Reviewerly/exHarmony huggingface dataset:
Reversible Data Hiding in Encrypted Images based on Interpolation Compression and Bits Distribution

Securing online data storage is paramount in the modern digital age, particularly when dealing with sensitive information. Reversible Data Hiding in Encrypted Images (RDHEI) emerges as a highly promising technological advancement that permits embedding confidential data within encrypted images. This study focuses on developing an RDHEI algorithm employing the interpolation method and bit distribution. Through the amalgamation of multiple interpolation algorithms, a comparative analysis is conducted to identify the optimal combination. This approach leverages interpolation for dimensional reduction within the image, thereby maximizing its storage capacity. Furthermore, the concealed data and the image can be restored to their original version utilizing the reverse algorithm. Experimental results demonstrated that linear interpolation and dimensional restoration using Lanczos interpolation yields the most favorable outcomes. These results showed significant achievements in an Embedding Rate (ER) score of up to 5,929 bits per pixel (bpp), a Structure Similarity Index (SSIM) score of 0.971, and a Peak Signal-to-Noise Ratio (PSNR) score of 38.722 dB. These results indicate that our proposed RDHEI algorithm can be a practical approach for data hiding.
Improving Performance of Eye Movements Classification Using CNN-Transformer Model

In the past decade, eye tracking has been a crucial approach for object selection in digital assistive technology as well as touchless digital signage. Accurate object selection depends on performance of eye movements classification. Many deep learning techniques have been proposed for eye movements classification. Despite of these numerous models, previous approaches have yet to achieve high classficiation accuracy—particularly when dealing with smooth pursuit eye movement. To bridge this scientific gap and improve the effectiveness of eye movement classification, we propose a hybrid CNN-Transformer model. We also incorporated Hyperband hyperparameter tuning to obtain the best parameter values of the model. We evaluated our approach in the GazeCom dataset. This dataset was enhanced with customized annotations designed to accommodate different types of eye movements. Our method yielded F1 scores of 0.9572, 0.9273, and 0.8358 for fixation, saccade, and smooth pursuit eye movements, respectively. The proposed method achieved superior F1 scores by a margin of 1% to 12.36% compared with the state-of-the-art Temporal Convolutional Network (TCN). A significant improvement was observed in the classification of smooth pursuit eye movement. The experimental results imply that the proposed method can serve as a guide for implementing the Transformer models for eye movements classification.
Improved Artificial Neural Network Method for Predicting Temperature of Solar Panel Output Performance

Solar energy in Indonesia has great potential for advancing solar panel technology as an electrical energy source. Photovoltaic (solar panel) technology is known as a technology that utilizes solar energy and then converts it into electrical energy. There is a wide array of strategies available to articulate PV models. Artificial Intelligence (AI) is one of the diagnosis methods used to model, control, forecast, diagnosis and classification detection and prediction. The application of artificial neural networks (ANNs) to renewable power systems has been developed many times before. Previous research has conducted studies on how to optimally size photovoltaic power systems and optimize installation costs. However, solar cell models have been developed to analyze the power characteristics of PV (photovoltaic) cell elements. Renewable power systems has been developed many times before. In this simulation of solar panel temperature prediction, measurements were taken including measurements of solar panel temperature, ambient temperature and irradiation as primary data. The training phase reveals a compelling regression plot, demonstrating an impressive R value of 0.8231. The minuscule error rate in the prediction system unequivocally affirms the exceptional quality of this ANN model.
Predicting Anger Proneness Using Deep Learning Techniques

High levels of anger proneness can affect the individuals and others around them in the society. Anger proneness or the likelihood of experiencing anger in response to certain stimuli or situations is influenced by a variety of factors, including personality traits, life experiences, and environmental factors. Accurately predicting the level of anger proneness in individuals can help mitigate a variety of risks, including relationship and mental health problems. In this paper, we tried to create a model that is capable of accurately predicting levels of anger proneness based on one’s answers to a personality questionnaire. To do so, we utilized a deep learning algorithm namely SAINT (Self-Attention and Intersample Attention Transformer), alongside with other three machine learning and deep learning algorithms as the comparisons, LSTM (Long Short-Term Memory), GRU (Gated Recurrent Unit), and XGBoost (EXtreme Gradient Boosting). The four models were trained with the same dataset and the same data preprocessing methods. All the four algorithms couldn’t manage to produce any models with satisfactory performances, with the highest performing model produced using the state-of-the-art machine learning classifier XGBoost only achieving 55.38% accuracy. Multiple data preprocessing methods such as resampling and feature selection with the help of XGBoost were applied to the dataset with negligible effects to the models’ performances. The results lead us to the conclusion that the dataset contains too many inconsistencies in its rows, as can be fairly expected from a dataset based on individuals’ responses to an online personality questionnaire. The inconsistencies affected all the models’ ability to find the patterns in between rows and columns and compromise their resulting efficacies to do the inferences.
Assistance of cybercrime:Practice in Indonesia

