Reviewerly/exHarmony huggingface dataset:
Research and implementation of a traceability algorithm for electric distribution data resources considering distributed new energy

The traceability and analysis of power distribution business data depend on the accurate description of the correlation network of power distribution data resources. In response to the above requirements, this paper first builds a multi-source heterogeneous power distribution and distributed new energy data flow topology network. Aiming at the complex relationship between power distribution and data resources and the flow characteristics of data, topology network construction technology is used to build bus type, star type, ring type, tree type, mesh and other topology networks according to the data association characteristics to achieve accurate description of the data association network. Then, based on the data association relationship network of power distribution and distributed energy, the data traceability must be calculated, and the traceability analysis can only be carried out if there is connectivity between the data. In response to the above requirements, the research on connectivity analysis algorithm based on power distribution and distributed new energy data network was carried out. By solving the connection path between the data nodes, the connectivity analysis requirements of the data nodes in the directed connected network and the directed connected network can be met. The data traceability of power distribution and distributed new energy is to trace the source of data in all business links, which plays an important role in the safe production, problem tracing, root cause analysis and other businesses of power distribution. In response to the above requirements, carry out research on tracing technology of allocated electricity and distributed new energy
Implicit sentiment analysis based on affective knowledge and event information

In recent years, implicit sentiment analysis, which aims to detect sentiment of a sentence that don’t contain obvious sentiment words, has become an attractive research topic. This paper focuses on event-centric implicit sentiment analysis, which utilizes the sentiment-aware event to infer the sentiment polarity of the sentence. Existing event-based implicit sentiment analysis methods typically treat entities or noun phrases in the text as events, or model contextual information to indirectly infer events using sophisticated models, but these methods fail to fully capture event information. To address these issues, this paper defines events as &lt;subject, predicate, object&gt;. Based on this event representation, neural tensor network was used to model the interaction between event elements and extract high-level semantic features of events. In addition, a novel affective enhanced graph model was proposed to capture sentiment-related dependencies between context words. Furthermore, this paper considers the case where a sentence contains multiple events, and constructs an event-centric implicit sentiment analysis dataset, where each sentence contains at least one event triplet. Experimental results on the constructed dataset demonstrate the effectiveness of our proposed approach.
PENGEMBANGAN BUKU AJAR MENULIS CERPEN BERBASIS KARAKTERISTIK BUDAYA LOKAL DENGAN TEKNIK MENERUSKAN CERITA UNTUK SMK KABUPATEN GROBOGAN

AbstracTraditional learning activities of writing simple essays do not provide instructions to make it easier for students to write essays. As a result, students clash in writing down the material that comes to mind, causing frustration and considering learning boring. The purpose of this study is to develop a textbook on how to write short stories that meet the needs of students with storytelling skills based on local cultural characteristics. The research was conducted in the following phases using modified Borg and Gal research and development methods. 1) We collect information. 2) planning; 3) initial product development; 4) the first experiment. 5) Product Revision.
Impacts of COVID-19 on the mental health of health professionals: An integrative review

Objective: To analyze the scientific evidence regarding the impacts of COVID-19 on the mental health of frontline healthcare workers. Methodology: integrative review study, which had as eligibility criteria: primary research, published between December / 2019 to October / 2020, without language restriction and that presented information on the subject. The research question was formulated following the assumptions of the acronym PICo, generating the question: What is the scientific evidence about the impacts on the mental health of health professionals who are on the front lines of the fight against COVID-19? The bibliographic survey took place in the following databases: MEDLINE via PubMed, CINAHL, EMBASE, SCOPUS, WOS, IBECS, LILACS, and BDENF via VHL. Results: 10 studies were analyzed, identifying the prevalence of studies published in the MEDLINE database, with the country of origin in China, cross-sectional studies, and having as participants doctors and nurses. The main impacts observed on the mental health of the professionals were: insomnia, depression, anxiety, fear, and stress, and these changes were more prevalent in females. Conclusion: the mental illness of health professionals during the pandemic reveals the need to strengthen strategies and actions that promote the mental health of these workers in the workplace, especially in periods such as these.
Confidentiality, integrity, authentication (CIA) model for secured storage and processing of personal health records using Merkle hash tree and recurrent neural networks (RNN)

