Industrial Circles Research Articles

Fake News: Mass Knowledge Illusion Under Pleasure Marketing

Fake news refers to deliberately fake news. It is not only a practical problem that puzzles the information audience, but also a blind spot and misunderstanding of the public's participation in current affairs. It also brings endless troubles to news work of social life. Therefore, it constitutes a problem that is widely concerned by the communication, journalism and cultural industry circles. This paper will profundly analyze the generation, essence and function of fake news, a knowledge illusion, from the perspective of the audience's consumption psychology, and on this basis, think about the psychological path for audience to penetrate the fake news knowledge illusion, hoping to solve the difficult problem of perplexing social life and social order through cultivation of the audience's own news literacy.The essence of fake news is that it provides knowledge illusion for the audience by pleasure marketing,which is somewhat like the true news. The knowledge illusion is materialization for audience’s “omniscience” desire by “peseudo-objective” symbolic system, to realize the producer’s utilitarian intention in political, economic and cultural aspects. To penetrate the knowledge illusion of false news, the audience should first be aware of the contradictions within the organizational structure of the false news knowledge illusion, then be soberly aware of the illusory nature of the illusion and self desire, and avoid objectifying self desire, so as to discover the gap between the news symbols and the intention of the false news producers and penetrate the false news knowledge illusion. When the audience really realizes the penetration of the false news knowledge illusion, he not only returns to the normal social transmission order, but also helps the public media to establish a healthy transmission ethics in social public space.

Rice plant disease classification using dilated convolutional neural network with global average pooling

The Indian economy is thought to be primarily dependent on agriculture. In plants with various climatic circumstances, illness is highly prevalent and natural. As a result, the quality of crop deteriorates. Getting the best quality and quantity of harvest is farmers' most challenging task due to recent changes in weather cycles. Crop diseases must be identified and prevented as soon as possible to improve productivity. Deep learning is an artificial intelligence branch. It has been actively discussed in academic and industrial circles in recent days, because of the advantages of autonomous learning and feature extraction. The use of learning-based algorithms in plant leaf disease recognition can help to strengthen the objectivity of plant leaf disease feature extraction, minimize the limitations of intentionally selecting disease spot features, and speed up the study. In this paper, we examine existing approaches to detecting plant leaf disease using deep learning and high-end imaging methods, as well as their challenges. We anticipate that our research will be useful to researchers interested in plant disease identification. The traditional CNN has the issue of using up too much computational power. To address this issue, this research developed a DCNN (Dilated Convolutional Neural Network) model with Global Average Pooling (GAP), which will be constructed by changing regular CNN convolution kernels with dilated convolution kernels and the fully connected layer in traditional CNN replaced by Global Average Pooling. The dilated convolution gives the advantages of less computational cost and reduced memory usage then GAP avoids overfitting. These two new concepts are implemented with CNN and the results of this method is compared with other learning and hybrid learning methods using performance metric such as precision, recall, f1-score and accuracy. The classification includes four classes such as bacterial blight, blast, brown spot and turgo. The performance metrics shows that, in the same experimental setup, the DCNN model with GAP improves training accuracy by 5.49 percent on average, compared to the classic CNN model and the results are compared with other learning and hybrid learning methods.

Convex Optimization for Trajectory Generation: A Tutorial on Generating Dynamically Feasible Trajectories Reliably and Efficiently

