Reviewerly/exHarmony huggingface dataset:
Communications in vocational education

Introduction . The article considers vocational education in the aspect of the relationship between the educational organization, the real sectors of the economy and the state. Purpose setting. The objectives of the study are to identify the reasons for employers» dissatisfaction with the quality of training of graduates of educational organizations and to search for options for modernizing the system of vocational education by establishing a correspondence between the needs of business and industry. Methodology and methods of the study . The research methodology is based on a philosophical understanding of the main components of vocational education that are responsible for the quality of graduate training. Results . As a result of the analysis of the content and organizational components of education, it was revealed that the state policy of the Russian Federation dictates the need to modernize the system of vocational education in terms of establishing close ties (professional communications) between universities, business, industries and the state. Conclusion . It is noted that such communications should be considered as business communications, since this makes it possible to consider professional education in the context of an interdisciplinary approach, in which not only professional and applied, but also supra-professional competencies necessary for employers are formed. A list of academic disciplines is given that reveals certain aspects of communications from the economic, informational, technological and organizational side. It has been established that the most important task is to train managers who will develop certain technological projects and then implement them in the form of specific products, systems, services. The main problems, tasks, principles and mechanisms of training and retraining of personnel are identified, and possible results are presented. It is noted that educational programs should be built not only on in-depth fundamental training, but also on the interdisciplinarity of projects, the development of programs in agreement with enterprises, and the organization of training specialists using project- oriented technologies. It has been proved that focus on the real sectors of the economy, interaction with industry enterprises will make Russian education an important tool for the scientific and technological development of the country.
UFODMV: Unsupervised Feature Selection for Online Dynamic Multi-Views

In most machine learning (ML) applications, data that arrive from heterogeneous views (i.e., multiple heterogeneous sources of data) are more likely to provide complementary information than does a single view. Hence, these are known as multi-view data. In real-world applications, such as web clustering, data arrive from diverse groups (i.e., sets of features) and therefore have heterogeneous properties. Each feature group is referred to as a particular view. Although multi-view learning provides complementary information for machine learning algorithms, it results in high dimensionality. However, to reduce the dimensionality, feature selection is an efficient method that can be used to select only the representative features of the views so to reduce the dimensionality. In this paper, an unsupervised feature selection for online dynamic multi-views (UFODMV) is developed, which is a novel and efficient mechanism for the dynamic selection of features from multi-views in an unsupervised stream. UFODMV consists of a clustering-based feature selection mechanism enabling the dynamic selection of representative features and a merging process whereby both features and views are received incrementally in a streamed fashion over time. The experimental evaluation demonstrates that the UFODMV model has the best classification accuracy with values of 20% and 50% compared with well-known single-view and multi-view unsupervised feature selection methods, namely OMVFS, USSSF, and SPEC.
Survey on Image and Text Encrypted Data with Authorized Deduplication in Cloud

Summaries are short summaries or abbreviated versions of the entire project, where we use machine learning techniques and algorithms to implement them. Cloud computing is a web-based computer system that is a large platform storage area that authorized users can access anytime and anywhere with a good internet or network connection. Cloud computing primarily provides shared resources and delivers hardware and software applications to devices on demand. It is like a remote server on the internet, which can store, manage and process data without using a computer. Therefore, the working time is faster compared to other local computers. Cloud computing is an information technology service and product. It supports virtualized resources based on IT infrastructure reusability. Cloud computing is the sequence of all required hardware, software, platforms, applications, infrastructure and storage with an online identity only. We use AES and MD5 algorithms for encryption and decryption after image and text encryption, then generate numbers and enter key to send emails
Avoidance of the term agile in software engineering: Necessary and possible

Abstract The term agile is frequently used in IT especially in the area of software engineering. The broad pervasiveness of the term agile is astonishing, because it is not well defined in the area of software engineering. The fuzziness of the term agile comes with the danger of ill‐founded decisions and ideological discussions. Both slow down the gain in knowledge in the domain of software engineering. The term agile should therefore not be used either in science nor in the industry. This article provides a critical discussion of the term agile. By using a three‐layered abstraction concept for software development processes, the use of the term agile can be avoided.
Feeling Small or Standing Tall? Height Manipulation Affects Speech Anxiety and Arousal in Virtual Reality

Social performance situations often constitute one of the most challenging communication tasks across different cultures. In today's work environments, giving presentations and performing in front of others are often essential and expected. Therefore, public speaking anxiety can have a serious impact on an individual's job performance, career choice, and prospects. Contemporary consumer virtual reality hardware has made it possible to practice public speaking anywhere in a safe and private virtual reality environment (VRE). As VREs offer the means to practice real-life scenarios, they also make it possible to go beyond what is "real"; to replace simulations with more dynamic and innovative training environments. Furthermore, with occupational life undergoing a significant shift toward technology-mediated working conditions, innovative tools and methods could also be used during virtually implemented real-time social interactions. This research aimed to study the ways in which an illusion of height, that is, perceived tallness versus perceived shortness, without any visible virtual body or representation, influences state speech anxiety and emotional responses of participants during simulation of a stressful speech task. The experiment followed a strictly controlled between-subject procedure, and both self-reported and psychophysiological data were collected. Results indicate that participants perceiving the illusion of tallness felt less anxious and had lower self-reported arousal compared with participants with the illusion of shortness. This implies that even simple, visual, first-person perspective manipulation of the VRE could help individuals to reduce their stress responses during a task-oriented situation.
URL filtering using machine learning algorithms

