Reviewerly/exHarmony huggingface dataset:
Implementation of data mining to enhance the performance of cloud computing environment

To deal with large scale computing events, the advantages of cloud computing are used extensively, whereby the possibility of machines processing larger data is possible to deliver in a scalable manner. However, one cannot ignore the challenges involved using the technology linked with large amount of data and internet applications (i.e., cloud). One of the biggest challenges cloud computing faces is security breaching and related issues. In this paper, an extensive mitigation system is proposed to achieve enhanced security and safer environment while using the applications of cloud computing. Using the decision tree model Chaid algorithm, it is proved to be a robust technique to classify and decision making by providing high end security for the cloud services. In this paper, it is proved that the standards, controls and policies are very important to the management processes for securing and protecting the data involved at the time of processing.
An Epistemic Logic for Modular Development of Multi-Agent Systems

Logic has been proved useful to model various aspects of the reasoning process of agents and Multi-Agent Systems (MAS). In this paper, we report about the last advances over a line of work aimed to explore social aspects of such systems. The objective is to formally model (aspects of) the group dynamics of cooperative agents. We have proposed and here extend a particular logical framework (the Logic of “Inferable” L-DINF), where a group of cooperative agents can jointly perform actions. I.e., at least one agent of the group can perform the action, either with the approval of the group or on behalf of the group. We have been able to take into consideration actions’ cost and the preferences that each agent can have for what concerns performing each action. Our focus here is on: (i) explainability, i.e., the syntax of our logic is especially devised to make it possible to transpose a proof into a natural language explanation, in the perspective of trustworthy Artificial Intelligence; (ii) the capability to construct and execute joint plans within a group of agents; (iii) the formalization of aspects of the Theory of Mind, which is an important social-cognitive skill involving the ability to attribute mental states, including emotions, desires, beliefs, and knowledge to oneself and to others, and to reason about the practical consequences of such mental states; such capability is very relevant when agents have to interact with humans, and in particular in robotic applications; (iv) connection between theory and practice, so as to make our logic actually usable by a system’s designers.
Monotone improvement of information-geometric optimization algorithms with a surrogate function

A surrogate function is often employed to reduce the number of objective function evaluations for optimization. However, the effect of using a surrogate model in evolutionary approaches has not been theoretically investigated. This paper theoretically analyzes the information-geometric optimization framework using a surrogate function. The value of the expected objective function under the candidate sampling distribution is used as the measure of progress of the algorithm. We assume that the surrogate function is maintained so that the population version of the Kendall's rank correlation coefficient between the surrogate function and the objective function under the candidate sampling distribution is greater than or equal to a predefined threshold. We prove that information-geometric optimization using such a surrogate function leads to a monotonic decrease in the expected objective function value if the threshold is sufficiently close to one. The acceptable threshold value is analyzed for the case of the information-geometric optimization instantiated with Gaussian distributions, i.e., the rank-$\mu$ update CMA-ES, on a convex quadratic objective function. As an alternative to the Kendall's rank correlation coefficient, we investigate the use of the Pearson correlation coefficient between the weights assigned to candidate solutions based on the objective function and the surrogate function.
Modules over monads and operational semantics (expanded version)

This paper is a contribution to the search for efficient and high-level mathematical tools to specify and reason about (abstract) programming languages or calculi. Generalising the reduction monads of Ahrens et al., we introduce transition monads, thus covering new applications such as lambda-bar-mu-calculus, pi-calculus, Positive GSOS specifications, differential lambda-calculus, and the big-step, simply-typed, call-by-value lambda-calculus. Moreover, we design a suitable notion of signature for transition monads.
Preparing Data for Machine Learning

As we saw in the previous chapter, data collection, preparation, and normalization is one of the primary steps in any machine learning experiment. Almost all of the machine learning algorithms (there are some interesting exceptions though) work with vectors of numbers. Even the other algorithms or tools would require data to be formatted in a specific way. So regardless of the kind of original data you are working with, you should know how to convert it into a usable format without losing necessary details.
Sentiment analysis using treebank filtered preprocess with relevant vector boost classifier

