Reviewerly/exHarmony huggingface dataset:
Blockchain System Implementation

This chapter focuses on the implementation aspects of blockchain technology. The chapter starts with a brief discussion of some potential organizational use cases such as the use of blockchain technology in disaster recovery, contract management, product monetization, supply chain monitoring, asset management and compliance management. Next, using the COBIT 2019 implementation approach as the framework, the chapter presents and discusses 10 general IT implementation challenges; then the reader is taken through the seven phases of the COBIT 2019 implementation by outlining roles and responsibilities, as well as each phase’s description, objectives, inputs, and outputs. Readers interested in using the COBIT 2019 implementation approach are highly encouraged to review several COBIT 2019-related publications, including COBIT 2019 Implementation Guide, COBIT 2019 Design Guide and the COBIT 2019 Governance and Management Objectives guide available on the ISACA website (ISACA.org), as this chapter aims to provide a high-level discussion of blockchain implementation considerations.
Contrastive explanations

In this chapter, we show how the techniques for generating adversarial examples can be used to generate a type of post-hoc local explanations for a given classifier, known as “contrastive explanations”. The technique applies to both white-box and black-box models, similarly to assumed knowledge of a target model in adversarial attacks.
Contextual Active Learning for Person Re- Identification

Active learning has been recently investigated in the field of Person Re-identijication to obtain informative samples for training. However, the current methods incur a fixed annotation cost as they do not explicitly incorporate the re-identification model’ confidence. To this end, we propose a novel human-in-the-loop context aware active learning method that helps the re-identification model improve with progressively collected data while annotating a few but effective samples. In our proposed method, a contex-tual bandit agent is trained to learn a policy to obtain the training samples about which the model is least confident and thus needs annotations. A binary reward is provided to the agent based on the actions and the confidence of the model given the current query image. On an average, our model achieves a boost of 9.13% mAP, 5.64% rank-1 improvement over the baseline and uses 32.3% less anno-tations compared to the previous best active learning approach on DukeMTMC-reID.
Vehicle detection in dark weather based on YOLO and MSRCR method

With the continuous growth of car ownership, intelligent transportation has penetrated into our daily life. As an important part of intelligent transportation, car detection has also been developed rapidly. It plays a vital role on the planning of urban public transportation and brings great convenience for citizens to commute. Due to the extremely complex urban conditions, car detection encounters many difficulties. By analyzing a large amount of vehicle color difference data, it draws the conclusion that illumination is the main factor of affecting detection and recognition. Based on the convolutional neural network framework, this paper focuses on low-light enhancement and car recognition, aiming to realize the task of car recognition in more complex low-light situations. Notably, in those scenarios, the YOLOv4 model with a basic training set can recognize automobiles well.
Caracterização espectrorradiométrica (VNIR-SWIR) do minério magnesítico da porção centro-sul do estado do Ceará

Due to the practicality, speed and low cost of operation, the use of spectroradiometry has become an important tool in determining the composition of materials, being used in the characterization of boreholes, support and orientation of mining fronts, and as support to interpretation of remote sensing images. To perform the spectroradiometric characterization of the magnesitic ore of the state of Ceará (Brazil), the FieldSpec Pro FR spectroradiometer (350 to 2500 nm) was used. The samples were collected from the mines Cabeça de Negro, Riacho Fundo and Torto, and subsequently cut and polished in order to obtain uniform surfaces, and thus minimize the effect of the sample relief and of any surface weather. Data acquisition was carried out in the laboratory, without any interference from external lighting, with three spectral measurements made at different points of each sample. The spectral features that characterized the analyzed ore were attributed to iron (350 to 750 nm, 1000 nm and 1700 nm), water/hydroxyl (1390 nm, 1440 nm and 1940 nm), to the Mg-OH bond (2070 nm and 2390 nm) and carbonate (1850 nm, 1970 nm, 2130 nm and 2300 nm), with the albedo and intensity of the features being influenced by the presence of organic matter and particle size. According to the spectral characteristics, the magnesitic ore studied could be classified into four distinct groups, called hydrated magnesite, anhydrous magnesite, ferrous magnesite and carbonaceous magnesite.
PROBLEMS OF DEVELOPMENT OF DIGITAL COMPETENCIES IN HIGHER EDUCATION TAKING INTO ACCOUNT WORLD TRANSFORMATIONS (ON THE EXAMPLE OF ARMENIA AND THE RUSSIAN FEDERATION)

