Reviewerly/exHarmony huggingface dataset:
Design of a Trust-Based Authentication Scheme for Blockchain-Enabled IoV System

The Internet of Vehicles (IoV) has become a remarkable vehicular communication network for exchanging information between vehicles and other infrastructures. It plays a significant role in the Intelligent Transport System (ITS) by improving vehicle safety and traffic efficiency. However, trust management in IoV suffers in existing systems, even utilizing blockchain. Blockchain is a decentralized and secure technology that improves the security and privacy of incorporating applications. Many researchers proposed various blockchain-enabled IoV applications to enhance trust in the IoV network. But these suffer from vehicle safety, secure authentication mechanism, and higher computational cost. This paper presents a review of existing Blockchain-enabled IoV systems and analyzes them. Therefore, a blockchain-enabled IoV framework has been proposed to increase the trust among all connected nodes in the blockchain network. A 3-step authentication algorithm has been presented to enhance trust management in the IoV system. The proposed framework can establish secure communication among all connected entities. The implementation of the proposed framework assures the trustworthiness of the IoV network.
The Dilemma of the Intellectual Property System in the Context of Artificial Intelligence and the Path of Change

The rapid development of artificial intelligence technology has brought a series of challenges to the intellectual property system.In terms of the subject system, granting AI subject status will impact on the system centred on natural persons.In terms of the object system, AI creations are caught in the dilemma of identifying the objects of intellectual property protection and innovative standards.In terms of the content of rights, granting intellectual property protection to AI creations will face difficulties in the protection of personal rights, the duration of rights and the limitation of rights.In this regard, China's intellectual property system should be changed to expand the scope of the subject matter, improve the criteria for determining innovativeness, and improve the system for the content of rights, with a view to balancing the interests of all parties and promoting the development of artificial intelligence.
"Deep Non-Parametic Learning Architecture: Performance Analysis Of Deep Learning Architecture"

This paper analyses the effectiveness of deep non-parametric learning architectures in the field of deep learning.Due to their ability to manage complex and high-dimensional data without requiring a fixed set of parameters, non-parametric models have gained in popularity.This article examines the effectiveness of deep non-parametric models in various deep learning applications.This paper begins with an overview of the concept of deep learning and its significance in a variety of disciplines.The benefits of non-parametric models over parametric models are then elaborated upon.Several deep non-parametric architectures are discussed, including the kernel-based approach, tree-based models, and graph-based architectures.In addition, we provide a comparative analysis of the effectiveness of these models in a variety of applications, including image classification, object detection, and natural language processing.Our research demonstrates that deep non-parametric models perform well with highdimensional data and are capable of learning complex patterns.In addition, we discovered that transfer learning and ensemble methods can improve the performance of these models.Additionally, statistical analysis supports our findings.This paper examines the effectiveness of deep non-parametric architectures in various deep learning applications.Researchers and practitioners in the field of deep learning can select appropriate models for their applications using these findings.
Novel adenocarcinoma esophagus prediction using deep belief network of machine learning

Adenocarcinoma esophagus detection on early stages is very difficult in traditional imaging detection methods as its spread area is complete esophagus.By using deep learning it become quite accurate than other methods but still in deep learning method scientist uses gradient descent based learning parameter to diagnosis of cancer cells of esophagus, which leads to false negative and positive datasets when high degree of class imbalance is there.In this article a technique which uses a Barrett's esophagus image to predict the adenocarcinoma esophagus with Raven Roosting Optimization algorithm is discussed.
CUBA: An Evolutionary Consortium Oriented Distributed Ledger Byzantine Consensus Algorithm

We propose a consortium-based distributed ledger (Blockchain) consensus algorithm overcoming the classical problems of Byzantine Fault Tolerant (BFT) consensus algorithms. The identified issues concerning Byzantine algorithms are scalability, performance, and attack resilience. These factors inspire us to conceive our novel consensus algorithm CUBA. CUBA expands to Contesting Utilitarian Byzantine Agreement which evaluates and valorizes each consensus action as a Utilitarian metric of the gamified participants in the network. The obtained utilitarian metrics are used as feedback to reorganize the network for faster performance of the network consensus or for being resilient to the malicious activity noticed. This consensus protocol is designed to sustain or increase the Utilitarian happiness in a Byzantine environment of identified participants for the network’s liveness, safety, performance, and scalability. Evaluation results show an improved throughput, scalability, and malicious resilience compared to Proof of Authority protocols like PBFT, IBFT, and QBFT, as well as comparable to Clique for consortium Distributed Ledger networks.
Artificial intelligence in the field of Education

