Reviewerly/exHarmony huggingface dataset:
Peer review declaration

All conference organisers/editors are required to declare details about their peer review. Therefore, please provide the following information: • Type of peer review: Double-blind • Conference submission management system: AI Scholar System • Number of submissions received: 146 • Number of submissions sent for review: 133 • Number of submissions accepted: 75 • Acceptance Rate (Number of Submissions Accepted/Number of Submissions Received X 100): 51.4% • Average number of reviews per paper: 2 • Total number of reviewers involved: 45 • Any additional info on review process : • Step 1. Each of selected paper will be reviewed by two/three professional experts in the related subject area. • Step 2. Review Reports received from the experts will be judged by one of the editors either Review Reports are logical or not? • Step 3. If not logical, then editor can assign new reviewer or can also judge at his/her own. • Step 4. If logical, then Review Reports will be sent to authors to modify the manuscript accordingly. • Step 5. Authors will be required to revise their papers according to the points raised. • Step 6. Revised version will then be evaluated by the editor for the incorporation of the points raised by the reviewers. • Step 7. Then the editor will send the revised manuscript to the reviewers again for re-evaluation. • Step 8. If the reviewers approve the revise version of the manuscript, then will be accepted for publication. • Contact person for queries : Xuexia Ye publication@keoaeic.org AEIC Academic Exchange Information Centre
The sel4 verification: the art and craft of proof and the reality of commercial support (invited talk)

The formal verification of the seL4 microkernel started as a research project in 2004 and has achieved commercial scale now, in the number of properties proven, the supported features and platforms, the adoption and deployment by industry and government organisations. It is supported by an open-source Foundation and a growing ecosystem. In this talk, I will reflect on the seL4 verification journey, past, present and future, and the challenges to combine the art and craft of proof with the reality of meeting industry demand for verified software.
Planning robotic agent actions using semantic knowledge for a home environment

Autonomous mobile robotic agents are increasingly present in highly dynamic environments, thus making the planning and execution of their tasks challenging.Task planning is vital in directing the actions of a robotic agent in domains where a causal chain could lock the agent into a dead-end state.This paper proposes a framework that integrates a domain ontology (home environment ontology) with a task planner (ROSPlan) to translate the objectives coming from a given agent (robot or human) into executable actions by a robotic agent.
Introducing 2D Game Engine Development with JavaScript

Video games are complex, interactive, multimedia software systems. They must, in real time, process player input, simulate the interactions of semiautonomous objects, and generate high-fidelity graphics and audio outputs, all while trying to keep players engaged. Attempts at building a video game can quickly become overwhelming with the need to be well versed in software development as well as in how to create appealing player experiences. The first challenge can be alleviated with a software library, or game engine, that contains a coherent collection of utilities and objects designed specifically for developing video games. The player engagement goal is typically achieved through careful gameplay design and fine-tuning throughout the video game development process. This book is about the design and development of a game engine; it will focus on implementing and hiding the mundane operations of the engine while supporting many complex simulations. Through the projects in this book, you will build a practical game engine for developing video games that are accessible across the Internet.
Activating hidden metrological usefulness

We consider bipartite entangled states that cannot outperform separable states in any linear interferometer. We show that these states can be more useful metrologically than separable states if several copies of the state are provided or an ancilla is added.
iClinic: Software specialized in management of clinics and medical offices

Introduction: iClinic is a company that develops software with the purpose of assisting the doctor in the business management of his office or clinic. We believe that healthcare professionals are increasingly intertwined with technology. With that in mind, we offer an efficient and intuitive system, allowing our users to increase their productivity, reduce costs, decrease the no show rate and save time so that they can focus on humanized service. Methodology: A study was structured in order to understand what the doctors’ difficulties were when they became entrepreneurs. In order to contribute to the health area and when realizing that professionals are unassisted in the face of technological advances in their routines, iClinic has developed software capable of managing essential medical care tasks, namely: administrative management, medical records and electronic prescription, agenda service, telemedicine and online appointment scheduling. Results: Through market studies, interviews, routine monitoring of offices and clinics, it was found that the stored information of patients was largely contained in paper charts, notebooks, diaries and spreadsheets. Therefore, the insertion of technology in the administrative routine of offices and clinics was extremely important. With the use of the software, doctors obtained greater control of information, better management of time, financial control, reduction in the show and increase in the number of consultations. Conclusion: The health sector has stood out over the years and iClinic has as a pillar to encourage the evolution of good practices using technology, so the software developed by iClinic contributes immensely to the doctor or professional who runs the clinic and / or office increase the level of organization and efficiency, as the use of technology in administrative activities is indispensable in the globalized world.
An Efficient and Fast Partial Template Matching Technique Enhancement in Normalized Cross Correlation

