Reviewerly/exHarmony huggingface dataset:
Application of IoT and AI in the Development of Smart Cities

About half of the global population now lives in cities, and this figure is rapidly increasing, posing immense challenges. A rise in population leads to more energy consumption which will increase pollution. Smart cities are expected to aid in the resolution of these issues. Smart cities are supposed to help cope with these problems. The main objective of smart cities is to improve people’s life by providing facilities like Smart Lighting, Smart Mobility, Smart Logistics, Smart Harvest, Smart Help. People must be able to express what kind of community they want to live in for the smart city to exist. Technical prerequisites are needed for these concepts to become a truth. The modern mobile connectivity standard 5G plays a key role. It drives the Internet of Things (IoT) and makes for massively integrated infrastructures. This chapter explicitly offers a detailed overview of the services of IoT and the use of Artificial Intelligence (AI) where we briefly explain Waste Management, Navigation System, Environment Monitoring, and so on. Furthermore, existing problems and potential technical solutions to the challenges are discussed like Security, Networking, and so on. Finally, in the field of IoT and AI, the article provides future research perspectives.
A radial distortion compensation method for artifact-free multi-adjacent-tile stitching/mosaicking in Mesoscopic Optical Microscopy

Mesoscopic optical imaging of an ultra-large biological sample demands a large enough field of view (FOV) in order to reduce the requirement of extensive digital image stitching operations. For an objective lens with a specific magnification, when we continue extending the FOV, the adverse effects of optical aberrations become prominent. One of such effects is radial distortion towards the edges and corners of a square-shaped FOV. Consequently, when we attempt to mosaic/stitch multiple of such radially distorted adjacent tiles, it often becomes challenging to avoid artifacts or discontinuity of structures especially at the joining of such adjacent tiles. To address the same, we apply a preestimated opposite radial distortion to each acquired tile prior to applying an alignment algorithm. Employing a bruteforced approach, we one-time quantify a suitable compensating radial distortion. We apply this method in a custom-built large-FOV nonlinear optical microscope (NLOM) system and demonstrate an artifact-free mosaic-stitching operation.
Influence of Management Efficiency of Sports Equipment in Colleges and Universities Based on the Intelligent Optimization Method

China has always considered the improvement of youth physical quality as one of the key steps in the talent training program, and universities have always followed the national policy to change the quantity and quality of sports equipment. This study mainly focuses on the development of sports equipment management system in colleges and universities. By building a scientific sports equipment management system, the efficiency of sports equipment management process is improved and the difficulty of managers’ work is reduced. The construction of the sports equipment management system is a comprehensive information service platform created specifically for sports administrators. The main function is to help sports equipment administrators obtain information on equipment management, and it is an electronic system that can obtain the status of sports equipment use through certain instructions, providing an example for sports equipment management in China’s universities.
Automatic Control of Mobile Industrial Robot Based on Multiobjective Optimization Strategy

In order to solve the optimal cascade mobile path selection problem when mobile industrial robots repair network coverage holes, a cascade mobile path selection optimization method considering the number and energy of intermediate cascade nodes is proposed. By calculating the energy availability of intermediate cascade nodes, this method further obtains the energy availability and decisive energy of each path, selects the optimal cascade mobile path from the perspective of multiobjective optimization, effectively balances the energy consumption of each mobile industrial robot, makes full use of the energy of the whole network, and prolongs the survival time of the network. Simulation results show that the optimization method has higher network energy efficiency than the standard cascaded mobile method.
Threats and Protection on E-sim: A Prospective Study

