Biblio

This paper proposes a vehicle violation determination system based on improved YOLOv5 algorithm, which performs vehicle violation determination on a single unit at a single intersection, and displays illegal photos and license plates of illegal vehicles on the webpage. Using the network structure of YOLOv5, modifying the vector output of the Head module, and modifying the rectangular frame detection of the target object to quadrilateral detection, the system can identify vehicles and lane lines with more flexibilities.

The heterogeneity of network traffic features brings quantitative calculation problems to the matching between network data. In order to solve the above fuzzy matching problem between the heterogeneous network feature data, a quantitative matching method for network traffic features is proposed in this paper. By constructing the numerical expression method of network traffic features, the numerical expression of key features of network data is realized. By constructing the suitable section calculation methods for the similarity of different network traffic features, the personalized quantitative matching for heterogeneous network data features is realized according to the actual meaning of different features. By defining the weight of network traffic features, the quantitative importance value of different features is realized. The weighted sum mathematical method is used to accurately calculate the overall similarity value between network data. The effectiveness of the proposed method through experiments is verified. The experimental results show that the proposed matching method can be used to calculate the similarity value between network data, and the quantitative calculation purpose of network traffic feature matching with heterogeneous features is realized.

Vulnerability assessment is an important process for network security. However, most commonly used vulnerability assessment methods still rely on expert experience or rule-based automated scripts, which are difficult to meet the security requirements of increasingly complex network environment. In recent years, although scientists and engineers have made great progress on artificial intelligence in both theory and practice, it is a challenging to manufacture a mature high-quality intelligent products in the field of network security, especially in penetration testing based vulnerability assessment for enterprises. Therefore, in order to realize the intelligent penetration testing, Vul.AI with its rich experience in cyber attack and defense for many years has designed and developed a set of intelligent penetration and attack simulation system Ai.Scan, which is based on attack chain, knowledge graph and related evaluation algorithms. In this paper, the realization principle, main functions and application scenarios of Ai.Scan are introduced in detail.

The latest, modern security camera systems record numerous data at once. With the utilization of artificial intelligence, these systems can even compose an online attendance register of students present during the lectures. Data is primarily recorded on the hard disk of the NVR (Network Video Recorder), and in the long term, it is recommended to save the data in the blockchain. The purpose of the research is to demonstrate how university security cameras can be securely connected to the blockchain. This would be important for universities as this is sensitive student data that needs to be protected from unauthorized access. In my research, as part of the practical implementation, I therefore also use encryption methods and data fragmentation, which are saved at the nodes of the blockchain. Thus, even a DDoS (Distributed Denial of Service) type attack may be easily repelled, as data is not concentrated on a single, central server. To further increase security, it is useful to constitute a blockchain capable of its own data storage at the faculty itself, rather than renting data storage space, so we, ourselves may regulate the conditions of operation, and the policy of data protection. As a practical part of my research, therefore, I created a blockchain called UEDSC (Universities Data Storage Chain) where I saved the student's data.

Document scanning aims to transfer the captured photographs documents into scanned document files. However, current methods based on traditional or key point detection have the problem of low detection accuracy. In this paper, we were the first to propose a document processing system based on semantic segmentation. Our system uses OCRNet to segment documents. Then, perspective transformation and other post-processing algorithms are used to obtain well-scanned documents based on the segmentation result. Meanwhile, we optimized OCRNet's loss function and reached 97.25 MIoU on the test dataset.

Artificial intelligence (AI) and machine learning (ML) have been used in transforming our environment and the way people think, behave, and make decisions during the last few decades [1]. In the last two decades everyone connected to the Internet either an enterprise or individuals has become concerned about the security of his/their computational resources. Cybersecurity is responsible for protecting hardware and software resources from cyber attacks e.g. viruses, malware, intrusion, eavesdropping. Cyber attacks either come from black hackers or cyber warfare units. Artificial intelligence (AI) and machine learning (ML) have played an important role in developing efficient cyber security tools. This paper presents Latest Cyber Security Tools Based on Machine Learning which are: Windows defender ATP, DarckTrace, Cisco Network Analytic, IBM QRader, StringSifter, Sophos intercept X, SIME, NPL, and Symantec Targeted Attack Analytic.

Network intrusion detection technology has been a popular application technology for current network security, but the existing network intrusion detection technology in the application process, there are problems such as low detection efficiency, low detection accuracy and other poor detection performance. To solve the above problems, a new treatment combining artificial intelligence with network intrusion detection is proposed. Artificial intelligence-based network intrusion detection technology refers to the application of artificial intelligence techniques, such as: neural networks, neural algorithms, etc., to network intrusion detection, and the application of these artificial intelligence techniques makes the automatic detection of network intrusion detection models possible.

