Reviewerly/exHarmony huggingface dataset:
PENGEMBANGAN WEB GIFFT.ME SEBAGAI MEDIA PEMBELAJARAN BAHASA INDONESIA

Today's rapid technological developments have an effect on the learning process as well as on learning materials and the way in which material is delivered in the process of teaching and learning activities. While learning media that utilize technological developments can provide a knowledge of learning experiences that attract attention and curiosity and improve student achievement, all of these are the objectives of this development research. The researcher wants to describe the design of the gift.me web, and to find out the effectiveness of using the gift.me web as a medium for learning Indonesian. The model used in this development research is the ADDIE model (analyze stage, design stage, development stage, implementation stage, and evaluation stage). The data in this study were collected by means of document recording and questionnaires. Then the researcher analyzed the data using qualitative descriptive analysis and quantitative descriptive analysis. From this research, it was found that creating a gift.me web is very easy to design, and the gift.me web is an effective web used for Indonesian language learning media.
Implementation of AES Algorithm

AES, which stands for Advanced Encryption Standard is the most used symmetric block cipher based on the secured symmetric encryption algorithm. It uses a block cipher method that encrypts data in 128-bit blocks and operates on the principle of substitution and permutation. This algorithm has the ability to deal with three different key sizes such as 128, 192, and 256 bit and each of these ciphers has a 128-bit block size. A key size of 128 bits is used which ensures a high level of security and makes it difficult for an attacker to break the encryption. AES 128-bit encryption is commonly used in various applications, such as secure communication, financial transactions, and cloud storage. Its efficiency, security, and widespread adoption make it an essential tool in modern-day cybersecurity. The simulation and implementation are done using Xilinx software and on a Nexys-4 board. AES made it easy in securing the data with a key of 128-bit size which is larger than traditional DES and became a replacement algorithm for DES.
P‐96: Importance of Data Structure Change by Applying Domain Knowledge in Industrial Tabular Data

Recently, AI has penetrated various fields in industrial settings. For example, the display industry is also actively researching AI applications, and the most advanced field is using inspection data. A measurement sensor from a processing facility or data from an inspection facility is used to detect defects at the display manufacturing site. However, in the case of raw material defects, the effects of minor defects can accumulate and cause abnormalities in subsequent processes, which are very difficult to detect. In this case, the accumulated data is analyzed, but there is no exact standard, and there is a limit to subjective judgment by the inspector. To solve this issue, we applied DBSCAN clustering, clustering the X and Y axes individually, coordinates with binning, and normalization as the preprocessing method to maximize the discriminative power of the defect. Moreover, for the final defect detection, an AI structure that detects defects was completed using a structure that combines DBSCAN and Isolation Forest. We minimized detection errors by using the clustering function of DBSCAN as a noise removal effect. As a result, it was possible to develop and mass‐produce ML with an F1 score of 0.998 for defect detection, which was difficult to automate in the past. AI performed this comparison, objectifying the existing engineer's resource‐based work and shortening the progress time.
Single-stage object detector with attention mechanism for squamous cell carcinoma feature detection using histopathological images

Abstract Squamous cell carcinoma is the most common type of cancer that occurs in squamous cells of epithelial tissue. Histopathological evaluation of tissue samples is the gold standard approach used for carcinoma diagnosis. SCC detection based on various histopathological features often employs traditional machine learning approaches or pixel-based deep CNN models. This study aims to detect keratin pearl, the most prominent SCC feature, by implementing RetinaNet one-stage object detector. Further, we enhance the model performance by incorporating an attention module. The proposed method is more efficient in detection of small keratin pearls. This is the first work detecting keratin pearl resorting to the object detection technique to the extent of our knowledge. We conducted a comprehensive assessment of the model both quantitatively and qualitatively. The experimental results demonstrate that the proposed approach enhanced the mAP by about 4% compared to default RetinaNet model.
Classification of broadband network devices using text mining technique

The Broadband Internet industry is highly competitive, with service providers investing heavily in network development to meet customer demands and competing on pricing. Effective cost management is crucial for profitability in this market. This work proposes a model for classifying broadband network devices based on text mining techniques applied to a device list from a leading broadband network company in Thailand. The device descriptions are used to generate a feature vector, which is then employed by a classification algorithm to categorize devices into core, access, and last mile hierarchies. Various algorithms including decision tree, naïve Bayes, Bayesian network, k-nearest neighbor, support vector machine, and deep neural network are compared, with support vector machine achieving the highest accuracy of 90.35%. The results are visualized to provide insights into network hierarchy, device replacement dates, and budget requirements, enabling support for cost management, budget planning, maintenance, and investment decision-making. The methodology outline includes,•Obtaining a device list from a major broadband network company and extracting device descriptions through text mining and generating a feature vector.•Using a support vector machine for classification and comparing algorithm performances.•Visualizing the results for actionable insights in cost management, budget planning, and investment decisions.
Numeracy development training for elementary school students at SD Muhammadiyah 2 Waru Sidoarjo

