Reviewerly/exHarmony huggingface dataset:
Security challenges and defense approaches for blockchain-based services from a full-stack architecture perspective

As an advantageous technique and service, the blockchain has shown great development and application prospects. However, its security has also met great challenges, and many security vulnerabilities and attack issues in blockchain-based services have emerged. Recently, security issues of blockchain have attracted extensive attention. However, there is still a lack of blockchain security research from a full-stack architecture perspective, as well as representative quantitative experimental reproduction and analysis. We aim to provide a security architecture to solve security risks in blockchain services from a full-stack architecture perspective. Meanwhile, we propose a formal definition of the full-stack security architecture for blockchain-based services, and we also propose a formal expression of security issues and defense solutions from a full-stack security perspective. We use ConCert to conduct a smart contract formal verification experiment by property-based testing. The security vulnerabilities of blockchain services in the Common Vulnerabilities and Exposures (CVE) and China Nation Vulnerability Database (CNVD) are selected and enumerated. Additionally, three real contract-layer real attack events are reproduced by an experimental approach. Using Alibaba's blockchain services and Identity Mixer in Hyperledger Fabric as a case study, the security problems and defense techniques are analyzed and researched. At last, the future research directions are proposed.
Joint Transfer Extreme Learning Machine with Cross-Domain Mean Approximation and Output Weight Alignment

With fast learning speed and high accuracy, extreme learning machine (ELM) has achieved great success in pattern recognition and machine learning. Unfortunately, it will fail in the circumstance where plenty of labeled samples for training model are insufficient. The labeled samples are difficult to obtain due to their high cost. In this paper, we solve this problem with transfer learning and propose joint transfer extreme learning machine (JTELM). First, it applies cross-domain mean approximation (CDMA) to minimize the discrepancy between domains, thus obtaining one ELM model. Second, subspace alignment (sa) and weight approximation are together introduced into the output layer to enhance the capability of knowledge transfer and learn another ELM model. Third, the prediction of test samples is dominated by the two learned ELM models. Finally, a series of experiments are carried out to investigate the performance of JTELM, and the results show that it achieves efficiently the task of transfer learning and performs better than the traditional ELM and other transfer or nontransfer learning methods.
Drones and ultraviolet radiation for the detection of scheelite mineral

Technological development and mineral extraction have always been close-knit. Devices have become dependent on dozens of elements with scant concentrations in the Earth's crust. In order to compensate for the scarcity, the search for reliable mineral survey techniques is crucial. It is possible to take advantage of the fact that many minerals are sensitive to the physical phenomena of electromagnetic radiation. The ultraviolet light shows dazzling results since it can excite some minerals to the point of causing them to fluoresce, as is the case of scheelite (main ore of tungsten), a critical mineral that is under a significant mining activity. The purpose of this article is to present a methodology both for collecting spectral data of scheelite, supported by airborne multispectral sensors, and for working with machine learning algorithms, in order to develop an accurate procedure for mineral mapping. The results show that scheelite is easily detectable with a characteristic spectral footprint by making it react with an ultraviolet light source, which could be used in further research to detect this mineral without a severe environmental impact.
Detection Method of Augmented Reality Systems Mosaic Stochastic Markers for Data-Centric Business and Applications

The paper proposes the method of detection of mosaic stochastic markers of augmented reality systems for data-centric business and applications. A description of the well-known markers of augmented reality systems, their advantages and disadvantages is given. Determined that the finding of angles or special areas of reference of well-known markers is a quick method, but requires unambiguous detection of all four points of well-known markers. The detection method of mosaic stochastic markers for augmented reality systems has been improved. The main stages of the method are: the preprocessing of the input image; the finding the marker area; to determine the bit container. Results of the experiments of the method of detection of mosaic stochastic markers for augmented reality systems has been given.
Deep learning-based bubble detection with automatic training data generation: Application to the PEM water electrolysis

Various fields, such as the paper industry, chemical engineering, and renewable energy, are faced with gas-liquid two-phase flows and are being studied by visualization and observation. Although it is necessary to quantitatively evaluate the characteristics of bubbles, there is a limitation in the amount of labor required for detection and measurement by human observation of images. There are no examples for bubbles in polymer electrolyte membrane water electrolysis (PEMWE), where the bubbles in PEMWE have heterogeneous backgrounds, unpatterned patterns, and unclear bubble contours. Existing methods for detecting these bubbles are not expected to be accurate enough. In this study, a deep learning-based bubble detection method using convolutional neural networks (CNN) was developed for bubbles in PEMWE. Our method has two novel approaches: first, we developed an algorithm that automatically draws a pseudo-bubble image based on an actual bubble image on an arbitrary background as a method for automatically generating training data for the CNNs. Second, we developed a CNN-based bubble detection method that uses the motion of bubbles, specifically, the difference between the bubble image and the image from one frame ago, as input. Finally, we tested the algorithm on bubble images in a PEMWE and achieved an F1 score of 0.83 or better for all current densities of 0.5, 1, 2, and 3 A/cm2, and an F1 score of 0.844 for the entire detection.
Hybrid framework for single-pointer meter identification

