Reviewerly/exHarmony huggingface dataset:
Pengentasan Buta Aksara melalui Program Keaksaraan Dasar di Desa Selebung Ketangga Lombok Timur

Basic Literacy Education (KD) is an important program to resolve the problem of illiteracy in Selebung Ketangga Village. The KD program is one of the educational services outside of school to develop community members' reading, writing and arithmetic skills in Indonesian. Therefore, it is important to know the motivation and benefits of the KD program for society. This research uses a descriptive qualitative approach. Through observation, interviews and documentation. The researcher looked at how the illiteracy eradication program was in Selebung Ketangga Village. Data collection was carried out using purposive sampling, namely the researcher interviewed research informants. The observation results show that in the process of mastering basic understanding the participants routinely participate in learning activities for three hours from 14:00-16:00 and every day the participants have learning goals that must be achieved. Meanwhile, during the activity which lasted for several days, participants had different memory abilities, such as participants' memory which was very low and there were also some participants who had quite good memory, this was due to the age of the participants and the inhibiting factors found by The researcher said that some participants were unable to take part in learning activities because they were busy with other things, namely work. Based on the results of interviews and document analysis, it is known that there is a development of abilities that take part in KD activities, especially in daily activities and active participation in learning activities.
The Use of Code Switching Among Waiters in Restaurant and Bar Operations at Amber Resort-Selong Belanak Central Lombok

The purpose of this study is to analyze what language elements are most widely used in code-switching among waiters at Amber Beach Resort restaurant. The data collection methods used in this research include document study, field research, collecting data by recording respondents and taking notes (Note-taking) on managers and F&amp;B Service staff at Amber Beach Resort Restaurant. The results found that the language elements that experienced the most code-switching were from Indonesian to English and Sasak language to English. Waiters/managers and other staff mostly use Indonesian to English elements
The Challenges in Paraphrasing Among English Language Students

Paraphrasing is the most challenging part of academic writing. This study explored the challenges encountered by English language students in one of the Higher Education Institutions in Ozamiz City during the second semester of the school year 2021-2022. This study utilized phenomenological research design. The participants were 14 third-year English major students and were chosen using purposive sampling. A semi-structured interview was used based on the interview guide to gather the needed information. Moustakas's transcendental phenomenology was used in analyzing the participants’ responses. Students faced different challenges when paraphrasing texts and results revealed four themes: 1.) lack of vocabulary skills, 2.) inaccuracy of paraphrasing tools, 3.) poor reading comprehension skills, and 4.) copying someone else's work. Researchers suggest that teachers can put much effort into helping students comprehend how to paraphrase English texts. Also, the students have to be aware of how crucial paraphrasing is in writing.
ANALISIS DAN PERANCANGAN PROSES BISNIS BERBASIS CRM PADA CAFE XY

Cafe XY is a business that has been running for quite a long time, and every year customers always increase. But the disadvantage of this café is that there is no visual picture of the processes and business models that are run and applied, then still using manual methods and when it wants to lead to digitalization, it is necessary to flow business processes based on CRM in service to customers and remember many business actors in the same field around where to run a business. CRM-based business process design is able to create and maintain good relationships and communication between customers and business people and create customer loyalty. The high increase in customers and the level of business competition make café xy to implement CRM-based business processes even though there has been no implementation towards mobile-based CRM. The method used is qualitative research with a descriptive type of thinking framework so that it is able to describe the design of business processes in Cafe xy using the UML framework and mobile-based display design using figma. So this research produces a business process model based and mobile CRM display that is needed in answering problem identification. The result of the research is the design of business process models: business CRM, data flow diagrams, class diagrams, use case diagrams and mobile-based display design. It is expected that further research will be able to develop and lead to the implementation of mobile-based information systems supported by E-CRM.
Convolution Neural Network Based Prediction for Eye Gaze Estimation

Levels of progress in progress have truly made it possible to get various kinds of biometric information from individuals, enabling bases on assessment of human conditions in cure, auto prospering, advancing, and various zones. These evaluations have particularly featured eye improvement as a convincing marker with respect to human conditions, and assessment on its applications is adequately being pursued. The contraptions as of now for the most part used for assessing eye overhauls rely on the video-oculography (VOG) procedure, wherein the course of look is outlined by managing eye pictures crushed a camera. Applying convolutional neural network (ConvNet) to the getting ready of eye pictures has been seemed to enable exact and unprecedented look assessment. Ordinary picture overseeing, in any case, is begun on execution using a PC, making it difficult to finish consistent look. We hence propose another eye picture overseeing framework that cycles look assessment and event disclosure starting with one fulfillment then onto the accompanying using a self-governing engineered lightweight ConvNet. This paper evaluates the course of action of the proposed lightweight ConvNet, the frameworks for learning and appraisal used, and the proposed methodology's ability to meanwhile see look heading and event occasion using a truly unassuming memory and at lower computational complex nature than standard ways of thinking.
HandMonizer: a case study for personalized digital musical instrument design

