Reviewerly/exHarmony huggingface dataset:
Ethereum Veri Tabanları İçin Bir Yazılım ve ESRI Dijital Haritalar Programlama Yazılımı ile Önerilen Uygulamalar İçin Bir Araştırma

Ethereum databases with their huge security and integrity can provide perfect mechanism for storing and sharing the data of the highly important projects. Qualitative and quantitative research methods were used in this paper to design software for Ethereum databases. Also, these methods were used to examine how and how much features can be added to the software. Designing the software considered the barriers and the limitations of Solidity smart contracts programming language and Ethereum blockchain platform. The achieved software can make any Ethereum database with parent-child relationships in between the tables. More professional features will be added in future research as soon as possible. The study used survey method to discuss recommended applications for the software integration with another software for ESRI digital maps programming. The recommended applications are the real estate business full automation and the transparent environmental management to protect the Mediterranean Sea from the pollution. However, most parts related to Ethereum databases software are covered. But to keep this paper in reasonable size, only the survey is included for the parts related to the software recommended applications with the digital maps software.
Measurement Systems for Wind, Solar and Hydro Power Applications

Wind, solar, and hydropower are major forms of the so-called renewable energies. Effective application of renewable energies to supply heat and electricity is weather dependent and needs short-term weather forecasts, as well as historical and climatological information. All relevant measurement sensors were introduced in previous chapters. Here, the special requirements of wind, radiation, and precipitation measurements for planning and operating renewable energy power plants are addressed. The cooling of conventional thermal power plants, the transmission of electricity in cables above ground, and the overall energy demand are weather dependent as well, and thus need atmospheric measurements.
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Distance metric learning has been an important technique in machine learning field recently due to its high effectiveness in improving the performance of distance related methods. In order to take advantages of both subspace learning and metric learning to overcome the limitations of metric learning, in this work we intend to learn a robust discriminative subspace and a distance metric simultaneously by maximizing the ratio of inter-class covariance to inner-class covariance using lp−norm (0<p≤2), where the lp−norm is used to enhance the robustness. The proposed model is a more general framework compared to the state-of-art algorithms. Moreover, a modified gradient ascending algorithm is designed to optimize the problem, and the convergence of the algorithm and complexity are analyzed. To verify the proposed method, we carry out numerical experiments on artificial data sets and benchmark data sets. Under different evaluation criterions, experiment results show that the proposed method achieves better performance than the state-of-art algorithms in most cases.
Combining Lexical and Dense Retrieval for Computationally Efficient Multi-hop Question Answering

In simple open-domain question answering (QA), dense retrieval has become one of the standard approaches for retrieving the relevant passages to infer an answer. Recently, dense retrieval also achieved state-of-the-art results in multi-hop QA, where aggregating information from multiple pieces of information and reasoning over them is required. Despite their success, dense retrieval methods are computationally intensive, requiring multiple GPUs to train. In this work, we introduce a hybrid (lexical and dense) retrieval approach that is highly competitive with the state-of-the-art dense retrieval models, while requiring substantially less computational resources. Additionally, we provide an in-depth evaluation of dense retrieval methods on limited computational resource settings, something that is missing from the current literature.
Finding the Optimal Virtual Machine Setup for Nested Virtualization

In this paper, we have conducted a thorough comparative analysis of the nested state between the two most popular hypervisors in the world today, which are Oracle's VirtualBox and VMware's workstation. The concept of Nested virtualization involves loading a virtual machine inside another virtual machine which could open a new dimension of multitasking and high-performance computing. In the present world scenario, this concept isn't as widely used as it has the potential to be. For our research, we set up the virtual machine on both VirtualBox and VMware workstation and measured the performance and did the same on the nested state. We then captured the slowdown of both the hypervisors and found out which virtual machine is better suited to perform the task of nested virtualization.
Multi-Depot Vehicle Fleet Planning with Trucks and Aerial Drones

In the context of parcel delivery, aerial drones have great potential that comes especially true in rural areas. In combination with truck deliveries drones can be launched from trucks, microdepots or a city distribution center (CDC). We develop a decision support model that decides on the cost-minimal integration of aerial drones in a current vehicle fleet with trucks of a logistics service provider. The problem setting is modeled as a mixed-integer linear program, which allows to assess the benefits of different transport concepts as well as the impact of mixing different delivery modes. We present a short numerical case study that demonstrates the applicability of the modeling and solution approach suggested.
How Teams Learn Agility, a Beginner’s Guide for Software Development

