Reviewerly/exHarmony huggingface dataset:
Applications of Blockchain Technology in Finance

For data analysis and processing, blockchain is a revolutionary technology that has the potential to combine financial resources efficiently and effectively. Data, rules, and application layers are used to design new financial forms or service models based on client needs to enhance the banking system's overall efficiency and the quality of financial services. As blockchain technology automates and improves the accuracy of customer credit conditions, financial markets may be transformed. Other uses include improving the efficiency of cross-border financial operations, amongst others. In this study, the authors comprehensively scrutinized the structure of blockchain technology and its use in the financial sector. The final section of the chapter included constructive suggestions to help the development of blockchain technology in cryptocurrencies, smart contracts, initial coin offerings, the clearing and settlement of financial market transactions, and the implications of these developments for the governance of publicly traded companies.
Anisotropic Chan–Vese segmentation

In this paper we study a variant to Chan–Vese (CV) segmentation model with rectilinear anisotropy. We show existence of minimizers in the 2-phases case and how they are related to the (anisotropic) Rudin–Osher–Fatemi (ROF) denoising model. Our analysis shows that in the natural case of a piecewise constant on rectangles image (PCR function in short), there exists a minimizer of the CV functional which is also piecewise constant on rectangles over the same grid that the one defined by the original image. In the multiphase case, we show that minimizers of the CV multiphase functional also share this property in the case that the initial image is a PCR function. We also investigate a multiphase and anisotropic version of the Truncated ROF algorithm, and we compare the solutions given by this algorithm with minimizers of the multiphase anisotropic CV functional.
Comparing collocations in translated and learner language

Abstract This paper compares use of collocations by Italian learners writing in and translating into English, conceptualising the two tasks as different modes of constrained language production and adopting Halverson’s (2017) revised Gravitational Pull Hypothesis as a theoretical model. A particular focus is placed on identifying a method for comparing datasets containing translations and essays, assembled opportunistically and varying in size and structure. The study shows that lexical association scores for dependency-defined word pairs are significantly higher in translations than essays. A qualitative analysis of a subset of collocations shared and unique to either mode shows that the former set features more collocations with direct cross-linguistic links (connectivity), and that the source/first language seems to affect both modes similarly. We tentatively conclude that second/target language salience effects are more visible in translation than second language use, while connectivity and source language salience affect both modes of bilingual processing similarly, regardless of the mediation variable.
Digital Communication Landscapes on Language Learning Telecollaboration

This chapter explores WhatsApp interactions and the benefits of mobile instant messaging (MIM) in teaching and learning processes within the context of an inter-university telecollaboration project. From a cyberpragmatic framework, this study analyses how the participants communicate by taking advantage of the possibilities of the medium and considering its constraints, as well as how the creative use of the multimodal elements fosters the development of learners' communicative competence. After categorising the learning activities, a mixed-method analysis was performed to identify which multimodal elements were used and what pragmatic effects they had in the analysed WhatsApp conversations. Of these multimodal elements, emojis have been explored in greater depth owing to their pragmatic relevance in MIM. The results show a rich communication landscape in which multimodal elements play a significant role in generating conversation topics, establishing a friendly and safe learning atmosphere, facilitating communication, and providing language learning opportunities.
Few‐shot object detection via class encoding and multi‐target decoding

Abstract The task of few‐shot object detection is to classify and locate objects through a few annotated samples. Although many studies have tried to solve this problem, the results are still not satisfactory. Recent studies have found that the class margin significantly impacts the classification and representation of the targets to be detected. Most methods use the loss function to balance the class margin, but the results show that the loss‐based methods only have a tiny improvement on the few‐shot object detection problem. In this study, the authors propose a class encoding method based on the transformer to balance the class margin, which can make the model pay more attention to the essential information of the features, thus increasing the recognition ability of the sample. Besides, the authors propose a multi‐target decoding method to aggregate RoI vectors generated from multi‐target images with multiple support vectors, which can significantly improve the detection ability of the detector for multi‐target images. Experiments on Pascal visual object classes (VOC) and Microsoft Common Objects in Context datasets show that our proposed Few‐Shot Object Detection via Class Encoding and Multi‐Target Decoding significantly improves upon baseline detectors (average accuracy improvement is up to 10.8% on VOC and 2.1% on COCO), achieving competitive performance. In general, we propose a new way to regulate the class margin between support set vectors and a way of feature aggregation for images containing multiple objects and achieve remarkable results. Our method is implemented on mmfewshot, and the code will be available later.
Consolidation of ideas about length in children of senior preschool age with mental retardation