The procedure of identifying and classifying opinions in a piece of text to find out whether customer reviews toward a particular product or service are positive, negative, or neutral is termed as sentiment analysis. Stock market prediction is one of the most attractive topics in academic and real-life business. Many data mining techniques about sentiment analysis are suffering from the inaccuracy of prediction. The low classification accuracy has a direct effect on the reliability of stock market indicators. Treebank filtering Data Preprocessing based Ochiai-Barkman Relevance Vector Linear Programming Boost Classification technique is used for stock market prediction using sentimental analysis with higher prediction accuracy and lesser classification time for enhancing accuracy of stock market based on product review. Initially, the customer reviews and feedback on services or products are collected from the large database. After that, the collected customer reviews are preprocessed by performing the process such as tokenization, stemming, filtering. In order to achieve sentimental analysis through classifying customer reviews as positive and negative, Ochiai-Barkman Relevance Vector Linear Programming Boost Classification algorithm is used. The Linear Programming Boost Classification algorithm constructs with an empty set of weak classifiers as the Ochiai-Barkman Relevance Vector machine. The customer reviews are classified based on the Ochiai-Barkman similarity coefficient. The ensemble technique combines the weak classification results into strong by minimizing the error. In this way, the classification performance gets improved and the prediction of the stock market is carried out in a more accurate manner. Experimental evaluation is carried out on factors such as prediction accuracy, sensitivity, specificity, and prediction time versus amount of customer reviews.
A Framework for Crowdfunding Platform Using Ethereum Blockchain Technology

Crowdfunding involves collecting money from a great many people to subsidize a single project. It makes use of extensive networks of people to bring in investments to the project. Rewards-based crowdfunding offers investors rewards based on the amount of money donated. Traditional Crowdfunding platforms involve intermediaries and result in processing fees levied on all transactions. This results in subsided capital for the beneficiary. Using a blockchain-based approach eliminates the demand for intermediaries hence mitigating the processing fee levied. A blockchain is fundamentally secure, decentralized, auditable, and transparent in nature. This provides a trusted atmosphere where investors and beneficiaries can work together in a genuine and controlled fashion. There have also been cases where the project was fraudulent. Such contretemps intimidate potential investors. Blockchain-based crowdfunding can mitigate such occurrences using smart contracts. It can hence be inferred that a blockchain-based approach to crowdfunding is propitious.
Transfer Learning for Autonomous Recognition of Swarm Behaviour in UGVs

Recent work has developed value functions that can recognize emergent swarming behaviour and distinguish it from random behaviour. To date, this work has been done in point-mass swarm simulations. This paper proposes a transfer learning approach that can improve the performance of a value system for recognising swarming in simulated and real robots from limited data without replicating the training. A source value function is trained on human-labelled point-mass boid data. A target tree is trained on a small amount of new domain specific data. It can recognise swarm behaviour of diverse agents not used in the original training. We test the value function on homogeneous swarms of simulated and real robots. Results show that this value function can detect swarming in at least 89% of cases.
Intuitionistic Fuzzy Evaluations for Analysis of the Proximal Humerus Fractures

The paper is devoted of the assessment with intelligent instrument the very often happened Proximal humerus fractures. For our research 42 patients divided into two groups are analysed: Group 1 with 19 patients, operated via angular-stable locking plate and polymethylmetacrylate augmentation, and Group 2 with 23 patients, where iliac crest bone graft has been used. The analyzed data have been collected in the period 2017–2019 in Department of orthopedics and traumatology in University hospital for active treatment in Burgas. A method for evaluation the degree of recovery of the patients with Proximal humerus fractures have been proposed. The assessment of the patient’ recovery is formed on the basis of a set of intuitionistic fuzzy estimations $$\left\langle {\mu ,\nu } \right\rangle$$ of real numbers from the set [0, 1] × [0, 1]. These intuitionistic fuzzy estimations reflect the degree of successful recovery μ, or non-successful recovery ν, for each patient. The degree of uncertainty π = 1 − μ − ν represents such cases wherein the treatment is not finished or we cannot provide information on the patient.
Compact instantaneous phase-shifting Sagnac interferometer for nanoscale tilt measurement

There is a large inter disciplinary need for high precision angular sensors in state-of-the-art technologies. This particularly applies to optical metrology in the field of Extreme Ultraviolet (EUV) or X-ray where the optics are commonly characterized in terms of slope errors at a nanoradian scale level. This paper presents the in-lab development of an angular sensor based on a single reflection compact Sagnac interferometer using instantaneous phase shifting. Our automated prototype and first results show less than 15 nrad RMS repeatability, at least 200 nrad sensitivity and 280 nrad reproducibility over 100 μrad angular range.
Cucumber: Renewable-Aware Admission Control for Delay-Tolerant Cloud and Edge Workloads

