Reviewerly/exHarmony huggingface dataset:
Aspect Based Sentiment Analysis Annotation Methodology for Group Decision Making Problems: An Insight on the Baseball Domain

Decision making is an important part of our lives, especially in the context of an organization where decisions affect their business, and in this modern era, it is increasingly important to make the best decisions and increasingly difficult to get people together to make said decisions. Because of this, the importance of Group Decision Support Systems keeps growing, especially those that are web-based since they allow a connection between people in different corners of the world. However, there isn’t much in terms of systems that can take online text-based discussions and use them to help a group of people reach a decision. This works addresses one of the aspects of this issue, that being the lack of annotated datasets that can provide a source of information to help in the creation of said systems. For this purpose, this work presents a methodology to be applied to unstructured text-based discussions found on the social web, to extract from them important information and organize it. In addition, a practical case study of this methodology is described, using Baseball domain discussions from Reddit as this case’s unstructured data. We concluded that the created methodology allows the structuring of different aspects of a given social web discussion, especially in Reddit, and could be applied to discussions found on several existing domains.
Knowledge Diffusion Trajectories of Agile Software Development Research: A Main Path Analysis

ContextThe dramatic growth of agile software development (ASD) research has resulted in a large number of diverse theoretical and empirical publications. The citation relationships among publications indicate knowledge dissemination across and within academics or scientists.ObjectiveThis study provides a comprehensive understanding of research development in the ASD literature by exploring the knowledge diffusion path through the citation network of publications that have made significant contributions to the development of ASD.MethodMain path analysis (MPA), a quantitative citation-based methodology, is used in this study to examine the citation relationship of 1431 scientific articles published in the Web of Science (WoS) between 2001 and 2021. MPA results are then visualized using Pajek software. Results: Our findings show that the growth of ASD publications is at the midpoint of development in 2022 and will reach maturity in the 2030s. Their growth rate will then slow down and reach a saturation rate in the 2040s. Key results present that 32 publications identified along the key-route main path are the most influential in the trajectories of ASD, divided into three development phases: introduction, evaluation, and deployment and expansion. We further uncover the publication trends from a set of recent papers using the multiple-global main path, which reveals four sub-themes of ASD publications: agile practices tailoring, large-scale agile context, challenges and success factors of large-scale organizations, and agile global software development.ConclusionsThe knowledge diffusion trajectories of the ASD domain are revealed clearly through utilizing MPA. It thus offers an alternative approach for researchers to systematically understand the knowledge flow and historical development of this domain.
A Hybrid Whale Genetic Algorithm for Feature Selection in Biomedical Dataset

One of the major concerns of biomedical datasets is high dimensionality. These dimensions may include irrelevant and redundant features that adversely affect the performance of classification algorithms. Extensive research has been done in the area of machine learning to handle high dimensionality. In literature, feature selection algorithms have been developed for this purpose. In this paper, a hybrid nature-inspired algorithm is proposed which is a combination of whale optimization algorithm and genetic algorithm for feature selection. The proposed algorithm is applied to four microarray datasets and one DNA sequence dataset and compared with classical feature selection methods. In all the algorithms decision tree classifier is mainly employed. To reach an approximate best solution and remove local solutions, the exploitation and exploration phases are balanced efficiently in the proposed algorithm. The convergence speed in the proposed algorithm is accelerated by the adaptive mechanisms. Overall results employ better performance on the majority of datasets.
An Application for Facial Image Beautification

In today's world, social media, virtual meetings, online demonstrations, etc. are rapidly growing in different parts of the world. With the rapid growth of the digital world, image and face beautification became one of the biggest concerns. Each human wants to look perfect and natural through the viewfinder. Therefore, appearance in an image became the most important factor. In this kind of situation, cosmetic process is not very promising all of the time and it is cost-effective. Therefore, the beautification application plays the important role in these circumstances. In this experiment, an image and face beautification application is created. Different tools and filters are created to make the input image more natural and dramatic. This application is fully in the choice of human. Every tool can be adjusted by the slider option. Finally, a user can easily beautify their image without any prior knowledge of the application.
Classification Rule Diagram to Diagnosis the Tuberculosis Based on Symptoms