Threats include a variety of risks, and threats are associated with embedded SIM technology, such as Internet of things (IoT) identity. IoT refers to the working capabilities that enable the allocation of unique identifiers (UID) in order to effectively connect with the related devices, thereby improving communication. An e-SIM application cannot generate reliable and accurate data that can be used to predict the subscriber's outcome. SIM technology does not provide any reliable data that the user can use to formulate some serious productive outcomes.Failure of the technology to process and automatically notify the user implies that there has been an infringement or hacking. Another significant threat to the embedded universal integrated connectivity card(e-UICC) is SIM-jacking. Due to failure protections within the operational environment, incompetent Log Rhythm Al Engine influences the fraudster hacking experience. The e-SIM technology system lacks timely threat, risk, and other vital operations predictability to respond to the fraudsters' experienced unbearable operations challenges. Similarly, the embedded SIM exposes service providers to insider threats by failing to ensure the much-needed privacy of an individual's vital information. Situations of personal data leakage have been observed during system operations.The e-SIM hijacking enables the fraudsters to secretly obtain the victim’s vital data of the subscriber, hijack, and receive the information intended to the individual to his/her personal phone. The process results to complete mobile account operations by the hacker resulting to further access to the victim’s bank information and transfer of cash. The other threat experienced by e-SIM users is the provision of false information. SIM subscribers are commonly duped into falling into the traps of fraudsters by receiving short messages (SMS) claiming assistance from service providers, thereby draining the victim's bank account. Other risks associated with the use of e-SIM include identity fraud and device poisoning. In general, the entire process of fraud invasion and victimisation influences the affected individuals' business decisions. Protection focuses on the embedded SIM, which provides greater security as well as a reprogrammable technological system, as opposed to the physical SIM card. The subscriber's personal information is not stored in the e-SIM, but rather with the service providers, which increases its effectiveness. An e-SIM allows consumers to switch carriers between T-Mobile and Sprint without physically moving, which is beneficial to security systems. Despite the security measures put into place, e-SIM like any other SIM card experiences information theft. As a result, service providers should prepare for emerging fraudster effects by properly monitoring the network system and enabling security restrictions. During operation, the system should impose strict conditions that allow for the evaluation and differentiation of IoT and non-IoT devices.
Archangel dataset: UAV-based imagery with position and pose metadata

Object detection on imagery captured onboard aerial platforms involves different challenges than in ground-to-ground object detection. For example, images captured from UAVs with varying altitude and view angles present challenges for machine learning that are due to variations in appearance and scene attributes. Thus, it is essential to closely examine the critical variables that impact object detection from UAV platforms, such as the significant variations in pose, range to objects, background clutter, lighting, weather conditions, and velocity/acceleration of the UAV. To that end, in this work, we introduce a UAV-based image dataset, called the Archangel dataset, collected with a UAV that includes pose and range information in the form of metadata. Additionally, we use the Archangel dataset to conduct comprehensive studies of how the critical attributes of UAV-based images affect machine learning models for object detection. The extensive analysis on the Archangel dataset aims to advance optimal training and testing of machine learning models in general as well as the more specific case of UAV-based object detection using deep neural networks.
An Intelligent Teaching Strategy of Cross-Cultural Business Communication Course Based on Big Data

The Internet's growth has opened up new avenues for educational reform, and educational informatization has shifted the traditional classroom-teaching model. Teachers' teaching techniques have changed dramatically because of the introduction of a variety of new mobile intelligent terminals to the campus and classroom. This paper examines the design content of intelligent teaching methods against the backdrop of big data, using a cross-cultural business communication course as an example to reform the classroom-teaching mode, with the goal of resolving the current classroom-teaching dilemma and improving teaching effectiveness through the new intelligent teaching mode.
Expert System Approach for Online Admissions

In current pandemic situation online admission system is the need of the hour. This research paper first presents importance of domain of Online Admission System. It mentions in brief research work and contribution of researcher in this domain required for this research paper. Paper briefs first what is prototype system and expert system. Researcher is presenting prototype system useful for stakeholders, organizations that conduct online admissions in framing the requirements for the system, and they can also get idea of what features in the system they are going to get at what approximate cost and third party organizations which are in design and development of the system. Three point formula is used for approximate cost estimation. Expert system asks questions to the user, according to input system suggests type of online admission system along with approximate cost.
MyData Personal Data Store Model(PDS) to Enhance Information Security for Guarantee the Self-determination rights

The European Union recently established the General Data Protection Regulation (GDPR) for secure data use and personal information protection.Inspired by this, South Korea revised their Personal Information Protection Act, the Act on Promotion of Information and Communications Network Utilization and Information Protection, and the Credit Information Use and Protection Act, collectively known as the "Three Data Bills," which prescribe safe personal information use based on pseudonymous data processing.Based on these bills, the personal data store (PDS) has received attention because it utilizes the MyData service, which actively manages and controls personal information based on the approval of individuals, and it practically ensures their rights to informational self-determination.Various types of PDS models have been developed by several countries (e.g., the US, Europe, and Japan) and global platform firms.The South Korean government has now initiated MyData service projects for personal information use in the financial field, focusing on personal credit information management.There is also a need to verify the efficacy of this service in diverse fields (e.g., medical).However, despite the increased attention, existing MyData models and frameworks do not satisfy security requirements of ensured traceability, transparency, and distributed authentication for personal information use.This study analyzes primary PDS models and compares them to an internationally standardized framework for personal information security with guidelines on MyData so that a proper PDS model can be proposed for South Korea.
The qualification improvement model for teachers of philology on the use of cloud technologies in pedagogical activity

