Reviewerly/exHarmony huggingface dataset:
Deepfake Detection Model Based on Combined Features Extracted from Facenet and PCA Techniques

Recently, the increase in the emergence of fake videos that have a high degree of accuracy makes it difficult to distinguish from real ones. This is due to the rapid development of deep-learning techniques, especially Generative Adversarial Networks (GAN). The harmful nature of deepfakes urges immediate action to improve the detection of such videos. In this work, we proposed a new model to detect deepfakes based on a hybrid approach for feature extraction by using 128-identity features obtained from facenet_CNN combined with most powerful 10-PCA features. All these features are extracted from cropped faces of 10 frames for each video. FaceForensics++ (FF++) dataset was used to train and test the model, which gave a maximum test accuracy of 0.83, precision of 0.824 and recall value of 0.849.
Practicum of Early Childhood Teacher Students in Pandemic Times: A Narrative Perspective

The Covid-19 pandemic affected teacher education; universities had to adapt quickly through emergency pedagogy. One problem that emerged was the relationship between schools and teacher students. The situation was more critical in early childhood teacher education than in others. The conditions forced them to adjust to the absence of children in the student teachers’ practicum. This article addresses the relational problem in a practicum in early childhood teacher education. Through a narrative inquiry with two early childhood education teacher students, we give an account of learning experiences in different educational spaces. During the analysis, two concepts emerged: ‘the classroom taste’ and ‘presence’ in teacher education. The research allows us to reflect on the essential focus of the practicum in early childhood teacher education from the perspective of these two concepts.
TextField and TextArea

Text fields are commonly used in a form to pass user-entered text data to the server. There are two variants (prior to HTML5): password fields and text areas. The characters in password fields are masked (shown as asterisks or circles). Text areas allow multiple lines of text. See Figure 5-1 for examples.
The Impact of Artificial Intelligence

The UKeiG Members’ Day, held at CILIP HQ on Friday 7th June 2019, showcased three experts: Michael Upshall (UNSILO), David Haynes (City, University of London) and Dr. Tony Russell-Rose (UXLabs, 2dSearch.) The event explored the impact of artificial intelligence on the knowledge, information management and library profession. Upshall kicked off the event by throwing down a gauntlet. Were taxonomies still relevant in an age when AI was transforming search by enabling concept clustering and semantic enrichment? Manual classification schemes, vocabularies, taxonomies and ontologies have always played an essential role in information retrieval but Upshall argues that they are expensive and fundamentally flawed; reactive not proactive. “They will never be complete. They will never be large enough.” Haynes went on to explore the potential impact of AI on the information resource management cycle, leaving Russell Rose to deliver an illuminating crash course on the linguistic phenomena that make natural language processing (NLP) such a complex and multi-faceted field of research. “Language is ambiguous,” he said, “and the key tenet of NLP is resolving that ambiguity.” There was a consensus amongst the delegates that there are huge challenges and opportunities ahead for the LIS profession; that information science is fundamental to AI.&#x0D; Keywords: AI, Artificial Intelligence, Alogorithms, Information Retrieval, Search, Machine Learning, Taxonomies, Natural Language Processing, NLP, Information Science.
Technical Report: Protocol for Characterizing Phenotype Variants Using Phenome-Wide Association Study (PheWAS) Utilizing the Nationwide Inpatient Sample 2020 in Individuals With Pancreatic Cysts and Lung Cancer

This technical report serves as a comprehensive guide for conducting a phenome-wide association study (PheWAS) utilizing data extracted from the Nationwide Inpatient Sample 2020. Specifically tailored to individuals diagnosed with pancreatic cysts and lung cancer, the report establishes a step-by-step workflow designed to assist researchers in uncovering potential associations within this specific cohort. The methodology outlined in the report ensures clarity and reproducibility by employing a curated cohort sourced from the GitHub repository and executed using R for robust data analysis. The code encompasses pivotal steps, including the utilization of a QQ plot as a crucial diagnostic tool aimed at identifying systematic biases or associations. Additionally, the report incorporates the creation of a Manhattan plot, delving into essential mathematical considerations to enhance the interpretability of the results. Notably, the report elucidates the handling of the International Classification of Disease version 10 (ICD-10) codes, providing a sample approach for their segmentation to analyze associations by diagnostic categories. The segmentation aligns with the guidelines outlined in the American Medical Association's ICD-10-CM 2022, the Complete Official Codebook with Guidelines (American Medical Association Press, 2021), ensuring a standardized and rigorous analytical process. This comprehensive guide equips researchers with the tools and insights needed to navigate the complexities of PheWAS within the context of pancreatic cysts and lung cancer, fostering transparency, reproducibility, and meaningful scientific exploration.
PKM Strategi Penguatan Literasi Digital Kritis untuk Pembinaan Perempuan di Lapas Kota Pontianak

