Reviewerly/exHarmony huggingface dataset:
Certificateless Hybrid Signcryption by a Novel Protocol Applied to Internet of Things

The rapid development of the Internet of Things (IoT) has accelerated the integration of science and technology with life, enabling the public to start enjoying the convenience brought by intelligent living. However, there are multiple resource-constrained sensing devices in IoT, which are always facing various external or internal attacks, making it difficult to ensure the secure transmission of sensitive data in IoT. Therefore, to address the problem of data transmission in resource-constrained devices in IoT, we propose a new certificateless hybrid signcryption scheme for IoT. It is a novel scheme that satisfies confidentiality and unforgeability, showing higher computational efficiency and lower overhead of transmission. To prove that it satisfies the efficent transmission of IoT, we conduct simulation experiments, and the experimental results show that our proposed scheme has higher efficiency than the existing schemes.
Super Resolution for Noisy Images Using Convolutional Neural Networks

The images in high resolution contain more useful information than the images in low resolution. Thus, high-resolution digital images are preferred over low-resolution images. Image super-resolution is one of the principal techniques for generating high-resolution images. The major advantages of super-resolution methods are that they are economical, independent of the image capture devices, and can be statically used. In this paper, a single-image super-resolution network model based on convolutional neural networks is proposed by combining conventional autoencoder and residual neural network approaches. A convolutional neural network-based dictionary method is used to train low-resolution input images for high-resolution images. In addition, a linear refined unit thresholds the convolutional neural network output to provide a better low-resolution image dictionary. Autoencoders aid in the removal of noise from images and the enhancement of their quality. Secondly, the residual neural network model processes it further to create a high-resolution image. The experimental results demonstrate the outstanding performance of our proposed method compared to other traditional methods. The proposed method produces clearer and more detailed high-resolution images, as they are important in real-life applications. Moreover, it has the advantage of combining convolutional neural network-based dictionary learning, autoencoder image enhancement, and noise removal. Furthermore, residual neural network training with improved preprocessing creates an efficient and versatile single-image super-resolution network.
The Application of Computer-Based Multimedia Technology in Cognitive Computing

With the continuous development of science and technology, people's research on computer-related technologies is gradually deepening. The proposal of artificial intelligence makes the research of intelligent AI in urgent need for seeking breakthroughs. Among them, cognitive computing methods are important for computers and human brain thinking. Model learning is even more meaningful. This article aims to study the application of computer-based multimedia technology in cognitive computing methods. For this, this article proposes a method of distributed multimedia technology and a method of two-way cognition for cognitive computing through this technology to deepen the rapid improvement of cognition. In addition, this article finally designed relating experiments to study it. The experimental results show that the cognitive accuracy of the improved cognitive computing method has increased by 32.9%, and the cognitive ability has also been greatly improved compared to the past.
Convolutional Siamese-RPN++ and Yolo-v3 based Visual Tracking Regression

Visual tracking is an implementation of moving object tracing from deep machine learning methods where system initially set the object and generate a unique identification or pattern for tracking the moving object at each frame of a video.Object tracking is the undertaking of automatically distinguishing objects in a video and deciphering them as a bunch of directions with high accuracy.This paper intended to propose a SiamRPN network which has been considered as offline network with having very large dataset.In this network there are so many sub networks are available to extract the features along with regression and classification.Here the Siamese-RPN++ has been reconciled with Yolo-v3 which is an object detection approach that enhances the feature extraction model for better visual tracking analysis.Prior recognition frameworks repurpose the classifiers or localizers to perform feature extraction.It applies the model to an image at various areas even while object scaling.System has been tested with various datasets/benchmarks including OTB50 and OTB100 and achieved 91.17 & 89.98 resp.percent of accuracies.
Towards developing impairments arabic speech dataset using deep learning

&lt;span&gt;The effective and efficient recognition of speech sounds errors for impaired children is important if a defective phonological process is early detected and corrected. This study deals with the topic of classification of speech sound errors in Arabic impairments children when Arabic letters and numbers are incorrectly pronounced. For 18 standard Arabic isolated numerals and characters, we created an impaired children speech recognition system. We utilized the Mel frequency cepstral coefficients throughout the feature extraction step. then deep long short-term memory network recognition phase. We used the developed model with the developed dataset and the classification accuracy was 97.99% and lose 0.18%, additionally, the results have been compared and yielded extremely intriguing results with previously existing recognition rates models.&lt;/span&gt;
Coldness Presentation Depending on Motion to Enhance the Sense of Presence in a Virtual Underwater Experience