Cybersecurity is emerging as one of the efforts to deal with cybercrime. The increase in the number of cybercrimes requires attention. Like conventional crime, cyber crime is also acknowledging the role of assistance. Cybercrime assistance in the Electronic Information and Transaction Law often refers to providing hardware, software, passwords, and access codes as stated in Article 34. Using normative juridical research and analysis of 47 court decisions in 2012-2022, this research found that cybercrime assistance does not only provide device facilities, computer passwords, and access codes. Assistance is also provided in other forms that may not correlate with technology such as providing information, opening a bank account and physical assistance. On condition that the perpetrator’s actions meet the requirements of aiding a crime in Article 56 of the Criminal Code, the perpetrator is classified as assisting. Thus, the scope of assistance has a broad scope as long to assist the activity classified as cybercrime as stated in the Electronic Information and Transaction Law. The result of this study can provide additional insight to understand cybercrime as crime that is often associated with the use of ICT. It helps for public legal awarness’s program that is required to minimize cybercrime.
User Experience Analysis on JIBAS Computer Based Exam Application Using Usability Testing Method

JIBAS Computer Based Exam is a mobile-based exam application created by Yayasan Indonesia Baca. This study analyzes and evaluates the User Experience (UX) in JIBAS Computer Based Exam (CBE) application. Usability testing and System Usability Scale (SUS) methods have been used to obtain the results. This application has 1,5 of 5,0 ratings on Google Play Store. The study participants were students who used the application. Through observations and SUS questionnaires, qualitative and quantitative data were collected and analyzed. The result identify the app’s strengths and weaknesses, and provide suggestions for improvements to improve the user experience. The average SUS is 65.9, a low level of satisfaction with the usability of the application. This study contributes to the development of JIBAS CBE by considering UX and system usability and can be used as a guideline for the improvement of these applications and usability testing of other computer-based applications.
The Comparison Of Lexicon-Based And Support Vector Machine Methods For Online Loan Sentiment Analysis On Twitter Social Media

The development of online lending has led to many reactions, opinions, discussions, and public debates on various social media, including Twitter. Different things conveyed through Twitter Tweets can be used to determine how social media users view an issue or problem through sentiment analysis or classification activities. Lexicon-based algorithms and Support Vector Machine (SVM) are used with the CRISP-DM methodology for data mining. Therefore, this research aims to compare the performance of those two methodologies. The results of testing the model using 610 test data produce an accuracy level of sentiment analysis of Online Loans on social media Twitter using Lexicon-Based by 65% and SVM by 85%. The predicted results of the SVM method use 9,710 tweet data to produce 5,255 clean tweet data. 2,885 tweets are neutral, 1,190 data are positive tweets, 1,180 data are negative tweets, and 4,455 data are duplicates and empty tweets. With these results, it was found that the tendency of Indonesian people to use Twitter social media has views or opinions that are neutral towards online loans. The results of a comparison of the two methods based on performance metric calculations show that the SVM method has better performance when compared to the Lexicon-based method, with the highest precision value in the linear kernel SVM method for the positive class of 92%.
The Experience of the Unconscious from Freud to Jung: on Telepathy and Synchronicity

Our object of study in this article, succinctly, shows the evolution of the concept of telepathy in the psychoanalytic field, as well as the concept of synchronicity in the Jungian field; For this we will verify concepts that are close to the latter, such as projective identification, transitional object, within the Kleinian and Winnicottian universe. Bion’s considerations on “Finite space and infinite space, such as Jung’s use of the concept of “participation mystique”. To accomplish this goal we intend to make a survey of the historical evolution of the concept of synchronicity. We will begin with the primary sources, and in a second moment we will make a bibliographic survey of the developments that occurred after the death of Jung until the present day, on these proposed themes, and the possible enrichment through interdisciplinarity with other sciences. We will try to identify at the end of our reflection, some epistemological consequences that may benefit the evolution of Freudian and Jungian metapsychology and its consequences for the epistemology of analytical practice. We intend to illuminate some objective and intersubjective aspects of therapeutics. What types of communication are possible? What kind of looks? What types of listening? What kinds of relationships can we establish between the forms we compare the forms of communication in transference, compared to telepathy and synchronicity?
Vision-Based DRL Autonomous Driving Agent with Sim2Real Transfer

