Reviewerly/exHarmony huggingface dataset:
Understanding User Satisfaction with Task-oriented Dialogue Systems

\beginabstract \AcpDS are evaluated depending on their type and purpose. Two categories are often distinguished: \beginenumerate* \item \acpTDS, which are typically evaluated on utility, i.e., their ability to complete a specified task, and \item open-domain chat-bots, which are evaluated on the user experience, i.e., based on their ability to engage a person. \endenumerate* What is the influence of user experience on the user satisfaction rating of \acpTDS as opposed to, or in addition to, utility ? We collect data by providing an additional annotation layer for dialogues sampled from the ReDial dataset, a widely used conversational recommendation dataset. Unlike prior work, we annotate the sampled dialogues at both the turn and dialogue level on six dialogue aspects: relevance, interestingness, understanding, task completion, efficiency, and interest arousal. The annotations allow us to study how different dialogue aspects influence user satisfaction. We introduce a comprehensive set of user experience aspects derived from the annotators' open comments that can influence users' overall impression. We find that the concept of satisfaction varies across annotators and dialogues, and show that a relevant turn is significant for some annotators, while for others, an interesting turn is all they need. Our analysis indicates that the proposed user experience aspects provide a fine-grained analysis of user satisfaction that is not captured by a monolithic overall human rating. \endabstract
“The Statistical Analysis of Compositional Data” by John Aitchison (1986): A Bibliometric Overview

This paper presents a complete bibliometric analysis of Aitchison’s 1986 seminal book “The Statistical Analysis of Compositional Data.” We have set three objectives. The first is to analyze the academic structure of Aitchison’s 1986 book. Results reveals that although the work has received citations uninterruptedly since its publication, the number of these has increased very significantly over the past 4 years. This is due to the significant increase in the number of publications on the theme of “Compositional Data Analysis” in fields related to “geoscience” over the last few years. The second objective is to determine which main journals Aitchison’s book has been cited in. The results highlight that the main journals are indexed under the following WoS category: “Geosciences, Multidisciplinary” and “Ecology.” Of these, “Mathematical Geosciences” and “Computers, Geosciences” stand out. The third objective is to determine the main topics analyzed in the principal papers published by authors citing Aitchison’s book. Our results show that the keywords in the main papers to have cited Aitchison’s 1986 book originate from the geoscience field, since many of them are related to concepts directly linked to this field and refer to terms related to “biodiversity,” “geodiversity,” “geoheritage,” and “georesources.” Lastly, the analysis shows how the CoDA methodology is now in a phase of exponential growth, expanding to other fields. This implies that geoscience is becoming consolidated in the scientific literature as one of the branches of modern science that has given rise to a new mathematical theory of great impact.
OtherTube: Facilitating Content Discovery and Reflection by Exchanging YouTube Recommendations with Strangers

To promote engagement, recommendation algorithms on platforms like YouTube increasingly personalize users' feeds, limiting users' exposure to diverse content and depriving them of opportunities to reflect on their interests compared to others'. In this work, we investigate how exchanging recommendations with strangers can help users discover new content and reflect. We tested this idea by developing OtherTube—a browser extension for YouTube that displays strangers' personalized YouTube recommendations. OtherTube allows users to (i) create an anonymized profile for social comparison, (ii) share their recommended videos with others, and (iii) browse strangers' YouTube recommendations. We conducted a 10-day-long user study (n = 41) followed by a post-study interview (n = 11). Our results reveal that users discovered and developed new interests from seeing OtherTube recommendations. We identified user and content characteristics that affect interaction and engagement with exchanged recommendations; for example, younger users interacted more with OtherTube, while the perceived irrelevance of some content discouraged users from watching certain videos. Users reflected on their interests as well as others', recognizing similarities and differences. Our work shows promise for designs leveraging the exchange of personalized recommendations with strangers.
A model text recycling policy for publishers

Because science advances incrementally, scientists often need to repeat material included in their prior work when composing new texts. Such &amp;ldquo;text recycling&amp;rdquo; is a common but complex writing practice, so authors and editors need clear and consistent guidance about what constitutes appropriate practice. Unfortunately, publishers&amp;rsquo; policies on text recycling to date have been incomplete, unclear, and sometimes internally inconsistent. Building on 4 years of research on text recycling in scientific writing, the Text Recycling Research Project has developed a model text recycling policy that should be widely applicable for research publications in scientific fields. This article lays out the challenges text recycling poses for editors and authors, describes key factors that were addressed in developing the policy, and explains the policy&amp;rsquo;s main features.
New Approach Based on Homomorphic Encryption to Secure Medical Images in Cloud Computing