Due to the SARS-COVID19 Pandemic, the need for information technology professionals has increased. At a global level, it is necessary to have teams in projects with agile methodologies. These methodologies allow permanent deliveries, risk management, and change management. Rapid application development, scrum and extreme programming stand out to develop software. It is proposed a model to identify main problems and guide beginner teams based on a set of good practices in developing and managing an agile project. Thus, it contributes to the quality of the software product and the satisfaction of the client. The methodology considers four stages. Firstly, the development team makes progress on the functionalities. Then, they are assessed to identify problems and difficulties to work with agile methodologies. Finally, the senior scrum master socialises good practices from the literature to be applied in the next iteration of the project. The research project lasted two years. The university involved 14 final year undergraduate students’ teams from systems engineering that developed an information and communications technology project. In a first cohort, 32 problems are detected in the implementation of scrum/XP, which impact the project’s objectives and the minimum viable product committed in each Sprint. The second cohort students applied good practices to minimise the problems when developing the software with scrum/XP, reducing more than 85% of the first cohort’s problems.
Traceable and Verifier-Local Revocable Attribute-Based Signature with Constant Length

Attribute-based signature (ABS) allows an authorised user to anonymously produced a signature on the document with satisfiable predicate. The predicate can be a threshold predicate, which supports only AND operator or can be a monotone predicate, which has AND and OR operator or can be a non-monotone predicate, which also includes NOT operator. Traceable ABS scheme additionally allows a designated authority to trace the identity of the real signer of ABS, whenever need arise. To make ABS more realistic a revocation feature need to be incorporated, which enables the designated authority to expel the user when found her signature on disputed document. There is one ABS scheme by J. Wei et al. supports traceability and revocability but with threshold predicate. Moreover, his signature length is variable in terms of the number of attributes. There are few more ABS schemes which either supports traceability or revocability, but not both. In this paper, we proposed a constant-size traceable ABS scheme with verifier-local revocation (VLR) feature and it supports monotone predicate. The scheme is secure in the standard model under well-known cryptographic assumptions.
Deflection and Stretching Techniques for Detection of Multiple Minimizers in Multimodal Optimization Problems

Multimodal optimization refers to problems where the detection of many local or global minimizers is desirable. A number of methodologies have been developed in the past decades for this purpose. Deflection and stretching are two techniques that can be integrated with any optimization algorithm in order to detect multiple minimizers by properly transforming the objective function. Requiring only a minimal number of control parameters, both techniques have been used with metaheuristics as well as gradient-based optimization algorithms, enhancing their performance while demanding only minor implementation effort. Up until now their applications span various scientific fields, ranging from game theory and numerical optimization to astrophysics, computational intelligence, and medical informatics. The present chapter offers a comprehensive presentation of the two techniques and demonstrates their use through simple examples. Also, their latest developments and applications of the past two decades are concisely reviewed.
An Improved EZW Algorithm for Image Compression

Image as an important information carrier, direct transmission and storage require high bandwidth and storage space, so proper compression is of great practical significance. Among many image compression algorithms, wavelet transform image compression can decompose the image with multi-level resolution, and can be progressive coding, which is very suitable for low bit rate image compression. Embedded Zerotree Wavelet (EZW) algorithm is an effective coding technique for low bit rate image compression, but it exits large redundancy. In this study, the authors improves the EZW algorithm and puts forward the IM-EZW algorithm. IM-EZW retain the characteristics of progressive, and increase the number of uncoded coefficients through new symbols, and use T compressors to reduce the number of bits. We use the peak-signal-noise ratio (PSNR) as the evaluation criteria and calculate it at the different compression rates. The experimental results show that IM-EZW has distinct advantages over the classical EZW and outperforms Set Partitioning in Hierarchical Trees (SPIHT).
A sequent calculus for first-order logic formalized in Isabelle/HOL

Abstract We formalize in Isabelle/HOL soundness and completeness of a one-sided sequent calculus for first-order logic. The completeness is shown via a translation from a semantic tableau calculus, whose completeness proof we base on the theory entry ‘First-Order Logic According to Fitting’ by Berghofer in the Archive of Formal Proofs. The calculi and proof techniques are taken from Ben-Ari’s textbook Mathematical Logic for Computer Science (Springer, 2012). We thereby demonstrate that Berghofer’s approach works not only for natural deduction but also constitutes a framework for mechanically checked completeness proofs for a range of proof systems.
Diffusion of Innovations for Optimizing the Information Technology Implementation in Higher Education

