Reviewerly/exHarmony huggingface dataset:
Implementasi Data Mining terhadap Pola Penjualan Bahan Material Bangunan di TB. Murah Rejeki Menggunakan Algoritma Apriori

The Murah Rejeki Building Store makes sales transactions every day, but these transactions are only used for reporting and data bookkeeping purposes. The Murah Rejeki Building Store has not conducted an analysis of the relationship between building material products purchased by consumers in the future. This study aims to process sales transaction data from consumer purchases using the Apriori Algorithm, which is one of the data mining processing methods. In this study, the authors use the Apriori Algorithm to find association rules by determining the Minimum Support and Minimum Confidence values. The results showed that with a Minimum Support value of 5% and a Minimum Confidence of 60%, 14 patterns of consumer purchase transactions were found. This information becomes very valuable in making decisions on preparing supplies of building materials and determining the layout of goods in TB. Cheap Fortune. The association rules formed can be used to plan sales strategies and layout of building materials at TB. Murah Rejeki.
A SURVEY OF BLUEBLUE INFORMATION DESIGN'S PROJECT EXAMPLES, DESIGN METHODOLOGIES, AND TOOLS IN THE FIELD OF INFORMATION VISUALIZATION

The aim of this thesis is to provide an in-depth study of case studies of Blue Blue Information Design's projects in the field of data visualisation, as well as the infographics design methodologies and tools employed when dealing with large amounts of data. By analysing actual projects, the researcher explores how information design firms can improve data communication through innovative data visualisation tools and the best practices adopted when dealing with large-scale data.Using case studies, non-participant observation, and interviews, this study focuses on the experiences and innovative approaches of commercial design firms.BlueBlue Design demonstrates excellence in information design through a systematic and structured design process, user-centred approach, innovative visual solutions, data-driven decision-making, and multimedia integration. Their emphasis on collaboration and communication with clients and teams ensures that design solutions meet expectations during the course of a project, reflecting efficient and close internal and external collaboration.
Enhancing Computer Digital Signal Processing through the Utilization of RNN Sequence Algorithms

With the increase in computing power and the availability of large amounts of data, deep learning techniques, especially convolutional neural networks (CNNS) and recurrent neural networks (RNNS), have become important tools for processing complex signals. These methods show excellent performance in speech recognition, image processing, natural language processing and so on. In this paper, we explore the application of recurrent neural network (RNN) sequence algorithms in the field of computer digital signal processing, highlighting current artificial intelligence techniques and their capabilities in solving complex signal processing problems. First, the paper reviews the basic principles and development of deep learning and RNN sequence algorithms, highlighting the advances these advanced technologies have made in simulating the way the human brain processes information. The practical application and effect of RNN sequence algorithm in computer digital signal processing are demonstrated through experimental data. By comparing with traditional algorithms, we demonstrate the efficiency and accuracy of RNN in processing complex signals, such as speech recognition in noisy environments and real-time video data processing. The experimental data not only demonstrate the effectiveness of RNNS in this field, but also highlight the unique advantages of deep learning methods when dealing with large and high-dimensional data. Through these empirical studies, this paper aims to provide researchers and engineers with an in-depth understanding of the potential of RNNS in digital signal processing applications, and looks forward to the future development direction of artificial intelligence technology in this field.
Overtake: Achieving Meltdown-type Attacks with One Instruction

In early 2018, the Meltdown attack was reported, which steals secret data by loading and then encoding them into the cache covert channel during the invisible transient executions. After that, a set of Meltdown-type attacks are proposed; those attacks largely threaten the security of modern processors.In this study, we review Intel’s x86-64 Instruction Set Architecture (ISA) and find two vulnerable instructions (CMPSB and SCASB) that can be exploited to achieve the Meltdown-type attacks with few instructions. Especially, the CMPSB instruction itself is enough to implement the core part of the Meltdown-type attacks. We design a special cache-based and Performance Monitor Unit (PMU)-based covert channel to recover the secret data for the two instructions. In our experiments, we demonstrate the availability of the two instructions by implementing the Meltdown and ZombieLoad attacks with them. Compared to the original Meltdown-type attacks, the proposed attack can be considered as an attack that does not rely on the transient executions from the perspective of the macro instruction level because no more macro instruction is executed after triggering the exception. Therefore, we name our attacks Overtake. Our experiments indicate that the average data leakage speed of Overtake attack could reach 770.1 KB/s with an error rate of 0.4 %.
APPLICATION OF AUTOMATED INTELLIGENT VIDEO SURVEILLANCE SYSTEM WITH DEEP LEARNING CAPABILITY FOR SAFETY MONITORING IN MANUFACTURING SECTOR