Template matching forms the basis of many image processing algorithms and hence the computer vision algorithms. There are many existing template matching algorithms like Sum of Absolute Difference (SAD), Normalized SAD (NSAD), Correlation methods (CORR), Normalized CORR(NCORR), Sum of Squared Difference (SSD), and Normalized SSD(NSSD). In general, as image requires more memory space for storage and much time for processing. The above said methods involves much computation. In any processing, efficiency constraints include many factors, especially accuracy of the results and speed of processing. An approach to reduce the execution time is always most appreciated. As a result of this, a novel method of partial NCC (PNCC) template matching technique is proposed in this paper. A block window approach is used to reduce the number of operations and hence to speed up the processing. A comparative study between existing NCC algorithm and the proposed partial NCC, PNCC algorithm is done. It is experimented and results proves that the execution time is reduced by 8 - 47 times approximately based on the various template images for different main images in PNCC. The accuracy of the result obtained is 100%. This proposed algorithm works for various types of images. The experiment is repeated for various sizes of templates and different sizes of main image. Further improvement in the speed of execution can be achieved by implementation of the proposed algorithm using parallel processors. It may find its importance in the real time image processing.
OptAGAN: Entropy-based Finetuning on Text VAE-GAN

Transfer learning through large pre-trained models has changed the landscape of current applications in natural language processing (NLP). Recently Optimus, a variational autoencoder (VAE) which combines two pre-trained models, BERT and GPT-2, has been released, and its combination with generative adversarial networks (GANs) has been shown to produce novel, yet very human-looking text. The Optimus and GANs combination avoids the troublesome application of GANs to the discrete domain of text, and prevents the exposure bias of standard maximum likelihood methods. We combine the training of GANs in the latent space, with the finetuning of the decoder of Optimus for single word generation. This approach lets us model both the high-level features of the sentences, and the low-level word-by-word generation. We finetune using reinforcement learning (RL) by exploiting the structure of GPT-2 and by adding entropy-based intrinsically motivated rewards to balance between quality and diversity. We benchmark the results of the VAE-GAN model, and show the improvements brought by our RL finetuning on three widely used datasets for text generation, with results that greatly surpass the current state-of-the-art for the quality of the generated texts.
KEY REFERENCE MARKS OF PROFESSIONAL ORIENTATION IN TEACHING MATHEMATICS AT SECONDARY VOCATIONAL EDUCATIONAL INSTITUTIONS OF TECHNICAL PROFILE

The authors are guided by the principle of professional orientation of teaching mathematics in the secondary vocational educational system. Professional orientation in training in an educational program is considered as a combination of content and activity (procedural) aspects. The article explains that forming the ability to deal with algorithms is the key reference mark for students of the technical profile. The content of these skills and proficiency levels for students of secondary vocational educational institutions have been outlined. The ability to work with algorithms involves the formation of the following actions: to establish the “essence” of the algorithm, to highlight the steps of the algorithm and establish links between them, to develop mechanisms for the implementation of each stage. Students need to learn how to highlight the essence of algorithmic phenomena, implement a given algorithm, control the progress of the algorithm, find errors, and make adjustments. When performing actions “as directed”, students are offered a ready-made algorithm of actions to solve certain problems. When performing “by reference points”, students are required to conduct an analysis associated with the rationalization of the proposed algorithm. The independent performance of actions is distinguished by the complete independence of the student from any instructions. It is assumed that when teaching mathematics in the system of secondary vocational education of a technical profile, the selection of pedagogical technologies must be oriented towards achieving the level of independent implementation of the algorithm.
Nearest-Neighbor-based Collision Avoidance for Quadrotors via  Reinforcement Learning

Collision avoidance algorithms are of central interest to many drone applications. In particular, decentralized approaches may be the key to enabling robust drone swarm solutions in cases where centralized communication becomes computationally prohibitive. In this work, we draw biological inspiration from flocks of starlings (Sturnus vulgaris) and apply the insight to end-to-end learned decentralized collision avoidance. More specifically, we propose a new, scalable observation model following a biomimetic nearest-neighbor information constraint that leads to fast learning and good collision avoidance behavior. By proposing a general reinforcement learning approach, we obtain an end-to-end learning-based approach to integrating collision avoidance with arbitrary tasks such as package collection and formation change. To validate the generality of this approach, we successfully apply our methodology through motion models of medium complexity, modeling momentum and nonetheless allowing direct application to real world quadrotors in conjunction with a standard PID controller. In contrast to prior works, we find that in our sufficiently rich motion model, nearest-neighbor information is indeed enough to learn effective collision avoidance behavior. Our learned policies are tested in simulation and subsequently transferred to real-world drones to validate their real-world applicability.
The challenge of ICT in education in times of pandemic

