Reviewerly/exHarmony huggingface dataset:
An Assessment of Digital Forensic Models and a Suggestion for Enhanced Operating Systems

Digital forensics is a significant area where research is still being undertaken on a wide scale, as the number of computer-assisted crimes continues to increase and the methods for investigating cybercrimes are in their infancy.Governments and corporate organizations struggle with the necessity to set up control systems as cybercrime becomes more ubiquitous in the present society. Parliaments and boards are working rapidly to draft legislation, regulations, and guidelines with an end goal to stop these crimes from going crazy. To specific degree, e-discovery and digital forensics are coordinated into the requirement strategies utilized to battle these cybercrimes. The people group of digital forensics has been significantly influenced by the speedy advancement of digital devices, and digital crimes have gone with the same pattern. Right now many cases are being heard in courtrooms all over the planet where, despite their subject matter and starting place, there is digital evidence present. Traditional cases including the dealing of unlawful substances, homicides, misrepresentation, and many different offenses increasingly vigorously depend on information/information stored on a digital gadget. Thusly, digital forensics approaches are expected to assemble digital evidence in the two situations where a wrongdoing is finished using a digital gadget and situations where the wrongdoing was not completely carried out using a digital gadget. As the evidence got is intrinsically exceptional from different sorts of evidence got in traditional “forensic” investigations, digital forensics offer issues. The essential distinction is that digital evidence is more susceptible to duplication and control by investigators (or not), whether on purpose or accidentally. This article describes how digital forensic processes can be used to gather the digital evidence needed to convict or acquit individuals accused of such crimes, and some of the things that need to be addressed. describes the key challenges of An integrated framework and methodologies for investigations using digital evidence are presented, including legal, technical, ethical, and educational aspects of digital forensics. The purpose of these drafts is to provide solutions to problems related to the collection and subsequent presentation of digital evidence in court. We also provide guidelines for strengthening this type of evidence when presented in court.
Analysis, Implementation and Performance Validation of Steganography based Movie piracy Identification using Alpha Blending and Triangular similarity to Location of the person Involved

Digitalization is transforming the world and the entertainment industry is not untouched from it. The significant increase in Over-The-Top (OTT) media services around the world has made the process of entertainment distribution much easier. But still, people prefer to download pirated copies of the movies and web-series from the internet to save money due to which film-makers and OTT owners incur huge losses every year. Piracy has become a widely spread crime in many countries which greatly affects the motion picture industry. Many techniques failed to identify the piracy location, but encryption appended in all the frames of movies, location frequency jammer, fixing IR sensors in every seat of theatre etc., did not prove the success of piracy detection or the violation of Intellectual Property due to advancements in technologies. In this research, a solution to estimate the position of the device used for In-theater recordings with few selected frames from the pirated copy has been proposed. A novel approach is analyzed using various transformation and alpha blending techniques along with triangular similarity algorithms is proposed to find the distance and angle inside the theatre to locate the position of the person while doing the crime.
Introduction to Deep Learning and its related case studies

This paper initially introduces deep learning. The next step in machine learning is deep learning. This paper make deep insightsinto the review of the literature related to deep learning. The papers used various deep learning approaches such as an Autoencoder (AE), convolutional neural network (CNN), deep belief network (DBN), recurrent neural network (RNN). Offshore wind farms are the subject of one case study of automatic bird identification. This case study uses convolutional neural network (CNN) for extracting features. On the other hand, another case study is of low-power hardware implementation of microscopy diagnostic support system. This case study used CNN and support vector machine methods.
Just-In-Time Compiler System in Aspect-Oriented Programming based Building Block Platform for constructing Domain-Specific Language for HPC Application

The recent complexity of high-performance computing (HPC) systems to create efficient programs is becoming more difficult because of the lagging of processors' performance. Domain Specific Language (DSL) is a promising approach that offers a productive environment and creates a high-efficient program. However, DSLs lack portability and make developers incur high costs. An Aspect-Oriented Programming (AOP) based platform for building DSL processing systems is proposed to solve the problem. By combining reusable aspect modules, DSL processing systems can be built into each HPC system to assist developers in creating programs with various systems config-urations. The platform is achieved by putting the application programming interface into the user's kernel code and controlling the kernel's execution according to the platform conventions. However, the platform has some problems. It is impossible to perform optimization that intervenes in the kernel code. It is also impossible to use SIMD and GPGPU efficiently. This study proposes a modification of the kernel code by introducing a Just-In-Time (JIT) compilation system into the platform and using SIMD and GPGPU. We also created benchmarks and confirmed a performance improvement of 94 % for reducing the execution time compared with the based platform and an improvement of 9% compared with handwritten code for auto compiler optimization.
An innovation development of smart data protection mechanisms and its related parameters in financial institutions

