Reviewerly/exHarmony huggingface dataset:
Mt. Random: Multi-tiered Randomness Beacons

Many decentralized applications require a common source of randomness that cannot be biased or predicted by any single party. Randomness beacons provide such a functionality, allowing parties to periodically obtain fresh random outputs and verify that they are computed correctly. In this work, we propose Mt. Random, a multi-tiered randomness beacon that combines Publicly Verifiable Secret Sharing (PVSS) and (Threshold) Verifiable Random Function (VRF) techniques in order to provide efficiency/randomness quality trade-offs with security under the standard DDH assumption (in the random oracle model) using only a bulletin board as setup (a requirement for the vast majority of beacons). Each tier provides a constant stream of random outputs offering progressive efficiency vs. quality trade-offs: true uniform randomness is refreshed less frequently than pseudorandomness, which in turn is refreshed less frequently than (bounded) biased randomness. This wide span of efficiency/quality allows for applications to consume random outputs from an optimal point in this trade-off spectrum. In order to achieve these results, we construct two new building blocks of independent interest: GULL, a PVSS-based beacon that preprocesses a large batch of random outputs but allows for gradual release of smaller “sub-batches”, which is a first in the literature of randomness beacons; and a publicly verifiable and unbiasable protocol for Distributed Key Generation protocol (DKG), which is significantly more efficient than most of previous DKGs secure under standard assumptions and closely matches the efficiency of the currently most efficient biasable DKG protocol. We showcase the efficiency of our novel building blocks and of the Mt. Random beacon via benchmarks made with a prototype implementation.
UMUSE: User Monitoring of the US Presidential Election

This paper presents the results of a new monitoring project of the US presidential election with the aim of establishing computer-based tools to track the popularity of the two main candidates. The innovative methods that have designed and developed allow us to extract the frequency of search queries sent to numerous search engines, social media, and e-shops. Based on these data, this paper demonstrates that Donald Trump is significantly more frequently searched than Joe Biden. When analyzing various political topics, it is observed that the US Internet users have shown a remarkable interest in subjects like Coronavirus, and Jobs in 2020. Other subjects such as Education or Healthcare were given less attention. Finally, some “flame” topics such as Black lives matter appeared to be very popular for a few days before returning to a low level of interest.
Technology of differentiated training bases of algorithmization and programming on the first course of the university on the basis of the DL.GSU.BY system of distance learning

The article describes the technology of teaching text-based programming on the basis of the DL.GSU.BY website. The main advantages of the technology include: “zero entry threshold”, training adapted to the student, many years of practical experience, efficiency, scalability. The following issues are consistently considered in the article: idealized goal setting; the use by students of a programming language of their choice from a variety of modern programming languages when performing practical tasks in the discipline; a clear verification of goal achievement; blended learning; effective personalization of the educational process; non-standard organizational and technical decisions; effectiveness of training. The idealized goal setting includes the need to teach students the following: algorithmic reformulation of the problem statement; knowledge of a set of basic language constructs, as well as basic builtin procedures and functions; the ability to use basic algorithms on one-dimensional and two-dimensional arrays, sets of plane points, lines, queues; the ability to develop and debug new algorithms. Effective personalization of the educational process is provided with the help of such techniques: at each lesson, the student is offered a choice of activities that correspond to current level of his training; automatic verification of solutions is provided with the test assignment service; the system of automatic differentiated learning is used.
Modifying the softening process for knowledge distillation

The prime focus of knowledge distillation (KD) seeks a light proxy termed student to mimic the outputs of its heavy neural networks termed teacher, and makes the student run real-time on the resource-limited devices. This paradigm requires aligning the soft logits of both teacher and student. However, few doubts whether the process of softening the logits truly give full play to the teacher-student paradigm. In this paper, we launch several analyses to delve into this issue from scratch. Subsequently, several simple yet effective functions are devised to replace the vanilla KD. The ultimate function can be an effective alternative to its original counterparts and work well with other skills like FitNets. To claim this point, we conduct several visual tasks on individual benchmarks, and experimental results verify the potential of our proposed function in terms of performance gains. For example, when the teacher and student networks are ShuffleNetV2-1.0 and ShuffleNetV2-0.5, our proposed method achieves 40.88%top-1 error rate on Tiny ImageNet.
University of Portsmouth library subject pages: a flexible in-house system for guidance and resource discovery

