Reviewerly/exHarmony huggingface dataset:
Design and Implementation of a Radioactive Source Intelligent Search Robot Based on Artificial Intelligence Edge Computing

Artificial intelligence is a very broad science, which consists of different fields, such as machine learning, and computer vision. In recent years, the world nuclear industry has developed vigorously. At the same time, incidents of loss of radioactive sources also occur from time to time. At present, most of the search for radioactive sources adopt manual search, which is inefficient, and the searchers are vulnerable to radiation damage. Sending a robot to the search an area where there may be an uncontrolled radioactive source is different. Not only does it improve efficiency, it also protects people from radiation. Therefore, it is of great practical significance to design a radioactive source search robot. This paper mainly introduces the design and implementation of a radioactive source intelligent search robot based on artificial intelligence edge computing, aiming to provide some ideas and directions for the research of radioactive source intelligent search robot. In this paper, a research method for the design and implementation of a radioactive source intelligent search robot based on artificial intelligence edge computing is proposed, including intelligent edge computing and gamma-ray imaging algorithms, which are used to carry out related experiments on the design and implementation of radioactive sources, an intelligent search robot based on edge computing. The experimental results of this paper show that the average resolution of the radioactive source search robot is 90.55%, and the resolution results are more prominent.
Making the most of cybercrime and fraud crime report data: a case study of UK Action Fraud

Researchers and public authorities are increasingly exploring the potential of administrative data to generate new insights. This includes recent work leveraging the opportunities of the crime report data collected by the UK's national reporting centre Action Fraud (AF). However, the quality of these data and its implications for data users have not been systematically analysed.This paper outlines challenges and opportunities of using AF data in cybercrime and fraud victimisation research and practice and makes recommendations to improve the quality of this dataset.The author has undertaken two studies using samples of AF data pertaining to crime reports within the Welsh police forces, between 2014 and 2020. Quality diagnostic checks, reflections and methodological decisions were considered across each study. These were reviewed, key themes were identified and discussed with data users and a broader group of researchers to finalise the recommendations presented.The strengths and limitations of AF data are discussed and grouped into themes, closely aligned with four quality dimensions widely used by statistical authorities. This includes an assessment of 1) the impact of under-reporting and 2) the purpose and rules of crime recording, on the relevance of the data to its users; 3) the accuracy and reliability of the data; 4) the consistency of recording and its impact on coherence and comparability; and 5) the accessibility and timeliness of the data.Recommendations are made to improve AF data to generate better quality insights across the dimensions of relevance, accuracy & reliability, coherence & comparability and the accessibility & timeliness of this dataset. Additionally, a data catalogue would enable frontline officers and researchers to make the most of this dataset, harnessing it to produce key insights for crime prevention, investigation, and victim support.
IoT: A Boon for Advancement of Technology

Abstract: Internet of Things (IoT) is a new model that has turned the traditional lifestyle into a high-tech lifestyle. Smart city, smart home, pollution control, energy saving, smart transportation, smart industry are such transformations due to IoT. A large number of important studies and surveys have been carried out to improve technology through IoT. However, there are still many challenges and problems that need to be addressed by the to reach the full potential of IoT. These challenges and issues need to be considered from different aspects of IoT such as application, challenge, enabling technology, social and environmental impact, etc. The main purpose of this review article is to provide a detailed discussion from both a technological and social perspective. Article discusses various key IoT challenges and issues, architecture, and key application areas. In addition, the paper highlights the existing literature and illustrates their contribution in different aspects of IoT. In addition, the importance of big data and its analysis in relation to IoT was discussed. This article will help readers and researchers understand IoT and its real-world applicability. Keywords: Internet of Things (IoT), IoT architecture, IoT challenges, IoT applications
SUPER RESOLUTION FOR SINGLE SATELLITE IMAGE USING A GENERATIVE ADVERSARIAL NETWORK

Abstract. Inspired by the immense success of deep neural network in image processing and object recognition, learning-based image super resolution (SR) methods have been highly valued and have become the mainstream direction of super resolution research. Base on the recent proposed state-of-art convolution neural network (CNN) super-resolution methods, this paper proposed a generative adversarial network for single satellite image Super Resolution reconstruction. It built on a trained deep residual network to generate preliminary SR images, combined with a discriminative network learns to differentiate preliminary SR images and High resolution samples. The experiments results show that our method can use existing model parameters to refine SR image performance.
CascadER: Cross-Modal Cascading for Knowledge Graph Link Prediction

