Reviewerly/exHarmony huggingface dataset:
Advances in Aerial Image Exploitation

The availability of low cost sensors, high bandwidth communication links and the growing popularity of Unmanned Aerial Reconnaissance Platforms like UAVs have resulted in dramatic new opportunities to conduct remote battlefield surveillance and reconnaissance missions from relatively safe Ground Control Stations. The sensors used in UAV explore the visible, infrared and microwave regions of the spectrum and the images are produced optically, electronically or by film. Aerial Image Exploitation is an innovative image utilization program that uses multi sensor, multi resolution and multi spectral imagery obtained from Aerial Reconnaissance Platform for the purpose of Extraction, Exploitation, Dissemination and Interpretation of Imagery Intelligence. Research in aerial image exploitation is focused to provide a total solution of the problem by combining both hardware and software at the system and subsystem level. Major operational goal of image exploitation technology is to extract the vital intelligence from a large storehouse of available imagery by filtering those which are most likely to produce valuable findings, to combine classical image/signal processing techniques with technologies available in other fields and to use specialized hardware wherever required. The first part of the paper describes the Ground Image Exploitation System and Imagery Intelligence Exploitation System being developed in ADE. The second part of the paper describes some of the basic and advanced research work being carried out in ADE, which is the backbone that lead to the development of these systems. Some of the areas discussed in this paper are adaptive preprocessing of imagery, image mosaicing, terrain classification and high-resolution reconstruction of target region.
Exploring Students’ Speaking Anxiety in Story Completion Technique In EFL Classroom

Speaking is a form of speech act in the form of sounds produced by the speech apparatus accompanied by body movements and facial expression. Unfortunately, many students find it difficult to practice this because of excessive anxiety. One example of anxiety that we often encounter is public speaking anxiety. Anxiety is a tendency where a person is reluctant to his abilities. In this research, the researcher will use case study design. The type of this research was descriptive qualitative. This research focused in investigated speaking anxiety with story completion technique in EFL Classroom. This research was conducted at a high school in West Bandung Regency. Researchers chose this school because researchers are alumni of this school.The results of the analysis, In short, we can consider that speaking is one of the instruments to talk to each of them others in everyday life, because human life speaks very important because we can provide insights, data, news, thoughts. However, speaking in English for students is difficult because students need to communicate in other dialects with high self-confidence.However, students must be active in the classroom because speaking skills can improve with practice. They must also pay attention and respect each other not laugh at anyone when he makes a mistake in speaking. Students must be aware that making mistakes means the teaching and learning process is ongoing.
USING MULTI ADAPTIVE NEURO FUZZY INFERENCE SYSTEMS TO IDENTIFY FRAUD BANK CHECKS

This research is to use multiple Adaptive Neuro Fuzzy Inference Systems (ANFIS) to design and build an image processing algorithm to quickly and easily detect a fraud or altered bank check in real time. With the help of MATLAB Fuzzy Logic and Image
Elevating Lexical Proficiency: Enhancing Students' Vocabulary Mastery with English Spelling Bee Challenges

This study aims to determine if spelling bee competitions aid in vocabulary growth for SMP Negeri 3 Banawa class VIII students. This study uses a quasi-experimental research approach using both controlled and experimental categories. Purposive sampling was used to choose the sample. Class VIII A has twenty-four students, while Class VIII B and the control class also have twenty-four students. Vocabulary tests were administered both before and after therapy to gather data. There is a pre-test and a post-test for the test. The control class received 39.27 points, whereas the experimental class had an average projected score of 50.83. The experimental class's post-test score was 73.63, whereas the control group scored 49.86. The data analysis findings indicate α = 0.05 and Sig. (2-tailed) based on the t-test, the predicated equality between the two means is 0.000. Sig.(2-tailed) &lt; α = 0.05. in light of this. Ho was turned down, while Ha was accepted. Calculations show that SMP Negeri 3 Banawa class VIII students' vocabulary mastery has increased dramatically due to using the English Spelling Bee.
Quaternion Elastic Net for Signal Recovery

Elastic Net (EN) has shown effectively performance in application to sparse signal recovery when variables displayed high levels of correlation. However, because quaternion multiplication is noncommutative, EN cannot be used directly to recover quaternion sparse signals. We develop a novel Quaternion Elastic Net (QEN) approach to recover the quaternion sparse signals. The quaternion signal's multi-channels are processed simultaneously by QEN rather than being restored one at a time, making full use of the correlation information of different channels. To get over the challenges of quaternion optimization, we also create a powerful optimization technique based on the ADMM framework and half-quadratic theory. Experimental results show that QEN is superior for recovering quaternion sparse signals.
Rotation Invariant Copy Move Forgery Detection using Discrete Cosine Transform

