Reviewerly/exHarmony huggingface dataset:
Contribution of Block Link to Issues in SME Financing

The block link technology has become the key to solve the financing problem, and has far-reaching impact on the long-term development of SMEs. In this study, this paper analyzes the background of SME financing difficulties and contributes to the block link technology and its response strategy, hoping to provide reference for solving the financing difficulties of related enterprises.
Multiple Granularity Person Re-identification Network Based on Representation Learning and Metric Learning

Abstract Powerful local features can be extracted from multiple body regions of a pedestrian. Early person re-identification research has focused on extracting local features by locating regions with specific pre-defined semantics, which is not effective and increases the complexity of the network. In this paper, we propose a multiple granularity person re-identification network based on representation learning and metric learning for learning discriminative representations of pedestrian images. Multiple granularity person re-identification network consists of a multiple granularity feature extraction part and a combined loss part. In particular, the multiple granularity feature extraction part extracts global features and local features of different granularities from the feature maps of Conv4 and Conv5 of the ResNet50 backbone network, respectively, the extracted feature information is more comprehensive and discriminative. The combined loss part employs a joint representation learning and metric learning approach for supervised learning, which enables the model to learn more optimal parameters. The experimental results show that the Rank-1 accuracy of the multiple granularity person re-identification network reaches 95.2% and 88.2% on the Market1501 dataset and DukeMTMC-reID dataset, respectively, which illustrates the effectiveness of the model.
A Trace Ratio Maximization Method for Parameter Free Multiple Kernel Clustering

Multiple Kernel Clustering (MKC) is helpful to leverage complementary information from various contexts and alleviate the difficulty of kernel determination. However, the key weighting strategies for optimal kernel learning around individual kernels are not derived from their optimization problems but embedded in a plug-and-play manner and lead to sub-optimal objective function value. More seriously, the hyper-parameters, introduced by the additive balance of these two coupled sub-tasks, are hard to determine in unsupervised learning scenarios and lead to inconsistent and less satisfying results. To avoid the problems mentioned above, we present a novel parameter-free MKC method with the trace ratio criterion (TRMKC in short), which minimizes the approximation errors between consensus and base kernels using the corr-entropy induced metric and maximizes the mean similarities based on the consensus kernel. The trade-off between these two coupled sub-procedures can be automatically balanced, and the performance could be mutually reinforced. To solve the trace ratio criterion and the corr-entropy induced non-quadratic function optimization problem, we present an alternative strategy with monotonic convergence proof, which reformulates it into a series of sub-problems with trace difference and quadratic programming by utilizing the half-quadratic optimization technique. Extensive MKC experimental results well demonstrate the effectiveness of TRMKC.
Teaching Resource Integration System of English Multimedia Network Based on Cloud Storage

In order to improve the efficiency of teaching resource integration, this paper designs an English multimedia network teaching resource integration system based on cloud storage. The hardware design completes the design of FPGA core board and the selection design of embedded microprocessor. In the software part, the functional module architecture of cloud storage system and the multimedia network resource integration database under cloud storage system are designed. The test results show that compared with the English teaching resource integration system without cloud storage test group, the coupling coefficient of the resource integration system designed in this paper is relatively high. It shows that the system has a strong ability to integrate English multimedia network teaching resources. At the same time, under the auxiliary cloud storage technology, the system has wider coverage and higher resource storage capacity, which has practical application value.
Rethinking crime, control, and channelling

This chapter rethinks the use of technologies for the purpose of channelling conduct, particularly by employing new tools in the criminal justice system. Having noted some examples of a regulatory approach to criminal law and criminal justice (the recognition of (strict liability) 'regulatory' offences; the targeting of high-risk 'dangerous' persons; and the willingness to adopt technologies and tools that will serve criminal justice purposes), the discussion focuses on more radical automation and 'smart prevention' of crime. Again, in each community, there will need to be a conversation about the social acceptability of these applications of technology as well as about how they sit with the community's values (such as security and justice).
Towards Automation of Topic Taxonomy Construction

The automation of taxonomy construction has increased in popularity recently. Such an interest for the domain has been motivated by the large number of new scientific papers published each year that implies a growing difficulty in following the new topics of the different scientific domains and their importance in the topic hierarchy. In this paper, we propose a way to automatically construct topic taxonomies from millions of scientific article abstracts and ways to automatically evaluate this construction. While, to our knowledge, other approaches rely on pipelines of models and human evaluation to validate them, we chose to rely on simple models that are easier to evaluate automatically and, thus, promote the improvement of our models thanks to a large number of iterations. The contribution of this paper is threefold: 1) the proposition of a new method to construct taxonomies from a large set of scientific papers, 2) a method to precompile taxonomy information into matrices that will be quickly queried, and 3) an objective method to automatically evaluate the constructed taxonomies without requiring human evaluation.
Worried about digital footprint Attitudes toward online behavioural advertising with the AdChoices icon

