Reviewerly/exHarmony huggingface dataset:
CREDERE: A Modular Blockchain Implementation for the Issuance, Sharing, and Verification of Digital Credentials

Shared loosely online through multiple unsecured platforms necessitates priority in the development of more secured tracking of issuance, sharing, access, and usage of digital credentials. This paper presents the design and development of CREDERE, a Blockchain-enabled platform for storage, processing, and sharing of digital credentials within the context of academic credentials and national identification. Blockchain technology is considered a potential solution to addressing gaps in existing credentials management systems. To set up the platform, a Hyperledger Fabric network was first deployed. A permissioned Blockchain model was selected because identity management is needed in maintaining the privacy of personal information and preventing unauthorized access to digital credentials. Chaincode was developed and deployed in specific Channels in the network. A web interface was also developed to allow users to interact with the Blockchain network and initiate transactions. The initial implementation shows that the development of a modular Blockchain-based web application for digital credentials is feasible. Two types of digital credentials were initially implemented in order to test the modularity of the Blockchain design. Future work includes the full deployment of a decentralized Hyperledger network, issuance of actual digital credentials, testing and validation on new types of digital credentials, and user validation and satisfaction studies.
A Survey on Current Speech to Text Analysis to Help Programmers Dictate Code

Abstract: The focus of this study is to survey the usage of Speech to Text in programming and the general application in other fields. The findings are n then applied to further the application of Speech to Text with coding. It was found that the state of modern Speech to Text is in constant motion. Research and development are done in this field to improve Speech to Text and apply it to various fields. It applies to medical fields, education, machinery control, and others. It is being seen that while being used, there is still a struggle with a user's accent if it differs from the native accent of the language. This study selects 31 articles and has been split into content, application, and Speech To Text (STT).
CMSDPP: Cloud-native Multi-source Streaming Data Processing Platform

As the digital transition process of enterprises continues to advance, streaming data processing platforms are becoming the most important part of the enterprise data infrastructure. Meanwhile, cloud-native, an emerging service pattern of cloud computing, gains more and more attention for its ability to reduce the cost of application deployment and maintenance and enhance the effectiveness. In this paper, we design and implement a Cloud-native Multi-source Streaming Data Processing Platform (CMSDPP). It provides various capabilities, such as data access, data aggregation, data analysis and other data processing capabilities, for streaming data processing platform on cloud environment. Besides, we provide a unified registry of data processing components to realize the management of various streaming data processing capabilities. Relying on the extensibility and flexibility of cloud, convenient deployment and management of data application are provided in CMSDPP.
Pruning Networks Using Filters Similarity Stability

Current filter pruning methods rely too much on pretrained weights and have many super parameters, resulting in obvious performance degradation and too long parameters adjustment time. In our research, we found that the cosine similarity distribution between filters can achieve stable in a few epochs during training. Therefore, a cluster pruning method named ECP(Early Cluster Pruning) based on the cosine similarity between filters in the early stage of training is proposed to compress the deep neural networks. First, in the early stage of training, the filters were clustered with a gradually increasing threshold, and then the reserved filters were selected randomly in each cluster. The pruned models could be obtained with only a few super parameters and a single training progress, leading to an obvious reduction in algorithmic complexity and large savings in training time. The experimental results on CIFAR-10 and CIFAR-100 datasets show that ECP method outperforms recent pruning methods in terms of model accuracy maintenance, training time, and model compression rate.
An Explainable Machine Learning-based Prediction Model for In-hospital Mortality in Acute Myocardial Infarction Patients with Typical Chest Pain

Acute myocardial infarction (AMI) is the leading cause of hospital admissions and death all over the world and chest pain is the most common presenting complaint of AMI. Therefore, this paper proposes a machine learning (ML)-based prediction model for the in-hospital mortality in AMI patients with typical chest pain. To understand the principle of the black-box prediction model, a Shapley additive explanations (SHAP) method is applied to the ML-based prediction model. The experimental framework mainly includes three steps. First, we extract the experimental data from the Korea Acute Myocardial Infarction Registry National Institutes of Health (KAMIR-NIH), and then preprocess the selected data with missing value imputation, data normalization, and splitting. Thereafter, two kinds of data sampling methods such as synthetic minority oversampling techniques (SMOTE) and Adaptive Synthetic (ADASYN), are applied to handle the class imbalance problem on the experimental data. Second, different ML models such as decision tree, random forest, extreme gradient boosting (XGBoost), support vector machine, and logistic regression, are trained and evaluated on the preprocessed AMI patient data. Finally, the SHAP method is used to explain the best ML-based prediction model. The experimental results showed that the logistic regression with the ADASYN approach achieved the highest performance. Moreover, the SHAP technique enhanced the transparency of the ML model and can be a good reference for doctors to support their decisions in real life.
An Extractive Text Summarization Based on Reinforcement Learning

