Reviewerly/exHarmony huggingface dataset:
Deep Learning-Based Optimization Algorithm for Enterprise Personnel Identity Authentication

Enterprise strategic management is not only an important part of enterprise work, but also an important factor to deepen the reform of management system and promote the centralized and unified management of enterprises. Enterprise strategic management is to study the major problems of survival and development of enterprises in the competitive environment from the overall and long-term point of view. It is the most important function of senior leaders of modern enterprises. Starting from the characteristics of the recognition object, this paper analyzes the individual differences of biometrics through intelligent face image recognition technology to identify biometrics, which can be used to identify different individuals. This paper studies the main problems of personnel identity authentication in the current enterprise strategic management system. Based on identity management and supported by face image recognition technology, deep learning, and cloud computing technology, the personnel management model of the management system is constructed, which solves the problems of personnel real identity authentication and personnel safety behavior control. Experiments show that the model can simplify the workflow, improve the operation efficiency, and reduce the management cost. From the perspective of enterprise system development, building a scientific enterprise strategic management system is of great significance to improve the scientific level of enterprise system management.
Control Physical and Logical Access to Assets

Managing access to assets is the prime theme of security, and access control is provided by many different security controls working together. Mostly, an asset conforms of information, systems, devices, facilities, and personnel which need to be secured.
Implementation of Forward Chaining Method in Expert System to Detect Diseases in Corn Plants in Muara Tami District

Diseases in plants are very disturbing and hinder to obtain maximum yields, corn plants are plants that are very easy to attack by diseases or pests, so expertise is needed in dealing with corn plant diseases. Problems arise when farmers do not have the ability and expertise to detect early corn plant diseases as experienced by farmers in the mura tami district. This study aims to design an expert system application as a form of solution for farmers so that they can detect plant diseases as early as possible. The expert system was developed using the forward chaining method which is a forward trace method. System development using the PHP programming language and mysql database. The results of this study conclude that the forward chaining method is suitable to be implemented in an expert system. The test results using the black box method stated that 100% of the system's functionality could work well, while testing using the user acceptance test (UAT) method stated that 84% of respondents strongly agreed that the application was implemented.
Interaction Design for AI Systems: An oriented state-of-the-art

In recent years, new web-based technologies have emerged, and mobile devices and applications access have become widespread, resulting in new paradigms in Human-Computer Interaction (HCI). These paradigms created several challenges in interaction design regarding customization, adaptability, and accessibility. Over the lifetime of a digital product, the user must adapt to updates, changes in the interface, and new functions, while having to provide attention to customization and configuration tasks in interfaces created for millions of individuals worldwide. Simultaneously, users' conditions change, and the system must adjust to new requirements, preferences, and needs in a fast-paced digital environment. And new functions, interfaces, and technologies that meet current market directions, such as Artificial Intelligence, Augmented Reality, Virtual Reality, and even Design without physical interfaces must be developed and implemented. This research work explores the importance of Interaction Design (IxD) in the present and future Artificial Intelligence (AI) systems, highlighting the prominent authors of User-Centered Design and usability principles, guidelines, and heuristics. Moreover, researching appropriate design principles for developing a positive, effective, and safe interaction between humans and computers. This study will also deepen and orient the study of the state-of-the-art to promote the exploration of HCI in a period of enormous challenges and opportunities in AI. As a result of this research work, a table summarizes, compares, and classifies the state of the art according to Usability & Design Principles. Outputting a matrix that aggregates the fundamental principles for designing interfaces in artificial intelligence systems, regardless of their interface. This matrix serves as a base for a framework to create a prototype, in future work, based on the guidelines suggested.
Speech Synthesis System for People with Disabilities

It is not uncommon for a person to lose the ability to speak over the course of his life due to certain diseases or injuries. In some cases, medicine can fully or partially restore this ability, but often a person is permanently speechless. In such cases, people use sign language or some technical means that can translate text into speech to communicate. However, these options do not fully compensate for the lost abilities. This paper proposes, as a solution to this problem, a speech synthesis system capable of generating speech in a voice most similar to the voice of a person that he had before he lost the ability to speak.
Biometric-based Remote Mutual Authentication Scheme for Mobile Device

