Reviewerly/exHarmony huggingface dataset:
A lower bound on the field size of convolutional codes with a maximum distance profile and an improved construction

Convolutional codes with a maximum distance profile attain the largest possible column distances for the maximum number of time instants and thus have outstanding error-correcting capability especially for streaming applications. Explicit constructions of such codes are scarce in the literature. In particular, known constructions of convolutional codes with rate <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k/n</i> and a maximum distance profile require a field of size at least exponential in <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</i> for general code parameters. At the same time, the only known lower bound on the field size is the trivial bound that is linear in <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</i> . In this paper, we show that a finite field of size Ω <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><i>L</i></sub> ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</i> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><i>L</i>-1</sup> ) is necessary for constructing convolutional codes with rate <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k/n</i> and a maximum distance profile of length <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</i> . As a direct consequence, this rules out the possibility of constructing convolutional codes with a maximum distance profile of length <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</i> ≥ 3 over a finite field of size <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">O</i> (n). Additionally, we also present an explicit construction of convolutional code with rate <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k/n</i> and a maximum profile of length L = 1 over a finite field of size <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">O</i> ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</i> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">min{<i>k</i>,<i>n</i>-<i>k</i>}</sup> ), achieving a smaller field size than known constructions with the same profile length.
Research, Development and Design for Product Development SAC: Interaction Design Mobile Web Application Using User Centered Design Methodology

In the era of Covid-19, people are more sensitive to their health and hygiene. SAC is present in the community as a service to clean each customer’s favourite shoes. Even though SAC has been around since 2013, until now they have not touched the digital realm. For this reason, innovation for SAC needs to be implemented so they are not left behind by competitors who are starting to become aware of digitalization. So that the implementation of digitalization can save costs and time, the approach to making digital products is through product development. To innovate through the use of product development in digital, a User-Centered Design (UCD) method approach with a Design Thinking (DT) mindset is used. The UCD process includes understanding context, specify user requirements, design solution, and evaluate. With the DT mindset, the process of emphatizing, defining, and ideation is applied to the understand context and specify user requirements so that this research will understand the usability side for users. With the addition of 5 UCD elements from Jesse James Garret, the digitalization process will be more in-depth and detailed in terms of creating interface design solutions. Primary data will be collected through In-depth Interview resulting abundant data that create persona and Minimum Viable Product(MVP). This research aims to analyze and identify the user experience flow for the prototype mobile web app product design SAC with the identifying the MVP and design prototype mobile website application and later do some usability testing to getting result that can meet the needs and uses for SAC customers. The result of this design approach method is a high-fidelity mobile web app prototype which was evaluated using Usability Testing (UT). The persona of each customer and the desire are the first key in development. Strong framework is formed with information architecture, low-fidelity, and high-fidelity prototypes. The final stage of the UCD iteration is to evaluate Usability Testing with a binary task success rate of 90%. Another UT results of quantitative data analysis with the Single Ease Question (SEQ) showed the number 6.42 and the System Usability Scale (SUS) showed the number 87.5 with quality A.
Low-Resource Scenario Classification Through Model Pruning Towards Refined Edge Intelligence

The implementation of Scenario Classification (SC) plays a pivotal role in various edge intelligence applications, notably in fields such as autonomous driving, navigation, and remote sensing. With recent advancements, deep learning (DL) techniques have substantially improved SC, delivering remarkable results in classification tasks. However, the integration of DL in SC brings significant computational demands, posing challenges for deployment on edge devices where resources are constrained. Addressing this issue, we propose a novel Low-Resource Scenario Classification (LR-SC) approach, primarily focused on model pruning. This strategy aims to reduce computational power and storage needs, thus optimizing resource utilization in edge intelligence applications. Our approach involves the application of an ℓ2 regularization and a threshold-based pruning method, which selectively eliminates non-essential connections. This is followed by a systematic process of alternating pruning and fine-tuning to mitigate any performance loss due to the pruning. Experimental evaluations of the LR-SC method have shown its effectiveness; it substantially lowers the parameter count to merely 24% of the original model, while simultaneously achieving a 0.42% increase in classification accuracy.
Digital Platform-Based Learning Innovation in Elementary Schools in The Industry 4.0 Era: Systematic Literature Review