The rapid evolution in technology has found its way to introducing novelty in today’s live music performances. In this context, the development of Digital Musical Instruments (DMIs) has obtained increasing attention in recent years. In this paper, we present the development of a DMI called Handmonizer, an interactive artist-oriented harmonizer for musical performance adapted to the needs of a specific singer. A key component of our work is the combination of hand motion recognition and audio signal processing to obtain a smoother interaction. We describe the development methodology, but we also focus on our collaboration with the artist to conceptualize and then refine this tool until the development of the final product. At the end of this paper, we define an evaluation strategy, collecting feedback with a questionnaire addressed to the singer. Our aim in presenting this evaluation strategy is to help other engineers keen to develop cutting-edge technologies by working in partnership with artists. While results are not definitive, we believe that the chosen methodology could be of interest to other DMI researchers. Moreover, the modular nature of the Handmonizer makes it easily adaptable to further developments concerning the Internet of Sounds (IoS) and Networked Music Performances (NMP).
PI2-CMA Based Trajectory Planning Method for the Tractor-Trailer Vehicle

The motion primitive-based state lattice trajectory planning method has been widely used in the field of trajectory planning. However, due to the limitations of the search space and resolution of motion primitives, and the lack of consideration of scenario information during offline generation of motion primitives, the tractor-trailer vehicles can easily fall into "jackknife phenomenon" when making large pose adjustments in narrow scenarios, which brings dilemma for trajectory planning. In this paper, a trajectory planning algorithm is proposed based on a general model of tractor-trailer vehicle, which combines global planning and local optimization. The method first generates a series of motion primitives, including forward and backward driving conditions, and then an A <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∗</sup> based global state lattice planner is used to obtain a planning result, which is imported as the initial solution into the local trajectory optimizer based on PI <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> − CMA (Path Integral Policy Improvement with Covariance Matrix Adaptation) algorithm and dynamic potential method. Experiments show that this method significantly improves the performance in the obstacle avoidance, "jackknife phenomenon" avoidance and curvature compared to traditional motion primitive-based state lattice planning methods.
Where do we stand on Ethics, Privacy, and Security for Scenarios of Remote Collaboration supported by eXtended Reality?

Scenarios of remote collaboration using eXtended Reality (XR) have seen significant growth in recent years, allowing distributed team members to overlay digital information into the physical world, making it an ideal platform for certain activities. As more organizations look for XR to maintain productivity and efficiency while collaborating remotely, aspects like ethics, privacy, and security, which have not been explored in detail, become paramount. In this paper, the state of these topics is described and discussed. This is important given that the success of XR-remote collaboration depends on collaborators’ trust in the technology, which must be developed and used in a way that protects user privacy and security while complying with regulations and mitigating risks. Therefore, ethics, privacy, and security play an important role in building and maintaining confidence. With this, we hope to mobilize the community to start questioning their works in search for how these matters impact and should inform their research.
Facial Emotion Recognition usign CNN

Nowadays where most of the works are being carried online the demand for face recognition technique is elevated. Computerized software is assisting in identifying human feelings such as happiness, sadness, anger, fear, disgust, etc. Over the decades, various research has been carried on the facial expression and emotion recognition. Emotion detection has extended applications. It is not merely related to any specific field nut instead the approach ranges from communication, advertising to hospital requisition and many more. To exist collective mechanisms through which we can accomplish the process of facial emotion recognition. In this paper we are using Convolutional Neural Network for the implementation. Upon exploring numerous datasets for the procedure of experiment we have chosen to go with Kaggle dataset.
E-Agricultural for Better Yield

Abstract: The advent of newer technologies had led scientists and researchers to monitor and control agricultural activities remotely by the incorporation of information and communication technology (ICT). Modern information technologies have the impact to potentially increase the agricultural productivity by transferring knowledge and information to agricultural practitioners from the rural communities. Since the present generation have knowledge and easy access to smart mobile phones so the setup of control system installed in field can be monitored and controlled by new generation giving new dimensions to agriculture also known as smart agriculture. The agriculture is playing a major role in the economy of nation developing; therefore the need for upliftment in Agriculture is very important. Smart agriculture or e-Agriculture is a one of such concepts that is playing an important role in the enhancement of processes involved in Agriculture. The major role of ICT in Agriculture is its potentiality to aid a wide access to information that will support knowledge sharing and decision making. The usage of Information and Communication Technology (ICT) in Agriculture is growing day by day and more youth are attracted towards e-agriculture for their better earnings.
The Neuroscience, Neurobiology of Alcohol and Drug Addiction: A Discussion