In recent years, there has been a growing demand for the adoption of cloud in healthcare to process medical data. Unfortunately, this emerging new paradigm faces several challenges, as customer data is stored on remote servers rather than on premise solutions. This is considered to be the main root cause of the major security vulnerabilities encountered in outsourced calculations. Indeed, to solve this problem we used encryption; customers should encode their sensitive data when considering adopting cloud services. One of the biggest challenges is establishing controls that completely protect secret data from insider attacks, while also supporting the computations. In addition, applying complex encryption schemes can have a negative effect on system performance.&#x0D; This study focuses in particular on homomorphic techniques and their main applications, as well as their limitations. Unlike traditional encryption methods, homomorphic schemes are used not only to reduce the security risks associated with cloud technology, but also to process cipher texts. However, current efforts in this area need to be further developed to strike the right balance between privacy risks and data utility. In this regard, we offer a hybrid approach that offers the possibility of quickly processing health records in a secure manner. Our main contribution is the proposal of a new method based on Hadoop MapReduce functions in conjunction with a multi-agent system to maintain data confidentiality. In addition, and to design intelligent distributed computing for efficient management of Virtual Machine (VM) workloads, we used a method based on the Bat Algorithm (BA).&#x0D; HIGHLIGHTS&#x0D; &#x0D; This article is about a system based on homomorphic encryption to secure medical images stored in cloud computing&#x0D; Its objective is to reduce the security risks associated with cloud technology, but also to process ciphertexts&#x0D; This system is based on the MapReduce function to benefit from parallelism during data processing and the Hadoop framework to develop and implement secure medical image processing applications in the cloud&#x0D; Hadoop and MapReduce are in conjunction with a multi-agent system to perform distributed data processing&#x0D; Our system uses a method based on the Bat (BA) algorithm to ensure efficient management of the workload of virtual machines (VMs) and thus guarantee load balancing&#x0D; &#x0D; GRAPHICAL ABSTRACT
Classification of Unwanted E-Mails (Spam) with Turkish Text by Different Algorithms in Weka Program

Abstract: Today, with the widespread use of the Internet, electronic communication tools have also been widely used. One of these tools is e-mails. E-mails are easy to use and provide the opportunity to reach thousands of people at the same time. This advantage causes some bad uses. E-mail users are faced with dozens of unsolicited mails (spam) against their will. In this study, 1017 mails collected from about 20 different Gmail and Hotmail accounts were classified as spam or regular e-mail using the algorithms in the Weka program, and the success of the algorithms was compared. In the study, 45 different algorithms were tested. The highest classification success was obtained with the NavieBayesMultinominal and NavieBayesMultinominalUpdateable algorithms with 94.7886% correct classification. Among other classifier algorithms, Trees RandomForest algorithm 93.6087%, Meta. MultiClassClassifier and Functions SGD 92.4287%, Functions SMO 91.7404%, Meta RandomCommittee 91.0521%, Bayes NavieBayes and Bayes NavieBayesUpdateable 90.3638% classification success.
Assessment of Heavy Metal Contamination in Urban Soils of selected areas in Hilla City, Babylon, Iraq

Modern cities suffer from heavy metal pollution due to urban expansion and population increase. Heavy metals have a great impact on human health. The objective is to determine the contamination level of heavy metals Cr, Mn, Ni, Cu, Zn, As, Zr and Pb at industrial and residential in Hilla city. The mean concentration of Cr, Mn, Ni, Cu, Zn, As, Zr and Pb enrichment factors of the investigated industrial soils are 3.43, 0.74, 6.45, 3.95, 5.60, 3.44, 1.17 and 11.44, respectively. The means of Cr, Mn, Ni, Cu, Zn, As, Zr and Pb in residential soils are 3.30, 1.09, 11.40, 0.94, 2.08, 5.39, 0.9 and 3.6, respectively. The I-geo mean values of heavy elements in the industrial area may be ordered in the following: Mn&gt; Pb&gt; Ni&gt; Zn&gt; Cu&gt; As&gt; Cr&gt; Zr. While in the residential area ordered Mn&gt; Ni&gt; As&gt;Cr&gt; Pb&gt; Zn&gt; Cu &gt; Zr. Integrated Pollution Load Index categories results showed high contamination in industrial and residential areas. The main sources of heavy elements pollution in the study area has been regarded as anthropogenic sources.
The impact of using the model of research supervising on the formation of research competence in students majoring in education