Healthcare is a major sector generating huge amount of data rapidly. It requires a large storage space for storing the personal health records (PHR), thereby insisting the use of the storage services of third-party cloud providers. But in cloud, the security of PHR is compromised since the user is not having direct control over the data. To handle this issue, we aim at proposing novel Confidentiality, Integrity, Authentication model (CIA model) to ensure the security of stored data in cloud. Here, confidentiality of data is achieved by encrypting the PHR using the 2-Keys Symmetric Encryption Algorithm. Data integrity is ensured by using Merkle hash tree (MHT). Authentication is done by using fingerprint because of its consistency and uniqueness. PHR can be retrieved only by the authenticated users. For authenticating the user, a signature is generated from the hash values of the user&apos;s fingerprint by using the MHT and is submitted to the cloud providers during registration. And for accessing the data, the user needs to submit one leaf of MHT. From the leaf, the provider is able to generate the signature of the user. So, it is not required to submit the fingerprint in its entirety, thereby reducing the misuse of fingerprint of the user. For data representation and retrieval, recurrent neural network (RNN) deep learning technique is used. The cloud environment is set up with OpenNebula cloud and the algorithms for confidentiality, integrity, and authentication algorithms are implemented in java. Cryptanalysis against different cryptographic attacks prove that the proposed method is contributing a secure and proficient mechanism for storing health information. After comparing the encryption time of our system with the standard encryption algorithms, it is inferred that this CIA model incurs less overhead for encryption. And also, better performance has been observed on the factors like error rate of 2% and accuracy rate of 98%, minimum overhead on computation, communication, and storage costs.
Governance, regulatory quality and financial institutions: emerging economies perspective

Purpose The study evaluates the effects of governance and other regulatory structures on the development of financial institutions in the subregion of sub-Saharan Africa (SSA). Design/methodology/approach Data for the analyses were compiled from relevant sources from 1996 to 2019 from a sample of 36 countries in the subregion. Empirical analyses were carried out using the Prais-Winsten panel corrected standard errors panel estimation technique augmented by pooled ordinary least squares with Driscoll and Kraay (1998) standard errors model. Findings Findings from the study suggest that governance and institutional quality index, as well as individual governance and regulatory variables, have positive effect on the development of financial institutions among economies in SSA. Further empirical estimates show that output growth volatility has negative moderating impact on the relationship between effective governance, control of corruption, rule of law, regulatory quality, voice and accountability, and development of financial institutions. Additionally, the results show that during periods of heightened macroeconomic risk, financial institutions could benefit from improved governance and effective regulatory structures. Originality/value Compared to related studies that have reviewed the discourse on financial institutions, this study rather focuses on how governance structures and institutions influence development of financial institutions instead of the impact of financial institution on the broader economy. The authors further augment this interaction by examining how the relationship in question may be moderated by macroeconomic shocks.
Sketching the vision of the Web of Debates

The exchange of comments, opinions, and arguments in blogs, forums, social media, wikis, and review websites has transformed the Web into a modern agora, a virtual place where all types of debates take place. This wealth of information remains mostly unexploited: due to its textual form, such information is difficult to automatically process and analyse in order to validate, evaluate, compare, combine with other types of information and make it actionable. Recent research in Machine Learning, Natural Language Processing, and Computational Argumentation has provided some solutions, which still cannot fully capture important aspects of online debates, such as various forms of unsound reasoning, arguments that do not follow a standard structure, information that is not explicitly expressed, and non-logical argumentation methods. Tackling these challenges would give immense added-value, as it would allow searching for, navigating through and analyzing online opinions and arguments, obtaining a better picture of the various debates for a well-intentioned user. Ultimately, it may lead to increased participation of Web users in democratic, dialogical interchange of arguments, more informed decisions by professionals and decision-makers, as well as to an easier identification of biased, misleading, or deceptive arguments. This paper presents the vision of the Web of Debates, a more human-centered version of the Web, which aims to unlock the potential of the abundance of argumentative information that currently exists online, offering its users a new generation of argument-based web services and tools that are tailored to their real needs.
Method of Facial De-identification Using Machine Learning in Real-Time Video

There is a growing trend of individuals or small teams creating and broadcasting personal streaming content through content creation, editing, and live streaming. However, there is a concern about inadvertently exposing the faces of non-public figures. In order to prevent the exposure of the faces of non-public figures, manual de-identification has been performed through editing rather than real-time video. This method is prone to mistakes and consumes a significant amount of time, and there is no way to prevent face exposure in real-time broadcasts. Therefore, this paper proposes an efficient real-time de-identification technique based on Facial Landmark that can solve the problem of loss of valid information in the video without compromising the quality of the video. The proposed technique detects only the facial landmark areas of people in the video in real-time and de-identifies them. It has been shown to achieve an average object recognition rate of 95.52% and can additionally secure valid information in the video by an average of 1.2–2.2%.
Blockchain technology integrity in health informatics using hyper ledger platform

