Reviewerly/exHarmony huggingface dataset:
Challenges to Internationalization in Russian Higher Education: The Impact of the COVID‑19 Pandemic on the International Student Experience

The COVID‑19 pandemic has essentially jeopardized the internationalization processes in higher education. International travel restrictions, financial insecurities, and the introduction of distance learning formats have been posing serious challenges for international students. The present study is based on data obtained in a countrywide survey of Russian university students conducted in June—July 2021 as part of the project Research and Instructional Design Support for the Development of a Quality Measurement System in Higher Education During the COVID‑19 Pandemic and Beyond. International students’ perceptions of distance learning, its quality and challenges are analyzed. Judging from international students’ responses to the questionnaire, the process of their adaptation to the new study conditions has been routinized. Among the benefits of online learning, international students name the logistic ones such as mobility, relative cost-effectiveness, and optimization of time. Perceptions of the communication constraints related to learning from a distance are largely negative. In addition, international students perceive themselves as a more vulnerable category compared to Russian students and obviously gravitate toward in-person learning. For the most part, international students are ready for blended learning, but remote formats of communication and learning should be applied to them with more caution than with Russian students.
3D Stereoscopy Video Production: Z-Depth Extraction and Optimized Rendering using Foundry Nuke

This paper proposed an effective and efficient 3D stereo Video production methodology for Stereo conversion of any image or video using the 3D compositing tool Foundry Nuke. For efficient S3D conversion, there are several pipelines uses by video production engineer into the industry. There are several technical issues during the 3D stereo production like spilling, reflection, translucency, occlusion, flickering and noise problems as well due to imperfect calibration. This paper presents a theoretical explanation of the principles of stereo vision systems, followed by a quick review of the state of the art. The research paper concludes with validating the assumption of 3D stereoscopy video conversion with film case studies to execute high-end 3d stereo video production quality with roto-based depth map extraction with optimized render conversion methodology.
A Verifiable Ring Signature Scheme of Anonymous Signcryption Using ECC

The ring signature does hide the member of actually signature in the all possible signer.This technique was introduced by Rivest, Tauman and Shamir in 2001.This paper presents elliptic curve cryptosystem using signcryption algorithm with the anonymity feature.In the present paper, combines ring signature scheme with signcryption method to produce the anonymity feature for the signcryption scheme and discuss the characteristic of the security.Because elliptic curve cryptosystem yields like as small bandwidth requirements, low computation load, and high security and methodology of ring signature gives without revealing the actual signer to all possible signers.Combining the benefits of these two methodology, the result is an efficient anonymous signcryption algorithm and highly secure.
Levenshtein Training for Word-level Quality Estimation

We propose a novel scheme to use the Levenshtein Transformer to perform the task of word-level quality estimation.A Levenshtein Transformer is a natural fit for this task: trained to perform decoding in an iterative manner, a Levenshtein Transformer can learn to post-edit without explicit supervision.To further minimize the mismatch between the translation task and the word-level QE task, we propose a two-stage transfer learning procedure on both augmented data and human postediting data.We also propose heuristics to construct reference labels that are compatible with subword-level finetuning and inference.Results on WMT 2020 QE shared task dataset show that our proposed method has superior data efficiency under the data-constrained setting and competitive performance under the unconstrained setting.
Research on image stitching method based on improved ORB and stitching line calculation

Image matching is one of the hot issues in recent years. Image matching determines the effect of image mosaic to a large extent. Therefore, a stable, accurate, and fast image matching algorithm is very important for the subsequent processing of image information. Most of the traditional image matching methods have the problems of high-error matching rate and difficulty in to eliminating false matching. To resolve the shortcomings listed here, an improved algorithm was proposed in which a combined measure of Hamming distance. Similarity measurement with Tanimoto similarity measurement was adopted to dispose binary feature vectors. In addition, as an important step in the process of image mosaics, the calculation of the best mosaics can eliminate the ghosting and ghosting that may appear in the mosaics. However, the traditional dynamic planning method is easy to pass through the obstacles and form obvious stitching traces when searching for stitching lines in images with large obstacles. In view of the aforementioned content, we propose a fast and robust method to search the image difference matrix and structure difference matrix of the overlapping area of the input image according to the search strategy to determine the pixel coordinates of the splicing line. Finally, from an optimal splicing line, the experimental results show that our algorithm performs well for eliminating error matching points. Compared with traditional algorithms, matching accuracy is improved by 25%. In terms of calculating the optimal splicing line, the method proposed is faster than the traditional method and has good robustness.
The Comprehension of ISAs