Guiding students to library resources, and supporting users in their discovery and use, is a key role of academic librarians. Subject pages are a traditional way of asynchronously managing some of the complexity that exists, with off-the-shelf software packages often a solution. The University of Portsmouth Library (https://library.port.ac.uk/) decided to provide this support through an addition to our existing in-house content management system (CMS). This would be flexible and powerful in allowing freedom of design for Faculty Librarians within a shell that provided some similarity – and thus familiarity – between subject areas. It would also be easy to maintain technically. The Library's Web Developer, in consultation with the six Faculty Librarians and the Enquiry team lead, has built a PHP/MySQL based application which is tightly integrated to our existing web resources providing enhanced delivery mechanisms allowing for open ended content development while retaining support for legacy web pages. This allows new pages to be quickly and easily added to the system in real time, existing pages to be either edited alone, ‘linked to’ or copied and modified. Resource lists can be simply constructed which rely on Library metadata rather than copies of data which need updating; support material can be added whether video, slides or images; guidance can be included to help students navigate their information literacy journey. Despite a pressured summer period of lockdown and working from home due to Covid-19, the rollout of the system has been successful from the point of view of the faculty librarian construction and development, as well as in usage by our enquiries team supporting students requiring help. Student feedback has been positive.
Fast Machine Learning-Based Signal Classification in Energy Constrained CRN: FPGA Design and Implementation

Cognitive Radio Networks (CRNs) is positioned as an appealing autonomous system to enhance spectrum scarcity by dynamic spectrum access and spectrum sharing across wireless networks. To operate at the highest performance level, the allocation and vacation process of primary and secondary users need to be accomplished rapidly. This issue motivates us to propose a fast machine learning-based processing algorithm, referred to as the Arithmetic Shifter-Based Support Vector Machine (ASB-SVM) classifier. The novelty of our proposed scheme is to increase the speed of signal classification by employing shift registers in a two multipliers feature mapping method instead of using multiplication blocks in the SVM classifier. The proposed ASB-SVM design is implemented in Xilinx Virtex-6 XC6VLX240T FPGA. By exploiting spectral features for the classifier, an overall accuracy rate of 98:2% is achieved for green modulated signals in CRNs. Experimental results show that given the feature vector, our proposed system is capable of classifying a blind modulated signal within just 3 ns in the classifier block of a CRN while achieving 30% resource reduction and 45% increase in speed compared to the conventional linear SVM implementation.
Analysis of Key Technologies of Cloud Computing Security Based on Trust Model

With the development of cloud computing, cloud computing provides more and more computing services. In today's fiercely competitive environment, the service flexibility and choice provided by this highly scalable technology are becoming more and more attractive to enterprises. However, behind the rapid development of cloud computing, its security and trust issues have become increasingly unignorable. The purpose of this paper is to analyze the key technologies of cloud computing security based on the trust model (TM). This paper first proposes a subjective and objective TM based on the ideal point method for the trust problem in the cloud environment, and verifies the performance of the model through experiments. This paper proposes a decentralized TM based on blockchain, and verifies the effectiveness of the model through experiments. This paper starts from the current problems of cloud computing, and on the basis of understanding the shortcomings of traditional security technologies, proposes a key technology of cloud computing security based on the TM. The solution proposed in this paper has a minimum average time consumption of 0.12 ms in the copy + label generation stage, while the minimum average time consumption of a traditional PDP is 16.3 ms. It is obvious that this solution has great advantages.
Robust time-varying Kalman smoothers with uncertain noise variances

This paper addresses the design of robust Kalman smoothers for the time-varying system with uncertain noise variances. According to the minimax robust estimation principle, and the unbiased linear minimum variance (ULMV) optimal estimation rule, based on the worst-case conservative system with the conservative upper bounds of noise variances, two robust Kalman state smoothing algorithms are presented by the augmented and non-augmented state approaches, respectively. Their robustness is proved by the Lyapunov equation approach, and their robust accuracy relations are proved. A simulation example is given to verify the robustness and the correctness of the robust accuracy relations.
Research on Instrument Image Generation Method Based on Conditional Generative Adversarial Network

