Reviewerly/exHarmony huggingface dataset:
Minimal degrees of algebraic numbers with respect to primitive elements

Given a number field [Formula: see text], we define the degree of an algebraic number [Formula: see text] with respect to a choice of a primitive element of [Formula: see text]. We propose the question of computing the minimal degrees of algebraic numbers in [Formula: see text], and examine these values in degree [Formula: see text] Galois extensions over [Formula: see text] and triquadratic number fields. We show that computing minimal degrees of non-rational elements in triquadratic number fields is closely related to solving classical Diophantine problems such as congruent number problem as well as understanding various arithmetic properties of elliptic curves.
Secure Multi-Subinterval Data Aggregation Scheme with Interval Privacy Preservation for Vehicle Sensing Systems

Road side units (RSUs) can act as fog nodes to perform data aggregation at the edge of network, which can reduce communication overhead and improve the utilization of network resources. However, because the RSU is public infrastructure, this feature may bring data security and privacy risks in data aggregation. In this paper, we propose a secure multi-subinterval data aggregation scheme, named SMDA, with interval privacy preservation for vehicle sensing systems. Specifically, our scheme combines the [Formula: see text] encoding theory and proxy re-encryption to protect interval privacy, this can ensure that the interval information is only known by the data center, and the RSU can classify the encrypted data without knowing the plaintext of the data and interval information. Meanwhile, our scheme employs the Paillier homomorphic encryption to accomplish data aggregation at the RSU, and the Identity-based batch authentication technology to solve authentication and data integrity. Finally, the security analysis and performance evaluations illustrate the safety and efficiency of our scheme.
Mobile Robot Path Planning Approaches: Recent Developments

Robotics is the current growing technology with various applications in many fields practiced so far. The bit-by-bit strategies are assembled to know the growth of way arranging systems in different environmental situations and to spot the research gap. It reviews the most important contributions to the mobile robot path planning field from classical approach to heuristic approaches. The classical approaches include cell decomposition method, road map method, artificial potential field method; heuristic approaches such as genetic algorithm (GA), neural network (NN), ant colony optimization (ACO), and particle swarm optimization (PSO) are studied. Because of the numerous computational problems, heuristic methods outperformed the classical methods and gained great reputation. The navigation of static and dynamic surroundings is also examined and it has been remarked that the heuristic approaches have executed efficiently in all aspects when differentiated with classical methods. The paper ended up with tabular knowledge, and graphs analyzed the regularity of separate navigational policies which can be utilized for particular implementation during robot navigation.
The Effectiveness of Video Messaging Campaigns to Use 2FA

For exploring messaging campaigns that motivate users to adopt a new security behavior and affect their security decisions, we designed different informational videos asking users to adopt Duo Two-Factor Authentication (2FA) on their university account. These videos used five different communication techniques: Authoritarian, Logic, Benefit, Personal Risk, and Enterprise Risk. During the two weeks of the messaging campaigns, our preliminary results showed that no significant differences were found between the treatment groups and the control group regarding adoption rate of Duo. However, we found that Authoritarian (20% of university employees enabled Duo 2FA on their university accounts) and Benefit (17%) had the highest percentages in enabling Duo 2FA compared to other groups. All groups stated that Duo 2FA is annoying but not difficult to use. In addition, we identified a preliminary list of improvements in the messaging design. Our findings suggest that including the improvements in the messaging will increase users’ willingness to adopt new security features.
GloSAT Historical Measurement Table Dataset

Understanding and extracting tables from documents is a research problem that has been studied for decades. Table structure recognition is the labelling of components within a detected table, which can be detected automatically or manually provided. This paper presents the GloSAT historical measurement table dataset designed to train table structure recognition models for use in downstream historical data rescue applications. The dataset contains 500 scanned and manually annotated images of pages from meteorological measurement logbooks. We enhance standard full table and individual cell annotations by adding additional annotations for headings, headers, and table bodies. We also provide annotations for coarse segmentation cells consisting of multiple data cells logically grouped by ruling lines of ink or whitespace in the table, which often represent data cells that are semantically grouped. Our dataset annotations are provided in VOC2007 and ICDAR-2019 Competition on Table Detection and Recognition (cTDaR-19) XML formats, and our dataset can easily be aggregated with the cTDaR-19 dataset. We report results running a series of benchmark algorithms on our new dataset, concluding that post-processing is very important for performance, and that page style is not as significant a feature as table type on model performance.
Towards Robust Graph Contrastive Learning