This study examined the efficacy of using the AdChoices icon, a symbol that indicates the use of online behavioural advertising (OBA) on websites. Despite the increased use of OBA for internet advertising, little is known as to its impact on user attitudes toward websites and advertisements that voluntarily display the icon. This study compared the effects of the icon in individuals with less favourable attitudes toward OBA in general to those in people holding more favourable attitudes toward OBA in general, in relation to their privacy concerns. For those with less favourable attitudes toward OBA, their attitudes toward the website displaying the icon increased significantly when the individual's perceived privacy concern was high. Those with more favourable attitudes toward OBA did not show much change regardless of individual privacy concerns. However, such an interaction effect was not observed for the advertisements themselves on websites displaying the icon.
A New Class of Quantum Codes Associate with a Class of Maps

If the cyclic sequence of faces for all the vertices in a map are of the same types, then the map is a semi-equivelar map. In particular, a semi-equivelar is equivelar if the faces are the same type. Topological quantum codes are being introduced as an alternative quantum code. The homological quantum code is a subclass of topological quantum code. We produce a technique to construct homological quantum codes associate with semi-equivelar maps. We also present homological quantum codes associate with maps on the surfaces with Euler characteristic $$-1$$ , $$-2$$ . Furthermore, we will present fifteen classes of homological quantum codes associate with covering maps of the class of maps on the surface with Euler characteristic $$-1$$ and $$-2$$ .
Visual Micro-Pattern Propagation

Statistic observations demonstrate that visual feature patterns or structure patterns recur high-frequently within/across homo/heterogeneous images. Motivated by the interdependencies of visual patterns, we propose visual micro-pattern propagation (VMPP) to facilitate universal visual pattern learning. Especially, we present a graph framework to unify the conventional micro-pattern propagations in spatial, temporal, cross-modal and cross-task domains. A general formulation of pattern propagation named cross-graph model is presented under this framework, and accordingly a factorized version is derived for more efficient computation as well as better understanding. To correlate homo/heterogeneous patterns, in cross-graph we introduce two types of pattern relations from feature-level and structure-level. The structure pattern relation defines second-order visual connections for heterogeneous patterns by measuring first-order visual relations of homogeneous feature patterns. In virtue of the constructed first-/second-order connections, we design feature pattern diffusion and structure pattern diffusion to prop up various pattern propagation cases. To fulfill different pattern diffusions involved, further, we deeply study two fundamental visual problems, multi-task pixel-level prediction and online dual-modal object tracking, and accordingly propose two end-to-end pattern propagation networks by encapsulating and integrating some necessary diffusion modules therein. We conduct extensive experiments by dissecting every diffusion component as well as comparing numerous advanced methods. The experiments validate the effectiveness of our proposed various pattern diffusion ways and meantime report the state-of-the-art results on the two representative visual problems.
Research on Sports Technical Action Feature Model Based on Human Body Attitude Estimation Algorithm

With the continuous improvement of sports competition level, scientific sports training has become a common concern. In traditional sports training, coaches often use intuitive training to improve athletes' skill level. However, with the development of science and technology, in the process of people's pursuit of constantly surpassing themselves, this traditional training method has obviously been difficult to adapt to and meet the training needs of sports athletes. In order to better grasp the essentials of sports and promote the standardization of sports actions, computer technology has been applied to sports learning, which has promoted the reform and development of sports undertakings and brought favorable conditions for scientific sports training. In order to standardize sports action and improve sports level, this paper summarizes the representation of sports action characteristics, discusses the methods of quantitative processing and pattern analysis of sports action characteristics, and establishes the action characteristic model based on human posture estimation algorithm, which provides scientific basis for physical education and training.
An Improved Filtering Method of Superfluous Relationship in Domain Model Based on Random Forest

Domain model is a beneficial tool to make the interpretation and refinement of natural language necessities extra accurate. Natural Language Processing (NLP) makes it viable to mechanically extract most of the data associated to domain modeling from requirements. However, in addition to the relevant information, NLP additionally extracts a giant quantity of records unrelated to the domain model from the requirements. The motive of this paper is to enhance the current domain redundancy filtering method. Therefore, this paper designs a technique primarily based on dual cost sensitive random forest. According the analysis of the statistics set, the training features are designed. According to the analyst’s comments on the relevance and redundancy of the extracted foremost model elements, the redundant relationships are categorized through the use of the comments information. In this paper, the demand of satellite components is taken as an instance to consider the method. The accuracy of experiment is 91.2%, which is 2.1% greater than the present methods.
Single Image Super-Resolution via Residual Dictionary Learning