The reason of this considers was to evaluate the effect of Manufactured Insights (AI) on education.Premised on a story and system for evaluating AI recognized from a preparatory investigation, the scope of the ponder was constrained to the application and impacts of AI in organization, instruction, and learning.A subjective inquire about approach, leveraging the utilize of writing survey as a investigating plan and approach was utilized and viably encouraged the realization of the study purpose.Artificial insights could be a field of study and the coming about advancements and advancements that have finished in computers, machines, and other artifacts having human-like insights characterized by cognitive capacities, learning, adaptability, and decision-making capabilities.The ponder found out that AI has extensively been embraced and utilized in education, especially by instruction teach, in several shapes.
An Assessment on Growth of Knowledge and Skills

This article explores the value of knowledge and skill development for both personal and professional growth. It emphasizes the significance of cultivating a growth mindset, the impact of technology advancements, and the transforming force of lifelong learning. In a world that is changing quickly, education and skill development are essential for people and societies to prosper. People can broaden their understanding, adjust to novel circumstances, and hone their critical thinking abilities through ongoing learning. As a result of technological advancements, there are now many new chances for education and improvement of skills. Having a growth mindset enables people to overcome obstacles, see setbacks as chances for learning, and embrace a desire for lifelong learning. Understanding how information and abilities develop is crucial for professional as well as personal achievement. In order to assist the growth of knowledge and skills, researchers can find successful techniques and strategies by examining the trans formative potential of continuous learning. The creation of creative educational devices and platforms can benefit from examining how technological advancements affect learning and skill acquisition. Additionally, understanding how a growth mindset fosters development of skills and knowledge can help people and organisations adopt a mentality that is supportive of lifelong learning and individual development. Knowledge of financial accounting, Financial Progress Knowledge Social obligations and ethics, Administration Information System, Development of the global business, Growth in microeconomics are the parameters used in this analysis. “The overall Cronbach's Alpha value for the model is .595 which indicates 60% reliability”
Encrypted Image Retrieval Scheme on Blockchain

Blockchainhas flourished in a variety of industries throughout a period known as the "digital economy", including finance and digital copyright. Blockchain is highlighting the storage issue more and more. In order to lessen the demand for node storage, the existing blockchain maintains block information in external storage devices. Blockchain transaction retrieval issue is brought on by the new blockchain storage approach. The issue arises when the user is required to download the decentralized blockchain ledger information from the external storage system. Since, they couldn't locate the node comprising that particular transaction, therefore which results in significant communication overhead. We take advantage of the data that is Blockchain is semi-structured for this issue and obtain the common traits of blockchain transactions like the Date and account address. Next, we create a method for retrieving blockchain transactions. Because of the absence of an effective account-specific secondary search data structure addresses, we suggest the scalable B+ tree. For encryption and decryption of Images we have used Elliptic curve Cryptography algorithm and AES algorithm. For Generating Hash value we have used ECDSA Algorithm.
Multi Trend Twitter Sentiment Analysis: Collaborative Approach for Improved Results

Twitter has a significant number of daily users through which tweets are utilized to communicate their thoughts in this era of growing social media users. This paper offers a way to haul out sentiments from tweets as well as a method for sorting out various tweets as optimistic, adverse, or unbiased. It refers to identifying and classifying the sentiments expressed in the text source. The existing Twitter APIs for data extraction are used to mine public Twitter data. Tweets would be chosen based on a few carefully chosen keywords related to the domain of our concern. In our proposed method, we collected various sentiment data from a variety of tweets to train and produce more precise and reliable sentiment classifiers for each trend. This method automatically extracts the key elements of subjects from online user evaluations. Since tweets are generally unstructured in format, they must first be converted into structured format. And after that, the data is fed into several models for training and used to rank the best sentiment classifier. The intention of this design is to arrive at a model that can classify sentiments of real-world data using Twitter.
Dynamic Confidence Sampling and Label Semantic Guidance Learning for Domain Adaptive Retrieval