In many cases, occupational and accidental injuries arise from the neglect of employees and the failure of upper management to ensure the constant adherence to safety regulations. Nevertheless, occupational accidents can also result from a combination of factors such as the presence of potential hazards in the workplace and unsafe actions or procedures. Traditionally, to mitigate safety risks in the workplace, security personnel are employed to carry out safety checks and monitoring, a process that can be arduous and inefficient, particularly in large spaces like factories. The objective of this article is to assess previous studies pertaining to the application of intelligent visual surveillance through deep learning methods in enforcing safety regulations. The paper also discusses the use of deep learning in safety management across various sectors. Notably, as far as our knowledge extends, there currently exists no comprehensive survey or initial assessment of deep learning for safety management specifically within manufacturing facilities. This survey is intended to serve as a catalyst for future research into the implementation of intelligent visual surveillance through deep learning for safety management and regulatory purposes.
Vortex Search Optimizer based Hybrid Watermarking Scheme for Securing the Medical Images

Preventing the duplication, modification, and claim of unauthorised ownership of digital content is one of the major issues caused by the growing use of computer capabilities. Watermarking is one way to put a visible or invisible watermark on a digital document in order to later show that the document is legitimate or belongs to its owner. concentrating on security issues in the storage and broadcast of images in the medical info scheme, as well as the special requirements for medical images for the protection of lesion regions. In this study, a hybrid watermarking strategy was presented utilising the Slantlet transform, randomised singular value decomposition, and optimisation methods (Vortex Search algorithm (VSA)) that are modelled after natural processes. The XOR encryption method is used to encrypt the watermark image. Extensive testing demonstrates that our novel approach works better than the current approaches based on the NC, SSIM, and PSNR. The PSNR of the suggested technique is between 58 and 59 dB, suggesting increased performance, and at a scale factor of 0.06, the SSIM and NC standards of the watermarked picture and the extracted watermark are nearly equal to. The recommended method outperforms past studies in the field in terms of usability, robustness, security, and invisibility, as well as its ability to repel local nonlinear geometric attacks.
Recognition of Scanned Handwritten digits using Deep Learning

The capability of ML (Machine Learning) algorithms to recognize images of handwritten numerals is known as HDR (Handwritten Digit Recognition). Because handwritten numerals are imperfect and can be generated with a variety of flavors, it is a difficult work for the machine. The answer to this issue is handwritten digit recognition, which uses an image of a digit to identify the digit that is contained in the image. Additionally, there may be various visual artifacts including intensity changes, blurred and noisy effects makes this process more difficult. A CNN (Convolutional Neural Network)-based deep learning technique for HDR is proposed to get beyond the aforementioned restrictions. The input photos are initially transformed to a grayscale image for the area of interest using a pixel resolution ratio of 0-255. For classification of written digits is trained for recognition. The proposed model has been tested using MNIST dataset to show its effectiveness, and it achieved higher accuracy value of 99%. The suggested method can recognize HDR images with diverse writing styles and variations of sizes numbers, with prediction in plots and predicted digits in results. Tensorflow with Keras module of Python was imported for training and implementation. Scaling the training data it gives 98.7% of accuracy.
Recognition of Human emotions using Semi-Supervised Technique with SHO based DTM

There are many different aspects to study in the relatively new field of facial expression recognition (FER). Credibility of these methodologies and their use in human-computer interaction for intelligent results have been bolstered by their applications in healthcare, security, safe driving, and so on. Computational FER is able to provide helpful indications that supplement speech in a manner reminiscent of human FER coding abilities. Similar FER approaches using deep learning and AI techniques have been built into edge modules to guarantee speed and accuracy. Many investigations have been conducted to learn more about FER. There is an immediate need to address the issue of insufficient generalizability of expression recognition models due to the small size of FER datasets, which hinders performance on the facial expression recognition job. We offer a semi-supervised learning approach for successful training of expression recognition models using unlabeled face data. To maximize the utility of the face recognition (FR) data for pseudo-label generation and hence enhance the model's capacity to predict facial emotions, we employ a dynamic threshold module (DTM) that may adaptively alter the threshold. Sea Horse Optimization (SHO) is used to find the best possible value for the confidence threshold. Validation results show that using a large number of unlabeled images helps boost facial expression recognition accuracy.
Social Engineering Threat: Phishing Detection using Machine Learning Approach

