Reviewerly/exHarmony huggingface dataset:
Design of High-Performance Computing System for Big Data Analytics

The design of a high-performance computing (HPC) system for big data analytics involves selecting hardware and software components that can handle the massive amounts of data generated by big data applications, while ensuring efficient processing and storage of the data.The hardware components of an HPC system typically include high-end servers with powerful processors, large amounts of memory, and high-speed storage devices such as solid-state drives (SSDs) or hard disk drives (HDDs) configured in a parallel or distributed architecture.In addition, specialized hardware such as graphical processing units (GPUs) or field-programmable gate arrays (FPGAs) can be used to accelerate the processing of certain types of data.The software components of an HPC system include the operating system, middleware, and applications.The operating system should be optimized for HPC workloads, with low overhead and high scalability.Middleware such as MPI (Message Passing Interface) can be used to facilitate communication between nodes in a distributed computing environment.Finally, applications should be designed to take advantage of the parallel and distributed processing capabilities of the HPC system, with efficient algorithms and optimized data structures.This paper proposes a design of highperformance computing system for Big Data Analytics.
Hierarchical federated learning algorithm based on efficient aggregation mechanism

The existing federated learning algorithm is affected by the heterogeneity of multiple clients and the frequent information interactions between clients and servers, resulting in low training efficiency. Therefore, the edge servers are introduced to the client and the center server in this paper, in order to construct a hierarchical federated learning framework that can effectively reduce the frequency and cost of communication between the client and the center server, which also improves the communication efficiency. At the same time, an efficient time-based hierarchical aggregation mechanism is designed for the hierarchical federated learning framework. The edge server receives local models within a specified period of time and achieves dynamic weighted aggregation according to the obsolescence of the local models obtained in each round, so as to improve the overall training efficiency and ensure the accuracy of the models. In experiments, it is shown that compared with the existing federated learning framework, the hierarchical federated learning framework based on efficient aggregation mechanism proposed in this paper can significantly improve the training efficiency on both MNIST and CIFAR-10 datasets.
Sub-Planck structures and sensitivity of the superposed photon-added or photon-subtracted squeezed-vacuum states

The Wigner function of the compass state (a superposition of four coherent states) develops phase-space structures of dimension much less than the Planck scale $\ensuremath{\hbar}$, which are crucial in determining the sensitivity of these states to phase-space displacements. In the present work we introduce compasslike states that may have connection to the contemporary experiments, which are obtained by either adding photons to or subtracting photons from the superposition of two squeezed-vacuum states. We show that when a significant quantity of photons is added (or subtracted), the Wigner functions of these states are shown to have phase-space structures of an area that is substantially smaller than the Planck scale. In addition, these states exhibit sensitivity to displacements that is much higher than the standard quantum limit. Finally, we show that both the size of the sub-Planck structures and the sensitivity of our states are strongly influenced by the average photon number, with the photon-addition case having a higher average photon number leading to the smaller sub-Planck structures and, consequently, being more sensitive to displacement than the photon-subtraction case. Our states offer unprecedented resolution to the external perturbations, making them suitable for quantum sensing applications.
Particle swarm optimization based on simulated annealing rules

Aiming at the problems of traditional particle swarm optimization (PSO) algorithm such as slow convergence speed and easy to fall into local optimum, this paper proposes to use the core idea of simulated annealing algorithm to improve the particle swarm optimization algorithm. First, the Metropolis criterion is used to select the probability of inertia weight, and for each particle in each iteration, the probability of inertia weight is selected to balance the search ability of particles; Secondly, by analyzing the information exchange of the optimal solution between each iteration, the mutation particles are constructed, and the position information of the next generation of global learning particles is determined through probability selection, which can effectively prevent the particle population from falling into the local optimal region. The simulation results show that the PSO algorithm based on simulated annealing rules has higher convergence accuracy and speed compared with other test algorithms, and can effectively improve the ability of PSO to find the optimal solution.
Big Data Provenance Using Blockchain for Qualitative Analytics via Machine Learning&amp;nbsp;