The use of information and communication technologies in education, including cloud technologies, has recently become an urgent issue. The main reasons for the growing interest in cloud technologies are the need to find alternative forms of learning to traditional ones, as well as the conformity of functional capabilities of cloud technologies to modern needs of optimisation of educational branch. Application of cloud technologies in educational process requires teachers to have appropriate qualification. The aim of this work was to develop a qualification improvement model for foreign language teachers, which could help them acquire the necessary theoretical knowledge, as well as practical skills and abilities to use cloud technologies in professional activities. This paper used methods of analysis of existing models of teacher training, as well as the survey and mathematical methods of processing the results of experimental research. The model of advanced training is created in the work, which helps teachers of Philology to acquire the necessary skills and abilities of productive use of cloud technologies. In addition, it allows you to gain new knowledge in the field of foreign philology, pedagogy and psychology. According to this study, as a result of using such a model, foreign language teachers acquire the necessary skills and abilities to use cloud technologies in the educational process. They allow them to achieve didactic goals in professional activities. As this study showed, foreign language teachers can achieve didactic goals in professional activities using cloud technology through the proposed model. It was found that the developed model is highly efficient. The results of this study can be useful to scientists and educators who are engaged in finding and researching effective learning models that rely on modern technology. Prospects for future research may be to improve existing teacher training models and develop new ones in accordance with the state of digital technologies.
An augmented reality-assisted visualization system for potential applications in prostate biopsy

Ultrasound-guided biopsy is widely used for disease detection and diagnosis. We plan to register preoperative imaging, such as positron emission tomography / computed tomography (PET/CT) and/or magnetic resonance imaging (MRI), with real-time intraoperative ultrasound imaging for improved localization of suspicious lesions that may not be seen on ultrasound but visible on other imaging modalities. Once the image registration is completed, we will combine the images from two or more imaging modalities and use Microsoft HoloLens 2 augmented reality (AR) headset to display three-dimensional (3D) segmented lesions and organs from previously acquired images and real-time ultrasound images. In this work, we are developing a multi-modal, 3D augmented reality system for the potential use in ultrasound-guided prostate biopsy. Preliminary results demonstrate the feasibility of combining images from multiple modalities into an AR-guided system.
An Enhanced Deep Learning Approach for Tectonic Fault and Fracture Extraction in Very High Resolution Optical Images

Identifying and mapping fractures and faults are important in geosciences, especially in earthquake hazard and geological reservoir studies. This mapping can be done manually in optical images of the earth surface, yet it is time consuming and it requires an expertise that may not be available. Building upon a recent prior study, we develop a deep learning approach, based on a variant of a U-Net neural network, and apply it to automate fracture and fault mapping in optical images and topographic data. We show that training the model with a realistic knowledge of fracture and fault uneven distributions and trends, and using a loss function that operates at both pixel and larger scales through the combined use of weighted Binary Cross Entropy and Intersection over Union, greatly improves the predictions, both qualitatively and quantitatively. As we apply the model to a site differing from those used for training, we demonstrate its enhanced generalization capacity.
Hierarchical Inductive Transfer for Continual Dialogue Learning

Pre-trained models have achieved excellent performance on the dialogue task.However, for the continual increase of online chit-chat scenarios, directly fine-tuning these models for each of the new tasks not only explodes the capacity of the dialogue system on the embedded devices but also causes knowledge forgetting on pre-trained models and knowledge interference among diverse dialogue tasks.In this work, we propose a hierarchical inductive transfer framework to learn and deploy the dialogue skills continually and efficiently.First, we introduce the adapter module into pre-trained models for learning new dialogue tasks.As the only trainable module, it is beneficial for the dialogue system on the embedded devices to acquire new dialogue skills with negligible additional parameters.Then, for alleviating knowledge interference between tasks yet benefiting the regularization between them, we further design hierarchical inductive transfer that enables new tasks to use general knowledge in the base adapter without being misled by diverse knowledge in task-specific adapters.Empirical evaluation and analysis indicate that our framework obtains comparable performance under deployment-friendly model capacity.
Universal Graph Filter Design Based on Butterworth, Chebyshev, and Elliptic Functions