Abstract: In recent years, with the rapid development of network information technology, network text information also presents an explosive growth trend. As an efficient information processing technology in the digital age, text summarization can bring the advantage of focusing on key information in all directions in massive text information. However, text summarization is still faced with some problems such as difficulty in extracting long text and information redundancy. Therefore, combining with the deep learning framework, this paper proposes an extractive text summarization that uses reinforcement learning to optimize the long text extraction process and uses the attention mechanism to achieve the effect of redundancy removal. On CNN/Daily Mail datasets, the automatic evaluation shows that our model outperforms the previous on ROUGE, and the ablation experiment proves the effectiveness of the de-redundant attention module.
An Artificial Neural Network Model based on Binary Particle Swarm Optimization for enhancing the efficiency of Software Defect Prediction

With the rise in the growth of the software industry, it is essential to identify software defects in earlier stages to save costs and improve the efficiency of the software development lifecycle process. We have devised a hybrid software defect prediction (SDP) model that integrates Binary Particle Swarm Optimization (Binary PSO), Synthetic Minority Oversampling Technique (SMOTE), and Artificial Neural Network (ANN). BPSO is applied as a wrapper feature selection process utilizing AUC as a fitness function, SMOTE handles the dataset imbalance, and ANN is used as a classification algorithm for predicting software defects. We analyze the proposed BPSO-SMOTE-ANN model's predictive capability using the AUC and G-mean performance metrics. The proposed hybrid model is found helpful in predicting software defects. The statistical results suggest the enhanced performance of the proposed hybrid model concerning AUC and G-mean values. Also, the hybrid model was found to be competitive with other machine learning(ML) algorithms in determining software defects.
Sentiment Analysis of Social Media: Techniques, Applications, and Reliability

Big data analytics can be used by smart cities to improve their citizens’ liveability, health, and wellbeing. Social surveys and also social media can be employed to engage with their communities, and these require sophisticated analysis techniques. Twitter and Reddit are ideal social media tools for natural language processing since they have predominantly text-based content. Data from these social media systems can be analysed to provide sentiment on issues of importance in near real-time for decision makers. Techniques such as word clouds can provide initial qualitative analysis while quantitative analysis can produce bar charts and time series of sentiment values. Access to the Twitter and Reddit APIs are described together with analysis techniques using Python libraries. The advantages and disadvantages of this type of analysis are discussed. Social media users tend to be concentrated in the more youthful and socially progressive social cohorts, which may cause bias.
Towards a General Black-Box Attack on Tabular Datasets

Recent studies have shown that machine learning is not secure and is vulnerable to adversarial attacks. While many industries, such as healthcare, finance, and cybersecurity, require tabular datasets, most research in adversarial machine learning has focused on homogeneous datasets (e.g., vision, sound, speech, text). In this work, we will explore a black-box attack designed for tabular datasets. We use the Feature Importance Guided Attack (FIGA) [9], an evasion attack algorithm designed for tabular datasets. Previous work has achieved a 94% success rate using FIGA in a light-grey box setting which assumes having access to the feature representation and summary statistics of the target dataset. These assumptions are unrealistic because it is unlikely to have such prior knowledge about the dataset. We design a black-box attack that requires only querying the target model to learn the labels for a stand-in dataset. Our experiments obtained on average a 70% success rate, with a maximum of 96% success. This demonstrates that FIGA is viable as a black-box attack against tabular datasets.
A Novel Blockchain-Based Smart Contract for Real Estate Management

Blockchain is trending in recent days as it is gaining ground in various technical fields. Smart contracts are the most reliable and secure technology to use when implementing a blockchain-based application. It can be used to maintain an asset record in a blockchain context and can automatically change ownership and attributes when a specific instance or condition is achieved. When it comes to buying or selling properties, real estate is one of the industries where most transactions take place on daily basis. When it comes to tracking real estate assets, blockchain technology seems to be more effective. Transparent ledgers simplify the process of managing inflows and outflows of real estate transactions. Therefore, the application of smart contracts in the real estate industry has the potential to bring about a radical transformation by making it more secure, dependable, and impenetrable. This study aims to develop a theoretical framework for deploying a novel blockchain-based smart contract for hassle-free real estate transactions. By observing this model, a person with little technical understanding can grasp the mechanism and operation of smart contracts in real estate management.
Bitcoin Price Prediction by Applying Machine Learning Approaches