Knowledge graph (KG) link prediction is a fundamental task in artificial intelligence, with applications in natural language processing, information retrieval, and biomedicine. Recently, promising results have been achieved by leveraging cross-modal information in KGs, using ensembles that combine knowledge graph embeddings (KGEs) and contextual language models (LMs). However, existing ensembles are either (1) not consistently effective in terms of ranking accuracy gains or (2) impractically inefficient on larger datasets due to the combinatorial explosion problem of pairwise ranking with deep language models. In this paper, we propose a novel tiered ranking architecture CascadER to maintain the ranking accuracy of full ensembling while improving efficiency considerably. CascadER uses LMs to rerank the outputs of more efficient base KGEs, relying on an adaptive subset selection scheme aimed at invoking the LMs minimally while maximizing accuracy gain over the KGE. Extensive experiments demonstrate that CascadER improves MRR by up to 9 points over KGE baselines, setting new state-of-the-art performance on four benchmarks while improving efficiency by one or more orders of magnitude over competitive cross-modal baselines. Our empirical analyses reveal that diversity of models across modalities and preservation of individual models' confidence signals help explain the effectiveness of CascadER, and suggest promising directions for cross-modal cascaded architectures. Code and pretrained models are available at https://github.com/tsafavi/cascader.
Deep understanding of everyday activity commands for household robots

Going from natural language directions to fully specified executable plans for household robots involves a challenging variety of reasoning steps. In this paper, a processing pipeline to tackle these steps for natural language directions is proposed and implemented. It uses the ontological Socio-physical Model of Activities (SOMA) as a common interface between its components. The pipeline includes a natural language parser and a module for natural language grounding. Several reasoning steps formulate simulation plans, in which robot actions are guided by data gathered using human computation. As a last step, the pipeline simulates the given natural language direction inside a virtual environment. The major advantage of employing an overarching ontological framework is that its asserted facts can be stored alongside the semantics of directions, contextual knowledge, and annotated activity models in one central knowledge base. This allows for a unified and efficient knowledge retrieval across all pipeline components, providing flexibility and reasoning capabilities as symbolic knowledge is combined with annotated sub-symbolic models.
Enhancement process of AES: a lightweight cryptography algorithm-AES for constrained devices

The restricted devices have a small memory, simple processor, and limited power. To secure them, we need lightweight cryptography algorithms, taking into account the limited specifications. Lightweight cryptography (LWC) algorithms provide confidentiality and maintain information integrity for devices with limited resources. This paper improves and enhances advanced encryption standard (AES) algorithm by reducing algorithm computation power and improving cryptography performance from the point of resource constraint devices. The proposed algorithm is fast and lightweight, which is essential for securing all kinds of data. Besides, the use of mix column overhead is dispensing with, and the ciphertext is processed by the mathematical function (continued fraction) to compress the ciphertext and make it more confusing and also to increasing the data transfer speed. The proposed lightweight cryptography-AES (LWC-AES) algorithm highly suitable for the timely execution of encryption and decryption (such as when encrypt text has (45.1 KB) encryption execution time for AES was (294 ms), while in LWC-AES was (280 ms), as well as suitable for the memory size of the resource-constrained devices for all types of data, than the AES algorithm. The proposed algorithm tested for security analysis using the Avalanche Effect parameter, and this test showed acceptable and within required security results
Object Tracking Based Surgical Incision Region Encoding using Scalable High Efficiency Video Coding for Surgical Telementoring Applications

Surgical telementoring is an advanced telemedicine concept where the expert surgeon guides the onsite novice present at the remote location.The efficient telementoring system requires the wireless transmission of highquality surgical video with less bitrate in less time.The bit rate of the surgical video can be decreased by segmenting the surgical incision region and removing the background region.The High Efficiency Video Coding (HEVC) standard has provided promising results for surgical telementoring applications.But the Rate-Distortion Optimization (RDO) search process in HEVC increases the complexity that in turn increases the encoding time.We propose the method which involves the segmentation of the surgical incision region using the Kernelized Correlation Filter (KCF) object tracking technique.The segmented region is encoded by the complexity-efficient Scalable HEVC (SHVC) to meet the resolution of an end-user device.The complexity of SHVC is decreased by using the Convolutional Neural Network (CNN) and Long-and Short-Term Memory (LSTM) to predict the Coding Tree Unit (CTU) structure.The results show that the proposed method decreases the bitrate significantly for segmented surgical video sequences without degradation in Peak Signal-to-Noise Ratio (PSNR).These results are obtained for the surgical video sequences with slow-moving objects.Furthermore, the CNN+LSTM approach reduces the encoding time of standard SHVC by 51% with negligible Rate-Distortion (RD) performance loss.
PROFESSIONAL MOTIVATION OF SCIENTIFIC AND PEDAGOGICAL PERSONNEL AS A FACTOR OF SOCIAL SECURITY