&#x0D; &#x0D; &#x0D; Introduction. ICT in the educational environment in times of the pandemic is a highly topical issue worldwide, a subject of study that is in full development, and consequently, it is little analyzed. Objective. Description of the scientific production on ICTs in education during 2020. Materials and methods. Bibliometric study, quantitative type, descriptive, retrospective design, with a final sample of 70 research articles, in indexed journals speaking in Spanish in the Scielo, Dialnet and Redalyc databases, to organize and process the collected information, Mendeley was used as bibliographic manager. Results. It was identified that, the database with the highest publication of journals is Scielo (28%), the highest production of journals at education levels occurs at the basic level (33%), in terms of the country with the highest production of articles It has been Ecuador (19%), in relation to the publications of the research type, it is the quantitative type (31%) that occupies the greatest study interest and on the most used research design is the descriptive one (34%). Discussion. According to the descriptive study on&#x0D; &#x0D; &#x0D;
A Comprehensive Comparison of Binary Archimedes Optimization Algorithms on Uncapacitated Facility Location Problems

Metaheuristic optimization algorithms are widely used in solving NP-hard continuous optimization problems. Whereas, in the real world, many optimization problems are discrete. The uncapacitated facility location problem (UFLP) is a pure discrete binary optimization problem. Archimedes optimization algorithm (AOA) is a recently develop metaheuristic optimization algorithm and there is no binary variant of AOA. In this work, 17 transfer functions (TF1-TF17) are used for mapping continuous values to binary values. 17 binary variants of AOA (BAOA1- BAOA17) are proposed for solving UFLPs. 16 to 100-dimensional UFLPs were solved with binary variants of AOA. Stationary and non-stationary transfer functions were compared in terms of solution quality. The non-stationary transfer functions were produced better solutions than stationary transfer functions. Peculiar parameter analyzes for binary optimization problems were performed in the best variant (BAOA9) produced with TF9 transfer function.
Natural Scene Chinese Recognition Based on Deep Learning

Abstract Text recognition is one of the research focuses in the field of computer vision. In recent years, many researchers have proposed recognition methods based on codec frameworks, such as CRNN, but most of these methods are based on English, and relatively few are based on Chinese. Chinese language, because of its large number of character types and contains a large number of similar characters, only using CNN to extract features will lose a large amount of information. Meanwhile, the method of giving combined attention is an emerging direction. In this paper, we study the recognition of the Chinese language and introduce attention to BiLSTM to obtain more contextual information. Compared with the original algorithm, the performance of this algorithm is improved on the synthetic dataset.
Remote Registration and Group based Authentication of IoT over 5G

Cellular networks are preferred as a backbone network for IoT environment, due to their wider coverage and high flexibility. Therefore, security and privacy in IoT over cellular networks has attracted the attention of several researchers. Recently, a number of authentication schemes have been proposed for improving the security and privacy in IoT over 5G cellular network using various techniques, like lightweight cryptography, digital signature, group based authentication, blockchain technology, etc. However, it is seen that none of these works are able to address all the security issues. Moreover, the mechanisms currently being used for security in 5G cellular network cannot be adopted as-it-is in IoT because of resource constraint of IoT devices. Therefore, we are working on a novel scheme for remote registration and group-based authentication of IoT devices in 5G mobile network. Security analysis and performance analysis of the scheme, in comparison to recent proposals in the literature, have found the scheme to be secured and efficient.
Financial system

The financial system is one of the most useful applications of blockchain technology in the banking sector. All the involved parties such as complex transactions can be on-boarded on a blockchain and the information can be shared by exporters, importers, and banks on one common distributed ledger. This ledger can store facts like, who owns a particular piece of land or any information transfer on the network. This technology offers transparency and verifiable financial transaction with ease. With blockchain in the financial industry, individuals, and banks can access their transactions and it helps to improve the system's efficiency. In this chapter, we have described the transaction management in the banking sector and the role of blockchain in the financial system.
Beeldvorming over archeologie, beeldvorming bij archeologen

