Reviewerly/exHarmony huggingface dataset:
A Machine Learning Framework for Document Classification by Topic Recognition Using Latent Dirichlet Allocation and Domain Knowledge

The quantity of unstructured text data in digital archive is continually expanding due to the exponential growth of information technology, so the tasks of analysing, organizing, classifying, and summarizing text have become a big challenge. Since the manual classification of text documents requires a lot of human resources, finance, and time, automatic text classification is obligatory. Latent Dirichlet allocation (LDA) is an unsupervised machine learning algorithm often used in topic modelling. The output of the topic modelling algorithm can be used logically to classify documents. The LDA model is plagued with domain-specific terms. A novel latent Dirichlet allocation (LDA) with domain knowledge framework for document classification was introduced. The experiment was carried out using a dataset with five different categories of data. The experiments showed that LDA with domain knowledge gives better results than standard LDA and LDA using the TF-IDF model. Precision, Recall, F1-score, accuracy, and Purity were all improved using the proposed framework.
Beyond legislation and technological design: The importance and implications of institutional trust for privacy issues of digital contact tracing

For proper implementation of digital contact tracing technologies for fighting against SARS-CoV-2, participants' privacy vulnerability and the uncertainty from the relevant institutions' side could be seen as two core elements that should be dealt with, among others. In this paper, we propose to understand the current approaches for preserving privacy, referred to as privacy by legislation and privacy by technological design, as distrusting strategies that primarily work to reduce participants' vulnerability by specifying and implementing privacy standards related to this digital solution. We point out that mere distrusting strategies are insufficient for the ethically appropriate development of this digital solution, nor can they eliminate the need for institutional trust that plays an essential role in fostering voluntary support for this solution. To reach well-grounded trust in both an ethical and epistemological sense, we argue that trust in institutions concerning personal data protection in the case of digital contact tracing ought to be built on the relevant institutions' and individuals' goodwill towards the public and their competence in improving the actual effectiveness of this solution. We conclude by clarifying three dimensions, including the purpose, procedure, and outcome, where the relevant trustees can work to signal and justify their intentions and increase their trustworthiness via an effective communication strategy. Given the complementary qualities shown by the distrusting and trusting strategies, a combined strategy including both sorts seems closer to what we expect from the responsible implementation of this digital solution, which could also improve the effectiveness of this institutional response.
Heterogeneous feature-aware Transformer-CNN coupling network for person re-identification

Person re-identification plays an important role in the construction of the smart city. A reliable person re-identification system relieves users from the inefficient work of identifying the specific individual from enormous numbers of photos or videos captured by different surveillance devices. The most existing methods either focus on local discriminative features without global contextual information or scatter global features while ignoring the local features, resulting in ineffective attention to irregular pedestrian zones. In this article, a novel Transformer-CNN Coupling Network (TCCNet) is proposed to capture the fluctuant body region features in a heterogeneous feature-aware manner. We employ two bridging modules, the Low-level Feature Coupling Module (LFCM) and the High-level Feature Coupling Module (HFCM), to improve the complementary characteristics of the hybrid network. It is significantly helpful to enhance the capacity to distinguish between foreground and background features, thereby reducing the unfavorable impact of cluttered backgrounds on person re-identification. Furthermore, the duplicate loss for the two branches is employed to incorporate semantic information from distant preferences of the two branches into the resulting person representation. The experiments on two large-scale person re-identification benchmarks demonstrate that the proposed TCCNet achieves competitive results compared with several state-of-the-art approaches. The mean Average Precision (mAP) and Rank-1 identification rate on the MSMT17 dataset achieve 66.9% and 84.5%, respectively.
Citizen science as a form of social empowerment of youth and volunteers’ engagement in research in the context of mobilized economy of knowledge

The article reflects the systematization of approaches to stimulating the participation of young people in scientific volunteering in an economy mobilized to produce and implement new knowledge and ensure the country’s leadership in global innovative growth. The needs of Russian society in involving young categories of the population in the implementation of citizen science have been studied within the framework of theoretical socioadministrative analysis and political economy. Open citizen science is based on the sociocultural regulation of the motives of young people to participate in national research projects, which makes it very important to systematize the motives and needs of young people to turn to citizen science. The article presents the results of a theoretical analysis of the motivation for the participation of young citizens in scientific projects, based on the analysis of the case study and secondary research, the main recommendations for organizing and stimulating the involvement of young people in citizen science are formulated. The article presents the specifics and requirements of the mobilization economy for the branches of knowledge production; the tasks of involving citizens in scientific projects are formulated; the essential characteristics of open science, citizen science are determined; the main approaches to attracting young categories of the population as volunteers in research activities based on their motivation are identified. The article also provides some practical recommendations on the use of managerial tools to stimulate youth participation in citizen science. Formed approaches to the motivation of young people’s participation in research activities.
Building Information Modelling and Augmented Reality for building energy systems visualisation

