Reviewerly/exHarmony huggingface dataset:
Artificial Neural Networks

Artificial neural networks (ANNs) have emerged as a powerful tool in the field of machine learning due to their ability to replicate the behavior of biological neurons.ANNs have found extensive applications in various domains, including image recognition, speech recognition, natural language processing, and predictive analytics.The main objective of this journal is to investigate the various applications of ANNs in these fields and assess their strengths and limitations.ANNs have transformed image recognition by achieving unprecedented accuracy in tasks such as object detection, segmentation, and classification.Convolutional neural networks (CNNs) have been particularly successful in image recognition, with several groundbreaking applications in healthcare, security, and autonomous vehicles.Similarly, ANNs have contributed significantly to the development of advanced speech recognition systems that can recognize different accents and dialects, making them suitable for global applications.In natural language processing, ANNs have shown great potential in tasks such as language translation, sentiment analysis, and text generation.However, the complexity of human language and the nuances of human communication remain a challenge for ANNs, and researchers are working to develop more sophisticated models to address these issues.Moreover, ANNs have been utilized for predictive analytics, where they can identify patterns in large datasets and predict outcomes.Despite their accuracy, the interpretability of ANNs remains a significant concern, particularly in critical fields such as healthcare, where decisions based on ANN predictions can have life-changing consequences.ANNs have transformed the landscape of machine learning and have become indispensable tools invarious fields.Despite their limitations, ANNs hold great promise for future applications, and ongoingresearch will continue to refine their capabilities and address their challenges.This journal provides a comprehensive overview of the various applications of ANNs and their potential for further advancement.
SUPERVISED LEARNING METODE K-NEAREST NEIGHBOR UNTUK PREDIKSI DIABETES PADA WANITA

Supervised learning is a technique of machine learning by doing learning that has a reference value to direct something, one of which is the K-Nearest Neighbor (KNN) method. This method is for object classification through learning data that is closest to the object (neighbor) using euclidean distance to calculate the distance. KNN can be used for data classification that already has a reference, in this case the dataset used is the diabetes mellitus dataset in women. DM is a disease that can cause complications in parts of the body that cause death. DM in women can be seen from several parameters such as glucose levels, blood pressure, skin thickness, insulin hormone, body index mass, age, number of pregnancies, and the number of family history of diabetes. In this research, KNN will be used for the classification of diabetes in women with two classes, namely DM Positive and DM Negative, in other words, a woman can be predicted to suffer from DM disease or not. This method will be implemented into a system with PHP programming language and Codeigniter Framework. KNN testing is carried out with three test scenarios, the 1st test with 150 test data gets an 82% accuracy rate, the 2nd data test with 200 test data gets an 84% accuracy rate, and the 3rd data test with 300 test data gets an 82% accuracy rate.
IMPLEMENTASI METODE CNN UNTUK KLASIFIKASI OBJEK

Objects can be interpreted as all inanimate and living things that have various shapes and sizes. For humans to determine the presence of objects, to classify and estimate the distance of objects around them is not difficult. But for a computer to do the work mentioned above with an accuracy level that reaches up to greater than 90% is not easy. Object detection is important in the field of computer vision because it is used to monitor and track objects, while robots that use cameras as sensors are used to avoid obstacles, follow objects, classify and so on. Therefore, the purpose of this study was to determine the level of accuracy of the CNN method in classifying objects. The steps used to complete this research were literature study, collecting digital image data, determining training, and testing data, designing the CNN program, conducting training and testing. From the results of testing the CNN method that has been carried out, it is known that the level of accuracy in classifying objects reaches 98%.
An Efficient and Overlap Free Urdhva Tiryagbyam Multiplier Using HDL

Numerous applications, including error-correcting codes and cryptography, heavily rely on arithmetic in the finite/Galois field.The two fundamental operations in the finite field GF are addition and multiplication.The bit parallel Karatsuba Multiplier over GF (2m) complexity (space) study and effective FPGA implementation are provided.Due to its carry-free characteristic, this is extremely intriguing for high performance systems.By using a Karatsuba multiplier, we can increase the process' efficiency.For tiny digital systems, this research suggests an optimal polynomial multiplication method.This idea suggests brand-new optimisations for the polynomial multiplier, the component of lattice-based cryptography that requires the greatest computing, with a focus on high-speed hardware.Polynomial multiplication is regarded as the most time-and space-consuming operation in ECC systems.This article suggests a novel hardware design for effective Finitefield multiplier implementation for ECC using field-programmable gate arrays (FPGAs).The original Karatsuba has been speed-optimized in the OKA.This approach divides inputs into odd and even orders rather than high and low sections in order to reduce the longest path time.In the proposed design, a 2-bit and a 4-bit binary polynomial multiplier with six input LUTs is implemented as an example DFG for an FPGA.This work can be improved by comparing the performance of the Karatsuba algorithm to that of other multiplication-related algorithms that have been developed.Additionally, an algorithm for improving multiplication operation efficiency and decreasing process cost, area, and latency can be created.
PERANCANGAN SISTEM INFORMASI PENJUALAN PADA YOURSBEAUTY STORE KOTA JAMBI BERBASIS WEBSITE