Artificial intelligence (AI) was engendered by the rapid development of high and new technologies, which altered the environment of business financial audits and caused problems in recent years. As the pioneers of enterprise financial monitoring, auditors must actively and proactively adapt to the new audit environment in the age of AI. However, the performances of the auditors during the adaptation process are not so favorable. In this paper, methods such as data analysis and field research are used to conduct investigations and surveys. In the process of applying AI to the financial auditing of a business, a number of issues are discovered, such as auditors' underappreciation, information security risks, and liability risk uncertainty. On the basis of the problems, related suggestions for improvement are provided, including the cultivation of compound talents, the emphasis on the value of auditors, and the development of a mechanism for accepting responsibility.

Nowadays big shopping marts are expanding their business all over the world but not all marts are fully protected with the advanced security system. Very often we come across cases where people take the things out of the mart without billing. These marts require some advanced features-based security system for them so that they can run an efficient and no-loss business. The idea we are giving here can not only be implemented in marts to enhance their security but can also be used in various other fields to cope up with the incompetent management system. Several issues of the stores like regular stock updating, placing orders for new products, replacing products that have expired can be solved with the idea we present here. We also plan on making the slow processes of billing and checking out of the mart faster and more efficient that would result in customer satisfaction.

Topic modeling algorithms from the natural language processing (NLP) discipline have been used for various applications. For instance, topic modeling for the product recommendation systems in the e-commerce systems. In this paper, we briefly reviewed topic modeling applications and then described our proposed idea of utilizing topic modeling approaches for cyber threat intelligence (CTI) applications. We improved the previous work by implementing BERTopic and Top2Vec approaches, enabling users to select their preferred pre-trained text/sentence embedding model, and supporting various languages. We implemented our proposed idea as the new topic modeling module for the Open Web Application Security Project (OWASP) Maryam: Open-Source Intelligence (OSINT) framework. We also described our experiment results using a leaked hacker forum dataset (nulled.io) to attract more researchers and open-source communities to participate in the Maryam project of OWASP Foundation.

Propagation delay in blockchain networks is a major impairment of message transmission and validation in the bitcoin network. The transaction delay caused by message propagation across long network chains can cause significant threats to the bitcoin network integrity by allowing miners to find blocks during the message consensus process. Potential threats of slow transaction dissemination include double-spending, partitions, and eclipse attacks. In this paper, we propose a method for minimizing propagation delay by reducing non-compulsory message broadcasts during transaction dissemination in the underlying blockchain network. Our method will decrease the propagation delay in the bitcoin network and consequently mitigate the security threats based on message dissemination delay. Our results show improvement in the delay time with more effect on networks with a large number of nodes.

This paper mainly studies an attack effectiveness evaluation method for AD hoc networks based on rough set theory. Firstly, we use OPNET to build AD hoc network simulation scenario, design and develop attack module, and obtain network performance parameters before and after the attack. Then the rough set theory is used to evaluate the attack effectiveness. The results show that this method can effectively evaluate the performance of AD hoc networks before and after attacks.

Generative Adversarial Networks (GANs) are machine learning methods that are used in many important and novel applications. For example, in imaging science, GANs are effectively utilized in generating image datasets, photographs of human faces, image and video captioning, image-to-image translation, text-to-image translation, video prediction, and 3D object generation to name a few. In this paper, we discuss how GANs can be used to create an artificial world. More specifically, we discuss how GANs help to describe an image utilizing image/video captioning methods and how to translate the image to a new image using image-to-image translation frameworks in a theme we desire. We articulate how GANs impact creating a customized world.

Nowadays, children, teens, and almost everyone around us tend to receive abundant and frequent advice regarding the usefulness of syllabification. Not only does it improve pronunciation, but it also makes it easier for us to read unfamiliar words in chunks of syllables rather than reading them all at once. Within this paper, we have designed, implemented, and presented a Turing machine-based syllable splitter. A Turing machine forms the theoretical basis for all modern computers and can be used to solve universal problems. On the other hand, a syllable splitter is used to hyphenate words into their corresponding syllables. We have proposed our work by illustrating the various states of the Turing machine, along with the rules it abides by, its machine specifications, and transition function. In addition to this, we have implemented a Graphical User Interface to stimulate our Turing machine to analyze our results better.