Abstract Augmented reality is an important aspect of today's technological environment, as it aids in the development of technical abilities in students and engineers. Construction is often the industry with the least relation to digitalization, and we are currently dealing with challenges with real-view objects. Because some Elements are buried beneath the fake ceiling and walls, these components, which include ventilation, air conditioning, heating, and lighting, are now only partially visible in the building environment[9]. As per this article, Owners can track the project and use it for predictive maintenance, facility management, and future refurbishment using a mobile.
An approach for applying natural language processing to image classification problems

A growing interest in applying Natural Language Processing (NLP) models to computer vision problems has recently emerged. This interest is motivated by the success of NLP models in tasks such as translation and text summarization. In this paper, we propose a new method for applying NLP to image classification problems. We aim to represent the visual patterns of objects by using a sequence of alphabet symbols and then train a Gated Recurrent Unit (GRU), Long Short-Term Memory (LSTM), or Transformer using these sequences to classify objects. An extensive experimental evaluation using a limited number of images for training has been conducted to compare our method with the ResNet-50 deep learning architecture. The results obtained by the proposed method outperform ResNet-50 in all test scenarios. In one test, the method achieved an average accuracy of 95.3% compared to 89.9% of ResNet-50. The source code ( http://git.inovisao.ucdb.br/inovisao/applying-npl-to-image-classification) and dataset ( https://doi.org/10.6084/m9.figshare.20055602.v1) are publicly available.
High-Performance Implementation of the Identity-Based Signature Scheme in IEEE P1363 on GPU

Identity-based cryptography is proposed to solve the complicated certificate management of traditional public-key cryptography. The pairing computation and high-level tower extension field arithmetic turn out to be the performance bottleneck of pairing-based signature schemes. Graphics processing units have been increasingly popular for general-purpose computing in recent years. They have shown a lot of promise in speeding up cryptographic schemes such as AES, RSA, and ECDSA. However, to our knowledge, the research on parallel implementation of pairings and identity-based cryptographic schemes on graphics processing units is somewhat outdated. Therefore, in this article, we implement the identity-based signature scheme in the IEEE P1363 Standard on a modern NVIDIA RTX 3060 card. We convert the pairing computation in signature verification into a product of pairings with fixed arguments and therefore avoid the scalar multiplication in 𝔾 2 . Then we employ the precomputation technique to improve the elliptic curve scalar multiplication, exponentiation in \(\mathbb {F}_{p^{12}}\) and the pairing computation. We also apply PTX ISA to multiple-precision arithmetic. Experiments demonstrate that our implementation can perform 43,856/46,753/39,798 pairings/sec for the Optimal Ate pairing, the pairing with a fixed argument, and two pairings with fixed arguments, respectively. Peak throughputs of signature generation and verification can achieve 322.6 and 40.6 kops/sec over the BN254 curve.
CATCH: Cloud Data Acquisition through Comprehensive and Hybrid Approaches

With the development of Internet technology, cloud-based services have improved the availability and usability of resources. Among them, cloud storage services enable users to remotely store, access, or share data over a network. Therefore, digital forensic investigators need to collect data stored in remote servers to comprehensively understand a suspect's activities. Although several well-known commercial digital forensic tools provide features for cloud data acquisition in order to support this requirement, fewer studies have addressed whether they have full access to cloud resources and collect all the data as expected. In this regard, our findings from this work show that those commercial tools do not completely identify and collect data that are obviously available through dedicated clients (e.g., web-browsers and desktop/mobile apps). In this paper, we propose an investigative framework, CATCH (Cloud Data Acquisition through Comprehensive and Hybrid Approaches), which is composed of four steps (Authentication, Exploration, Filtering, and Collection). CATCH collects authentication data to access cloud resources and then, explores, filters, and collects all accessible metadata as well as contents from remote cloud servers by using Open and Internal APIs. To demonstrate our proposal, the CATCH framework is applied to collect a user's Microsoft OneDrive storage from digital forensics perspectives. We then evaluate data collection results generated from a self-developed tool based on the proposed framework, by comparing them to results from commercial digital forensic tools.
Weakest preconditioned goto axiom

This paper presents an axiom for goto statements in terms of Dijkstra's weakest (liberal) precondition model. We give a detailed comparison of our axiom with Arbib and Alagic's approach and argue that the latter is actually flawed even if it offers a goto axiom that has widely been cited and adopted.
Automated Paragraph Detection Using Cohesion Network Analysis

