Reviewerly/exHarmony huggingface dataset:
A Study on Cross-Media Teaching Model for College English Classroom Based on Output-Driven Hypothetical Neural Network

In the field of education, the development of educational big data has become an important strategic choice to promote the construction of the digital campus and educational reform, and educational big data has become a new driving force in the field of education that cannot be ignored. Based on the theoretical basis of output-driven hypothesis neural network, and combining the media spanning of contemporary art and cross-media association effect, this study changes the status quo of English teaching through traditional methods such as grammar-translation method and deductive method and constructs a new cross-media university English teaching model. Based on the existing feature learning model of two-way attention, combined with existing techniques such as generative adversarial networks and semantic hashing, the semantic association between different media data is deeply mined, and feature learning is integrated with adversarial learning and hash learning to build a unified semantic space for different media data. In this paper, we focus on the structure and characteristics of convolutional neural networks through the study of deep learning theory, discuss three classical convolutional neural network models, such as AlexNet, VGG, and GoogLeNet, and propose a convolutional neural network model applicable to cross-media teaching in college English classroom and carry out experimental validation, and the results show that the proposed neural network model is based on output-driven hypothesis. The following research has been added to the abstract: to address the key problem of the semantic gap that is difficult to cross in cross-media semantic learning, a cross-media supervised adversarial hashing model based on two-way attentional features is proposed. Based on the existing two-way attention-based feature learning model, we combine existing techniques such as generative adversarial networks and semantic hashing to deeply explore the semantic association between different media data and integrate feature learning with adversarial learning and hashing to build a unified semantic space for different media data. The results show that the proposed neural network model of cross-media teaching in college English classrooms based on the output-driven hypothesis can not only promote the improvement of students' English literacy skills but also have a certain promotion effect on their overall performance improvement.
Confirming the Consistency of QoS-based Web Services Ranking by Logic Scoring of Preference Method

The Web services selection based on qualitative characteristics is a complex and relevant task, which is often solved using multi-criteria decision-making (MCDM) methods. Among the MCDM methods, the Logic Scoring of Preference (LSP) method is noticeably less applied. This study aims to assert the consistency and expediency of applying the LSP method for Web service selection. The consistency LSP method is evaluated by comparing its Web services ranking result with ones obtained by several other MCDM methods. The Spearman's rank-order correlation, Average Overlap, Discounted Cumulative Gain and Rank Consistency Index measures are used as a criterion of rankings' coincidence.
Bug-Transformer: Automated Program Repair Using Attention-Based Deep Neural Network

In this paper, we propose a novel transformer-based deep neural network model to learn semantic bug patterns from a corpus of buggy/fixed codes, then generate correct ones automatically. Transformer is a deep learning model relying entirely on attention mechanism to model global dependencies between input and output. Although there are a few endeavors to repair programs by learning neural language models (NLM), many special program properties, such as structure and semantics of an identifier, are not considered in embedding input sequence and designing model effectively, which results in undesired performance. In the proposed Bug-Transformer, we design a novel context abstraction mechanism to better support neural language models. Specifically, it is capable of 1) compressing code information but preserving the key structure and semantics, which provides more thorough information for NLM models, 2) renaming identifiers and literals based on their lexical scopes, structural and semantic information, to reduce code vocabulary size and 3) reserving keywords and selected idioms (domain- or developer-specific vocabularies) for better understanding code structure and semantics. Hence, Bug-Transformer adequately embeds code structural and semantic information into input data and optimize attention-based transformer neural network to well handle code features in order to improve learning tasks for bug repair. We evaluate the performance of the proposed work comprehensively on three datasets (Java code corpora) and generate patches to buggy code using a beam search decoder. The experimental results show that our proposed work outperforms the-state-of-art techniques: Bug-Transformer can successfully predict 54.81%, 34.45%, and 42.40% of the fixed code in these three datasets, respectively, which outperform the baseline models. These success rates steadily increase along with the increase of beam size. Besides, the overall syntactic correctness of all patches remains above 97%, 96%, and 50% on the three benchmarks, respectively, regardless of the beam size.
(Retracted) Image processing technology in Shanghai Stock Exchange options based on state-space model