In virtual reality (VR), there is consumer demand for underwater world experiences.To address this demand, previous studies have presented coldness to enhance the sense of presence in virtual underwater experiences.We considered that we could further improve the sense of presence and the experiential quality of underwater VR simulations by making the coldness presentation more realistic.We also considered that methods of presenting coldness continuously would have a wider application range.In this research, with reference to actual water behavior, we propose a method that presents coldness depending on whether it is moving.We conducted an experiment comparing the sense of presence between the steady-state temperatures of 24.9 ± 1.9 • C / 27.8 ± 2.7 • C and the proposed method, the temperature of which moves between the aforementioned temperatures as the user moves and stops.The results show that the proposed method yielded higher scores than the higher steady-state temperature in ''closeness of overall sensation'' and ''closeness of thermal sensation''.For the lower steady-state temperature, the proposed method achieved the same sense of presence improvement while removing less heat.This extends the experiential duration.Meanwhile, for those who noticed the temperature change, the proposed method yielded higher scores than the lower steadystate temperature in ''realness'' and ''closeness of thermal sensation''.To further improve effectiveness of the proposed method, it is important to not only change the temperature but also to notice the temperature change.This method is suitable for several virtual underwater applications, such as education and training.
Convergence Analysis of BIM &amp; Blockchain Technology in Construction Industry Informatization

Informationization is an important trend in the development of the current construction industry. Ensuring the efficient combination of BIM technology and construction engineering will not only help improve the quality of engineering intelligent information management, but also have a significant impact on the acquisition of engineering construction benefits. On the basis of clarifying the concept of blockchain technology, analyze the characteristics of blockchain technology, and predict the possible application scenarios of blockchain technology in the construction industry, in order to provide a reference for the new development of the construction industry. The article explains BIM and block Based on the chain technology, analyze its specific application in the construction industry information management, and point out its practical application value.
A Three-step Method for Three-way Clustering by Similarity-based Sample’s Stability

Clustering is an important research field in machine learning. Traditional clustering approaches are not very effective in dealing with clusters having overlapping regions. To better capture the three types of relationships between a cluster and a sample, namely, belong-to fully, belong-to partially and not belong-to fully, we propose a theory of similarity-based sample’s stability and develop a three-step method for three-way clustering by integrating similarity-based sample’s stability into the idea of three-way clustering in this paper. In the proposed theory, the similarity of two samples is used to define the frequencies of two samples and the samples stability is calculated based on the defined frequencies and determinacy function. With this stability, the universe is divided into stable set and unstable set. The samples in the stable set are assigned into the core region of each cluster by using traditional clustering algorithm. The samples in the unstable set are assigned into the fringe region of corresponding cluster according to distances between the elements and the centers of the cluster core regions. Therefore, a three-way clustering is naturally formed. Experimental results on datasets show that this method can improve the structure of the clustering results.
Distortion Calculation Method Based on Image Processing for Automobile Lateral Mirrors

The automobile lateral-view mirrors are the most important visual support for driver safety; therefore, it is important they have robust quality control. Typically, the distortion of a lateral-view mirror is measured using the JIS-D-5705 standard; however, this methodology requires an expert person to perform the measurements and calculations manually, which can induce measurement errors. In this work, a semi-automatic distortion calculation method based on image processing is presented. Distortion calculations of five commercial mirrors from different manufacturers were performed, and a comparative study was carried out between the JIS-D-5705 standard and the proposed method. Experimental results performed according to the JIS-D-5705 standard showed that all mirrors have a distortion lower than 5%, indicating that all meet the standard. On the other hand, the proposed method was able to detect that one of the mirrors presented an important distortion, which was not detected by the methodology proposed in the standard; therefore, that mirror should not meet the standard. Then, it was possible to conclude that the proposed distortion calculation method, based on image processing, has higher robustness and precision than the standard. In addition, an appropriate and effective behavior against changes in scale, resolution, and, unlike the standard, against changes in image rotation was also shown.
Adopting the cybersecurity concepts into curriculum: the potential effects on students’ cybersecurity knowledge