Digital images occupy a crucial part in various realms of our lifestyle nowadays. However, a digital image may be easily tampered with the help of various image editing applications. Hence digital image forensics holds immense importance among all multimedia-based research topics. The simplest image forgery is Copy-Move Forgery, and for this reason, it is the most prominent forgery method. Several post-processing steps like rotation, blurring, and colour reduction make detecting this kind of forgeries difficult. This paper introduces a novel method of identifying and localizing copy move forgery robust to the rotation of the forged areas in an image. Feature matching using block-based Discrete Cosine Transform and the Euclidean distance between the cumulative block Discrete Cosine Transform values are effective parameters that detect replicated regions and rotation of these regions in the image. The experimental results like accuracy, precision, recall and F1 score provide better intuition into the efficiency of this method compared to the other related works in Copy-Move forgery detection involving rotation detection.
A Robust Data-Driven Predictive Maintenance Framework for Industrial Machinery using Explainable Machine Learning Techniques

The best performance and longevity of industrial machinery are dependent on predictive maintenance. The proposed framework is a reliable and effective predictive maintenance tool that makes use of the widely used NASA-bearing dataset. It uses advanced machine learning algorithms in a multi-step procedure that includes data preparation, anomaly identification, and fault categorization. Then we tried to learn the underlying patterns in the data using an autoencoder, a potent unsupervised learning technique, and then set a threshold to detect anomalies. Anomalies are defined as deviations from the reconstructed signal, which the autoencoder is trained to recreate from the input data. Next, we employed k-shape clustering, a state-of-the-art time-series clustering method, to group similar patterns together in the latent representation obtained from the autoencoder. This enables us to obtain more accurate anomaly labels and improve fault classification accuracy. To further enhance the accuracy of the framework, we extracted several features from the data, including the poisson process, latent representation, frequency, and time domain features. These features are then used as inputs to a decision tree classifier, a popular machine-learning algorithm that can handle both categorical and numerical data. Additionally, we experimented with the gradient boost classifier, a boosting algorithm that improves the performance of the decision tree classifier. The experimental results demonstrate that our proposed framework achieves an accuracy of 95 percent using the gradient boost classifier, outperforming traditional approaches. The combination of the autoencoder-based anomaly detection and the decision tree classifier with the extracted features provides a powerful predictive maintenance framework that can improve the reliability and performance of machinery, reduce downtime and maintenance costs, and ultimately enhance overall productivity in various industrial settings.
Automated Power Point Generation using Natural Language Processing Techniques

Presentations have become an important part of the work life whether it is a student or an employee or a businessman. But creating a presentation is a time consuming process. The proposed method aims to reduce the amount of work put into creating a presentation by automatically generating an outline or a framework of the presentation based on text input. The system is a website that will be able to convert a text file or a pdf document into a presentation. If it is a text document, segments of text are created by analyzing the text and grouping sentences that belong to the same topic. Then, each of these segments will be summarized and key points will be generated. These points will be distributed over several slides. Appropriate titles will be generated for each slide based on the contents. If it is a pdf document, the titles are extracted from the document itself. The segments are already present as pdf documents and have a structure. The presentation can be exported as a.pptx file which lets the user to edit the file easily and make any changes if necessary. The system uses Google App Script to generate a file with.pptx, and uses API’s to summarize text, generate key points and to generate titles.
Humans in Intelligent Environment: Near Future