In the present-day market, the use of various cryptocurrencies is getting popular, and out of them, Bitcoin is very much accepted by the user. It is an application of blockchain that is based on decentralized peer-to-peer technology that leads to the development of cryptocurrency. As it is highly volatile, there is a necessity for the proper prediction of the price of Bitcoin for the users. Existing studies indicate a good number of articles on the price prediction of Bitcoin with higher accuracy using various methodologies. This study aims to extend research work based on effective as well as efficient models to predict Bitcoin prices using other machine learning and deep learning techniques. Various machine learning models such as Facebook’s prophet, Linear regression, Autoregressive Integrated Moving Average (ARIMA), random forest, support vector regression (SVR), and deep learning model long short-term memory (LSTM) have been considered in this study for effective price prediction of Bitcoin. Later, the performance of various techniques is critically assessed using several standard parameters.
Performance-Based Evaluation for Detection and Classification of Breast Cancer in Mammograms

The breast cancer is considered as one of the deadly disease since last three decades. Among several types of cancer, breast cancer causes death of millions of women in every year. The effective death rate of breast cancer is still in increasing order irrespective of having assistance from highly developed computational tools and techniques. Sometimes, noise or micro-calcification in mammary gland in breast misleads the detection procedure. In this research, the enhanced mammogram images are segmented and the intensity of segmentation is compared using both conventional and optimal threshold techniques to identify the affected breast tissue or dense lesion patch on breast. The performance of both threshold segmentation technique is evaluated by comparing the obtained results using some segmentation score parameters as accuracy, DICE, sensitivity specificity, and Matthews correlation coefficient (MCC). The performance analysis recommends that optimum threshold is better than conventional threshold segmentation method, which can be implemented for computer aided detection (CAD) for detection abnormal patches in low contrast mammogram images toward classification of normal breast to benign or malignant types.
SRGAN with 3D CNN Model for Video Stabilization

The Video Stabilization (VS) has been an energetic place of study for many years. Video stabilization is a technique that improves the video’s quality, eliminating or reducing undesired and unwanted camera motions. Non-professionals taking videos will have unintended consequences, like visual blurring, image distortion, etc. Therefore, many researchers study these shortcomings to raise the caliber of video. In this paper, a model for smoothing down jerky videos is put forward. Applying a 3D convolution to both the spatial and temporal domains, we proposed a model 3D convolutional generative adversarial networks GAN with Gaussian Mixture, we reduce spatiotemporal data for unstable background subtraction. Our model can simultaneously collect the spatiotemporal information that is stored in nearby frames, directly enhancing the performance of video stabilization. The input video is recognized and processed before being optimized and stabilized using a generator in the GAN (generative adversarial network) architecture, which also employs a content loss in addition to an adversarial loss for effective adversarial training. The Gaussian distribution model is revised for dynamic changes to determine the background process. In the foreground, detection removes moving substances from frames. Background and foreground are fundamental problems of video study. Our method produces both visually and quantitatively improved results, for KITTI data set was used to compare peak signal-to-noise ratio (PSNR) and structural index similarity (SSIM). This model produced stable output video and successfully eliminated distortion from videos recorded under various conditions.
Pembelajaran Bahasa Daerah (Jawa) Secara Daring dengan Menerapkan Penggunaan Google Classroom