The article deals with what is relevant today for post-Soviet countries, and what is relating to both the development of intellectual hu- man capital and to following the need to transform higher education du-ring the COVID-19 pandemic. Based on research in current trends, the ar- ticle considers the competencies required today in the field of higher education (HE), which can transform crises into opportunities. Digital competencies are becoming a necessity and are developing not only due to the dictation of the widespread dissemination of new digital technologies in the modern world edu- cational environment, but also of the social, physical and political realities, ig- noring which will lead to lagging behind and the loss of competitiveness of the university. Media and information literacy is recognized as one of the most im- portant competencies in the current realities of the society digital transforma- tion the neglect of which can lead to chaos in the perception of the world. Ta-king into account the current challenges, the authors outlined important tasks facing the sphere of higher education in post-Soviet countries, using the exam- ple of the Republic of Armenia. Following the analysis of world trends, scientific literature and expert opinions, they also revealed the issues that our coun- tries face in the field of innovative transformation of the educational sphere into a free common space, contributing to the development of intellectual hu- man capital in the context of digitalization. Among other things, the digitalisa- tion processes and rapid development of the information and communication technologies make an issue of mastering the media and the information literacy competences relevant. The article is concluded with some possible steps ad- dressing the challenges facing the education system. The research results can be used in the educational process by educational specialists and the digitaliza- tion participants in order to improve the educational policy of the RA.
New Local Binary Pattern Feature Extractor with Adaptive Threshold for Face Recognition Applications

This paper represents a feature extraction method constructed on the local binary pattern (LBP) structure. The proposed method introduces a new adaptive thresholding function to the LBP method replacing the fixed thresholding at zero. The introduced function is a Gaussian Distribution Function (GDF) variation. The proposed technique uses the global and local information of the image and image blocks to perform the adaptation. The adaptive function adds to the on-hand im-age’s features by preserving the information of the amplitude of the pixel difference rather than just considering the sign of the pixel difference in the process of LBP coding. This feature improves the accuracy of the face recognition system by providing additional information. The proposed method demonstrates a higher recognition rate than other presented techniques (%97.75). The proposed method was also tested with different types of noise to demonstrate its effectiveness in the presence of various levels of noise. The Extended Yale B dataset was used for the testing along with Support Vector Machine (SVM) as classifier.
MapIntel: Enhancing Competitive Intelligence Acquisition Through Embeddings and Visual Analytics

Competitive Intelligence allows an organization to keep up with market trends and foresee business opportunities. This practice is mainly performed by analysts scanning for any piece of valuable information in a myriad of dispersed and unstructured sources. Here we present MapIntel, a system for acquiring intelligence from vast collections of text data by representing each document as a multidimensional vector that captures its own semantics. The system is designed to handle complex Natural Language queries and visual exploration of the corpus, potentially aiding overburdened analysts in finding meaningful insights to help decision-making. The system searching module uses a retriever and re-ranker engine that first finds the closest neighbors to the query embedding and then sifts the results through a cross-encoder model that identifies the most relevant documents. The browsing module also leverages the embeddings by projecting them onto two dimensions while preserving the original landscape, resulting in a map where semantically related documents form topical clusters which we capture using topic modeling. This map aims at promoting a fast overview of the corpus while allowing a more detailed exploration and interactive information encountering process. In this work, we evaluate the system and its components on the 20 newsgroups dataset and demonstrate the superiority of Transformer-based components.
Exploring the Teaching of English Reading in Junior High School Based on Core Literacy

With the introduction of core literacy, it has been received great interest that the development and pedagogical exploration of core literacy in reading teaching.In order to improve students' core literacy, the teaching concept of core literacy needs to be continuously implemented throughout English teaching.And how should core literacy be cultivated and concretely implemented in English reading teaching?In this paper, we conduct a research and exploration based on core literacy in junior high school English reading teaching.It is hoped that this will better improve students' core literacy, better promote the development of students' comprehensive quality, and better improve junior high school English reading teaching.
Correlation analysis of motivation and willingness to teach in provincial public-funded normal students