The current relevant models for the analysis of Shanghai Stock Exchange (SSE) options, whether for the study of theoretical algorithms or the application of verification, are still at the beginning of the research stage. Based on this, we combine the status quo of China’s SSE options to construct a state-space model with a certain flexibility and combine image processing technology to extract model features. At the same time, we obtained the experimental data of this study through the network data collection method and analyzed the performance of the algorithm by comparison method, recorded the data obtained by the model operation, and turned the result into a visually identifiable feature result through image processing. The research indicates that the state-space model has certain effects on the analysis of the SSE option and can provide theoretical reference for subsequent related research.
A Study on the Prospects of Using Schnorr Digital Signature in Blockchain

A purely peer-to-peer version of electronic cash would allow online payments to be sent directly from one party to another without going through a financial institution.Digital signatures provide part of the solution, but the main benefits are lost if a trusted third party is still required to prevent double-spending.We propose a solution to the double-spending problem using a peer-to-peer network.The network timestamps transactions by hashing them into an ongoing chain of hash-based proof-of-work, forming a record that cannot be changed without redoing the proof-of-work.The longest chain not only serves as proof of the sequence of events witnessed, but proof that it came from the largest pool of CPU power.As long as a majority of CPU power is controlled by nodes that are not cooperating to attack the network, they'll generate the longest chain and outpace attackers.The network itself requires minimal structure.Messages are broadcast on a best effort basis, and nodes can leave and rejoin the network at will, accepting the longest proof-of-work chain as proof of what happened while they were gone.
Real Time Detection of the Various Sign of Ageing Using Deep Learning

One of the most promising fields where the technology of deep learning and CNN can thrive are the cosmetic and dermatology industries. Detection of conditions like premature ageing can be made easy by deep learning procedures like facial detection and recognition. This project is based on improving the technology principally in these domains. A deep learning model utilizing CNNs is built, and the network is equipped with hand-crafted characteristics like wrinkles, acne and blemishes. The model will be able to distinguish these features concurrently and has diverse applications. It is computationally efficient compared to previous models, and it uses special convolution and pooling operations and performs parameter shifting. An overall accuracy of 94.11 &#x0025; was achieved.
Dynamic Target Tracking Method Based on Medical Imaging

The cross fusion of rehabilitation medicine and computer graphics is becoming a research hotspot. Due to the problems of low definition and unobvious features of the initial video data of medical images, the initial data is filtered and enhanced by adding image preprocessing, including image rotation and contrast enhancement, in order to improve the performance of the tracking algorithm. For the moving barium meal, the discrete point tracking and improved inter frame difference method are proposed; for the position calibration of tissues and organs, the Kernel Correlation Filter (KCF) and Discriminative Scale Space Tracker (DSST) correlation filtering method and the corresponding multi-target tracking method are proposed, and the experimental results show that the tracking effect is better. The two algorithms modify each other to further improve the accuracy of calibration and tracking barium meal flow and soft tissue organ motion, and optimize the whole swallowing process of moving target tracking model.
Enhancement of Images Using Optimized Gamma Correction with Weighted Distribution Via Differential Evolution Algorithm

Whenever the acquired images having flaws such as poor visual appearance to the eyes, noise, or low quality which degraded the quality of the image. In order to increase the visual appearance, image enhancement should be used. The primary goal of image enhancement is to suppress blemishes in an image while retaining useful information. Many researchers suggested different kinds of enhancement processes that produced positive results. The conventional Histogram Equalization (HE) is a popular technique for refining the quality of a taken image. However, it is possible that unwanted contrast enhancement may occur. As a result, we have an unnatural presence in a processed image, as well as visual objects. To address this, we developed a novel hybrid algorithm called Optimized Gamma Correction with Weighted Distribution (OGCWD), which combines the Differential Evolution algorithm and Adaptive Gamma Correction with Weighted Distribution. The proposed method is an automated transformation process that aids in improving the brightness of a lowered image. The proposed OGCWD algorithm outperforms state-of-the-art image enhancement techniques in terms of structural Similarity Index (SSIM), Mean Square Error (MSE), and Peak Signal to Noise Ratio (PSNR).
Genesis of uranium containing groundwater in the Brahmaputra floodplain of Assam: Hydrogeochemistry and its associated health risk

