Reviewerly/exHarmony huggingface dataset:
A Way of Implementation of Block Chain Technology in The Field Finance With The Evidence of Result

Bit coin, a virtual money with a rising worldwide user base, provides an exciting basis for blockchain networks. A new generation of security technology called block chain provides a secure method of recording transactions by encapsulating operations in a sequence of blocks. It is dispersed to signatories or any intended audience that take involved in the system, and it goes well beyond bitcoin. It appeals because it does it without the help of a centralized authority. The existing financial system is centralized, making it vulnerable to mass defaults and frauds. Global banking has adopted blockchain technology, which is critically required for governance and the avoidance of such frauds. In order to allow decentralized banking and do away with all sorts of authoritarian monitoring, we are using block chain technology. In addition to the RBI-established requirements for secure and decentralized financial transfers, the solution we are proposing combines block chain as a component of the Net banking (National Electronic Fund Transfer) method using the Wide range of input (Indian Financial System Code). Our blocks will be composed of the computations made by Java micro services. The ledgers will be linked together using centralized systems.
Medical IoT Dynamic Service Switching

Power-efficient approaches are essential to achieving adequate battery lifetimes as the Internet of Things (IoT) becomes an actuality. We suggest a design that constantly adapts the information level flexibly depending on the present healthcare status of the patient under watched, in contrast to present healthcare IoT sensors that generally capture information with a fixed grade. Because transmitting & processor account for a sizable portion of power usage, the drop-in connection congestion as well as computing efforts brought on by this a modification leads in a decrease in the gadgets’ power consume. For instance, the surveillance is carried out with higher resolution & more thorough analysis when abnormalities in the observed information are found. Additionally, not all information produced by the healthcare.
Neuroevolutionary algorithms driven by neuron coverage metrics for semi-supervised classification

In this paper, we address the question of how to evolve neural networks for semi-supervised problems. We introduce neuroevolutionary approaches that exploit unlabeled instances by using neuron coverage metrics computed on the neural network architecture encoded by each candidate solution. In our neuroevolutionary approach, we define fitness functions that combine classification accuracy computed on labeled examples and neuron coverage metrics evaluated using unlabeled examples. Our results show that the use of neuron coverage metrics helps neuroevolution to become less sensitive to the scarcity of labeled data, and can lead in some cases to a more robust generalization of the learned classifiers.
Visual Content-Agnostic Novelty Detection Engine with 2.4 pJ/pixel Energy and Two-Order of Magnitude DNN Activity Reduction in 40 nm

An engine to identify frames with novel content in a video stream is proposed as additional vision pipeline stage following conventional saliency detection. Based on connected component analysis with mean-center tracking, its complexity is reduced to linear compared to quadratic in prior art. This introduces frame-level temporal sparsity for subsequent DNN activity/power reduction (177X beyond saliency detection). 2.4 pJ/pixel energy is achieved in 40 nm.
The Impact of Machine Learning (ML) Optimization of supply chain management domain

The paper explores the impact of machine learning on supply chain optimization and its potential to improve profitability. The study highlights the benefits of utilizing machine learning algorithms in areas such as demand forecasting, inventory management, and logistics optimization. The results show that incorporating machine learning techniques can lead to significant improvements in accuracy, speed, and efficiency in these areas, thereby leading to an increase in profitability. The research also points out the limitations of traditional approaches and the need for continuous improvement and adaptation of machine learning models. The paper concludes by emphasizing the potential of machine learning in driving transformative change in the supply chain and its critical role in enabling organizations to stay ahead in a rapidly changing business environment.
Method for Recognizing Hand Gestures in Real- Time Across Multiple Sensors Using Machine Learning

The field of human-computer interaction includes multimodal gesture detection as a key task. In human-machine interface, wearable sensors for hand gestures consume additional potential than ambient detection due to their short price, weightiness, and wide applicability. Despite the fact that research on hand motions with wearable sensors already exists, the results are rarely transferable from one scenario to another. And when it comes to collaboration between humans and technology, there is still no agreed-upon protocol for hand signals. In this study, we introduce a new technique of using hand gestures to command the environment. An affordable Internet of Things (IoT) sensor-pluggable device that has an integrated accelerometer and gyroscope is used to collect the data for analysis. Inputs to the feature extractor network include the raw data from the RGB sense modality, the visual flow sense modality, and the disassembled information from the hands. This work provides an additional modality, feature extraction based on temporal differences in RGB sense modality. The characteristics of these modes are learned using a pre-trained version of the Googlenet Caffe model. All of these components need to come together as a whole for the fusion procedure to be successful. When doing a classification, the bidirectional LSTM network is used. The new approach is put through its paces using the IPN Hand dataset. When first starting out, each modality is employed independently. Then, we employ such mixtures to boost classification precision even further. Combining the segmented indicator sense modality with the optical movement mode and the chronological transformation of the RGB sense modality yielded the greatest results. The suggested method for multimodal gesture recognition outperforms the benchmark methods, as shown by the simulation results
Standards for Big Data Management

