Reviewerly/exHarmony huggingface dataset:
Multi-level Federated Learning Mechanism with Reinforcement Learning Optimizing in Smart City

While taking account into data privacy protection, federated learning can mine local data knowledge and gather data value, which has been widely concerned by the Smart city and Internet of Things. At present, a large amount of data is generated by the massive edge network in the smart city, but the resources of the edge side are limited. How to reduce the communication overhead between the edge and the centralized cloud server, improve the convergence speed of data model, and avoid resource waste caused by synchronized blocking of federated learning has become the core issue for the integration of federated learning and the Internet of Things in the smart city. For this reason, this paper designs a multi-level federated learning mechanism in the smart city, and uses reinforcement learning agents to select nodes to offset the influence of the non-IID data that is not independent and identically distributed. At the same time, asynchronous non-blocking updating method is used to perform model aggregation and updating of federated learning to release the resources of faster devices and improving the efficiency and stability of federated learning. Finally, simulation results show that the proposed method can improve the efficiency of federated learning tasks in edge network scenarios with a lot of devices in the smart city.
A Hypothetical Agent-Based Model Inspired by the Abstraction of Solitary Behavior in Tigers and Its Employment as a Chain Code for Compression

In this paper, we design an agent-based modeling simulation that represents the solitary behavior in tigers and utilizes it in encoding image information. Our model mainly depends on converting the digital data to a virtual environment with paths classified based on the allocation of the data in the original image. Then, we introduce virtual tigers to the environment to begin the encoding process. Tiger agents are separated from each other, and the algorithm monitors their movements and keeps them away from each other. This separation in the virtual environment allows tigers to encode information that exists in different image parts. Additionally, tigers follow a relative movement style that encodes each tiger’s movement direction based on the previous one. This encoding approach allows particular movements that occur in different directions to be encoded in a similar way. After that, we apply Huffman coding to the chain of movements, the purpose of which is to reduce the size and have a new representation. The experimental findings reveal that we could obtain better results than leading standards in bi-level image compression, including JBIG family methods. Our outcomes strengthen the findings of previous studies that incorporated biological behaviors within agent-based modeling simulations and provide a new abstraction to be utilized in information processing research.
FAIRification of Citizen Science Data

Citizen Science (CS) initiatives encourage citizens to collect local data, contributing to knowledge creation and scientific development. However, these CS initiatives do not follow metadata nor data-sharing standards, which hampers their discoverability and reusability out of the scope of them. To improve this scenario in CS is crucial to consider Findable, Accessible, Interoperable and Reusable (FAIR) guidelines for research data sharing. This work proposes a FAIRification process (i.e. making CS initiatives more FAIR compliant), enhancing data sharing capacities in the CS context. It will be considered the adoption of Web standards, Web application programming interfaces (APIs) and Web augmentation. This approach contributes to the production of FAIR data in CS for data consumers. As preliminary results this paper explains the FAIRification process. The research objectives and plan are also presented.
Analysis of Fine Needle Aspiration Images by Using Hybrid Feature Selection and Various Machine Learning Classifiers

Women die of breast cancer most often worldwide. Breast tissue samples can be examined by radiologists, surgeons, and pathologists for evidence of this cancer. Fine needle aspiration cytology (FNAC) can be used to detect this cancer through a visual microscopic examination of breast tissue samples. This sample must be examined by a cytopathologist in order to determine the patient's risk of breast cancer. To determine if a tumor is malignant, the nuclei of the cells must be characterized by their chromatin texture patterns. A machine learning method is used in order to categorize FNA images into two classes, respectively ‘Malignant’ and ‘Benign.’ For detecting abnormalities, numerous feature collection methods and machine learning means are applied here. Using features extracted from the FNA image set, UCI machine learning datasets are used to validate the proposed approach. This paper compares three classification methodologies, namely random forests, Naive Bayes, and artificial neural networks, by examining their accuracy, specificity, precision, and sensitivity, respectively. With the ANN and PCA along with the Chi-square selection method, 99.1% of the classifiers are correctly classified.
Construction of Event Annotation Corpus for Political News Texts

The extraction of event elements from political news texts can lay the foundation for the construction of knowledge graph of political news events, but the construction of event annotation corpus as the basis of event extraction research is not perfect. In this paper, we discuss the construction of an Event Annotation Corpus for Political News texts (EACPN). The initial corpus used in this paper comes from the 2019 political news text. Based on the investigation of the existing news event annotation corpus, and combined with the characteristics of the political news text, an annotation schema has been established. The schema covers five categories of event elements and sub-categories: visit, conference, investigation, telegram and letter, and foreign affairs activity. Accordingly, we formulated a complete specification for the annotation of political news texts, and then used the distributed annotation platform to carry out multiple rounds of data annotation. EACPN contains more than 20,000 pieces of political news with 128,000 event elements in about 290,000 sentences, and high consistency is achieved in the results.
〈2〉-exponents of shift register transformations nonlinearity dipgraphs