Remote user authentication schemes provide a system to verify the legitimacy of remote users' authentication request over insecure communication channel. In the past years, many authentication schemes using password and smart card have been proposed. However, password might be guessed, leaked or forgotten and smart card might be shared, lost or stolen. In contrast, the biometrics which utilize biological characteristics, such as face, fingerprint or iris, have no such weakness. With the trend of mobile payment, more and more applications of mobile payment use biometrics to replace password and smart card. In this paper, we propose a biometric-based remote authentication scheme substituting biometric and mobile device bounded by user for password and smart card. This scheme is more convenient, suitable and securer than the schemes using smart cards on mobile payment environment.
61‐3: <i>Invited Paper:</i> Research on Switchable Privacy Mode Applied to Automotive Displays

The concept of switchable privacy display has been successfully applied to notebooks and other consumer display products [1]. However, when applied to automobile display modules, their unique application scenarios, such as display to the co‐driver's side or CID, should also be considered (see Figure 1‐1 for viewing angle requirements). And BOE has developed display modules for application in automotive displays.
Multiple Instance Learning with Mixed Supervision in Gleason Grading

With the development of computational pathology, deep learning methods for Gleason grading through whole slide images (WSIs) have excellent prospects. Since the size of WSIs is extremely large, the image label usually contains only slide-level label or limited pixel-level labels. The current mainstream approach adopts multi-instance learning to predict Gleason grades. However, some methods only considering the slide-level label ignore the limited pixel-level labels containing rich local information. Furthermore, the method of additionally considering the pixel-level labels ignores the inaccuracy of pixel-level labels. To address these problems, we propose a mixed supervision Transformer based on the multiple instance learning framework. The model utilizes both slide-level label and instance-level labels to achieve more accurate Gleason grading at the slide level. The impact of inaccurate instance-level labels is further reduced by introducing an efficient random masking strategy in the mixed supervision training process. We achieve the state-of-the-art performance on the SICAPv2 dataset, and the visual analysis shows the accurate prediction results of instance level. The source code is available at https://github.com/bianhao123/Mixed_supervision .
XDataExplorer: A Three-Stage Comprehensive Self-Tuning Tool for Big Data Platforms

To meet the challenges of massive data, many big data platforms have been used in practice. In these data processing platforms, there are many correlated parameters that have an impact on processing performance; thus, it is challenging to configure these parameters properly for users with different roles. This paper proposes XDataExplorer, a new comprehensive self-tuning tool for big data platforms, which is based on a three-stage optimization approach that optimizes performance successively at the system level, application level and fine-grain tuning. System-level optimization is guided by expert knowledge that is used to update the system variables. Based on the metrics that are computed by collecting recent application history, application-level optimization is achieved using rule-based heuristic methods. The last step of fine-grain tuning uses a hill-climbing algorithm to iterative examine combinations of system-level and application-level parameters. Several performance optimization best practices, expertise, and heuristic rules are also summarized in this paper. Through different stages of gradual tuning, the proposed tool can rapidly improve the productivity of a big data platform and make processes run more efficiently. System evaluations show that, with the suggested configurations, performance can be improved by between 15% and 60% for different workloads compared to default configurations.
Bayesian Network Structure Learning and Application

With the continuous development of artificial intelligence technology and information technology, a large number of background data are constantly generated. How to obtain effective and useful data in a large and complex data group becomes important and meaningful. The traditional Bayesian network can represent the probability distribution of data variables from a large number of data based on graphical models. It has relatively clear and reliable reasoning ability and decision-making mechanism. However, the traditional Bayesian network structure has serious shortcomings in the recognition accuracy of corresponding key data, so the efficiency of the corresponding algorithm is seriously low. Based on this, this study adds an adaptive genetic algorithm with causality to the original Bayesian structure, so as to optimize the strategy of its structure operation, quantitatively describe the order of the corresponding data nodes, creatively arrange the corresponding data nodes in order by using the node priority, and initialize the initial architecture of Bayesian network based on this. Finally, the network is initialized through information exchange and data score correction, so as to get the final learning results. In this study, the convolution neural network algorithm in a database is verified in the experiment. The experimental results show that the accuracy of the experimental results given by the Bayesian network structure proposed in this study is about 10% higher than the traditional accuracy, and its corresponding learning results basically cover the important algorithms, hypotheses, and verification of convolution neural network, from this level; the algorithm proposed in this study has obvious advantages in bibliometrics.
Optimization of Particle Swarms for Travelling Salesman Problem