Covid-19 pandemic has caused the decline in the numeracy ability of low-grade students at SD Muhammadiyah 2 Waru Sidoarjo. Students’ ability dropped by 50%, which cause teacher unable to continue to a higher numeracy level. The purpose of this program is to develop the numeracy skills of low-grade elementary school children at SD Muhammadiyah 2 Waru Sidoarjo. The method used in the form of training for low-grade students (grade 1, 2, and 3) which begins with a pre-test and ends with a post-test. Training is carried out by providing a stimulus for learning numeracy using modern methods through applications on gadgets and conventional methods and exercises. The result obtained from this training were that low-grade students felt interested and enjoyed learning numeracy with modern media than conventional methods. Training using modern numeracy media can increase students’ abilities and interest in learning numeracy.
Multi-Modal Clustering Discovery Method for Illegal Websites Based on Network Surveying and Mapping Big Data

With the development of internet technology, the number of illicit websites such as gambling and pornography has dramatically increased, posing serious threats to people’s physical and mental health, as well as their financial security. Currently, the governance of such illicit websites mainly focuses on limited-scale detection through manual annotation. However, the need for effective solutions to govern illicit websites is urgent, requiring the ability to rapidly acquire large volumes of existing website data from the internet. Web mapping engines can provide massive, near real-time web data, which plays a crucial role in batch detection of illicit websites. Therefore, in this paper, we propose a method that combines web mapping engine big data to perform unsupervised multimodal clustering (MDC) for illicit website discovery. By extracting features based on contrastive learning methods from webpage screenshots and OCR text, we conduct feature similarity clustering to identify illicit websites. Finally, our unsupervised clustering model achieved an overall accuracy of 84.1% on all confidence levels, and a 92.39% accuracy at a confidence level of 0.999 or higher. By applying the MDC model to 3.7 million real web mapping data, we obtained 397,275 illicit websites primarily focused on gambling and pornography, with 14 attributes. This dataset is made publicly.
A Comprehensive Study of Deep Learning and Performance Comparison of Deep Neural Network Models (YOLO, RetinaNet)

This paper presents the latest advances in machine learning techniques and highlights deep learning (DL) methods in recent studies. This technology has recently received great attention as it can solve complex problems. This paper focuses on covering one of the deep learning algorithms (deep neural network) and learning about its types such as convolutional neural network (CNN), Recurrent Neural Networks (RNN), etc. We have discussed recent changes, such as advanced DL technologies. Next, we continue analyzing and discussing the challenges and possible solutions of machine learning, such as big data and object detection, studying more papers in deep learning, and knowing the main experiments and future directions. In addition, this review also identifies some successful deep learning applications in recent years. Moreover, in this paper, one of the deep learning methods, convolutional neural networks, is applied to detect objects in images by using the You Only Look One model and comparing it with RetinaNet using pre-trained models. The results found that CNN (using YOLOv3) is a more accurate model than RetinaNet.
Intelligent Performance Evaluation in Rowing Sport Using a Graph-Matching Network

Rowing competitions require consistent rowing strokes among crew members to achieve optimal performance. However, existing motion analysis techniques often rely on wearable sensors, leading to challenges in sporter inconvenience. The aim of our work is to use a graph-matching network to analyze the similarity in rowers’ rowing posture and further pair rowers to improve the performance of their rowing team. This study proposed a novel video-based performance analysis system to analyze paired rowers using a graph-matching network. The proposed system first detected human joint points, as acquired from the OpenPose system, and then the graph embedding model and graph-matching network model were applied to analyze similarities in rowing postures between paired rowers. When analyzing the postures of the paired rowers, the proposed system detected the same starting point of their rowing postures to achieve more accurate pairing results. Finally, variations in the similarities were displayed using the proposed time-period similarity processing. The experimental results show that the proposed time-period similarity processing of the 2D graph-embedding model (GEM) had the best pairing results.
Postcolonial Trace in the Translation of Bumi Manusia