The matrix-graph approach is used to estimate the set of essential and non-linear variables of coordinate functions of the product of transformations of vector spaces. For essential variables, estimates are obtained by multiplying binary mixing matrices (or digraphs) of multiplied transformations, for non-linear variables - by multiplying ternary non-linearity matrices of multiplied transformations or their corresponding non-linearity digraphs, the arcs of which are labeled by the numbers of the set {0,1, 2}. For degrees of a given transformation, the area of non-trivial estimates is limited: for a set of essential variables, by the exponential of the mixing matrix (digraph); for a set of nonlinear variables, the 〈2〉-exponent of the matrix (digraph) of nonlinearity. For the class of transformations of binary shift registers, an attainable estimate of 〈2〉-exponents is obtained, expressed in terms of the length of the shift register and the set of numbers of essential and nonlinear variables of the feedback function. For register transformations whose non-linearity digraph has a loop, an exact formula for the 〈2〉-exponent is obtained. The results can be used to evaluate the nonlinearity characteristics of cryptographic functions built on the basis of iterations of register transformations.
Laplace Operator in Connection to Underlying Space Structure

Laplace operator is a diverse concept throughout natural sciences. It appears in many research areas and every such area defines it accordingly based on underlying domain and plans on follow-up applications. This operator attracts a lot of attention e.g. in signal and image processing applications [6]. However, signals, in general, can be defined not only on Euclidean domains such as regular grids (in case of images). There are cases when underlying space is considered to be e.g. a non-regular graph or even a manifold, but the Laplace operator is still closely bound to the space structure. Therefore, we investigated this operator from point of view of spaces, where distance may not be explicitly defined and thus is being replaced by more general, so-called, proximity. Our goal was to find such a representation, that would be simple for computations but at the same time applicable to more general domains, possibly to spaces without a notion of a classic distance. In this article, we will mention some of the various ways in which this operator can be introduced in relation to the corresponding space. Also, we will introduce the formula for the Laplace operator in the space whose structure is determined by a fuzzy partition [8]. And we will investigate the properties of this kind of representation in parallelisms to standard well-known versions.
Sentiment Analysis of Moroccan Dialect Using Deep Learning

In recent years, with the proliferation of the internet and the social media, there are massive numbers of users who share their contents over wide range of social networks. Thus, a huge volume of electronic data is available on the Internet containing the users’ thoughts, attitudes, views and opinions towards certain products, events, news or any interesting topics. Therefore, Sentiment Analysis (SA) became one of the most active research areas in Natural Language Processing (NLP). Most research efforts in the area of opinion mining deal with English text and little work is done with Arabic text. In this paper we presents a tool for sentiment analysis comments written in Moroccan dialect. This tool is based on an approach combining the use of the Convolutional Neural Network (CNN) model and Word2Vect. We evaluated our approach using a dataset of moroccan dialect. Our result obtained achieves a score of 60%.
A Quantitative Study on the Measure Index of Syntactic Complexity in Textbooks for Chinese as a Second Language

Syntactic complexity is an important index to measure the quality of text. This study identified the quantitative measure indicators of syntactic complexity from the three dimensions of aggregate, length, and relative indexes, and examined the feasibility of these indexes as syntactic complexity measure indicators. The results showed that the aggregate index of the text shows statistical differences; as the basic index to describe syntactic complexity, the aggregate index can be used to assess the suitability of textbook compilation and learners’ language output level. Moreover, in the length indexes, only the length of the T-unit and of clauses showed good differences and growth trends in the primary stage. Lastly, in the relative indexes, the number of clauses in the T-unit can be used to examine the syntactic complexity of each grade. The above results contributed to the Chinese as a Second Language teaching or in the compilation of teaching materials.
Repository of research data as a new vector for open archives

The data repositories and academic libraries offer researchers recommendations on how to manage data and to promote open science. The librarians have to get deep understanding of repository structure, to provide services of research data depositing and to get new knowledge in analyzing these data. The repositories specialize or aggregate generalized data in the large knowledge domains. The register re3data (https://www.re3data.org/) is among the trusted portals to search specialized repositories in, by various criteria. As of the late September 2021, it enlisted 3,404 repositories, 22 of them with Russian research organizations participation. The national repositories of research data registered in re3data are reviewed. The authors conlude: despite insignificant ratio of Russian repositories, their content, volume and authority in academic environment are highly rated. The libraries have to get deeper understanding of the character and potential of different repositories to meet user demands and to be engaged in preparing recommendations for data management. The article is prepared within the framework of the State Order No. 075-01300-20-00 “Design and development of the system of Open Archive of Integrated Information Resources of Russian National Public Library for Science and Technology as the modern system of knowledge in digital environment: On the way to open science” for the years 2020–2022.
A Multi-criteria Decision-Making Approach to Analyze Python Code Smells

