Reviewerly/exHarmony huggingface dataset:
Secure Deduplication with Dynamic Updates in Multi-Tenant Cloud Environment

Cloud storage service providers accommodate customers by offering adequate storage based on customer requirements. Multi-tenant architecture to provide low-cost resource provision. In a multitenant system single instance shares with multiple customers, similar applications data has been used multiple customers so Deduplication techniques eliminate redundant data to improve storage efficiency and Bandwidth. To improve the confidentiality of the user privacy data, convergent encryption with the deduplication techniques has been proposed to encrypt the user data before uploading it to the third-party CS Sometimes Confidentiality of the user data leads to issues with this deduplication method. To overcome these issues this paper proposed dynamic ownership management of server-side deduplication with the blowfish encryption algorithm. In this model to support tenant block-level deduplication. The proposed method computational storage efficiency and security level.
Imaging Technology for Promoting Live Streaming in Thai Traditional Long-Boat Racing

This paper proposes initial concepts in promoting sport-watching, especially racing sports such as Thai traditional long-boat racing, through live streaming with imaging technology. The technique used is an amalgamation of Artificial Intelligent (Deep learning and image processing), computer graphic (CG), and live-broadcasting (Streaming) through the online platform. The preliminary experiments from YOLO3 reached the results 76.4% in precision and 95.7% in recall. Moreover, the research showed good results in real-time mapping graphic-items to the image. This proposed system will elevate the standards of Thai local sport-watching as it has an impact on the Thai society and local economy. The researchers hope that this system will become a benchmark for practice, competition, and judgment for other Thai sports in the future as well.
The Software Component Reusability: An Efficient Strategy to Develop an Enhanced Software through Using Service-Oriented Architecture (SOA)

In the years hate, aspects of reusability software are very strange and cannot be avoided by building new software arms. Service-oriented architecture (SOA) is a model for organising and leveraging distributed capabilities that may be controlled by several domains. This is a type of software design in which application components use network communication protocols to provide services to other components. In software components, the reusable handling project team is generally very familiar with the use of the source code database, so they can find the code they need and easily enter it into the application. Developers can also apply code to the repository for analysis, approval and inclusion. Developers can also monitor which applications using the UX version received. This is allowed because the library section is permitted. The software development process includes development time, development costs and human power requirements and so on. To reduce these things, programmers can apply technology called Reusability software components. The process of implementing or modifying software systems using existing software components is known as software component use. which can reduce human power, costs, and time. In this paper applying a new framework for SOA architecture in this architecture ensures the reuse of user services, easy maintenance to software, independent platforms, reliability, and scalability. In this framework for reuse and reduce the time and cost of software components to develop enhanced projects in an efficient way.
Customized Facial Expression Analysis in Video

<i>Existing computer vision-based emotion recognition systems are trained to classify images of faces into a very limited number of emotions. In this work, we take a different approach and train a convolutional neural network (CNN) that is able to distinguish subtle differences in facial expressions appearing in both individual images and videos. For this effect, we train a feature embedding network that maps a facial image into a position in embedding space, such that it is positioned close to similar facial expressions compared with other expressions. We train the feature embedding network using triplet loss on the publicly available facial expression comparison (FEC) data set. The proposed facial expression model is lightweight (4.7M parameters) and obtains a triplet prediction accuracy of 84.5&#x0025;&#x2014;very close to the average human performance of 86.2&#x0025;. A key contribution of this work is adapting the model that has been trained on images to work reliably on video content. To this end, we propose a novel automatic face quality assessment (FaceQA), which allows us to filter out faces that are unsuited for expression analysis. We validate the proposed approach on several applications, including searching video content for a specific expression from a single query face, generating facial expression statistics of various actors appearing in a video, as well as generating facial expression summaries</i>.
Writer Identification From Historical Documents Using Ensemble Deep Learning Transfer Models

Handwriting recognition is a challenging task and with the advancements in the development of the deep learning such task can be performed even for very limited documents. This paper aims to perform writer identification and retrieval from historical documents using an ensemble of convolutional neural network models that were built using the Inception-ResNet-v2 pre-trained architecture. The dataset comprises 170 images grouped in 34 classes. The results prove that the ensemble model outperforms single pre-trained models, obtaining an accuracy of 96%.
Combination of data mining and artificial intelligence algorithms for efficient web page recommendation

