Reviewerly/exHarmony huggingface dataset:
An improved context-aware analysis for sentimental Grass Hopper Optimization algorithm and its post affects on Twitter

Social media platforms, namely Instagram, Facebook, Twitter, YouTube, etc. have gained a lot of attention as users used to share their views, and post videos, audio, and pictures for social networking. In near future, understanding the meaning and analyzing this enormously rising volume and size of online data will become a necessity in order to extract valuable information from them. In a similar context, the paper proposes an analysis model in two phases namely the training and the sentiment classification using the reward-based grasshopper optimization algorithm. The training architecture and context analysis of the tweet are presented for the sentiment analysis along with the ground truth processing of emotions. The proposed algorithm is divided into two phases namely the exploitation and the exploration part and creates a reward mechanism that utilizes both phases. The proposed algorithm also uses cosine similarity, dice coefficient, and euclidean distance as the input set and further processes using the grasshopper algorithm. Finally, it presents a combination of swarm intelligence and machine learning for attribute selection in which the reward mechanism is further validated using machine learning techniques. The comparative performance in terms of precision, recall, and F-measure has been measured for the proposed model in comparison to existing swarm-based sentiment analysis works. Overall, simulation analysis showed that the proposed work based on grasshopper optimization outperformed the existing approaches for Sentiment 140 by 5.93% to 10.05% SemEval 2013 by 6.15% to 12.61% and COVID-19 tweets by 2.72% to 9.13%. Thus, demonstrating the efficiency of the context-aware sentiment analysis using the grasshopper optimization approach.
Computational medicine: Grand challenges and opportunities for revolutionizing personalized healthcare

SPECIALTY GRAND CHALLENGE article Front. Med. Eng., 31 March 2023Sec. Computational Medicine Volume 1 - 2023 | https://doi.org/10.3389/fmede.2023.1112763
Maneuvering target tracking based on an adaptive variable structure interactive multiple model filtering and smoothing algorithm

For maneuvering target tracking, the interactive multiple model (IMM) algorithm and its variants have shown good performance, among which the variable structure IMM (VSIMM) algorithm is the most widely studied one. The approximation degree of the algorithm and the matching degree of the model subset will affect the performance of the VSIMM. In addition, considering that smoothing can provide better estimation of the target state, a new adaptive VSIMM filtering and smoothing (AVSIMMFS) algorithm is proposed in this paper. First, an accurate model of the VSIMM algorithm is established, in which the IMM is run in parallel with and independently from the different model subsets, and the state estimation of the model subset with the highest probability is selected as the final estimation result. Then, an adaptive VSIMM (AVSIMM) algorithm was designed based on the VSIMM algorithm. The adaptation is reflected in the construction of a new model subset from the original model subsets, which improves the matching degree between the model subset and the actual maneuvering model of the target. Finally, by smoothing the filtering data of the AVSIMM algorithm, the AVSIMMFS algorithm is obtained. Because of the combination of forward filtering and backward smoothing, the target tracking accuracy is further improved. Simulation results show that the tracking performance of the AVSIMMFS algorithm is better than that of other methods.
High-precision single-axis rotation measurement method of multi-rudders based on monocular vision

To evaluate the assembly accuracy of rudders during the production of aerospace vehicles, a measurement method based on monocular vision is proposed in this paper. Compared with existing methods that adopt cooperative targets pasted manually, the proposed method avoids pasting cooperative targets on the surface of rudders and calibrating the original position of rudders in advance. First, we use two known position markers on the surface of the vehicle and multiple feature points of the rudder to solve the relative pose between the camera and the rudder by employing the PnP algorithm. Then, we measure the rotation angle by converting the change of the camera's pose to the rotation angle of the rudder. Finally, a tailored error compensation model is introduced into the proposed method to increase the accuracy of the measurement. Experiment results show that the average measurement absolute error of the proposed method is less than 0.08° overall, which is remarkably superior to existing methods and satisfies the requirement of practical industrial production.
The Effect of Design Elements on Simplicity, Usability, and Product Preference: A Video Conference Application Case Study

Abstract Simplicity achieved by minimizing the number of controls is a major guideline for designing usable products. Still, restricting the number of controls in interfaces to achieve simplicity may not be a wise strategy for communicating usability. Our model suggests that although the number of objects in designs helps determine visual simplicity, design elements influence the perceived simplicity of products. Design elements that enhance orientation and structure enable many otherwise-described complex objects to be perceived as simple. To test the effect of simplicity on usability, both visual and perceived simplicity are analyzed. We report a qualitative pilot study and a subsequent experimental study that helped test our model. While the number of objects helped determine the level of simplicity, adding design elements moderated the perceived complexity of control-laden designs. Designs with either a medium or a large number of both objects and design elements were judged as highest for perceived usability.
A heuristic-based package-aware function scheduling approach for creating a trade-off between cold start time and cost in FaaS computing environments

