speed reading technique.