Blockchain (BC) is an unchangeable ledger that stores data in blocks in a decentralized manner. This technology can benefit any industry that generates and transfers data. The advanced hyper ledger fabric is made up on the BC network and infrastructure/platform, offering decentralized management, robustness, security, and privacy. The hyper ledger explorer is a block-chain module that displays blocks, transactions, as well as any other relevant data that has been stored in the ledger. This state-of-the-art technology has the potential to upend a variety of data-driven industries, including healthcare. This work aims to evaluate the existing technology and proposed the novel frame work using blockchain technology to improve processes in health informatics. However, BC is potentially viable in health informatics applications. Currently, BC is not widely used in the healthcare business. This chapter focuses on the proposed conceptual frameworks and implementation of BC technologies with IoT using hyper ledger on potential applications in healthcare. A novel framework based on the most popular Ethereum and hyper ledger of fabric platforms/frameworks in this domain was proposed. In addition, the present status of IoT-based healthcare models in the domain, as well as an analysis of IoT and BC applications in healthcare is also discussed. IoT and BCT are used to improve medical image quality and storage or transmission for healthcare application challenges and issues. Medical image diagnosis benefits from the current future direction in this domain. Overall, the use of BCT in the healthcare domain is growing at an exponential rate. There are some areas of the healthcare domain where BCT has the potential to have a significant impact.
Modelling and performance analysis of tracked vehicle with coupled pump-motor steering system based on MATLAB

Aiming at the tracked vehicle with coupled pump-motor hydraulic steering system, the vehicle system coupling model based on MATLAB/Simulink was established under the braking, steering and straight driving scenario. The mathematical model of hydraulic steering regulation system with variable pump and quantitative motor is established. After analyzing the kinematics and dynamics of the tracked vehicle, the vehicle system coupling model under the above working scenario was established. The main performance parameters of the vehicle, such as track speed, steering radius, steering driving force and steering high pressure, are studied under the conditions of different variable pump swash plate inclination angle (different pump displacement), different vehicle braking force and different engine speed. The stability of the combined pump-motor hydraulic steering system is evaluated, and the correctness of the established model is verified, which has certain theoretical guiding significance for the research of the steering control strategy of tracked vehicles.
Research on a smart home system based on short-range wireless communication technology

With the development of economy and the continuous progress of science and technology, people are increasingly pursuing a highly safe and comfortable living environment and intelligent and diversified information services. Smart home system is generally based on old and new houses, and through a series of wireless wired communication technology, network integration technology and automation application technology, a family management system composed of environmental data collection, terminal control of household appliances, security alarm and other related aspects is established, thus providing a highly integrated and comfortable home living space. In line with this trend, we designed DCA-3000 smart home system, which is suitable for the middle and high-end market. This system can realize the functions of home-based security and remote meter reading. However, the Zigbee communication module, a data acquisition sub-node, adopted at that time, has high power consumption and no sleep function, so it is difficult to supply power with batteries for a long time. On the basis of analysis and design, a temperature acquisition subsystem of smart home wireless network based on ZigBee technology is implemented. Through the implementation of the subsystem and relevant experimental tests, the feasibility of the overall scheme design of the smart home system in this paper is verified. The network composition based on ZigBee technology can meet the requirements of the smart home system.
Research on the influence of artificial intelligence technology based on big data analysis on modern art creation

Today's society is in the era of rapid development of science and technology. With the continuous updating and iteration of artificial intelligence science, visual art has ushered in a new chapter of digitalization and intelligence with the help of powerful computer and network technology. Modern painting is a kind of painting form that gradually matures in the period of extreme development of industrial society. The anti-aesthetic trend of thought embodied in modern painting is a recognized judgment of modern painting. Therefore, this paper will focus on the possibility of diversified expression of visual arts with the help of artificial intelligence related technologies. The art talent education system, newspaper and magazine communication system, art association system, and art criticism system possessed by modern Chinese art all constitute an important part of the art system. The guidance and restriction of the art system enable Chinese art to transcend the imagination and monologue of the individual soul in literati paintings, move towards the value orientation of life and socialization, and form a new realistic tradition of realism, life and popularity. This paper selects a technical branch of the field of artificial intelligence - the field of deep learning. Combining with the manifestation of visual art, it explores the characteristics of intelligent visual art in the context of deep learning, finds its advantages that can be combined with artistic expression, and creates a new form of visual art creation expression based on deep learning.
Hardware feature extraction technology for resource aggregation control communication channel and signal source