YoursBeauty is one of the business ventures engaged in sales that still uses notebooks and agendas. YoursBeauty Jambi City is located at Jalan Amir Hamzah No.76, Sungai Kambang, Jambi. In the process of managing sales data and making reports using notes for sales and agenda books for sales reports. YoursBeauty Jambi City carries out promotional activities and product buying and selling transactions are carried out by requiring customers or buyers from far away to come just to find out what products are offered, besides those customers cannot place orders remotely to choose what products to buy, as well as promoting word of mouth and social media such as Instagram. Therefore, this study aims to provide solutions to problems that occur by offering sales applications using a programming language, namely PHP and MySQL database where the system development is using the waterfall method and using a unified model language system model approach using use case diagrams, activity diagrams, class diagrams and flowchart diagrams. Providing convenience for the store in providing the latest product information and services provided by the YoursBeauty store, the new system produces output that displays structured sales data and cost data and displays the required reports to facilitate YoursBeauty Jambi City in data processing.
https://ejurnal.methodist.ac.id/index.php/methomika/issue/view/146

In this modern era, of course, every good company has a company standard that greatly affects all operational and non-operational activities such as selling goods. PT Jomini Coco Internasional is a company that was established in 2013, PT Jomini Coco Internasional is a company engaged in charcoal briquettes. However, the current sales system at PT. Jomini coco Internasional still uses the conventional system because in the process of processing sales data and making reports still using computers in the form of standard applications, namely Microsoft Word and Microsoft Excel, recording customer documents still using record books and also stored in different computers so that it can cause problems. such as damage and loss of important documents. Therefore PT Jomini Coco Internasional requires a web-based information system that aims to manage data on sales of goods and can provide convenience for every user in accessing information on goods and managing data. The software development method used is using a prototype. This website-based Charcoal Briquette Sales Information System is expected to facilitate transactions and the preparation of useful reports for PT Jomini Coco Internasional.
SISTEM INFORMASI MONITORING STOK MOTOR LISTRIK ALAT PRODUKSI BERBASIS MOBILE ANDROID

PT. Musim Mas is a business entity engaged in the processing of palm oil and soap in the city of Medan. In carrying out the processing, PT. Musim Mas uses several means of production. Every production tool used requires an electric motor to drive the tool, so PT. Musim Mas must provide stock of the electric motorbikes that will be used. In processing electric motor stock data, PT. Musim Mas still uses the manual method. Electric motor stock data is recorded and processed in a book by officers. In this way, the problem persists. Recording and processing data requires a significant investment of time and effort on the part of staff. The author conducted research to design an Android-based mobile information system that can be used to process and record electric motor stock. The waterfall method is used for system development, while the Kodular framework and Airtable database are used to build information systems. With this information system, it can help the warehouse staff of PT. Musim Mas in processing and recording as well as facilitate monitoring the stock of electric motor online via an Android smartphone.
Perancangan Sistem Pendukung Keputusan Pemilihan Guru Terbaik Dengan Pendekatan User Centered Design

Evaluation of teachers is a common activity in the education world with the aim of assessing and encouraging the performance of teachers in the learning process. At Pesantren Miftahul Jannah, the evaluation and assessment of teachers has not been fully implemented due to subjective evaluations conducted manually, often resulting in human errors and making it difficult to determine the best teachers. User-centered design method was used in this research to create a system solution based on user needs. This research discusses the design of a decision support system for selecting the best teachers by applying the user-centered design method to help evaluate the best teachers, reduce subjectivity and errors in evaluation, and provide transparency in the evaluation process. The results of this research provide a system design and prototype. The prototype that has been built was tested using the System Usability Scale (SUS) method with a score of 71.6 and received an adjective rating of B or good.
Robust and Fast Vehicle Detection using Augmented Confidence Map