To achieve fully autonomous driving, vehicles must be capable of continuously performing various driving tasks, including lane keeping and car following, both of which are fundamental and well-studied driving ones. However, previous studies have mainly focused on individual tasks, and car following tasks have typically relied on complete leader-follower information to attain optimal performance. To address this limitation, we propose a vision-based deep reinforcement learning (DRL) agent that can simultaneously perform lane keeping and car following maneuvers. To evaluate the performance of our DRL agent, we compare it with a baseline controller and use various performance metrics for quantitative analysis. Furthermore, we conduct a real-world evaluation to demonstrate the Sim2Real transfer capability of the trained DRL agent. To the best of our knowledge, our vision-based car following and lane keeping agent with Sim2Real transfer capability is the first of its kind. We have made the codes and the videos of the simulation and real-world evaluation accessible online <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> Code and videos are available on: https://github.com/DailyL/Sim2Realautonomous_vehicle.
An Efficient Driver Anomaly State Detection Approach Based on End-Cloud Integration and Unsupervised Learning

For current autonomous vehicles, real-time monitoring of driver states and prompt identification of abnormal behaviors during the driving process are of paramount importance for safety. This paper proposes an innovative edge-cloud fusion driver anomaly detection system to address the issues of unstable anomaly detection performance and high computational demands of existing driver anomaly detection systems. Our system achieves instantaneous anomaly detection on the edge side while transmitting crucial facial features of the driver to the cloud for long-term anomaly detection. The proposed method employs unsupervised learning techniques to identify challenging and ill-defined abnormal patterns. Through deployment testing on actual hardware platform, our edge-cloud detection system achieved a latency less than 100ms.
Collective PV-RCNN: A Novel Fusion Technique using Collective Detections for Enhanced Local LiDAR-Based Perception

Comprehensive perception of the environment is crucial for the safe operation of autonomous vehicles. However, the perception capabilities of autonomous vehicles are limited due to occlusions, limited sensor ranges, or environmental influences. Collective Perception (CP) aims to mitigate these problems by enabling the exchange of information between vehicles. A major challenge in CP is the fusion of the exchanged information. Due to the enormous bandwidth requirement of early fusion approaches and the interchangeability issues of intermediate fusion approaches, only the late fusion of shared detections is practical. Current late fusion approaches neglect valuable information for local detection, this is why we propose a novel fusion method to fuse the detections of cooperative vehicles within the local LiDAR-based detection pipeline. Therefore, we present Collective PV-RCNN (CPV-RCNN), which extends the PV-RCNN++ framework to fuse collective detections. Code is available at https://github.com/ekut-es
A Human Feedback-Driven Decision-Making Method Based on Multi-Modal Deep Reinforcement Learning in Ethical Dilemma Traffic Scenarios

Ethical decision-making in autonomous vehicles has been a significant area of research since the emergence of the Trolley Problem. However, current studies fail to effectively incorporate the operative state of the vehicle and instead rely exclusively on sociological attributes for decision-making. This paper establishes three ethical traffic scenarios that reflect the most typical ethical dilemmas. Based on this, we examine the ethical decision-making of autonomous vehicles in each scenario. Firstly, to enable the decision-making system of autonomous vehicles to solve ethical dilemmas, a coupled ethical reward function model is innovatively proposed based on human feedback that integrates knowledge from sociology, economics, and vehicle dynamics. Furthermore, an ethics-driven multi-modal network model is proposed to extract morphological features and dynamic features from perceptual information and road test data, respectively. Finally, an ethical simulation experiment is conducted, which demonstrates that the decision-making strategies generated by the proposed model in the ethical traffic scenario are more aligned with human intentions compared to those of the control group.
Rollout-Based Interactive Motion Planning for Automated Vehicles *

Longitudinal and lateral motion planning poses a significant challenge to achieving full autonomy in automated vehicles. This work focuses on studying the motion planning problem for automated vehicles specifically in a highwaymerging scenario. The problem is modeled as an infinite horizon optimal control problem, taking into account finite control sets for the ego agents and uncontrolled state components of surrounding traffic. For this type of control problem, obtaining a real-time solution that meets both high safety and efficiency requirements can be difficult. In this study, we employ the rollout approach, which involves online optimization following the simulation of a known baseline policy instead of relying solely on extensive offline training. We compare the performance of one and multistep lookahead rollout algorithms against several state-of-the-art benchmark policies in simulation. The simulation results indicate that the rollout algorithm significantly enhances safety while simultaneously maintaining a high average speed within the merging scenario. Furthermore, we conduct simulation studies to assess the rollout methods in adapting to varying behaviors of surrounding vehicles. Additionally, we investigate the impact of different horizon settings and the introduction of terminal cost approximation.
DESIGN OF PROTOTYPE OF LEARNING INFORMATION SYSTEM OF WEB-BASED MODERN SHIP NAVIGATION TOOLS