Aiming at the shortcomings of traditional learning-based super-resolution (SR) reconstruction algorithms, single image super-resolution via residual dictionary learning is proposed. This method adds residual image learning to the super-resolution algorithm of beta process joint dictionary learning for coupled feature spaces. The residual dictionary pairs are learned by combining the high-resolution (HR) and low-resolution (LR) images in the external training set, which can improve the reconstruction quality and speed up the dictionary training. According to the experimental results, compared with these traditional algorithms, the peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) of the proposed algorithm are significantly improved, and the visual effect is also improved.
SARAH-3 &amp;#8211; a new satellite-based Climate Data Record of Surface Solar Radiation parameters

&amp;lt;p class=&amp;quot;western&amp;quot;&amp;gt;&amp;lt;span lang=&amp;quot;en-US&amp;quot;&amp;gt;The EUMETSAT Satellite Application Facility on Climate Monitoring (CM SAF) generates and distributes high quality long-term climate data records (CDR) of energy and water cycle parameters, which are freely available.&amp;lt;/span&amp;gt;&amp;lt;/p&amp;gt; &amp;lt;p class=&amp;quot;western&amp;quot;&amp;gt;&amp;lt;span lang=&amp;quot;en-US&amp;quot;&amp;gt;In 2022, a new version of the &amp;amp;#8220;Surface Solar Radiation data set &amp;amp;#8211; Heliosat&amp;amp;#8221; will be released: SARAH-3. As the previous editions, the SARAH-3 climate data record is based on satellite observations from the first and second METEOSAT generations and provides various surface radiation parameters, including global radiation, direct radiation, sunshine duration, photosynthetic active radiation and others. SARAH-3 covers the time period 1983 to 2020 and offers 30-minute instantaneous data as well as daily and monthly means on a regular 0.05&amp;amp;#176; x 0.05&amp;amp;#176; lon/lat grid.&amp;lt;/span&amp;gt;&amp;lt;/p&amp;gt; &amp;lt;p class=&amp;quot;western&amp;quot; align=&amp;quot;left&amp;quot;&amp;gt;&amp;lt;span lang=&amp;quot;en-US&amp;quot;&amp;gt;In this presentation, an overview of the SARAH climate data record and their applications will be given. A focus will be on the SARAH-3 developments and validation with surface reference observations. Further, SARAH-3 will be used for a first analysis of the climate variability and potential trends of global radiation in Europe during the last decades. &amp;lt;/span&amp;gt;&amp;lt;span lang=&amp;quot;en-US&amp;quot;&amp;gt;The data record reveals that there is an increasing trend of surface solar radiation in Europe during the last decades, which is superimposed by decadal and regional variability.&amp;lt;/span&amp;gt;&amp;lt;/p&amp;gt;
A Study on Syllabification of Intervocalic Consonants Based on Computer Corpus

In order to meet the needs of learners' English pronunciation training, the idea of computer-based English pronunciation corpus construction and language evaluation is proposed. Noise reduction technology is introduced into computer technology to extract characteristic English pronunciation syllables. Vowel syllables can be marked, main syllable information can be counted, and corresponding vowels and intervocalic consonants can be marked, It also analyzes the construction principles of English syllable corpus. This method needs to collect representative syllable corpus, update the corpus regularly and analyze the data with new software. It is found that the corpus can realize different combinations of horizontal and vertical through computer programs, meet the application needs of different individuals or groups of students, comprehensively evaluate the vowels in the syllable, and greatly improve the application efficiency of the corpus, The original evaluation time is reduced from 2 min to 30 s, which proves that the English phonetic corpus constructed by the design idea proposed in this paper can effectively improve students' mastery and application ability of English pronunciation.
Blockchain-Based System to Support the Authorisation and Execution of the Transport of Dangerous Waste Across Europe