The Traveling Salesman Problem is a story application of Atom Swarm Optimizations in this research. We have developed several novel techniques intended for solving TSP with PSO. Additionally, we introduced the notions to Swap Operative and Swap Chronological sequence and redefining the remaining operatives their foundation; that way, the study created unique PSO. Research prove it can produce satisfactory outcomes. The aim of this paper be there to assess the functioning of particle swarm optimization, for the going salesman issue TSP. The solution to this trouble is common to be NP-hard, it has N! permutations. The study's goal is to examine the capacity of both algorithms to solve intercontinental and other benchmark problems. Overall, the results suggest that used algorithms know how to understand good quality explanations than PSO algorithm, but they are not good enough in terms of normal generation.
The Design of Personalized Education Resource Recommendation System under Big Data

With the advent of the Internet and the era of big data, education is increasingly dependent on data resources to support product and business innovation, and the lack of data resources has severely limited the areas involved. As a general information filtering method, personalized recommendation systems analyze the historical interaction data between users and items to build user interest models in an environment of "information overload", allowing users to discover and recommend information that interests them. However, the explosive growth of information in the network makes users wander in the sea of information, and it is increasingly difficult to find the information they really need, i.e., information overload. This has given rise to personalized recommendation systems, which currently have more mature applications in industries such as e-commerce, music services, and movie services. To this end, this paper studies and implements a customized educational resource recommendation system that can handle big data. The results show that the values of different similarity calculations all fluctuate with the gradual increase of the number of nearest neighbors, and the algorithm in this paper is maximum at the number of neighbors around 60; then, it is inferred that applying the calculation method to the recommendation algorithm will improve the recommendation accuracy. Therefore, education uses the concept of big data to process the huge amount of education data and find some correlations and laws in education, so as to realize "teaching according to the material, teaching according to the material".
SFedChain: blockchain-based federated learning scheme for secure data sharing in distributed energy storage networks

The intelligence of energy storage devices has led to a sharp increase in the amount of detection data generated. Data sharing among distributed energy storage networks can realize collaborative control and comprehensive analysis, which effectively improves the clustering and intelligence. However, data security problems have become the main obstacle for energy storage devices to share data for joint modeling and analysis. The security issues caused by information leakage far outweigh property losses. In this article, we first proposed a blockchain-based machine learning scheme for secure data sharing in distributed energy storage networks. Then, we formulated the data sharing problem into a machine-learning problem by incorporating secure federated learning. Innovative verification methods and consensus mechanisms were used to encourage participants to act honestly, and to use well-designed incentive mechanisms to ensure the sustainable and stable operation of the system. We implemented the scheme of SFedChain and experimented on real datasets with different settings. The numerical results show that SFedChain is promising.
Intelligent Deployment and Development Strategy of Agricultural Farmland Based on Improved Architecture of Internet of Things

Agricultural modernization has gradually become the direction of future agricultural development. With the improvement of the technical capacity of the Internet of things, a new agricultural development model, namely, intelligent agriculture, has gradually emerged. It can not only improve the production efficiency of agriculture but also improve the efficiency of resource utilization. The main work of the software design of the terminal node is the IoT network connection, the sensor work acquisition, and the packaging of the collected data for transmission and acceptance control to fundamentally alleviate the pressure on food security and ensure the sustainable development of China's agriculture. The experiment demonstrates that the curve temperature cycle of the agricultural greenhouse monitored by the system in this paper is normal, and the maximum temperature has been constant under different time cycles. For example, the maximum values of 5, 15, and 20 are all 0.67.
An Overview on Solar Tracking Systems