Automated identification of single-pointer meter identification in substations is widely used in the construction of digital substations and it must accurately identify the value of the pointer meter. Current single-pointer meter identification methods are not universally applicable and can only identify one type of meter. In this study, we present a hybrid framework for single-pointer meter identification. First, the input image of the single-pointer meter is modeled to gain a priori knowledge, including the template image, dial position information, the pointer template image, and scale value positions. Based on a convolutional neural network to generate the input image and the template image feature points, image alignment is then applied through a feature point match to mitigate slight changes in the camera angle. Next, a pixel loss-free method of arbitrary point image rotation correction is presented for rotation template matching. Finally, by rotating the input gray mask image of the dial and matching it to the pointer template to get the optimal rotation angle, the meter value is calculated. The experimental findings demonstrate the method's effectiveness in identifying nine different types of single-pointer meters in substations with various ambient illuminations. This study provides a feasible reference for substations to identify the value of different types of single-pointer meters.
Privacy Preserving Keyword Search Over Hash Encrypted Data in Distributed Environment

In trusted public cloud, allowing data consumers to recover the information with full integrity. The most of research efforts were focused on a single data owner for safe cloud-based encryption of encrypted data. Additionally, searchable encryption makes it possible for a data user to locate a specific encrypted document from a cloud of encrypted data by searching for keywords (KS). This study isn't useful for retrieving relevant results from keyword searches. Such issues may be overcome using the secure and privacy-preserving keyword search retrieval (SPKSR) technology, which allows users to recover hashed encrypted files from cloud data that is also hashed encrypted. Three distinct entities are included in the proposed system: (a) cloud server (CS), (b) data owner (DO), and (c) data users (DU). It is the CS that generates a searchable index tree for the owner’s hashed encrypted files. The CS keeps the index tree structure and the hashed encrypted document set in its own storage. The encrypted data is hashed and DU executes the “search” over it. Analyses and comparisons of the experimental findings of the suggested system with the other existing system demonstrate its superiority.
An Analysis of COVID Effected Patients

Due to COVID-19 pandemic, public health emergency was created throughout the world. So, we took the base data and perform analysis on how the effect of vaccination on the human lives in terms of recovery, severity, side effects, and deaths on the globe. We also analyzed the country wise vaccination to understand the scenarios in the world, because the COVID virus is transforming in different countries in different ways, therefore the understanding the mutations of the virus and the use of the drug analysis also very much important for the future generations and also useful to face the future COVID virus mutations.
Fluctuating Small Data Imputation with Lagrange Interpolation Based

The data imputation process, the subject of this research, is a solution needed in the current era of big data. In addition to big data, small data often experiences similar problems such as data loss, especially on time series-based data. Several things can cause data loss, one of which is the data communication problem caused by the network on the Internet of Things (IoT) system. Losing data in data conditions that are quite volatile is also quite a challenge. Not all methods can produce good accuracy in dealing with fluctuating data cases. The method used to impute small volatile data in this study is Lagrange interpolation. Several methods used for the data imputation process consist of Lagrange interpolation of degree one (linear), degree two (quadratic), and degree three (cubic). Lagrange cubic interpolation generally provides better accuracy with data loss percentages of 10%, 30%, and 50% compared to several other types of polynomial interpolation indicated by the MSE value. Indirectly, the results of this research can predict that if the application of Lagrange interpolation on fluctuating data has good accuracy, then Lagrange interpolation can have the same accuracy as non-fluctuating data.
Workpiece Recognition Technology Based on Improved SIFT Algorithm

