Reviewerly/exHarmony huggingface dataset:
Applied remote sensing technique, and GIS to choose suitable Geopark in south east Iraq

Sentinel satellite images are enhanced using Erdas-2014 and analyzed by GIS techniques to distinguish the main geological features in the study area. The enhancement process and correct satellite image with Topography, Geomorphology, and Lithology characterize for landscape classify. GIS method environmental, geological, hydrological, and topographic analysis .to identified and assess the suitable location of Geopark in the eastern masian area. This study shows the east misan area has many suitable sites that can be developed as Geopark. By GIS method can choose the suitable Geopark that achieves the basic requirements set by UNESCO for the establishment of Geopark. It includes The existence of wildlife or watery Presence of geological, geophysical, or hydrological factors Hydrogeology is of importance in the region, and high Geodiversity, the area is of importance to ecotourism, The presence of archaeological sites in the region.
Implementation of conceptual learning in STEM Technology for Higher education through JADE multiagent system

In the education system, teaching and learning takes place through various modes. In teaching mode, various types of strategies have beenfollowed and in learning, various types of strategies have been followed. Generally, teaching and learning takes place in traditional modes and online modes where traditional methods of learning and teaching methods are used to share and acquire knowledge which helps the individual to organize and use a particular set of skills in the order to accomplish the task effectively and efficiently. In this paper, we focus to improve the learning capability of an individual through STEM Technology using Conceptual learning through the JADE multiagent system for better understanding of subject. In conceptual learning learner must remember less and understand more to relate different concepts. Conceptual learning encourages active participation since it requires students to participate actively in the process, whereas traditional types of learning require the teacher to explain information. The student is then expected to pay attention and practice the techniques while the dynamics remain unclear. However, when it comes to conceptual learning, both the student and JADE Multiagent play a key role; with the teacher providing tasks or difficulties that help students fully develop an idea. Thus, encouraging active participation of students. Through multiagent system, we have achieved high precision and recall.
A 0.35-μm subthreshold CMOS ASIC for a Smart Contact Lens Eye-Tracker

This paper describes a camera-less eye-tracker using an instrumented contact lens fitted with photodetectors and illuminated by eyewear. The gaze direction is computed on the lens using the photo-currents by means of a mixed signal 0.35$-\mu$m CMOS ASIC. NFC is used to power the ASIC and to transmit the gaze direction to the eyewear. Experimental measurements are performed using a prototype scleral contact lens mounted on a mock-up eye. The measurements show that an accuracy of 0.2$^{\mathrm{\circ}}$ is achievable, i.e. 2.5 times better than current mobile video-based eye-trackers, for a power consumption of 170 $\mu$W. Several tests were carried out on several ASICs, demonstrating system reliability despite process variations, operating time, and supply voltage variations.
An Optimization of Adaptive Computing-plus-Communication for Multimedia Processing

Abstract: Cloud data centers become more and more powerful, energy consumption becomes a major challenge both for environmental concerns and for economic reasons, Towards this aim, we present model of social network website and will optimize server in such way that old data server should run on minimum cost. Also this implementation finds out the pattern of data consuming of users and according to that the graphs get generated. And after generating user data consumption data patterns optimize server in such way that whichever server data have less traffic should stay on sleep mode.
Sensing and Detection of Traffic Signs Using CNNs: An Assessment on Their Performance

Traffic sign detection systems constitute a key component in trending real-world applications such as autonomous driving and driver safety and assistance. In recent years, many learning systems have been used to help detect traffic signs more accurately, such as ResNet, Vgg, Squeeznet, and DenseNet, but which of these systems can perform better than the others is debatable. They must be examined carefully and under the same conditions. To check the system under the same conditions, you must first have the same database structure. Moreover, the practice of training under the same number of epochs should be the same. Other points to consider are the language in which the coding operation was performed as well as the method of calling the training system, which should be the same. As a result, under these conditions, it can be said that the comparison between different education systems has been done under equal conditions, and the result of this analogy will be valid. In this article, traffic sign detection was done using AlexNet and XresNet 50 training methods, which had not been used until now. Then, with the implementation of ResNet 18, 34, and 50, DenseNet 121, 169, and 201, Vgg 16_bn and Vgg19_bn, AlexNet, SqueezeNet1_0, and SqueezeNet1_1 training methods under completely the same conditions. The results are compared with each other, and finally, the best ones for use in detecting traffic signs are introduced. The experimental results showed that, considering parameters train loss, valid loss, accuracy, error rate and Time, three types of CNN learning models Vgg 16_bn, Vgg19_bn and, AlexNet performed better for the intended purpose. As a result, these three types of learning models can be considered for further studies.
Value and Risk in the Fourth Informational Revolution