In recent years, generative adversarial networks have achieved great success in image synthesis.They have amazing ability to generate clear and realistic images. However, as we all know, GAN networks are difficult to adapt to different data sets, for example, in the process of image coding,the images of instrument are complex, instrument characters vary in size, and characters of different sizes need different receptive fields, so new requirements are put forward for the GAN networks. In this paper, we propose UYF-Net, an instrument image generator network, to solve this problem.The network uses Y-Fusion module many times, and fuses high-level and low-level semantic features according to the importance of each channel. Experiments show that the network achieves better performance in Mish activation function, mixed loss function of L1 with MS-SSIM and LayerNormal normalization.
Approach to the Automated Development of Scientific Subject Domain Ontologies Based on Heterogeneous Ontology Design Patterns

Our practice has shown that for the development of ontologies of scientific subject domains (SSD), the use of ontology design patterns (ODPs) is especially effective. This is due to the fact that the ontology of any SSD contains, as a rule, a large number of typical fragments that are well described by the ODPs. In addition, since these patterns greatly facilitate the development of an SSD ontology, it is possible to involve experts in the modeled SSD not possessing the skills of ontological modeling, which, in turn, speeds up the development of an SSD ontology. In order to obtain an ontology that describes a given SSD fully enough, it is necessary to process a huge number of publications related to this SSD. We can facilitate and accelerate the process of populating an ontology with information from these sources using lexico-syntactic patterns. The paper presents an approach to automating the development of the SSD ontologies based on a set of heterogeneous ontology design patterns. This set includes two kinds of patterns: (a) patterns intended for ontology developers and (b) lexico-syntactic patterns automatically built on the basis of (a), capable of automatically populating the ontology with the information extracted from natural language texts.
Do I-PASS for FAIR? Measuring the FAIR-ness of Research Organizations

Given the increased use of the FAIR acronym as adjective for other contexts than data or data sets, the Dutch National Coordination Point for Research Data Management initiated a Task Group to work out the concept of a FAIR research organization. The results of this Task Groups are a definition of a FAIR enabling organization and a method to measure the FAIR-ness of a research organization (The Do-I-PASS for FAIR method). The method can also aid in developing FAIR-enabling Road Maps for individual research institutions and at a national level. This practice paper describes the development of the method and provides a couple of use cases for the application of the method in daily research data management practices in research organizations.
Carrier Robust Reversible Watermark Model Based on Image Block Chain Authentication

Digital watermarking technology protects information security from the dimension of the existence of hidden information. At present, most watermarking algorithms only focus on the embedding and extraction of secret information, which usually brings permanent distortion to the carrier. This poses many challenges in scenarios that require high data authentication, such as medical image processing and military communications. This paper proposes a robust reversible watermarking algorithm for carriers based on block chain authentication. It is designed to mark the tampered location of the carrier with a fragile watermark, and restore the tampered information through authentication before and after, so that the peak signal-to-noise ratio of the restored carrier can be greatly improved when tampering occurs. The experimental results show that the scheme has good recovery ability in the scene of tampering with the watermark image.
Modelling Dynamic Item Complementarity with Graph Neural Network for Recommendation

Relationships among items, especially complementarity, have shown great potential to empower the performance and explainability of recommender systems. However, there are two key limitations: 1) Most previous methods use co-occurrence to quantify item complementary relationship, which lacks theoretical support and overlooks the fact that co-occurrence is only a necessary but not sufficient condition to identify item complementarity. 2) Most studies do not consider the time-sensitive nature of item complementarity, which does exist in real scenarios.In this study, we propose a Graph Neural Network (DCGNN) to model the dynamic item complementarity for the recommendation. First, to improve the reliability of item relationships, complementary item pairs are mined according to the ‘cross elasticity of demand’ concept in economic theory and the mined relationships are applied to enrich the user-item graph. Second, considering the time-sensitive nature of item complementarity, we design a time-transfer mechanism to distillate historical knowledge of item complementarity by using graph neural networks. Finally, extensive experiments and analysis were conducted on two real-world data sets, which demonstrate the effectiveness of DCGNN in capturing dynamic item complementarity and recommendation.
Software Analysis and Design

Software analysis and design are relative terms, often used in order to identify software quality attributes and the requirements. It can be considered as a set of yardsticks to measure software quality. The main idea behind writing the technical note on Software analysis and design is to ensure that software professionals develop appropriate quality attributes behaviour and knit software of high quality which comply with fundamental attribute requirements. The software designer, enabled with the insight of software analysis and design requirement, can also assess the software quality for any given application, and produce report for possible improvement areas, design faults etc. It is of utmost importance that the designer should also be well aware about the architecture, notations, programming language and technology stacks which is used to develop the application.
Augmented reality in orthodontics: The way ahead