Groundwater uranium (U) content and the major hydrogeochemical processes were evaluated for six districts of Assam, lying mostly on the central and northern region of the Brahmaputra floodplain. A total of 504 groundwater samples were collected using the grid sampling technique. Uranium concentrations analysed using LED fluorimeter model LF-2a, ranged from below detection limit (BDL) to 13.5 μgL−1. All the U concentrations were found to be well below the prescribed limits of AERB, WHO and USEPA. The highest groundwater U was recorded in a sample from Nagaon, a central Brahmaputra floodplain district. The SEM-EDX spectra of the sediment samples revealed geogenic presence of U in the floodplain. A comparative assessment between the relationships of U with other groundwater parameters revealed that shallow aquifers provided more feasibility for elemental interactions and hence more mobility of U species. The correlation statistics in shallow aquifers depicted the importance of alkalinity on U release and mobilization. Uranium release during weathering of the geological strata was signified by the relationship of U with EC, TDS and salinity. The role played by pH and ORP in dissolution of U in the floodplain was not strong. The dominant water type in the study was Mg–HCO3, which was associated with higher U concentration. This revealed the positive role of carbonate dissolution towards U release in the floodplain. Hydrogeochemical investigation further implied for greater silicate weathering and ion exchange compared to that of carbonate weathering and reverse ion exchange processes. The calculated undersaturation of calcite, dolomite and gypsum explained their role as source minerals in the floodplain. Uranium dissolution and mobilization was found to have positive association with carbonate weathering, as depicted by its significant relationship with calcite, dolomite and gypsum. Health risk, both radiological and chemical, relating to U ingestion through drinking water was absent within the floodplain.
Research on the Reform of Higher Education Training Mode under the Background of Big Data and Internet of Things

With the rapid development of information processing, storage, and transmission technologies, the demand for talents from all walks of life in the society has increased, which has put forward higher requirements for the diversity of Chinese higher education. In China’s higher education, undergraduate education is in a fundamental position. China’s undergraduate education has undergone several reforms in talent training, but there are still many problems in the innovative talent training model. With the gradual improvement of the Internet of Things infrastructure, various applications characterized by big data are deeply embedded in people’s daily life. In the era of big data, the amount of data is larger and the structure is more complex. It penetrates into all fields of human life and shows unprecedented vitality and vigor. Especially in the field of higher education, the era of big data provides more information resources for education and learning and also brings new opportunities for the cultivation of applied talents in local undergraduate colleges and universities. This paper deeply studies the training mode of applied talents in local undergraduate colleges and universities under the background of big data and the Internet of Things. Based on the data analysis results of big data application technicians and industry talents, professional ability-course structure data, this paper completes the design of the corresponding talent training mode analysis system. Under the application of the system designed in this paper, it is possible to realize the analysis of the differences in the knowledge structure requirements of talents in various positions and the needs of talents’ ability, as well as the optimization and reform of the talent training curriculum system. The design of this paper can play an important guiding role in talent training in the environment of big data and Internet of Things.
Multilabel Emotion Recognition Technique considering the Characteristics of Unstructured Conversation Data

Due to the recent increase in non-face-to-face services due to COVID-19, the number of users communicating through messengers or SNS (social networking service) is increasing. As a large amount of data is generated by users, research on recognizing emotions by analyzing user information or opinions is being actively conducted. Conversation data such as SNS is freely created by users, so there is no set format. Due to these characteristics, it is difficult to analyze using AI (artificial intelligence), which leads to a decrease in the performance of the emotion recognition technique. Therefore, a processing method suitable for the characteristics of unstructured data is required. Among the unstructured data, most emotion recognition in Korean conversation recognizes a single emotion by analyzing emotion keywords or vocabulary. However, since multiple emotions exist complexly in a single sentence, research on multilabel emotion recognition is needed. Therefore, in this paper, the characteristics of unstructured conversation data are considered and processed for more accurate emotion recognition. In addition, we propose a multilabel emotion recognition technique that understands the meaning of dialogue and recognizes inherent and complex emotions. A deep learning model was compared and tested as a method to verify the usefulness of the proposed technique. As a result, performance was improved when it was processed in consideration of the characteristics of unstructured conversation data. Also, when the attention model was used, accuracy showed the best performance with 65.9%. The proposed technique can contribute to improving the accuracy and performance of conversational emotion recognition.
Hardware Accelerated FrodoKEM on RISC-V