This chapter explains main cyber and data risks in the Fourth Informational Revolution. It explains that long-standing models of data’s origin, collection, ownership, protection, monetization, regulation, and litigation are not merely at the foundation of established business and technical practices: they are also embedded, often only by implication, in many aspects of law and precedent. It highlights how data is a defining and durable asset and a medium of exchange. The chapter describes how technology changes our relationship with knowledge, thereby affecting our understanding of risk. The chapter provides key takeaways, including: when responsibility for data begins and ends; not falling into the trap of assuming your data situation looks to be well in hand; and, understanding the implications of audit and data-transfer contracts as well as artificial intelligence (AI). Even when rapidly evolving laws and regulations may be infeasible or even logically impossible to satisfy in full, a reasoned and responsibly considered posture of good-faith effort is (one may argue) the best that can be achieved: it is the goal of this chapter to support that process of informed consideration and prudent choice.
China's virtual reality plans go beyond the metaverse

Significance The plan actually covers not only the fully immersive digital environments that the term 'virtual reality' (VR) refers to in English, but also 'augmented reality' (AR), in which digital objects and images are superimposed on the physical world. Impacts Application trials will take place in a wide range of areas, both in consumer-facing activities and the underlying hardware and science. VR and AR are central to most visions of a 'metaverse': a system of shared, interactive online 3D virtual environments. The growing skills and capabilities created by China's VR and AR efforts will create opportunities for foreign tech companies. Foreign sanctions, in particular US sanctions, might impose constraints on China's development and application of VR and AR technologies.
Investigating the Use of COVID-19 Contact Tracing Applications in South Korea and China: An Agency Theory Perspective

Contact tracing applications are one of the most promising tools to slow the spread of coronavirus. Since being first documented in China, the coronavirus disease 2019 (COVID-19) has affected all aspects of society in serious and long-term ways. Tragically, the COVID-19 pandemic has caused more than six million deaths and even set back the progress of world economy by decades. In this study, we investigated the uptake of COVID-19 contact tracing applications in South Korea and China. In particular, drawing on agency theory, we examined the determinants of application use and compared the differences between these two countries. We also identified the moderating effects of privacy concerns and the mediating effects of implementation intention. We conducted a survey-based cross-country field study, and recruited undergraduate and graduate students from South Korea and China, separately. Results showed a large gap regarding the uptake of COVID-19 contact tracing applications between the two countries. In South Korea, self-regulation was the primary determinant, whereas government regulation was the primary factor in China. Our study contributes to the literature on both public health and information privacy, and more significantly, suggests how we can motivate more people to use such applications to prevent the spread of COVID-19.
Multi-Microworld Conversational Agent with RDF Knowledge Graph Integration

We live in an era where time is a scarce resource and people enjoy the benefits of technological innovations to ensure prompt and smooth access to information required for our daily activities. In this context, conversational agents start to play a remarkable role by mediating the interaction between humans and computers in specific contexts. However, they turn out to be laborious for cross-domain use cases or when they are expected to automatically adapt throughout user dialogues. This paper introduces a method to plug in multiple domains of knowledge for a conversational agent localized in Romanian in order to facilitate the extension of the agent’s area of expertise. Furthermore, the agent is intended to become more domain-aware and learn new information dynamically from user conversations by means of a knowledge graph acting as a network of facts and information. We ensure high capabilities for natural language understanding by proposing a novel architecture that takes into account RoBERT-contextualized embeddings alongside syntactic features. Our approach leads to improved intent classification performance (F1 score = 82.6) when compared with a basic pipeline relying only on features extracted from the agent’s training data. Moreover, the proposed RDF knowledge representation is confirmed to provide flexibility in storing and retrieving natural language entities, values, and factoid relations between them in the context of each microworld.
Reputation Driven Distributed Power Transaction Blockchain Model for Prosumers

Given the increasing penetration of distributed renewable energy, the distributed energy trading among prosumers can improve the uptake of renewable energy. But some market subjects are prone to default due to their selfishness, such behaviors will affect the economic benefits of other prosumers. In this paper, we investigate distributed energy trading blockchain model driven by the reputation. First, this paper introduces a reputation scoring model to describe how each prosumer performs the contract. Second, a transaction matching mechanism based on reputation ranking is adopted for increasing the priority of high reputation users. Finally, a price update strategy based on adaptive aggressive is employed to reach a contract quickly. The proposed model is tested on the IDE-Remix and Ganache platforms. The simulation results demonstrate the effectiveness of the proposed distributed energy trading model.
Metaphor in Chelsea vs. Arsenal Football Commentary Speeches: A Pragmatic Perspective