Frequent modifications in the software leads to the issues in the code which deteriorates software quality. The suitable approach to rectify it is to refactor it. Consequently, this research devises a ranking using the MCDM approach for prioritizing Python-based code smells. This order ranking has been studied over 10,552 classes for five generic code smells diffused in 20 Python software. The software metrics extracted over these python classes have been considered as multiple criteria which are processed using rule-based machine learning approach. In this study, an approach providing compromised solution—VIKOR have been practiced along with TOPSIS and WASPAS methods. Additionally, the MCDM approaches have utilized Shannon’s Entropy and CRITIC method as weight estimation techniques. The extracted vital software metrics achieved over 90% accuracy. The prominent outcome portrays that the most dominant smell is Collapsible “IF” which needs to be refactored at the early stage. This should be accompanied by Cognitive Complexity smell so as to attain sustainable development environment.
ACHIEVEMENT BY STUDENTS OF METADISCIPLINARY EDUCATIONAL RESULTS: INTERACTION BETWEEN GENERAL AND HIGHER EDUCATION ORGANIZATIONS

The need to effectively solve innovative problems facing the modern system of domestic education requires the transfer of cooperation between schools and universities to a new level of networking. One of these problems, involving the integration of intellectual, scientific, methodological, personnel, program-informational, reputational resources of general and higher education, is the achievement of meta-subject educational results by schoolchildren. In this regard, an infrastructure project was developed and implemented, which rallied the interested participants in the educational process. The main methods of interaction between teachers and school leaders with teachers of a pedagogical university were project and process consulting. These methods make it possible to effectively integrate the potential of school and university workers.
DesignEva: A Design-Supported Tool with Multi-faceted Perceptual Evaluation

Perceptual design evaluation helps designers recognize how others perceive their work and iterate their design process. Organizing user studies to gather human perceptual evaluation is time-consuming. Thus, computational evaluation methods are proposed to provide rapid and reliable feedback for designers. In recent years, the development of deep neural networks has enabled Artificial Intelligence (AI) to conduct perceptual quality evaluation as human beings. This article proposes to utilize AI to provide designers with real-time evaluations of their designs and to facilitate the iterative design. To achieve this, we developed a prototype, DesignEva, a design-supported tool to offer multi-faceted perceptual evaluation on design works, including aesthetics, visual importance, memorability, and sentiment. In addition, based on designers’ current works, DesignEva also searches for similar examples from the material library as references to inspire designers. We conducted a user study to verify the effectiveness of our proposed prototype. The experimental results showed that DesignEva could help designers reflect on their designs from different perspectives in a timely way.
NFR Evaluation in IoT Applications: Methods, Strategies and Open Challenges

Internet of Things (IoT) is a paradigm that allows physical objects to interact and work together over the Internet. IoT applications will be increasingly common in our lives and influence the way we perform daily activities in the near future. This type of application has particular characteristics, such as context awareness, interconnectivity, and heterogeneity, and it presents a new type of interaction, the interaction between devices (called thing-thing interaction). These characteristics represent the expectation around the system and are also known as Non-Functional Requirements (NFRs). However, these NFRs often increase the complexity of IoT application development and evaluation. Then, this paper aims to identify, through a literature review, how the scientific community has addressed the evaluation of NFRs for IoT applications, how these evaluations have been performed, what artifacts have been used, which NFRs are considered during these evaluations, and what are the main challenges faced by evaluators. We use a systematic mapping methodology to provide a comprehensive overview of the subject and we strengthen the results by conducting a snowballing procedure (backward and forward). As a result, we identified a set of 48 NFRs for IoT applications as well as seven tools, six approaches, four methods and one process that can be considered for evaluating IoT applications. Furthermore, we list the main challenges related to the evaluation of NFRs for IoT applications and provide a summary of the scientific community’s view on the topic.
An E2ED-based Approach to Custom Robot Navigation and Localization

