Reviewerly/exHarmony huggingface dataset:
Non-Classical Logics in Modern Science

Non-classical logicians have significantly expanded the traditional field of using logical methods. The first of them was the three-digit logic of Y. Lukasevich. Next came the three-digit logic of A. Bochvar, the "quantum logics" of G. Reichenbach and P. Detush-Fevrier, infinite-valued, probabilistic and other logics. The possibilities of non-classical logics have become widely used in various branches of scientific knowledge. Polysemantic, fuzzy, intuitionistic, modal, relevant and paranoherent, temporal and other non-classical logics are widely used today in physics, computational mathematics, computer science, linguistics, jurisprudence, ethics and other fields of natural science and socio-humanitarian knowledge. The recently increased interest in non-classical logics is explained, first of all, by the fact that various philosophical, syntactic, semantic and metalogical problems that were previously discussed in the scientific community are being replaced by practical interests. The main source of such interest is their wide application in computer science, artificial intelligence and programming. The logic of causality is used in the interpretation of the concepts of "law of nature", "ontological necessity" and "determinism"; temporal modal logics - for modeling, specification and verification of software systems of logical control; logics with vector semantics, combining the features of fuzzy and para-contradictory logics - in solving problems of dynamic verification of production knowledge bases and expert systems.
Metadata creation practices at the Lilongwe University of Agriculture and Natural Resources library’s institutional repository

Purpose The purpose of this study was to investigate metadata creation practices in a functional academic institution repository in Malawi, with a specific focus on the Lilongwe University of Agriculture and Natural Resources (LUANAR) library. Design/methodology/approach The study used a qualitative approach with a case study design. The study adopted a case study strategy that focuses on the in-depth, holistic and in-context examination of one or more cases. The researcher used non-probability purposive sampling to include all three LUANAR Digital Repository (LDR) staff at LUANAR library because they were thought to be knowledgeable about the LDR metadata work. The three library staff members directly involved in repository metadata were investigated for the study. Data collection techniques used in a case study approach included semi-structuring face-to-face interviews and documentary analysis. Data from interviews and documentary reviews were manually analyzed and presented in thematic categories based on the study’s objectives. Findings Qualified Dublin Core (DC) was chosen by all participants as the only metadata structure scheme that they will use to create and implement metadata in the repository. DC application profile was the only scheme used to enforce uniform naming and capitalization conventions in the application of Qualified DC element definitions. The scheme, however, was discovered to be the Qualified DC default format in the DSpace system. All participants indicated that the Agricultural Organization of the United Nations Vocabulary is used. Participants highlighted that institutional repository system compatibility, the subject matter of the resources, resource types and staff expertise influenced the selection criteria for the metadata schemes. The repository policy had been developed but had yet to be adopted by the LUANAR management. Research limitations/implications The current study was limited to LUANAR library. A wider study across public and private universities in Malawi is needed to ascertain the role of metadata policy, technical knowledge and metadata specialist institutional repositories. Practical implications Metadata policy is to aid in the understanding of the data, ensuring that appropriate security measures are used to protect the data and for metadata harvesting purposes. Social implications Academic libraries should lobby for management support towards metadata policy for institutional repositories. Originality/value Very little is known about challenges affecting the growth of institutional repositories and standards adopted, including metadata harvesting. This paper bridges the gap in metadata standards for institutional repositories in developing countries.
Metaverse and Contradiction Practice of Jazz

A world where the contradiction of the metaverse becomes non-contradiction has arrived. However, there is a tendency to ignore the metaverse because there is no place to learn the nature of the metaverse, the philosophy of the metaverse, or the ethics of the metaverse. Since the metaverse is not a simple device, it should not be approached from an instrumental point of view, but rather it should be understood from a conceptual point of view. Therefore, the role of literature is greater than ever. However, it would be more effective to go through other media with literary quality rather than simply the form of literature. It is Jazz. Because jazz, like the metaverse, is a contradiction in itself. Jazz improvisation makes us experience the virtuality, and jazz"s impromptu performance becomes a level-shifting ability called contradiction practice, where one can understand ‘This side soon becomes the other, so the life of one of them is the life of the other’. The metaverse has its potential in the human brain"s ability to form relationships that will be activated by recognizing these technological structures as natural and social environments. Since the brain"s cognitive ability to displace real-simulation, this side-that side, and inside-outside is the key, the dichotomous worldview that has dominated mankind so far will be neutralized in the world of metaverse. In this paper, I suggest we need the power of the human brain to replace it. Consequently, I assert Jazz fosters this power.
Systems and software product lines of the future