The rapid and massive development of technology is a challenge for Higher Education to continue adapting and innovating to maintain academic quality. Therefore, this study aims to analyze the diffusion of innovation models to increase the use of information technology so that every application developed in higher education can run optimally following the design objectives. The method used in this study is an innovation diffusion model, which includes an analysis of the essence of communication channel innovation, the reception process, and social systems. Technology acceptance is assessed based on the innovation-decision process stage, which includes the degree of knowledge, persuasion, decision-making process, implementation, and confirmation of the benefits of using technology. The results show that the process of innovation diffusion can run effectively when paying attention to the profile of human resources (brain-ware), technological conditions (software and hardware), organizational policies (orga-ware), documentation (info-ware), and environmental dynamics (social systems).
A Modern Greek readability tool

The aim of this paper is to develop an automatic readability analysis tool that focusses on Modern Greek as a foreign language. Based on previous work done in the Centre for the Greek Language (CGL), we offer an enhanced methodology in readability prediction for Modern Greek texts matching the adequacy level (A1 to C2) according to the Common European Framework of Languages. The proposed tool is based on several stylometric indices inspired by work done in the field of quantitative linguistics. The resulting feature vectors train a Random Forest, a robust and accurate machine learning algorithm that predicts readability in our testing dataset with 0.943 accuracy, surpassing all previous readability tools for Modern Greek. Further, analysis of the results with advanced visualization methods reveals the complex and fluid dynamics of the features used and their readability predictions.
Prototype of a Conversational Agent for the Catholic University of Cuenca

The article analyses tools and applies the base for processes to the areas of artificial intelligence that are Immersed in the daily lives of people, companies, institutions, and the industry, in constant interaction, without the human beings realizing the process. For achieving this, work has been done on the subarea of conversational agents that offers new ways and experiences of communicating with users. Software modules that have been designed to recognize the patterns of human writing generating an appropriate response. This research presents an analysis of the evolving needs of users and the support of emerging technologies concerning automated communication and the implementation of a prototype of a conversational agent or chatbot that uses artificial intelligence and is focused on natural language processing; the prototype can establish a conversation with students naturally.
Squeezed states generation in an array of Linear and Nonlinear Waveguides

Abstract We demonstrate the generation of squeezed states of light due to the second harmonic generation and Kerr effect in an array of nonlinear waveguides mediated through a linear one. We characterized the electromagnetic field by a quantum mechanical Hamiltonian and the density operator time evolution is obtained from the Von-Neumann equation of motion. Using the quasiprobability positive P of phase space representation, the classical Fokker-Planck equation is obtained from the master equation and translated to its classical matching set of nonlinear differential equations. We showed that because of the new possibilities of correlation between the linear and nonlinear channel waveguides, highly nonclassical light may be produced.
Di lessico e altre quisquilie: autopercezione ed eteropercezione linguistica nel social networking

Digital writing and handwriting are cognitively different activities; thus, it is interesting to verify what happens to the written language when it is conveyed through a keyboard and no longer a pen. On the Net, where the text is stripped of all non-strictly linguistic clues such as tone of voice, facial expression, proxemics, gestures, etc., written words assume a large part of the communicative burden: in other words, we are what we type. Despite this, it often seems that users are not fully aware of how much their linguistic choices affect the construction of their reputation on the net. In this contribution, we reflect on the current state of the typed Italian, trying to hypothesize some future strategies to improve the condition of millions of novice and unprepared users.
A Custom Classifier to Detect Spambots on CRESCI-2017 Dataset

In recent times, social networks are actively emerging as an encouraging platform for democratic discussions on social, media, and political issues. However, the growing popularity of social networks leads to a surge of concerns on the quality of the shared data. Initially, bots are created with good intentions. Nowadays, bad bots are created and powered by AI to perform malicious activities in social network sites. Statistics say that 94.2% of the entire websites in the world are being affected by the Bots. They are more sophisticated, intelligent enough to mimic like humans and can change their signatures dynamically. It's hard to detect such kind of bots with the existing techniques as the new bots keep evolving. Social networks are growing exponentially. As a result, big data is growing unconditionally. So, we need more computationally efficient algorithms running at a higher detection rate to handle big data. For our study, we used the real-time dataset from the bot repository released by Indiana University. In our work, we propose a new custom bot classifier algorithm to predict the bot accounts. The algorithm detects the bots with an accuracy rate of 96.13%. Furthermore, we made a comparative analysis of our algorithm with existing machine learning algorithms using various classification metrics.
Scene Understanding Technology of Intelligent Customer Service Robot Based on Deep Learning