Evolutionary computation (EC) is a kind of advanced computational intelligence (CI) algorithm and advanced artificial intelligence (AI) algorithm. EC algorithms have been widely studied for solving optimization and scheduling problems in various real-world applications, which act as one of the Big Three in CI and AI, together with fuzzy systems and neural networks. Even though EC has been fast developed in recent years, there is an assumption that the algorithm designer can obtain the objective function of the optimization problem so that they can calculate the fitness values of the individuals to follow the “survival of the fittest” principle in natural selection. However, in a real-world application scenario, there is a kind of problem that the objective function is privacy so that the algorithm designer can not obtain the fitness values of the individuals directly. This is the privacy-preserving optimization problem (PPOP) where the assumption of available objective function does not check out. How to solve the PPOP is a new emerging frontier with seldom study but is also a challenging research topic in the EC community. This paper proposes a rank-based cryptographic function (RCF) to protect the fitness value information. Especially, the RCF is adopted by the algorithm user to encrypt the fitness values of all the individuals as rank so that the algorithm designer does not know the exact fitness information but only the rank information. Nevertheless, the RCF can protect the privacy of the algorithm user but still can provide sufficient information to the algorithm designer to drive the EC algorithm. We have applied the RCF privacy-preserving method to two typical EC algorithms including particle swarm optimization (PSO) and differential evolution (DE). Experimental results show that the RCF-based privacy-preserving PSO and DE can solve the PPOP without performance loss.

In the Swarm Intelligence domain, the firefly algorithm(s) is the most significant algorithm applied in most all optimization areas. FA and variants are easily understood and implemented. FA is capable of solving different domain problems. For solving diverse range of engineering problems requires modified FA or Hybrid FA algorithms, but it is possible additional scope of improvement. In recent times swarm intelligence based intelligent optimization algorithms have been used for Research purposes. FA is one of most important intelligence Swarm algorithm that can be applied for the problems of Global optimization. FA algorithm is capable of achieving best results for complicated issues. In this research study we have discussed and different characteristics of FA and presented brief Review of FA. Along with other metahauristic algorithm we have discussed FA algorithm’s different variant like multi objective, and hybrid. The applications of firefly algorithm are bestowed. The aim of the paper is to give future direction for research in FA.

Numerical simulation of fluid flow is a significant research concern during the design process of a machine component that experiences fluid-structure interaction (FSI). State-of-the-art in traditional computational fluid dynamics (CFD) has made CFD reach a relative perfection level during the last couple of decades. However, the accuracy of CFD is highly dependent on mesh size; therefore, the computational cost depends on resolving the minor feature. The computational complexity grows even further when there are multiple physics and scales involved making the approach time-consuming. In contrast, machine learning (ML) has shown a highly encouraging capacity to forecast solutions for partial differential equations. A trained neural network has offered to make accurate approximations instantaneously compared with conventional simulation procedures. This study presents transient fluid flow prediction past a fully immersed body as an integral part of the ML-CFD project. MLCFD is a hybrid approach that involves initialising the CFD simulation domain with a solution forecasted by an ML model to achieve fast convergence in traditional CDF. Initial results are highly encouraging, and the entire time-based series of fluid patterns past the immersed structure is forecasted using a deep learning algorithm. Prepared results show a strong agreement compared with fluid flow simulation performed utilising CFD.

Application of computational intelligence methods in data analysis and optimization problems can allow feasible and optimal solutions of complicated engineering problems. This study demonstrates an intelligent analysis scheme for determination of optimal operating condition of an internal combustion engine. For this purpose, an artificial neural network learning model is used to represent engine behavior based on engine data, and a metaheuristic optimization method is implemented to figure out optimal operating states of the engine according to the neural network learning model. This data analysis scheme is used for adjustment of optimal engine speed and fuel rate parameters to provide a maximum torque under Nitrous oxide emission constraint. Harris hawks optimization method is implemented to solve the proposed optimization problem. The solution of this optimization problem addresses eco-friendly enhancement of vehicle performance. Results indicate that this computational intelligent analysis scheme can find optimal operating regimes of an engine.

Earlier around in year 1880’s, Industry 2.0 marked as change to the society caused by the invention of electricity. In today’s era, artificial intelligence plays a crucial role in defining the period of Industry 4.0. In this research study, we have presented Computational Intelligence based Machine Condition Monitoring system architecture for determination of developing faults in industrial machines. The goal is to increase efficiency of machines and reduce the cost. The architecture is fusion of machine sensitive sensors, cloud computing, artificial intelligence and databases, to develop an autonomous fault diagnostic system. To explain CI-MCMs, we have used neural networks on sensor data obtained from hydraulic system. The results obtained by neural network were compared with those obtained from traditional methods.