This article provides tasks for the educational area &amp;quot;Cognitive Development&amp;quot; in the sections &amp;quot;Sensory Development&amp;quot; and &amp;quot;Formation of Elementary Mathematical Representations&amp;quot;, aimed at consolidating the concept of length in children of older preschool age with mental retardation.
Single-shot dense depth sensing with a tricolor RGB fringe pattern

A single-shot fringe pattern composed of red (R), green (G), and blue (B) is proposed for dynamic scene depth acquisition. A tricolor binary fringe is adopted and filtered in the frequency domain to obtain sinusoidal fringes. A Gabor filter is employed to estimate the wrapped phase. To overcome the phase ambiguity, we use the color-encoded De Bruijn sequence to denote the period of the phase information and assign the sequence to two channels of the pattern, while the third channel is equal to the inverse of the composition of the two channels. Benefiting from this coding strategy, two disparity maps with different phase shifts can be acquired in only one shot, and a disparity consistency check is used to eliminate errors. Quantitative and qualitative experiments are conducted to verify the performance of the proposed method and prove that it can achieve a high accuracy and dense depth map.
A Multi-User Collaborative Access Control Scheme Based on New Hash Chain

As the threats to the Internet of Things (IoT) continue to increase, access control is widely used in various IoT information systems. However, due to the shortcomings of IoT devices such as low computing power, it is impossible to use high-performance methods to control user access. Although the emergence of the blockchain provides another way of thinking for access control, the implementation based on the blockchain requires the device to complete the proof of work (PoW) and requires the device to have high computing power. At the same time, most access control schemes existing today are intended for users to use alone, which cannot be applied to the field of multi-user coordinated access. Therefore, this paper proposes a multi-user collaborative access control scheme based on a new hash chain, which uses the identity information of multiple users as the seed value to construct the hash chain, and uses the hash chain as the PoW of the blockchain. An efficiency analysis showed that this method requires only a small amount of hash value calculation and can be applied to IoT systems with low computing power. The security analysis shows that the scheme can resist a variety of attack methods and has high security.
Chinese Named Entity Recognition Model Based on Multi-Task Learning

Compared to English, Chinese named entity recognition has lower performance due to the greater ambiguity in entity boundaries in Chinese text, making boundary prediction more difficult. While traditional models have attempted to enhance the definition of Chinese entity boundaries by incorporating external features such as lexicons or glyphs, they have rarely disentangled the entity boundary prediction problem for separate study. In order to leverage entity boundary information, the named entity recognition task has been decomposed into two subtasks: boundary annotation and type annotation, and a multi-task learning network (MTL-BERT) has been proposed that combines a bidirectional encoder (BERT) model. This network performs joint encoding and specific decoding of the subtasks, enhancing the model’s feature extraction abilities by reinforcing the feature associations between subtasks. Multiple sets of experiments conducted on Weibo NER, MSRA, and OntoNote4.0 public datasets show that the F1 values of MTL-BERT reach 73.8%, 96.5%, and 86.7%, respectively, effectively improving the performance and efficiency of Chinese named entity recognition tasks.
Different lexicons make different rivals

Analogy-based theories assume that in situations of affix competition, language users create novel word forms on the basis of similar existing forms in their Mental Lexicons ( Baayen et al. 2011 ; Daelemans &amp; van den Bosch 2005; Skousen 1989 ). Interestingly, however, simulation studies employing computational implementations of analogical theories have almost invariably adopted a rather abstractionist view of the Mental Lexicon, representing the word stock of the language, and abstracting away from differences between individual speakers (see, e.g., Arndt-Lappe 2014 ; Chapman &amp; Skousen 2005 ; Eddington 2006 ; Nieder et al. 2021 ). This is a problem because it precludes the possibility of testing a central prediction of analogical theories: if affixes are assigned on the fly on the basis of similar words in the lexicon, then speakers with different lexicons should make different choices. The present paper provides a proof-of-concept study addressing this issue for the form-based rivalry between the two English adjectival suffixes - ic and - ical. Analogical Modeling of Language (AML; Skousen et al. 2013 ) is used as a computational model. On the basis of a survey of the distribution of derivatives in different registers in the British National Corpus, predictions of the analogical model are compared for a simulated speaker with a large vocabulary (including academic words) and a simulated speaker with a small vocabulary that is based mainly on words from spoken language. The statistical analysis of the simulations reveals that, while sharing some basic properties, the two models make very clear – and testable – predictions about speaker differences.
Service-Action-Objects