The prerequisites for the development of human capital and ensuring social security are created through the institutions of the family and the public education system. It is here that the foundations of moral, mental and physical health of a person and society are laid, a system of universal values is formed, professional orientation takes place, knowledge and skills are acquired. One of the determinants of the development of human capital and ensuring social security is the sustainable and harmonious development of the personnel potential of educational institutions, while a significant place belongs to the motivation of scientific and pedagogical personnel and job satisfaction. This study is aimed at revealing the content of professional motivation of scientific and pedagogical personnel as an important factor of social security. The scientific contribution consists in the fact that job satisfaction is considered as an important socio-psychological phenomenon and a condition for ensuring social security; the factors of job satisfaction are systematized; the main value orientations of a university teacher are determined; the nature of a teacher's professional motivation is revealed and the factors that determine it are characterized. The results of the study are useful to the founders and heads of educational organizations in order to more effectively manage the staff, and in general, the development strategy of institutions
THE RESULTS OF THE USE OF CLOUD TECHNOLOGIES IN THE EDUCATIONAL PROCESS OF PEDAGOGICAL UNIVERSITIES IN A PANDEMIC

The research relevance ties with the necessity to form future teachers’ digital competence under forced blended learning conditions due to COVID-19 preventive quarantine measures implementing. The purpose of the article is to reveal the content and analyze the results of the study on the experience in cloud technologies usage while teaching pedagogical courses to students in a modern pedagogical higher education institution. The methods are: theoretical analysis of the cloud technologies usage in higher education institutions, documentation analysis in the European Higher Education Area, information collection, questionnaires for students and lecturers, observation, evaluation, systematization of the results. The results are: the experience in cloud technologies usage while teaching pedagogical courses to future teachers is determined; issues with the information and communication technologies usage are identified: constant access to the network, poor service, lack of equipment. It has been clarified that students gained an enough experience in cloud technologies usage before the quarantine same as during it, identified the factors that contributed to it (inclusion in distance learning, studying the course “Digital Technologies”, testing of cloud services and technologies). It has been established that the lecturers used to have small experience in cloud technologies usage before the quarantine because there was no special necessity, but during it, these indicators have changed (forced distance learning and the conditions for its implementation contributed to the rapid capture of cloud services, attending courses, testing the experience in cloud technologies usage). The most popular cloud technologies are Classroom, Zoom, Meet, Moodle have been lined out.
Social media feedback. a different look at instagram as a collaborative learning tool

This study pretends to characterize the comments of instagram users to have an approval base from the conceptual and aesthetic point of view, to find out what effect has the feedback of this social network in the creation of creative concepts. We worked with the account "@distritovisual" where graphic pieces of bachelor’s students of Graphic Design in a private university in Guayaquil city where exhibited. A mixed approach with an exploratory-descriptive-causal scope was applied, an interview with an expert on the subject and the application of a rubric for the review and quantification of the comments of those who visited the profile, later these were analyzed by students for the respective conclusion in the decision making on the pedagogical relevance of instagram as a tool of collaborative learning that involves didactic interaction empowering education through feedback.
Long-term Control for Dialogue Generation: Methods and Evaluation

Current approaches for controlling dialogue response generation are primarily focused on high-level attributes like style, sentiment, or topic. In this work, we focus on constrained long-term dialogue generation, which involves more fine-grained control and requires a given set of control words to appear in generated responses. This setting requires a model to not only consider the generation of these control words in the immediate context, but also produce utterances that will encourage the generation of the words at some time in the (possibly distant) future. We define the problem of constrained long-term control for dialogue generation, identify gaps in current methods for evaluation, and propose new metrics that better measure long-term control. We also propose a retrieval-augmented method that improves performance of long-term controlled generation via logit modification techniques. We show through experiments on three task-oriented dialogue datasets that our metrics better assess dialogue control relative to current alternatives and that our method outperforms state-of-the-art constrained generation baselines.
The Application of AI Technology in GPU Scheduling Algorithm Optimization