The growing electricity demand of cloud and edge computing increases operational costs and will soon have a considerable impact on the environment. A possible countermeasure is equipping IT infrastructure directly with on-site renewable energy sources. Yet, particularly smaller data centers may not be able to use all generated power directly at all times, while feeding it into the public grid or energy storage is often not an option. To maximize the usage of renewable excess energy, we propose Cucumber, an admission control policy that accepts delay-tolerant workloads only if they can be computed within their deadlines without the use of grid energy. Using probabilistic forecasting of computational load, energy consumption, and energy production, Cucumber can be configured towards more optimistic or conservative admission. We evaluate our approach on two scenarios using real solar production forecasts for Berlin, Mexico City, and Cape Town in a simulation environment. For scenarios where excess energy was actually available, our results show that Cucumber's default configuration achieves acceptance rates close to the optimal case and causes 97.0% of accepted workloads to be powered using excess energy, while more conservative admission results in 18.5% reduced acceptance at almost zero grid power usage.
Bayesian Models of Human Sentence Processing

Human language processing relies on many kinds of linguistic knowledge, and is sensitive to their frequency, including lexical frequencies (Tyler, 1984; Salasoo & Pisoni, 1985; Marslen-Wilson, 1990; Zwitserlood, 1989; Simpson & Burgess, 1985), idiom frequencies (d’Arcais, 1993), phonological neighborhood frequencies (Luce, Pisoni, & Goldfinger, 1990), subcategorization frequencies (Trueswell, Tanenhaus, & Kello, 1993), and thematic role frequencies (Trueswell, Tanenhaus, & Garnsey, 1994; Gamsey, Pearlmutter, Myers, & Lotocky, 1997). But while we know that each of these knowledge sources must be probabilistic, we know very little about exactly how these probabilistic knowledge sources are combined. This paper proposes the use of Bayesian decision trees in modeling the probabilistic, evidential nature of human sentence processing. Our method reifies conditional independence assertions implicit in sign-based linguistic theories and describes interactions among features without requiring additional assumptions about modularity. We show that our Bayesian approach successfully models psycholinguistic results on evidence combination in human lexical, idiomatic, and syntactic/semantic processing.
The Impact of Artificial Intelligence and Data Fusion Technology on the Accounting Industry and Its Countermeasures

With the continuous development of artificial intelligence technology, big data is also constantly infiltrating the development process of the accounting industry, and it is playing an increasingly important role in enterprises. Big data and cloud computing are key requirements for information generation, processing and utilization in the current rapid economic growth process. This article first introduces the current status of the accounting field for the development of artificial intelligence billing systems under the background of the intelligent era; then puts forward relevant suggestions for the current financial work environment in our country; finally, conducts a questionnaire on the accounting industry in enterprises. The experimental results show that the impact of artificial intelligence technology on the accounting industry is more positive. Building a platform based on big data to achieve real-time interactive accounting and remote financial management is conducive to the development of the accounting industry.
Research on the design of large data storage structure of database based on Data Mining

With the continuous development of China's social economy, the informationization function of power grid companies is becoming increasingly powerful, and its effectiveness is very remarkable. However, in the related business of power system, the performance of database cannot be guaranteed. In the process of power marketing, a large amount of data will be generated. In the process of processing a large amount of data, it is necessary to constantly optimize the database to improve the structure of the database. Data mining is a promising subject frontier of database system and new database application. In this paper, a large data storage structure of database based on data mining is designed. The ETL tool in signaling data warehouse is used to realize the integrated design of signaling data storage and application.
Research on the Application of Artificial Intelligence in Piano Exercise Using Internet and Computer Software and Hardware Technologies

With the gradual deepening of the "Internet +" trend and the rise of artificial intelligence to the national strategy, it has become an inevitable trend to use new Internet and computer software and hardware technologies to serve music communication and music education. In the entire system of intelligent piano teaching, it mainly includes hardware construction methods, software design methods, music presentation forms, teaching methods, etc. Based on the existing technology and theories, different methods have different characteristics in the entire teaching. This article elaborates on the demand analysis, design, implementation and testing of the piano assistant practice system. This system can provide more convenience and better teaching effect for traditional piano teaching, provide a piano learning auxiliary tool for students, and provide a tool for teachers to supervise and manage students' piano learning.
Organisational excellence as a marketing tool: Greater Amman Municipality as a case study