Information and communication technology advances have been utilized in many sectors of human life, including educational practices. This study aims to reveal the innovation practices of app-based digital application platforms. This research was conducted using a systematic literature review (SLR) approach. Data sources come from research journal articles and books by the theme of the discussion. The data analysis technique uses content analysis. The finding or result of this research is that learning can utilize digital platform features as part of learning innovation. Digital platforms that can be utilized are such as Microsoft Teams and Edmodo applications. Both applications have many services, such as video conferencing, presentations, assignments, and online assessments. The utilization of digital platforms in schools in the Industrial Age 4.0 aligns with the development and challenges of the rapidly changing times. Digital platforms are part of the innovations that can be used as alternative media and real solutions for technology-integrated learning. Learning with digital platform media can increase student participation and collaboration and provide new, in-depth learning experiences.
Efficiency Assessment of the Permutation-Based Frame Synchronization Method

The objective of this study is to assess comparatively the efficiency of the permutation-based frame synchronization method for the syncword length of 16 and 8 elements. The proposed method employs a permutation of elements encoded using a fixed-length code. In this method, the maximum value of the minimum Hamming distance between the binary representation of permutation and each of its circular shifts is used. The results show that, for a syncword of length 16, the maximum value of the minimum Hamming distance is 30. A computer simulation model for a frame synchronization system is developed. The indicators for synchronization are determined using the parameters calculated for bit error rate values of 0.4 and 0.495, a correct synchronization probability not less than 0.9997, and a false synchronization probability not greater than 3E-4. The results show that a syncword of length 16 increases the number of bits required in comparison with a syncword of length 8. However, a larger syncword length is required to ensure the cryptographic security, especially, for a three-pass permutation-based protocol.
PLURAL: 3D Point Cloud Transfer Learning via Contrastive Learning With Augmentations

Unlocking the power of 3D point cloud machine learning models can be a challenge due to the need for extensive labeled datasets, which presents a challenge when applying these models to new domains. Transfer learning can help overcome this challenge by utilizing data from related tasks to enhance model performance. However, traditional (2D) transfer learning methods struggle with 3D point cloud domain adaptation, due to differences in physical environments and sensor configurations. To address this issue, we propose PLURAL, a novel 3D point cloud transfer learning methodology based on contrastive learning with augmentations. Our approach is inspired by the notion that high-level shape features are more transferable than low-level geometry features. We propose a co-training architecture that includes separate 3D point cloud models with domain-specific parameters, as well as a module for learning domain-invariant features. Additionally, PLURAL extends the approach of contrastive instance alignment to 3D point cloud modeling by considering physics-informed hard sample mining. Our experiments on simulation and real-world datasets demonstrate that PLURAL outperforms state-of-the-art transfer learning methods by a significant margin, effectively reducing the domain gap. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —The usage of 3D point cloud machine learning models is currently limited by the need for extensive labeled data. With our proposed framework, data from related tasks can be utilized to enhance the model performance on new applications or domains. PLURAL explicitly considers the acquisition of 3D point clouds by diverse sensors and in diverse environments. The method is highly adaptable and includes separate models with domain-specific parameters, making it applicable to a wide range of applications and domains.
PEMBANGUNAN KEAHLIAN PUBLIC SPEAKING MELALUI KEGITAN KHITOBAH DI PONDOK PESANTREN ASAASUNNAJAAH KESUGIHAN CILACAP

This study aims to determine the public speaking skills development program for students through khitobah activities and to find out the results of khitobah activities on the development of students' public speaking skills. The method used in this research is a qualitative descriptive approach that explains, describes systematically based on the phenomena that occur. Data collection techniques used are observation, interviews, and documentation. Meanwhile, to analyze the researchers used data reduction, data presentation, and drawing conclusions.&#x0D; The results of this study indicate that the program for developing public speaking skills for students is through khitobah activities, namely: first, being the host or MC at the khitobah activity. Second, to be a welcoming speech at the sermon activity. Third, being a speaker at a sermon activity. By frequently participating in sermon activities for students and always practicing and continuing to practice when they become officers at sermon activities, students' public speaking skills can be built. While the results of the sermon activity on the development of students' public speaking skills are: first, students become more confident. Second, the students have the courage to appear in public. Third, be mentally strong. Fourth, mature yourself. Fifth, useful for the future of students.
Breast cancer detection employing stacked ensemble model with convolutional features