Purpose: The purpose of this paper is to discuss the neuroscience and neurobiology of alcohol and drug addiction. The study of neuroscience and neurobiology of alcohol and drug addiction will help to understand the relationship between addiction and brain functioning.&#x0D; Methodology: The study utilized a systematic review of literature to study alcohol and drug addiction by identifying parts of the human brain responsible for addiction, stages of addiction, elements of addiction, and effects of different drugs on the brain. The main theories of addiction guiding the research were the Incentive-Sensitization theory and the Disease Theory.&#x0D; Findings: The outcome from the paper showed addiction is a chronic illness that contributes to significant impairments in social function, health, and in lack of control over drug use. The three stages of addiction are intoxication/ binge, withdrawal, and anticipation. Also, it was found out basal ganglia, extended amygdala, and the prefrontal cortex are the main parts of the brain responsible for drug addiction.&#x0D; Unique Contribution to Theory, Practice and Policy: The outcome from the paper has contributed towards advancing the knowledge of drug addiction by establishing evidence-based approaches for identifying the neurobiology and neuroscience of drug addiction. Findings can be utilized in developing policies and frameworks that guide the use of drugs, especially prescription drugs like opioids. Moreover, findings help in advancing the practice in medicine by recommending new pharmacological and non-pharmacological treatments to prevent and control addiction.
Robust Zero-Watermarking Algorithm for Medical Images Based on Direction Gradient Histogram and DCT

With the rapid development of Internet technology, using the Internet to transmit medical images has gradually become an efficient and convenient transmission method. It improves the efficiency of medical services, enabling medical institutions and doctors to share medical images and diagnostic information quickly. However, there are also some potential security risks in transmitting medical images over the Internet, such as data leakage and tampering. This article proposes a medical image zero-watermarking algorithm based on the direction gradient histogram (HOG) and discrete cosine transform (DCT) to solve the above problems. The proposed algorithm first extracts the feature vector of the medical image and then utilizes the perceptual hash algorithm to obtain its corresponding feature sequence. The watermark image is then encrypted using chaotic scrambling through logical mapping. The zero-watermarking technique is employed for the embedding and extraction of the watermark. Experimental results demonstrate the effectiveness of the proposed algorithm in successfully extracting and restoring the watermark even under challenging conditions.
Ludo-pedagogical Module for Emotion Regulation of Undergraduate Students

This demo presents an educational e-learning module that teaches emotion regulation to medical students in a 3D virtual environment. The scenario focuses on the pressure and stress experienced by students when studying for an exam. The survey results based on 50 university students have shown that the immersive tool is effective to elicit stress and anxiety emotions. Although the majority of students were able to regulate their emotions, half of them used inappropriate strategies to manage their negative emotions.
Pathologic complete response prediction in breast cancer lesion segmentation and neoadjuvant therapy

Predicting whether axillary lymph nodes could achieve pathologic Complete Response (pCR) after breast cancer patients receive neoadjuvant chemotherapy helps make a quick follow-up treatment plan. This paper presents a novel method to achieve this prediction with the most effective medical imaging method, Dynamic Contrast-enhanced Magnetic Resonance Imaging (DCE-MRI).In order to get an accurate prediction, we first proposed a two-step lesion segmentation method to extract the breast cancer lesion region from DCE-MRI images. With the segmented breast cancer lesion region, we then used a multi-modal fusion model to predict the probability of axillary lymph nodes achieving pCR.We collected 361 breast cancer samples from two hospitals to train and test the proposed segmentation model and the multi-modal fusion model. Both segmentation and prediction models obtained high accuracy.The results show that our method is effective in both the segmentation task and the pCR prediction task. It suggests that the presented methods, especially the multi-modal fusion model, can be used for the prediction of treatment response in breast cancer, given data from noninvasive methods only.
Development Study of Learning Space for Social Studies Education Virtual Geographic Fieldwork: Focused on Drone Mapping and Metaverse Technology Application