Imaging of archaeology, imaging by archaeologists. Among the general public and in the popular media, archaeology has a quite positive image, but one that is far from the realities of the everyday work of professional archaeologists. In this paper, I explore how that biased image became established and what role media professionals and archaeologists play in maintaining it. Further, I discuss what effect the image of excavation as the central, if not the only, aspect of archaeology has and has had on the careers of female archaeologists. Finally, I argue for self-reflection about our professional identities and the way we present our work.
A Study of Debugging Sequence on Multi-Bug Program

Debugging behavior measurement is a traditional field. There are many studies on debugging behaviors, but most of the measurement materials are programs with a single bug, and there are rarely multiple bugs in one program. In a multi-bug test scenario, unlike a single bug, we need to consider the connection between multiple bugs, i.e. the relationship between the order or performance of bug fixation and the code in which bugs are located. There is little basic knowledge about the research based on multiple bugs at present. In this paper, we tentatively perform a measurement study based on debugging behaviors in a multi-bug program. We concentrate on discovery and solution sequences and temporal performance of bugs, as well as the relationship between complexity and debugging behaviors for different bugs. Our results show that debugging behaviors have some correlations with the bug complexity if the code indentation is cited to describe the complexity of bugs. Our work can be used as a reference for the future in-depth study of code complexity and debugging behaviors.
Interaction of a teacher and a child as a factor in the development of the creativity of educational process subjects

The article deals with the development of creativity of a teacher and a child in joint activities to transform the subject space of an educational organization. The level of preschool education is chosen as the level of education, since working in a preschool organization requires constant creativity from the teacher — in communicating with children, in developing events, in planning joint activities. Improvisational characteristics of the professional activity of a kindergarten teacher are one of its leading features. The most important role in the organization of interaction between the teacher and the pupils is played by the developing subject space of the kindergarten group. This article deals with the specifics of the development of creativity of the subjects of the educational process in the conditions of joint visual activity. This aspect is not chosen by chance. The visual activity of a child has great opportunities in the formation of many mental spheres of a preschooler: attention, perception, memory, imagination, motor development, self-management, self-realization and other properties and qualities of intelligence and personality. This applies not only to preschool age, but is also valuable for a younger student who creates more complex drawings, collages, products of application, modeling. At the same time, the works performed by children and presented to others (peers, parents, teachers) create a special atmosphere of joy and the desire of children to improve their achievements, creativity. The teacher is constantly looking for new ideas in the design of the group space, thereby stimulating the process of children's creativity, developing his own pedagogical creativity. The article also presents one of the options for evaluating the activity of preschoolers in the process of joint creation of a kindergarten group with a teacher developing a spatial-subject environment.
Knowledge as an Intangible Factor of Growth in Papua, Indonesia: Examining the Role of Education, Vocational Program, Information and Communication Technology

Knowledge is capable to produce economic growth, but also the most challenging to capture. By endogenous growth theory, it can sustain the long-term wealth through the increase in capital and goods. In this study, knowledge as an intangible factor, described by duration of formal education, the application of information technology and culture. The aim of this study is to examine whether the duration of formal education, the application of information technology and culture can produce growth in less developed region. The analysis applied through Fixed Effect Model on panel data from 25 regencies and city in Papua Province, Indonesia, from 2012-2017. The results show that there are only few indicators such as years of schooling, numbers of people who have the computers and internets which had significant effect on growth.
Fast spectral clustering with self-weighted features

As one of the mainstream clustering methods, the spectral clustering has aroused more and more attention recently because of its good performance, especially in nonlinear data sets.However, traditional spectral clustering models have high computational complexity.Meanwhile, most of these models fail in distinguishing the noisy and useful features in practice, which leads to the limitation of clustering performance.In this paper, we propose a new fast spectral clustering with self-weighted features (FSCSWF) to achieve good clustering performance through learning and assigning optimal weights for features in a low computational complexity.Specifically, the FSCSWF selects anchors from original samples, then learns the weights of features and the similarity between anchors and samples interactively in a local structure learning framework.This interactive learning makes the learnt similarity can better measure the relationship between anchors, and samples due to the optimal weights make the data points become more discriminative.Moreover, the connectivity constraint are embedded to make sure that the connected components of bipartite graph constructed by the learnt similarity can indicate clusters directly.In this way, the FSCSWF can achieve good clustering performance and has a low computational complexity, which is linear to the number of samples.Extensive experiments on synthetic and practical data sets illustrate the effectiveness and efficiency of the FSCSWF with respect to state-of-the-art methods.