In this chapter, I discuss the main experimental issues bearing on the comprehension of ISAs. A first question concerns differences in processing between direct and indirect SAs, and a second question relates to the differences between the indirect uses and the literal/direct uses of ISA constructions. Another issue is whether the understanding of an utterance as an ISA necessarily implies the derivation of the direct meaning of the construction used, and what properties of the construction make such a direct meaning more or less likely to be inferred.
Advances in Distant Diplomatics: A Stylometric Approach to Medieval Charters

The quantitative analysis of writing style (stylometry) is becoming an increasingly common research instrument in philology. When it comes to medieval texts, such a methodology might be able to help us disentangle the multiple authorial strata that can often be discerned in them (issuer, dictator, scribe, etc.). To deliver a proof of concept in 'distant diplomatics,' we have turned to a corpus of twelfth-century Latin charters from the Cambrai episcopal chancery. We subjected this collection to an (unsupervised) stylometric modelling procedure, based on lexical frequency extraction and dimension reduction. In the absence of a sizable 'ground truth' for this material, we zoomed in on a specific case study, namely the oeuvre of the previously identified dictator-scribe known as 'RogF/JeanE.' Our results offer additional support for the attribution of a diplomatic oeuvre to this individual and even allow us to enlarge it with additional documents. Our analysis moreover yielded the serendipitous discovery of another, previously unnoticed, oeuvre, which we tentatively attribute to a scribe-dictator 'JeanB.' We conclude that the large-scale stylometric analysis is a promising methodology for digital diplomatics. More efforts, however, will have to be invested in establishing gold standards for this method to realize its full potential.
The experience of the transition of a law university to distance learning during the pandemic: an analysis of students’ and teachers’ opinions

Introduction. The spread of coronavirus infection COVID-19 has led to a forced and rapid transition of educational institutions, including law universities, to distance learning, which has become its predominant and even exclusive form in some periods. The research purpose is to study the features of the transition from traditional to distance learning during the COVID-19 pandemic based on the opinions of students and teachers at a law university. Materials and methods. The survey was attended by the teaching staff and students of Kutafin Moscow State Law University. In June 2020, eighty (80) teachers and sixty-five (65) students and in December 2020 – one hundred and thirty (130) teachers and four hundred and sixty-three (463) students took part in the survey. The survey was conducted remotely using Google Forms. Methods of mathematical statistics were used: descriptive methods (percentages, graphical data presentation), Spearman’s rank correlation coefficient. Research results. The forced large-scale transition of practicing lawyers to the work with digital technologies in the context of the spread of coronavirus infection revealed the lack of digital competencies in many of them and demonstrated the need to prepare law students for future work in the digital environment. A two-stage survey of teachers and students at a law university made it possible to reveal a significant change in attitudes towards the electronic educational environment and distance learning technologies. An improvement in the quality of education in the second period was noted by 43.0% of students and 41.5% of teachers. Due to the transition to learning with the use of distance learning technologies, teachers began to communicate more often with students and colleagues (r = 0.203; p &lt; 0.05), as a result of which the emotional perception of the educational process in many teachers changed for the better (r = 0.236; p &lt; 0.01). Discussion and conclusion. Learning with the use of distance learning technologies allows students to form new digital competencies that are necessary and in demand in the modern world. In this case, an important role is given not only to remote lecture- and seminar-type classes, but also to remote academic training, which is close to real conditions of the activities of practicing lawyers.
One small step for ‘artificial intelligence’ and a giant leap for the Australian patent system? The Federal Court decision in <i>Thaler v Commissioner of Patents</i>

Thaler, the applicant in relation to patent application no. 2019363177, named an artificial intelligence system—referred to as DABUS1—as the inventor of the subject invention. The application was a PCT application. S29A(5)(a) Patents Act (Cth) 1990 (‘the Act’) obliges an applicant of a PCT application to ‘file the prescribed documents and pay the prescribed fees’.2 Reg 3.2C(2)(aa) of the Patents Regulations 1991 (Cth) (‘the Regulations’) relating to the formalities for a PCT application requires that the ‘applicant must provide the name of the inventor of the invention to which the application relates’. The Deputy Commissioner took the view that both s15(1) of the Act—dealing with who is entitled to be granted a patent—and Reg 3.2C(2)(aa) were inconsistent with an artificial intelligence system being named as the inventor. Thaler, as the applicant in the Federal Court, sought judicial review of these decisions under s5(1) of the...
On the Performance of the ORTHOMADS Algorithm on Continuous and Mixed-Integer Optimization Problems

