Reviewerly/exHarmony huggingface dataset:
Impact of Active Scanning Tools for Device Discovery in Industrial Networks

The number of networked devices for automation in industrial environments is growing constantly. Therefore, network operators face the task of managing and securing these networks, as attacks and failures in these systems tend to have serious consequences. The decentralized nature of Operational Technology (OT) networks and the usage of legacy network equipment make passive scanning not feasible for device identification. To manage these networks properly, active network scans would be a suitable tool. However, these may influence or damage the fragile industrial devices and the physical processes they control. Nevertheless, device identification and asset management is increasingly important in industrial networks. An understanding of the impact of active scans on those devices is necessary to minimize the risk of negative effects. In this paper, we analyze the impact of five active scanning tools, capable of identifying Industrial Control System (ICS) components, on seven Programmable Logic Controllers (PLCs). Most devices show measurable influences during the scans, these range from few milliseconds to several hundred milliseconds. However, we are also able to show that it is possible to scan PLCs without influences at all, while gathering the required information. The results of the experiments can be used to evaluate the potential risks of these tools and whether or how they should be used in industrial environments.
Multi-echelon Vehicle Routing Problem in Sensor-Cloud Architecture with Mobile Sinks

The sensor-cloud architecture rises the opportunity to overcome intrinsic shortages of wireless sensors, such as computing capacity, storage space and communication range. However, before realizing these complementary effects of cloud computing, there is a challenge of how to plan efficient routes for mobile sinks to gather distributedly sensed data to centralized computing resources on cloud, especially where practical environment limits the travelling range of mobile sinks. This paper models the route planning problem into multi-echelon vehicle routing problem, and formulates it into integer linear programming. To solve this problem, a GPU-based parallel algorithm is proposed. The experimental results verify the accuracy and efficiency of the algorithm.
Sand Table Design of Virtual Reality Psychotherapy

With the rapid pace of life and social pressure, the number of patients with mental disorders has increased, and the demand for psychological counseling has increased. The sand table therapy is a popular psychotherapy method on the market, which is suitable for a wide range of people, based on the analysis of the development and current situation of the sand-play therapy in China, combining the Internet resources and the rapid development of virtual reality technology, integrating the new media technology and the limited interactive computer database into the psychological sand-play therapy, it can not only accurately and vividly complete the subconscious inner expression and spiritual release of patients, but can also reduce the work pressure of psychiatrists, and this design is an expansion and innovation of the traditional sand table game mode.
UAV multi-dynamic target points path planning with obstacles based on SOM-DAPF

In this paper, the unmanned aerial velhicle (UAV) path planning problem with obstacles is disscussed for an air-ground collaborative system which is composed of a messenger UAV and multiple unmanned ground vehicles (UGVs). The messenger UAV needs to visit each UGV to collect and share information. The aim of this problem is to achieve a shortest path which makes the UAV fly over all UGVs while avoiding obstacles. By the Dubins model, the path planning problem is formulated as a Dynamic Dubins Traveling Salesman Problem with Neighborhood (DDTSPN) under obstacle constraints. To adapt to the change of UGVs' locations and reduce the complexity of tuning global path for obstacle avoidance, a moving horizon optimization strategy is porposed. After the UAV visits a UGV, the rest of UGVs' access sequence and access locations are determined using SOM method according to the real-time locations of UGVs. Further, the DAPF algorithm is presented to replan the path which enters the obstacle regions. Simulation results show the advantages of the proposed path planning algorithm.
Artificial intelligent based fuzzy game analysis of migrant students’ studying based on performance and fairness

Artificial Intelligence based Fuzzy Importance Performance Analysis Model (AFIPA)With the transformation of economic system, migrant workers transfer employment from underdeveloped areas to economically developed areas, and children’s reading has become one of the important things for migrant workers and families. With the increase of children in the immigration area year by year, the pressure on education in the immigration area has gradually increased, which has become one of the social problems to be solved by the government in the immigration area. The speed of school construction cannot keep up with the growth of children, and because it takes up a lot of resources, funds and manpower, the government is not very enthusiasticin the immigration area. The stakeholders involved are always playing a game. How to achieve the best state is a difficult problem. After the central government’s policywereissuedabout solving the enrollment problem of migrant workers’children in school, local acceptance criteria for admitting migrant children to school were formulated. However, when lowering the admission threshold, all localities always look to other areas for fear of falling too low to become a basin of confluence, which cause an unbearable burden. In this paper fuzzy based analytic theory has been analysed Through the game analysis of various stakeholders, and taking Shengze Town, Jiangsu Province as an example, this paper puts forward some fuzzy policy suggestions, hoping to provide some new experience in promoting migrant children’s education in the future.
Sequential Inverse Covariance Intersection Fusion Kalman Filter for Multi-sensor Systems with Packet Dropouts and Multiplicative Noise