To solve the problem of workpiece image matching and recognition in complex environments such as rotation, scaling, and partial occlusion, and to solve the problems of slow running speed and low matching accuracy of SIFT algorithm, a scheme of reducing the dimension of feature point descriptor and selecting the best feature point matching is proposed. A concentric ring with a radius of 2 pixels, 4 pixels, and 6 pixels is constructed with the feature point as the center. According to the rule, the pixels close to the feature points significantly impact the features. The pixels far from the feature points have a minor impact on the features. They are divided into 4, 8, and 16 partitions, respectively, generating 56-dimensional descriptors. The feature points are divided into two sets in image matching: maximum and minimum. The distance between the feature points of the same typeset between the two images is calculated to select the best matching points, which reduces the amount of image matching calculation and saves the algorithm time consuming. The experimental results of workpiece image recognition show that the improved algorithm of feature point descriptor dimensionality reduction and image matching can effectively improve the accuracy and reduce the image matching time.
Taxonomy for Classification of Cloud Service (Paas) Interoperability

The decreasing cost of hardware has opened the doors to the development of various concepts like big data and huge storage spaces. The development of the cloud is one major development in this string of developments. The economic and other important contract features like the locking time gave rise to the usage of multi-clouds by various companies for the same or similar applications. The extended form of this scenario leads to operation on data on different clouds giving rise to interoperability. The Infrastructure as a service, Platform as a service, and Software as a service in the cloud force us to define taxonomy based on various views. Each view addresses its own customized problem. There is a need to generalize the taxonomy of interoperability. This paper presents a taxonomy for the classification of the cloud for Platform as a Service.
A Novel Hierarchical Face Recognition Method Based on the Geometrical Face Features and Convolutional Neural Network with a New Layer Arrangement

This paper represents the face recognition system utilizing a hierarchical arrangement for feature extraction and classification. The proposed method uses the active appearance graph model (AMM) as the first feature extractor and CNN as the second extractor. Also, for each stage, a classifier is considered. The paper investigates the results of two different types of classifiers, SVM and Softmax. The support vector machine shows superior function and outcomes. For the first stage, the AMM extracts six points from the face, then calculates three axillary points based on those to later measure and create five axes or distances on the face as the feature map for each image. It is worth mentioning that the axillary axes are divided by half to eliminate the effect of different facial orientations by the algorithm. In the second stage, for the CNN to give desired results, various forms of data augmentation, such as horizontal flip, shift, scaling, and rotation, are implemented into the algorithm. The color FERET database is used in this research for evaluation. The AMM divides 1208 classes of the FERET into 64 new classes. The biggest new class has 42 members. Applying the algorithm to the FERET database, we compare the accuracy of the system to the other existing methods. Our method shows a better accuracy of 96.35% with Softmax and 97.68% with SVM. Also, compared to the other techniques, the number of required data augmentation is reduced drastically, which translates to lower computational complexity and faster process.
Evaluation and Techniques of Automatic Text Summarization

A progression of script summarization of compress original manuscript and produces a summary containing significant sentences without changing the meaning of source manuscript and includes all significant vital information from the source manuscript. Now-a-days, text summarization has become very popular and applicable for search engine, business analysis, market review, medical records, news article summary, government officials, and researchers. The most indispensable concepts, while viewed commencing the summary conclusion, refer the term taking out script sorting along with conceptual script sorting. An extract the scripts with highly rank statement review is designation in addition to enlargement as well as at this moment investigate in addition with transmitted collaborate with conceptual summarization and instantaneous summarization. Even though nearby enclose been accordingly an assortment of accomplishment within the attainment of data sources, mechanism, and approaches available, in attendance be nix several credentials with the purpose of be capable of endow with an expansive representation of the contemporary circumstances of investigate pasture. This article makes accessible a bulky and competent assessment of investigate in the pasture of text summarization available from preceding ten existence as the consequence of extraction of investigate studies for the acknowledgment and investigation to portray investigate focus or topical trends, data sources, preprocessing, features, investigation studies, methods, costing, lacunas, and future scope.
An Efficient Detection and Classification of Sarcastic by Using CNN Model

There have been a lot of new ways to figure out how people feel about things on Twitter and other microblogging platforms in the last few years. Using both types of semantics, experiments are carried out to see how they perform in three popular tasks of sentiment investigation on Twitter: sentiment analysis at the level of entities, sentiment analysis at the level of tweets, and context-sensitive sentiment lexicon adaptation. These three tasks include as follows: Find and categorize sarcastic texts were the objectives of this module. After the initial sentiment classification, texts that were initially classified as positive will be classified as moreover positive sarcastic or true positive. Texts classified as negative by the primary sentiment classification module can be confidential in two ways. Negative sarcasm and true negativity are two different types of negativity. In this research, a CNN model integrates the implicit as well as explicit representations of short text for sarcastic classification and is abbreviated as CNN-SC model. The proposed classification model incorporates three main processes, namely feature engineering, FS, and classification. Using data from Amazon and Twitter, it was tested to see if it worked. Detailed information on the dataset, experiments, and findings is presented in the section on experimental setup and results.
Deep and Transfer Learning in Malignant Cell Classification for Colorectal Cancer