Cyber-attacks using malicious uniform resource locator (URL) propagation are very common and serious. Statistics indicate that there is a need to research and apply techniques and methods for identifying and preventing malicious URLs. The main objective of this research is to train machine learning models on selected dataset to predict phishing websites based on URL-related features. The accuracy level of each model is measured and compared. Finally, the best performing model will be used to develop a web application that provide internet users with an easy way to check suspicious URLs. We have used five different machine learning models to classify URLs as legitimate or phishing, these models are eXtreme Gradient Boosting (XGBoost), k-nearest neighbors (KNN), support vector machine (SVM), Decision Tree, and Random Forest. Finally, we used Voting Classifier to combine the work of Random Forest (RF) algorithm with other two models, Gaussian Naive Bayes, and Logistic Regression, to check if we can increase the accuracy of RF as suggested in the literature, but we found that the accuracy of RF alone was higher than the accuracy of the combined models. This project can be implemented as a browser extension or mobile application to classify suspicious URLs to legitimate or phishing with the use of the saved model.
Multi-Scale Cost Attention and Adaptive Fusion Stereo Matching Network

At present, compared to 3D convolution, 2D convolution is less computationally expensive and faster in stereo matching methods based on convolution. However, compared to the initial cost volume generated by calculation using a 3D convolution method, the initial cost volume generated by 2D convolution in the relevant layer lacks rich information, resulting in the area affected by illumination in the disparity map having a lower robustness and thus affecting its accuracy. Therefore, to address the lack of rich cost volume information in the 2D convolution method, this paper proposes a multi-scale adaptive cost attention and adaptive fusion stereo matching network (MCAFNet) based on AANet+. Firstly, the extracted features are used for initial cost calculation, and the cost volume is input into the multi-scale adaptive cost attention module to generate attention weight, which is then combined with the initial cost volume to suppress irrelevant information and enrich the cost volume. Secondly, the cost aggregation part of the model is improved. A multi-scale adaptive fusion module is added to improve the fusion efficiency of cross-scale cost aggregation. In the Scene Flow dataset, the EPE is reduced to 0.66. The error matching rates in the KITTI2012 and KITTI2015 datasets are 1.60% and 2.22%, respectively.
Language typology as a discursive affordance in digital discourse

With the rapid development of participatory social media, these spaces have become new arenas for producing political discourse. However, critical analyses of digital discourse that examine the importance of language typological variation for the (re)production of political discourse are relatively new. This chapter investigates linguistic creativity in non-elite political communication by combining a critical discursive approach with language typology. The analysis highlights the importance of language typology in political communication as political actors exploit the typological affordances of Polish. This chapter demonstrates how language affordances are mobilised for political reasons to (re)produce discriminatory political messages. The chapter suggests combining critical discourse analysis and language typology to reveal novel perspectives on the intersection of language and politics on the Internet.
Falsehoods, Foreign Interference, and Compelled Speech in Singapore

Abstract Online misinformation endangers the infrastructure of fact essential to public discourse and presents an even greater threat where it is being utilised as a weapon by hostile state actors. In recognition of these dangers, Singapore has implemented legal measures to combat online misinformation, enacting in quick succession the Protection from Online Falsehoods and Manipulation Act (POFMA) and the Foreign Interference (Countermeasures) Act (FICA). These statutes open up novel frontiers of development for Singapore's free speech jurisprudence. Indeed, these statutes confer upon government authorities the power to compel the authors of certain material to display notices stating that the material contains falsehoods or originated from a hostile information campaign. Yet, should one accept that the constitutional right to freedom of speech extends to the freedom not to speak, the compulsion of such expressions may well be unconstitutional under Singapore's free speech guarantee. This article will study the theoretical justifications for a prohibition against compelled speech to evaluate whether Singapore free speech jurisprudence ought to recognise such a prohibition, propose a doctrinal framework to analyse compelled expressions by reference to US, UK, and Canadian jurisprudence, and critically assess how the POFMA and FICA would fare under such a doctrine.
A practical approach to explaining defect proneness of code commits by causal discovery