Introduction. The article investigates the problem of increasing the efficiency of educational process at university with the main focus on supervising students’ research. The purpose of the study is to evaluate the influence of using the model of research supervision on developing research competences of students majoring in education. Materials and Methods. The study is conducted on the basis of theoretical analysis and generalization of scholarly literature on supervising students’ research, experimental methods of educational research, and methods of mathematical statistics for processing empirical data (Wilcoxon test). Results. The authors identified the following components within the research supervision model: choosing the research topic, determining the research methodology; diagnostic materials for identifying the effectiveness of the research investigation (criteria, indicators, levels of results assessment, choosing diagnostic methods); participation in research conferences, seminars, forums, etc.; design of experimental research (formative experiment program, short-term academic placement program); working in the academic library, preparing references and literature reviews; writing research articles). The study has revealed a positive correlation between using the research supervision model and the increase in research competence of students majoring in education. Conclusions. The authors conclude that the use of the model for research supervision improves students' research competence. The obtained results can be used to improve the efficiency of educational process at university.
Toward Sharing Brain Images: Differentially Private TOF-MRA Images With Segmentation Labels Using Generative Adversarial Networks

Sharing labeled data is crucial to acquire large datasets for various Deep Learning applications. In medical imaging, this is often not feasible due to privacy regulations. Whereas anonymization would be a solution, standard techniques have been shown to be partially reversible. Here, synthetic data using a Generative Adversarial Network (GAN) with differential privacy guarantees could be a solution to ensure the patient's privacy while maintaining the predictive properties of the data. In this study, we implemented a Wasserstein GAN (WGAN) with and without differential privacy guarantees to generate privacy-preserving labeled Time-of-Flight Magnetic Resonance Angiography (TOF-MRA) image patches for brain vessel segmentation. The synthesized image-label pairs were used to train a U-net which was evaluated in terms of the segmentation performance on real patient images from two different datasets. Additionally, the Fréchet Inception Distance (FID) was calculated between the generated images and the real images to assess their similarity. During the evaluation using the U-Net and the FID, we explored the effect of different levels of privacy which was represented by the parameter ϵ. With stricter privacy guarantees, the segmentation performance and the similarity to the real patient images in terms of FID decreased. Our best segmentation model, trained on synthetic and private data, achieved a Dice Similarity Coefficient (DSC) of 0.75 for ϵ = 7.4 compared to 0.84 for ϵ = ∞ in a brain vessel segmentation paradigm (DSC of 0.69 and 0.88 on the second test set, respectively). We identified a threshold of ϵ <5 for which the performance (DSC <0.61) became unstable and not usable. Our synthesized labeled TOF-MRA images with strict privacy guarantees retained predictive properties necessary for segmenting the brain vessels. Although further research is warranted regarding generalizability to other imaging modalities and performance improvement, our results mark an encouraging first step for privacy-preserving data sharing in medical imaging.
A model for blockchain-based privacy-preserving for big data users on the internet of thing

Recently, with the emergence and growth of the IoT as a promising vehicle for sustainable development, the concept of ‘smart cities’ has advanced significantly. However, many challenges inhibit the development of using IoT applications in smart cities, such as issues of privacy, scalability, trust, security, and centralisation. On a daily basis in smart cities, the IoT generates a large amount of data (big data) which could potentially be used for questionable or suspect purposes by attackers. The weight of the security issues surrounding big data must be acknowledged as the associated technology is continuously developing. To solve this issue, a strategy that secures important and potentially sensitive user information on a distributed blockchain and transmits non-sensitive information to the primary system by controlling the size of the blockchain is proposed. This solution cannot be achieved in traditional blockchain because it requires too many resources. The model is composed of three proposed algorithms: the first aims to allocate data to each user; the second performs the process of searching for data, and the third confirms the communication process. Experiments have proved that this proposed protocol for blockchain has excellent byzantine fault tolerance. The final experimental results of the proposed model established that the algorithms effectively meet the performance requirements.
Realization of c-Inference as a SAT Problem

Semantically based on Spohn’s ranking functions, c-representations are special ranking models obtainedby assigning individual integer impacts to the conditionals in a knowledge base R and by defining the rank of eachpossible world as the sum of the impacts of falsified conditionals. c-Inference is the inference relation taking allc-representations of a given knowledge base R into account. In this paper, we show that c-inference can be realizedas a boolean satisfiability problem (SAT), which in turn allows c-inference to be implemented by a SAT solver. Weprovide a stepwise transformation of the characterization of c-inference as as constraint satisfaction problem (CSP),into a solvable-equivalent SAT problem. We present a SAT-based implementation of c-inference using the SMTsolver Z3, demonstrating the feasibility of the approach. Up to now, there has been only one previous implementationof c-inference; this previous implementation utilizes a Prolog-based CSP solver. First evaluation results demonstratethat our SAT-based implementation outperforms the previous CSP-based implementation, providing a promising basis forfurther developing this approach.
Multilayer perceptron artificial neural networks-based model for credit card fraud detection