This study aims at providing a deeper understanding of current status of organisational excellence (OE) from executives' perspectives in Greater Amman Municipality (GAM). More specifically, the study investigates OE benefits, tools, challenges, and evaluation criteria from executives' perspectives in GAM. The study employs the qualitative case study design; semi-structured interview is conducted as data collection instrument. Seven executives from GAM were selected for interview based on the purposive sample. Thematic and textual analysis of each interview transcript has been employed. To build a holistic view of the case study, cross-interview analysis has been adopted to highlight the study themes. The study concludes that OE has not been implemented effectively in GAM, although there are planned methodologies for each implementation dimension. Moreover, the common challenge encountered by OE implementation is lack of top management support and low available resources. The study recommends that continuous evaluation of customer satisfaction can be pursued by GAM based on clear, measurable, and reliable criteria. The study provides important lessons in applying OE models for practitioners and policy makers in governmental organisations generally and GAMS particularly.
Maximal velocity and amplitude decrement angle: a novel parameter for finger tapping instrumental evaluation in Parkinson disease

In our current knowledge-driven society, many information systems encourage users to utilize their online social connections’ information collections actively as useful sources. The abundant information-sharing activities among online social connections could be valuable in enhancing and developing a sophisticated user information model. In order to leverage the shared information as a user information model, our preliminary job is to determine how to measure effectively the resulting patterns. However, this task is not easy, due to multiple aspects of information and the diversity of information preferences among social connections. Which similarity measure is the most representable for the common interests of multifaceted information among online social connections? This is the main question we will explore in this paper. In order to answer this question, we considered users’ self-defined online social connections, specifically in Citeulike, which were built around an object-centered sociality as the gold standard of shared interests among online social connections. Then, we computed the effectiveness of various similarity measures in their capabilities to estimate shared interests. The results demonstrate that, instead of focusing on monotonous bookmark-based similarities, it is significantly better to zero in on more cognitively expressible metadata-based similarities in accounting for shared interests.
Development of High-performance Polymer Electret for Energy Harvesting based on De Novo Molecule Generation

We report development of a new polymer electret CTX-A/APDEA based on Cyclic Transparent Optical Polymer (CYTOP, AGC Chemicals). With a 15-μm-thick film, the surface potential of the synthesized electret kept at -3 kV for more than 800 hours and the peak temperature under thermally stimulated discharge (TSD) measurement of it was 187 °C, which both outperformed all commercialized CYTOP polymer electrets. In our study, deep reinforcement learning is employed, where density functional theory (DFT) is used to characterize the material property and ChemTS is employed for exploring the unknown chemical space. Besides, functional group enrichment of the generated molecules is analyzed statistically for incorporating interpretable knowledge while solid-state quantum chemical analysis based on PCM-DFT is conducted for studying CTX-A/APDEA. Our results are the first to demonstrate the successful application of machine learning in polymer electret design and the combination of expert knowledge with artificial intelligence.
Online Collaborative Learning: Analysis of the Current State

The transition to online education in times of Covid-19 has led to the use of educational innovations in order to ensure relevant learning. In this new and challenging pandemic context, the objective of this study is to diagnose the current state of Collaborative Online Learning (ACL) in teachers and students at the university level. The methodology adopted in the study is descriptive, inferential and cross-sectional. A new theoretical model of online collaborative learning is shown, which will include the design, validation and application of a questionnaire in 90 students and 20 teachers from different universities in Lima (Peru). The applied instrument showed high internal consistency with a Cronbach’s Alpha of 0,943 for teachers and 0,914 for students. The results obtained show pending challenges in digital skills and internal regulation of teams; potentialities related to the exchange of information, socio-emotional skills for collaboration and positive interdependence online. Thus, the implementation of innovative methodologies such as collaborative online learning should not be a spontaneous act, but rather reflective and conscious, based on a managerial-mediator role of the teacher and a leading role of the student, as indicators of pedagogical autonomy in these educational actors in the classroom context, in order to ensure its effectiveness in virtual environments and the achievement of relevant learning, as a basis for the formation of a global and ethical citizenship.
Identifying natural images and computer-generated graphics based on convolutional neural network

Aiming at the identification of natural images and computer-generated graphics, an image source pipeline forensics method based on convolutional neural network (CNN) is proposed. In this method, Inception-v3 is used as the basic network, and the pre-trained model parameters in ImageNet are adopted. The top-level classification layer of Inception-v3 is replaced by two fully-connected Softmax classifiers. With the transfer learning, a new network model is constructed. The network is fine-tuned by a database with 10,000 images to identify natural images and computer-generated graphics. Experimental results and analysis show that it can effectively identify natural images and computer-generated graphics, and it is robustness against JPEG compression, scaling, rotation, noise and other post-processing operations. Furthermore, the effect of Softmax classifier and SVM classifier on the experimental results are analysed.