We study the problem of adversarially robust self-supervised learning on graphs. In the contrastive learning framework, we introduce a new method that increases the adversarial robustness of the learned representations through i) adversarial transformations and ii) transformations that not only remove but also insert edges. We evaluate the learned representations in a preliminary set of experiments, obtaining promising results. We believe this work takes an important step towards incorporating robustness as a viable auxiliary task in graph contrastive learning.
A Fault-Resistant Architecture for AES S-box Architecture

This paper introduces a high-Speed fault-resistant hardware implementation for the S-box of AES cryptographic algorithm, called HFS-box. A deep pipelining for S-box at the gate level is proposed. In addition, in HFS-box a new Dual Modular Redundancy based (DMR-based) countermeasure is exploited for fault correction purpose. The newly introduced countermeasure is a fault correction scheme based on DMR technique (FC-DMR) combined with a version of the time redundancy technique. In the proposed architecture, when a transient random or malicious fault(s) is detected in each pipeline stage, the error signal corresponding to that stage becomes high. The control unit holds the previous correct value in the output of our proposed DMR voter in the other pipeline stages as soon as it observes the value ‘1’ on the error signal. The previous correct outputs will be kept until the fault effect disappears. The presented low-cost HFS-box provide a high capability of fault resistance against transient faults with any duration by imposing low area overhead compared with similar fault correction strategies, i.e. 137%, and low throughput degradation, i.e. 11.3%, on the original S-box implementation.
UoB_UK at SemEval 2021 Task 2: Zero-Shot and Few-Shot Learning for Multi-lingual and Cross-lingual Word Sense Disambiguation.

This paper describes our submission to Se-mEval 2021 Task 2. We compare XLM-RoBERTa Base and Large in the few-shot and zero-shot settings and additionally test the effectiveness of using a k-nearest neighbors classifier in the few-shot setting instead of the more traditional multi-layered perceptron.Our experiments on both the multi-lingual and cross-lingual data show that XLM-RoBERTa Large, unlike the Base version, seems to be able to more effectively transfer learning in a few-shot setting and that the k-nearest neighbors classifier is indeed a more powerful classifier than a multi-layered perceptron when used in few-shot learning.
Maintaining Social Distancing Using Artificial Intelligence

I spearheaded the development of a model that can detect whether people have breached the six-foot distance rule and alert each user who has. The software is installed in the hardware of CCTV cameras and can perform effective contact tracing. The application goes through the video footage and does the following process for every frame in the video. It uses Python libraries such as Keras-RetinaNet to detect which objects are human and where they are located in the frame. Then, it uses the law of similar triangles to find the distances between people, whose accuracy is improved by 30% using the law of cosines to find a more accurate distance. The application then uses Multi-task Cascaded Convolutional Neural Networks to grab people’s faces from an image and then uses a face recognizer to recognize the names of people in the frame. The application then alerts every user that broke the social distancing rule through email and logs the instance into a database from that frame. For every frame, the application uses cv2 to create an edited image of the original that displays which people broke the social distancing rule, by writing names on top of their bounding boxes and drawing red lines between the people who broke it. In the end, the application outputs an edited video clip of the original, which in reality is a collection of all the edited frames. Already it has begun to show promise, with an 85 percent accuracy rate.
Design a Hybrid FPGA Architecture for Visible Digital Image Watermarking in Spatial and Frequency Domain

Hybrid field programmable gate array (FPGA) implementation is proposed to improve the performance of visible image watermarking systems. The visible watermarking process is implemented as pixel by pixel operation under a spatial domain or vector operation in the frequency domain. The proposed approach is mainly designed for watermarking the images taken from digital cameras of various sizes. The padding technique is used for unequal sizes of the watermark image and original host image. The architecture data path consists of eight and six stages of pipeline capable of watermarking on the pixel-based operation and vector-based operation, respectively. The dual image watermarking architecture data path consists of a 13-stage pipeline. Pipeline and parallelism mechanisms are used to improve throughput. To improve the performance in discrete cosine transform operations at the frequency domain, the shift-add technique replaces the conventional multipliers. The clock gating technique is employed to reduce the power by preventing unnecessary switching in a path. Hardware implementation of the algorithm is tested in Intel Cyclone FPGA with the device of EP4CGX22CF19C6, with which the throughput achieved is 1.27[Formula: see text]Gbits/s with a total area utilization of 35[Formula: see text]digital signal processing (DSP) blocks, 378 look-up tables (LUTs) and 486 registers.
Should I Tear down This Wall? Optimizing Social Metrics by Evaluating Novel Actions