E-learning which is an electronic media used as a learning medium can certainly facilitate the implementation of online learning. E-learning provides an easy-to-use method, namely the Google Classroom application, which is an application and can provide online classes. With the Google Classroom application, students / students can easily learn anywhere. As is the case at this time where the COVID-19 pandemic is still not over and must carry out teaching and learning activities through distance learning, an online system is needed. which is also useful as a learning tool in Javanese language subjects. The approach is descriptive qualitative. Meanwhile, the data collection used in this research is in the form of observation, interviews and documentation. To analyze the researchers did namely; collecting data, reducing data, providing data, which then makes conclusions and data validity. To strengthen the data, the researchers conducted data verification using triangulation techniques (synthesis of truth). In this study, it resulted in a response that by implementing the use of online applications (google classroom) that could be affordable in its implementation online during this pandemic. The implementation of Javanese language learning using the Google Classroom application is a fact that makes it easier to find material even though it has been previously conveyed by educators/teachers. The collection of practice questions and assignments through the Google Classroom application is considered safe by students / students, because these exercises and assignments can be stored directly in Google Drive. Even so, the implementation of Javanese language learning using the Google Classroom application still has its drawbacks. The shortcoming lies in students who feel unable to concentrate on learning caused by external disturbances, for example, in this case, noise. Other external disturbances can be in the form of an internet signal when the weather is bad, or the amount of internet credit that he does not have. In this case, in the implementation of Javanese language learning with these obstacles, educators/teachers can provide direction to students/students about solving the problem (solosinya).
Reinforcement Learning based position control of shell-fetching manipulator with extreme random trees

Abstract The automatic loading system of artillery includes the gun-fetching manipulator, which is crucial. The stability and control accuracy of the manipulator, however, are comparatively subpar when following the position trajectory as a result of the changes in the settings of the permanent magnet synchronous motor. This research suggests a reinforcement learning-based strategy for controlling a gun manipulator’s motor behavior in light of the current scenario. In this algorithm, the state variable is the feedback output of the control variable, and the reward function is utilized to calculate the associated reward value. The reinforcement learning agent then makes decisions based on the environment’s state and reward value, adjusting the manipulator’s trajectory in real-time. By comparing different simulation results, the gun-taking manipulator can achieve more accurate position trajectory control based on reinforcement learning.
Book Heritage in the Collection of the National Museum of the Komi Republic

The Scientific Library of the National Museum of the Komi Republic is one of the oldest libraries in the region. For more than 110 years of its history, a unique book collection has been formed, containing rare printed editions of the mid-18 th - early 20 th centuries from private collections of famous scientists, public figures, writers, teachers, as well as books and periodicals of scientific, educational and other government institutions of both the Komi region and Russia. The purpose of the article is to outline the main stages in the history of the Scientific Library of the National Museum of the Komi Republic collection, designed to identify and expose its unique rare editions for visitors. The first stage (1911-1919) is connected with the activities of the Ust-Sysolsk local historians and the Arkhangelsk Society for the Study of the Russian North, the second (1920-1930) was conducted by the Society for the Study of the Komi Territory. The third stage is limited by the period of the Great Patriotic War. Archaeographic expeditions for to collect handwritten books and early printed publications became significant for the fourth (1960s-1970s) period. In 2011, a new stage began in the life of the library, associated with the transfer of the book stock to the building of the Literary Museum named after I. Kuratov, one of the departments of the museum. When creating the space of the Literary Museum, a special place was assigned to the scientific library: the combination of the educational resource of the exposition and the scientific library with the reading room and book storage. Research activity is one of the main directions in the work of the library. Studies are actively carried out aimed at scientific and bibliographic description and examination of rare editions, identification, attribution and cataloging of book signs. Publications covering the results of researches become the concluding summing up both at the regional and Russian levels. The priority direction is a wide demonstration of their collections using computer technologies, which contributes to the expansion of opportunities for public access to rare book collections and allows expanding the list of visitors.
2EPV-ApproCom: Enhanced Effective, Private, and Verifiable Approximate Similarity Computation with Server Aided over Scalable Datasets for IoT

In big data analytics, Jaccard similarity is a widely used block for scalable similarity computation. It is broadly applied in the Internet of Things (IoT) applications, such as credit system, social networking, epidemic tracking, and so on. However, with the increasing privacy concerns of user’s sensitive data for IoT, it is intensively desirable and necessary to investigate privacy-preserving Jaccard similarity computing over two users’ datasets. To boost the efficiency and enhance the security, we propose two methods to measure Jaccard similarity over private sets of two users under the assistance of an untrusted cloud server in this paper. Concretely, by leveraging an effective Min-Hash algorithm on encrypted datasets, our protocols output an approximate similarity, which is very close to the exact value, without leaking any additional privacy to the cloud. Our first solution is under a semihonest cloud server, and our enhanced solution introduced the consistency-check mechanism to achieve verifiability in malicious model. For efficiency, the first solution only need about 6 minutes for billion-element sets. Furthermore, as far as we know, the consistency-check mechanism is proposed for the first time to achieve an effective verifiable approximate similarity computation.