This chapter reviews major standards that are fit for remote sensing big data. Two groups of standards are reviewed: metadata standards and data standards. The International Organization for Standardization (ISO) standards are discussed as the major standard efforts in enabling storing, managing and transporting of remote sensing data. The Open Geospatial Consortium (OGC) and other major community specifications are reviewed for data discovery and access. Data formats include HDF (Hierarchical Data Format) and HDF-EOS (Hierarchical Data Format—Earth Observing System), netCDF (Network Common Data Form), GeoTIFF (Geographic Tagged Image File Format), JPEG2000 (Joint Photographic Experts Group format 2000), OGC GMLCov (Geography Markup Language Coverage), and NITF (National Imagery Transmission Format). Discovery services include OGC CSW (Catalog Service for the Web), OpenSearch, and Z39.40 Geo-profile. Data access services include OGC Web Coverage Service (WCS), Web Map Service (WMS), Web Feature Service (WFS), Sensor Observation Service (SOS), and Open-source Project for a Network Data Access Protocol (OPeNDAP).
Encoder-Decoder Model For Detecting Spammed Tweets on Twitter

Accompanied by the desire for communal engagement expanding in nowadays high society, a well-known social media website called Twitter plays a vital role in enabling every citizen to communicate socially, whether by tweeting a tweet on someone else’s behalf or researching many subjects in the running world. But these days, some spammers have infected this platform, connecting their URLs to informative tweets to drive traffic to their spam websites even in spite of no connection between the material in the URL and the tweet message. In this paper with the help of encoder-decoder methodology combined with a vectorizer converter on the stated threads and their connected URLs, this study offers a novel method to determine if a tweet written by the other person is spam or ham, which results in the finding of a similarized vectors between them.
Eigenfaces and LEM Face Recognition Technique

Among the most major uses of image analysis is face recognition. Face recognition has received a lot of attention due to its wide range of commercial, security, and forensic applications, as well as the accessibility of new media technologies. Several computer vision algorithms have been created over the last few decades. However, no technique exists to date that offers a comprehensive remedy to all situations and applications that identify people may confront. The presentation discusses the various challenges encountered during photo face recognition, such as pose or view angle, illumination, occlusion, facial expression, and time delay between image acquisition, as well as the various image-based approaches. Despite the fact that a large number of systems have emerged that can achieve recognition accuracy larger than 90% under controlled circumstances, major applications in real-world conditions remain difficult. The presentation also covers two robust face recognition techniques: Line Edge Map Detection (LEM) and Eigenfaces.
Quantum Enhancements for AlphaZero

Reinforcement learning algorithms including AlphaZero are powerful artificial intelligence (AI) algorithms, but are known to be resource intensive and unable to train within a reasonable budget. Speeding up learning would be valuable to further expand application areas for real world problems. In this paper, we investigate two quantum computing methods to enhance AlphaZero, with the goal of speeding up training. We evaluate the results by playing the board game Othello, an adversarial multi-agent turn based game similar to the game Go. First, parameterized quantum circuits (PQC) have been shown to train hybrid quantum-classical reinforcement learning agents in standard benchmark environments. With this inspiration, we replace the classical neural network with a PQC quantum neural network (QNN) in the AlphaZero architecture. Second, tensor-network quantum circuits have been used to extract important features for convolutional neural networks (CNNs) in image classification tasks. Using this as inspiration, we use a tree tensor network (TTN) to extract features from the Othello game board, generating a new set of feature vectors for a classical neural network to estimate the policy and value. Results show both novel methods converge to master the game and achieve the same level of play compared to the classical AlphaZero agent.
Relevance of Load Balancing in Cloud Computing-A Critical Study

The "cloud computing" paradigm of today describes the provision of services over the Internet. Load balancing is an essential component of cloud computing because it keeps certain nodes from being overworked while others are idle or just performing minimal work. Load balancing improves resource utilisation, throughput, performance, cost, and response time. This study looks at the necessity for load balancing, load balancing matrices, load balancing rules, load balancing algorithms, as well as challenges and issues with load balancing.
Sentiment Analysis of Tweets: A Machine Learning Approach