Data mining is a vast area in research and applications where it is applicable for many real-time problems hence it proves to be beneficial for large data handling. Especially, when it comes to medical diagnosis, using data mining algorithms always proves to be exemplary where it helps to quickly diagnose diseases using available data based on classification algorithms. For example, diagnosing tuberculosis can be done using knowledge discovery patterns on uncovering various symptoms and associated patterns of behavior which is cost-efficient instead of diagnosing using various medical tests. In this paper, we are employing a classification diagram which is a key element of a much more elaborative process called knowledge discovery, to provide some indicators that are mainly responsible for the occurrence of special disease—tuberculosis and symptoms that are highly correlated with the positive examination test based on various rule-based conditions. Classification rules can be used to infer the defining characteristics based on information relevance of different test components and hence discover hidden knowledge, unexpected patterns, and new rules from the database by using the classification rule diagram to diagnose tuberculosis.
Learned Dense Representations for Ranking

Arguably, the single biggest benefit brought about by modern deep learning techniques to text ranking is the move away from sparse signals, mostly limited to exact matches, to continuous dense representations that are able to capture semantic matches to better model relevance (see Section 1.2). With so-called dense retrieval techniques, the topic of this chapter, we can perform ranking directly on vector representations (naturally, generated by transformers). This approach has the potential to address the vocabulary mismatch problem by directly performing relevance matching in a representation space that “captures meaning”—as opposed to reranking the output of keyword-based first-stage retrieval, which still relies on sparse exact match signals (document and query expansion techniques discussed in Chapter 4 notwithstanding).
A Statistical Analysis of CVD using Binary Logistic Regression

Background: As a chronic disease, cardiovascular disease (CVD) has greatly affected people's quality of life.Limited studies have investigated the relationship between many risk factors and infecting CVD, and few of them use a large sample size to conduct the study.We investigated the relationship between several different risk factors, such as blood pressure and cholesterol, and CVD.Methods: A dataset which contains 70000 samples is obtained from Kaggle, and binary logistic regression analysis was used to investigate the impact of clinical and lifestyle factors on the prevalence of CVD.Variable selection, such as forward selection and backward selection, was performed to build the final model.Results: The result of forward-backward selection and backward selection show that all features, except gender, are significant under 0.1% level, and the AIC of them are both 77216.The percentage of accuracy of the model is 0.7267.The proportion of actual positive samples identified correctly (recall/sensitivity) is 0.663, the proportion of predict positive samples identified correctly (precision) is 0.755, and the proportion of actual negative samples identified correctly (specificity) is 0.789.Conclusion: Our study suggested that age, height, weight, systolic blood pressure, diastolic blood pressure, cholesterol, glucose, smoke, alcohol intake, and physical activity are the most important factor in determining CVD.The findings from our study have important public health implications and call for future studies to explore the potential mechanism of these findings.CCS Concept•Mathematics of computing  Probability and statistics  Statistical paradigms  Regression analysis
Modal Feature Contribution Distribution Strategy in Visual Question Answering

The results of the visual question answering (VQA) task are obtained by the joint inference of the information of the image and text modalities, and its performance is affected by multiple factors, such as the modal feature extraction method and the modal feature fusion process.The current popular VQA models do not undertake processing after extracting the modal features from images and texts.Instead, feature fusion is executed straight without considering the modality's feature contribution, which is debatable.To reveal the relationship between the contribution distribution of image and text modal features, and the performance of VQA task, a plug-and-play contribution distribution strategy of modal features was proposed based on nonlinear functions.By globally operating the image and text features on the basis of extracting the features of the two modalities, the feature weight distribution in each mode was processed and analyzed based on the nonlinear function and the global features of the two modes.Extensive experiments were carried out on the basis of the existing model to fully verify the effectiveness of this strategy.Results show that the contribution distribution of the extracted image and text features before the feature fusion stage of the modality harmonizes the relationship between the image and text modalities and strengthens the effective features in the respective modalities.At the same time, the strategy further improves the performance of existing VQA models.This study provides a certain reference on how to reprocess features in VQA tasks.
Efficient Extended Chaotic Map-Based IBE for Industrial Environment

Identity-based encryption is a public key-based method of encryption that enables communicating identities to use some individual and unique information, such as their physical IP addresses and MAC addresses, to identify them and as public keys. The scheme does not require the extra device to store long-term public keys. So, it is convenient for use in practical applications, including smart industry and smart manufacturing. This study develops a novel, efficient, and secure identity-based encryption scheme using an extended Chebyshev chaotic map that does not require time-consuming modular exponential computations or scalar multiplications on elliptic curves. Cryptography that is based on chaotic maps has recently been demonstrated to outperform traditional cryptography. Besides demonstrating that the proposed scheme satisfies the security requirements of identity-based encryption, the simulation results of this study show that the proposed scheme requires less response time than related identity-based encryption schemes. Due to hardware limitations, not all industrial devices can load heavy computations. Therefore, the proposed identity-based encryption scheme outperforms related identity-based encryption approaches, and is suitable for industrial environment.
A hybrid deep learning network for non-linear time series prediction

