Reviewerly/exHarmony huggingface dataset:
Module Research of Blockchain-based Power Equipment Inspection and Data Asset Sharing

Using blockchain technology to realize equipment management and patrol inspection in power enterprise industry based on blockchain "non tamper ability, data uniqueness, smart contract and decentralization", At the same time, build the research on the mode of data asset sharing of power enterprises, and confirm the right of data assets and provide data sharing services for government departments, credit investigation departments, public security and law departments and environmental protection departments. On the one hand, based on the blockchain, the scientific equipment management and patrol inspection management within power enterprises are realized, and the data analysis is carried out through big data and artificial intelligence technology to improve the performance and utilization of equipment and reduce the carbon emission of power enterprises. On the other hand, build a social oriented data asset sharing platform for power enterprises, realize the intelligent service of power enterprise data, improve the internal value of data, and provide reliable data services for the government, society and other institutions and departments. In terms of system architecture, firstly, the hyperledger fabric based on Linux foundation is used to build the alliance chain, which can be initiated and managed by power enterprises. Power enterprises provide alliance chain management service nodes, including sorting nodes and Ca nodes. All participating institutions register nodes in the alliance chain provided by power enterprises and obtain and apply desensitization data through interface authorization. The application of the scheme will improve the equipment utilization rate of power enterprises, provide the sharing ability of data assets of power enterprises.
Privacy Enabled Dynamic Regimentation of Photo Posting on Online Social Networks

Security has become a major concern in today&#x2019;s IT scenario because of the surge in use of technology aids and wide usage of publically available social networks among people because of its economic viability. Due to these concerns, common users are facing lot of issues which has become a life threatening issue. Public in terms of application provider needs some advertisement business model to offer the application at free of cost, even though the application which are already available have some security features, they completely lag in preserving user privacy, and vice versa. The user of such applications are not aware of the privacy settings configured in the application. Apparently, when a user (X) of an application takes and shares picture from a public place (Museum), the photo may have other users (Y) image as well, without their (Y&#x2019;s) knowledge it may be available online. Hence, the basic idea behind the solution is to use some face recognition algorithm to identify the other (Y&#x2019;s) face in the current user (X&#x2019;s) picture and to intimate the other user (Y) about the occurrence of their picture. With this, the user can provide their decision on whether their picture could be shared or not so as to have control on their access privilege. The proposed approach solves the above problem and provides privacy in posting of photos in online sources. This provides an intimation to the users on their photo sharing.
A Technique For Analyzing Banking Transactions To Identify Fraudulent Activities In E-commerce

The article presents a technique for analyzing banking transactions using machine learning methods and graph algorithms to identify fraudulent activities in e-commerce. The first stage of the proposed technique involves training an ensemble of classifiers on a labeled transaction data set. The second stage of the methodology involves launching the Personalized Page Rank (PPR) algorithm on the constructed directed transaction graph to search for counterparties who interact with fraudulent or affected by fraudulent counterparties more than others. Interpreting the PPR results allows one to identify potential victims of fraudsters. An approach to identify fraudulent schemes for cashing out of funds through individual entrepreneurs is also proposed, based on the search in large graphs for sub graphs satisfying a given property. The application of the technique is illustrated on a publicly available dataset of transactions of European bank card holders. The estimates of the work of the classifiers (precision, completeness, F -measure) are given. To build a transaction graph, the dataset was artificially extended with the payer and recipient fields. The results of the search for potential victims of fraud using the PPR algorithm are discussed, as well as the approach for construction of queries in Apache Spark to search for cash-out chains of a given length.
A digital signal processor‐efficient accelerator for depthwise separable convolution

Recent researches on deep convolution neural networks have proposed some compact networks, such as MobileNet, but its main computation, depthwise separable convolution (DWC), which reduces the reusable data and improves the requirement of data loading efficiency. Although DWC can effectively reduce the amount of network computation, it needs a special accelerator to enhance the inference speed. This paper proposes a high-performance accelerator for DWC based on the commonly used acceleration platform field-programmable gate array. The proposed accelerator supports the computation of both standard convolutions (SCs) and DWC as well as two activation functions. In addition, two data storage formats are used to maintain the data loading efficiency for different input requirements of SC and DWC under high parallelism. Furthermore, a processing unit that can execute two 8 × 8-bit multiplications inside one digital signal processor (DSP) is designed to make the best use of the DSP hardware resources. Finally, the accelerator is implemented on ZYNQ ZC706 at 200 MHz. Consuming only 392 DSPs, the accelerator achieves 134.5 giga operations per second (GOPS) and 209.4 frames per second (FPS) on MobileNet V1 as well as 96.4 GOPS and 250.4 FPS on MobileNet V2. Experimental results demonstrate that this design provides a better DSP efficiency than previous works.
Dynamic hyperchaotic key generation using optical orthogonal frequency division multiplexing‐based visible light communication networks