Prison women are a group of people who are trained so that they do not repeat crimes when they reintegrate into society. Therefore, we conducted Tular Reason education activities for female Pontianak prison residents. The aim of the Tular Nalar digital literacy training at the Pontianak City women's prison is to (1) increase critical awareness to avoid crimes originating in the media, such as hoax traps and fraud, as well as incitement, hate speech, etc. (2) Provide the ability to check simple facts using several methods and tools. (3) Become a digital literacy agent in their environment. The activity participants are residents of the Pontianak Women's Penitentiary, most elderly. This activity was attended by 50 older women in prisons consisting of 45 prison residents and five prison officers. The participants were divided into five groups, each accompanied by one facilitator. The facilitators used the activity implementation guide module for the facilitator and the implementing team, templates of training props such as Stickers, Flyers, etc., which can be adjusted according to needs. The material presented to the participants included: (1) Understanding the basics of anti-hoax advocacy. (2) Understanding how to identify hoaxes, (3) Discussing digital fraud, (4) Discussing hate speech. The stages of the activity are using presentations, video screenings, and group discussions, practicing checking news hoaxes/facts, and filling out questionnaires to find out the participants' understanding accompanied by facilitators. The activity results were that the participants could identify hoaxes from the teaching materials used. The participants could present the results of group reflections and carry out simple fact checks.
Real-time Pedestrian Tracking Based on YOLOv3 and Prototype Clustering

Although the accuracy of existing neural network models is high, in pedestrian tracking tasks, due to the uncertainty of targets, when tracking new targets, it is necessary to fine-tune the model, which further requires large computing and storage resource overhead. Therefore, its application on some lightweight platforms, such as robots and UAVs, is limited. Pedestrian tracking by robots and UAVs still faces great challenges in occlusion, multi-target, target loss, etc. This paper mainly solves the problem of real-time pedestrian tracking by object detection model of robot lightweight, which is mainly based on YOLO network to detect pedestrians, and then proposes a novel lightweight model and prototype clustering algorithm. Numerous experiments on the ETH dataset validate the superiority and effectiveness of our approach.
DIAGNOSTIC STUDY: TRANSFORMING RESEARCH GOVERNANCE AT UNIVERSITAS JAMBI – A COMPARATIVE ANALYSIS WITH LPPM OF SEVERAL INDONESIAN UNIVERSITIES

The Tridharma of Higher Education, encompassing Education, Research, and Community Service, serves as the cornerstone of higher education's tripartite mission. These integral functions are interdependent and mutually reinforcing, positioning universities as pivotal institutions for fulfilling societal needs in terms of quality human resources, the generation of new knowledge, and providing solutions to a myriad of societal challenges. This study aims to engage in a comparative analysis with other higher education institutions involved in Research and Community Service (LPPM) - specifically USK, UB, UNPAD, and UNHAS - to enhance the performance and governance of UNJA's LPPM. The methodology employed in this research is an interview survey. The findings indicate that USK excels in supporting researchers and offering substantial incentives. UNPAD is noted for its robust foundational data, which forms a critical basis for decision-making. Additionally, UNPAD ensures efficient facilitation of research activities and publication. The standout feature of UB is its user-friendly system, greatly simplifying processes for stakeholders. Finally, UNHAS is commended for its clear direction in research and service execution, alongside well-defined annual targets. This comparative study is instrumental in identifying best practices and potential areas for improvement in UNJA's LPPM, thereby contributing to the broader goal of enhancing the effectiveness of higher education institutions in fulfilling their Tri Dharma. Keywords: Diagnostics, Comparison, LPPM Governance
Lightweight Authentication for Devices in Internet of Thing Environment: A Survey

Internet of Thing (IoT)" is a major and emerging technology where different devices are associated together to operate smartly deprived of human’s intervention. IoT has a great effect on economic, social and commercial life. The extremely large number of associated devices together through different protocols make it vulnerable to be threatened by several types of attacks, such as; Unauthorized Access and Authentication Attacks, Network Attacks, Data Privacy and Integrity Attacks, and Supply Chain Attacks. Building and developing lightweight authentication systems for IoT devices are indeed crucial due to several compelling reasons, including; Resource Constraints, Security Concerns, Scalability, Bandwidth Efficiency, Latency Considerations, Cost Reduction, Long Lifecycle and the overall User Experience. Blockchain and “Machine Learning (ML)” are emerging technologies that may be exploited in solving different security problems. This paper presents a survey of lightweight authentication protocols and schemes that are adopted and applied for IoT environment. The security issues in IoT environment are also discussed besides the different types of attacks that may face the network. The paper held a comparison between the selected works and studies in terms of different criteria including the benefits, the results, the used applications and technologies or methods.
Reconciling the Divides: A Dynamic Integrative Analysis of Variability and Commonality in (Pre)primary School English Development in Switzerland