Artificial intelligence (AI) in its various forms finds more and more its way into complex distributed systems. For instance, it is used locally, as part of a sensor system, on the edge for low-latency high-performance inference, or in the cloud, e.g. for data mining. Modern complex systems, such as connected vehicles, are often part of an Internet of Things (IoT). This poses additional architectural challenges. To manage complexity, architectures are described with architecture frameworks, which are composed of a number of architectural views connected through correspondence rules. Despite some attempts, the definition of a mathematical foundation for architecture frameworks that are suitable for the development of distributed AI systems still requires investigation and study.In this paper, we propose to extend the state of the art on architecture framework by providing a mathematical model for system architectures, which is scalable and supports co-evolution of different aspects for example of an AI system. Based on Design Science Research, this study starts by identifying the challenges with architectural frameworks in a use case of distributed AI systems. Then, we derive from the identified challenges four rules, and we formulate them by exploiting concepts from category theory. We show how compositional thinking can provide rules for the creation and management of architectural frameworks for complex systems, for example distributed systems with AI. The aim of the paper is not to provide viewpoints or architecture models specific to AI systems, but instead to provide guidelines based on a mathematical formulation on how a consistent framework can be built up with existing, or newly created, viewpoints. To put in practice and test the approach, the identified and formulated rules are applied to derive an architectural framework for the EU Horizon 2020 project “Very efficient deep learning in the IoT” (VEDLIoT) in the form of a case study.
The Cybersecurity Counteroffensive

Cybersecurity is the art of protecting networks, devices, and data from unauthorized access or criminal use and the practice of ensuring confidentiality, integrity, and availability of information. The increasing connectedness and the ongoing digitization of many aspects of life continued to offer cybercriminals new opportunities to exploit. Next-gen cybersecurity uses different approaches to increase detection of new and unprecedented threats, while also reducing the number of false positives.
Ant-Man Vision in the Museum with Interactive and Immersive Surreal Experience Based on Machine Learning

In this paper, we propose an immersive and interactive way to explore the virtual space of a future museum using Mediapipe body pose tracking solution, Unity 3D game engine, and a machine learning model. Users can explore the built virtual space by movement translating and scaling changing for the virtual avatar. Without wearing any wearable devices or operating any joysticks, according to the trained LSTM machine learning behavior models, users can control the avatar in the built virtual space.
Tech4Good: Artificial Intelligence Powered Chatbots with Child Online Protection in Mind

This review paper explores literature focused on technology for good also known as “tech4good”, that outlines topics of chatbots and Artificial Intelligence (AI) for Child Online Protection (COP). Texts dated from 2017 – 2022 have been surveyed that covers subject of “chatbots”, “artificial intelligence solutions”, “ethical guidelines” and “child online protection”. This paper defines the themes discussed in literature that places an emphasis on technology for good such as the importance of ethical guidelines when designing AI based solutions. Technology for good’s vision, is applying these technologies for a beneficial goal within the technological ecosystem, to make a difference to society and ensure safety of all its users online. With further advancement in technology, a wide range of users such as children are also tapping into technological space, either for educational purposes, gaming and other online entertainment. However, do technologists, entrepreneurs, key-industry players, and other stakeholders keep children’s safety and wellbeing in mind in the online world? As many companies invests in AI powered solutions, how many of these solutions are there to empower, educate, raise awareness and are community focused? In addition, this paper will showcase Chatbots, and AI based solutions that contributes to the safety of children navigating online.
Deployment of Asynchronous Traffic Shapers in Data Center Networks

With the proliferation of distributed applications in data centers and the increase in latency-sensitive flows, it has become increasingly important to provide deterministic latency guarantees for business flows. The main component of business flow latency in the data center is queuing latency. Existing congestion control methods work in a passive reaction working approach, which can neither determine the queuing delay nor guarantee zero data loss. In this situation, this paper proposes a mechanism to deploy asynchronous traffic shapers on the Layer 3 switches of the data center network from the network side perspective. This mechanism shapes the data flow at each hop switch, which can prevent buffer overflow and packet loss, thus improving the deterministic performance of the data center network latency guarantee. We built a large Layer 2 data center network for simulation to verify the asynchronous traffic shaping mechanism based on laboratory conditions and we found that the shaping mechanism has deterministic delay guarantee capability, which can improve the delay deterministic performance of the network.
Hardware-Software Codesign of DNN Accelerators Using Approximate Posit Multipliers