One of the biggest problem facing the world today's is the energy crisis, the fossil fuels are one of the energies that the world consumes significantly and it is known that this energy is available in limited quantities. In the past 40 years ago, fossil fuels have been used excessively, which has greatly reduced their quantities, so researchers have head to search for new reliable energy sources, available in abundance as well as renewable. There are a lot of renewable energies, for example: solar energy, wind energy and others. Solar energy is one of the most cost-effective renewable energies to generate energy, where converting solar energy into electrical energy using photovoltaic panels is one of the areas of interest to researchers. But, it was noted that the efficiency of solar panels to producing electricity is low because the panels are fixed position and knowing that the angle of the sun changes throughout the day. This problem can be solved by putting a system called the solar tracking system, the main task of this system is changes the position of the angle of solar panels when angle of sun become change. The solar tracking system will increase the efficiency of electricity production because it will help make the solar panels face the sun throughout the day. This paper provides thorough review of the latest research and literature in the field of solar tracking systems using photovoltaic panels. In addition, the review will be completed with a realistic presentation of modern tracking system projects.
Headhunting System Using Blockchain Technology

In the job market, there is a significant problem with job switching. Employees can change jobs or be employed by other companies, but there is a chance that the present employer will find out about the job move, which could put the employee in a difficult situation. But with blockchain technology, these problems can be overcome. Around 3 billion people work in a variety of fields all over the world. Individuals with a wide range of skills were required for various jobs. As a result, the organization's growth and development require an ideal candidate. Because so many qualified people apply for the same job, the interviewer has a difficult time choosing the best candidate. Persons with a good reputation in the previous company, the necessary skillset, and a reference from a well-known person are given more weight in the hiring process. But, there is a possibility that the data will be modified and tampered based on the comments and concerns provided by the participants. With the use of blockchain technology, these data may be made more safe and protected. The goal of this research is to create a blockchain-based framework for finding and hiring high-level employees without revealing their identities until a specific time period has passed. Employees can provide feedback and ratings to their current organization's peers while maintaining their anonymity and being able to switch jobs. This research paper shows a process on how to develop a headhunting system using blockchain technology. The interviewer will find and hire a top-level employee with proper identification and more securely. This system will be more dependable and secure for selecting the best applicant. Moreover, this input will be more valuable to the company's development and progress.
Practices for Supervising Master’s Theses in Company Context: An Anti-Pattern Approach

Software Engineering (SE) university students often work part-time during their studies. In this setup the students can reform the practices of companies by transferring what they have learned to companies and correspondingly utilize what they have experienced at work in their studies. This symbiosis often continues as the students begin to work towards their thesis. The topic of the thesis relates to the problems in the company. These topics often solve a practical problem, which are not always in a perfect match with academic expectations. On the one hand the employer has certain expectations in terms of working for the company, whereas the supervising professor needs to follow the university guidelines. In this paper, we study this tension by focusing on the problems appearing in MSc thesis process in company context. We propose ways to act so that the different stakeholders -- the student, the professor, and the company -- reach the best possible results. We have analyzed the problems and their root causes. We have also taken the first steps toward anti-patterns for analysis and salvaging of the problems. The study is based on the authors’ collective supervision experience, which covers over 1000 MSc theses, with the combined supervision experience of over 100 years.
A Proposal for Translation Between Franca and Android Interface Definition Language

Great advancements in the automotive industry increased demands for variety of services. Android has become the operating system of choice for different infotainment applications. Nonetheless, infotainment domain is not explicitly based solely on Android. In various systems, such as Linux and QNX, inter-process communication (IPC) is done via Franca framework, unlike the Android system which implements the IPC mechanisms via Android Interface Definition Language (AIDL). From this reason, switching from one standard to another requires a large amount of effort. This paper proposes the generator whose purpose is to translate Franca Interface Definition Language (FIDL) and Android Interface Definition Language (AIDL) models between each other. By creating the proposed generator, we can speed up the process of migration from one standard to another in either automotive system or any other heterogenous system which contains Franca and Android.
Overview of Blockchain Privacy Protection

In recent years, blockchain technology has attracted extensive attention in academia and industry by virtue of its unique advantages such as decentralization, tamper proof, openness and traceability. The combination of blockchain and data protection has always been the focus of scholars. This paper first analyzes the system structure and characteristics of the blockchain itself, and introduces the development process of the blockchain. Secondly, it analyzes the privacy threats faced by data protection in the blockchain, and introduces the relevant blockchain privacy protection technologies. Finally, it discusses how to combine blockchain and other technologies to enhance privacy protection.