Due to the obvious unstable increase in information, the web is saturated with data, which makes the data search a complicated task. Existing web-based recommendation systems include shortcomings such as a lack of capability as well as scalability when dealing with online data, and blockages created by traffic while utilising the website during peak hours. Web recommendation systems help consumers find the right content and make the information search process easier. Web usage mining is regarded as the primary source for web recommendation, and it is used in conjunction with association rule mining and the C4.5 algorithm to recommend online pages to the user. The Google search engine has been widely enhanced the likelihood on the system's suggested structure. A web log is created when a user enters a search query into a search engine. This query would be compared to the web logs by the proposed system. The associate rule mining technique helps in matching the user's search query to the online log. The C4.5 algorithm is linked to a priority based on reviews, which obviously ranks the search based on priority for greater validation result.
Relationship between Physical Training and Tactical Training in Sports Training Relying on Boosting and Bagging Algorithms

With the development of sports, competition is becoming more and more confrontational and modern sports are developing in the direction of fast speed, fast rhythm, good skills, and high-altitude combat. And, this development requires good conditions and technology as a guarantee. Sports denote a competitive physical activity, which follows four aspects of competition: technology is the foundation, tactics are the means, the body is the cornerstone, and the psychology is the decisive point. Among these four factors, if the athlete does not have a good body, all techniques and know-how are empty talks. In this paper, two algorithms are introduced to track the athletes’ physical training and tactical training videos and the relevant data in the game are counted to obtain the physical indicators required by the athletes in each position. In a certain game, the sprint distance in the first round was 1979 m, the high-speed running distance was 2426 m, the high-intensity running distance was 4398 m, and the jogging distance was 1267 m.
Multi-Style Printed Sinhala Character Recognition and Digitalization Using Artificial Neural Network

Sinhala is the main language that is being used by over 75% of people as the first language and nearly 20% of people as a second language. Also, the Sinhala script is used to communicate and do official and non-official works all over Sri Lanka. Because of the technology developed in the world new trend was begin that digitalize printed documents because digitalizing documents are safe, easy to find, and can create editable files easily. Recognizing Sinhala characters are more complex mainly because of the shape and the character similarity. When digitalizing Sinhala printed documents using machine-trained models, it&#x2019;s hard to detect similar characters with multi-style. Past researches are also done for printed Sinhala character recognition but for the training and testing, they used regular font styles only. The problem of those researches is whether the recognition was successfully done or not when testing special font styles like bold, italic, and underline characters. The objective of this research is to recognize multi-style printed Sinhala characters with their features using an Artificial Neural Network (ANN) based on Universe of Discourse and Self Organization Map methods. The developed model is tested with MATLAB 2015. Overall character recognition accuracy is over 85%. Normal Sinhala character recognition accuracy is the same as previous researches (over 90) but fonts with feature recognition accuracy levels have been increased over 80%.
Hand Sign Language Interpreter using Machine Learning Model

Hand gestures are those gestures that are very expressive. Hand gestures are the non-verbal method of communication. These hand gestures involve different movements or positioning of fingers which convey a message. These hand gestures may represent any short messages like numbers, greetings, or any other message. Here, a machine learning model which classifies images of various gestures has been built and trained. This model is implemented in many types of convolution neural networks (CNN) and obtain an accuracy graph. The best convolution neural network with the best results is chosen and that model is deployed in the Django framework by converting it to a (.h5) file. Hence the output will be in a web application.
Efficient Multibit Function Encryption for Data Security in Internet of Things

The development of the Internet of Things (IoT) has been facing severe security threats, and the security and fine-grained access control of data in the IoT is one of the security problems that urgently need to deal with. Attribute-based encryption (ABE) schemes over lattice can not only achieve fine-grained access control but also resist quantum attacks. However, most schemes are single-bit encryption, which is inefficient. In this study, a multibit inner product predicate encryption (PE) scheme over lattice is proposed, which effectively expands the plaintext space. The scheme can realize multibit attribute-based encryption with the hidden access structure for data security in the IoT and support And-gate operation in the access structure with multiattribute. The fine-grained access control of ciphertext data can be realized under the condition of ensuring data privacy. The security of the scheme is based on LWE problem, and it can resist quantum attacks, that is, CPA security under the standard model.
Radial Basis Function Networks for Image Restoration with Stochastic Normalizations as Bayesian Learning in Deep Conventional Neural Network