This paper concerned with the hybrid network system with state multiplicative noises, packet dropouts and linear correlation additive white noises. Firstly, the dimension of the original system is augmented by the method of augmented matrix. By introducing the fictitious noises to compensate the uncertainty of multiplicative noises and transform the augmented system into a standard system with only determined noise variance. Then, based on the standard system, local steady-state Kalman estimators are designed by using Lyapunov equation approach, and the SICI steady-state Kalman fusers (predictor and filter) are presented by using the sequential inverse covariance intersection algorithm. In this paper, two SICI steady-state Kalman fusers are presented combined with a hybrid networked system, which expand the application fields of the new ICI information fusion algorithm, which overcomes the shortcomings that the large amount of calculation given by the original CI fusion algorithm, and overcomes the large conservation of the original SCI fusion algorithm. It is proved that the accuracy of SICI fuser is higher than that of SCI fuser. Finally, some simulation results are given to verify the effectiveness of the proposed algorithm.
A RDBMS-Based Bitcoin Analysis Method

Due to the proliferation of Bitcoin and Ethereum, over 1000 cryptocurrencies have appeared in the market. As hundreds of thousands of cryptocurrency transactions are taken place per day, cryptocurrency exchange, service operators, or government agencies have to observe user transaction activities for legal concerns or economic purposes. Cryptocurrencies generally use the blockchain structure where every transaction is connected in the linked list with the public key hash function. In order to examine specific transaction, address or a group of addresses, called a cluster, we need an efficient cryptocurrency data analyzer and scalable storage. Though a few studies on cryptocurrency analysis tools have been presented, they do not generally satisfy all the requirements. In this paper, we propose an extensible and user-friendly Bitcoin analysis software based on RDBMS. From extensive Bitcoin experiments, we demonstrate that RDBMS queries are useful to perform analysis of Bitcoin transaction, cluster and graph. In addition, we show that the indexed SQLite3 database provides quick response time and the extensible Bitcoin storage. This study contributes to a method of analyzing Bitcoin blockchain data using an easy-to-use RDBMS.
Research on Reversible Information Hiding Algorithm in Encrypted Domain Based on Histogram Translation

In order to ensure the security of the carrier owner's privacy and restore the original carrier image and secret information losslessly, a reversible information hiding algorithm based on histogram translation in the encrypted domain is proposed. The algorithm first encrypts the original carrier image and secret information, and then Use the histogram translation method to embed and extract the secret information to ensure that the original carrier image will not be affected during the embedding and extraction process, and in the recovery stage, this algorithm can either decrypt the image before extracting the secret information, or extract the secret first the image is then decrypted. The experimental simulation results show that this algorithm can not only directly embed and extract secret information in the encrypted domain, but also achieve complete separation of information extraction and image decryption, and improve the peak signal-to-noise ratio of the directly decrypted image. Ensure the security of reversible hiding.
The Development of Information System Security Operation Centre (SOC): Case Study of Auto Repair Company

Auto repair is a series of activities that engage in the field of body repair and painting services of car and motorcycle. The ultimate goal is to provide the best service and quality standards to customers by running the proper response processes, which lead the increased customer satisfaction to be achieved. Importantly, every company required the security system and mechanism to maintain the existing system as an effort to protect and overcome problems related to data privacy, transactions and communication. This study explores the problem and challenges faced by the respected company to generate an access control matrix for logical and physical assets as well as service operation procedure. The focus primarily in the development of Security Operation Center (SOC) as the front gate of the company to offer various services to the customers. SOC is a key enabler for operators who aspire to differentiate themselves based on customer experience and demand. A successful SOC application can help operators to reduce the pressure and improve operational efficiency.
A knowledge recommendation algorithm based on tacit knowledge relation calculation

The rapid development of the Internet and the increase in the magnitude of the dissemination of knowledge and information have made it difficult for people to quickly find knowledge and information that suits them from the huge range of knowledge. Traditional knowledge recommendation algorithms often face many deficiencies in their use, and perform poorly in actual applications. This paper proposes a knowledge recommendation algorithm that calculates the correlation through the tacit knowledge relationship. It uses vector quantification of the implicit knowledge relationship, and uses the concept of information entropy to calculate the maximum recommendable range, uses expert samples to learn to assist decision-making, and adjusts the results through variable factors. Experiments show that the algorithm proposed in this paper can effectively improve the accuracy and efficiency of recommendation and ensure the diversity of recommendation results.
Obstacle Avoidance Algorithm of Simulation Robotic Fish Based on Ant Colony Algorithm