&lt;span&gt;This study examines the effect of adopting cybersecurity concepts on the information and technology (IT) curriculum and determines the potential effect on students' knowledge of cybersecurity practices and level of awareness. To this end, a pilot study was first conducted to measure the current level of cybersecurity awareness. The results revealed that students do not have much knowledge of cybersecurity. Thus, a four-step approach was proposed to infuse the relevant cybersecurity topics in five matched courses based on the latest cybersecurity curricular guidelines (CSEC2017). A sample of 42 students was selected purposively without prior knowledge of cybersecurity and divided identically into experimental and control groups. Students in the experimental group were asked to take five consecutive courses over five semesters. In each course, groups went through a pre-test for the infused topics. Then, the experimental group taught the corresponding infused topics. A post-test was administered to both groups at the end of each course, and the t-test was conducted. The results found significant differences between marks of prior and post-tests for 11 out of 14 infused topics. These satisfactory results would encourage universities to infuse cybersecurity concepts into their curriculum.&lt;/span&gt;
Engineering Education and Learning outside the Classroom through MOOCs: Student Assessment

The development of engineering students is more often associated with classroom learning, but there is an increasing need for online learning. This study tests students' impressions of a specialized massive open online course. Engineering students from the Russian Federation and Ukraine (n = 295) were involved to the empirical part. According to the results, 84% of the participants had difficulty in using the knowledge gained during the course. In this paper, the authors suggest ways of improving online courses for students so that what they learn can be put into practice. On average, it will take universities 4.5 - 8 years to completely switch from face-to-face classes to the use of MOOCs.
Crypto Currency Market Price Prediction Using Data Science Process

Abstract: Cryptocurrency is commonly called as digital currency where the coin ownership records are stored in an electronic ledger existing in a form of a computerized database using strong cryptography approach which is used to maintain the creation and updation of an addition coin in a market, it is also used to check and maintain the current ownerships of the coins. Nowadays cryptocurrency is used in largescale and there is a sudden rise or decrease in their share and it is difficult to predict the price of the crypto currency. In this project a machine learning model is built to predict the price of crypto currency. The application of data science process is applied for getting the better model for predicting the outcome. Variable identification and data understanding is the main process in building the successful model. Different machine learning algorithms are applied on the pre-processed data and the accuracy are compared to see which algorithm performed better other performance metrics like precision, recall, scores are also taken in consideration for evaluating the model. The machine learning model is used to predict the crypto currency outcome Keywords: crypto currency, Decision Tree, Prediction, Random Forest, Logistic regression, Linear Regression, LASSO Regression, Ridge Regression.
Combining extended Kalman filtering and rapidly-exploring random tree: An improved autonomous navigation strategy for four-wheel steering vehicle in narrow indoor environments

Autonomous navigation in narrow indoor environments such as indoor factory, warehouse and laboratory environments, and so on requires higher flexibility and navigation accuracy of the vehicle. This article presents an autonomous navigation method for four-wheel steering vehicle which combines extended Kalman filtering (EKF) and rapidly-exploring random tree (RRT) to improve the precision and flexibility of autonomous navigation of the vehicle in narrow indoor environments. The four-wheel steering model was established by the key parameters such as shape size and minimum angle of rotation of the experimental vehicle. Considering the problem that the uncertainty of pose estimation increases with time during autonomous navigation, an error model is schemed by adding noise to the output terminal of the analog odometer sensor. In order to suppress the accumulation of the uncertainty and keep it stable for a long time, the prediction and update steps of Kalman filter are introduced to filter the error. Then, the simultaneous positioning and mapping are established. Based on accurate positioning, a set of driving paths to reach the target is generated by RRT sampling algorithm. The simulation results show that positioning uncertainty remains stable over time, which verifies the effectiveness of the method. The overall positioning percentage error is 0.21%. Compared with traditional dead reckoning algorithm, the positioning accuracy is improved by 73.1% and the vehicle flexibility is increased by 68.6%. The four-wheel steering vehicle can find an ideal trajectory in narrow indoor environments, which assures the efficiency of the autonomous navigation and the traveling quality of the navigation route. Finally, the experimental results are consistent with the simulation results, which further verifies the effectiveness of the proposed algorithm.
Variable Decomposition for Large-Scale Constrained Optimization Problems Using a Grouping Genetic Algorithm