This paper aims to provide an overview of the process of learning navigational tools on boardby making a prototype design of a website-based learning information system for learningmodern ship navigation tools. This type of research is descriptive qualitative. Data collectiontechniques in this writing using observation, interviews, literature studies, and documentation.Interviews were conducted in-depth and structured with the captains of the ships who are alsoprofessional lecturers in the Maritime Higher Education Nautics study program. The resultsinclude the design of a prototype menu consisting of a sub-menu on principles and navigationdevices as well as a sub-menu on the advantages and disadvantages of each navigation device,both modern and conventional navigation devices. The prototype can be implemented on thesubject and consists of various navigation devices, functions and uses of modern navigationdevices; principles and mechanisms of modern navigation devices; and the strengths andweaknesses of modern navigation tools. The conclusion is that designing a prototype learninginformation system for modern web-based navigation devices for determining ship locations hasa communicative design. The design of the Information System prototype for learning ModernNavigation tools is capable of creating an information system for learning website-basedmodern navigation tools
Consequences of Cybercrime Among Teenagers

Globalization process and development of the modern civilization led to the transition of industrial society to information society and introduction of modern information technologies creates new, unique opportunities for more active and efficient development of economy, politics, country, society, social consciousness and a citizen (Vladimir Golubev [1]).
Development Of Edible Starch-Based Paper

The study is aimed to develop an edible film from maize and rice flour. The film is prepared using corn starch, CMC, MC, LG, and glycerol in various combinations. The 15% glycerol showed excellent binding and elasticity qualities. Broken rice flour inclusion resulted in a significant reduction in fractures and stickiness. The edible paper made from starch (3%), rice flour (22%), sodium carboxymethyl cellulose (0.5%), microcrystalline cellulose (0.5%), glycerol (20%), liquid glucose (5%), and xanthan gum (1%) had good physical and structural qualities. The thickness of the formed paper was 138 ±2 µm, with a solubility of 89-92 percent. Edible paper is slightly diverse in character, according to SEM data. The edible paper is amorphous in nature, according to the X-ray diffraction analysis.
An Entity Extraction pipeline for Medical Text Records Utilizing Large Language Models: An Analytical Study (Preprint)

The study of disease progression relies on clinical data, including text data, and extracting valuable features from text data has been a research hot spot. With the rise of large language models (LLMs), semantic-based extraction pipelines are gaining acceptance in clinical research. However, the security and feature hallucination issues of LLMs require further attention.This study aimed to introduce a novel modular LLM pipeline, which could semantically extract features from textual patient admission records.The pipeline was designed to process a systematic succession of concept extraction, aggregation, question generation, corpus extraction, and question-and-answer scale extraction, which was tested via 2 low-parameter LLMs: Qwen-14B-Chat (QWEN) and Baichuan2-13B-Chat (BAICHUAN). A data set of 25,709 pregnancy cases from the People's Hospital of Guangxi Zhuang Autonomous Region, China, was used for evaluation with the help of a local expert's annotation. The pipeline was evaluated with the metrics of accuracy and precision, null ratio, and time consumption. Additionally, we evaluated its performance via a quantified version of Qwen-14B-Chat on a consumer-grade GPU.The pipeline demonstrates a high level of precision in feature extraction, as evidenced by the accuracy and precision results of Qwen-14B-Chat (95.52% and 92.93%, respectively) and Baichuan2-13B-Chat (95.86% and 90.08%, respectively). Furthermore, the pipeline exhibited low null ratios and variable time consumption. The INT4-quantified version of QWEN delivered an enhanced performance with 97.28% accuracy and a 0% null ratio.The pipeline exhibited consistent performance across different LLMs and efficiently extracted clinical features from textual data. It also showed reliable performance on consumer-grade hardware. This approach offers a viable and effective solution for mining clinical research data from textual records.
Web-Based Online Booking Service System Application Design using Software Development Life Cycle Method

The purpose of this research is to design a web-based online booking service system application to become a structured booking service data management system. The information obtained will be developed towards computerization through the internet network, which so far is still done conventionally. Data management that can be done by the admin is managing admin data, customer data, booking data, mechanical data, page data and contact data. This application also supports the creation of payment invoices. This web-based online booking service system application has two application interfaces, one for the customer and the other for the administrator. Based on the results of tests carried out both offline and online, this online booking service system application can process booking service data and this application successfully displays information about car services, both booking packages, additional packages, estimated costs and payment invoices. Based on testing using a questionnaire on a web-based online booking service system application, the results obtained are that this application is quite helpful in managing vehicle service booking data and this application can assist the repair shop in providing information related to vehicle service to customers.