The ability to express yourself concisely and coherently is a crucial skill, both for academic purposes and professional careers. An important aspect to consider in writing is an adequate segmentation of ideas, which in turn requires a proper understanding of where to place paragraph breaks. However, these decisions are often performed intuitively, with little systematicity in sequencing ideas. Thus, an automated method of detecting the optimal hierarchical structure of texts using quantifiable features could be a valuable tool for learners. Here, we aim to define a framework grounded in Cohesion Network Analysis to establish the structure of a text by modeling paragraphs as clusters of sentences. The analogy to clustering enables us to identify paragraph breaks that maximize inter-paragraph separation while ensuring high intra-paragraph cohesion. Our approach consists of two steps acted on texts without paragraph breaks. First, the number of paragraphs is automatically inferred with an absolute error of 1.02 using a Recurrent Neural Network, which relies on text features and cohesion flow. Second, paragraph splits are detected using two algorithms: top k which selects the largest cohesion gaps between adjacent utterances, and divisive clustering which iteratively splits the text into paragraphs. Silhouette scores are used to assess performance and the obtained values denote adequately inferred structures.
Solar Radiation Prediction Using the Random Forest Regression Algorithm

Solar radiation data is critical for solar energy research and development. It is extremely valuable for design and simulation of solar thermal technologies and solar photovoltaic applications because it offers essential data about the energy that impacts the earth. Because conventional energy sources are rapidly decreasing, sustainable energy sources such as solar, biomass, and wind are growing more prominent. Solar energy is the most generally acknowledged renewable energy source since it is abundant all around the planet. As a result, this project examines the model and methodologies utilized to forecast solar radiation. Random forest algorithm is used in solar radiation prediction; this algorithm combines the benefits of both techniques utilized, resulting in faster calculation and convergence to optimal results.
Experimental Comparative Analysis on Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) on Aspect-Level Sentiment Analysis

Sentiment analysis is a process of inferencing subjective text with sentiments. Huge data generated by social media, business, medical industry, e-commerce, engineering, and academia are analyzed to understand customer satisfaction, trends, experience, opinion, and influence. Various algorithms have been proposed in the literature to analyze the generated data. This paper summarizes various types of sentiment analysis and the process of sentiment analysis and discusses the application of two main deep learning architectures convolutional neural networks (CNN) and recurrent neural networks (RNN) in sentiment analysis. The experimental analysis also has been carried out on these two architecture and performance metrics, viz., accuracy, loss, precision, and recall, which have been compared.
Digital Transformation for the University of the Future: A Perspective

The university of the future looks bright for those who engage in digital transformation. This article should be of great interest to university leaders, students, faculty, and education practitioners in the field.
Bitcoin and gold investment strategy based on LSTM and Markowitz portfolio investment theory

Bitcoin, as a new mean of circulation, has a great impact on gold market. The investment of Bitcoin and Gold has recently attracted intensive attention from financial institutions, and the capital markets. This paper proposes an investment strategy based on Title C of the 2022 Mathematical Contest. This paper establishes an LSTM time series forecasting model. After data processing and training with indicators such as MACD as features, a forecasting model and the predicted data are obtained. For the decision strategy, this paper builds a model that seeks the maximum return as well as the minimum risk based on Markowitz portfolio investment theory and Sharpe ratio. Calculated by genetic algorithm, the final return are calculated to be 207,612.96 USD.
Applications of singular value decomposition in data reduction

The singular value decomposition (SVD) is broadly used in the dimensional reduction due to its universality for all matrices. We have deduced the mathematical formula of the SVD and logical thinking of SVD truncation. Dimensional reduction just uses the SVD truncation to select N biggest singular values. We have experimentally found that image compression used by SVD could greatly save memory without losing too much accuracy. The effectiveness of SVD could be directly shown in image recognition with complex data hierarchy. We also combined SVD with another useful dimensional reduction method named Principle Component Analysis (PCA). Our analysis indicates that the accuracy of both SVD and PCA could be guaranteed when processing a large number of data. Taken together with the data science and machine learning, SVD appears to be a fundamental tool of data processing. Our results establish a basic understanding of SVD in the application of dimensional reduction.
A Local Quadratic Embedding Learning Algorithm and Applications for Soft Sensing