This paper introduces an optical orthogonal frequency division multiplexing (OFDM)‐based hyperchaotic key generation encryption approach that can improve confidentiality in visible light communication (VLC) networks. Using a hyperchaotic four‐dimensional method, the bipolar real‐valued OFDM signal can be used for constructing dynamic cipher keys modified at every frame over the communication time, resulting in a superior degree of protection against malicious attacks. In accordance with our findings, this approach decreases the ratio of peak‐to‐average power of the transmitted signal, and enhances the bit error rate efficiency and secrecy capacity of the OFDM‐based VLC network, which improves confidentiality. © 2022 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.
Sentiment Analysis of Indonesian Government Policies In Handling Covid 19 Through Twitter Data

This paper presented a sentiment analysis of the Indonesian government&#x0027;s policies in overcoming Covid 19 through twitter data using several classification methods, namely SVM, Naive Bayes, and LSTM. Based on the analysis of the twitter data, it was found that the twitter community in Indonesia gave negative sentiments to government policies in handling Covid 19. From the experimental results, it was found that SVM gave the best sentiment results compared to Na&#x00EF;ve Bayes and LSTM by providing an accuracy of 88.5&#x0025;.
Improving Code Summarization Through Automated Quality Assurance

The code summarization task aims to generate brief descriptions of source code automatically. It is beneficial for developers to understand source code. However, almost all of current code summarization approaches may generate low-quality (BLEU4<40) summaries, which will mislead developers. Previous work has shown that it is possible to conduct quality assurance for document generation (QA4DG) and improve the practicability of document generation approaches. Code summarization can also be regarded as a document generation task. This work aims to investigate whether QA4DG approaches can be leveraged to improve code summarization. Specifically, we first investigate whether existing QA4DG approaches can be plugged in code summarization approaches. We find that an automated quality assurance framework for commit message generation named QACom performs best. In-spired by the idea behind QAcom, we propose an ensemble code summarization approach called Ensum. Precisely, given a code snippet, Ensum first uses current code summarization approaches to generate candidate summaries. Then, Ensum predicts the quality of each candidate summary using a collaborative filtering-based component and a retrieval-based component and selects the best candidate summary as the output. Experimental results on two public datasets show that Ensum outperforms three state-of-the-art single approaches and one ensemble approach for code summarization in terms of BLEU-4, METEOR, and ROUGE-L.
Towards a low-cost embedded vehicle counting system based on deep-learning for traffic management applications

This paper explores the feasibility of using a low-cost embedded system for real-time vehicle detection and counting through the use of deep neural networks. It compares the performance of two different object tracking methods, the Kalman filter with the Hungarian algorithm and the centroid tracking algorithm. The experimentation proved that the efficiency of the implemented algorithms was above the 92% and 98% for the centroid tracking algorithm and Kalman filter with the Hungarian algorithm, respectively. Also, the Kalman filter produced fewer errors overcoming the centroid tracking algorithm.
Study of Stochastic Approach Impact on Accuracy of Sequence Element Classification Task

In some tasks, it is necessary to represent a process as a sequence of stages that should be classified. It is important to get correct stage classes because it impacts the performance of the entire task solution. In this paper, I consider the problem of low classification accuracy in those cases when stages of different classes may be similar to each other. I consider the solution to the problem in the example of the abnormal human action recognition task. In the solution, an action as is presented a sequence of discrete states where every state is described by a human pose. Also, the action process is considered as a stochastic process. In this paper, I propose an approach, the main new idea of which is that stochastic data are used in addition to image data describing a human action for recognition. In this approach, a stochastic graph of discrete stages is used as auxiliary data to describe the action process. The graph is parameterized by stage transition probabilities. These probabilities are computed explicitly from the training data. The proposed approach was tested on synthetic data. As a baseline, I use the performance of the classifier trained on the visual data only. The study shows that the approach doesn&#x0027;t outperform the baseline in most cases. This shows that the way stochastic data is used in this work is inefficient. The application of this approach to solving real-life problems requires additional research. The results of the work will be used to find other ways to use stochastic information to improve human abnormal actions recognition.
Digital Market using Blockchain Technology in Cloud Environment