Graph filters are crucial tools in processing the spectrum of graph signals. In this paper, we propose to design universal IIR graph filters with low computational complexity by using three kinds of functions, which are Butterworth, Chebyshev, and elliptic functions, respectively. Specifically, inspired by the classical analog filter design method, we first derive the zeros and poles of graph frequency responses. With these zeros and poles, we construct the conjugate graph filters to design the Butterworth high-pass graph filter, Chebyshev high-pass graph filter, and elliptic high-pass graph filter, respectively. On this basis, we further propose to construct a desired graph filter of low pass, band pass, and band stop by mapping the parameters of the desired graph filter to those of the equivalent high-pass graph filter. Furthermore, we propose to set the graph filter order given the maximum passband attenuation and the minimum stopband attenuation. Our numerical results show that the proposed graph filter design methods realize the desired frequency response more accurately than the autoregressive moving average graph filter design method, the linear least-squares fitting-based graph filter design method, and the Chebyshev FIR polynomial graph filter design method.
Terminology and history of digital libraries

The proposed work examines the history, current state and rates of development of the electronic library, analyzes the advantages and disadvantages of the electronic library. There is has been made a classification of the electronic library. The features of the types of electronic libraries are considered. Special attention was paid to foreign and domestic libraries. The first is, an overview of the development, history of the electronic library was made. The analysis of this study of the XX century’s electronic library was carried out. A comparative study of an electronic library and traditional library was carried out, also there were analyzed two types of libraries. The electronic library was seen as a modern tool providing fast and efficient access to modern knowledge. The proposed work also considers the possibility of pro- viding readers with national and international projects of open digital libraries. The main directions of the traditional library on the Internet today are considered. This paper analyzes the terminological system, which will reveal the modern content of the term “electronic library”. The importance of the electronic library is revealed, as an increase in the efficiency of the use of traditional and electronic information resources. The work contains the main content of im- portant domestic and foreign sources of information, which creates theoretical prerequisites for the trans- formation of traditional libraries into electronic ones. The practical significance of this work is mainly the improvement of information services and bibliographic services through electronic libraries and opens a new way for further large-scale research in the electronic library. Key words: electronic library, digital library, scientific electronic library, international project, tra- ditional library.
Role of Artificial Intelligence-Based Technologies in Healthcare to Combat Critical Diseases

Artificial intelligence (AI) and allied technologies are increasingly sophisticated at doing what humans do, but more efficiently, more quickly and at a lower cost. Several organizations from healthcare of all sizes, types, and specializations are progressively fascinated by how AI and its applications enable supreme healthcare delivery that provides patient-level recommendations based on identification, stratification models and care while also lowering costs and enhancing efficiency. For instance, AI has been used for the assessment and earlier diagnosis of continual chronic wound status by achieving a maximum yield of 85.67% efficiency and accuracy by utilizing several computational learning algorithms like Linear Discriminant Analysis, Decision Tree etc., respectively. In this chapter, we discuss recent advances in AI technology and its biomedical applications, which have the potential to enhance the healthcare industry by enhancing aspects of healthcare systems. Despite the enormous promise of cutting-edge AI research and innovation in the realm of healthcare, the ethical issues induced by its applications have been addressed.
A Comparative Study of Pre-trained Encoders for Low-Resource Named Entity Recognition

Pre-trained language models (PLM) are effective components of few-shot named entity recognition (NER) approaches when augmented with continued pre-training on taskspecific out-of-domain data or fine-tuning on in-domain data.However, their performance in low-resource scenarios, where such data is not available, remains an open question.We introduce an encoder evaluation framework, and use it to systematically compare the performance of state-of-the-art pre-trained representations on the task of low-resource NER.We analyze a wide range of encoders pre-trained with different strategies, model architectures, intermediate-task fine-tuning, and contrastive learning.Our experimental results across ten benchmark NER datasets in English and German show that encoder performance varies significantly, suggesting that the choice of encoder for a specific low-resource scenario needs to be carefully evaluated.