Abstract. Bumi Manusia as a postcolonial novel became a vehicle for Toer to articulate his national identity as Indonesian during the Dutch colonialism. Its translation into This Earth of Mankind posts two questions of whether or not power relations are brought in the translation and how the translator represents Indonesian culture in English. In order to answer the two, postcolonial translation approach was employed. This research is qualitative for it aims at analyzing the text. Seven data were selected based on two indicators representing the most relation between the colonized and colonizer or postcoloniality of the Dutch East Indies and were analyzed employing Baker’s translation strategies. The findings reveal that there are unequal relations between the source language (Indonesian) and the target language (English). In This Earth of Mankind, the translator does not show Indonesian cultural representation. There are great loss of information, omission, biased information, and great loss of impactful meaning of the source language in the target language that create another colonial domain and leave colonial trace in the target language.Keywords: Postcolonial Translation, Bumi Manusia, Postcolonialism, Colonialism, Power Relations
Development Mobile Learning Application : Clinical Education Management Information System (Cemis) Among Health Science Students In Indonesia

Background: Most of the clinical learning process in Indonesia is still conventional and not yet integrated between needs assessment and mobile learning outcomes of clinical education, so it requires learning media based on mobile learning applications that are not limited by space and time and can be accessed anytime and anywhere. Methods: This type of research is Research and Development (R&amp;D). Research product development design using the ADDIE model with 3 research instruments, namely; Product validation tests by technology experts and material experts and product practicality tests by users. The sampling technique was porposive sampling with a case study of 30 students practicing the clinical profession of nurses, 5 material experts and 3 technology experts. Results: Mobile learning application Clinical Education Management Information System (CEMIS) with users namely academic Preceptors, clinical Preceptors and students and has 12 features which include components of Cognitive ability, Psychomotor ability, Affective ability, Clinical Education Learning Outcomes and Supporting Factors. While the results of product validity tests by technology experts amounted to 87.90% (Very feasible) and material expert assessments amounted to 88.52% (Very feasible) while the product practicality test results were 85.05% (Practical). Conclusion: CEMIS's mobile learning application is declared feasible and practical to use as a clinical learning medium for academic preceptors, clinical preceptors and students in accordance with the learning outcomes and clinical learning standards in Indonesia.&#x0D;
MacoCaml: Staging Composable and Compilable Macros

We introduce MacoCaml, a new design and implementation of compile-time code generation for the OCaml language. MacoCaml features a novel combination of macros with phase separation and quotation-based staging, where macros are considered as compile-time bindings, expression cross evaluation phases using staging annotations, and compile-time evaluation happens inside top-level splices. We provide a theoretical foundation for MacoCaml by formalizing a typed source calculus maco that supports interleaving typing and compile-time code generation, references with explicit compile-time heaps, and modules. We study various crucial properties including soundness and phase distinction. We have implemented MacoCaml in the OCaml compiler, and ported two substantial existing libraries to validate our implementation.
Siamese Transformer for Saliency Prediction Based on Multi-Prior Enhancement and Cross-Modal Attention Collaboration

Visual saliency prediction has improved dramatically since the advent of convolutional neural networks (CNN). Although CNN achieves excellent performance, it still cannot learn global and long-range contextual information well and lacks interpretability due to the locality of convolution operations. We proposed a saliency prediction model based on multi-prior enhancement and cross-modal attention collaboration (ME-CAS). Concretely, we designed a transformer-based Siamese network architecture as the backbone for feature extraction. One of the transformer branches captures the context information of the image under the self-attention mechanism to obtain a global saliency map. At the same time, we build a prior learning module to learn the human visual center bias prior, contrast prior, and frequency prior. The multi-prior input to another Siamese branch to learn the detailed features of the underlying visual features and obtain the saliency map of local information. Finally, we use an attention calibration module to guide the cross-modal collaborative learning of global and local information and generate the final saliency map. Extensive experimental results demonstrate that our proposed ME-CAS achieves superior results on public benchmarks and competitors of saliency prediction models. Moreover, the multi-prior learning modules enhance images express salient details, and model interpretability.
Communication Skill-Based Writing Instruction (Csbwi) to Promote Students Writing Ability

This study examined the effect of communication skill-based writing instruction on the student writing ability. The researcher used a pretest-posttest experimental group design. The research was conducted from April to May 2023 in the Jinarakkhita Buddhist College Bandar Lampung. The researchers used the Statistical Package for Social Science (SPSS) version 25 program to analyze the data. A rubric score was used to analyze and measure the students' narrative text writing ability before and after treatment. The student's writing ability improved from 49.9 to 65.1. In addition, the researcher found that students who previously struggled to write now have a growing understanding of how to gather information and confidently use CSBWI in their writing. Therefore, it is critical to use a suitable and effective method to assist students in overcoming the difficulty of their writing achievement.
Quantum Cloud Computing from a User Perspective