This paper aims to investigate the metaphorical images of Chelsea and Arsenal football commentary speeches. To achieve this aim, the data is going to be analyzed pragmatically. It is hypothesized that metaphor is widely used by commentators. The procedure adopted is to survey the language of sport and metaphor in sports commentaries theoretically first, and to analyze pragmatically certain sports texts second. &#x0D; Among the important conclusions the study comes up with are: The appearance of metaphorical images is quite clear. Metaphor is widely used by commentators. The metaphorical images are one of the important factors that make the commentaries exciting.
Advent of central bank digital currency and the future of monetary sovereignty

With the advent of modern digital monetary transactions, the idea of digital money and cash money has become synonymous. But digital currency concept is one step ahead of digital money. Owing to technological advancement, central banks across the world have come up with the idea of central bank digital currency (CBDC) which will serve two purposes primarily: (i) facilitate high volume monetary transactions in no time, without printing new currency notes or generating liabilities to banks and (ii) counter cryptocurrency acceptability as the new mode of money. But this idea has its bearing on the working of monetary policy in particular and the monetary sovereignty of a country at large. The present study is a narrative review-based study, covering various aspects of CBDC. A vast array of working papers, reports, and empirical research papers were studied to capture the idea of how to define the term CBDC, why CBDC is required, and how it will impact the various macroeconomic scenario in the future under its effect on monetary policy of the country. This piece of research also highlights the usefulness of CBDC in managing state welfare-oriented policy in a more optimal way. But in hindsight, one can visualize that CBDC could infringe the choice of privacy of the general public and may remain susceptible to cyber risk, which solely can undermine the existence of CBDC. Moreover, the chapter also highlights the future challenges the commercial banks may also face when central bank-controlled digital currency would arrive.
The meaning of decentralised finance (DeFi)

Since the appearance of Bitcoin in 2009, and then Ethereum in 2015 with its first decentralised applications, various blockchain implementations have already rooted in financial markets. Moreover, it is dynamically developing: (1) creating new business models and applications of blockchain technology, (2) creating alternatives to financial services provided traditionally in a centralised manner and (3) extending the possibilities of providing traditional financial services. This chapter focuses on defining the concept of “decentralised finance” (DeFi), defining its scope, initial classification of services available under DeFi as well as the state and legal challenges resulting from the specificity of DeFi. Having existing definitions collected and analysed, the authors’ own definition of DeFi was elaborated. Moreover, 4 dimensions of decentralisation of blockchain and 13 groups of DeFi services were identified.
Signal processing

In this chapter MATLAB is applied to examine periodic data sets. Harmonic analysis is introduced to examine periodic data sets. Next, the fast Fourier transform (FFT) method is applied to examine the temporal evolution of measured or computed data. These procedures are two of the important methods in the field of signal processing.
Pendampingan Penggunaan Pengusulan Jenjang Jabatan Akademik Dosen menggunakan Aplikasi Sikito

The development of the stages of promotion of functional positions of lecturers includes certification, development of professional competencies through further studies, promotion of academic positions and the development of scientific papers, research or scientific publications. So it is important to provide training that can provide understanding for lecturers in the environment of the Bina Husada College of Health Sciences (STIK) in proposing an increase in the level of academic positions, namely through the SIKITO application. The results of the participants' proposals with guidance and assistance through training on the use of the cycloto application from 4 (four) lecturers have received the JJA Lektor Decree and 2 (two) lecturers are in the JJA proposal to the Head Lector, and there are 14 (fourteen) lecturers in the process of verifying files and assessing credit numbers by the JJA LLDIKTI 2 team.
Journalism Ethics at the Crossroads: Democracy, Fake News, and the News Crisis, Roger Patching and Martin Hirst (2022)

Review of: Journalism Ethics at the Crossroads: Democracy, Fake News, and the News Crisis , Roger Patching and Martin Hirst (2022) Abingdon: Routledge, 308 pp., ISBN 978-0-36719-727-8, h/bk, AUD 252.00, ISBN 978-0-36719-728-5, p/bk, AUD 69.99, ISBN 978-0-42924-289-2, e-book, AUD 56.69
Crowd abnormal event detection based on motion entropy and dual support vector data description