Reliable and efficient trajectory generation methods are a fundamental need for autonomous dynamical systems. The goal of this article is to provide a comprehensive tutorial of three major convex optimization-based trajectory generation methods: lossless convexification (LCvx) and two sequential convex programming algorithms, successive convexification (SCvx) and guaranteed sequential trajectory optimization (GuSTO). Trajectory generation is defined as the computation of a dynamically feasible state and control signal that satisfies a set of constraints while optimizing key mission objectives. The trajectory generation problem is almost always nonconvex, which typically means that it is difficult to solve efficiently and reliably onboard an autonomous vehicle. The three algorithms that we discuss use problem reformulation and a systematic algorithmic strategy to nonetheless solve nonconvex trajectory generation tasks using a convex optimizer. The theoretical guarantees and computational speed offered by convex optimization have made the algorithms popular in both research and industry circles. The growing list of applications includes rocket landing, spacecraft hypersonic reentry, spacecraft rendezvous and docking, aerial motion planning for fixed-wing and quadrotor vehicles, robot motion planning, and more. Among these applications are high-profile rocket flights conducted by organizations such as NASA, Masten Space Systems, SpaceX, and Blue Origin. This article equips the reader with the tools and understanding necessary to work with each algorithm and know their advantages and limitations. An open source tool called the SCP Toolbox accompanies the article and provides a practical implementation of every numerical example. By the end of the article, the reader will not only be ready to use the lossless convexification and sequential convex programming algorithms, but also to extend them and to contribute to their many exciting modern applications.

Incremental Production of Urban Public Green Space: A ‘Spiral Space’ Building Typology

This paper addresses a challenging global problem, insufficient accessible urban public green space, based on building typology. Offering sufficient public green spaces and maintaining the equalities of citizens’ accessibility in high-intensity urban cities has been increasingly challenging. Thus, providing adequate and accessible green public spaces by 2030 is a sub-goal of SDGs No. 11. Solving this urban problem is commonly considered the responsibility of urban planning; however, the potential of buildings is scarcely discussed in academia. Luckily, in the industry, many top architecture firms (studios) have tapped the particular potential of buildings via design practice. This practice-led research aims to understand the efforts made by industrial circles. Based on the fieldwork worldwide, this study proposes a ‘spiral space’ building typology to work as a conceptual framework for this emerging field. The key benefit of this building typology—incremental production of public green spaces—is qualitatively verified, and the good flexibility and international acceptance of this building typology are demonstrated based on global cases. This work could serve as a basis for future research on how buildings could play a greater role in supporting urban sustainability, such as enhancing the residents’ accessibility to public green space in metropolises. In addition, the building typology and corresponding design strategies discussed herein could also serve as references for future design practice for architects.

Effect of green hybrid fillers loading on mechanical and thermal properties of vinyl ester composites

AbstractThe need for eco‐friendly materials has been attracted due to renewability, abundance availability, low cost, and so on. Therefore, the search for bio fillers for the fabrication of bio‐based composite materials is gaining more and more attention in both academic and industry circles because it promotes sustainability. The present study represents the utilization of biomass solid waste in the hybrid form of Tamarind Seed and Date Seed Filler (TSF/DSF) into polymer reinforced composite which has been explored for the first time by a compression molding technique. These fillers are bio‐waste that can be obtained at a minimal cost from renewable sources. An attempt has been made to use these hybrid fillers to reinforce the matrix ranging from 0 to 50 wt%, and their physical, mechanical, and thermal properties were investigated. In general, the inclusion of hybrid fillers increases mechanical properties, although the addition of hybrid fillers had only a minor impact on thermal properties. When compared to the pure vinyl ester resin, the hybrid fillers reinforced composites revealed a significant enhancement in tensile, flexural, impact, and hardness properties, with improvements of 1.51 times, 1.44 times, 1.87 times, and 1.46 times respectively, at 10 wt% filler loading. Filler matrix interaction of fractured mechanical testing samples was evaluated by scanning electron microscope. Based on the findings, hybrid filler reinforced composites may be suitable for applications where cost is a consideration and where minor compromises in thermal qualities are acceptable.

A variable-trust threshold-based approach for DDOS attack mitigation in software defined networks.