The main goal of this paper is to suggest a combination of data analyses – notably generalized additive models, time-series clustering methodology, visual methods for significance testing and qualitative analyses – that relate to Complex Dynamic Systems Theory (CDST). To this end, we report on findings from a larger project conducted in a bilingual (pre)primary school in Switzerland, aiming to elucidate the complex ways that L2 English development emerges over time in 45 children who received German/English bilingual instruction over a period of eight years (age 5-12) in combination with emerging extracurricular exposure to English. The results reveal that increased extracurricular activities in English are particularly noticeable during periods of rapid development, but the effects seem temporally limited to the end stages of testing and strongly hinge on the cluster in question (i.e., learners with trajectorial similarities). We relate the findings to the “authenticity gap” between English inside and outside of school, as clusters who perceive a discrepancy between in- and out-of-school encounters with English also show rapid development that is characterized by increased English exposure during extracurricular activities. Methodological implications of adopting non-linear models, which can model complex dynamic relationships in order to better reconcile generalizability, variability, and individuality, are discussed.
Emotion analysis of social network data using cluster based probabilistic neural network with data parallelism

Social media contains a huge amount of data that is used by various organizations to study people’s emotions, thoughts and opinions. Users often use emoticons and emojis in addition to words to express their opinions on a topic. Emotion identification from text is no exception, but research in this area is still in its infancy. There are not many emotion annotated corpora available today. The complexity of the annotation task and the resulting inconsistent human comments are a challenge in developing emotion annotated corpora. Numerous studies have been carried out to solve these problems. The proposed methods were unable to perform emotion classification in a simple and cost-effective manner. To solve these problems, an efficient classification of emotions in recordings based on clustering is proposed. A dataset of social media posts is pre-processed to remove unwanted elements and then clustered. Semantic and emotional features are selected to improve classification efficiency. To reduce computation time and increase the efficiency of the system for predicting the probability of emotions, the concept of data parallelism in the classifier is proposed. The proposed model is tested using MATLAB software. The proposed model achieves 92 % accuracy on the annotated dataset and 94 % accuracy on the WASSA-2017 dataset. Performance comparison with other existing methods, such as Parallel K-Nearest Neighboring and Parallel Naive Byes Model methods, is performed. The comparison results showed that the proposed model is most effective in predicting emotions compared to existing models.
Adversarial guesswork with quantum side information

The guesswork of a classical-quantum channel quantifies the cost incurred in guessing the state transmitted by the channel when only one state can be queried at a time, maximized over any classical pre-processing and minimized over any quantum post-processing. For arbitrary-dimensional covariant classical-quantum channels, we prove the invariance of the optimal pre-processing and the covariance of the optimal ost-processing. In the qubit case, we compute the optimal guesswork for the class of so-called highly symmetric informationally complete classical-quantum channels.
Artificial Intelligence and Innovative Applications in Special Education

Artificial intelligence can be defined as systems or machines that mimic human intelligence to perform tasks and can iteratively improve itself based on the data they gather. Artificial intelligence applications, which are among the innovative educational technologies, have been frequently used in the field of education as well as in other fields such as entertainment, medicine, cyber security, transportation, tourism, e-commerce, banking, and finance. Developments in artificial intelligence applications have brought different innovations in the field of special education as well as in the other areas of education. The current study aims to examine the current situation of artificial intelligence applications to meet the needs and desires of individuals with special needs and to provide with a broader perspective on this issue. Narrative review method is used to fulfill the purpose of this study since it neither requires predetermined research question nor specific search strategy. Through narrative review, it can be concluded that, artificial intelligence applications are successfully applied to meet the needs and desires of individuals with special needs. Artificial intelligence, which is personalized according to the needs of individuals, determines their deficiencies, and offers the advantage of personalized support for their development. In addition, thanks to artificial intelligence applications, individuals with special needs express their feelings more easily by interacting with their parents, teachers, psychologists, and others around them. These applications also provide individuals with the opportunity to discover their talents. It is also seen that artificial intelligence offers some important advantages such as saving time and cost, creating more efficient and effective learning environments.
Chatbot for MindTech Digital Solutions