Abstract As a value-added service that improves the efficiency of online customer service, customer service robots have been well received by sellers in recent years. Because the robot strives to free the customer service staff from the heavy consulting services in the past, thereby reducing the seller’s operating costs and improving the quality of online services. The purpose of this article is to study the intelligent customer service robot scene understanding technology based on deep learning. It mainly introduces some commonly used models and training methods of deep learning and the application fields of deep learning. Analyzed the problems of the traditional Encoder-Decoder framework, and introduced the chat model designed in this paper based on these problems, that is, the intelligent chat robot model (T-DLLModel) obtained by combining the neural network topic model and the deep learning language model. Conduct an independent question understanding experiment based on question retelling and a question understanding experiment combined with contextual information on the dialogue between online shopping customer service and customers. The experimental results show that when the similarity threshold is 0.4, the method achieves better results, and an F value of 0.5 is achieved. The semantic similarity calculation method proposed in this paper is better than the traditional method based on keywords and semantic information, especially when the similarity threshold increases, the recall rate of this paper is significantly better than the traditional method. The method in this article has a slightly better answer sorting effect on the real customer service dialogue data than the method based on LDA.
Researcher Field Work Notebook

This study focuses on organizing different type of data collection neatly in Researcher Field Work Notebook mobile application. The researcher uses multiple devices to capture different type of data collection and this makes the researcher confused and the collected data will be more complicated. Therefore, this notebook application is proposed to solve the problem in different typeof data collection. This application has the features on adding text, inserting photo and inserting audio recording. The report is generated to store all type of data collection by different medias in one application. It helps to minimize the time spent by the researcher on organizing the data collection. Besides, the development of Researcher Field Work Notebook will adapt the Agilemethodology whereby it emphasizes on quick releases in prototyping and iteration implemented through the development to constantly make improvements on the application. There are no maintenance and deployment phase as the project is a fully-functional prototyping. The significant of Researcher Field Work Notebook is intended for its users to organize their data collection neatly based on the demands of user. Data synchronization is available to save and upload the generated report to a cloud server, Dropbox for backup storage. The highlighted part for Researcher Field Work Notebook is the application serves with offline mode that bring benefits especially to those researchers who have their field work in weak or no internet connection area.
A closer look at the multilinear cryptography using nilpotent groups

In Kahrobaei et al. [Multilinear cryptography using nilpotent groups, Proceedings of Elementary Theory of Groups and Group Rings, and Related Topics conference. Conference held at Fairfield University and at the Graduate Center, CUNY, New York, NY, USA, November 1–2, 2018, De Gruyter, 2020, pp. 127–133] we generalized the definition of a multilinear map to arbitrary groups and introduced two multiparty key-exchange protocols using nilpotent groups. In this paper we have a closer look at the protocols and will address some incorrect cryptanalysis which has been proposed in Roman'kov [Discrete logarithm for nilpotent groups and cryptanalysis of polylinear cryptographic system, Prikl. Diskretn. Mat. Suppl. (12), (2019), pp. 154–160].
Breast Cancer Prediction with Gaussian Process Using Anthropometric Parameters

In recent years, the breast cancer classification has piqued attention in the area of health-care informatics which is the second leading cause of mortality in women due to cancer. The spread of Breast Cancer growth and its high casualty has prodded a ton of research for contemplating its causes and medicines. The enormous number of genes and their entwining relations requires progressed AI models, instead of simple statistical and correlation analysis. Having the objective to propel the present status of knowledge concerning the early determination of breast cancer, we utilized Random Forest (RF), Logistic Regression(LR), Support Vector Machine(SVM), and Gaussian Process(GP) classifiers, joined with testing unique and novel biomarkers. This paper presents a correlation of these Machine Learning (ML) algorithms by estimating their order test exactness, and their sensitivity and specificity esteem. For the execution of the ML calculations, the dataset was divided into 80% for the training phase, and 20% for the testing phase. Results show that Gaussian Process performed well with 90% test exactness on the order task.