Confidential information is of great interest to competing firms. This causes aggression with attacks. Many problems underestimate the importance of the threat, and as a result, it can lead to collapse and bankruptcy for the company. Even a single case of malpractice can result in millions in damages and loss of customer trust. Threats are subject to data on organization structure, status and operations. Sources of such threats are its competitors, corrupt officials and criminals. In this paper, a smart data protection mechanism was proposed to enhance the data security in the financial institutions. They are introduced with certain value protected information as well as modify it in order to cause financial damage. Even 20% of such a decision can provide information leakage. Sometimes loss of company secrets can be due to inexperience of employees or lack of security systems.
Dialogue System to Make Humor by Tatoe-Tsukkomi

It is essential to design dialogue systems such that users and dialogue systems can develop a friendly relationship to increase user satisfaction and dialogue continuation. This study focuses on humor expressions and proposes a method to generate Japanese tatoe-tsukkomi texts considering the emotions and thoughts of the interlocutors.
Space reservation technique at distributed level for hiding reversible data in the encrypted images

Reversible information hiding (RDH) in protected images is now receiving greater attention from the study team. Since it safeguards the privacy of supplemental information while still being a covered media, it is attractive for application in analytics, online clouds, diagnostic devices, and other disciplines. This study proposes an original method for irreversible data hiding in secret picture (RDH-EI). After securing your cover image, our method uses the tactic of assigning adequate space for the additional data. We begin by identifying suitable blocks for data occlusion in various parts of the image. Before the photo is encoded, some of the S-box of these chunks are supported into other areas of the image to employ a high-performance traditional RDH approach. Once the image has been encrypted, these LSB-planes are used to hide further data. Restoration of the original cover image and error-free supplementary information extraction are often guaranteed. The proposed strategy is quite straightforward and simple to comprehend. Experiments' results show that our method is superior to traditional ones for embedding concealment in protected images.
Studying human behavior, perception, and cognition to controlled stimulus in a virtual Environment

Exploring the possibilities for gathering and analyzing user data from virtual simulations (Metaverse) on a macro level & mapping user behavior data from these virtual simulations to gather relevant information for inferential statistics, creating a taxonomy to classify and group data. Biological cybernetics exposes subjects to artificially controlled environments to study and research their action-to-perception loop, which influences human behavior. The study employs experimental & qualitative research methodology, where we will be introducing a particular sample population of candidates to a controlled social environment in a virtual simulation, studying correlation in the cross-sections of the data by observing the users' spontaneous responses to various controlled stimuli in the background.
Analysis of Modern and Ancient Chinese Literature Using a Chinese Emotional Expression Analysis System

Emotional analysis techniques for text are very useful for analyzing public opinions on social media, and user reputation and opinions about products and services on electronic commerce sites. The Chinese Emotional Word Dictionary (CEWD) was created and has a very rich vocabulary for the fine-grained (11 emotion categories) emotional analysis of Chinese text. Additionally, the Chinese Emotional Expression Analysis System (CEEAS) that uses CEWD was developed to analyze emotions in Chinese text relatively accurately and quickly. In this study, we apply CEEAS to the emotional analysis of modern Chinese literature to investigate the characteristics of the appearance of emotional categories in modern Chinese literary works. Then, we apply CEEAS to modern translations of ancient Chinese literary works to perform emotional analysis, and compare the appearance characteristics of emotional expressions with those of modern literary works. Furthermore, we report on the changes in the appearance characteristics of emotional expressions between modern Japanese novels and their Chinese translations. We are convinced that literary research using the emotional analysis techniques proposed in this study will be useful as a new digital approach to literary research.
Toward a training of CNNs on a multi-FPGA system