For hundreds of thousands of years since the appearance of humans on the face of Earth, humans have been interacting with their environment. The interactions have always been either with living species such as other humans or organisms from other species such as dogs and cats and horses and cows and bulls and so on, as well as trees. Humans have also been interacting with non-living objects such as rocks and soil and water, etc. When humans interact with living species (except probably trees), they always expect some form of active response in return. If we run after a dog, it might run away or may come back charging at us. If we talk with another human, they may talk back or laugh or get scared, etc. Humans are accustomed to such responses. The interaction with trees can be a bit tricky. With no locomotive organs, most of the interaction may seem like one with non-living things, but we can observe that trees grow from seeds, they shed leaves in fall and grow back in spring, they bear flowers and fruits, etc. In general, though, responses to non-living things are always passive in nature. If you hit a rock, you will get hurt, but the rock is not going to actively respond in any way. Also, when left alone, the rock is not going to show any changes to its appearance. Same applies to all the other non-living things. This is what has been programmed into human brains since eternity. It started to change a little with the introduction of machines into their lives around 7000 years ago as we saw in earlier chapters. These machines were capable of moving things and reacting to human actions in a way that was different than a typical non-living thing, but it was still quite deterministic and predictable. There was no element of surprise or uncertainty in their behavior and humans quickly adapted to work with such machines. For thousands of years from then till the late twentieth century, these interactions were still about the same in spite of vast improvements in the type of machines and their capabilities. The machines were still quite deterministic and predictable. They were still not unpredictable, and no original or intelligent response would be expected from them. The definition of intelligence is deeply rooted in the behavior of living species that come with uncertainty and unpredictability along with logical thinking. As the machine technology reached a point where they started outsmarting humans in complex heuristic calculations (still quite deterministic and predictable) in the form of expert systems, an inevitable comparison with human intelligence started to surface. However, with reference to the notion of human-like intelligence, the machines were still rather dumb.
Automated Inverse Kinematics Configuration Selection for Path Planning of a 6-DOF Robot

This article presents an automated technique for selecting suitable inverse configurations for the path planning of robots with six degrees of freedom (6 DOF). Traditionally, robots were limited to a single fixed configuration for movement, but now there's a growing need for robots that can adapt to different situations, especially in unknown environments. In this study, we introduce an innovative approach designed to assist a small industrial robot known as AKB-IRV1. This approach helps the robot determine and automatically select the most suitable configuration to move, which is essential for effective path planning. We simplify a complex problem related to how the robot's joints work through geometric analysis, breaking it down into two stages: calculation and selection of the best joint angles for movement. We also use computer simulations to assess the robot's workspace, considering joint angles as constraints. Our findings reveal that taking joint angles into account significantly reduces the robot's effective workspace. We also present a method for the robot to automatically choose the right configuration when planning its path, especially in uncertain situations. This ability allows the robot to change its configuration as needed, aligning with the goal of minimizing configuration changes. This method has promising applications for intelligent robots operating in unfamiliar environments.
Quality Assisted Three Fringe Photoelasticity for Autonomous Evaluation of Total Fringe Order

Three fringe photoelasticity (TFP) is a technique for determining the total fringe order from a single colour isochromatic fringe field. In TFP, the total fringe order is obtained by comparing the colour of the unknown photoelastic fringe pattern with a calibration specimen. Comparison is conventionally done by minimising the colour difference error. This leads to incorrect evaluation of fringe order in some regions showing itself as noise when these values are plotted as an image. In this paper, a new autonomous quality based approach is developed to identify and remove this noise. The method is explained by evaluating the total fringe order for the problem of a disc under diametral compression. The application of this methodology is demonstrated by solving two other example problems.
Implementing a Unique Identification System for Improved Traceability and Prevention of Counterfeit Drugs in the Supply Chain

Block chain technology provides a secure and impenetrable platform for locating and tracking counterfeit medications within the supply chain of the pharmaceutical industry. This research proposes a novel Block chain-Enabled Unique Identification System (BEUIS) designed to revolutionize traceability and combat counterfeit drugs. BEUIS leverages block chain technology to create an immutable and transparent ledger, ensuring the authenticity of pharmaceutical products at every stage of the supply chain. Through the integration of smart contracts, it automates verification processes, significantly enhancing efficiency and reliability. The decentralized nature of block chain minimizes the risk of data manipulation and establishes a trustworthy environment for all stakeholders. BEUIS also promotes interconnectivity, allowing seamless integration with existing systems, thus offering a scalable and sustainable approach to addressing the global counterfeit drug epidemic. This research presents a comprehensive framework, emphasizing the implementation of BEUIS as a pivotal solution to safeguard public health and restore trust in the pharmaceutical sector. Its transparency and immutability make it a promising solution for improving public health. By utilizing block chain technology, the system offers an extremely secure and unalterable platform for the system provides a highly secure and tamper-proof platform for detecting and tracking counterfeit drugs. Its transparency and immutability make it a promising solution for improving public health outcomes.
Federated Machine Learning for Cardiac Disease Detection Using Internet of Medical Things