Inspired by the tremendous achievements of meta-learning in various fields, this paper proposes the local quadratic embedding learning (LQEL) algorithm for regression problems based on metric learning and neural networks (NNs). First, Mahalanobis metric learning is improved by optimizing the global consistency of the metrics between instances in the input and output space. Then, we further prove that the improved metric learning problem is equivalent to a convex programming problem by relaxing the constraints. Based on the hypothesis of local quadratic interpolation, the algorithm introduces two lightweight NNs; one is used to learn the coefficient matrix in the local quadratic model, and the other is implemented for weight assignment for the prediction results obtained from different local neighbors. Finally, the two sub-models are embedded in a unified regression framework, and the parameters are learned by means of a stochastic gradient descent (SGD) algorithm. The proposed algorithm can make full use of the information implied in target labels to find more reliable reference instances. Moreover, it prevents the model degradation caused by sensor drift and unmeasurable variables by modeling variable differences with the LQEL algorithm. Simulation results on multiple benchmark datasets and two practical industrial applications show that the proposed method outperforms several popular regression methods.
Optimal Tensor Bipartite Graph Learning

are concerned in this paper with a multi-view clustering framework based on bipartite graphs. And we propose an efficient multiview clustering method, Optimal Tensor Bipartite Graph Learning for multi-view clustering (OTBGL). Our model is a novel tensorized bipartite graph based multi-view clustering method with low tensorrank constraint. Firstly, to remarkably reduce the computational complexity, we leverage the bipartite graphs of different views instead of full similarity graphs of the corresponding views. Secondly, we measure the similarity between bipartite graphs of different views by minimizing the tensor Schatten p-norm as a tighter tensor rank approximation and explore the spatial low-rank structure embedded in intra-view graphs by minimizing the l1,2-norm of learned graphs. Thirdly, we provide an efficient algorithm suitable for processing large-scale data. Extensive experimental results on six benchmark datasets indicate our proposed OTBGL is superior to the state-of-the-art methods.
Playing Lottery Tickets in Style Transfer Models

Style transfer has achieved great success and attracted a wide range of attention from both academic and industrial communities due to its flexible application scenarios. However, the dependence on a pretty large VGG-based autoencoder leads to existing style transfer models having high parameter complexities, which limits their applications on resource-constrained devices. Compared with many other tasks, the compression of style transfer models has been less explored. Recently, the lottery ticket hypothesis (LTH) has shown great potential in finding extremely sparse matching subnetworks which can achieve on par or even better performance than the original full networks when trained in isolation. In this work, we for the first time perform an empirical study to verify whether such trainable matching subnetworks also exist in style transfer models. Specifically, we take two most popular style transfer models, i.e., AdaIN and SANet, as the main testbeds, which represent global and local transformation based style transfer methods respectively. We carry out extensive experiments and comprehensive analysis, and draw the following conclusions. (1) Compared with fixing the VGG encoder, style transfer models can benefit more from training the whole network together. (2) Using iterative magnitude pruning, we find the matching subnetworks at 89.2% sparsity in AdaIN and 73.7% sparsity in SANet, which demonstrates that Style transfer models can play lottery tickets too. (3) The feature transformation module should also be pruned to obtain a much sparser model without affecting the existence and quality of the matching subnetworks. (4) Besides AdaIN and SANet, other models such as LST, MANet, AdaAttN and MCCNet can also play lottery tickets, which shows that LTH can be generalized to various style transfer models.
Impact of Content on Subjective Quality of Experience Assessment for 3D Video

Ongoing improvements in the field of visual entertainment may incline users to display 3D video content on various terminal devices provided satisfying Quality of Experience. This study aims to determine whether the cognitive features of appealing and yet uncommon 3D content may obfuscate subjective QoE measurements performed under different bitrates. To test the hypothesis two 3D video databases are compared in terms of a perceived QoE under an innovative scenario. The reference database is GroTruQoE-3D (VQEG) including short artificial clips. The authorial 3D video database DJ3D contains longer clips from feature films with a proved substantial level of cognitive features. The 3D video content features are operationalised by three cognitive attributes (attractiveness, interestingness, 3D effect experience). Gradation of video quality is introduced by streaming at four bitrate levels. The collected subjects' scores are statistically analysed with a stochastic dominance test adjusted to a 5-point Likert scale. The obtained results show that quality assessment scores depend on the intensity of the cognitive attributes of the content. Sequences commonly used in subjective QoE experiments are more vulnerable to the intensity of subjective content attributes (visual attractiveness, interestingness, and 3D effect experience) than sequences from commercial feature films and documentaries. Moreover, it is shown that the test material commonly used in research is assessed higher for lower bitrates. In view of key results QoE researchers should consider to use test material originating from commercially available content to minimize content impact on QoE assessment scores collected during subjective experiment. The research contributes to the QoE best practices by paying attention to 3D cognitive attributes that may obfuscate subjective scores. The innovative scenario for comparing video databases with the stochastic dominance test adjusted to the ordinal scale is proposed. The approach may be useful in a broader context when an emerging service operator wants to ascertain whether subjective QoE tests are not substantially biased by the service novelty.