In cyberspace, social engineering attacks are common, well-known, and simple to use. The overwhelming majority of hostile attempts against both physical and virtual IT systems were founded on or started utilizing social engineering techniques, according to historical evidence of such attacks. It is conceivable that the lockdown had an impact on crime rates because COVID-19 altered the opportunity for crime. This implies that trends in crime-related activities would likewise be impacted by the societal changes brought on by COVID-19. Social engineering attacks come in a variety of forms, such as phishing, pretexting, baiting, and tailgating. In a phishing attack, the victim is asked to enter important information or click on a harmful link in an email or message that appears to be from a reputable source, such a bank or social media site. This paper illustrates the machine learning (ML) approaches for social engineering (SE) assault threat identification. Cybercrime of the phishing variety has increased in recent years. It is a type of social engineering assault where an attacker fools people by sending emails or messages through social media platforms. Phishing attempts either enable the installation of malicious software or steal users' private data. Because attackers can create phishing messages that appear legitimate to a user, they are difficult to spot, and it is a security risk that has negative impacts on both the persons and the brands it targets. The literature provides a wide range of strategies to address this problem, particularly the identification of phishing websites. This paper presents results obtained in each model's data pre-processing, extraction of features, model design and the results are used to evaluate cutting-edge machine learning techniques for identifying URL-based and phishing assaults in depth.
Detection of underwater objects using Deep Learning and You Look Only Once Algorithm

Underwater object detection plays a pivotal role in various applications ranging from marine biology research to underwater exploration and surveillance. This paper presents a novel approach to underwater object detection using the YOLOV5 algorithm. The proposed underwater object detection model leverages the capabilities to efficiently and accurately identify and localize objects in underwater imagery. We used the aqua pretrain dataset for training and detecting distinct fish species in different water bodies and achieved highest mean average precision value of 0.861 for jelly fish class. This proposed method is helpful in identifying the debris or structures located in the depth of oceans and dealing with underwater calamities.
Segmentation-Based Classification Deep Learning Model for Breast Cancer Detection using Mammogram images

Breast cancer has emerged as a leading cause of mortality, responsible for an extensive number of deaths in recent years. The current imaging-based diagnostic methods adopted for the detection of breast cancer, such as mammography, has shown inadequate effectiveness in clinical environments due to their tendency for significant mistake rates. This paper introduces an effective methodology that uses segmentation based on deep learning classifiers for classification in order to perform an automated, productive, and precise diagnosis of breast cancer. In order to enhance the best model combination, a hybrid approach was employed, integrating deep learning segmentation models with VGG-19 classification models. The performance of the proposed methodology was evaluated using several statistical metrics, such as accuracy, precision, recall, f1-score, and receiver operating characteristics (ROC), along with cross-entropy loss function. The proposed methodology showed outstanding results compared to other segmentation-based classification model techniques. The research concluded that the UNet segmentation method, along with an improved VGG-19 classifier, had an improvement of 2.25% as compared to the VGG-19 (Pre-Trained) model.
Implementation of a Deep Learning Modulation Classifier in GNU Radio

This article examines the implementation of a neural network that allows to recognize different types of modulation of radio signals in real time in the GNU Radio software environment. The current article does not present the results of the research of the implemented neural network. The results of such research are planned to be published in future articles.
Virtual Mouse Using Face Gesture and NLP

The mouse plays a very important role in interacting with laptop’s and computer system, to perform necessary operations. In this paper, we are proposing a method to carry out mouse operations on the computer system without a physical mouse by utilizing voice commands and face-motion gestures. The system employs an internal webcam to detect facial gestures and an internal microphone to receive voice commands from the user. By employing a virtual mouse based on face gestures and natural language processing (NLP), we can accomplish all the fundamental functionalities of a mouse by issuing voice commands to the computer system. The system accepts these commands and executes corresponding mouse events such as "click," "right-click," "double-click," "scroll-up," "scroll-down," "minimize," and "close." The mobility of the mouse cursor on the monitor is achieved through face motion gestures, enabling the cursor to move up, down, left, and right, respectively.
Enhancing Communication through Speech to Sign Language Converter

There are millions of people suffering from speaking and hearing disabilities. Sign language is the only way to communicate for such people to convey their message. The advancement in technology has highly impacted this section of society, hence there is a need to deal with them. This paper describes the Application, "Speech to Sign Language Converter" for communicating with people having hearing and speaking impairments. The primary function of the Application is to convert input sentences into Indian Sign Language (ISL) actions in real time. It takes input in the form of speech or text and produces corresponding sign language using an Avatar which acts based on SiGML code. It can also be used to convert PDF based texts into sign language. In addition, the users can learn Indian Sign Language (ISL) using the tutorials provided within it. The application focuses mainly on converting local languages such as Kannada into ISL.
Sentiment Analysis of Textual Data using Word Embedding and Deep Learning Approaches