The majority organizations associate blockchain technology with the financial services manufacturing, it has concealed use within the manufacturing, government, healthcare, and education sectors as well. The Block chain is an unlock and dispersed ledger. The Blockchain contract between two or more parties is confirmable and everlasting documentation. The Proposed System is smart market system, in this system Producers can sell the product directly in the market under the municipal counsil supervision or through online using block chain technology in cloud environment. The planned work is to intend a decentralized cloud server. We are powerful authentication where the information regarding all the products is encrypted. Those who have registered in the system come to know the product details about selling and purchasing. A highly developed method in cryptography can be used to address the issue of confidentiality, and some system could even monitor how much contact persons have. The blockchain technology is united with cloud computing to offer the enlarged transparency and efficient market system. The proposed research work can apply merkle tree in the designed algorithm on smart contracts performance optimization and automatic repair. We acquire benefits of the scattered confirmation security of the Blockchain technology for financial transaction verification and influence smart contracts for protected service organization. In this system bitcoin, cryptocurrencies or any other money transfer mechanism is used for transaction. Without a third party dealings may get more profit for both sides. Different types of on-site circumstances are collected and checked by installing sensors that are IoT devices in different services, and on-site calculating devices are restricted slightly if required.
Economic Thinking of Blockchain

In the previous chapter, the exploration of value theory in digital economics was presented through a discussion of the value of blockchain. Indeed, we argue that the intrinsic logic of value networks is possessed by the trusted network qualities in the technology of blockchain. The logic of this discussion is not only because blockchain technology has the specificity of the Internet of Value, but also because the most important change brought about by blockchain technology with the development of the information economy refers to the replacement of the commercial contractual regime by a technological contractual regime in order to allow the fundamental logic of the digital economy to be changed.
Machine Learning

This last chapter discusses a relatively new method applied in atmospheric and climate science: machine learning. Put simply, machine learning refers to the use of algorithms allowing the computer to learn from the data and use this learning to identify patterns or draw inferences from the data. The chapter describes briefly the flavour of machine learning and discusses three main methods used in weather and climate, namely neural networks, self-organising maps and random forests. These algorithms can be used for various purposes, including finding structures in the data and making prediction.
Photovoltaic System Ensemble Prediction System

Solar energy is the major renewable energy in Gulf area. The yearly solar irradiance is one of the highest in the world with more than 2000kWh/m<sup>2</sup>. Consequently, nations in the Gulf area have intended their energy investing on solar energy aggregation, especially Photovoltaics (PV). Photovoltaics (PV) power output is tremendously contingent on environmental situations. PV generation power prediction models are important to study the effects of unreliable environmental circumstances and approve solar power converters&#x2019; optimum performance whilst meeting highest demand across numerous environmental circumstances. The ecological data which is examined and reviewed in this paper are air temperature, relative humidity, Photovoltaics (PV) surface temperature, irradiance, dust, wind speed, and output power. The model suggested in this paper adjusts and trains three prediction algorithms, including Artificial Neural Network (ANN), Multi-Variate (MV), and Support Vector Machine (SVM). The model exploits three well-known forecast algorithms and voting algorithm to determine the optimal likelihood of PV generation power. Additionally, the ensemble algorithm proves high forecast precision of the output power because of the ecological circumstances. The Mean Square Error (MSE) for the Artificial Neural network (ANN), Multivariate (MV), and Support Vector Machine (SVM) are 98, 81, and 82, respectively. In comparison, Mean Squared Error (MSE) of the voting algorithm is considerably lower which is just above 53. The anticipated PV power generation forecast algorithm establishes consistent result with respect to the environmental conditions in Qatar. This tool is likely to support in the design process of Photovoltaics (PV) plants design where energy generation is highly predictive using proposed voting algorithm.
Domain-invariant Feature Refinement Model for Generalizable Person Re-Identification