With the migration of enterprise applications to microservices and containers, cloud service providers, starting with Amazon in 2014, announced a new computational model called function-as-a-service. In these platforms, developers create a set of fine-grained functions with shorter execution times instead of developing coarse-grained software. In addition, the management of system resources and servers is delegated to cloud service providers. This model has many benefits, such as reducing costs, improving resource utilization, and helping developers focus on the core logic of applications. But, still faces many challenges such as cost/usage balance, optimizing performance, programming models, using current development tools, containers' cold start problem, saving data in caches, security issues, privacy concerns, and scheduling challenges like execution time prediction. In this paper, we focus on scheduling and cold start problems. Reducing the cold start time can result in better response times and hence a better experience for end-users. Compromise occurs when keeping warm operating environments which can reduce cold start times while increasing resource usage costs. The cold start problem is wildly studied, but in this work, we propose a novel dynamic waiting-time adjustment approach in which the waiting time of a container will change dynamically according to the situation. In this study, four different types of decisions for changing the waiting time are introduced and investigated. We aim to create a trade-off by using these decisions at runtime using a heuristic method. Functions' invocation frequency, function dependency graph, maximum merging time, correct mergers, cost, and timeline are considered for the decision-making process. The performed evaluations demonstrate that the proposed approach results in a 32% improvement over the fixed-time method (i.e., the method used by Apache OpenWhisk). This comparison is made from the cumulative measurement viewpoint which is a combination of response time, turnaround time, cost, and utilization.
Orthogonal stochastic configuration networks with adaptive construction parameter for data analytics

Abstract As a randomized learner model, SCNs are remarkable that the random weights and biases are assigned employing a supervisory mechanism to ensure universal approximation and fast learning. However, the randomness makes SCNs more likely to generate approximate linear correlative nodes that are redundant and low quality, thereby resulting in non-compact network structure. In light of a fundamental principle in machine learning, that is, a model with fewer parameters holds improved generalization. This paper proposes orthogonal SCN, termed OSCN, to filtrate out the low-quality hidden nodes for network structure reduction by incorporating Gram–Schmidt orthogonalization technology. The universal approximation property of OSCN and an adaptive setting for the key construction parameters have been presented in details. In addition, an incremental updating scheme is developed to dynamically determine the output weights, contributing to improved computational efficiency. Finally, experimental results on two numerical examples and several real-world regression and classification datasets substantiate the effectiveness and feasibility of the proposed approach.
A Systematic Literature Review of Reinforcement Algorithms in Machine Learning

Reinforcement learning (RL) is learning from interactions with the environment in order to accomplish certain long-term objectives connected to the environmental condition. Reinforcement learning takes place when action sequences, observations, and rewards are used as inputs, and is hypothesis-based and goal-oriented. The purpose of the research was to conduct a systematic literature review of reinforcement algorithms in machine learning in order to develop a successful multi-agent RL algorithm that can be applied to robotics, network packet routing, energy distribution, and other applications. The robotics-related RL techniques of value-based RL, policy-based RL, model-based RL, deep reinforcement learning, meta-RL, and inverse RL were examined. As a result, the robotics-related RL techniques of value-based RL, policy-based RL, model-based RL, deep RL, meta-RL, and inverse RL were discussed in this research work. The asynchronous advantage actor-critic algorithm (A3C) is one of the best reinforcement algorithms. A3C performs better on deep RLchallenges and is quicker and easier to use.
A Breakthrough With Machine Learning in Real-Time Environments

Over the past few years, machine learning (ML) has seen potential growth globally and has contributed a huge role in technological advancements. With improved learning mechanisms, the ML algorithms have provided huge breakthroughs and enhancements in solutions for the real-time global challenges that persist. With large amounts of digital data evolving each day, ML algorithms have evolved as the right-to-go approach, to provide technological breaks though by overruling the traditional methods. The chapter aims to briefly review the overview of ML algorithms and highlight the different applications under various segments where ML has contributed to providing breakthrough solutions. The applications that are to be discussed from different segments are healthcare, businesses, government, security, agriculture, industry, and the educational sector. The objective of the chapter is to provide the readers with a better understanding of how ML applications are used in different segments and the advancements it brings to modern technology.
Machine Learning-Based Data Analytics With Privacy