Several real optimization problems are very difficult, and their optimal solutions cannot be found with a traditional method. Moreover, for some of these problems, the large number of decision variables is a major contributing factor to their complexity; they are known as Large-Scale Optimization Problems, and various strategies have been proposed to deal with them. One of the most popular tools is called Cooperative Co-Evolution, which works through a decomposition of the decision variables into smaller subproblems or variables subgroups, which are optimized separately and cooperate to finally create a complete solution of the original problem. This kind of decomposition can be handled as a combinatorial optimization problem where we want to group variables that interact with each other. In this work, we propose a Grouping Genetic Algorithm to optimize the variable decomposition by reducing their interaction. Although the Cooperative Co-Evolution approach is widely used to deal with unconstrained optimization problems, there are few works related to constrained problems. Therefore, our experiments were performed on a test benchmark of 18 constrained functions under 100, 500, and 1000 variables. The results obtained indicate that a Grouping Genetic Algorithm is an appropriate tool to optimize the variable decomposition for Large-Scale Constrained Optimization Problems, outperforming the decomposition obtained by a state-of-the-art genetic algorithm.
Study on Risk Analysis and Decision-Making of Small- and Medium-Sized Enterprises on BP Neural Network Algorithm

Artificial neural network algorithm has strong nonlinear mapping function. BP neural network algorithm is the representative of artificial neural network algorithm. Due to the relatively small scale of small, medium, and micro enterprises and lack of mortgage assets, the biggest market risk they usually face is credit risk. In order to avoid huge economic losses for banks, the impact of risk analysis on credit decision-making is the key. Therefore, the establishment of a scientific and complete credit risk assessment system has become the primary task. This paper mainly uses BP neural network algorithm to analyze the risk of small and medium-sized enterprises and solve the problem of credit decision-making. First, analyze the various influencing factors that cause credit risk, and layer the factors according to the degree of risk; secondly, use the fuzzy comprehensive evaluation method in mathematical modeling to rank each factor; then, use the fuzzy analytic hierarchy process to calculate the weight of each indicator factor as calculated by establishing the risk value function, using the function expression of the annual interest rate and the total credit formula to calculate the total credit, which solves the problem of the loan line of each enterprise, and use the optimal linear programming to give priority to the selection of the line and the interest rate. Enterprises: finally, for enterprises that do not have a credit rating, reasonable adjustments are made to the quota by establishing lending qualification expression, taking into account the impact of unexpected external factors, establishing an improved annual interest rate function, and achieving an optimized credit strategy through the establishment and solution of the model. The goal of optimizing credit strategy has been achieved.
DTS-Depth: Real-Time Single-Image Depth Estimation Using Depth-to-Space Image Construction

As most of the recent high-resolution depth-estimation algorithms are computationally so expensive that they cannot work in real time, the common solution is using a low-resolution input image to reduce the computational complexity. We propose a different approach, an efficient and real-time convolutional neural network-based depth-estimation algorithm using a single high-resolution image as the input. The proposed method efficiently constructs a high-resolution depth map using a small encoding architecture and eliminates the need for a decoder, which is typically used in the encoder–decoder architectures employed for depth estimation. The proposed algorithm adopts a modified MobileNetV2 architecture, which is a lightweight architecture, to estimate the depth information through the depth-to-space image construction, which is generally employed in image super-resolution. As a result, it realizes fast frame processing and can predict a high-accuracy depth in real time. We train and test our method on the challenging KITTI, Cityscapes, and NYUV2 depth datasets. The proposed method achieves low relative absolute error (0.028 for KITTI, 0.167 for CITYSCAPES, and 0.069 for NYUV2) while working at speed reaching 48 frames per second on a GPU and 20 frames per second on a CPU for high-resolution test images. We compare our method with the state-of-the-art methods on depth estimation, showing that our method outperforms those methods. However, the architecture is less complex and works in real time.
Network traffic clustering with QoS-awareness

Network traffic classification is essential in supporting network measurement and management. Many existing traffic classification approaches provide application-level results regardless of the network quality of service (QoS) requirements. In practice, traffic flows from the same application may have irregular network behaviors that should be identified to various QoS classes for best network resource management. To address the issues, we propose to conduct traffic classification with two newly defined QoS-aware features, i.e., inter-APP similarity and intra-APP diversity. The inter-APP similarity represents the close QoS association between the traffic flows that originate from the different Internet applications. The intra-APP diversity describes the QoS variety of the traffic even among those originated from the same Internet application. The core of performing the QoS-aware feature extraction is a Long-Short Term Memory neural network based Autoencoder (LSTM-AE). The QoS-aware features extracted by the encoder part of the LSTM-AE are then clustered into the corresponding QoS classes. Real-life data from multiple applications are collected to evaluate the proposed QoS-aware network traffic classification approach. The evaluation results demonstrate the efficacy of the extracted QoS-aware features in supporting the traffic classification, which can further contribute to future network measurement and management.