The proliferation of massive amounts of raw textual data, particularly through social media, has made sentiment analysis more challenging in the age of big data. Due to the significance of consumer feedback, sentiment analysis has become increasingly important in a variety of industries, including retail, dining, and travel. This study investigates the use of deep learning techniques for sentiment analysis, including bidirectional long short-term memory (Bi-LSTM), and gated recurrent unit (GRU). The study also investigates how well word embedding techniques like BERT and RoBERTa capture the subtleties of sentiment, giving academics and business experts insights into how to use deep learning techniques for sentiment analysis in practical contexts. To evaluate the performance of these techniques, we consider two datasets: Twitter and IMDB movie reviews. The sentiment analysis process aims to classify the sentiment of the texts as positive or negative, while also determining the sarcasm score associated with each sentiment.By contrasting the outcomes of different deep learning architectures and word embedding techniques on diverse datasets, this research sheds light on their effectiveness in sentiment analysis tasks. The findings contribute to a better understanding of the strengths and limitations of each approach, offering insights for researchers and practitioners seeking to leverage deep learning techniques for sentiment analysis in real-world applications.
The Effect of Electronic Module Simulation (EMS) Injury Handling on Student Preparedness in Injury Handling at SMP Muhammadiyah 8 Surakarta

Wounds from mishaps in the school climate by and large happen in the outer muscle framework and should be dealt with rapidly and properly. Injury will cause dying, bone disfigurement or inability and even passing. Support dressing alleviation can be performed by any prepared layman. One of the laypeople prepared at school is an understudy who has gotten essential crisis instruction. Nursing fundamental instruction is given through the Adolescent Red Cross (PMR) extracurricular. There should be an expansion in treating breaks in understudies with online reenactments utilizing the Electronic Recreation Module (EMS). The motivation behind this study was to decide the impact of Electronic Module Reproduction (EMS) of Injury Dealing with on Understudies' Readiness in Taking care of Wounds at SMP Muhammadiyah 8 Surakarta. This research of exploration is a quantitative report utilizing a semi exploratory examination plan with a pre-post test without control which is completed via doing a mediation in one gathering without a correlation. The examination test was 43 understudies of SMA Al Islam 1 Surakarta. Information assortment was done two times at the time before treatment and after treatment. Information examination was performed utilizing the Wilcoxon test. The consequences of the Wilcoxon test on readiness when the mediation got a p worth of 0.000 so there was an impact of the Electronic Module Reproduction (EMS) of Injury The executives on Understudy Readiness in Dealing with Wounds at SMP Surakarta. There is an Impact of Electronic Reenactment Module (EMS) Injury Taking care of on Understudies' Readiness in Dealing with Wounds at SMP Muhammadiyah 8 Surakarta with a p worth of 0.000.
Reconfigurable Cell-Based Systolic Array Architecture for CNN Training Accelerator for Mobile Applications

Systolic array is widely employed in hardware accelerators for Convolutional Neural Networks (CNNs). Since most of previous systolic arrays designed for mobile neural network processors are designed only for inference of CNN models restricting their systolic arrays to support only the forward propagation. Therefore, they cannot process backward propagation which require gradient calculations for weights and feature data. In this paper, we propose a reconfigurable systolic array architecture employing 3x3 cell structures, which can process either forward or backward propagation without requiring additional datapath blocks. It can be configured by on-chip microcode to calculate three convolutional modes: forward convolution, weight gradients, and feature data gradients which are the key components of CNN training process. To evaluate the performance of the proposed architecture, we designed a CNN accelerator consisting of 2304 processing elements (PEs). We implemented it on Xilinx VU19P FPGA and tested it for an object detector CNN, YOLOv2-tiny. We achieved a peak throughput of 921 GFLOPS at a system clock of 150 MHz. Simulation results demonstrated that the utilization of PEs can reach nearly 100% during the convolutional operations.
Web-Based Geographic Information System for Member Distribution Mapping and Honey Sales at Inspirator Lebah Madu Indonesia (ILMI) With Prototype Methods

Nowadays, information systems are one of the most important things in an organization, especially in managing business processes. One of the businesses that can be assisted by the existence of an information system in managing its business processes is Inspirator Lebah Madu Indonesia (ILMI). ILMI is an association of honey bee practitioner organizations that has the intention to provide education to traditional breeders, and forest honey hunters on how to obtain and produce good, quality honey so that good benefits will be obtained for anyone who consumes it, but ILMI still uses the traditional system and do not have an information system that runs well. This research aims to create a web-based geographic information system for ILMI that can manage member distribution mapping and honey sales. The research method used in this study is a qualitative descriptive research method. This research resulted in a web-based application that manages applicant data, the map distribution of members, views the product, and generated revenue in hopes to assist ILMI for better productivity. In the end, this research is expected to increase the productivity of the people of West Java, especially the people who are involved in bee production.