The transportation of dangerous good and especially of dangerous waste across several countries is a process that is standardised in the EU by law. This process describes the information that must be provided in the application form by the requester and requires that each country through which the waste is moved agrees before an authorisation for the transportation can be given. It also tells which entities must be informed at arrival and later after the processing of the waste in the treating facilities. Currently, most communication between the involved institutions does not based on IT systems. This paper deals with the question, if and how a fully digitised solution on the base of a blockchain framework could work. It discusses the question on how the balance between confidentially and transparency of data that is used during the authorisation process, during the transportation and in emergency situations can be ensured. We analysed the various options of on-chain and off-chain use of the managed data as well as encryption and discuss the advantages and disadvantages of each option. One of the big challenges that we had to face is the unknown set of involved parties of each new transportation request: How can confidentially be ensured for that specific group of participants that is not known during the deployment of the process in the blockchain? Without a proper solution it might be not possible to manage confidential data on base of a blockchain solution in general for this class of use case. As the most promising framework that could help, we took a special look on the Hyperledger Fabric framework. It provides two options to share confidential data between a set of participants: channels and private data collections. We analyse if one of these options could be a solution for our main challenge.
Research on Police Texts Keyword Extraction Method Based on BTM-ALBERT

Police text data has the characteristics of short length and professional vocabulary, so how to mine and analyze it and find its potential information is always a difficult problem for public security organs. The key to deal with the massive police short text is to extract the most concerned problems of users. It is the most effective method to pry the theme of the whole text through keywords. In order to effectively extract the potential key information of a large number of police short text data, a keyword extraction model based on BTM-ALBERT technology is proposed. The paper used more than 6 million cases data in a city, to construct a domain pre-training model for the police ontology word representation model, and take the text data of burglary cases as an example, appling the proposed model to carry out a police key intelligence extraction experiment. It shows that the proposed model compared with traditional machine learning methods, the effect of extracting keywords is significantly improved,the overall average similarity was 31.2%, 21.5% and 8.3% higher than LDA, textrank and BTM.It is necessary to further test the generalization ability of the model on more police thematic data or large-scale data sets in more fields.the proposed method verifies the effectiveness of the proposed model in short text police data intelligence mining and field application value.
Game equivalence and expressive power of game description languages: a bisimulation approach

Abstract Bisimulations are a key notion to study the expressive power of a modal language. This paper studies the expressivity of Game Description Language (GDL) and its epistemic extension Epistemic GDL (EGDL) through a bisimulation approach. We first define a notion of bisimulation for GDL and prove that it coincides with the indistinguishability of GDL formulas. Based on it, we establish a characterization of the definability of GDL in terms of $k$-bisimulations. Then we design novel notions of bisimulation for EGDL and obtain characterizations of the expressive power of EGDL in terms of them. These characterizations provide a powerful tool to identify the expressive power of game description languages. Finally, we demonstrate with real games that bisimulation can be generalized to capture a wide range of game equivalence.
Online Deep Metric Learning via Mutual Distillation

Deep metric learning aims to transform input data into an embedding space, where similar samples are close while dissimilar samples are far apart from each other. In practice, samples of new categories arrive incrementally, which requires the periodical augmentation of the learned model. The fine-tuning on the new categories usually leads to poor performance on the old, which is known as “catastrophic forgetting”. Existing solutions either retrain the model from scratch or require the replay of old samples during the training. In this paper, a complete online deep metric learning framework is proposed based on mutual distillation for both one-task and multi-task scenarios. Different from the teacher-student framework, the proposed approach treats the old and new learning tasks with equal importance. No preference over the old or new knowledge is caused. In addition, a novel virtual feature estimation approach is proposed to recover the features assumed to be extracted by the old models. It allows the distillation between the new and the old models without the replay of old training samples or the holding of old models during the training. A comprehensive study shows the superior performance of our approach with the support of different backbones.
Industry initiatives across edge computing

To accelerate the evolution and adoption of Edge computing various standard bodies, open-source projects and industry consortia have come together in recent times to revolutionize Edge compute. This chapter goes through various initiatives across the world that have major traction in terms of collaboration, collateral produced and industry impact. Architecture, collateral produced and details of the projects involved are described.
Common and Special Embedding-Based Collaborative Filtering

Information overload makes a big obstacle for multimedia services. To alleviate the burden, collaborative filtering has been actively studied in the recommendation field to help users find satisfactory content. However, current methods fail to comprehensively predict users’ interactions since users’ interests are complex and multifaceted. We argue that the prevalent graph-based models pay attention to shared characters among neighboring node users or items while ignoring their specific characters. In this work, we investigate common and special characters hidden in users’ interactions with the help of semantic knowledge to model users’ interests. We propose a dual-channel common and special embedding-based collaborative filtering model (CSE) for personalized recommendation. CSE adopts a divide-and-conquer strategy to capture commonality and semantic speciality with graph convolutional learning and knowledge translation. Ultimately, common and special embeddings of users’ interests participate in pairwise user-item matching to score their interaction for personalized recommendation. Extensive experiments on two real-world datasets demonstrate the rationality of the proposed CSE. It verifies that the proposed CSE can comprehensively model user interests with common and special characters and improve recommendation performance.