In order to realize fast obstacle avoidance and tracking of robotic fish in the competition situation, and realize the rapid and accurate adjustment of robotic fish in the water environment without special intervention, a motion strategy of obstacle avoidance and tracking based on ant colony algorithm is proposed. And, the method is verified by 2D simulation platform. The results show that the simulation robotic fish can find the optimal path according to the algorithm, achieve the combination optimization of speed and direction under the premise of obstacle avoidance, and find the target point with the shortest time and distance. This shows that the obstacle avoidance and tracking method based on ant colony algorithm has strong adaptability, and can meet the requirements of motion strategy of simulation robotic fish.
Recognizing Bangla Handwritten Numerals: A Hybrid Model

Images of handwritten are non-identical to machine-printed ones. Orientation, distortion, writing style, similarity among digits and capturing angle of the image bring the confusion to detection. To ease the problem of human-computer interaction for handwritten digits, a hybrid model has been proposed in this paper. The model classifies digit image applying global, local and geometric features. To read an image more crystalline, the procedure is introduced with image blurring, otsu's binarization and thinning. After preprocessing, features are extracted including thin based local binary pattern, radon transform and radon cumulative distribution transform (RCDT), junction and endpoint. Tangent feature is extracted from junction and endpoint. Then all these features are scaled by min-max normalization. In the proposed architecture, the SVM classifier has been used. This technique has shown its supremacy regarding recognizing Bangla handwritten digits by achieving 97.25% accuracy.
Normalized Data Technique Performance for Covid-19 Social Assistance Decision Making - case: student’s internet data social assistance during learning from home due covid19

The student internet data assistance program is an effort by educational institutions to support online learning from home during the Covid-19 pandemic.A series of tests are applied to determine the optimization of decision making on the social assistance program performance.This study aims to evaluate the performance of students' internet data assistance programs using a confusion matrix approach, in particular on the performance of simple, linear and vector normalized data analysis techniques.The representation normalized techniques performance for simple data using SAW, linear data is VIKOR and vector using the MOORA method.The study results found that there were differences in performance in the process of selecting preferences for ranking potential social assistance recipients, as well as a differential in the confusion matrix performance values on the accuracy, precision, recall and error rate values on each method.
Performance-enhancing of RSA public key via three-dimensional hyperchaotic system

Every day the number of attacks increases to break the encryption algorithms. Therefore, it is considered necessary for designing a new algorithm with higher security. Any cryptographic system must have some fundamental features to resist any effective attack, preserve the transmitted information, and robust the statistical characteristics. This article aims to improve the RSA algorithm using a three-dimensional chaotic dynamic system with sophisticated attributes, which proposed for this purpose. It is derived from Memristive's and Sprott's chaotic systems. We call it a 3D-hyperchaotic Memristive Sprott system (3D-HMSS). This new system is adopted in generating keys for the RSA encryption algorithm. Some tests are used to show the efficiency of the proposed cryptosystem.
Loss-tolerant concatenated Bell-state measurement with encoded coherent-state qubits for long-range quantum communication

The coherent-state qubit is a promising candidate for optical quantum information processing due to its nearly-deterministic nature of the Bell-state measurement (BSM). However, its non-orthogonality incurs difficulties such as failure of the BSM. One may use a large amplitude ($\alpha$) for the coherent state to minimize the failure probability, but the qubit then becomes more vulnerable to dephasing by photon loss. We propose a hardware-efficient concatenated BSM (CBSM) scheme with modified parity encoding using coherent states with reasonably small amplitudes ($|\alpha| \lessapprox 2$), which simultaneously suppresses both failures and dephasing in the BSM procedure. We numerically show that the CBSM scheme achieves a success probability arbitrarily close to unity for appropriate values of $\alpha$ and sufficiently low photon loss rates (e.g., $\lessapprox 5\%$). Furthermore, we verify that the quantum repeater scheme exploiting the CBSM scheme for quantum error correction enables one to carry out efficient long-range quantum communication over 1000 km. We show that the performance is comparable to those of other up-to-date methods or even outperforms them for some cases. Finally, we present methods to prepare logical qubits under modified parity encoding and implement elementary logical operations, which consist of several physical-level ingredients such as generation of Schr\"odinger's cat state and elementary gates under coherent-state basis. Our work demonstrates that the encoded coherent-state qubits in free-propagating fields provide an alternative route to fault-tolerant information processing, especially long-range quantum communication.
Lightweight Feature Enhancement Network for Single-Shot Object Detection