Breast cancer is a major cause of female deaths, especially in underdeveloped countries. It can be treated if diagnosed early and chances of survival are high if treated appropriately and timely. For timely and accurate automated diagnosis, machine learning approaches tend to show better results than traditional methods, however, accuracy lacks the desired level. This study proposes the use of an ensemble model to provide accurate detection of breast cancer. The proposed model uses the random forest and support vector classifier along with automatic feature extraction using an optimized convolutional neural network (CNN). Extensive experiments are performed using the original, as well as, CNN-based features to analyze the performance of the deployed models. Experimental results involving the use of the Wisconsin dataset reveal that CNN-based features provide better results than the original features. It is observed that the proposed model achieves an accuracy of 99.99% for breast cancer detection. Performance comparison with existing state-of-the-art models is also carried out showing the superior performance of the proposed model.
Optical Microscopy Images Enhancement Using YCbCr Color Space Based on Nonlinear Mapping

Improving medical images captured by the optical microscope plays an essential role in many medical, radiological, and other applications in the medical field. In this study, the microscope image was improved based on the color conversion (Ycbcr) and the transformation nonlinear mapping, where the lighting component (Y) was processed based on the nonlinear transformation and fuzzy technique, and the color component was improved using (CLAHE) algorithm. The proposed algorithm was compared with several modern algorithms based on non-referenced quality measures. Analyzing the proposed results has obtained the best quality measures with high values of EN (7.0701), AG (14.6901), and MSD (55.8363).
A New Feature Selection Metric Based on Rough Sets and Information Gain in Text Classification

In text classification, taking words in text documents as features creates a very high dimensional feature space. This is known as the high dimensionality problem in text classification. The most common and effective way to solve this problem is to select an ideal subset of features using a feature selection approach. In this paper, a new feature selection approach called Rough Information Gain (RIG) is presented as a solution to the high dimensionality problem. Rough Information Gain extracts hidden and meaningful patterns in text data with the help of Rough Sets and computes a score value based on these patterns. The proposed approach utilizes the selection strategy of the Information Gain Selection (IG) approach when pattern extraction is completely uncertain. To demonstrate the performance of the Rough Information Gain in the experimental studies, the Micro-F1 success metric is used to compare with Information Gain Selection (IG), Chi-Square (CHI2), Gini Coefficient (GI), Discriminative Feature Selector (DFS) approaches. The proposed Rough Information Gain approach outperforms the other methods in terms of performance, according to the results.
Breast Cancer Detection using Machine Learning Algorithms

Machine learning employs classification methods on datasets. The Machine Learning repository provided the cancer datasets that were used in this study, which were used for categorization. Breast cancer databases come in two varieties. There are various numbers of characteristics dispersed among these datasets. Breast cancer observes around 14\% of all female cancers. One in every 28 women will develop breast cancer. To analyse patterns in datasets, machine learning algorithms like SVM, KNN, and decision trees are used. Computers are able to ``learn'' from their past mistakes and come up with solutions that are difficult for humans to come up with. According to the study, there are many effective algorithms for analysing the properties of data sets. This study compares and implements several well known classification methods, including Decision Trees, K Nearest Neighbor, SVM, Bayesian Network, and Naive Bayes on the Wisconsin Diagnostic dataset by calculating its classification accuracy, and its sensitivity and specificity value.
Sentiment Analysis on IMDB Review Dataset