Vehicle detection in real-time scenarios is challenging because of the time constraints and the presence of multiple types of vehicles with different speeds, shapes, structures, etc. This paper presents a new method relied on generating a confidence map-for robust and faster vehicle detection. To reduce the adverse effect of different speeds, shapes, structures, and the presence of several vehicles in a single image, we introduce the concept of augmentation which highlights the region of interest containing the vehicles. The augmented map is generated by exploring the combination of multiresolution analysis and maximally stable extremal regions (MR-MSER). The output of MR-MSER is supplied to fast CNN to generate a confidence map, which results in candidate regions. Furthermore, unlike existing models that implement complicated models for vehicle detection, we explore the combination of a rough set and fuzzy-based models for robust vehicle detection. To show the effectiveness of the proposed method, we conduct experiments on our dataset captured by drones and on several vehicle detection benchmark datasets, namely, KITTI and UA-DETRAC. The results on our dataset and the benchmark datasets show that the proposed method outperforms the existing methods in terms of time efficiency and achieves a good detection rate.
Causal State Estimation and the Heisenberg Uncertainty Principle

The observables of a noisy quantum system can be estimated by appropriately filtering the records of their continuous measurement. Such filtering is relevant for state estimation and measurement-based quantum feedback control. It is therefore imperative that the observables estimated through a causal filter satisfy the Heisenberg uncertainty principle. In the Markovian setting, prior work implicitly guarantees this requirement. We show that any causal estimate of linear observables of a linear, but not necessarily Markovian, system will satisfy the uncertainty principle. In particular, this is true irrespective of any feedback control of the system and of where in the feedback loop -- inside or outside -- the measurement record is accessed. Indeed, causal estimators using the in-loop measurement record can be as precise as those using the out-of-loop record. These results clarify the role of causal estimators to a large class of quantum systems, restores the equanimity of in-loop and out-of-loop measurements in their estimation and control, and simplifies future experiments on measurement-based quantum feedback control.
Hybrid Deepfake Detection Utilizing MLP and LSTM

The growing reliance of society on social media for authentic information has done nothing but increase over the past years. This has only raised the potential consequences of the spread of misinformation. One of the growing methods in popularity is to deceive users using a deepfake. A deepfake is an invention that has come with the latest technological advancements, which enables nefarious online users to replace their face with a computer generated, synthetic face of numerous powerful members of society. Deepfake images and videos now provide the means to mimic important political and cultural figures to spread massive amounts of false information. Models that can detect these deepfakes to prevent the spread of misinformation are now of tremendous necessity. In this paper, we propose a new deepfake detection schema utilizing two deep learning algorithms: long short term memory and multilayer perceptron. We evaluate our model using a publicly available dataset named 140k Real and Fake Faces to detect images altered by a deepfake with accuracies achieved as high as 74.7%
UHRNet: A Deep Learning-Based Method for Accurate 3D Reconstruction from a Single Fringe-Pattern

The quick and accurate retrieval of an object height from a single fringe pattern in Fringe Projection Profilometry has been a topic of ongoing research. While a single shot fringe to depth CNN based method can restore height map directly from a single pattern, its accuracy is currently inferior to the traditional phase shifting technique. To improve this method's accuracy, we propose using a U shaped High resolution Network (UHRNet). The network uses UNet encoding and decoding structure as backbone, with Multi-Level convolution Block and High resolution Fusion Block applied to extract local features and global features. We also designed a compound loss function by combining Structural Similarity Index Measure Loss (SSIMLoss) function and chunked L2 loss function to improve 3D reconstruction details.We conducted several experiments to demonstrate the validity and robustness of our proposed method. A few experiments have been conducted to demonstrate the validity and robustness of the proposed method, The average RMSE of 3D reconstruction by our method is only 0.443(mm). which is 41.13% of the UNet method and 33.31% of Wang et al hNet method. Our experimental results show that our proposed method can increase the accuracy of 3D reconstruction from a single fringe pattern.
Interactive Greybox Penetration Testing for Cloud Access Control using IAM Modeling and Deep Reinforcement Learning

Identity and Access Management (IAM) is an access control service in cloud platforms. To securely manage cloud resources, customers are required to configure IAM to specify the access control rules for their cloud organizations. However, incorrectly configuring IAM may be exploited to cause a security attack such as privilege escalation (PE), which can cause severe economic loss. To detect such PEs due to IAM misconfigurations, third-party cloud security services are commonly used. The state-of-the-art services apply whitebox penetration testing techniques, which require the access to complete IAM configurations. However, the configurations can contain sensitive information. To prevent the disclosure of such information, the customers have to put lots of manual efforts for the anonymization. In this paper, we propose a precise greybox penetration testing approach called TAC for third-party services to detect IAM PEs. To mitigate the dual challenges of labor-intensive anonymizations and potentially sensitive information disclosures, TAC interacts with customers by selectively querying only the essential information needed. Our key insight is that only a small fraction of information in the IAM configuration is relevant to the IAM PE detection. We first propose IAM modeling, enabling TAC to detect a broad class of IAM PEs based on the partial information collected from queries. To improve the efficiency and applicability of TAC, we aim to minimize the interactions with customers by applying Reinforcement Learning (RL) with Graph Neural Networks (GNNs), allowing TAC to learn to make as few queries as possible. Experimental results on both our synthesized task set and the only publicly available task set IAM Vulnerable show that, in comparison to state-of-the-art whitebox approaches, TAC detects IAM PEs with competitively low false negative rates, employing a limited number of queries.
Optimal partition of feature using Bayesian classifier