Multi-FPGA systems have been receiving a lot of attention as low-cost and energy-efficient systems. Pattern recognition using CNN(Convolutional Neural Network) is a common application of such multi-FPGA systems, and various design examples have been presented, while the training phase of CNN is executed on GPUs in the cloud. However, when a multi-FPGA system is used for the MEC (Multi-access Edge Computing), the execution of the training phase is worth trying even if the absolute performance is smaller than that with GPUs in the cloud. In this paper, we implemented the backpropagation of sparse convolutional neural networks on the FiC system and estimated its performance and resource consumption with HLS simulation.
Extraction of Preference and Classification Rules in Floor Plan Databases using Answer Set Programming

The preference criteria for real estate rental prop-erties strongly depend on the form of use and purposes. If it is possible to acquire the preference criteria on each purpose of use in a human-understandable form, they can be useful information for each of user, lenders and developers of rental properties. In this research, using the framework of preference and classification learning on answer set programming (ASP), we attempt to acquire preference criteria from floor plans of rental properties. Specifically, from a ranking of rental properties on intended purpose, inductive preference learning of ASP is applied to extract weak constraints corresponding to ranking functions, and classification learning is performed to obtain rules representing distinguish features between high and low ranked properties. The usefulness of our ASP-based analysis is evaluated by examining the results of the learnings for three real rankings of nine rental properties.
Analysis of Variance of Searching Time for Serial Concatenation of Graph-Clique based Cryptocurrency Mining

Cryptocurrency involves the search for a solution to a computationally difficult problem when conducting transactions. This is called mining, and by its nature, the time required for mining can vary significantly. This is undesirable from a security perspective. Existing research has proposed mining using graph cliques and methods for linking multiple mines with the aim of reducing the variance in mining time. In this study, we evaluated and implemented the possibility of further reducing the variance of mining time by combining the existing studies. The results showed that the combination of the two methods was more effective than conventional mining and existing research to achieve small variance.
Proposal and Evaluation of a Method for Secure Division Sum Using the Secret Sharing Scheme by Rounding Off Real Numbers

Although many secure computations in finite fields are known, few secure computations that can be performed on real numbers have been proposed. Particularly, division of real numbers is difficult for secure computations in a finite field, and there are many inefficient processes (e.g., repeated approximations). In this study, we propose a secure computation method that can directly compute four arithmetic operations, including division, using real numbers. By multiplying the secret information by a random number and rounding off real numbers, the proposed method can evaluate the calculation error in the case of secure computation using real numbers in detail, which has not been done in previous studies. We show that the proposed method has a low error rate and achieves efficient and fast processing, particularly in division.
Design of Monitoring System for Water Levels and Turbidity Water Canals Based on Nodemcu

Indonesia has two seasons, namely the dry season and the rainy season. Floods occupy the first position in the sequence of natural disasters with a total of 1,067 floods in Indonesia. The flood caused a lot of losses for the community, this is because the garbage that has accumulated in the waterways makes it difficult for the water to flow and when it rains enough it can cause the canal to be unable to accommodate a lot of water so it overflows onto the road and causes floods to occur. From this condition, a system is needed that aims to monitor the height and turbidity of the water. This research will be designed using NodeMCU as a microcontroller and communication with android applications via the internet. The method used is a literature study by collecting data related to the problem, analyzing needs for research, designing tools, testing, analyzing systems and documentation, and compiling research reports. The results of this study are a prototype tool that is used as a monitoring system for water level and turbidity using an android application as an early flood warning.
A Web-Based Accounting Information System Application using CodeIngniter Framework: (A Case Study Approach)

The design of the Financial Accounting Information System at the Operational and Financial Unit aims to analyze the system and procedures for filing, recording and accountability of ongoing down-payment, designing a system that can make submissions, recording and accountability of down-time computerized web-based, as well as evaluating application system submission, recording and down payment accountability that is built as well as compiling an internal control system of the proposed system. Data collection was done by interviews and direct observation of ongoing business processes which then described qualitatively in flowchart and narrative. Based on analysis of the current system, shows that the systems and procedures for filing and accountability ongoing “imprest” has not been effective and still has several problems. System analysis model is described using an object-oriented approach with UML diagrams are represented by use case diagram while the phases of system development using the waterfall model in the SDLC. Information system designed web-based and developed using framework CodeIgniter v3 with the PHP programming language and MySQL database that can generate a report containing the amount of the down-payment that must be accounted for, the amount of the down payment that has been accounted for and the amount of the down payment that has not been accounted for in certain periods.
A Study on Subjective Information in Deep Neural Network Models