The growth and advancement in social network platforms increase the number of users noticeably. Social network platforms, like Twitter, grant users the ability to interact and express their emotions about events. Since Twitter platform involves all ages with a fair representation of gender, the sentiment analysis of Twitter data reflects the general feelings of people about a particular event. The sentiment analysis is a natural language processing (NLP) method that mainly focuses on deciding whether the sentiment is positive, negative, or neutral. Additionally, it is referred to as material polarity or mining of opinions. In the context of sentiment analysis, various approaches can be applied such as the Lexicon and machine learning (ML) approaches. Compared with lexicon approach, ML approach is considered simple and more efficient. In this study aims at Performing sentiment analysis of Twitter data related to COVID19 using the ML approach. Four ML models are used in this study namely, linear support vector classification (Linear SVC), logistic regression (LR), decision tree (DT), and random forest (RF). The performance of the above-mentioned models is tested using various metrics such as accuracy, recall, precision, and F1 score. The results released that the Linear SVC model has superior performance among the other models.
Resource Management allocation in the CC Environments with Multi-Objective Optimization system

These days, the topic of cloud computing has become a very popular paradigm for providing services online. For the cloud data center to deliver cloud infrastructure services in response to user demand, a set of virtualized resources are required. In order to lower the degree as a result of resource loss and SLA violations, the necessity for optimum resource management becomes a crucial problem in cloud data centers. These issues are classified as NP-hard, and meta heuristics approaches are well suited to solving them. The article develops a multi-objective hybrid fruit fly optimization (MOHFO)-based method for SLA-conscious dynamic resource management in cloud data centers. For the fruit fly optimization technique, the Bald Eagle Search (BES) optimization behavior search capabilities. To balance resource waste and SLA breaches, the proposed solution employs dynamic consolidation and deployment of virtual machines (VMs) mechanism. The suggested technique utilizing Clouds to simulate different data center designs, using QoS measures to assess the experimental outcomes. The proposed MOHFO technique is assessed and contrasted to existing optimization strategies in terms of resource wastage, energy use, communication costs, and migration frequency in order to provide cloud computing with QoS-aware optimal resource provisioning.
Learning-based multi-start iterated local search for the profit maximization set covering problem

The profit maximization set covering problem is a general model able to formulate practical problems including in particular an application in the mining industry. As a variant of the partial set covering problem, the studied problem is to select some subsets of elements to maximize the difference of the total gain of the covered elements subtracting the costs of the chosen subsets and their associated groups. We investigate for the first time a learning-based multi-start iterated local search algorithm for solving the problem. The proposed algorithm combines a multi-restart mechanism to enhance robustness, an intensification-driven local search to perform intensive solution examination, a learning-driven initialization to obtain high-quality starting solutions and a learning-based strategy to select suitable perturbations. Experimental results on 30 benchmark instances show the competitiveness of the algorithm against the state-of-the-art methods, by reporting 18 new lower bounds and 12 equal results (including 7 known optimal results). We also perform additional experiments to validate the design of the algorithmic components.
Explainable AI for paid-up risk management in life insurance products

Explainable artificial intelligence (xAI) provides a better understanding of the decision-making processes and results generated by black-box machine learning (ML) models. Here, we outline several xAI techniques in order to equip risk managers with more explainable ML methods. We illustrate this by describing an application for the more effective management of paid-up risk in insurance savings products. We draw on a database of real universal life policies to fit an initial logistic regression model and several tree-based models. We then use different xAI techniques, including a novel approach that leverages a Kohonen network of Shapley values, to offer valuable perspectives on tree-based models to the end-user. Based on these findings, we show how non-trivial ideas can emerge to improve paid-up risk management.
On construction of maximal parabolic centralizers of root elements and the Weyl group of type F4 in E6(K) for fields K of characteristic 2

The purpose of this paper is to give an elementary and explicit construction of maximal parabolic centralizers of root elements in the Chevalley group E6(K) and to show that the centralizer of a Seigel involution in the Weyl group W of type E6(K) is the Weyl group of type F4, or equivalently it is the stabilizer of a totaly singular line L in W, using properties of the generalized quadrangle (Ω,L) of type O6−(2). The construction here of the maximal parabolic centralizers of root elements in E6K for fields K of characteristic 2, is somewhat novel, begining as it does, with root bases of Levi-type and root bases of unipotent type, giving an explicit construction of the group generated by these two types.
Enhancing interaction representation for joint entity and relation extraction