With the rapid development of integrated circuit technology, GPU computing capabilities continue to improve. Due to the continuous improvement and improvement of GPU programming capabilities, functions, and performance, GPUs have been widely used in the field of high-tech general-purpose computers. This article is aimed at studying the optimization of GPU scheduling algorithm based on AI technology. Through a combination of theoretical analysis and simulation experiments, the concepts of artificial intelligence technology and GPU scheduling are explained, and the impact of GPU architecture and GPGPU load on the energy efficiency of GPGPU is explained. On the basis of comprehensive analysis of GPU cluster characteristics, a new GA-TP scheduling algorithm based on genetic algorithm was designed, and based on the energy efficiency of the cluster, a simulation verification platform was built for the accuracy of simulation. Experimental results show that the acceleration rate of the GA-TP algorithm is significantly lower than that of the HEFT algorithm, the average acceleration rate is reduced by nearly 25%, and the scheduling efficiency of the GA-TP algorithm is higher.
Topical Issues of Expert Identification in the Forensic Authorship Studies

Within the school of forensic expertology, in the context of modern challenges of digitalization, the current problems of forensic and expert identification are considered to establish the author of a controversial speech work posted on the Internet, an information and media language personality. Based on the analysis of the main stages of the process of expert identification of the author of the text, the need for their correction and addition is revealed in connection with significant changes in the linguistic personality manifests itself mainly in a virtual environment. The necessity of developing a classifier of the author’s idiolect features with an indication of their identification significance, as well as the use of quantitative statistical methods for analyzing speech works using computer technology, is substantiated.
Interdisciplinary Community Based Learning to Enhance Competence of Digital Citizenship of Social Studies Pre-Service Teacher’s in Thai Context: Pedagogical Approaches Perspective

The objectives of this research were to 1) synthesize competency of digital citizenship and concepts of interdisciplinary community-based learning and 2) propose guidelines of interdisciplinary community-based learning management for promoting digital citizenship of pre-service teachers in the Thai context. The research was qualitative research conducted by using content analysis according to the grounded theory to obtain study results according to the objectives. The study results were as follows: 1. The results of synthesizing competency of digital citizenship and concepts of interdisciplinary community-based learning revealed that digital competencies which led to digital citizenship were as follows: Digital Access, Digital Literacy, Digital Commerce, Digital Safety and Resilience, Digital Participation and Agency, Digital Emotional Intelligence, Digital Creativity and Innovation, Digital Communication, Digital Ethics, Digital Health, and well-being. For concepts of interdisciplinary community based learning through pedagogical approaches, they consisted of Academically Based community Service / Civic learning / Environmental Education / Placed-based Education / Service Learning / Work-based Learning/Tech Pre /Ethical Case Studies/Social Engagement/Discipline-Based Model Problem-based Learning / Capstone course (a short course emphasizing self-study from actual and complicated problems) /Service Internship / Youth Apprenticeship / Community-Based Action Research Model /Experience-Based Career Education Volunteerism / Pre-Service Teacher /Field Education / Independent Study Modal and 2. Guidelines of managing interdisciplinary community based learning for promoting digital citizenship of pre-service teachers in the Thai context revealed that important issues leading to interdisciplinary community based learning management consisted of Contextaul Knowlegde / Academic study /Citizenship/ Community-based Learning/ Interdisciplinary approach/ Environmental Education/ Experiential Learning / Lifelong Learning.
Maximally informed Bayesian modelling of disc galaxies

Dissecting the underlying structure of galaxies is of main importance in the framework of galaxy formation and evolution theories. While a classical bulge+disc decomposition of disc galaxies is usually taken as granted, this is only rarely solidly founded upon the full exploitation of the richness of data arising from spectroscopic studies with integral field units. In this work we describe a fully Bayesian estimation method of the global structure of disc galaxies which makes use of the wealth of photometric, kinematic, and mass-to-light ratio data, and that can be seen as a first step towards a machine-learning approach, certainly needed when dealing with larger samples of galaxies. Ours is a novel, hybrid line of action in tackling the problem of galactic parameter estimation, neither purely photometric nor orbit-based. Being rooted on a nested sampler, our code, which is available publicly as an online repository, allows for a statistical assessment of the need for multiple components in the dissecting process. As a first case-study the GPU-optimized code is applied to the S0 galaxy NGC-7683, finding that in this galaxy a pseudo-bulge, possibly the remnant of a bar-like structure, does exist in the center of the system. These results are then tested against the publicly available, orbit-based code DYNAMITE, finding substantial agreement.