Non-linear time series prediction is highly significant because most of the practical situations deals with time series which are non-linear in nature. This study suggests a new time series prediction CEEMDAN-SVDLSTM model amalgamating Complete Ensemble EMD with Adaptive Noise, Singular Value Decomposition and Long Short Term Memory network. Non-linear and non-stationary data can be analysed by deploying the above model. CEEMDAN stage, SVD stage and LSTM stage are the main parts of the model. The break down of the data into some IMF components plus a residue is carried out by CEEMDAN in the first stage. Each IMF component and residue so obtained is de-noised by SVD in the second stage. Third stage deployed LSTM to predict all the de-noised IMF components. The foretold values of the actual data is then obtained by adding all the predicted IMF components and residue. We compared the model with other models such as LSTM model, EMD-LSTM model, EEMD-LSTM model, CEEMDAN-LSTM model and EEMD-SVD-LSTM model. The results show that the suggested CEEMDAN-SVD-LSTM model works better than other models in terms of efficiency in predicting future values.
Whale Optimization Algorithm Based on Artificial Fish Swarm Algorithm

The traditional whale algorithm has some shortcomings such as low solution accuracy and falling into local optimization easily. Aiming at these, a new whale optimization algorithm (NWOA) based on the artificial fish swarm algorithm is proposed, which combined with the advantages of artificial fish swarm algorithm that the crowding factor is used to limit the cluster size to promote the algorithm to be more optimized widely. The new whale optimization algorithm introduces the concept of crowding in the artificial fish swarm algorithm (AFSA) into the whale algorithm and changes characteristics of whale populations, and it overcomes the shortcoming of the traditional whale algorithm that individual whales perform different foraging behaviors according to the size of the random probability number. By calculating the degree of crowding around the prey, the foraging behavior of individual whales is designed to improve the convergence speed and optimization accuracy of the whale algorithm and enhance the stability of the algorithm. The simulation experiment is conducted by selecting 9 benchmark test functions. The experimental results show that the convergence speed and algorithm stability of the algorithm in this paper are significantly improved compared to the traditional whale algorithm.
Design of an Image Transmission System Employing a Hybridization of Bit-Plane Slicing, Run-Length Encoding and Vector Quantization Based Visual Cryptography Scheme

The exponential growth in sharing information over public network holds a major portion of image communication. In the domain of secure image transmission, visual cryptography scheme (VCS) evolved as a potential tool for its simple formulation. But, the noise-like multiple share images, generated after image encryption, augment the transmission overhead and may be captured by curious hackers to reveal the secret. A VCS based image transmission system is proposed in this paper to lower the transmission overhead using a hybridization of bit-plane slicing, run-length encoding, and vector quantization techniques. The proposed scheme also transforms the noise-like shares in coded numeric values to secure its transmission. The proposed scheme is successfully applied to the benchmark images and the results demonstrate its usefulness in secure image transmission.
Analysis of Safety-Critical Communication Protocols for On-Premise SIL4 Cloud in Railways

In this paper, we address the question of how SIL4 railway applications within on-premise cloud environments can communicate safely with internal and external systems. The EN 50159 standard is the railway standard applicable for safety-related communication. For IT/OT Security IEC 62443 can be considered. Most standardized protocols developed according to EN 50159 are peer-to-peer protocols involving two communication partners. To leverage the cloud environment, contemporary applications of other domains use a different communication scheme, namely publish-subscribe, to connect internal and external components and enable scalability. Based on this challenge, the goal of this paper is to investigate emerging communication protocols from different domains and their suitability for the railway system. We will first determine the requirements for the railway communication infrastructure and applications executed in a SIL4 cloud, i.e., an environment provided by on-premise data centers utilizing technologies such as virtualization and with other cloud-like features, such as scalability and flexible usage of resources. Furthermore, a brief comparison of the potential application-layer communication protocols from industrial domains with railway-specific safety-critical protocols will be presented. Finally, we will present a system architecture that demonstrates how safe communication can be realized by middleware protocols such as DDS or OPC UA and how they fulfill the previously established requirements for the railway system.
How Much Randomness is Needed to Convert MA Protocols to AM Protocols?