As the "reserve army" and "talent pool" of teachers in primary and secondary schools, publicfunded normal students play a key role in revitalizing rural education and promoting the development of education in my country.The motivation of the provincial public-funded normal students to apply for the examination plays a predictive role in their expectation of engaging in the teaching profession in the future.Accordingly, through a questionnaire survey of 122 provincial public-funded normal students in Qufu Normal University, it is found that there is a close relationship between the motivation of publicfunded normal students to apply for the examination and their willingness to teach.This provides detailed and reliable first-hand data for the next step of the implementation and improvement of the public-funded normal student policy, as well as a reference for the future public-funded normal students to apply for the examination and the training of public-funded normal students.
The idea of artificial intelligence guidance algorithm suitable for multiple robot platforms

This article aims to propose an artificial intelligence guidance algorithm that can be applied to multiple robot platforms and address the navigation needs of the blind.With this idea, the blind guide device can be more efficient, more convenient, and less expensive.
Design and Implementation of Flexible-Specified Retrieval in a Unified Framework for Management System of Diverse Information

We have designed and implemented a diversed-information management system for both staff and department information based on .NET framework, B/S structure, Sql Server database and Visual Studio Develop Environment of Microsoft Corporation are adopted.Many kinds of personal and department-related information tables are created in the Sql Server database which results to difficulties in uniform retrieve and analysis.In traditional information retrieval applications, designers generate sql sentence from several fixed inputs and show the results in a fixed view.To enable flexible-specified advanced query of all that sorts of information in a unified framework, we import extra tables for describing information table columns called 'AllTableInfo' and extra views for personal information integration.Besides, query strings can be generated and transferred to the background processing program when the user selects the info-fields to output in specified form and sequence and sets the where condition.Sub query strings can be added to fulfill all-around queries using all kinds of where condition permutation and combination.Once specifying the query instance, the query string can be chosen as memorized so that the user can choose it easily and only set a few where conditions instead of specifying the query instance allover again.Through our design, the query input view and query results view can be generated dynamically and flexibly in a unified framework according to user's choice of query need.What's more, our work make it possible for user to set the query need accurately covering all conditions.In this paper we will explain how all these are designed and implemented.
Prediction of Earth polar motion in short time interval

The accuracy characteristics of mathematical models of the Earth pole motion in a short time intervals have been investigated. The modeling was carried out using two approaches: a numerical-analytical model of the Earth’s rotational motion relative to the center of mass, based on the weighted least squares method and a neural network model for predicting the Earth pole motion. A comparison is made between the standard deviations of short-term forecasts of the Earth pole motion for 1–2 days according to the developed models and the standard deviations of forecasts published by the International Earth Rotation Service in Bulletin A. The models under consideration did not use data smoothing. The model based on neural network approach showed a higher accuracy of short-term forecasting.
MSFANet: Crowd Density Estimation Based on Multi-scale Feature Adaptation

To solve the problem that the number of heads cannot be accurately extracted due to scale changes in crowd counting, a crowd density estimation based on multi-scale feature adaptive network was proposed(MSFANet).Firstly, deep convolution neural network is used to extract semantic features of different scales.Then, the expansion convolution network is introduced, and the expansion convolution in the scale-up unit is combined with the traditional convolution to further increase the receptive field and reduce the information loss caused by channel competition.Finally, channel attention is introduced to enhance the ability of multi-scale feature extraction and ensure the integrity of relevant and important information in the image.Experiments on datasets (ShanghaiTech, UCF_CC_50 and WorldExpo10) show that this algorithm is more accurate and robust than the current mainstream crowd counting algorithms.
Analysis of risks and regulatory issues in the development of blockchain finance

In recent years, a wave of blockchain technology development has been launched in China.Blockchain technology promotes the development of finance, but there are also technical risks, economic risks, artificial risks and legal risks, so it is necessary to supervise blockchain finance.The supervision of blockchain finance should rely on the "sandbox supervision" model, pay attention to the characteristics of blockchain, innovate traditional supervision, establish special supervision, and transform blockchain financial supervision into functional and technical supervision; at the same time, clarify supervision The main body, rationally divide supervisory powers, and accelerate the integration of technical and legal supervisory means from the aspects of basic legislation, supervisory law enforcement, industry law-abiding, and financial justice.The article focuses on blockchain finance, analyzes its development risks and practical problems according to its development characteristics, and based on this, puts forward suggestions for future development.
A Study on Resident Participation in the Changing Environment of the Community: Focusing on volunteering and resident autonomy