Colorectal cancer has shown wide spread over a decade, projected number of cancer cases in 2022 will be almost 71% as per ICMR and NCBI data (Kather et al., 100,000 histological images of human colorectal cancer and healthy tissue (Version v0.1) (2018)) due to lifestyle and changing dietary habits. If diagnosis in its early stages, then will significantly boost survival rate of patient. Computer integrated system had positive influence on smoothing out the process of detection or classification. Furthermore, learning methods added more accuracy and details in this process. In this paper, deep learning and transfer learning methods were experimented and analyzed to know the impact of various parameters and model-related factors in identification and classification of malignant cells for colorectal cancer on whole slide stained tissue image samples.
GAN to Produce New Faces and Detection Expression

In a world where data is the most significant item. Image has now set off to be the most valuable data. Along with the evolving technologies, image is being bifurcated and used in the field of machine learning for various operations. From the recent past, image is bifurcated into segments and could be seen to be used everywhere, whether it is predicting market patterns or imitating the real world in a form of virtual grid. Segmentation of images is the most critical part of developing a machine learning model. Better is the training of the model, better will be the results, which may result in a successful machine learning model. A recent innovative proceeding has been introduced related to image segmentation, generative adversarial network (GAN), which will surely bring huge achievements in this field. In this study, the function of GAN in producing new faces and subsequently detecting facial expressions is elaborated. This study can be divided into two phases, the first is to generate faces using GAN, and the second phase is to detect expressions of the faces generated. For the first phase, the aim is to train the generator model with the existing face dataset and after the successful training, the generator will be able to produce new faces. In the second phase, a model is prepared which can extract the facial features and detect expressions based on these features.
Student Placement Analyser and Predictor Using Machine Learning and Data Analytics

Improving the placement success of the students is one of the main difficulties that higher education institutions confront nowadays. As educational entities become more complicated, so does the placement analyser and prediction. It is crucial that educational institutions strive for more effective technology that may support improved administration, decision-making processes and help them create new strategies. Introducing fresh information about procedures and entities that contribute to management education is one of the most efficient ways to handle these issues and improve quality. Information regarding historical student data is contained in the dataset for system implementation who may have graduated from that educational institute. The knowledge can be drawn out of the operational and archival data stored in the databases of the educational organisation using machine learning techniques. These data are utilised to train the rule identification model and test the classification model. This essay offers a categorization and prediction system that assigns students to one of the six placement classes—Classes A, B, C, D, E, and F—based on their academic performance. With the use of this model, an organisation’s placement cell may recognise potential students and focus on and enhance both their necessary technical and interpersonal skills. This will allow the students to work on developing these talents. The institutions will be informed of the criteria used by the businesses when choosing their employees. Additionally, students can use this system to determine their particular placement status, where they stand, and what they are most likely to accomplish in the both their B. Tech. course’s pre-final and final years. With this, people can work harder and focus on their weak areas in order to be hired by better firms.
Difference Equations, Stationary and Non-stationary Discrete Systems in Block Ciphers

In this article, for Markov ciphers, we prove that they are resistant to differential cryptanalysis and some statements made for MS are obtained. The upper estimates of the probabilities of integer differentials are significantly improved when compared to previously known results. Our differential cryptanalytic algorithm finds weak subkeys that have more than 80 bits and 128 bits for 128-bit keys.
Time-Optimal Path Planning in an Evolving Ocean Wave Field Based on Reachability Theory

We consider the navigation of surface vessels in an evolving ocean wave field, where the detrimental extreme waves are treated as moving and deforming obstacles. We propose a new time-optimal path planning algorithm with avoidance of such obstacles based on the reachability theory. In our framework, the mathematical optimization problem is boiled down to: 1) forward propagation of reachable set described by a variational inequation (VI), which is numerically solved through the level set method combined with a prediction-correction scheme and 2) a backtracking procedure to find the optimal path. The new path planner is tested in a number of cases including one with realistic ocean waves. We demonstrate that our algorithm can provide correct paths avoiding high waves and achieve multiple optimal (equivalent in time) paths when available. Finally, the framework can be straightforwardly extended to other problems involving collision avoidance with moving and deforming obstacles.