Software-defined networks offer a new approach that attracts the attention of most academic and industrial circles due to the features it contains. However, some loopholes make such modern networks vulnerable to many types of attacks. Among the most important types of these attacks is the Distributed Denial of Service (DDoS) attack, which in turn affects the network’s performance and delays many real user requests. As one of the main features of SDN is the centralization of all the control plane in the SDN controller, it becomes a central point of attack that may compromise the whole network. Hence, in our proposed approach, we aim to mitigate the DDoS attack that maybe launched to compromise the SDN controller, flood the control plane and cripple the entire network. Many DDoS mitigation scheme have been proposed, however, determining the threshold between legitimate requests and malicious requests is still a challenging task. Our proposed approach relies on a two-phases algorithm that assigns a variable trust value for every user. This trust value is compared with schemes relying on a threshold value that changes dynamically and assists in detecting the DDoS attack. The first phase of our two-phases algorithm is Header fields extraction, and the second phase is calculating the trust value based on header fields information. Our proposed approach shows better performance than related detection schemes in terms of accuracy, detection rate, and false-positive rate. Where the accuracy of the system reaches up to 98.83% which is high compared to other traditional methods.

Constructing WO3/TiO2 heterojunction with solvothermal-sintering for enhanced photocatalytic activity under visible light irradiation

Developing high-efficiency and low-cost photocatalysis are of important significance for large-scale organic waste disposal technologies. Here, a versatile and simple solvothermal-sintering route is proposed to construct WO3/TiO2 heterojunction for catalytic oxidation of origin dyes under visible light irradiation. Tightly bonded bimetal centers and well-designed heterojunction lead to significantly reduced band gap of the photocatalysis. Furthermore, significantly enhanced photocatalytic activity are achieved for degrading various organic dyes under visible light. In particular, the degradation rate of RhB with the heterojunction reaches 95.31% under visible light for 80 min, which is considerably higher than that of 34.81% with TiO2. Besides, WO3/TiO2 heterojunction still maintains excellent photocatalytic activity after three cycles of degradation. This work proposes a simple, low-cost but effective route to fabricate WO3/TiO2 heterojunction, which can be used for large-scale organic wastewater disposals across industrial circles.

Plant Leaf Disease Predictor Using Deep Learning

Abstract: Artificial intelligence includes deep learning as a subset. Due to its advantages, autonomous learning and feature extraction have been hotly debated in academic and industrial circles in recent years. Image and video processing, voice processing, and natural language processing have all benefited from it. Simultaneously, it has grown into a centre for agricultural plant protection research, which includes, among other things, plant disease recognition and insect range evaluation. Deep learning can assist avoid the drawbacks of artificially selecting disease spot features, improve the objectivity of plant disease feature extraction, and accelerate research and technological change. In this review, we look at how deep learning technology has progressed in the field of agricultural leaf disease detection in recent years. The current trends and challenges in using deep learning and sophisticated imaging techniques to detect plant leaf disease are discussed in this paper. Our findings are expected to be valuable to researchers interested in detecting plant diseases and insect pests. We also discussed some of the current problems and issues that need to be addressed. Keywords: Analysis, Deep Learning, Prediction, Plant Leaf, Resnet 50

CCPIN: Classification and Combine Parallel Interaction Network for CTR Prediction

The study of feature interactions in deep neural network-based recommender systems has been a popular research area in industry and academic circles. However, the vast majority of parallel CTR prediction models do not classify the input features but instead feed them into the model. This way not only reduces the accuracy of the model but also ignores the effectiveness of learning individual feature interactions. In addition, the majority of parallel CTR prediction models only focus on the submodel intersections of their parallel models, ignoring the importance of the external intersection. To address the shortcomings, this paper proposes the CCPIN model on the basis of the XdeepFM model. In the CCPIN model, it can not only learn different category feature interactions but also learn individual feature interactions. Through the classification gate, adaptive features are maximized to improve the performance of the submodel. Through the Combine layer, the interaction of submodel results can be learned while retaining the original output. Through comparison experiments with other models on two datasets, it is demonstrated that the CCPIN model has an average increase of 0.93% in AUC and a decrease of 0.47% in Logloss compared to other models.