An important aspect that has recently been confirmed in Korean society is interest in the local community(eup, myeon, dong). The emergence of ‘eup-myeon-dong’, which is the closest living space to the lives of residents, but has received little attention, is an important change in the social welfare field. The emphasis on ‘eup, myeon, dong’ differs from the existing method in that resident participation is important. Therefore, the recently confirmed interest in the local community can only be understood as an interest in the residents. One of the most important contents required in this process is citizen participation.&#x0D; It is a problem that the ‘status of residents’ is still ambiguous despite the current change in which resident participation is emphasized. Resident participation is inevitably used in the form of ‘volunteer service’ and sometimes ‘resident autonomy’ as needed without clear standards. This in turn creates a risk of being reduced to a means of mobilization rather than participation. This study was conducted with the purpose of examining resident participation through ‘volunteer service’ and ‘resident autonomy’. In the sense that the current situation is not caused by individuals, new institutionalism is used as a theoretical framework. Through this, it was possible to distinguish resident participation (volunteer service, resident autonomy) amid changes in the community environment. Finally, this study suggests the need for accurate recognition and application of resident participation, and the role of social welfare.
Neural-network-based estimation of normal distributions in black-box optimization

The paper presents a novel application of artificial neural networks (ANNs) in the context of surrogate models for black-box optimization, i.e. optimization of objective functions that are accessed through empirical evaluation.For active learning of surrogate models, a very important role plays learning of multidimensional normal distributions, for which Gaussian processes (GPs) have been traditionally used.On the other hand, the research reported in this paper evaluated the applicability of two ANN-based methods to this end: combining GPs with ANNs and learning normal distributions with evidential ANNs.After methods sketch, the paper brings their comparison on a large collection of data from surrogate-assisted black-box optimization.It shows that combining GPs using linear covariance functions with ANNs yields lower errors than the investigated methods of evidential learning.
The Weakest Security Link—People

Generally speaking, the weakest link in information technology security isn’t how the devices are configured or what solutions have been configured. The weakest link is the general office staff. The company can put hundreds of thousands of dollars into protecting the computing environment, but that is no match for Fred in customer service who finds a USB thumb drive on the ground in the parking lot and plugs it into their company computer to see what’s on it.
Map-Based Virtual-Structure Fleet Maneuvering in Cluttered Environments

Some practical applications have a specific working environment that remains almost unchanged over time, and do not allow installation of localization hardware. In such applications, a good choice is to generate the map of the environment in advance and localize vehicles by matching this map afterwards. This chapter will consider vehicle fleet maneuvering in such scenarios. In addition, a more important issue, collision avoidance, will be discussed as well. To address this issue, this chapter introduces a multi-objective resolution strategy, which prioritizes different objectives in different situations. Real UGV experiments have been conducted to validate the effectiveness of the proposed approach.
Final Project, Agile Career Progression, and Interview Tips

The goal of the final project is to give a chance to the students and the teams they are part of to present the project they have been working on throughout this course in a cohesive and informative way. This section also includes advice on Agile career progression and interview tips.
Tangible Interaction with In-Car Smart Intelligence

Interacting with a car was once a tactile experience, which is on the decline with the rise of car assistants, where the dominant form of interaction is through screen displays and voice recognition. These interaction modalities within a car are not the only options available. In this paper, we discuss reintroducing tactility into the automotive experience. This work presents a tactile embodiment of an intelligent car system, different from previous studies, to improve engagement and emotional connection between users and future intelligent cars. A prototype tool was designed to embody an intelligent car system. It was used to investigate how to interact with and control a smart-comfort system to improve user comfort. The tool invited users to interact through touch. Users could use their hands to physically agree or disagree with changes made by the system with the system moving in response, creating a bi-directional interaction symbiosis that re-prioritises tactility.
A Gaze- vs. Joystick-Based Interaction Method for a Remote Reconnaissance Task

Increasing automation and artificial intelligence in every domain requires resolving complex human machine interactions. In the context of remote reconnaissance, an operator is likely to be supported by computer vision technology, whilst still making the final assessment, with the speed and precision of the classification being key. Within a vast design space with multiple design options, different interaction concepts were explored. One concept is based on gaze, which is tracked using eye tracking technology. It is then indicated if the operator's gaze is on a software identified object and allows an interaction through a secondary input device. To evaluate the concept, a simulator experiment was designed, comparing it to a joystick input. Expert users will complete two equal scenarios using both interaction methods, whilst metrics such as task success are recorded and interviews are conducted, for a balanced analysis of qualitative and quantitative, as well as subjective and objective data.