One of the fundamental challenges of governance is deciding when and how to intervene in multi-agent systems in order to impact group-wide metrics of success. This is particularly challenging when proposed interventions are novel and expensive. For example, one may wish to modify a building’s layout to improve the efficiency of its escape route. Evaluating such interventions would generally require access to an elaborate simulator, which must be constructed ad-hoc for each environment, and can be prohibitively costly or inaccurate. Here we examine a simple alternative: Optimize By Observational Extrapolation (OBOE). The idea is to use observed behavioural trajectories, without any interventions, to learn predictive models mapping environment states to individual agent outcomes, and then use these to evaluate and select changes. We evaluate OBOE in socially complex gridworld environments and consider novel physical interventions that our models were not trained on. We show that neural network models trained to predict agent returns on baseline environments are effective at selecting among the interventions. Thus, OBOE can provide guidance for challenging questions like: “which wall should I tear down in order to minimize the Gini index of this group?”
How Byzantine is a Send Corruption?

Consensus protocols enable n parties, each holding some input string, to agree on a common output even in the presence of corrupted parties. Recent work has pushed to understand the problem when a majority of parties may be corrupted thus providing higher resilience, and under various forms of corruptions. Zikas, Hauser, and Maurer introduced a model in which receive-corrupt parties may not receive messages sent to them, and send-corrupt parties may have their sent messages dropped. Otherwise, receive-corrupt and send-corrupt parties behave honestly and their inputs and outputs are constrained by the security definitions. Zikas, Hauser, and Maurer gave a perfectly secure, linear-round protocol for $$n > t_{\mathsf {rcv}}+t_{\mathsf {snd}}+3t_{\mathsf {byz}}$$ , where $$t_{\mathsf {rcv}}$$ , $$t_{\mathsf {snd}}$$ , and $$t_{\mathsf {byz}}$$ represent thresholds on receive-, send-, and byzantine-corruptions. We present the first expected constant-round protocol in the general corruption model tolerating $$n > t_{\mathsf {rcv}}+2t_{\mathsf {snd}}+2t_{\mathsf {byz}}$$ . In comparison, all current sublinear round consensus protocols fail if there exists even a single party which cannot communicate with some honest parties, but whose output must be consistent with the honest parties. While presenting our protocol, we explore the pathology of send-corruptions and characterize the difficulty of dealing with them in sublinear-round protocols. As an illustrative and surprising example (even though not in sublinear rounds), we show that the classical Dolev-Strong broadcast protocol degrades from tolerating $$t_{\mathsf {byz}}< n$$ corruptions in the byzantine-only model to $$t_{\mathsf {byz}}< n/2 - t_{\mathsf {snd}}$$ when send-corrupt parties’ outputs must be consistent with honest parties; we also show why other recent dishonest-majority broadcast protocols degrade similarly. We prove that our new consensus protocol achieves an optimal threshold of $$n > t_{\mathsf {rcv}}+t_{\mathsf {snd}}+2t_{\mathsf {byz}}$$ when we constrain the adversary to either drop all or none of a sender’s messages in a round (we denote this model by spotty send corruptions). To our knowledge, our protocol for the spotty send corruption model is thus the first sublinear-round consensus protocol for a majority of online faulty parties in any model. Because we are unable to prove optimality of our protocol’s corruption budget in the general case, we leave open the question of optimal corruption tolerance for both send-corruptions and byzantine-corruptions.
Insights and Lessons Learned from the Design, Development and Deployment of Pervasive Location-Based Mobile Systems “in the Wild”

This paper, based on a reflective approach, presents several insights and lessons learned from the design, development, and deployment of a location-based social network and a location-based game. These are analyzed and discussed against the life-cycle of our studies, and range from engaging with the participants, to dealing with technical issues while on the field. Overall, the insights and lessons learned illustrate that one should be prepared and flexible enough to accommodate any issues as they arise in a professional manner considering not only the results of the study but also the participants and the researchers involved. The aim of this work to inform researchers and designers about some of the key challenges we encountered during our research into pervasive location-based mobile technologies in the wild.
Reinforcement learning for robust radar tracking

In complicated environments with strong clutter and dense targets, radar tracking may encounter the issue of ambiguities in association. This paper proposes a reinforcement learning approach to reduce the number of misassociations in radar tracking. For each existing track, the characteristics of the target are learned to form a classifier, and then the classifier is employed for detection and association for this track. The parameters of each classifier will be updated using the feedback of the updated track. Experimental results on synthetic data and real data verify the effectiveness of our method.
Comparison of the State-of-the-Art Image and Video Quality Assessment Metrics

