Reviewerly/exHarmony huggingface dataset:
Effective Development and Deployment of Domain and Application Conceptualization in Wordnet-Based Ontologies Using Ontology Repository Tool

Ontology Repository Tool is a piece of software aimed to build wordnet-based ontologies which are an example of an information language designed to represent knowledge of various kinds, including general, domain, and application knowledge. This language extends the structure of the wordnet with new types of relations that make it possible to build synset hierarchies parallel to the wordnet structure. Ontology Repository Tool is equipped with functionalities that facilitate the development and management of polyhierarchical and polyrelational knowledge structures. Moreover, the software is intended for integration with information systems such as e-learning content repositories and information systems with multimodal data in which the wordnet-based ontology is deployed and expanded while indexing the documents stored there.
Computer Technology of Environmental Design Specialty in Cloud Computing Environment

In the field of computer technology (CT), cloud computing first originated in foreign countries, and is gradually accepted and recognized by people. With the development of the times, social progress, and technological innovation, information technology has developed rapidly and has begun to become the mainstream. Information technology has penetrated into all aspects, which has improved the ability of data processing, shared use of information resources, and the cloud computing platform provides users with more high-quality, convenient and efficient services, so that it can play a huge role in various fields and develop rapidly. With the development of modern CT, the design performance methods and tools of environmental design (ED) show a diversified and multi-dimensional development trend. This article adopts the questionnaire survey method and data analysis method to understand the development trend of CT in the environment design under the cloud computing environment through the questionnaire survey. According to the survey results, most of the interviewees believe that the application of CT in ED is necessary, and it has produced very significant practical effects.
Delimitation of Benign and Malignant Masses in Breast Ultrasound by Clustering of Intuitionistic Fuzzy Superpixels Using DBSCAN Algorithm

In this study, we propose a scheme to delimit benign and malignant masses in breast ultrasound images. It consists of two stages: superpixels extraction by an Intuitionistic Fuzzy algorithm, which considers the local information of the image to develop a local segmentation; and clustering the superpixels by means of DBSCAN algorithm. The proposal does not require preprocessing of noise reduction inherent to this type of medical images or enhancement of features. The effectiveness of our proposal is verified by quantitative and qualitative results.
Inclination of NLP Applications Towards Stereotypical and Gender Biased Results

Gender prejudice exists in all aspects of life, including schools, colleges, corporations, and government agencies. As a result, women are underrepresented in a variety of professions. In many cases, Artificial Intelligence (AI) and Natural Language Processing (NLP) models learning from these underrepresented real-world datasets amplify the bias, reinforcing conventional biases. In this report, we examine how gender bias and under representation of women in a variety of sectors lead to the amplification of bias in NLP applications such as applicant tracking system, machine translation, text prediction, information retrieval systems, speech recognition and sentiment analysis. Future prospects of our work would include possible solutions to the existing problem at hand would include debiasing existing word embeddings to mitigate the bias in information retrieval systems and having guidelines for AI-NLP systems to have more ethical and transparent standards.
No-Reference Underwater Image Quality Assessment Based on Quality-Aware Features

Underwater image is a key representative form of underwater information and plays a crucial role in the exploitation of marine resources and other subsequent tasks. However, due to the scattering and absorption effects, underwater images often suffer from low visibility, color distortion, and blurring, severely affecting the interpretation of image content. Therefore, it is highly demanded to achieve objective quality assessment. In this paper, we present a novel training-based no-reference quality evaluation method for underwater image, dubbed NCCSS. Technically, our NCCSS extracts and fuses a set of quality-aware features of an image from five aspects including naturalness, color, contrast, sharpness, and structure, yielding a 56-dimensional feature vector. Specifically, considering the server color degradation of underwater images and human visual perception scheme, we propose a novel color-cast weighted colorfulness measurement as well as color consistency measurement to characterize color, and design a saliency-weighted contrast measurement to improve the distinguishing ability of measuring contrast. Additionally, more powerful features such as locally mean subtracted and contrast normalized, maximum local variation, and local entropy are also incorporated measure naturalness, sharpness and structure, respectively. Afterward, we use the feature vector extracted from the training set to build a prediction model to predict the image quality. Moreover, we collect an underwater image dataset of different scenes for testing the generalization ability of our method. The qualitative and quantitative experimental results illustrate that our NCCSS has a superior prediction accuracy comparing with the state-of-the-art underwater image quality evaluation methods and is highly consistent with human visual perception.
Using Intelligent Multi-Objective Optimization and Artificial Neural Networking to Achieve Maximum Solar Radiation with Minimum Volume in the Archetype Urban Block