In the distributed resource access scenario, the growing number of terminal devices brings pressure and test to the wireless communication network. Unauthenticated terminal access to the network will bring huge security risks. This paper focuses on the hardware features of resource aggregation control communication channel and signal source. For the distributed power system with complex environment, this paper proposes a hardware feature extraction technology for resource aggregation-oriented control communication channel and signal source. On the basis of the research on the theory of generic feature extraction of hardware fingerprint, the specific feature extraction methods of several common communication methods in typical distributed resource aggregation and control scenarios are studied. This paper designs a hardware fingerprint implementation scheme in a typical distributed resource aggregation and control scenario. Experiments show that the proposed fingerprint extraction scheme can effectively distinguish different devices according to different characteristics of devices. It provides a terminal access method with high security and low complexity in the distributed edge aggregation control network of power system.
Design, implementation, and automation of a risk management approach for man-at-the-End software protection

The last years have seen an increase in Man-at-the-End (MATE) attacks against software applications, both in number and severity. However, software protection, which aims at mitigating MATE attacks, is dominated by fuzzy concepts and security-through-obscurity. This paper presents a rationale for adopting and standardizing the protection of software as a risk management process according to the NIST SP800-39 approach. We examine the relevant constructs, models, and methods needed for formalizing and automating the activities in this process in the context of MATE software protection. We highlight the open issues that the research community still has to address. We discuss the benefits that such an approach can bring to all stakeholders. In addition, we present a Proof of Concept (PoC) decision support system that instantiates many of the discussed construct, models, and methods and automates many activities in the risk analysis methodology for the protection of software. Despite being a prototype, the PoC’s validation with industry experts indicated that several aspects of the proposed risk management process can already be formalized and automated with our existing toolbox and that it can actually assist decision making in industrially relevant settings.
A real-time collision-free maneuver generation algorithm for autonomous driving

In this paper we propose a real-time maneuver generation algorithm for Autonomous Vehicles (AVs). Given a planar road geometry with static and moving obstacles along it, we are interested in finding collision-free maneuvers that satisfied the AV’s dynamics and subject to physical and comfort limits. Based on longitudinal and transverse coordinates, we propose a novel collision avoidance constraint and formulate a suitable maneuver regulation optimal control problem. Maneuver regulation has intrinsic robustness with respect to standard trajectory tracking that stems from the requirement of following a desired path with a desired velocity profile assigned on it. The optimization problem is solved by using a nonlinear optimal control technique that generates (local) optimal trajectories. We demonstrate the efficacy of the proposed algorithm by providing numerical computations on two different scenarios. Finally, experimental results are presented to demonstrate the efficiency of the proposed algorithm both in terms of computational effort and dynamic features captured.
Improving knowledge tracing via a heterogeneous information network enhanced by student interactions

Knowledge tracing (KT) aims to evaluate the knowledge state of students based on their coursework interactions in an intelligent tutoring system (ITS). It is the most fundamental and challenging task in the system. Existing KT models have achieved promising results. However, considerable interactions between the student entity and the other two types of entities (i.e., questions and skills) in ITS have not been well utilized, which restricts the performance of these models. To this end, we propose HIN-KT, a novel heterogeneous information network (HIN)-based pre-processing model, to further enhance the performance of existing KT models. An HIN is first built to model student–question–skill interactions. Subsequently, HIN-KT employs the interaction information modeled by HIN to pre-train embeddings for questions and then adopts pre-trained question embeddings to enhance the performance of recent deep KT models. Experimental results over three public KT datasets demonstrate that large gains in knowledge tracing can be achieved when the proposed HIN-KT model is used to pre-train question embeddings for state-of-the-art deep KT models. In particular, the two state-of-the-art deep KT models, deep knowledge tracing (DKT) and convolutional knowledge tracing (CKT) successfully improved their performance by an average of 7.88% with the help of the HIN-KT model, which is significant progress made in the knowledge tracing domain.