Data analytics is a very common word today. Data is collected from various sources and analyzed for decision making. The decisions help for growing business, for healthcare support, as well as to keep track of some useful information on communication media. For the same data may be shared, stored, and analyzed. Each of these three processes involves threat of data leakage to hacker. To prevent this, privacy preservation algorithms are used. This chapter discusses about privacy preserving techniques right from data collection to analytics through data storage. The data classification techniques are also discussed for understanding of machine learning data analytics. At the end open issues in privacy preserving are also discussed.
An Introduction to Data Visualisation

Data visualization is the process of producing interactive visualizations to study patterns and variances and extract useful insights from data. Any technique that enables the identification of insights inside a dataset is considered data visualization. A data analyst will assess a dataset and produce charts based on the results. Data visualizations consist of bar graphs, heat maps, world maps, and line graphs. Typically, data visualization is used for data verification and cleansing, exploration and discovery, and communicating business outcomes to important stakeholders. The vast majority of data scientists neglect visualizations and rely solely on frequently misleading numerical calculations. Thus, the proposed chapter has addressed the most prevalent data visualization approaches.
Data protection in heterogeneous big data systems

Modern Big Data systems are notable for their scale and, often, their distributed organization. A feature of a number of Big Data systems is the use of heterogeneous data processing tools. These are different DBMS and streaming processing tools with different data granularity. The input information may be repeatedly fragmented and re-grouped as it moves between storage locations. In this case, the problems of data integrity, access control and auditing cannot be solved by traditional methods. This paper considers the security of heterogeneous Big Data systems based on distributed ledger technologies and verifiable zero-knowledge operations.
International revenue share fraud prediction on the 5G edge using federated learning

Abstract Edge computing and multi-access edge computing (MEC) are two recent paradigms of distributed computing that are growing due to the rise of the fifth-generation (5G) of broadband cellular networks. The development of edge computing and MEC architectures involves the hosting of applications close to the end-users, allowing: an improved privacy, given that critical data is not shared with other systems; a reduced communication latency; an improved application speed; and a more efficient energy use. However, many applications are challenged by edge computing and MEC. In the case of machine learning (ML) applications, there can be privacy rules that do not allow data to be shared among distinct edges. Additionally, the devices used to train ML models might present lower computational capabilities than traditional computers. In this work, we present a Federated ML architecture that uses decentralized data and light ML training techniques to fit ML models on the 5G Edge. Our system consists of edge nodes that train models using local data and a centralized node that aggregates the results. As a case study, an international revenue share fraud task is addressed by considering two real-world datasets obtained from a commercial provider of Telecom analytics solutions. We test our architecture using two iterations of a Federated ML method, then compare it with a centralized ML model that is currently adopted by the provider. The results show that the Federated Learning decentralized approach produces an excellent level of class discrimination and that the main models maintain the performance across two rounds of decentralized training and even surpass the existing centralized model. After validating the results with the Telecom provider, we have built a prototype technological architecture that can be deployed in a real-world MEC scenario.
Data block decomposition and intelligent secure acquisition of microdata

P-sets (P stands for Packet) is a set model with dynamic characteristics, which is obtained by introducing dynamic characteristics into Cantor set and improving Cantor set. According to the fact that the characteristics of class I big data are completely consistent with the basic characteristics of P-sets, this paper gives research on theory and application on class I big data from the view of mathematics. Here we introduce Class I big data which need some new definitions of data block, microdata and data link. Based on these concepts, decomposition theorem of data block and microdata relation theorem are given, and then attribute reasoning theorem and microdata intelligent discovery and the intelligent secure acquisition algorithm of microdata are also proposed. By using these theoretical results, the applications of secure acquisition of microdata are presented. In summary, P-sets mathematical model provides a new theory and method for studying class I big data.
Determinantal polynomials and the base polynomial of a square matrix over a finite field

Purpose The author studies forms over finite fields obtained as the determinant of Hermitian matrices and use these determinatal forms to define and study the base polynomial of a square matrix over a finite field. Design/methodology/approach The authors give full proofs for the new results, quoting previous works by other authors in the proofs. In the introduction, the authors quoted related references. Findings The authors get a few theorems, mainly describing some monic polynomial arising as a base polynomial of a square matrix. Originality/value As far as the author knows, all the results are new, and the approach is also new.
Concepts of Interpolation in Stratified Institutions