The Naive Bayesian classifier is a popular classification method employing the Bayesian paradigm. The concept of having conditional dependence among input variables sounds good in theory but can lead to a majority vote style behaviour. Achieving conditional independence is often difficult, and they introduce decision biases in the estimates. In Naive Bayes, certain features are called independent features as they have no conditional correlation or dependency when predicting a classification. In this paper, we focus on the optimal partition of features by proposing a novel technique called the Comonotone-Independence Classifier (CIBer) which is able to overcome the challenges posed by the Naive Bayes method. For different datasets, we clearly demonstrate the efficacy of our technique, where we achieve lower error rates and higher or equivalent accuracy compared to models such as Random Forests and XGBoost.
An Efficient Ensemble Explainable AI (XAI) Approach for Morphed Face Detection

The extensive utilization of biometric authentication systems have emanated attackers / imposters to forge user identity based on morphed images. In this attack, a synthetic image is produced and merged with genuine. Next, the resultant image is user for authentication. Numerous deep neural convolutional architectures have been proposed in literature for face Morphing Attack Detection (MADs) to prevent such attacks and lessen the risks associated with them. Although, deep learning models achieved optimal results in terms of performance, it is difficult to understand and analyse these networks since they are black box/opaque in nature. As a consequence, incorrect judgments may be made. There is, however, a dearth of literature that explains decision-making methods of black box deep learning models for biometric Presentation Attack Detection (PADs) or MADs that can aid the biometric community to have trust in deep learning-based biometric systems for identification and authentication in various security applications such as border control, criminal database establishment etc. In this work, we present a novel visual explanation approach named Ensemble XAI integrating Saliency maps, Class Activation Maps (CAM) and Gradient-CAM (Grad-CAM) to provide a more comprehensive visual explanation for a deep learning prognostic model (EfficientNet-B1) that we have employed to predict whether the input presented to a biometric authentication system is morphed or genuine. The experimentations have been performed on three publicly available datasets namely Face Research Lab London Set, Wide Multi-Channel Presentation Attack (WMCA), and Makeup Induced Face Spoofing (MIFS). The experimental evaluations affirms that the resultant visual explanations highlight more fine-grained details of image features/areas focused by EfficientNet-B1 to reach decisions along with appropriate reasoning.
Review on Effect of IoT in Inventory Management System

Abstract: Inventory management plays a crucial role in every business organization that works on production. Appropriate control mechanism of inventory is essential to ensure stock levels of all the raw materials that are required for production of good. With the application of emerging technologies like IoT and sensors, the management of the stock levels has become more efficient in both industrial and domestic level. This has not only reduced the man power that is required for supervision of inventory, but also made the lifestyle of people more comfortable and easier.
Comparison of Machine Learning Algorithms for Classification of Ultraviolet Index

Indonesia is a tropical climate country which has the potential for high intensity of sunlight exposure. Several types of exposure that receives are ultraviolet rays. According to the National Agency for Meteorology, Climatology and Geophysics, it provides information regarding the impact on human activities in an ultraviolet index which has a risk scale. The aim of this research was creating a recommender system based on the ultraviolet index category in providing daily activity advice to users. The methods used the K-nearest Neighbor algorithm and Support Vector Machine with a Collaborative Filtering Model-Based approach that could recommend items based on the results of a model trained to identify input data patterns. The stages carried out in this study included data collection, data pre-processing, data division into test data and train data, dataset testing, analysis of the results of models that had been trained in the accuracy values using the algorithm tested. The results of the confusion matrix calculation produced test evaluations in accuracy values, precision values, and recall values. The comparison of result had the highest performance in K-nearest Neighbor with an accuracy value of 99.69%, a precision value of 99.00%, and a recall value of 96.20%. In research used the Support Vector Machine showed the lowest performance with an accuracy value of 97.91%, a precision value of 93.20%, and a recall value of 86.40%.