With the diversity of digital platforms available nowadays, captur- ing, integrating, and analyzing user interactions across multiple platforms in a uniform way is critical for determining the over- all system's success. We present recommendations for a cross- platform system for logging interaction data. The ideal logging system should be: expressive, customizable and future-ready, struc- tured and consistent, developer-friendly, unobtrusive, and easy- to-interpret and convey to others. Based on these recommenda- tions we propose Service-Action-Objects, an approach that cap- tures interaction data on each platform as events in the form of a Service, a distinct feature such as a \blog," an Action, an action that a user has performed within the service such as \wrote an article," and Objects, an object or set of objects being acted on by the user such as the identi er of the blog article written. We discuss the strengths and limitations of this approach and present examples.
Edge Intelligence-Based OCS Fault Detection in Rail Transit Systems

The Overhead Contact System (OCS) is critical infrastructure for train power supply in rail transit systems. OCS state monitoring and fault detection are indispensable to guarantee the safety of railway operations. The existing human-based OCS state monitoring and fault diagnosing method has some inherent drawbacks, such as poor real-time capability, low detecting precision, and waste of human resources. Edge Intelligence (EI) can perform complex computing tasks offloaded from trains within a little delay, and it is believed to help empower the OCS. In this paper, we propose an EI-based OCS state monitoring and fault detecting system. The latest Computer Vision (CV) model YOLOv5s is used to detect the OCS faults using the collected images. Edge Computing (EC) is used to perform the CV model inference. The EC system receives the OCS images taken by the train cameras and calculates the real-time fault detection results. The consistency and scalability of running jobs on edge devices are also addressed in our approach. Extensive experimental results demonstrate that the proposed EI-based system can detect OCS faults in real-time. The adopted YOLOv5s achieves a high fault detection rate, outperforming other models.
Brine reflux as a possible first order control on the alkali geochemistry of baseline sedimentary rocks in the Proterozoic North Australian Zinc Belt

The Proterozoic McArthur Basin is economically significant because it contains the world-class McArthur River camp of stratiform Zn-Pb deposits hosted in shales in the c. 1640 Ma Barney Creek Formation. Despite periods of strong interest from minerals explorers and research groups over the past forty years, significant additional resources have not yet been discovered in the terrane. High potassium (K) concentrations in rocks in the McArthur Basin are noted since the earliest literature. One hypothesis for high K concentrations in the Barney Creek Formation is the development of authigenic K-feldspar (K-feldspathisation) by exposure to a low temperature brine reflux flow system during early diagenesis. This is significant to minerals exploration because the genetic link between low temperature brine reflux and sediment-hosted ore formation is an emerging topic in economic geology. K-feldspathisation of sedimentary sequences may be a key identifier of regional potential for development of McArthur River type mineralisation. Gianfriddo et al. (2022) characterise K-feldspathised and non-feldspathised Barney Creek Formation using SEM imagery and whole rock inorganic geochemistry. Here we demonstrate that public domain geochemistry data for basinal sedimentary rocks in wider Proterozoic northern Australia broadly conform to the same K-feldspathised and non-feldspathised fields defined by Gianfriddo et al. (2022). Based on the assumption that the alkali signature in the regional data reflects the presence/absence of authigenic K-feldspar analogous to that described in the Barney Creek Formation case study, and the interpretation that this is controlled by K-feldspathisation during brine reflux, these data indicate a preliminary framework for extensive fluid flow that is geographically coherent and consistent with current understanding of the tectono-sedimentary evolution of Proterozoic northern Australia. We caution that our model is speculative at this stage and requires detailed studies of the mineralogy and facies of the samples for validation. While readers are encouraged to consider this approach in their mineral exploration or academic work, other components such as lithology, facies, mineralogy, diagenesis, and provenance need necessarily also be considered especially at the local scale.
EFL Students’ Challenges in the Integration of Reading and Writing in their Writing Classes

&lt;p style="text-align: justify;"&gt;This study focused on investigating English as a foreign language (EFL) gifted high school students’ challenges in classes when the integration of reading and writing was used. 103 grade 11 students from three gifted high schools in Mekong Delta were invited to participate in this study. The study adopted a descriptive, mixed method research design which employed the questionnaire and semi-structured interviews to collect data. The questionnaire, together with semi-structured interviews was used to collect data about EFL gifted high school students’ challenges of integration of reading and writing on their writing skills. The results of the study proved that students faced difficulties in integrating reading into writing lessons at a medium level, in relation to materials and integration tasks. However, the participants faced the greatest challenge in terms of personal backgrounds.&lt;/p&gt;
Some Features of AnyLogic Integration into the University’s Education System