ORTHOMADS is an instantiation of the Mesh Adaptive Direct Search (MADS) algorithm used in derivative-free and blackbox optimization. We investigate the performance of the variants of ORTHOMADS on the bbob and bbob-mixint, respectively continuous and mixed-integer, testbeds of the COmparing Continuous Optimizers (COCO) platform and compare the considered best variants with heuristic and non-heuristic techniques. The results show a favourable performance of ORTHOMADS on the low-dimensional continuous problems used and advantages on the considered mixed-integer problems. Besides, a generally faster convergence is observed on all types of problems when the search phase of ORTHOMADS is enabled.
Exploring study profiles of Computer Science students with Social Network Analysis

Information technology is widely adapted in all levels of education.The extensive information resources facilitate enhanced human capacity and the social environment to support learning.In particular, Social Network Analysis (SNA) has been broadly used in teaching and learning practices.In this paper, we perform community detection analysis to identify the learning behavior profiles of undergraduate computer science students in a Nordic university.The social network was created using 273 responses to an online survey.The students themselves provided their social connections at the university, and node attributes were created based on responses to questions regarding Educational Values, Goals Orientation, Self-efficacy, and the university teaching methods.We analyze the biggest communities to identify the factors that characterize the learning strategy and preferences of undergraduate computer science students.
Strings

A lot of software deals with text in one form or another. Here, we learn about the String data type, which is the workhorse of text analysis and manipulation in Kotlin.
Streaming Regular Expression Membership and Pattern Matching

Regular expression search is a key primitive in myriads of applications, from web scrapping to bioinformatics.A regular expression is a formalism for compactly describing a set of strings, built recursively from single characters using three operators: concatenation, union, and Kleene star.Two basic algorithmic problems concerning such expressions are membership and pattern matching.In the regular expression membership problem, we are given a regular expression R and a string T of length n, and must decide whether T matches R.In the regular expression pattern matching problem, the task to find the substrings of T that match R.By now we have a good understanding of the complexity of regular expression membership and pattern matching in the classical setting.However, only some special cases have been considered in the practically relevant streaming setting: dictionary matching and wildcard pattern matching.In the dictionary matching problem, we are given a dictionary of d strings of length at most m and a string T , and must find substrings of T that match one of the dictionary strings.In the wildcard pattern matching problem, we are given a string P of length m that contains d wildcards, where a wildcard is a special symbol that matches any character of the alphabet, and a string T , and must find all substrings of T that match P .Both problems can be solved in the streaming model by a randomised Monte Carlo algorithm that uses O(d log m) space [Golan and Porat (ESA 2017), Golan, Kopelowitz and Porat (Algorithmica 2019)].In the general case, we cannot hope for a streaming algorithm with space complexity smaller than the length of R for either variant of regular expression search.The main contribution of this paper is that we identify the number of unions and Kleene stars, denoted by d, as the parameter that allows for an efficient streaming algorithm.This parameter has been previously considered in the classical setting, and it has been observed that in practice it is significantly smaller than the length of R. We design general randomised Monte Carlo algorithms for both problems that use O(d 3 polylog n) space in the streaming setting.A crucial technical ingredient of our algorithms is an adaptation of the general framework for evaluating a circuit with addition and convolution gates in a space-efficient manner [Lokshtanov and Nederlof (STOC 2010), Bringmann (SODA 2017)], initially designed as a key component of a pseudopolynomial time algorithm for the subset sum problem.We show how to replace the Extended Generalised Riemann Hypothesis in [Bringmann (SODA 2017)] by an application of the Bombieri-Vinogradov theorem to achieve the same bounds (but unconditionally), which might be of independent interest.
The Need to Revise Copyright Law to Reflect the Changing Costs and Benefits of Modern Digital Reuse of Artistic Creations

Copyright law has always sought to maximize the quantity of valuable creative works available to society.While protecting the creative artists is essential, it is in some sense incidental; the reason to protect the artist is that without them, there would be nothing to copy.As new digital technologies for transforming artistic works gain in capability, the ease of producing innovative and valuable works based on the reuse of prior work increases, meaning that society can now benefit from an increased supply of works based on the reuse of others.This suggests that restrictions on reuse that were considered optimal in the past should now be relaxed.We suggest changes to copyright law to achieve this new optimum.We suggest that artistic merit should once again be considered relevant
DEDUCTION AND ANALYSIS OF THE HUMAN IMAGE: A PHILOSOPHICAL AND AESTHETIC INVESTIGATION OF SHERLOCK HOLMES METHOD

The article devoted to the 75th anniversary of the famous Russian Petersburg philosopher Boris Vasilyevich Markov investigates the perspectives of the development of modern philosophical and visual anthropology as the aesthetics of the human image. To concretize and actualize this approach, an unexpected context is chosen – a rethinking of the analytics and deduction of the human image in Sherlock Holmes' method of investigation, which is assessed from the standpoint of modern personality-oriented visual philosophy and aesthetics.
TabPert : An Effective Platform for Tabular Perturbation