In normal generalization work, domain-variant information is always ignored by the most method. In this paper, we propose a domain-invariant feature refinement model to solve this issue, a two-branch framework for dealing with domain generalization problems, which uses the instance normalization layer to split the global feature into domain invariant feature and domain variant feature. This method can make effective use of Domain-invariant features and further optimize the performance of the task in person re-identification. This paper is also performed several ablation experiments to verify the improvement of model performance, Through the experimental results, we can see that Feature splitting is the key to the effectiveness of our method.
T-HIBE: A Novel Key Establishment Solution for Decentralized, Multi-Tenant IoT Systems

The Internet of Things (IoT) devices has evolved considerably in the past few years and is expected to grow exponentially in the next decade. This exponential growth makes key management in an IoT ecosystem very challenging. Traditional IoT systems are often centralized and grouped into an ecosystem. However, this type of centralized architecture is not always compatible with practical IoT deployments. This paper proposes T-HIBE, a secure key establishment and agreement solution for a decentralized multi-tenant IoT system with multiple security domains. T-HIBE relies on principles of identity-based cryptography for key transport between intra and inter-domain devices while avoiding the inherent key-escrow problem. Furthermore, we have demonstrated our proposed architecture on an ARM Cortex-M4 microcontroller and evaluated the performance to show that T-HIBE does not have a significant energy and performance cost.
Vessel Detection Based on Deep Learning Approach

An effective monitoring system to observe vessel activity is essential to provide accurate vessel position information regarding vessel activity and movement at all times. Triggered to support the current VMS and AIS monitoring systems, Vessels monitoring by applying object detection methods to find all objects of interest in an image has a chance to be implemented. This study presents a deep learning approach for processing remote sensing images to detect the presence of vessels utilizing the Faster R-CNN network as a backbone, with the extractor feature modified using the inception-v2 network. Our experiments reveal that our method yields promising results in reasonable accuracy in detecting and identifying vessels images. It achieves an accuracy of 94.4&#x0025; and 0.971 for the F1Score.
Author Matching Classification on a Highly Imbalanced Bibliographic Data using Cost-Sensitive Deep Neural Network

One of the stages before classifying the author matching is to combine the data, in this case the resulting data becomes highly imbalanced dataset, between the author who matches or the author who does not match. This paper presents a method to solve the highly imbalanced problem in author matching classification. The method used Cost-Sensitive Deep Neural Network (CSDNN). CSDNN will consider costs that vary from the type of data misclassification. As text feature similarity measures, we use cosine similarity. And we use Digital Bibliography & Library Project (DBLP) data as a dataset. The result is outstanding in terms of specificity 0.99, precision 0.95, recall 0.96, f1-score 0.96, and accuracy 0.99.
Distributed Storage System for Dispatch Information Supported by Blockchain Technology

Blockchain technology can provide a trusted, traceable and tamper-proof data storage model. This paper first analyzes the technical characteristics of blockchain and its application in information storage, and then analyzes the key technologies that need to be used in dispatch blockchain. According to the data types and characteristics in the dispatch system, by improving the storage scalability of the blockchain, the blockchain technology is applied to the dispatch work, and the data in the work process is stored on the chain to ensure the integrity of all dispatch information, Continuity and consistency, make the dispatch work process transparent, ensure that the data cannot be tampered with, and the work records can be traced and checked at any time, effectively promoting the traditional dispatch operation mode to a comprehensive, intelligent and, improve the efficiency of collaborative work and ensure the safety of power grid operation.
High-Frequency Trading and the Material Political Economy of Finance

Over the last three decades, most face-to-face financial markets have shifted to electronic trading. Since around 2000, many markets have made the further transition to automated trading, including ultrafast, algorithmic high-frequency trading, or HFT. This chapter applies a perspective that the author calls “material political economy” to HFT, focusing on (a) the “signals” (patterns of data) that inform how HFT algorithms trade, (b) algorithms that usually “make” or “provide liquidity” (i.e., add orders to exchanges’ electronic order books that other algorithms or human beings can execute against), (c) algorithms that “take liquidity” (execute against orders already in order books), and (d) how HFT algorithms interact and exchanges’ material interventions in that interaction. The chapter examines how material features of HFT, its “signals,” and the technical systems that make HFT possible interweave in complex ways with power, politics, and finance’s mundane moneymaking.