Simultaneous mapping and localization or SLAM is a basic strategy used with robots and autonomous vehicles to identify unknown environments.It is of great attention in robotics due to its importance in the development of motion planning schemes in unknown and dynamic environments, which are close to the real cases of application of a robot.This is why, in parallel with research, they are also important in specialized training processes in robotics.However, access to robotic platforms and laboratories is often complex and costly, with high demands on time and resources, particularly for small research centers.A more efficient and affordable approach to working with autonomous algorithms and motion planning schemes is often the use of the ROS-Gazebo simulator, which allows high integration with customized non-commercial robots, and the possibility of an end-to-end design (E2ED) solution.This research addresses this approach as a training and research strategy with our ARMOS TurtleBot robotic platform, creating an environment for working with navigation algorithms, in localization, mapping, and path planning tasks.This paper shows the integration of ROS into the ARMOS TurtleBot project, and the design of several subsystems based on ROS to improve the interaction in the development of service robot tasks.The project's source code is available to the research community.
Beyond Classical Domain Adaptation

To develop learning methods across domains it is usual to make some simplifying assumptions. In standard domain adaptation the source consists of samples drawn from a single data distribution. The same holds also for the target, and the two domains share exactly the same label set. However, in the real world those conditions are often violated and several studies have formalized the sub-problems that arise when relaxing these assumptions. For example, when increasing the number of available domains (multi-source and multi-target DA), or reducing the access to manual annotation (zero-shot DA, predictive DA, online DA) or to source data (source-free and federated DA). The label set might also be different between source and target with more classes in the source (partial DA), in the target (open-set DA) or a more general a partial overlap combining these cases (universal DA). In this chapter we provide an overview of the techniques proposed to tackle all those challenging scenarios.
Deep Active Learning Approach for Traffic Sign and Panel Guide Arabic-Latin Text Content Annotation in Natural Scene Images

The detection and recognition of road traffic signs and panel guides content has become challenging in recent years. Few studies have been made to solve these two issues at the same time especially in Arabic language. Additionally, the limited number of datasets for traffic signs and panel guide content makes the investigation more interesting. In our work, we propose a Deep Active Learning Approach for Traffic Sign and Panel Guide Arabic-Latin Text Content Annotation in Natural Scene Images. Convolution neural network (CNN) and active learning are combined in the proposed system to detect and recognize traffic sign and multilingual scene text from panels guides, particularly those with Arabic Latin characters. The annotation system based on active learning method is applied to the Natural Scene Traffic Sign and Panel Guide Arabic-Latin Text dataset (NaSTSArLaT) of Tunisian highway road. The defined dataset contains initially 3000 collected images with only 181 annotated samples. Progressively, un-annotated training images, are automatically annotated when they provide high confidence with the YOLOv5 detection model and only the less confident examples are manually annotated. The annotation output consists of a class label and its bounding box for every traffic sign and panel guide text content. The performance of the proposed system is done on test data that represents 10% of the available annotated set. The proposed active learning strategy gives 62.7% in terms of mean Average Precision (mAP) only with the manual annotation of about 1 ⁄4 of the samples, whereas if the learning is performed using all the data after a complete annotation, the obtained performance is 69.2% which is comparable to our approach.
A SCIENTOMETRIC REVIEW OF THE IMPACTS OF DIGITAL TECHNOLOGIES ON HEIS DURING COVID-19 PANDEMIC

Ponte Academic JournalJun 2022, Volume 78, Issue 6 A SCIENTOMETRIC REVIEW OF THE IMPACTS OF DIGITAL TECHNOLOGIES ON HEIS DURING COVID-19 PANDEMICAuthor(s): Aderinsola Kayode ,Obiyemi O. ObiseyeJ. Ponte - Jun 2022 - Volume 78 - Issue 6 doi: 10.21506/j.ponte.2022.6.7 Abstract:Research has shown that the use of Digital Technologies (DT) in enhancing productivity and performance in higher education institutions (HEIs) can never be underestimated, especially during the COVID-19 pandemic. This strange incidence shook the whole world with pronounced impacts on every aspect of human life, and the education system was not spared. Though a large volume of research on COVID-19 pandemic has been published as a result of this outbreak, few studies have considered the impact of DT on education/learning. Educators and students of HEIs need to embrace the use of DT in delivering lectures and as well as learning to keep afloat. Unfortunately, educators and students as major HEIs stakeholders have not taken full advantage of its benefits, hence the motivation for this research. The methodology adopted entails a comprehensive, systematic, and expository review of selected published articles on DT in journals, books, and conference proceedings from different databases including Web of Science, Scopus, Google Scholar, Academia.edu and Research gate. This study presents the findings from a scientometric review of literature on the impact of deploying DTs in delivering lectures, especially from the beginning of the COVID-19 outbreak. The outcome would add to the body of knowledge, attending to concerns about whether or not the adoption of DTs has had any significant impact on teaching, learning, and assessment, as well as techniques for leveraging DT even for learners in hard-to-reach communities in this new normal.\n\nKeyword: Scientometric Review, Impacts of Digital technology, Higher Educational Institution, COVID-19 Download full text:Check if you have access through your login credentials or your institution Username Password