The objective of the image restoration process is to improve the high-quality visual resolution from degraded images using various techniques in Artificial Intelligence., utilizing a normalization method, a method of centers and weights that has been adapted from the Radial Basis Function Network (RBFN) is initiated to the Stochastic Normalizations method as a Bayesian Learning algorithm. The normalized form of the RBFN-SB algorithm is derived, and the RBFN input data vector is from a damaged image array raised using Taylor's expansion. Following the dynamics of the normalized technique appeared to be tactful in comparison with the RBFN-SB. The test performance comparable to BN and, concomitantly, better validation losses suitable for ensuring results unpredictability estimation through approximate Bayesian posterior are procured. To test the outcome of the proposed system gave speed up the learning process, with minimum mean squared error (MSE) and produced better results of restoration images from damaged images. In the same form, the normalization accuracy can also be improved notably by the Bayesian learning algorithm.
Application of Web Data Mining and Information Combination Technology in E-commerce

This paper conducts preliminary research on the application of data mining technology in the emerging e-commerce recommendation system field. I mainly complete the following tasks: First, systematically discuss the current research status of data mining, e-commerce and recommendation systems at home and abroad. The second is to briefly describe the application of data mining technology in e-commerce enterprises. The third is to explain the recommendation algorithm and technology used in the recommendation system, and the e-commerce order rate increased by the fast trend. The fourth is to design an e-commerce recommendation system based on data mining based on information combination technology. The recommendation system is the focus of this paper. A model is designed based on the comparison of the latest methodologies, and the comparison analysis has reflected the overall performance of the model designed.
Using Wireless Sensor Networks to Improve Coastguard Productivity Improvements in Transmissions with Cognition

Scientists have a range of good wireless sensor devices in the present Internet of Things network infrastructure, including Joule, Wlan, Pan, and Beta. These limited devices. The greatest choices for transferring and storing data are wireless sensors is to gather information. They are, however, all subject to severe blocking. Limited operations are not cost-effective for hacker. In all of these circuits, there are transmitter sources. Having many entry points offering alternate sensors connection would also benefit these connections to prevent catastrophic disruption. The sensors modules are housed in a compact flying buoy with a user interface for transmitting alerts to the cell tower. Sensor networks are built for disaster situations, expenditure, and deployment in the impacted area using a centrifuge distribution method dropped from a rotor. A vehicular arbitrary networking is created by optimizing and using cross Clusters for long connectivity with the Cell Tower. The modelling variables were developed in the context of recent Indian rural floods. Models are used to test the cable network exergy statistics to other networks. The cable network sensor data improves utilization, evacuation plan, and urgent prioritization, saving people from jammers.
A Method for Reconstructing 3D Skeleton of Coronary Artery from 2D X-ray Angiographic Images

X-ray angiographic imaging is commonly used for diagnosis and treatment planning of coronary artery disease. However, it is produced via perspective projection principle, causing two-dimensional (2D) views with vessel segments overlapping and shortening, which prevents physicians from observing the vascular structure clearly. Reconstructing a three-dimensional (3D) skeleton of coronary artery from 2D X-ray angiographic images is able to improve the accuracy and efficiency for diagnosis of coronary heart disease. Therefore, we propose a novel method to reconstruct the accurate 3D coronary artery skeletons from 2D X-ray angiographic images. Specifically, the 3D coronary artery skeleton is represented with a point-cloud, the impact of rigid motions including device and patient movement are both considered in our method. Additionally, an iterative correction method is introduced to refine the coarse reconstruction results. Evaluation with 10 cases of clinical data show that average reprojection error of our reconstructed models is 0.114 ± 0.051 mm, which is significantly reduced compared with that of related methods, and meets clinical requirements.
Monitoring System For Tillandsia Ornamental Plants Using Fuzzy Algorithm

The national floriculture industry will continue to grow, along with the interest and demand for ornamental plants by the public the author intends to design a tool in the field of communication technology by utilizing the Internet of Thing (IoT) Platform so that it can be monitored remotely by covering the cultivated land of tillandsia ornamental plants. Temperature and humidity data will be analyzed using a fuzzy algorithm. Then the system gives a callback to the actuator (relay on the water pump) to work so that the temperature and humidity reach the expected conditions stably so that the care and maintenance of tillandsia plants is optimal and maintains the water conditions of tillandsia plants. In this research, found results with an average error of 0.4% on the temperature sensor with 7 tests, 0.83% on the light sensor with 3 trials. This system has succeeded in monitoring and controlling the prototype of the IoT-based mini greenhouse condition that can monitor remotely and automatically manage the conditions of temperature, humidity, and water light intensity to remain stable, making it easier for cultivation managers to monitor land or greenhouse conditions even though they are far away from the place of cultivation for the tools and pond managers are connected to the internet network.
Big Data Mining Algorithm Based on Semantic Segmentation on Heterogeneous Multi-Core Platforms