SMALL INNOVATIVE BUSINESS DEVELOPMENT PROBLEMS IN GEORGIA

The sustainable development of small innovative entrepreneurship is quite a difficult task and requires systematic work, which must involve both the state, academic and industrial circles. It is necessary to create connecting rings between these areas. Based on these trends, a whole set of measures should be developed and implemented, which should be aimed at the mass acquisition of innovations. It should be noted that only with the fruitful interaction of science, entrepreneurship, society, and state and municipal bodies will it be possible to ensure the rapid pace of economic development of the country and the establishment of an innovative economy. Georgia has a positive attitude towards expected innovations along with the development of the country's economy. The relationship between income level (GDP per capita) and innovation is positive. A positive trend line indicates expected innovations in terms of revenue levels. Georgia's performance is in line with expectations of its level of development, the relationship between the input and output of innovation is negative. Georgia produces less innovative products compared to the level of innovative investments.

Detection and Classification of Plant Disease with Deep Learning

Abstract: Deep learning is a branch of artificial intelligence. In recent years, with the benefits of automatic learning and feature extraction, it's been wide involved by educational and industrial circles. It has been wide utilized in image and video processing, voice processing, and natural language processing. At a similar time, it's conjointly become an enquiry hotspot within the field of agricultural plant protection, such as plant disease recognition and pest range assessment, etc. the application of deep learning in disease recognition will avoid the disadvantages caused by artificial choice of illness spot options, make plant disease feature extraction additional objective, and improve the analysis potency and technology transformation speed. This paper provides the analysis progress of deep learning technology within the field of crop plant disease identification in recent years. during this paper, we tend to present this trends and challenges for the detection of plant leaf disease with deep learning and advanced imaging techniques. we tend to hope that this work are going to be a valuable resource for researchers UN agency study the detection of plant diseases and bug pests. At a similar time, we tend to conjointly mentioned some of the challenges and issues that require to be resolved. Keywords: Deep Learning, Disease Detection, Classification, Artificial Intelligence, Leaf Disease Detection;

Research on Government Regulation of Social Credit System — From the Perspective of Business Ethics

Institution system is an important part of modern social governance. Government, market and social relations within the system, as well as the corresponding institutional arrangements, can be said to be the top priority. Corporate social responsibility is an outstanding case that embodies the relationship and institutional arrangement of the three in practice.Government regulation of social credit system is a good example to analysis it. The research on government regulation of social credit system itself is put forward from the perspective of government, so in the field of government regulation of social credit system, should enterprises play a role? Especially in the era of big data, a large number of data are provided by private enterprises. Without these data, it is difficult to complete the construction of big data system. If enterprises should play their role, what’s the contribution of the government? Academic and industrial circles have been expressing their own opinions on these issues.Therefore, this paper attempts to build a game model among government, enterprise and society to solve the following problems: how to solve the relationship problem among government, enterprise and society? How to build the corresponding system? What’s the corresponding government regulation of China’s social credit system go from the perspective of business ethics?Through the game model analysis, the author finds that shareholders can reduce the threat of CEO replacement and collude with the management staff members to make the cake (including the interests of stakeholders) smaller. But on the basis of the loss of stakeholders’ rights and interests, shareholders’ wealth is increasing, which also explains why more and more senior executives are trying to form collusion with stakeholders in the campaign against antitakeover and loose audit system. Shareholders’ rights and interests and stakeholders’ rights and interests present a seesaw phenomenon. The response of the stakeholders and shareholders to a better corporate governance model is consistent. That is to say, the increase of shareholders’ income is not necessarily based on the loss of stakeholders. The game model also shows that the protection of formal laws and regulations is the main factor to increase the benefits of stakeholders. Through the case of the social credit system, we will conclude that the formal laws and regulations cannot fully ensure the increase of stakeholders’ income, but also need to strengthen the transformation of institutional advantages into governance advantages, and strengthen the law enforcement. It will finally promote the further improvement of social credit system.

Bibliometric Analysis of Artificial Intelligence in Textiles.