Jointly extracting entities and their relations from texts is an important task in information extraction. Despite the great success, traditional models suffer from two problems. First, the same token embeddings are shared in two subtasks. It ignores the difference between semantic granularities, in which named entities are more dependent on local features and relations are semantic expressions relevant a whole sentence. Second, the interaction between two subtasks is rarely considered, which is important to encode semantic dependencies relevant to two named entities. To address the above problems, we presents a novel joint entity and relation extraction model. It constructs two independent token embedding modules for encoding features about entities and relations respectively. It enables to encode semantic representations with different granularities for named entities and entity relations. Then, a cross-attention is used to capture the interaction between two subtasks for learning semantic dependencies in a relation instance. The experimental results demonstrate that our model outperforms previous state-of-art models on several public datasets. Extensive additional experiments further confirm the effectiveness of our model.
The creativity and limitations of AI neural machine translation

Abstract This study examines the performance of the neural machine translation system DeepL in translating Shakespeare’s plays Coriolanus and The Merchant of Venice . The aim here is to explore the strengths and limitations of an AI-based English-Chinese translation of literary texts. Adopting a corpus-based approach, the study investigates the accuracy and fluency rates, the linguistic features, and the use of various methods of translation in the Chinese translations of Shakespeare’s plays conducted via DeepL. It compares these to the translations by Liang Shiqiu, a well-known Chinese translator. The study finds that DeepL performs well in translating these works, with an accuracy and fluency rate of above 80% in sampled texts, showing the potential of the use of neural machine translation in translating literary texts across distant languages. Our research further reveals that the DeepL translations exhibit a certain degree of creativity in their use of translation methods such as addition, explicitation, conversion and shift of perspective, and in the use of Chinese sentence-final modal particles, as well as Chinese modal verbs. On the other hand, the system appears to be limited in that a certain amount of translation errors are present, including literal translations.
Neural machine translation and human translation

Abstract By comparing the Chinese, English and French versions of “Exhortations of Learning” and “On Building a Human Community with a Shared Future,” translated by human translators and the neural machine translation systems respectively, this essay finds out that human translators have addressed the political and ideological factors more tactfully while the working mechanism of the neural machine translation system lacks the formers’ judgment, consideration, flexibility and subjectivity. Moreover, unlike human translators, the neural machine system is not capable of activities such as summarizing the source texts, making comments or annotating. But on the other hand, the neural machine translation system has the advantages of its own. Not affected by bias like human translators, it could perform the translation faster and with a rather objective stance. All in all, there is still a long way to go before it can reveal the political and ideological factors in ways as human translators can achieve.
Deep Neural Network Ensembles Using Class-vs-Class Weighting

Ensembling is a popular and powerful technique to utilize predictions from several different machine learning models. The fundamental precondition of a well-working ensemble model is a diverse set of combined constituents. Rapid development in the deep learning field provides an ever-increasing palette of diverse model architectures. This rich variety of models provides an ideal situation to improve classification accuracy by ensembling. In this regard, we propose a novel weighted ensembling classification approach with unique weights for each combined classifier and each pair of classes. These weights allow us to account for the different abilities of individual classifiers to distinguish between pairs of classes. First, we analyze a theoretical scenario, in which our approach yields optimal classification. Second, we test its practical applicability on computer vision benchmark datasets. We evaluate the effectiveness of our proposed method and averaging ensemble baseline on an image classification task using the CIFAR-100 and ImageNet1k benchmarks. We use deep convolutional neural networks, vision transformers, and an MLP-Mixer as ensemble constituents. Statistical tests show that our proposed method provides higher accuracy gains than a popular baseline ensemble on both datasets. On the CIFAR-100 dataset, the proposed method attains accuracy improvements ranging from 2% to 5% compared to the best ensemble constituent. On the Imagenet dataset, these improvements range from 1% to 3% in most cases. Additionally, we show that when constituent classifiers are well-calibrated and have similar performance, the simple averaging ensemble yields good results.
Self-Healing Control: Review, Framework, and Prospect

The strong coupling between the components of a modern system and the increasing complexity of the system increase the demand for intelligent control and maintenance of the system become higher and higher.Self-healing control extends the scope of intelligent control, which is an inevitable trend in the development of intelligence for automated systems.Self-healing technology has a wide range of applications, but its theoretical system has not yet been developed.First, the characteristics, functions, definitions, and theoretical framework of self-healing control are clarified by reviewing and summarizing the research status of self-healing technology and self-healing control in various fields.Second, the differences and relationships between self-healing control and control methods, such as fault-tolerant control and adaptive control, are analyzed and discussed.Finally, the development tendency of self-healing control is summarized.The research status of self-healing technology in related fields is analyzed and summarized, and the basic framework of self-healing control is extracted to lay the foundation for the theoretical exploration of self-healing control.