Emerging data intensive AI/ML workloads encounter memory and power wall when run on general-purpose compute cores. This has led to the development of a myriad of techniques to deal with such workloads, among which DNN accelerator architectures have found a prominent place. In this work, we propose a hardware-software co-design approach to achieve system-level benefits. We propose a quantized data-aware POSIT number representation that leads to a highly optimized DNN accelerator. We demonstrate this work on SOTA SIMBA architecture, extendable to any other accelerator. Our proposal reduces the buffer/storage requirements within the architecture and reduces the data transfer cost between the main memory and the DNN accelerator. We have investigated the impact of using integer, IEEE floating point, and posit multipliers for LeNet, ResNet and VGG NNs trained and tested on MNIST, CIFAR10 and ImageNet datasets, respectively. Our system-level analysis shows that the proposed approximate-fixed-posit multiplier when implemented on SIMBA architecture, achieves on average ~2.2× speed up, consumes ~3.1× less energy and requires ~3.2× less area, respectively, against the baseline SOTA architecture, without loss of accuracy (~±1%)
The Rogue Scholar: An Archive for Scholarly blogs

Digital object identifiers (DOIs) and relevant metadata have been used for 20 years to help preserve the scholarly record by maintaining stable links to scholarly publications and other important scholarly resources, combined with long-term archiving by publishers and libraries. Lots and tools and services have been built around this infrastructure to make it easier for scholars to consume and contribute to this scholarly record.
Misconception of reading literacy and its impacts on literacy acculturation in school

Literacy competencies are important for students to keep abreast of the pace in the 4.0 industrial age. Misunderstanding of the concept of literacy may result in the incorrect application of literacy acculturation. This study is aimed at 1) describing teachers’ misconceptions of reading literacy and 2) describing the implementation of students’ reading literacy acculturation at school. Data were collected from teachers’ and students’ statements and activities during the literacy acculturation activities in the participating schools from 19 regencies in East Java, Indonesia. Additional data were in the forms of documents and field notes obtained from observation, interviews, questionnaires, and documentary studies. The results show that: 1) the concept of literacy is understood as literal and mechanistic reading, and 2) schools tend to apply formal literacy activities by asking students to read texts 15 minutes before classes begin. Several problems are found in most research sites in which there is no follow-up on the given free reading assignments, teachers and school principals neither become literacy models, the availability of fiction and non-fiction books is limited, and students are not highly motivated to follow the reading literacy program. This study implies the urgent need to improve all school stakeholders understanding of the literacy concept and provide better activities for the implementation of the reading literacy acculturation program in schools.
A new approach for coordinating generated agents’ plans dynamically

In this work, we propose a new approach for coordinating generated agents’ plans dynamically. The purpose is to take into consideration new conflicts introduced in new versions of agents’ plans. The approach consists in finding the best combination which contains one plan for each agent among its set of possible plans whose execution does not entail any conflict. This combination of plans is reconstructed dynamically, each time agents decide to change their plans to take into account unpredictable changes in the environment. This not only ensures that new conflicts are likely to be introduced in the new plans that are taken into account but also it allows agents to deal, solely, with the execution of their actions and not with the resolution of conflicts. For this, we use genetic algorithms where the proposed fitness function is defined based on the number of conflicts that agents can experience in each combination of plans. As part of our work, we used a concrete case to illustrate and show the usefulness of our approach.
Is the Quantity Theory Dead? Lessons from the Pandemic

Many policymakers and economists are surprised by the recent high and persistent inflation. This naturally raises questions about what caused it and why it was so unexpected. This paper argues that the quantity theory of money provides a useful framework for forecasting inflation. Anyone equipped with the rather crude forecasting model would have predicted the high and persistent inflation in 2021 and 2022. The failure to foresee such an occurrence was due to the lackof money in monetary policy analysis.
Optimized Deep Neuro-Fuzzy Network with MapPeduce Architecture for Acute Lymphoblastic Leukemia Classification and Severity Analysis

The most common life-threatening disease, acute lymphoblastic leukemia (ALL), can be lethal within a few weeks if untreated. The early detection and analysis of leukemia is a key dilemma in the field of disease diagnosis, and the methods available for the classification process are time-consuming. To overcome the issues, this paper develops a robust classification technique named Horse Herd Whale Optimization-enabled Deep Neuro-Fuzzy Network (HHWO-enabled DNFN method) for ALL classification and severity analysis using the MapReduce framework. The input image is first preprocessed and segmented, and the useful features necessary for improving the classification performance are extracted during the mapper phase, known as HHWO, which incorporates Horse Herd Optimization Algorithm (HOA) and Whale Optimization Algorithm (WOA). Finally, severity analysis of ALL is done to classify the levels of leukemia to offer optimal treatment. As a result, the developed method performed better than other existing methods, achieving superior performance with a greater testing accuracy of 0.959, sensitivity of 0.965, and specificity of 0.966, respectively.
Research on deep neural network physical education training model under flipped classroom mode