Augmented Reality is a favorable and definitive pedagogical approach for integrating formal learning. E-learning combined with Augmented Reality (AR) technology can surpass traditional learning methods if the system is tailored to include components of smart learning and evaluation of knowledge on devices ubiquitously in the absence of tutors. AR is predicted to transform and become the fundamental user interface of the twenty-first century. Augmented reality, instead, generates an interaction between the real environment and virtual objects. The use of augmented reality permits direct visualization bypassing the last transfer step, which means, on a large scale, to avoid data and time loss. Visualization of digital data directly on the patient means the possibility of achieving great advantages in digital procedures. This review briefly summarizes history, definitions, features and components of augmented reality technology and discusses its avenues and applications in orthodontics.
Establishment of Economic Term Bank Under the Background of Artificial Intelligence

Economic terminology is the most cores and basic part of economics, and it is the product of knowledge precipitation in the field of economics. The construction and application of term base is one of the necessary links in large-scale translation projects. As an important, professional, systematic and complete knowledge application platform, the establishment of term base is very important. Under the background of artificial intelligence, this paper studies the establishment of economic terminology database. In the process of research, this paper uses the control experiment method, that is, under the same conditions, under the background of artificial intelligence, the use of artificial intelligence technology to establish the economic term library and the use of traditional methods to establish the economic term library, and compares the two methods, mainly compares the establishment process and application effect of the two methods. The results show that under the background of artificial intelligence, the average score of the economic term database established by artificial intelligence technology is 94.33, and the average score of the economic term database established by traditional methods is 81.67, with a difference of 12.66. It can be seen that the overall application effect of the economic term database established by artificial intelligence technology is better than that established by traditional methods the application effect of the corpus. Moreover, the efficiency of establishing economic term bank under the background of artificial intelligence is higher than that of traditional methods.
Development of Logical Thinking in Creative Computer Science Lessons Using Inventive Problem-Solving Technology

Modern school education should provide the best conditions for realising students’ potential and reveal all the natural inclinations inherent in them. The use of technology for solving inventive problems in creative Computer Science lessons organises thinking and promotes students’ critical thinking development. Therefore, the child develops logical thinking, increases motivation to learn, and independence. This study aims to develop methodological aspects of using inventive problem-solving technology to develop students’ logical thinking in creative Computer Science lessons. The following approaches were used in the study: theoretical analysis and generalisation of scientific data on the subject matter; observation, questionnaires, testing, pedagogical experiment and methods of statistical processing of findings. In the process of research, the features of the formation of logical thinking of students in Computer Science lessons in primary school were analysed, the expediency of using techniques of technology for solving inventive problems as a means of developing logical thinking of primary school students in creative Computer Science lessons was substantiated. A system of tasks for the development of logical thinking in 8th-grade students was developed and implemented in the educational process, which allowed experimentally testing the pedagogical effectiveness of the level of formation of logical thinking and its development in 8th-grade students. The study of the level of formedness of logical thinking in 8th-grade students in such components as the level of development of the ability to analyse the conditions of the problem (the “logical problems” method) and the level of development of logical thinking (the “logical and quantitative relations” method) is carried out. The results of the pedagogical experiment confirmed the hypothesis that the development of logical thinking of 8th-grade students during creative Computer Science lessons is influenced by the technology of solving inventive problems
MDNet: A Multi-image Input Real-Time Semantic Segmentation Model with Decoupled Supervision

Current state-of-the-art Real-Time Semantic Segmentation Model is still not fast enough. They spend too much time on processing images in a deep CNN to grab the spatial and context information. Somehow, this information may not be so deterministic. In this work, we come up with a multi-image input real-time semantic segmentation model with decoupled label supervision. It can decrease the computational time and keep a relatively high precision of semantic segmentation meanwhile. The novelty of our model lies is picking up the decoupled label supervision to be our loss function and combining it with a multi-branch image processing framework. The edge detection module can not only improve the recognition of the differences between object body and edge but also guarantee the processing procedure of our network to be faster enough. Apart from this, the multi-branch image processing framework is not a burden of running time. Our network is trained on difficult datasets like CamVid and has favourable quality in real-time testing. The mean class IoU of our network is 66.6. It is the highest one among all of the other comparisons.