Abnormal event detection is a popular research direction in the field of intelligent transportation and public safety. The features that characterize abnormal events are extracted from given video sequence through computer vision technology. Then the abnormal events in the video are automatically detected through the classification model. In order to describe the motion characteristics of events more accurately, a new feature based on motion entropy is proposed in this paper. The entropy value of motion pixels in the video frame is calculated as the input feature of the classification model. Motion entropy is suitable to regard as a feature to distinguish normal events from abnormal events due to the big differences between normal and abnormal events. In addition, an abnormal event detection model based on motion entropy and dual support vector data description (ME-DSVDD) is presented to solve the problem of insufficient sample diversity. The standard data set is tested to analyze the performance of the proposed model. The experimental results show that the proposed method can effectively improve the performance of the abnormal event detection model.
Digital Archiving Best Practice and Future Challenges

This article will set out best practice and future challenges of digital archiving.
Visual Analysis of Research on Blockchain

With the development of big data, people focus on data security and privacy, blockchain has been paid more and more attention. Compared to pre-2013, the number of blockchain literature publications has increased. Scholars from different research fields have deeply explored blockchain technology. In this paper, we collected blockchain literature data from 2013 to 2020, and then we conducted a quantitative analysis of publications, author groups, research content and future research content. For the current research status, we also discussed the problems facing blockchain. We started by mining representative authors, and revealed current status of blockchain research, author’s team cooperation, and blockchain’s development content. We can provide research objectives and inspiration for new researchers, and give research routes for the future development of blockchain.
NewsThumbnail: Automatic Generation of News Video Thumbnail

Reading news is an important way for people to obtain information. People can quickly sort out the context of events through a short news video. However, there are numerous news generated around the world every day. It’s challenging to locate the interesting video. Thumbnails are often used as video covers and play an important role in displaying video content and driving views. Video owners can choose from individual images or elaborate thumbnails to upload to the site. But manually selecting from a large number of frames is time-consuming, and customizing thumbnails requires a high degree of expertise. Therefore, this paper proposes an automatic generation method of news video thumbnail, which can screen out semantically similar contents according to user query and combine them into a thumbnail. In order to facilitate the screening of graphic materials, we also propose a video content structuring method based on multiple cues, which can accurately segment the video into theme units. At the same time, we designed a visual system to display thumbnails and designed a user survey to investigate the performance of this method in news retrieval and understanding. Compared with peer methods, the thumbnails generated by our method can help users better understand the video content and locate the videos they are interested in.
Ensemble Learning for Retail Product Recognition with a Large Number of Classes

Under the recent trend of unmanned economy, the retail stores have reduced the manpower for service and cashier gradually. The retail product recognition becomes one essential problem for unmanned shopping. Although the success of deep neural network makes the object recognition feasible in various applications, it is still difficult to perform well on a large number of classes. This paper presents an ensemble learning approach to deal with recognition for the growing number of retail products. In the proposed technique, the object classification networks are first improved with feature extraction and block attention. The ensemble model is then constructed by integrating the multiple network models with the loss selection as model weights. In the experiments, the feasibility of our ensemble recognition method is validated with a number of production items. The results have demonstrated the effectiveness compared to the state-of-the-art recognition algorithms.
Classification of Canine Skin Disease using Deep Learning Algorithms

Dogs were the most common pet in India in January 2022, with a share of 68 percent, a share of 34 percent cats are the second-most popular pet, according to a Rakuten Insight poll on pet ownership. Canine interaction has been identified as a risk factor for several zoonotic illnesses caused by bacteria, fungi, parasites, and viruses through direct or indirect contact. Additionally, the doctor needs to have a list of infection diagnoses and routinely inquire about the existence of dogs in the home and their health. We intend to deploy a machine learning model to decrease the time and effort necessary for establishing a consistent infection because these procedures take time and a high level of skill set. We studied and compared various approaches researchers have used in similar situations. Key Words: Dermatophytosis, zoonoses, image classification, deep learning, MobileNet, CNN, KNN, LSTM, MLP.
Specific features of gold ore formation&#x0D; in the southeastern Baikal-Patom metallogenic province (Dzhalagun ore field)

In the course of geological prospecting fulfilled with participation of the FGBI TsNIGRI within the Chayangro ore cluster of the Dodykhta-Uryakh ore region in the southeastern Baikal-Patom metallogenic province, the Dzhalagun lode gold prospect within the ore field of the same name was studied and its economic significance has been proved. It has been substantiated that the Dzhalagun gold prospect is closely similar to the typical gold ore deposits of the Bodaibo ore region in sense of the ore formation phasing, the character of the hydrothermal metasomatic processes, and the structure of the mineralized vein-veinlet zones. At the same time, a series of specific geological features of the Dzhalagun ore field and the prospect have been determined as to the structure and composition of the ore-hosting rocks, the intrusive magmatism, the hydrothermal metasomatic processes, and the mineral composition of the veinveinlet mineralization.