To accurately retrieve similar objects from different domains, domain adaptive retrieval method is applied to cope with the domain shift problem in information retrieval. However, existing methods still have two problems: a) they fail to filter out low-confidence samples, leading to error accumulation; and b) they ignore the negative effect of domain discrepancy. To address these two issues, we propose an efficient method called Dynamic Confidence Sampling and Label Semantic Guidance Learning (DCS-LSG). First, Dynamic Confidence Sampling (DCS) is employed to dynamically select high-confidence samples from the target domain so as to improve the effectiveness of learning. Second, Label Semantic Guidance (LSG) learning is presented to enhance the label semantics of features during domain adaptive retrieval. In addition, we introduce a Dual-Projection Relaxation (DPR) strategy to learn more effective features on two specific projection spaces. At last, a two-step hashing strategy is used to generate high-quality hash codes. Experiments on multiple cross-domain retrieval datasets demonstrate that the proposed DCS-LSG can achieve a significant performance improvement. The source code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/Kybin-kb/DCS-LSG-code/tree/master</uri> .
Dual Circle Contrastive Learning-Based Blind Image Super-Resolution

Blind image super-resolution (BISR) aims to construct high-resolution image from low-resolution (LR) image that contains unknown degradation. Although the previous methods demonstrated impressive performance by introducing the degradation representation in BISR task, there still exist two problems in most of them. First, they ignore the degradation characteristics of different image regions when generating degradation representation. Second, they lack effective supervision on the generation of both degradation representation and super-resolution result. To solve these problems, we propose the dual circle contrastive learning (DCCL) with the high-efficiency modules to implement BISR. In our proposed method, we design the degradation extraction network to obtain the degradation representations from different texture regions of LR image. Meanwhile, we propose DCCL coupled with the degrading network to guarantee the obtained degradation representation to contain the degradation of LR image as much as possible. The application of DCCL also makes the SR results contain degradation as little as possible. Additionally, we develop an information distillation module for our proposed BISR model to guarantee the SR images with high quality. The experimental results demonstrate that our proposed method achieves the state-of-the-art BISR performance.
Cloud Monitoring and Observability

The objective of this chapter is to highlight the basic concepts behind cloud monitoringCloud monitoring. The reader will come to know how monitoring plays a crucial role in achieving the fundamental characteristics of cloud computing and optimizing the cloud. In the recent past, the concept of cloud observability gains a lot of interest. Auto-remediation is the direct result of observability. Deeper automation of various operations can be initiated and implemented with the maturity and stability of the observability process, product, patterns and practices.
Application of quantitative research to psychology dissertations—An analysis of self-efficacy in mediating tests of perceived doctor-patient relationship and work engagement in medical psychology

Quantitative research involves examining the quantitative relationship between each parameter of the research object and conducting comparative analysis of certain properties, characteristics, and interrelationships among multiple objects in a quantitative manner.Mediated effect analysis is a commonly used research tool in domestic quantitative research.This paper extensively explores the proper implementation of mediated effect analysis using the questionnaire method and data processed by SPSS.It analyzes the current data results and proposes relevant suggestions.
20 Mitigating the risk of quantum computing in cyber security era

The existence of quantum computers poses a huge threat to the privacy and safety of information transmitted via the internet. Once large-scale, error-tolerant quantum physics is discovered, the methods of encryption that are currently employed by the majority of people will become outdated. Consequently, it is not only important but also opportune to take action in order to prevent this threat. This chapter presents the utilization of cyber security modeling using quantum algorithm. The model is designed using quantum CNN mechanism that enables better prediction of attacks from input datasets. Testing of the model shows a better prediction of the attacks than with other methods.
A Singularity-Free Guiding Vector Field for Robot Navigation

In robot navigation tasks, such as UAV highway traffic monitoring, it is important for a mobile robot to follow a specified desired path.
Transformers and Visual Transformers

Abstract Transformers were initially introduced for natural language processing (NLP) tasks, but fast they were adopted by most deep learning fields, including computer vision. They measure the relationships between pairs of input tokens (words in the case of text strings, parts of images for visual transformers), termed attention. The cost is exponential with the number of tokens. For image classification, the most common transformer architecture uses only the transformer encoder in order to transform the various input tokens. However, there are also numerous other applications in which the decoder part of the traditional transformer architecture is also used. Here, we first introduce the attention mechanism (Subheading 1) and then the basic transformer block including the vision transformer (Subheading 2). Next, we discuss some improvements of visual transformers to account for small datasets or less computation (Subheading 3). Finally, we introduce visual transformers applied to tasks other than image classification, such as detection, segmentation, generation, and training without labels (Subheading 4) and other domains, such as video or multimodality using text or audio data (Subheading 5).
SIGCSE Technical Symposium 2024: Call for Submissions