Nowadays, &lt;span&gt;credit card fraud has emerged as a major problem. People are becoming increasingly using credit cards to pay for their transactions, it has become more popular and essential in our lives. Fraudsters are developing new strategies and techniques over time, and it is not easy for humans to manually check out all transactions. The cost of fraudulent transactions is significant and without prevention mechanisms it is rising. Finding the best methodology to detect fraudulent transactions is a crucial asset to the industry to reduce the fraud financial loss. Artificial neural networks (ANN) technique is considered as one of the effective techniques that has proved its efficiency in detecting credit card fraud transactions with high precision and minimum cost. In this paper, we propose a multilayer perceptron (MLP) ANN-based model solution to improve the accuracy of the detection process. The performance of the methodology is measured based on the precision, sensitivity, specificity, accuracy, F-measure, area under curve (AUC) and root mean square error (RMSE). Moreover, we illustrate the performance results of these measures with a descriptive analysis. Experimental results have shown that the proposed ANN-based model is efficient and does improve the accuracy of the detection of fraudulent transactions.&lt;/span&gt;
Lifelong Generative Modelling Using Dynamic Expansion Graph Model

Variational Autoencoders (VAEs) suffer from degenerated performance, when learning several successive tasks. This is caused by catastrophic forgetting. In order to address the knowledge loss, VAEs are using either Generative Replay (GR) mechanisms or Expanding Network Architectures (ENA). In this paper we study the forgetting behaviour of VAEs using a joint GR and ENA methodology, by deriving an upper bound on the negative marginal log-likelihood. This theoretical analysis provides new insights into how VAEs forget the previously learnt knowledge during lifelong learning. The analysis indicates the best performance achieved when considering model mixtures, under the ENA framework, where there are no restrictions on the number of components. However, an ENA-based approach may require an excessive number of parameters. This motivates us to propose a novel Dynamic Expansion Graph Model (DEGM). DEGM expands its architecture, according to the novelty associated with each new database, when compared to the information already learnt by the network from previous tasks. DEGM training optimizes knowledge structuring, characterizing the joint probabilistic representations corresponding to the past and more recently learned tasks. We demonstrate that DEGM guarantees optimal performance for each task while also minimizing the required number of parameters.
THE USE TECHNOLOGY BASED ON ARTIFICIAL INTELLIGENCE IN ENGLISH TEACHING AND LEARNING)

Artificial Intelligence (AI) is a human intelligence simulation based on computers and designed to function as human beings. AI is one of the drivers of the 4.0 industrial revolution to facilitate education in teaching and learning. This research is to know the role of AI in ELT and to investigate AI technologies in ELT. This is library research. The result shows that AI offers a good learning atmosphere for English learning. It has considerable ability to create a personalized atmosphere in which learners use their senses to concurrently exercise English skills depending on their current level of English, vocational needs, or interests. AI provides a real simulation dialog platform such as spoken English and increases practical skills such as written. It increases the practice of students’ capacity and optimizes the teaching impact of English in ELT. Learning English become easier with the development of technology and platforms. AI’s technology offers the opportunity to improve English language skills. The existence of various kinds of learning technology makes it easier for students to understand English. Many choices of ELT applications are based on AI technology that can be used by the students. These technologies as smart machines that think and behave like people with the ability to simulate intelligence and make decisions identical to human reasoning through a process both computers and cell-phones such as Google Translate, Text to speech (TTS), English Able, Orai, Elsa, Chatbot, Duolingo, Neo platforms, and many more.
A Smart Contract for Coffee Transport and Storage With Data Validation

Recently there has been an increase in the use of Blockchain technology for multiple purposes; one of them has been food traceability. This technology has increased quality control, safety, and reliability. So, the producers are looking for better ways to trace the products at any supply chain stage to ensure their quality. A smart contract is a transaction protocol that execute automatically when a predefined set of conditions are met. In this paper, we propose a smart contract to monitor the status of the coffee beans in the transport and storage stages with data validation. Using the Hyperledger Fabric Blockchain tool, we deploy a test network of two actors, also known as organizations. The organizations come together to form a channel in the network. Each has a valid identity that helps them verify their signatures over any transaction. We selected JavaScript to write our proposed smart contract for experimental and evaluation purposes. To evaluate the smart Contract, we use Hyperledger Caliper, obtaining an average throughput of 10.4tps and average latency of 0.7s, being fast enough to be used in a real environment, considering the current control conditions of the coffee beans.