urban planners and designers should employ solar energy analysis as a requirement to propose efficient solutions. This research aims to determine the maximum annual solar radiation with the least volume in various patterns of urban blocks and convert solar radiation modeling and analysis from simulation to emulation for types of urban blocks. As a result, simulation approaches such as MOO and ANN were presented to characterize the influence of different urban scales on solar access and reach the optimal dimensions of the block. In Kermanshah, different patterns of urban blocks were investigated for the study on the urban block scale. The height, direction, number of plots, and scales impact access to solar radiation energy and optimize the block's dimensions. A study of 34 different urban blocks found that the ideal size is roughly 100*100 meters, while the best height is between 10-15 meters. Furthermore, the findings of predicting the data training models revealed that three outputs, the rate of solar radiation absorption, volume, and area of the urban block, can be predicted with an R2 score of 98% using the research input variables. Thus, radiation absorption estimations utilizing AI algorithms can ultimately replace traditional approaches and time-consuming large-scale energy estimates.
Pre-assessment of Machine Learning Approaches for Patient Length of Stay Prediction

Patient length of stay (LOS) in ICU and hospital’s general care unit is one of the important indicators that is widely measured and benchmarked to improve the quality and efficiency of patient care. There are many studies both on statistical testing of the LOS outcome to determine factors associated with it and the outcome predictive modeling using machine learning algorithms. However, there are still fewer studies of the LOS outcome predictive modeling using local datasets. Therefore, an initial study of assessing supervised machine learning approaches for regression and classification tasks on predicting the LOS outcome has been conducted using an aggregated Multiparameter Intelligent Monitoring in Intensive Care-III (MIMIC-III) public dataset to obtain an overview of the prediction framework and to compare the predictive performance of the machine learning models. This is as a preparation for developing an outcome calculator of the LOS outcome and other operative outcomes in patients based on local data. The LOS was predicted using 10 classification and 15 regression models assessed using accuracy, precision, recall, and F1 scores for the classification task and root mean squared error (RMSE) for the regression task. The results showed that the Extreme Gradient Boosting classifier and Extreme Gradient Boosting regressor presented the highest validation and good testing performances for the LOS prediction. Although model overfitting trend was detected in both models’ learning curves, in this respect, the study may serve as a useful starting point for an extended work associating with predictive modeling on the LOS and other operational outcomes.
Found in Translation: Co-design for Security Modelling

Background. In increasingly complex and dynamic environments, it is difficult to predict potential outcomes of security policies. Therefore, security managers (or other stakeholders) are often challenged with designing and implementing security policies without knowing the consequences for the organization. Aim. Modelling, as a tool for thinking, can help identify those consequences in advance as a way of managing decision-making risks and uncertainties. Our co-design approach aims to tackle the challenges of problem definition, data availability, and data collection associated with modelling behavioural and cultural aspects of security. Method. Our process of modelling co-design is a proposed solution to these challenges, in particular for models aiming to incorporate organizational security culture. We present a case study of a long-term study at Company A, where using the methods of participatory action research, humble inquiry, and thematic analysis, largely shaped our understanding of co-design. We reflect on the methodological advantages of co-design, as well as shortcomings. Result. Our methodology engages modellers and system stakeholders through a four-stage co-design process consisting of (1) observation and candidate data availability, (2) candidate model design, (3) interpretation of model consequences, and (4) interpretation of domain consequences. Conclusion. We have proposed a new methodology by integrating the concept of co-design into the classical modelling cycle and providing a rigorous methodology for the construction of models that captures the system and its behaviours accurately. We have also demonstrated what an attempt at co-design looks like in the real-world, and reflected upon necessary improvements.
Light Field Image Super-Resolution Based on Raw Data with Transformers