To truly grasp reasoning ability, a Natural Language Inference model should be evaluated on counterfactual data.TABPERT facilitates this by assisting in the generation of such counterfactual data for assessing model tabular reasoning issues.TABPERT allows a user to update a table, change its associated hypotheses, change their labels, and highlight rows that are relevant for the hypothesis classification.TABPERT also captures information about the techniques used to automatically produce the table, as well as the strategies employed to generate the challenging hypotheses.These counterfactual tables and hypotheses, as well as the metadata, can then be used to explore an existing model's shortcomings methodically and quantitatively.
CCT Analysis and Effectiveness in e-Business Environment

The priority of the business is to ensure that both the clients and staff working around the business are all safe. All the assault cases within the business will be treated with the seriousness they deserve. The rules and regulation of the business criminalizes any form of violence within the business premises. The installed security cameras will be ideal in monitoring the behavior and the conduct of all the individuals within the business. In case it is noted that someone is behaving contrary to the rules, the security team within the building needs to act fast to get such individual to the state authorities. The relationship between the staff should remain cordially, and all workers need to respect each other. The business management does not condone the use of violence against the staff members. Anybody found to use violence against a fellow staff will be interdicted and face the assault charges.
Fakeheader: A Tool to Detect Deceptive Online News Based on Misleading News Headlines and Contents

Online news has been the primary source of news content for newsreaders. Unfortunately, based on several findings, readers tend to judge on specific events based on the news headlines rather than its contents. With the advancement of mobile and web technologies, it is easier to spread the news to others with these unhealthy habits that can cause negative impacts on individuals, organizations, or nations that are victimized by the news.In the proposed work, a tool to detect deceptive news based on misleading headlines or content is developed. The tools incorporate data veracity framework for online news with Support Vector Machine and proposed combination of features. The experimental results show the proposed tool managed to produce high performance results with more than 90% precisions and recalls.
Digital tools towards transplant first policy: Improving health literacy

End stage kidney disease (ESKD) is a prevalent health issue across the world and it is well established that kidney transplantation (KT), specifically from a living donor, is currently the best value-based treatment method. Ergo, it is crucial to invest heavily in making this care strategy the best possible option for the majority of eligible patients. Barriers to the kidney transplant process are mainly related to education and accessibility: awareness of the clinical and societal relevance of the therapy must be promoted at the level of patients, clinicians and health providers. A health-literacy-focused website has been developed as part of this, aiming to provide the general population, particularly potential organ receptors and donors, with better access to trustworthy information and data. This was meant to be the effective expression of knowledge diffusion and innovation as a result of an academic master thesis. This communication-focused digital resource is designed to combat these obstacles and to create a long-lasting, positive and significant impact.
Approximation of a marine ecosystem model by artificial neural networks

Marine ecosystem models are important to identify the processes that affect for example the global carbon cycle.Computation of an annually periodic solution (i.e., a steady annual cycle) for these models requires a high computational effort.To reduce this effort, we approximate an exemplary marine ecosystem model by different artificial neural networks (ANNs).We use a fully connected network (FCN), then apply the sparse evolutionary training (SET) procedure, and finally apply a genetic algorithm (GA) to optimize, inter alia, the network topology.With all three approaches, a direct approximation of the steady annual cycle is not sufficiently accurate.However, using the mass-corrected prediction of the ANN as initial concentration for additional model runs, the results are in very good agreement.In this way, we achieve a runtime reduction by about 15%.The results from the SET algorithm are comparable to those of the FCN.Further application of the GA may lead to an even higher reduction.
Unsupervised Learning Boost Person Re-identification and Real World Application

Person re-identification (Re-ID) is a retrieval problem based on computer vision, playing an important role in surveillance applications where we tried to identify the same person among surveillance photographs. At present, most person re-identification technologies and methods are based on convolutional neural networks (CNNs). Vision Transformers are merged recently and tend to displace pure CNNs in various computer vision tasks. In this paper, we first try ResNet to accomplish the task, with MGN and other tricks to improve the precision, then we explore the Swin Transformer, a pure transformer-based model. We make a large scale unlabeled dataset in which people acting various activities by cutting pictures in videos from YouTube, DINO and MoBY are employed for ResNet and Swin Transformer separately to perform unsupervised pre-training for improving the generalization ability of the learned person re-identification feature representation. Finally, We also make a dataset based on border inspection BoderCheck by using DBSCAN to cluster unlabeled pictures with several adaptations, a multi-datasets training method and a data augmentation are proposed to tackle the half-length vs full-body matching problem. Extensive experiments indicate that our method achieves state-of-the-art results on several mainstream benchmarks and behave well on BoderCheck dataset.