Abstract: Technological advancements have opened the door for businesses to automate customer service through artificial intelligence (AI) chatbots. While these digital assistants offer a range of potential benefits, interactions with them can often feel robotic and sterile. AI chatbots have undoubtedly revolutionized customer service. They offer 24/7 availability, handle repetitive tasks with lightning speed, and can access vast stores of information. However, their purely functional nature can leave customers feeling disconnected and yearning for a more personal touch. This study explores the concept of using humour in AI chatbots as a way to bridge this gap and create a more humanized customer experience.
The Effect of Process Integration on Organizational Culture for Strategic Collaboration in Automobile Industry

A strong partnership in new vehicle retail management is a long-term commitment made by automakers and dealers toward mutualbenefit. Manufacturers and dealers share a shared purpose in selling things, which makes everyone involved will benefit from acollaborative effort to sell a model automobile. This paper studies the aspects of trust that are built between the manufacturer and dealerwhich is affected by process integration, cultural understanding, and contract flexibility. The methodology followed was quantitative innature in which cross-sectional data from the same manufacturing company dyad was employed. The findings of the research show apositive association between process integration with cultural understanding and contract flexibility and together these affect the trust thatis built between the parties.
Improving Speaking Skills through Student Group Discussions at High School in Central Maluku

Language is all about communication and the people they communicate with most are their friends and family. Language is a system for communicating with others using sounds, symbols, and words in conveying meanings, ideas, or propositions. This study is going to have qualitative research implementing a case study as the methodology of research. It aims to know about something in-depth, focusing on a single unit, such as one individual, one group, one organization, or one program. For more intensive research and better results. The number of respondents is 35 students. The instrument of the research is as follows observation checklist, questionnaire, test, and interview. the pupils were exposed to novel experiences, such as technology integration, which heightened their enthusiasm. Second, instructors' preparations from creating the learning process to presenting some answers to the issues experienced by previous group discussion implementers until the evaluation before the learning program began all contributed to the learning process's success. Third, after their projects were completed, the students' improvement in speaking abilities and other related skills may have been prompted by the input in English and topic materials they received from their teachers, as well as their learning from a variety of sources. Fourth, by their hands-on participation in completing the job, students gained many exposures to English through meaningful interactions with actual information. As a result, it may be concluded that group discussion is valuable for instructors to use in the classroom, particularly in EFL learners.
Content Analysis of Cybercrime Infographic

Malaysia is one of the vulnerable regions of cyberattack in which cybercrimes continue to increase at alarming rate. Cyber criminals are attacking not only organisations but also individuals. Various types of cybercrimes that have resulted in significant financial losses have been reported in the media. Despite the daily coverage of cybercrime incidents, people continue to fall victims. Keeping the public informed of the potential online threats should not be taken for granted. This study argues that effective communication of cybersecurity information is essential to enhance public’s resilience in dealing with cybercrimes. Infographic is a useful tool for conveying cybersecurity information. Infographic is a visual communication that is often used to communicate complex messages that include science related information. Literature on infographics often emphasizes on its key components that include; visual, content and knowledge. As a visual form of communication, it makes information appear to be appealing and easier to understand to diverse types of publics. The format could be easily shared with others through different types of social media platforms. This study examined infographics related to cybercrimes from CyberSecurity Malaysia’s Facebook account. Visual content analysis has been adopted to analyse more than 20 infographics based on content, visual and knowledge. The study found the content of infographics was mainly text and graphic with instructive guidelines. However, the visual elements appear cluttered, potentially compromising the efficacy of the conveyed message. Keywords: Infographic, cybercrimes, scam, CyberSecurity Malaysia, cyberthreat.
THE USE OF DUOLINGO TO INCREASE THE STUDENTS’ VOCABULARY MASTERY AT JUNIOR HIGH SCHOOL

The purpose of this research was to obtain empirical evidence about the effect of using Duolingo to increase students' vocabulary mastery. This research used a quantitative type with a pre-experimental method involving 24 seventh grade students of SMP Nahdiyat Makassar as a research sample. Data collection techniques in this research used a pre-test and a post-test. The results of this research indicate that in the pre-test results the mean value was 56.67 which indicates that the students' ability in the pre-test was still lack while the mean value in the post-test is 67.29 which indicates that students' vocabulary mastery was starting to increase. The results of the data were analyzed using an independent sample t-test, showing that Sig. (2-tailed) was 0.000 which was lower than 0.05 so that the alternative hypothesis (Ha) was accepted and the null hypothesis (H0) was rejected. Research proves that there was an increase in students' vocabulary mastery through the use of the Duolingo application. Based on the results of this research, it can be concluded that the use of Duolingo can increase the vocabulary mastery of seventh grade students at SMP Nahdiyat Makassar.