Light field (LF) cameras can provide both spatial and angular information to promote image super-resolution (SR). However, current LF image SR methods only consider LF color data, without paying attention to the LF raw data. To fully use the raw data, a new data generation pipeline is developed to collect LF raw data. In addition, an end-to-end convolutional neural network architecture (namely, LF-RawSR) is proposed for LF image SR. Specifically, an aggregated module is first used to fuse the angular information based on a volume Transformer with plane sweep volume. Then the aggregated feature is warped to all LF views using a cross-view Transformer for non-local dependencies utilization. Experimental results demonstrate that our method outperforms the existing state-of-the-art methods with comparative computational cost, and fine details and clear structures can be restored.
Expanding the Competencies of the Tax Specialist of the Future in the Context of Digital Educational Realities

The education system was forced to quickly change in connection with the pandemic and the use of distance learning technologies, which were considered as additional opportunities and competitive advantages in the market of additional educational services yesterday, today has become the only possible option to ensure the sustainability of all levels of the educational process. The accelerated introduction of distance learning technologies has exacerbated the problems of matching the levels of digital literacy and digital competence of subjects of the educational process and their correlation with competencies in demand on the labor market. All this justifies the relevance of designing recommendations for the development of educational and methodological tools for the formation of the competencies of future specialists. The article analyzes the possibilities of existing distance educational technologies, identifies their weaknesses and strengths, and determines the possible options for their development on the example of educational programs of economists at a technological university. The results obtained from an analytical basis for substantiating the prospects for expanding the competencies of tax specialists of the future.
From Sensing to Acting—Can Pervasive Computing Change the World?

Computing technology has indeed become pervasive. Taking a quick look around me, I see computing systems in literally everything—in the cars, televisions, smartphones, restaurants, ski-lifts, heating systems, sports trackers, medical devices, etc. This has been realized by a tremendous development in hardware and software technology in terms of CPUs, memory, sensors, operating systems, network, display, etc. However, looking back at this technology development—and the research done in the field—it strikes me that something is missing. One of the grand visions was to make the computer “invisible,” as framed by Weiser. But it seems like instead of computing becoming more invisible, it is taking up more of the user’s attention. In this article, I argue that this is because (pervasive) computing has only come halfway. Much effort has been done in terms of sensing and understanding the world around the user, while much less effort has been put into helping the user actually doing anything. By providing examples mostly taken from the medical domain, this article discusses if moving from “sensing” and “thinking” to actually “doing” something is possible and what challenges are associated with this movement.
Bmsmlet: Boosting Multi-Scale Information on Multi-Level Aggregated Features for Salient Object Detection

Nowadays, salient object detection methods based on deep learning have become a research focus. Therefore, how to reveal the representation mechanism and association rules of features at different levels and scales in order to improve the accuracy of salient object detection is a key issue to be solved. This paper proposes a salient object detection method to boost multi-scale information on multi-level aggregated features, which can accurately and flexibly aggregate multi-scale feature information via effective multi-level feature utilization. The experimental results on four public datasets show that the performance of the proposed method has improved in contrast to the state-of-the-art methods.
Visual Question Generation

To explore how questions regarding images are posed and abstract the events caused by objects in the image, the visual question generation (VQG) task has been established. In this chapter, we classify VQG methods according to whether their objective is data augmentation or visual understanding.
Post-error Correction for Quantum Annealing Processor Using Reinforcement Learning

Finding the ground state of the Ising model is an important problem in condensed matter physics. Its applications spread far beyond physic due to its deep relation to various combinatorial optimization problems, such as travelling salesman or protein folding. Sophisticated new methods for solving Ising instances rely on quantum annealing, which is a paradigm of quantum computation. However, commercially available quantum annealers are still prone to errors, and their ability to find low energetic states is limited. This naturally calls for a post-processing procedure to correct errors. As a proof-of-concept, this work combines the recent ideas revolving around the DIRAC architecture with the Chimera topology and applies them in a real-world setting as an error-correcting scheme for D-Wave quantum annealers. Our preliminary results show how to correct states output by quantum annealers using reinforcement learning. Our approach exhibits excellent scalability, as it can be trained on small instances. However, its performance on the Chimera graphs is still inferior to Monte Carlo methods.
Blockchain Framework for Agro Financing of Farmers in South India