The amount of data is increasing rapidly as more and more devices are being linked to the Internet. Big data has a variety of uses and benefits, but it also has numerous challenges associated with it that are required to be resolved to raise the caliber of available services, including data integrity and security, analytics, acumen, and organization of Big data. While actively seeking the best way to manage, systemize, integrate, and affix Big data, we concluded that blockchain methodology contributes significantly. Its presented approaches for decentralized data management, digital property reconciliation, and internet of things data interchange have a massive impact on how Big data will advance. Unauthorized access to the data is very challenging due to the ciphered and decentralized data preservation in the blockchain network. This paper proposes insights related to specific Big data applications that can be analyzed by machine learning algorithms, driven by data provenance, and coupled with blockchain technology to increase data trustworthiness by giving interference-resistant information associated with the lineage and chronology of data records. The scenario of record tampering and big data provenance has been illustrated here using a diabetes prediction. The study carries out an empirical analysis on hundreds of patient records to perform the evaluation and to observe the impact of tampered records on big data analysis i.e diabetes model prediction. Through our experimentation, we may infer that under our blockchain-based system the unchangeable and tamper-proof metadata connected to the source and evolution of records produced verifiability to acquired data and thus high accuracy to our diabetes prediction model.&amp;nbsp;
The Fractional W Transform

Summary We have developed a novel fractional-order W transform, which is a generalization of the standard W transform. The fractional-order W transform combines the merits of the fractional-order Fourier transform and the W transform. The introduction of parameters introduces rotation of the time-frequency plane. The optimal choice of the fractional parameter is based on the maximum of kurtosis and the choice of and are based on maximum concentration measure. In the manuscript, the inverse frational-order W transform is also derived in the manuscript. Numerical experiments on synthetic and field seismic data indicates the validity and effectiveness of the proposed algorithm.
CNN segmentation as a basis for the processing of the cuttings data

Summary The project aims to create an integrated machine learning (ML) workflow for the cuttings data at Norwegian Continental Shelf, recently made available in the RWI-project. The algorithms and the data are hosted by Glex. The RWI database covers 1900+ wells, with hundreds of samples per well. There are white and UV light high-res photos of every cutting sample, along with chemical and mineral composition data. The format is unified, the data is geo-tagged by its nature, and can be combined with other data from the shelf, available in the DISKOS database. The cuttings data contain unprecedented amount of digital information about the subsurface and is perfectly suited for (multi-stage) ML-analysis. The tasks may range from pattern recognition in the sample photos, to both targeted and regional mineral prediction trained on pre-labelled data. Regional mineral search, including recently emerged seabed mineral exploration, search for new plays, petrophysical analysis – these are just a few areas where the findings could be directly applied. We will briefly discuss motivation for our ML workflow, provide the project updates, and demonstrate examples from exploration. Preliminary results were reported at DIGEX 2022 conference last year.
DDS-Based Information Interaction System for Ship Cloud Storage Data Center

As ship information network systems continue to increase in payload scale, problems such as difficult information storage and low interaction rate have become increasingly prominent. To address this, this paper proposes a distributed system for a ship cloud storage data center based on DDS, with a focus on system architecture and information interaction flow. A simulation platform for the warship system is built, and different types of information are tested to assess the information interaction and storage capabilities of the system. The experimental results demonstrate that DDS has good applicability in the distributed system of a ship cloud storage data center and has significant advantages in reliability.
FPGA based Vending Machine For Logical Gates

Recently we have seen many Vending machines dispense things like toys, chocolates, shakes, snacks, lottery tickets, etc. Vending machines automatically dispense the different products when a consumer puts a currency or colored or different size token. The requirements for modern vending machines are increasing rapidly due to their ease of use. This research work intends to design a vending machine to sell eight different integrated circuits (ICs). Users can select the desired product and quantity of the product while inserting the currency. The proposed model also shows the available stock and total amount for the entered product. Here, FPGA is used to design the proposed Vending Machine (VM) as FPGAs are more flexible than embedded systems. The proposed vending machine is mainly used to vend the logical gates. It has better advantages as the quantity and amount can be entered according to the user requirements. The developed vending machine can be used in schools, research laboratories etc. FPGAs can be reprogrammed any number of times, consume less power and work faster than CMOS based Vending machines. Finally, the proposed design is simulated using Xilinx 14.7 and then implemented using FPGA kit artic 7.
Twitter Sentiment Analysis for Bitcoin Price Prediction

Cryptocurrencies, like Bitcoin, have become increasingly popular over the last decade. The price of Bitcoin has gone through several cycles of highs and lows. As a result, it is a widely discussed topic, especially on platforms like Twitter. Sentiment analysis is a research area of Natural Language Processing. It is used to determine whether the text is positive, negative, or neutral. Twitter tweets are more challenging to analyze when compared to other forms of text, due to the presence of irregular grammar, emoticons, and sarcasm. This study intends to analyze the effect of tweets on the stock price of Bitcoin. In order to study the effect, the sentiment associated with each tweet is calculated using VADER, and also the profession and follower coun <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t</sup> associated with verified users who tweet about bitcoin is found. Following this, a model is trained and tested using a combined dataset of tweet related data and historical bitcoin price data. It was found that the sentiment of tweets does correlate with the shift in the price of bitcoin.
Machine Learning Prospects: Insights for Social Media Data Mining and Analytics