The SIGCSE TS is a forum for educators and researchers to share new results and insights around developing, implementing, or evaluating computing programs, pedagogy, curricula, and courses. The conference will be held in Portland, Oregon, from March 20 to 23, 2024 mostly in-person with a few limited online participation opportunities available to accommodate a broader range of attendees. Details will be available at the conference website: https://sigcse2024.org.
Contour Tracking and detection of heart chambers in an echocardiogram video sequence using UNET

Standard ultrasound is used in an echocardiogram, sometimes referred to as an ECG, a non-invasive imaging procedure, to examine the heart.It is one of many clinical imaging techniques that can be used to evaluate the human heart.Today, echocardiography is a common diagnostic procedure used by cardiologists to assist them find people who could have cardiac issues.It significantly affects how patient concerns are managed and tracked as well.Because of its efficacy, it has emerged as one of the most popular tests in the field.The information it provides-such as heart size, shape, pumping capacity, and the degree of tissue damage-proves to be quite useful when evaluating a patient.Ultrasound is used during the ECG test to evaluate the heart's chambers and valves, assisting medical professionals in assessing cardiovascular health.It aids in the diagnosis of a number of conditions, including changes in heart chamber size, blood clots, fluid accumulation around the heart, aortic troubles, systole/diastole problems, and heart valve function.The left ventricle and its shape on the ECG video are to be recognized during the examination.
Identification Of Blood Cell Types Using CNN

White blood cells (leukocytes) play an important role in the human body by increasing immunity and fighting infectious diseases.The classification of White Blood Cells is critical in detecting disease in an individual.The classification can also help with the identification of diseases caused by immune system anomalies, such as infections, allergies, anaemia, leukaemia, cancer, and Acquired Immune Deficiency Syndrome (AIDS).This classification will aid haematologists in distinguishing the types of White Blood Cells found in the human body and determining the root cause of diseases.There is currently a lot of research going on in this field.Given the importance of WBC classification, we will use a deep learning technique called Convolution Neural Networks (CNN) to classify images of WBCs into four subtypes: neutrophils, eosinophils, lymphocytes, and monocytes.We will present the results of various experiments performed on the Blood Cell Classification and Detection (BCCD) dataset using CNN in this paper.
Natural Language Processing in India

Named Entity Recognition (NER) is a vital task in natural language processing (NLP) that involves identifying and classifying named entities in text.This paper focuses on the comparison of four different algorithms of NER techniques in the context of India.The four different algorithms are CRF (Conditional Random Fields), Stanza, Spacy, and Nameparser.The accuracy will be compared with the expected accuracy of all four methods of NER.Accuracy will be obtained after running the method on a dataset or text.To calculate the accuracy of four algorithms a large dataset of Indian names will be used.A comparison graph was obtained to display the accuracy graph of all four algorithms."NLP-Named Entity Recognition in India" which focuses on detecting person names, states, and districts, and suggesting names based on religion.Named Entity Recognition (NER) is a Natural Language Processing (NLP) task that identifies and classifies named entities in text.Named entities are typically proper nouns, such as person names, organization names, and location names.NER is a challenging task for Indian languages, due to the diversity of Indian languages and the lack of resources for training NER systems.The proposed system can be used to improve the accuracy and efficiency of a variety of NLP applications in India.
A Clustering Approach Combining Lines and Text Detection for Table Extraction

Table detection is a crucial step in several document analysis applications as tables are used to present essential information to the reader in a structured manner. In companies that deal with a large amount of data, administrative documents must be processed with reasonable accuracy, and the detection and interpretation of tables are crucial. Table recognition has gained interest in document image analysis, particularly in unconstrained formats (absence of rule lines, unknown information of rows and columns). This problem is challenging due to the variety of table layouts, encoding techniques, and the similarity of tabular regions with non-tabular document elements. In this, paper, we make use of the location, context, and content type, thus it is purely a structure perception approach, not dependent on the language and the quality of the text reading. We evaluate our model on invoice-like documents and the proposed method showed good results for the task of table extraction.
A DCNN method for phase unwrapping

Two-dimensional phase unwrapping is a key step for interferometric synthetic aperture radar to obtain elevation, and it is also an indispensable step in the process of elevation inversion. However, under the condition of strong noise, traditional unwrapping methods cannot achieve high quality phase recovery. To solve the problem, we used a neural network to unwrap the phase. The proposed method is validated compared with several conventional methods, and the experimental results show that the neural network method can obtain better effect under the condition of strong noise.