At present, the one-stage detector based on the lightweight model can achieve real-time speed, but the detection performance is challenging. To enhance the discriminability and robustness of the model extraction features and improve the detector’s detection performance for small objects, we propose two modules in this work. First, we propose a receptive field enhancement method, referred to as adaptive receptive field fusion (ARFF). It enhances the model’s feature representation ability by adaptively learning the fusion weights of different receptive field branches in the receptive field module. Then, we propose an enhanced up-sampling (EU) module to reduce the information loss caused by up-sampling on the feature map. Finally, we assemble ARFF and EU modules on top of YOLO v3 to build a real-time, high-precision and lightweight object detection system referred to as the ARFF-EU network. We achieve a state-of-the-art speed and accuracy trade-off on both the Pascal VOC and MS COCO data sets, reporting 83.6% AP at 37.5 FPS and 42.5% AP at 33.7 FPS, respectively. The experimental results show that our proposed ARFF and EU modules improve the detection performance of the ARFF-EU network and achieve the development of advanced, very deep detectors while maintaining real-time speed.
Automatic Summarization of Stock Market News Articles

Stock market news articles published by leading companies are read by every trader to carry out their trading activities as they provide real time and reliable information about the organization. These news articles help in analyzing and identifying essential facts in trading. If these facts can be quickly captured from the articles could lead to look for more articles for better accuracy on their decision making. This research focuses on the single document based abstractive summarization of stock market investment news articles for traders. A summarization tool to extract the salient sentences from stock market investment news article on trading is developed in this research. In methodology, A keyword based weighting in extracting the sentences are used to enrich the domain relevancy. Domain is one of the deterministic factors in summarization which helps to correctly interpret the words. Finally an efficient graph algorithm is used to obtain the fluent summary. Then these summaries were compared with the domain expert summary to identify how far the summarization is useful for the traders.
Design and Development of Augmented Reality Application for Manufacturing Industry

In the past decade, the field of education has transformed tremendously with the advent of technology. Due to the advance of Smartphones, a new paradigm is known as M-learning has been developed. M-learning is an interactive learning technique by which students use a smartphone to study. In the presented work M-learning using the Augmented reality process has been discussed. The software development kit used for augmented reality development in Unity and Vuforia. The work focuses on the development of the interactive book and vital factors like dimensions of the page, the distance of the book page from VST display, orientation, and effect of curvature caused by binding. Experiments are conducted and results were obtained on the mentioned parameters. From the obtained results the work suggests the desired which works more effectively than a traditional layout followed for preparation of the interactive book for mobile augmented reality.
Evaluation of autoencoder for CBIR system in deep learning

In this world of growing Internet, the number of web search queries count per day has reached more than 5 Billion as of the year 2020. Growing numbers could also indicate previous unsatisfactory results or a lack of proper language used by the user. Language is never a barrier on the Internet, but sometimes searches related to photos and images are not correctly indexed. Image-based search engines were discovered to solve this problem. Through this work, we extend the Content-Based Image Retrieval (CBIR) system by developing and evaluating a Deep Learning approach by making use of an Autoencoder for quicker and relevant results.
Sharing Enterprise Cloud Securely at IBM

Cloud platforms have been relieving users from managing their own infrastructures and simplifying the task of application development. In recent years, containerized cloud platforms further accelerate the development process and provide a fine-grained billing model. For enterprise users, many challenges exist when migrating their applications to cloud platforms. The challenges include resource isolation, resource sharing, and usability. This article presents our approaches to securely and effectively share the already-shared cloud resources at IBM Chief Information Office. Several key approaches are discussed, such as cluster isolation, cluster automation, and identity and access management. We also collect various types of metrics as we are enabling the shared cloud. Our work shows great promise for migrating enterprise users and their data to the cloud.
Research on Generating Adversarial Examples in Applications

Abstract The deep neural network is a highly expressive model, which plays an extremely critical role in modern artificial intelligence applications. Adversarial samples can change the prediction results of neural networks by imposing imperceptible perturbations on the original images, which brings new challenges to deep learning. In this paper we summarize the methods of generating adversarial samples in recent years, intuitively feel the development of adversarial samples from the time of publication, and briefly classify them from the perspective of algorithm principles. At the same time, for practical applications, the advantages and disadvantages of the algorithm are analyzed and summarized, and the conditions for the algorithm to be suitable for application are proposed: no need to know the specific structure of the algorithm, high quality of the adversarial samples and convenient migration. The analysis points out that among the typical methods, MI-FGSM, ONE-PIXEL, and methods using GAN are more practical and worthy of further study.