Social network has increased surprising consideration in the most recent decade. Social network deals with enormous volume of composite as well as unstructured data and they are very hard to handle. Due to expanding dimensions and demand, one of the encouraging and interesting research field becomes social network. Data Mining affirms to get knowledge by discovery patterns among data. We have discussed social media mining and Social Media analytics. We have insights on the social media effect of our lives, some facts and reports from various sources. We have Integrated this growing research field of social networks with Machine Learning with one simple example of sentiment analysis of Twitter data using Machine Learning. We have also proposed the algorithms to improve the social media analytics results using Machine Learning. In this paper, we will exhibit how machine learning will utilizing for social networking systems like Twitter. In this procedure, a framework is proposed that will collect the tweets messages from the and we will inspect the item’s input to show the positive, negative, or nonpartisan tweets, for this this purpose we have proposed new machine learning algorithms Naive Bayes, maximum entropy to find these outputs. Our proposed Model will help new researchers, companies, Industries, business community, practitioners, new integrated application designers, and the global community to solve the new research problem and may reducing design failure rate of 80% by large through social media mining and networks.
PENGARUH DISIPLIN DAN MOTIVASI BELAJAR TERHADAP HASIL BELAJAR PADA MATA PELAJARAN EKONOMI KELAS XII IPS SMA NEGERI 2 MUARO JAMBI

This study aims to find out the effect of discipline and learning motivation on learning outcomes in economics class XII IPS SMA Negeri 2 Muaro Jambi. This research is a quantitative study, the total population of this study were 123 students and the number of samples was 95 students. With a significant level of 5%. Selection of this sample using Proportional Random Sampling Technique. Data collected using questionnaires or questionnaires and documentation. The method used is descriptive analysis and multiple regression analysis. The results showed from the test of the coefficient of determination, it can be seen from the R Square table of 0.708, which means that learning outcomes are influenced by learning discipline and learning motivation by 0.708 or 70.8% while the remaining 29.2% is influenced by other variables not examined in this study. Based on table 4.13 above, it is known that the sig value is 0.024 &lt;0.05 and it can also be seen that the calculated F value of 3.906 is greater than the F table value of 3.09. Then H0 is rejected and Ha is accepted, which means that learning discipline and learning motivation simultaneously have a significant effect on learning outcomes. Keywords : Discipline, Learning Motivation, Learning Outcomes
The Utilization of Gamification of Hijaiyyah Letters as an Effort to Improve the Reading Ability of Elementary School Students

As an effort to print a generation of Muslims who have an insight into the Qur'ran, educating starting from the age of children so as to instill a high love of Al-Qur'an and try to learn it well.The Qur'an as the holy book of the Muslim converts acts as a source by of law in earth, the basis of from science and as a benchmark in shaping behavior children, through learning the basics of the Qur'an such as reading and write it down, children recognize the good values contained in it Al-Qur'an.In teaching reading the Qur'an it is necessary to have the right media, so that students easily understand the lesson and with this media students are expected to be able to improve learning outcomes.In teaching reading The Qur'an has many learning media that we can apply.Gamification is also defined as the process of improving services with affordability for a pleasant experience.The most important application of gamification elements during learning is the accuracy of the concept, clear and constructive goals for students, and can attract students' interest in learning during the learning process.The problem want be discussed in this studying for is how to improve the ability and by to read the Qur'an in elementary school for the students class through gamification-based android learning media.The purpose of this research is to knowing the ability to read the Qur'an the ability to read the Qur'an in elementary school students through output gamification-based android learning media.
AgBFPN: Attention Guided Bidirectional Feature Pyramid Network for Object Detection