In order to improve the ability of accurate big data mining with multi-core heterogeneous boards, a semantic segmentation-based method for accurate big data mining with multi-core heterogeneous boards is proposed. The fuzzy information detection model proposed in this paper for constructing big data with multi-core heterogeneous boards based on semantic segmentation improves the feature matching model of data mining by nearly 7%. Construct a feature matching output model for big data with multi-core heterogeneous boards, and use semantic segmentation algorithm extraction methods for feature extraction and statistical analysis of big data with multi-core heterogeneous boards. Establish a semantic dynamic feature analysis model for big data on a heterogeneous multi-core platform, and extract statistical features of big data on a heterogeneous multi-core platform.
Learning and generalization of one-hidden-layer neural networks, going beyond standard Gaussian data

This paper analyzes the convergence and generalization of training a one-hidden-layer neural network when the input features follow the Gaussian mixture model consisting of a finite number of Gaussian distributions. Assuming the labels are generated from a teacher model with an unknown ground truth weight, the learning problem is to estimate the underlying teacher model by minimizing a non-convex risk function over a student neural network. With a finite number of training samples, referred to the sample complexity, the iterations are proved to converge linearly to a critical point with guaranteed generalization error. In addition, for the first time, this paper characterizes the impact of the input distributions on the sample complexity and the learning rate.
Retracted: Reform of Smart Exhibition English Guiding from the Perspective of New Media based on Data Association Analysis Model

Taking the exhibition English curriculum reform of the college as an example, from the aspects of course positioning, course objectives, teaching content design, and course evaluation and assessment, explore the teaching mode of exhibition English curriculum based on data association analysis model, and use multi-dimensional information data space, data development, The relationship between data analysis and data association analysis is used to discuss the visual presentation design, and follow the research ideas of "data collection/data statistics/analysis/import/preprocessing/data mining" to construct the intelligent exhibition English guidance reform under the new media technology.
Research on Application of Adaptive Matching Optimization and Artificial Intelligence System in Modern Chinese Network

There are many drawbacks in traditional Chinese teaching and Chinese learning, including narrow semantics and lower teaching efficiency than modern Chinese networks. This article analyzes modern Chinese networks based on artificial intelligence algorithms and pattern matching. First, it introduces machine learning and depth in artificial intelligence algorithms. Learning improves the accuracy of Chinese semantics and machine translation; then, based on pattern matching and adaptive matching optimization algorithms, the words, rare words and information networks in the Chinese network are refined and fully optimized, resulting in an increase of 6.2%. Experimental data shows that artificial intelligence algorithms combined with adaptive matching have improved modern Chinese.
A New Bound for Privacy Loss from Bayesian Posterior Sampling

Differential privacy (DP) is a state-of-the-art concept that formalizes privacy guarantees. We derive a new bound for the privacy loss from releasing Bayesian posterior samples in the setting of DP. The new bound is tighter than the existing bounds for common Bayesian models and is also consistent with the likelihood principle. We apply the privacy loss quantified by the new bound to release differentially private synthetic data from Bayesian models in several experiments and show the improved utility of the synthetic data compared to those generated from explicitly designed randomization mechanisms that privatize posterior distributions.
Adaptive Directional Lifting Transform based Optimization to Extreme Visual Recovery

Rank minimization is giving the solutions to the problems in image denoising, machine learning, computer vision and compressed sensing applications. The corrupted image will not convey the original information and during transmission the information may further tampered. The image is corrupted with small amount of noise, it may be recovered with the conventional methods. The amount of noise in the image is more, the existing methods are unable to recover the corrupted image. Here an Adaptive Directional Lifting Wavelet Transform (ADLWT) is used along with the familiar Alternating Direction Method of Multipliers optimization method. The proposed method is able to recover the corrupted image that contains high percentage of missing observations. The objective of the work is to provide an efficient method to recover the damaged pixels in the image and to provide the optimal λ value which can recover with high accuracy and efficiency with respect to quality metrics. The proposed method is evaluated with the familiar assessment measures like Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR) and Structural Similarity (SSIM). The recorded values are better when compared with the existing methods.