Deep learning technology has advanced remarkably in the last decade, and so has natural language processing (NLP) technology. Pre-learning models have achieved significant advances and are now being used very actively. As pre-trained models have been actively used, the knowledge contained in the models has been actively studied in order to reveal the basis for their decisions. However, the research on the investigation of such knowledge is mainly for objective knowledge, and there is not enough studies on the investigation of the subjective knowledge that the model contains. In this paper, we focus on GPT-3, which is one of the most popular pre-trained models, and investigate the subjective knowledge contained in its model by making GPT-3 generate sentences in English or Japanese. For investigation, we checked whether it generates documents favorable to each political party. The results showed that the model has subjective knowledge about each political party, which indicates whether the party is good or bad. We also found that subjective knowledge could be extracted more by making the model generate documents in English than in Japanese.
ConvUNeXt: A Lightweight Convolutional Neural Network for Watercolor Image Translation

Image-to-image transformation is the task of transforming an image from one domain to another. It includes the task of converting an image to an artistic style such as oil painting, tile art, and watercolor. These conversion techniques have rapidly advanced with the development of machine learning in recent years. This paper proposes a method for generating watercolor paintings using machine learning technologies. The proposed method uses conditional generative adversary networks (cGANs) to convert the style of input images from real to watercolor. Specifically, we propose a ConvUNeXt generator that generates high-quality watercolor images while preserving the color information of the original image. It employs an encoder-decoder structure with skip connections and introduces multiple ConvNeXt blocks to learn image features. Experimental results show that the proposed model significantly outperforms traditional rendering methods in the time consumption of image translation while achieving comparable translation performance.
Application of Certainty Factor Method to Diagnose Venereal Diseases Using Confusion Matrix for Multi-Class Classification

Public awareness about health is still deficient due to limited public knowledge in the health sector. Another problem is the limited availability of experts, especially in sexual health. Currently, health clinics that provide consultation for patients with venereal disease are still very limited. Awareness of venereal disease in Indonesia is still lacking, and there is still a reluctance to seek treatment or consult with venereal disease. Sexually transmitted diseases can be transmitted through sexual and non-sexual intercourse. Based on these problems, this study aims to build an expert system for diagnosing venereal diseases using the Certainty Factor method with Confusion Matrix for Multi-Class - Classification. The results of the study based on the accuracy test with the Confusion Matrix for Multi-Class Classification approach of 95% with an RMSE level of 0.091897
Road Segmentation with U-Net Architecture Using Jetson AGX Xavier For Autonomous Vehicle

Autonomous Vehicle is a technology that has been often discussed in the last few years in the category of research and industry. This technology is able to sense the surrounding environment and control the vehicle autonomously without any human intervention. In its implementation, this technology requires a lot of information, especially the road track that will be passed. Because of that, the thing that must be considered is to segment the road first. The aim of this research is to develop a method that can segment the roads to produce a model that can recognize the road track as well. This research uses Convolutional Neural Network (CNN) with U-Net architecture. The datasets have a form of car trips video recordings from the dashboard camera, which are then extracted into a frame. After this process, it is annotated or manual segmentation using Supervisely to be used as a reference for training and testing. From the results of the calculation process with the confusion matrix, the accuracy of the U-net architecture gets a value of 95%, precision value is 81%, recall value is 92%, F1-Score value is 86% IOU value is 76%. Followed by testing the model in real-time using Jetson AGX Xavier, this tool is specially designed to develop artificial intelligence with high specifications. The test is carried out with two types of testing. The first test with an RGB background produces an FPS of 0.17, and the second test without an RGB background gets an FPS in the range of 0.55-0.67.
Individual And Eligibility Verifiability Method For Verification Mechanism of Voter On E-Voting System

Elections conducted with the system are the most effective and efficient steps compared to conventional voting. However, in this case, the low level of trust between voters and candidates in the E- Voting system, such as voters being unable to verify their choices to ensure the choices have been recorded and counted correctly and the low level of confidence of candidates in the validity of voters involved in the voting process so that they prefer conventional selection. The verifiability factor is one aspect that affects the confidence level of voters and candidates in the results and processes of the electronic voting system. Individual and eligibility verifiability for voters for the verification mechanism in the E- Voting system is an E- Voting system created to be able to verify voter choices. and verification of voters to increase confidence from both the voter's and candidate's point of view of the E- Voting system.