The extension of the (ordinary) institution theory of Goguen and Burstall, known as the theory of stratified institutions, is a general axiomatic approach to model theories where the satisfaction is parameterized by states of models. Stratified institutions cover a uniformly wide range of applications from various Kripke semantics to various automata theories and even model theories with partial signature morphisms. In this paper, we introduce two natural concepts of logical interpolation at the abstract level of stratified institutions and we provide some sufficient technical conditions in order to establish a causality relationship between them. In essence, these conditions amount to the existence of nominals structures, which are considered fully and abstractly.
A Generalization Gap Estimation for Overparameterized Models via the Langevin Functional Variance

This paper discusses the estimation of the generalization gap, the difference between generalization performance and training performance, for overparameterized models including neural networks. We first show that a functional variance, a key concept in defining a widely-applicable information criterion, characterizes the generalization gap even in overparameterized settings where a conventional theory cannot be applied. As the computational cost of the functional variance is expensive for the overparameterized models, we propose an efficient approximation of the function variance, the Langevin approximation of the functional variance (Langevin FV). This method leverages only the 1st-order gradient of the squared loss function, without referencing the 2nd-order gradient; this ensures that the computation is efficient and the implementation is consistent with gradient-based optimization algorithms. We demonstrate the Langevin FV numerically by estimating the generalization gaps of overparameterized linear regression and non-linear neural network models, containing more than a thousand of parameters therein.
Machine Learning for Natural Language Processing: Techniques and Evaluation

The purpose of this research study is to analyse the approaches that are used in machine learning for the solution of natural language processing (NLP) issues. Also, the assessment criteria that are used in the performance analysis of these models are looked at as part of this investigation. We investigate the challenges that come with using machine learning for natural language processing (NLP), including the issue of data bias and the need that these models have the capacity to be explained. Research is conducted into a variety of supervised and unsupervised learning approaches, including as neural networks, topic modelling, and clustering. Also, a number of evaluation metrics, such as accuracy, precision, recall, and F1 score, are discussed. As the research comes to a conclusion, it is highlighted how important it is to find solutions to problems like data bias and the inability to explain results. This is done in order to ensure that the results generated by machine learning models for natural language processing are accurate and unbiased.
INTS-Net: Improved Navigator-Teacher-Scrutinizer Network for Fine-Grained Visual Categorization

Fine-grained image recognition, as a significant branch of computer vision, has become prevalent in various applications in the real world. However, this image recognition is more challenging than general image recognition due to the highly localized and subtle differences in special parts. Up to now, many classic models, including Bilinear Convolutional Neural Networks (Bilinear CNNs), Destruction and Construction Learning (DCL), etc., have emerged to make corresponding improvements. This paper focuses on optimizing the Navigator-Teacher-Scrutinizer Network (NTS-Net). The structure of NTS-Net determines its strong ability to capture subtle information areas. However, research finds that this advantage will lead to a bottleneck of the model’s learning ability. During the training process, the loss value of the training set approaches zero prematurely, which is not conducive to later model learning. Therefore, this paper proposes the INTS-Net model, in which the Stochastic Partial Swap (SPS) method is flexibly added to the feature extractor of NTS-Net. By injecting noise into the model during training, neurons are activated in a more balanced and efficient manner. In addition, we obtain a speedup of about 4.5% in test time by fusing batch normalization and convolution. Experiments conducted on CUB-200-2011 and Stanford cars demonstrate the superiority of INTS-Net.
Mastering of subject-oriented technologies by undergraduate students

Currently, due to the processes of development and modernization of education that are taking place in Russian society, the requirements for a graduate of a pedagogical university are increasing, new guidelines in pedagogical activity are being determined. The organization of training sessions at the university should first of all involve the construction of an educational process that takes into account the educational needs and capabilities of students, and the teaching of disciplines is carried out based on the existing subjective experience. The results of the survey of students and teachers show that currently there is no holistic system in the activities of universities to prepare students for the use of subject-oriented technologies in professional activities. In this regard, an urgent problem is the development of a comprehensive and purposeful preparation of students for the use of subject-oriented technologies at the university. The article describes the experience of studying the course “Technologies of educational activityˮ, during which students master subject-oriented technologies. The logic of studying the discipline as a whole and individual classes using subject-oriented technologies is presented. This work was carried out in undergraduate student groups of the Faculty of Natural Geography of the Yaroslavl State Pedagogical University named after K.D. Ushinsky (Russia, Yaroslavl). The obtained research results confirm the dynamics of students' readiness to use subject-oriented technologies in professional activities. The conducted research touches upon modern aspects of higher and secondary vocational education.