Generally, comprehensive documents are needed to provide the research community with relevant details of any research direction. This study conducted the first descriptive bibliometric analysis to examine the most influential journals, institutions, and countries in the field of artificial intelligence in textiles. Furthermore, bibliometric mapping analysis was also used to examine diverse research topics of artificial intelligence in textiles. VOSviewer was used to process 996 articles retrieved from Web of Science—Core Collection from 2007 to 2020. The results show that China and the United States have the largest number of publications, while Donghua University and Jiangnan University have the highest output. These three themes have also appeared in textile artificial intelligence publications and played a significant role in the textile structure, textile inspection, and textile clothing production. The authors believe that this research will unfold new research domains for researchers in computer science, electronics, material science, imaging science, and optics and will benefit academic and industrial circles.

A Summary of Security Techniques-Based Blockchain in IoV

With the rapid development of the informatization and industrialization of the Internet of Vehicles (IoV), the number and application of connected vehicles are growing rapidly. The safety problem is related to the property and life of human beings, which has attracted extensive attention from academic and industrial circles. Based on the study of high-quality literature published in the past decade and other high-level research works, this paper first analyzes the forms of attack against the Internet of Vehicles from the two aspects of attack mode and target. Then, it summarizes the existing blockchain-based system framework of the Internet of Vehicles (BIoV) and then discusses the security solutions of blockchain-based vehicles from the aspects of authentication, privacy, trust management, access control, and so on, to support the distributed system architecture and solve the security challenges of the Internet of Vehicles. Finally, the technical difficulties and the direction of further research of BIoV are summarized.

The effect of practicum learning based audiovisual on students’ learning outcomes in Indonesian vocational secondary school

<span>This research aimed to examine the effectiveness of practicum learning based audiovisual in improving students’ learning outcomes in vocational secondary schools. At the practical level, teachers play an important role in facilitating students by designing an appropriate method, media, and material of learning which represent the actual condition of workplaces. Thus, students can gain their learning skills and experiences. Quasi-experimental research was conducted in vocational secondary schools in Surakarta city, Indonesia. The research participants were 230 students categorized into the control group and the experimental group. The results showed the effectiveness of practicum learning based audiovisual in improving students’ learning outcomes effectively. The effectiveness can be proved by the average pretest score and post-test score between the control group and the experimental group. Considering its effectiveness, practicum learning based audiovisual can be used as an advanced model of learning in vocational secondary school especially in preparing students before joining the industrial circle. This research has shown the effectiveness of practicum learning based audiovisual in improving students’ learning outcomes in vocational secondary schools. It can be proved by the result of an independent t-test that shows significant differences in students’ learning outcomes in the control group and experimental group. Based on the results of research, the model of practicum based audiovisual learning can become an advanced model of practicum learning in vocational secondary schools, especially in non-engineering and technic majors.</span>

SBRAC: Blockchain-based sealed-bid auction with bidding price privacy and public verifiability

Electronic auction (e-auction) has become one of the most popular approaches to allocate resources or services in contemporary society because of removing physical limitations of traditional auctions. The rapid expansion of e-auction enhances work efficiency but incurs some new and distinctive challenges such as low-robustness, data privacy, and lack of trust. To address the above concerns, various auction designs have been proposed by researchers from the academic and industrial circles. However, most of them fail in simultaneously achieving decentralization (i.e. auctioneer-free), privacy-preserving (i.e. bids privacy), collusion-resistance, and secure-channel free. In this paper, we present an effective smart contract-based verifiable privacy-preserving sealed-bid reverse auction scheme, which not only has no need for trusted third parties, but also achieves robustness and privacy protection of losing bids even in the face of overwhelming collusion. In this scheme, the central auctioneer is replaced with some carefully designed smart contracts for decentralization, while zero-knowledge proof and anonymous veto networks are used to avoid the secret communication channel, support the public verifiability and detect the malicious behaviors of bidders. More practically, the complexity of the designed scheme is independent of the number of bidders. Finally, we implement a prototype of our design based on a locally simulated Ethereum test network. The extensive experimental results demonstrate the scheme is the high efficiency and practicality.