With the rapid progress and development of network information technology, electronic products such as computers, smart phones, and iPads have gradually become popular among students, bringing people a lot of convenience. This study uses the data of the physical education training process to build a deep neural network prediction model. Firstly, analyze and research the physical education process; Secondly, conduct research on the use of flipped classroom model and deep neural network model; Finally, realize the analysis of targeted physical education and sports training process. The experimental results show that the proposed research has certain practical applicability. It is believed that in the near future, flipped classroom will exist as an important teaching mode of physical education.
Crowd Density Estimation Using Neural Network for COVID-19 and Future Pandemics

The population’s vulnerability is exacerbated by the lack of effective treatment drugs and immunity to COVID-19. The only viable strategy for combating this pandemic is social separation. In order to automate the task of monitoring social separation using surveillance footage, this study presents a neural network-based crowd density estimation for COVID-19 and future pandemics. The suggested framework employs the object identification model to distinguish persons in the scene, as well as the deep sort technique to track recognized people with issued IDs. The obtained results of the proposed work are compared in terms of loss values defined by object classification and localization, frames per second (FPS), and mean average precision (mAP). The proposed method yields good results against faster region-based convolutional neural network (RCNN) and single-shot detector (SSD).
An Affective Multi-modal Conversational Agent for Non Intrusive Data Collection from Patients with Brain Diseases

This paper presents Zenon, an affective, multi-modal conversational agent (chatbot) specifically designed for treatment of brain diseases like multiple sclerosis and stroke. Zenon collects information from patients in a non-intrusive way and records user sentiment using two different modalities: text and video. A user-friendly interface is designed to meet users’ needs and achieve an efficient conversation flow. What makes Zenon unique is the support of multiple languages, the combination of two information sources for tracking sentiment, and the deployment of a semantic knowledge graph that ensures machine-interpretable information exchange.
Drawing and utilization of artifacts through 3D scanning

Archaeological data, called drawings of artifacts, are created through the accumulated experience of archaeol- ogists, and are the most efficient form of showing artifacts with three-dimensional shapes on a two-dimensional report page. Drawing of artifacts is finally completed through the stages of measurement, drafting, and editing of artifacts. In the process of drawing, actual measurements have been made through human hands in the past, but now a method through 3D scanning is additionally used.&#x0D; Existing measurement requires a lot of time and labor of the researcher, and there is a difference depending on the skill level of the researcher, and errors may occur for relics with high difficulty of measurement. If the actual measurement drawings completed in this way are drafted, errors in the drafting stage are added to express the result that is separated from the original form of the artifact. 3D scanning to replace this shows the advantage of efficient drawing by reducing time and errors compared to work that goes through human hands.&#x0D; In the archaeological field, conversion from analog to digital has been carried out in many ways. If this base spreads, it is thought that it will be possible to create a new research area through the management of digitized archaeological data and the establishment of big data.
Construction and evaluation of a domain-specific knowledge graph for knowledge discovery

Purpose This study aims to evaluate a method of building a biomedical knowledge graph (KG). Design/methodology/approach This research first constructs a COVID-19 KG on the COVID-19 Open Research Data Set, covering information over six categories (i.e. disease, drug, gene, species, therapy and symptom). The construction used open-source tools to extract entities, relations and triples. Then, the COVID-19 KG is evaluated on three data-quality dimensions: correctness, relatedness and comprehensiveness, using a semiautomatic approach. Finally, this study assesses the application of the KG by building a question answering (Q&amp;A) system. Five queries regarding COVID-19 genomes, symptoms, transmissions and therapeutics were submitted to the system and the results were analyzed. Findings With current extraction tools, the quality of the KG is moderate and difficult to improve, unless more efforts are made to improve the tools for entity extraction, relation extraction and others. This study finds that comprehensiveness and relatedness positively correlate with the data size. Furthermore, the results indicate the performances of the Q&amp;A systems built on the larger-scale KGs are better than the smaller ones for most queries, proving the importance of relatedness and comprehensiveness to ensure the usefulness of the KG. Originality/value The KG construction process, data-quality-based and application-based evaluations discussed in this paper provide valuable references for KG researchers and practitioners to build high-quality domain-specific knowledge discovery systems.