Objectives The purpose of this study is to develop a learning environment for social studies education that incorporates drone mapping and metaverse technology to enhance the sense of immediacy in virtual geographic fieldwork.&#x0D; Methods To achieve this, the first step involved conducting a quantitative and qualitative analysis of previous studies related to virtual geography exploration to understand the research trends. Following that, the components necessary for the development of a metaverse learning environment were derived. Subsequently, a prototype for a learning space designed for virtual geography exploration was developed based on these identified components. Finally, a validation process involving field teachers was used to carry out modifications and enhancements on the developed prototype, resulting in the final product.&#x0D; Results According to the research findings, drone mapping facilitated the development of a metaverse space with higher qualitative standards compared to conventional 3D data. This enhancement increases the authenticity of virtual geographic fieldwork, augments opportunities for interaction, strengthens self-directed learning abilities required in future education, and provides avenues for integration into STEAM (Science, Technology, Engineering, Arts, and Mathematics) curricula.&#x0D; Conclusions These results demonstrate that conventional virtual geographic fieldwork can no longer remain at the level of indirect geographical observation but can evolve into a distinctive teaching and learning approach by integrating with fourth industrial revolution technologies. Furthermore, learners can enhance their deep inquiry capabilities within metaverse spaces resembling the real world, connecting with local contexts for learning and thereby contributing to the uniqueness of social studies education.
Interplay of Cryptocurrencies with Financial and Social Media Indicators: An Entropy-Weighted Neural-MADM Approach

In the rapidly evolving domain of digital finance, the interplay between cryptocurrencies and external variables such as financial and social media indicators warrants thorough examination. This investigation employs a novel, entropy-weighted Multiple Attribute Decision Making (MADM) model to decipher these intricate relationships. The study's foundation is an expansive dataset, meticulously compiled to encompass a broad spectrum of financial data alongside diverse social media indicators. Central to this analysis is the employment of the Stepwise Weight Assessment Ratio Analysis (SWARA) method, meticulously applied to ascertain the relative importance of various social media indicators. Complementing this, the Complex Proportional Assessment (COPRAS) methodology is adeptly utilized to derive utility functions for each cryptocurrency under scrutiny. The analytical prowess of neural network regressions is harnessed to delineate the influence exerted by a multitude of financial indicators on these utility functions. The findings of this research are pivotal in understanding the dynamics within the cryptocurrency market. Bitcoin and Ripple emerge as pivotal entities, primarily functioning as primary conduits for market shocks. In contrast, Ethereum is identified as a stabilizing force, predominantly absorbing such fluctuations. A nuanced aspect of this study is the differential impact of social media indicators on various cryptocurrencies. Bitcoin and Ethereum display a negative correlation with these indicators, suggesting a complex, possibly inverse relationship with social media dynamics. Conversely, Litecoin, Dogecoin, and Ripple exhibit a positive responsiveness, indicating a heightened susceptibility to social media attention, sentiment, and prevailing uncertainty.
Beyond Securing Networks and Storage: Emerging Attacks and Defences to Machine Intelligence

Over the past three decades most research efforts in security and privacy have focused on network and storage security. Recently, Deep Neural Network (DNN) classifiers gain wide adoption in different complex tasks, including natural language processing, computer vision and cyber security. However, the underlying assumption of attack free operating environment has been defied by the introduction of several attacks such as adversarial examples and Trojan backdoor attacks. In Adversarial attacks the adversary perturbs the input examples during inference to force the DNN to misclassify while the adversary in the Trojan Backdoor operates in both training and inference phases. In the training phase the adversary trains the DNN in a way such that it behaves normally when the Trojan trigger does not exist, and it misclassifies if the trigger exists. Given that only the adversary knows the trigger, the users of the DNN will be fooled to trust the DNN model. The adversary can now attach the Trigger to the input examples during inference causing the DNN model to misclassify. In this talk we will discuss our development of several computationally efficient defense approaches for the Adversarial attacks enabling real-time detection of the attack for the first time. We will also discuss our development of an adaptive black-box defense approach for the Trojan Backdoor attack that outperforms the state-of-the-art by studying the relationships among the prediction logits of the DNN. After that we will discuss our recent follow up work in which we show how to jointly combine the above two adversaries to practically launch a new stealthy attack, dubbed AdvTrojan. AdvTrojan is stealthy because it can be activated only when: 1) a carefully crafted adversarial perturbation is injected into the input examples during inference, and 2) a Trojan backdoor is implanted during the training process of the model. We leverage adversarial noise in the input space to move Trojan-infected examples across the model decision boundary, making it difficult to detect. The stealthiness behavior of AdvTrojan fools the users into accidentally trusting the infected model as a robust classifier against adversarial examples. We will also discuss our future research that is focused on expanding the attack and defense mechanisms to new areas such as malicious domain detection, federated learning setting, personalized federated learning, and Graph Neural Networks. We will also discuss several application domains of adversarial as well as Trojan Backdoor attacks.