The abundant availability of medical data from healthcare organizations increases the application of Machine learning (ML) techniques in medical field. Reliable and trustworthy ML models needs to be developed in order to maintain the efficacy of the healthcare providers. ML models are used extensively to make reliable disease predictions while also safeguarding the data collected through Internet of Medical Things (IoMT) devices. Federated Learning (FL) techniques are suitable for preserving IoMT data since it retains only the trained models and progresses with information from dispersed consumers. FL techniques have the caliber to drastically transform medical industry by making prompt disease diagnosis to enhance efficacy of therapy. The accuracy of these FL techniques decreases due to the massive data being exchanged between local and remote sites. In order to mitigate this issue, a novel approach FedEDFA, that combines Federated Machine learning with meta-heuristic optimization algorithm is proposed. Enhanced Dragonfly Optimization algorithm is utilized to optimally choose the relevant characteristics and employ it further for disease prediction. This approach improves the resilience of the system in insecure networking environments. The performance of the proposed FedEDFA is assessed by applying it to UCI Cleveland dataset for predicting cardiovascular disease in a secure and privacy preserving way. The accuracy of the proposed approach is 98.3% which is superior to the other existing methods taken for comparison.
Zone-based Privacy-preserving Billing for Local Energy Market based on Multiparty Computation

We propose a zone-based privacy-preserving billing protocol (ZPBP) for local energy markets that takes into account energy volume deviations of market participants from their bids. Our protocol incorporates participants’ locations on the grid for splitting the deviations cost. The proposed billing model employs multiparty computation so that the accurate calculation of individual bills is performed in a decentralised and privacy-preserving manner. We also present a security analysis as well as performance evaluations for different security settings. The results show superiority of the honest-majority model to the dishonest majority in terms of computational efficiency. They also show that the billing can be executed for 5000 users in less than nine seconds in the online phase for all security settings, demonstrating the feasibility of ZPBP to be deployed in real local energy markets.
Suprasegmental Phoneme Learning in Deaf Students in TK B Class (Descriptive study at SLB B Pangudi Luhur DKI Jakarta)

This study seeks information about Suprasegmental Phoneme Learning in Deaf Students in TK B at SLB B Pangudi Luhur. This research employs a qualitative case study method involving data collection through interviews and documentation studies. Data analysis is conducted using the Miles and Huberman approach. Data validity is ensured through triangulation from various sources and maintained throughout the research process. The results of this study show that suprasegmental phoneme learning applies two forms of learning activities with different processes and applications in each form of activity, namely found in speech development learning (whose material is obtained from conversations/visualizations that are clarified with curved phrases to familiarize how students can read rhythmically with the Maternal Reflective Method (MRM) in class) and PKPBI. The learning of suprasegmental phonemes set in the TK B class of SLB B Pangudi Luhur is called an ‘accent’ (a sentence spoken with a certain tone/pressure. Some factors influence the learning of suprasegmental phonemes, namely supporting factors, including student focus, student dB, facial direction, oral motor, student motivation, and support from parents. On the other hand, the factors inhibiting the process include students who are sick, a limited amount of time, teachers who have not mastered the material, and a lack of support from the parents.
Chess Piece Classification Via Convolutional Neural Networks

Chess, one of the oldest and most intellectually demanding board games, continues to captivate players and researchers alike. Chess piece classification, the task of automatically recognizing and distinguishing different chess pieces on a chessboard, is a fundamental problem in computer vision with applications in chess analysis, game recording, and chess engine development. In recent years, Convolutional Neural Networks (CNNs) have emerged as a powerful tool for image classification tasks, achieving remarkable performance in various domains. The proposed research study highlights the importance of chess piece classification via Convolutional Neural Networks (CNNs) and explores its potential benefits. By leveraging the ability of CNNs to extract complex features from images, chess piece classification can be significantly improved. CNNs can capture local spatial patterns and hierarchical representations, allowing for the accurate identification of chess pieces with high precision and recall. The advantages of CNN-based chess piece classification extend beyond accurate piece recognition. These models can provide valuable insights into game analysis, facilitating move prediction, board evaluation, and player behavior analysis. Furthermore, CNN-based classification can enhance chess recording and broadcasting, enabling real-time annotation of chess games and enhancing the viewing experience for spectators. Overall, this research study underscores the significance of chess piece classification via Convolutional Neural Networks, showcasing its potential to revolutionize various aspects of chess analysis, game recording, and chess engine development, ultimately advancing the understanding and enjoyment of this timeless game.