In India, small scale farmers face challenges acquiring crop loans and insurance. The government of India has many schemes for both. However, due to lapses in processing, these commercial bank loans are difficult to avail. Thus farmers approach Microfinance Institutions (MFI) or moneylenders, who charge high interest rates. To eliminate this difficulty, a novel blockchain based module is proposed in this paper, incorporating farmers’ credibility, verifying insurance claims, and assured guaranteed repayment of loans to investors. The inherent property of blockchain ensures trust and transparency. The proposed blockchain module has four participating entities, the farmer, investors, customers, and the government agent, governed by the smart contract rules. The concept of trusted oracles is used to gather weather data, and the smart contract decides the payout and the subsidized loan repayment. This module is implemented and tested in the Ethereum live test network. Performance evaluation shows that the proposed module has an average latency of 30 s and minimal fees.
Data science

In the last few decades we have witnessed an acceleration of the process that aims to essentially render every aspect of reality in a form suitable for computational analysis, i.e., data. Thanks to the development of technologies capable of carrying out a massive number of tests and immediately acquiring a massive amount of data, even the biological sciences have undergone a profound transition from mainly driven-by-hypotheses disciplines to data-driven sciences. Data science is the field of study that encompasses the scientific principles, methodologies, and techniques suitable for the collection and the computational analysis of data, in order to draw from these new insights, i.e., novel information that is understandable, not obvious and not known a-priori, that can be useful in decision-making processes. We will introduce data science, briefly discussing the stages of a general data science process. Finally, various platforms and programming languages for data science will be presented.
Design and Implementation of Interactive Network Education Platform Based on Campus Network

This paper analyzes the problems existing in the process of basic education informatization in China. The application of campus network in education is not in-depth, and high investment fails to produce high efficiency. In view of this problem, this paper designs a general network education platform based on campus network, which creates an ideal environment for teachers’ teaching and students’ learning, so as to promote the in-depth application of campus network in education and teaching. This paper analyzes the characteristics of network education and summarizes the educational functions of campus network. Based on the constructivism learning theory, this paper discusses the requirements of the “learning based” teaching method to the network teaching environment; Based on the theory of distance learning circle, this paper discusses the relationship between the elements of network teaching, and expounds the essential attribute of network teaching interaction.
Analysing the Cryptocurrency Regulatory Environment of Malawi Using Pathetic Dot Theory

Cryptocurrencies are gaining popularity as a means of electronic payment across the globe. The aim of this paper is to analyse how financial regulatory environment can leverage the adoption of cryptocurrencies and enhance financial inclusion. The study used case of Malawi as an example of a developing country that was considering regulating cryptocurrencies. Using Pathetic Dot Theory to analyse secondary data, the findings showed that the country had substantial ICT infrastructure concentrated in urban areas that may support cryptocurrencies services. However, the country did not have adequate legal frameworks to regulate use of cryptocurrencies. The demand-side of payment systems e.g. the cryptocurrency market was still underdeveloped and required awareness of cryptocurrencies services to support financial inclusion. The study contributes to the understanding of cryptocurrencies regulatory environment in the context of developing countries.
Improving Entity Disambiguation Using Knowledge Graph Regularization

Entity disambiguation plays the role on bridging between words of interest from an input text document and unique entities in a target Knowledge Base (KB). In this study, to address the challenges of global entity disambiguation, we proposed Conditional Masked Entity Model Using Knowledge Graph Regularization (CMEM-KG), based on a conditional masked language model, in which multiple mentions in a context can be disambiguated in one forward pass. In addition, to address the long-tailed distribution of global entity disambiguation, we proposed a link prediction regularization, in which the entity embeddings were jointly learned to predict knowledge graph links to prevent the model from overfitting. Compared to other global entity disambiguation models, the model proposed in this study exhibited improved performance on six public datasets without an iterative decoding.
Smart Contracts: Between the Attractiveness of a Registry Catalog Implementation and Convenience of a Jurisdictional Background

Smart contracts are a common topic in scientific discussions held by computer scientists. Due to the dynamic nature of its applications, smart contracts must be considerably more adaptable, responsive and controllable. Yet the long-standing focus on security issues has affected negatively the development of smart contracts in the transfer of ownership of the real estate. In this paper, we study the possibilities and the limitations of how to transfer properties on the block chain using a digital record, as a back-support in case of litigation. We propose storing a registry catalog to handle the property historical data and criminal record of the owners. The execution of the smart contract is enforced using a digital identity of the two parties. We design the components of what the registry should contain to integrate with block chain infrastructure and we also analyze and discuss the government endorsement needed for the success of the implementation.