The Merlin-Arthur class of languages MA is included into Arthur-Merlin class AM, and into PP. For a standard transformation of a given MA protocol with Arthur’s message (= random string) of length a and Merlin’s message of length m to a PP machine, the latter needs O(ma) random bits. The same holds for simulating MA protocols by AM protocols: in the resulting AM protocol the length of Arthur’s message (= random string) is O(ma). And the same holds for simulating heuristic MA protocols by heuristic AM protocols as well. In the paper [A. Knop, Circuit Lower Bounds for Average-Case MA, CSR 2015] it was conjectured that, in the transformation of heuristic MA protocols to heuristic AM protocols, O(ma) can be replaced by a polynomial of a only. A similar question can be asked for normal MA and AM protocols, and for the simulation of MA protocols by PP machines. In the present paper we show that, relative to an oracle, both latter questions answer in the negative and Knop’s conjecture is false. Moreover, the same is true for simulation of MA protocols by AM protocols in which the error probability is not bounded away from 1/2, the so called PP $$\cdot $$ NP protocols. The latter protocols generalize both AM protocols and PP machines .
PhyMCons: Physical Machine Consolidation for Minimizing VM Migration and Energy Consumption in Fog Center

Virtualization technologies have revolutionized how businesses use their server resources in their fog centers. Rather than using dedicated physical machines for individual applications, virtualization makes it possible to view resources as a unified resource pool, simplifying management, and lowering complexity. One of the most common applications of virtualization in fog centers is physical machine consolidation, which involves lowering the number of physical machines used by consolidating applications. The latter technique aids in the more efficient use of computing resources and has numerous advantages, including lower power and cooling costs, and thus imparts to the Green IT program. In a data center environment that is always evolving, where virtual machines (VMs), lowering the number of physical machines utilized can be accomplished by migrating applications in a non-disruptive manner, a technique termed live migration. But since live migration is an expensive procedure, it’s challenging to figure out how to execute periodic physical machine consolidation in a migration-aware manner. We present the PhyMCons physical machine consolidation algorithm, which reduces both the overall number of physical machines in operation, as well as the number of migrations which in turn reduces the energy consumption. We demonstrate the practicality of our approach as well as its scalability.
Conversational AI for Positive-sum Retailing under Falsehood Control

Retailing combines complicated communication skills and strategies to reach an agreement between buyer and seller with identical or different goals.In each transaction a good seller finds an optimal solution by considering his/her own profits while simultaneously considering whether the buyer's needs have been met.In this paper, we manage the retailing problem by mixing cooperation and competition.We present a rich dataset of buyer-seller bargaining in a simulated marketplace in which each agent values goods and utility separately.Various attributes (preference, quality, and profit) are initially hidden from one agent with respect to its role; during the conversation, both sides may reveal, fake, or retain the information uncovered to come to a final decision through natural language.Using this dataset, we leverage transfer learning techniques on a pretrained, end-to-end model and enhance its decisionmaking ability toward the best choice in terms of utility by means of multi-agent reinforcement learning.An automatic evaluation shows that our approach results in more optimal transactions than human does.We also show that our framework controls the falsehoods generated by seller agents.The code and dataset are available on https://github.com/ckiplab/Fruit_Stand.
High-Dimensional Constrained Fuzzy C-Means (HCFCM) Algorithms for Automatic First-Arrival Picking

Summary Picking the first-breaks is an important step in seismic processing. The large volume of the seismic data calls for automatic and objective picking. In this paper, we formulate the first-breaks picking as constraint optimization problems. By designing a convex set projection operator and high-dimensional membership function on the basis of conventional Fuzzy C-Means Algorithms, the improved method considers the lateral spatial variation of seismic data to ensure the picking of first-breaks is more accurate and automatic. Testing this approach on the field seismic data reveals its properties and shows it can accurately do the automatic seismic events picking, especially the complex waveforms.
Introduction

The goal of text ranking is to generate an ordered list of texts retrieved from a corpus in response to a query for a particular task. The most common formulation of text ranking is search, where the search engine (also called the retrieval system) produces a ranked list of texts (web pages, scientific papers, news articles, tweets, etc.) ordered by estimated relevance with respect to the user’s query. In this context, relevant texts are those that are “about” the topic of the user’s request and address the user’s information need. Information retrieval (IR) researchers call this the ad hoc retrieval problem.1