Quantum computing is a rapidly progressing field: quantum computers are becoming more powerful and an increasing number of functionalities are offered via various quantum platforms and quantum software packages. Current quantum computers can be used almost exclusively via cloud services. It is expected that this will remain the case, at least in the near-term future. For successful adoption of quantum computing by the market, quantum cloud services should be user-centric. To that end, we explore quantum cloud computing from a user perspective. We describe a standardised overview of quantum cloud computing as a whole to create a common language to strengthen research and collaboration on quantum cloud computing. Specifically, we identify different types, information flows and relevant user functionalities of quantum cloud computing, based on their counterparts in classical cloud computing. Combined, this gives an overview of quantum cloud computing for the best user experience, paving the way towards user-centric quantum cloud computing.
Multirobot Target Searches in Unknown Environments Via Waypoint Planning System

Target searches by multiple robots in unknown environments have been widely applied in practical applications, such as rescues in hazardous areas, environmental monitoring, and locating leak sources. However, in existing methods, it is difficult to find all the information-lack targets in the unknown environment efficiently. To fill this gap, we propose a multirobot waypoint planning system consisting of an area decomposition-based local waypoint planner and a submap-based global waypoint planner. The local waypoint planner generates efficient coverage waypoints for every robot and accelerates the acquisition rate of global information. In contrast, the global planner optimizes the planning of waypoints using global information and prevents the multiple robots from duplicating searches. The proposed planning system benefits from the two planners by simultaneously guaranteeing a high coverage rate and searching efficiency in unknown environments. The effectiveness and advantages of the planning system are verified through both simulations and experimental results.
A Study on Robustness Evaluation of Weave Filters against Reverse Image Search Attacks on CAPTCHA

In recent years, CAPTCHAs have been used in a variety of online services. A typical example is reCAPTCHA v2, which uses a system to select the correct answer from a set of images given in a question text. However, there is a possibility that a computer program can break through the CAPTCHA by automatically recognizing each image in the image set and deriving the subject of the image. In this paper, we propose a CAPTCHA that uses processed images that are difficult for search engines and AI to recognize. Experiments will be conducted to verify and evaluate the proposed method’s high accuracy rate for humans and low accuracy rate for Google’s reverse image search and Google lens. We will discuss the issues that have been found.
Countermeasure against Backdoor Attack for Deep Learning-Based Phishing Detection

As online services have become increasingly popular and convenient in recent years, the number of phishing scams has increased. To prevent phishing scams, deep learning-based phishing detection methods have been proposed and shown to be effective. Recently however, backdoor attacks, in which deep learning intentionally induces false positives by falsifying training data, have appeared. In this paper, we propose a countermeasure against backdoor attacks that is suitable for deep learning-based phishing site detectors. In our countermeasure, the values of the layer before the output layers of the neural network are used as feature vectors, and the values of these feature vectors are monitored. The number of trigger data is much smaller than the number of normal data and can be detected as outliers. To detect outliers, our countermeasure uses HDBSCAN (Hierarchical Density-Based Spatial Clustering of Applications with Noise), which is a density-based clustering method. The feature vectors are clustered using HDBSCAN, and data suspected to be triggers are classified as noise. Through experiments with poisoned data, we show the effectiveness of our countermeasure against backdoor attacks.
Research on the Optimization Algorithm of Dynamic Obstacle Avoidance Path Planning for Unmanned Vehicles

As one of the key parts of the core technology of autonomous driving, path planning has become a focus of research today. In this paper, we study the path planning by artificial potential field method, solve the problem of unreachable target, improve the repulsion potential field function to make the nonlinear attenuation of repulsion, and guide the vehicle to reach the target point smoothly.Aiming at the local optimal problem, the invasive weed algorithm is introduced, designs and establishes the fitness function, and guides the vehicle to escape from the trap by establishing the escape potential field.The dynamic objective point method is proposed to obtain a set of dynamic elements of subobjective points, and to establish the dynamic gravitational potential field optimization path planning process.Finally, the effect of the scheme is modified by simulation in a dynamic obstacle motion scenario.
Action Recognition with Multiple People Using Long Short-term Memory

Human motion recognition technology has started to be applied in many important research fields, for example to assist inpatient rehabilitation and for the perception of dangerous activity. Hence, improving the accuracy of human motion recognition is a very important research topic. At present, human action recognition systems have only been developed for actions by a single person. In this paper, we propose to use LSTM technology for multi-person human action recognition model training and identification. We compare the performance of our approach with that of the ST-GCN human action recognition framework, and find that it improves the recognition accuracy by 49.69%.