FrodoKEM is an alternative finalist in the currently running standardization process for post-quantum secure cryptography, initiated by the National Institute of Standards and Technology (NIST). It is based on the well studied plain Learning With Errors (LWE) problem, leading to a high confidence in security. Its conservative design approach, however, makes it less performant when compared to other lattice-based candidates. In this work, we assemble a RISC-V based HW/SW codesign of FrodoKEM to speed up its computation. Our design supports all three parameter sets of the NIST submission. Compared to plain SW implementations on RISC-V, our accelerated design achieves speedup factors of up to 8.13.
Saliency and Transmission Feature Extraction from Underwater Images Using Level Set Method

In contrast to the widespread use of object segmentation on land, very few methods for detecting and segmenting objects in underwater settings have been presented. In addition, the segmentation procedures used on land are ineffective in underwater conditions. Object recognition and image segmentation aid in the separation of underwater images into object and background categories. Existing methods have flaws in terms of obtaining proper object outlines and determining the difference between object and background. We want to apply an unique level set based approach in this project to identify the pop out of the object region from the background.
Analyzing Emotional Stability on Social Media Post using Machine Learning Approach

Depression or emotion is the most important concern of health organizations today. We consider the capability of influencing social media postings as a new type of mirror in understanding depression and also the type of personality in populations. Social network analysis is the study of a group of people and the relationships that exist between them. It has become so important in our lives that if I want to know anything about a stranger, I can find out with the help of social media websites. The arrival of various social media networking sites has helped everyone to easily express and share their opinions and feelings about anything with millions of people around the world. Social media is a valuable resource for identifying an individual&#x0027;s personality traits based on their posts, comments, or activities on social media. The proposed methodology, we have developed the application of a web extension to connect with social media networks to extract the post by the individual person. Extracted post has been used to identify the emotional stability of a person. NLP and Machine Learning algorithms are used to classify individual emotional stability as stable, depressed, or tending towards depression. According to our study, significant feature selections and their combinations were considered. Hence it improves the performance and accuracy of classification.
GAIA: Great-Distribution Artificial Intelligence-Based Algorithm for Advanced Large-Scale Commercial Store Management

Today, the intelligent management of market stores in the large distribution field represents one of the most difficult tasks to address, considering the various problems to be managed. Specifically, from the classic issues of managing out-of-stock to the reconstruction of customer sentiment and the optimal management of shelves, scientific research has placed considerable effort on producing robust and efficient solutions to the aforementioned problems. In this context, modern deep learning techniques have allowed for the development of intelligent and adaptive systems capable of automating and significantly improving the management of a large-scale distribution market. Specifically, the authors have designed and implemented an innovative full pipeline that integrates modern deep learning technologies. More in detail, an innovative pipeline embedding a visual AI-based engine for customer sentiment assessment merged with a deep framework for stock management and market store cashflow monitoring is proposed. The innovative proposed system has been tested and validated in a large-scale distribution supermarket, confirming the effectiveness of the proposed solution. Specifically, in the performed testing sessions, the designed pipeline was able to show ad hoc visual customer sentiment assessment with an accuracy of 95% as well as intelligent stock monitoring with an accuracy of 93% in cross validation.
Image Enhancement Using Gain-Limited Contrast Stretching Technique

The main problems in enhancing the contrast of images during their pre-processing are the emergence of unwanted artifacts and distortions, loss of information, the need to adjust the parameters of transformations, which complicate the transformation of images in real-time. Among such undesirable effects, there is excessive contrast when transforming images. Therefore, in the paper, we propose a new approach to image enhancement to avoid this effect - gain-limited contrast stretching (GLCS). This gives the possibility to enhance the image by limiting the contrast of objects with excessive contrast and redistributing the contrast in the image. This approach is not only independent in itself to improve the contrast of images, but can also be used to correct the occurrence of unwanted artifacts as a result of use for pre-transformation with other methods.
Assessment of Image Quality Based on Deep Neural Networks

In the paper, the use of deep neural networks and deep learning technologies to quantify image quality is considered. As a software tool, Keras package with TensorFlow as BackEnd was used, the database TID2013 of images was used for computer evaluation, some set of images was used for training the neural network, and the other set of images was used for testing. The use of a neural network with learning from the beginning is considered, as well as a pre-trained basic neural network VGG16 with additional layers of neurons for which training was carried out. The structures of the used neural networks, illustrations of learning procedures, test results end their comparing are given.