Today, thanks to the developing technology, changes are experienced in many areas and these changes sometimes bring some problems, issues that need to be solved or improved. In the field of digital image and video processing, the viewer experience has been greatly improved with the development of image and video coding methods and technologies in recent years. However, due to these developments, the importance of video and image quality evaluation techniques has increased. In the field of imaging technology, although the human visual system and human judgment experiments will give us the most accurate results, this rapidly growing field has made human judgment experiments unsuitable with the increasing content. At this point, effective and automatic quality assessment metrics are needed to evaluate and optimize advanced compression technologies in terms of visual perception quality. In this paper, our aim is to compare the performances of some of the new generation image and video quality assessment metrics.
A Hashing Power Allocation Game with and without Risk-free Asset

Abstract Miners in various blockchain-backed cryptocurrency networks compete to maintain the validity of the underlying distributed ledgers to earn the bootstrapped cryptocurrencies. With limited hashing power, each miner needs to decide how to allocate their resource to different cryptocurrencies so as to achieve the best overall payoff. Together all the miners form a hashing power allocation game. We consider two settings of the game, depending on whether each miner can allocate their fund to a risk-free asset or not. We show that this game admits unique pure Nash equilibrium in closed-form for both settings.
The Role Position of Teachers in the Professionalization of the Educational Process in a Smart Environment

The aim of the study is to develop a set of organizational and pedagogical conditions for integrated training of working teachers and students of pedagogical institutions of higher education to perform labor functions, highlighting the role of teachers in the professionalization of the educational process in a Smart Environment. The article considers the main directions of transformation of the role position of future teachers in the professionalization of the educational process. The article has presented structurally functional model development of role positions of teachers in the formation of professional competencies of students. The development of the role position of the teacher has considered in relation to his professional and personal development and self-awareness as a subject of the educational process under the influence of a Smart Environment. The mechanism of realization of such model has offered by the integrative program which provides integration of formal and informal education and formation on such basis of new competences and development of components of a role position of the future teacher.
The efficacy of a dual-axis solar tracking device in tropical climate

The demand for energy and the pressure for reducing environmental impacts is increasing in developing countries, mainly in agricultural areas. The generation of electricity from photovoltaic panels can be economically and environmentally advantageous as a source of renewable energy and the ability to reach remote consumers. The present study aimed to evaluate the performance of a photovoltaic system equipped with a sun-tracking device, comparing to a fixed panel. The test compared two panels of a photovoltaic cell system, one used a rotation module in two-axis, and the other a fixed one (control), for capturing solar energy throughout the day in a tropical region of Brazil. Solar energy data were obtained in the two photovoltaic panels with data continuously recorded six months, with a weather characteristic of high cloudiness and rainfall indexes. The commissioning of the tested photovoltaic panels was done on bright days. Power results indicated that the two-axis tracker system was useful during the test, presenting an increase of 26% when compared to the fixed panel. It was found that when the cloudiness and the rain index are very high, the sun tracking system might not be as efficient as foreseen. Rainfall and cloudiness index are essential factors for determining the feasibility of using a tracker device in tropical regions.
NRG: A Network Perspective on Applications' Performance.

Running an application in a data center, users have an important goal: performance.Even though mismatched application requirements and network resource assignments can lead to significant performance loss, the understanding of dynamic networking effects on data center applications' performance is still limited. In this paper we present NRG, an open source toolset that enables reproducible experimentation and provides a networking perspective on applications' performance. NRG is a portable and programmable solution operating at line rate, and enabling users to recreate data center network conditions as a black box within controlled, small-scale experimentation environments. We demonstrate the potential of NRG to solve performance issues and provide insights on several applications.
APPLICATION OF 3D ANALYSIS TECHNOLOGY OF VISION SYSTEM IMAGE IN SPORTS MEDICINE

ABSTRACT Background: Objective: The study of sports biomechanics in sports medicine usually requires a special image analysis system (software) to obtain 3D kinematics data. Taking the swimming project in sports medicine as an example, 3D water images in water have always been relatively complicated and difficult. As light travels in different media, it will refract and reflect. When testing underwater movements, if only a land camera or an underwater camera is used for testing, the error caused by light refraction will be larger, which will affect the accuracy of the test data even more. Methods: Taking breaststroke movement as an example, a three-dimensional measurement method based on the Kwon3D movement analysis system is introduced. This method is different from the simple underwater camera test. It is a three-dimensional test method combining a land camera and an underwater camera. Two underwater cameras and two land cameras were used to simultaneously calibrate the water and underwater space with the same calibration frame in the experiment after analyzing and verifying the accuracy of 3D reconstruction. Results: The comprehensive reconstruction error is small, and the average relative error is less than 1%. Conclusions: The application of three-dimensional image analysis technology of vision systems in sports medicine is reasonable and worth promoting. Level of evidence II; Therapeutic studies - investigation of treatment results.