Pooling together samples and testing the resulting mixture is gaining considerable interest as a potential method to markedly increase the rate of testing for SARS-CoV-2, given the resource limited conditions. Such pooling can also be employed for carrying out large scale diagnostic testing of other infectious diseases, especially when the available resources are limited. Therefore, it has become important to design a user-friendly tool to assist clinicians and policy makers, to determine optimal testing pool and sub-pool sizes for their specific scenarios. We have developed such a tool; the calculator web application is available at https://riteshsingh.github.io/poolsize/. The algorithms employed are described and analyzed in this paper, and their application to other scientific fields is also discussed. We find that pooling always reduces the expected number of tests in all the conditions, at the cost of test sensitivity. The No sub-pooling optimal pool size calculator will be the most widely applicable one, because limitations of sample quantity will restrict sub-pooling in most conditions.

Intelligent design has been an emerging important area in the design. Existing works related to intelligent design use objective indicators to measure the quality of AI design by comparing the differences between AI-generated data and real data. However, the level of quality and efficiency of intelligent design compared to human designers remains unclear. We conducted user experiments to compare the design quality and efficiency of advanced design methods with that of junior designers. The conclusion is advanced intelligent design methods are comparable with junior designers on painting. Besides, intelligent design uses only 10% of the time spent by the junior designer in the tasks of layout design, color matching, and video editing.

Human Resource (HR) Analytics has been increasingly attracted attention for a past decade. This is because the study field is adopted data-driven approaches to be processed and interpreted for meaningful insights in human resources. The field is involved in HR decision making helping to understand why people, organization, or other business performance behaved the way they do. Embracing the available tools for decision making and learning in the field of computational intelligence (CI) and Artificial Intelligence (AI) to the field of HR, this creates tremendous opportunities for HR Analytics in practical aspects. However, there are still inadequate applications in this area. This paper serves as a survey of using the tools and their applications in HR involving recruitment, retention, reward and retirement. An example of using CI and AI for career development and training in the era of disruption is conceptually proposed.

One of the biggest problems in the current society is people's safety. Safety measures and mechanisms are especially important in the case of vulnerable social groups, such as migrants, homeless, and victims of domestic and/or sexual violence. In order to cope with this problem, we witness an increasing number of personal alarm systems in the market, most of them based on panic buttons. Nevertheless, none of them has got widespread acceptance mainly because of limited Human-Computer Interaction. In the context of this work, we developed an innovative mobile application that recognizes an attack through speech and gesture recognition. This paper describes such a system and presents its features, some of them based on the emerging concept of Human-in-the-Loop Cyber-physical Systems and new concepts of Human-Computer Interaction.

Over the past decade, an infotelecommunication technology has made significant strides forward. With the advent of new generation wireless networks and the massive digitalization of industries, the object of protection has changed. The digital transformation has led to an increased opportunity for cybercriminals. The ability of computational intelligence to quickly process large amounts of data makes the intrusions tailored to specific environments. Polymorphic attacks that have mutations in their sequences of acts adapt to the communication environments, operating systems and service frameworks, and also try to deceive the defense tools. The poor protection of most Internet of Things devices allows the attackers to take control over them creating the megabotnets. In this regard, traditional methods of network protection become rigid and low-effective. The paper reviews a computational intelligence (CI) enabled software- defined network (SDN) for the network management, providing dynamic network reconfiguration to improve network performance and security control. Advanced machine learning and artificial neural networks are promising in detection of false data injections. Bioinformatics methods make it possible to detect polymorphic attacks. Swarm intelligence detects dynamic routing anomalies. Quantum machine learning is effective at processing the large volumes of security-relevant datasets. The CI technology stack provides a comprehensive protection against a variative cyberthreats scope.

Countries are putting cyber-security at the forefront of their national issues. With the increase in cyber capabilities and infrastructure systems becoming cyber-enabled, threats now have a physical impact from the cyber dimension. This paper proposes an analytical framework for national cyber-security profiling by taking national governmental and technical threat modeling simulations. Applying thematic analysis towards national cybersecurity strategy helps further develop understanding, in conjunction with threat modeling methodology simulation, to gain insight into critical infrastructure threat impact.