Anglo-Soviet Trade Relations on the Eve of the Severance of Diplomatic Relations in 1927

During the 1920s, the USSR tried to establish both trade and diplomatic relations with England. In the conditions of the growing economic crisis of 1925, representatives of British business circles were interested in creating favorable conditions for the development of mutually beneficial trade and economic ties with Soviet Russia. The foreign trade turnover between the two countries was actively developing. At the same time, the trade balance was in favor of the UK. Meanwhile, the Conservative cabinet was clearly moving towards a break with the USSR. Soviet financial assistance to striking miners in 1926, as well as material support for the national revolutionary movement in China, contributed to the deterioration of Soviet-British relations. Representatives of some of the British commercial and industrial circles were extremely interested in trade with Russia. They rightly believed that England would suffer more damage from the rupture than the Soviets. One of the steps towards easing tensions and creating favorable conditions for the development of trade and economic relations was the conclusion in May 1927 of an agreement between the delegation of the USSR and the board of Midland Bank on crediting Soviet orders of British goods. However, the subsequent police raid on the premises of the joint stock company “Arkos Limited” led to the termination of diplomatic relations with the USSR. As a result of the breakdown of relations, English firms suffered heavy losses. Orders were lost for the amount of the loan, on the provision of which an agreement was reached with Midland Bank. The termination of relations with the USSR had negative consequences for the British economy. The volume of Soviet-British trade has significantly decreased. The gradual improvement of Soviet-British relations led to the restoration of lost trade and economic relations. However, the issue of granting large loans, including from Midland Bank, remained unresolved.

Quality and Energy Aware Multipath Routing in Wireless Multimedia Sensor Networks

In Wireless Multimedia Sensor Networks (WMSNs), the sensor nodes are capable of acquiring the multimedia information like wildlife tracking, traffic accidents as well scalar data. Because of these factors, the WMSNs have a bright future in both academic and industry circles. Multimedia applications, on the other hand, generate a lot of network traffic, which causes a lot of delay and wastes a lot of energy. In WMSNs, both network duration and quality of service are critical, hence an effective routing algorithm that can manage more data while simultaneously extending network lifetime is necessary. Towards this objective, in this paper, we proposes a Quality Aware Multipath Routing (QAMR) that selects multiple paths based on three reference metrics; they are Expected Transmission Count, Energy and Delay. For a given source and sink node, the QAMR constructs a composite metric by combining these three metrics and selects node disjoint multiple paths. Since the multiple paths with common nodes has less efficiency in data forwarding, we opt for node disjoint paths in which no two paths have one common node. On the proposed system QAMR model, extensive simulation tests are performed, and the outcomes are evaluated using a variety of metrics such as energy consumption, throughput, delay, and packet delivery ratio.

The Reform of the UN Security Council: How to Balance Legitimacy and Efficiency

With the development of multi-polarization in the world, the UN Security Council plays an increasingly important role in maintaining international peace and promoting international cooperation, which has attracted much attention from the academic and industrial circles. However, the current structure of five permanent and ten non-permanent members of the Council does not maximize efficiency and legitimacy, making it more difficult to resolve international conflicts on time. This article first has carried on the extensive search for relevant theories and finishing, and on this basis, using the survey method, literature research, interdisciplinary research method, qualitative analysis method, and other research methods. The goal is to balance the efficiency and the legitimacy of the UNSC, especially improving the council’s legitimacy. This paper draws the following conclusions: 1. Increase the number of seats on the Council by twenty-four (five permanent members, eight four-year renewable seats, and 11 non-permanent members) 2. It is impossible to abolish the veto completely, but permanent members should be urged to reduce the use of the veto through legislation. Given the political conflicts of interest and the complexity of revising the UN Charter, governments of all countries should promote internal negotiations as soon as possible, conduct in-depth dialogue and consultation on core major issues, clarify the principles and methods of reform, and determine practical solutions.

Talent Management in Educational Institutions Between Reality and Expectation (A Spot Light on Competencies Industry Circle in the Educational Institutions)

Talent Management in Educational Institutions Between Reality and Expectation (A Spot Light on Competencies Industry Circle in the Educational Institutions)