Anylogic is a simulation tool that can be used to build up a model of systems of any complexity. One of the advantages of simulation is a wide range of applications for education and training purposes. The development and use of simulation modeling allow students to see and "play out" real processes and situations on the model. With AnyLogic, the animation of a process in a telecommunication system can be created. At the same time it is possible to get diagrams of processes and states of different devices, as well as flowcharts of processes occurring in the systems. It can be used to generate a wide range of lab projects for undergraduate students. The principles of simulation modeling in telecommunication systems with the AnyLogic software are studied at Moscow Technical University of Communications and Informatics by students of Infocommunication Technologies and Communication Networks speciality.
Synthetic multi-view clustering with missing relationships and instances

Multi-view learning not only excavates the supplement information thoroughly, but also avoids noisy features and redundancies to improve down-stream tasks, e.g. Cluster, by constructing the fusing model. However, in practices, it is difficult to obtain complete multi-view data, which is summarized as both of Partially Sample-Missing Problem (PSP) originating from any view and Partially View-unaligned Problem (PVP). SURE (robust multi-view clustering with incomplete information) builds up the novel loss function to remove the noisy samples in the embedding space and presents the aligning method to solve both of PVP and PSP. However, generated instances and relationships contain relatively little discriminate information and semantic consistency, moreover, it needs a large size of complete samples to pre-train. Aiming to above problems, we propose the novel framework (termed Synthetic Multi-view Data Clustering, SMDC). For PSP problem, we induce feature-distillation in each feature-map level to generate features containing more semantic information helping with the guidance of complete feature-maps, furthermore, exploits self-distillation to strengthen the discriminability information of deeper levels supervised from the contrastive constraint in the embedding space. Moreover, for PVP problem, we leverage attention mechanism to adaptively fuse multi-view features and auto-align instances located in different views. The proposed model is verified in images and video clustering tasks, respectively. Comparing with popular methods achieve satisfactory performances. The code of model is published in: https://github.com/LNNU-computer-research-526/SMDC.
Attention‐based hierarchical pyramid feature fusion structure for efficient face recognition

Abstract Deep convolutional neural networks (CNN) have become the main method for face recognition (FR). To deploy deep CNN models on embedded and mobile devices, several lightweight FR models have been proposed. However, multi‐scale facial features are seldom considered in these approaches. To overcome this limitation, an attention‐based hierarchical pyramid feature fusion (AHPF) structure was proposed in this paper. Specifically, hierarchical multi‐scale features were directly extracted from the backbone based on its pyramidal hierarchy, and the bidirectional cross‐scale connection was used to better combine the high‐level global features with low‐level local features. In addition, instead of simple concatenation or summation, an attention‐based feature fusion mechanism was used to highlight the most recognizable facial patches, and to address the unequal contribution to the output during the fusing process. Based on the AHPF structure and efficient backbones, multiple sizes of lightweight FR models were presented, called HSFNet. After an extensive experimental evaluation involving 10 mainstream benchmarks, the proposed models consistently achieved state‐of‐the‐art FR performance compared to other lightweight FR models with same level of model complexity. With only 0.659M parameters and 94.94M FLOPs, our HSFNet‐05‐M exhibited a performance competitive with recent top‐ranked FR models containing up to 4M parameters and 500M FLOPs.
Problems of modern patriotic education in Russia

Analysis of the psychological aspects of the formation of patriotism in modern Russia is associated with the need to take into account the maximum number of factors in the successful educational policy of the state. Among the basic ideas of the author are the following: interethnic tolerance is formed only on the basis of national identity; patriotism begins with respect for one's nation, one's people; it is necessary to take into account the laws of formation of optimal tolerance; patriotism is a consequence of the presence in the structure of personal characteristics of optimal interethnic tolerance.
Difference sets and three-weight linear codes from trinomials

We confirm a conjecture of Cunsheng Ding claiming that the punctured value-sets of a list of eleven trinomials over odd-degree extensions of the binary field give rise to difference sets with Singer parameters. In the course of confirming the conjecture, we show that these trinomials share the remarkable property that every element of the value-set of each trinomial has either one or four preiamges. We also give the partial resolution of another conjecture of Cunsheng Ding claiming that linear codes constructed from those eleven trinomials are three-weight.
On the Implications of COVID-19 Pandemic-Induced "Work From Home" in the Software Industry

Work From Home (WFH) became the only viable option for software engineers to continue working during the COVID-19 pandemic if only because they were not deemed to be "essential workers". This note explores what the future might hold for different types of WFH from the perspectives of employers as well as employees.