A computational method known as sentiment analysis is employed to ascertain the emotional undertone or attitude of a text document, such as a review, tweet, or news story. Using machine learning models, deep neural network models, and natural language processing, the method entails examining the text to determine whether it expresses positive or negative sentiment. In this study, models like Naive Bayes, Logistic Regression, LSTM, LSVM, Decision tree, and BiLSTM are utilized to conduct a sentiment analysis (SA) study on the IMDB dataset. The goal of the investigation is to evaluate how well these models perform in retrospect on movie reviews, categorizing them as positive or negative. The study investigates the effects of data pre-processing methods and hyperparameter tuning on the models’ accuracy. The final results demonstrate that the BiLSTM model outperforms the other models in terms of recall, precision, and accuracy, followed by the LSTM, Logistic Regression, LSVM, Decision Tree, and Naive Bayes models. The research emphasizes the potential of deep learning models—in particular, BiLSTM in sentiment analysis tasks, as well as the significance of hyper-parameter tuning and pre-processing methods in achieving high accuracy.
COMPARATIVE STUDY OF CHAOTIC SYSTEM FOR ENCRYPTION

Chaotic systems leverage their inherent complexity and unpredictability to generate cryptographic keys, enhancing the security of encryption algorithms. This paper presents a comparative study of 13 chaotic keymaps. Several evaluation metrics, including keyspace size, dimensions, entropy, statistical properties, sensitivity to initial conditions, security level, practical implementation, and adaptability to cloud computing, are utilized to compare the keymaps. Keymaps such as Logistic, Lorenz, and Henon demonstrate robustness and high-security levels, offering large key space sizes and resistance to attacks. Their efficient implementation in a cloud computing environment further validates their suitability for real-world encryption scenarios. The context of the study focuses on the role of the key in encryption and provides a brief specification of each map to assess the effectiveness, security, and suitability of the popular chaotic keymaps for encryption applications. The study also discusses the security assessment of resistance to the popular cryptographic attacks: brute force, known plaintext, chosen plaintext, and side channel. The findings of this comparison reveal the Lorenz Map is the best for the cloud environment based on a specific scenario.
Detection and Classification of Malaria Parasite Based on Improved YOLOv5 Model

At present, malaria is still one of the major threats to human health. Timely diagnosis of malaria is critical to reducing both transmission and mortality. Malaria blood smear microscopy is the standard for malaria detection, but due to the cumbersome steps of manual evaluation, this diagnostic method is time-consuming and prone to missed and false positives, even in the hands of experienced physicians. Deep learning-based neural network models have achieved great success in object detection, but state-of-the-art models have not been widely used in biological image data. In response to this challenge, we have introduced an enhanced model based on YOLOv5 to accurately identify malaria blood smear cells and detect their infected stages. Based on the original YOLOv5, CA module and BiFPN are used. The experimental results demonstrate that the enhanced YOLOv5 model achieves an average accuracy of 79.3% on the public Plasmodium vivax (malaria) infected human blood smear dataset.
Policy Strategy for Transaction Speed, Data Security and Regulation in the Banking Industry: A Case Study on Bank Jatim Karesidenan Kediri and the Impact of its Contribution to Bank Performance

Bank Jatim Karesidenan Kediri needs to improve customer data security by developing the right policy strategy. Policies that need to be implemented include multi clouds with homomorphic encryption, managerial security awareness with Big Data Solution vendor reputation, and the use of holistic data warehouses in data privacy health architectures. In addition, Bank Jatim needs to pay attention to banking regulations and restrictions on access to activities between countries to minimize data security risks and increase customer trust. This study uses quantitative design and interview and observation instruments to obtain reviews on delay conditions in transactions, customer data security, banking regulations, and benefits for Bank Jatim Karesidenan Kediri. implement appropriate policy strategies such as multi clouds with homomorphic encryption, managerial security awareness with Big Data Solution vendor reputation, and using holistic data warehouses in data privacy health architectures. In addition, Bank Jatim also needs to pay attention to banking regulations and limit access to activities between countries to minimize data security risks and increase customer trust. Through the implementation of the right policy strategy, it can improve financial performance, expand the reach of customer services, and maintain its position in the midst of today's banking industry competition. The implementation of policy strategies must be carried out accurately and agilely in order to optimize bank performance and performance.