Object detection is increasingly in demand in IoT service applications. Deep learning based object detection algorithms are now in fashion. As the most popular multi-scale object detection network at present, Feature Pyramid Network achieves feature augmentation by fusing features of neighboring layers. It is widely used in the most advanced object detectors to detect objects of different scales. In this paper, we propose a new attention mechanism guided bidirectional feature pyramid architecture named AgBFPN to enhance the transfer of semantic and spatial information between each feature map. We design Channel Attention Guided Fusion(CAGF) Module and Spatial Attention Guided Fusion(SAGF) Module to enhance feature fusion. The CAGF mitigates the loss of information induced by channel reduction and better transfers the semantic information from high-level to low-level features. The SAGF passes the rich spatial information of shallow features into deep features. Our experiments show that AgBFPN achieves higher Average Precision for multi-scale object detection.
Ontology-based Framework for Boundary Verification of Safety and Security Properties in Industrial Control Systems

As part of Industrial Control Systems (ICS), the control logic controls the physical processes of critical infrastructures such as power plants and water and gas distribution. The Programmable Logic Controller (PLC) commonly manages these processes through actuators based on information received from sensor readings. Therefore, boundary checking is essential in ICS because sensor readings and actuator values must be within the safe range to ensure safe and secure ICS operation. In this paper, we propose an ontology-based approach to provide the knowledge required to verify the boundaries of ICS components with respect to their safety and security specifications. For the proof of concept, the formal model of the Programmable Logic Controller (PLC) is created in UPPAAL and validated in UPPAAL-API. Then, the proposed boundary verification algorithm is used to import the required information from the safety/security ontology
Agent-Based Medical Image Processing Using Multi-stage Distributed Neural Network

In this paper, we describe possible applications of early exit deep neural networks in magnetic resonance imaging, aiming to improve patient scan times and reduce processing costs. The solutions rely on deep neural network layers deployed over a hierarchical processing environment of embedded, edge-based and shared cloud-based resources. Coordination among processing environment layers is achieved by representing different processing resources as agents, effectively forming a multi-agent system. The described approach combines the least-required processing property of the early exit deep neural network and the resource usage optimisation of the multi-agent system in the dynamic and open environment.
Anonymous (Hierarchical) Identity-Based Encryption from Broader Assumptions

Döttling and Garg (CRYPTO 2017) introduced a non-black-box approach to identity-based encryption (IBE). This paves the way for the first and still the only anonymous IBE (AIBE) scheme from the computational Diffie–Hellman (CDH) assumption of Brakerski et al. (EUROCRYPT 2018). This paper revisits the blinding technique of Brakerski et al. and introduces a suite of blind primitives, extending chameleon encryption, hash encryption, and one-time signature with encryption. Using them, we propose an AIBE scheme from CDH with improved efficiency compared to Brakerski et al., especially in the decryption time. We also propose the first anonymous hierarchical IBE (AHIBE) scheme from CDH and the first AIBE and AHIBE schemes from the $$\phi $$ -hiding assumption, with similar efficiency as their non-anonymous counterparts.
UPAYA MENINGKATKAN MOTIVASI BELAJAR DAN PEMAHAMAN KONSEP IPS SISWA MELALUI MEDIA GAME INTERAKTIF WORDWALL

&#x0D; In the era of the 4.0 revolution, technology is developing very rapidly, so it is necessary for a teacher to create creative, innovative and fun learning media. This study aims to increase students' learning motivation and understanding of social studies concepts by using wordwall interactive game media. The research used was classroom action research, using three cycles, namely cycle I, cycle II, and cycle III and involving 6 Class V students at SDN Ngaglik 2 as objects. The data needed are the results of observations, interviews, questionnaires, and documentation such as documents of student learning outcomes. The results of this study prove that using wordwall interactive game media can increase students' learning motivation and understanding of social studies concepts. This can be proven by the learning outcomes obtained by students in social studies subject matter of economic activity which increases in each cycle. This research can also assist teachers in improving the quality of education in schools and increasing teacher competence in creating learning media, besides that teachers can also apply the wordwall interactive game media as an innovative medium for use in the learning process.
A Rule-Based Predictive Model for Estimating Human Impact Data in Natural Onset Disasters—The Case of a PRED Model

Background: This paper proposes a framework to cope with the lack of data at the time of a disaster by employing predictive models. The framework can be used for disaster human impact assessment based on the socio-economic characteristics of the affected countries. Methods: A panel data of 4252 natural onset disasters between 1980 to 2020 is processed through concept drift phenomenon and rule-based classifiers, namely the Moving Average (MA). Results: Predictive model for Estimating Data (PRED) is developed as a decision-making platform based on the Disaster Severity Analysis (DSA) Technique. Conclusions: comparison with the real data shows that the platform can predict the human impact of a disaster (fatality, injured, homeless) with up to 3% error; thus, it is able to inform the selection of disaster relief partners for various disaster scenarios.