Explainable software defect prediction is practical for software quality assurance. However, it is hard to explain the predictions made by obscure machine learning models because of the lack of transparency and the unreliability of the explanations for such predictions from existing explanation methods. To provide a better understanding of the causes of defect proneness of code commits, we propose an approach called CausalDefect to construct a causal graph of defects, which provides a visual way to understand the causal relationships between different features of defects. Based on the transparent causal graph, software engineers can naturally predict defect proneness of code commits, explain the resulting predictions, and finally take feasible action to reduce defect proneness. To verify the usefulness of the generated causal graph, we conduct evaluations, and the evaluation results suggest that our causal graph can effectively and efficiently predict defect proneness of code commits and explain the resulting predictions, even if the graph is not optimized for specific software projects. Most importantly, CausalDefect is the first attempt to employ causal discovery algorithms to investigate the explainability of defect proneness by establishing a causal graph of software defects.
Three-Dimensional Human Pose Estimation from Sparse IMUs through Temporal Encoder and Regression Decoder

Three-dimensional (3D) pose estimation has been widely used in many three-dimensional human motion analysis applications, where inertia-based path estimation is gradually being adopted. Systems based on commercial inertial measurement units (IMUs) usually rely on dense and complex wearable sensors and time-consuming calibration, causing intrusions to the subject and hindering free body movement. The sparse IMUs-based method has drawn research attention recently. Existing sparse IMUs-based three-dimensional pose estimation methods use neural networks to obtain human poses from temporal feature information. However, these methods still suffer from issues, such as body shaking, body tilt, and movement ambiguity. This paper presents an approach to improve three-dimensional human pose estimation by fusing temporal and spatial features. Based on a multistage encoder-decoder network, a temporal convolutional encoder and human kinematics regression decoder were designed. The final three-dimensional pose was predicted from the temporal feature information and human kinematic feature information. Extensive experiments were conducted on two benchmark datasets for three-dimensional human pose estimation. Compared to state-of-the-art methods, the mean per joint position error was decreased by 13.6% and 19.4% on the total capture and DIP-IMU datasets, respectively. The quantitative comparison demonstrates that the proposed temporal information and human kinematic topology can improve pose accuracy.
Serious and casual leisure in public library makerspaces: The two-audience conundrum and research agenda

Many public libraries provide makerspace services, where people can share tools and knowledge to create tangible items, yet the audience for these spaces is sometimes challenging to identify or maintain. Sometimes the spaces are less inclusive than intended or desired. This comparative case study examines user and library staff experiences in three makerspaces to determine the types of leisure the makerspaces support, and the types of makerspaces community members want. The study found that these spaces serve two audiences: those committed to a serious leisure use of the space, and those interested in casual leisure. These audiences each need different support, programs, and policies. Neither type of leisure was well supported in all three cases. This study situates the benefits of library makerspaces in the lives of the users and asks a series of questions orienting LIS toward a research agenda concerning serious and casual leisure.
Hierarchical parallel multi-scale graph network for 3d human pose estimation

Graph convolutional network (GCN) is a widespread architecture for 2D-to-3D human pose estimation (HPE). Vanilla graph convolution is the key in GCN for body joints feature extraction by aggregating features from the first-order neighbors of each joint at single-scale. However, the nodes features updated in graph are gradually assimilated with the network deepens This makes it difficult to model the powerful feature representation for joints and has an adverse effect on resolving the uncertainty caused by occlusion or depth ambiguity. To address these problems, we propose a hierarchical parallel multi-scale graph convolutional network (HPM-GNet) for 3D HPE in this paper. The proposed network is composed by multi-scale sub-graph networks in parallel framework, guided by the geometric constraint of human body. Firstly, a well-designed weight masked graph convolutional (WMGConv) layer is used as a fundamental unit to construct the parallel multi-scale sub-graph convolutional network module (PMGCN) in HPM-GNet, which corporately captures the features of target nodes and neighbor nodes with weight assignment. Then, a cross-scale features exchange fusion block (CFEB) is designed to aggregate multi-scale features from local and global perspective with considering the geometric information from different parts of human body. Finally, we conduct experiments on two challenging 3D human pose benchmark datasets to evaluate the effectiveness of the proposed model. The experimental results demonstrate that our model achieves state-of-the-art performance.
Crowdsourcing Thumbnail Captions: Data Collection and Validation

Speech interfaces, such as personal assistants and screen readers, read image captions to users. Typically, however, only one caption is available per image, which may not be adequate for all situations (e.g., browsing large quantities of images). Long captions provide a deeper understanding of an image but require more time to listen to, whereas shorter captions may not allow for such thorough comprehension yet have the advantage of being faster to consume. We explore how to effectively collect both thumbnail captions—succinct image descriptions meant to be consumed quickly—and comprehensive captions—which allow individuals to understand visual content in greater detail. We consider text-based instructions and time-constrained methods to collect descriptions at these two levels of detail and find that a time-constrained method is the most effective for collecting thumbnail captions while preserving caption accuracy. Additionally, we verify that caption authors using this time-constrained method are still able to focus on the most important regions of an image by tracking their eye gaze. We evaluate our collected captions along human-rated axes—correctness, fluency, amount of detail, and mentions of important concepts—and discuss the potential for model-based metrics to perform large-scale automatic evaluations in the future.
Short Text Sentiment Classification Using Bayesian and Deep Neural Networks

The previous multi-layer learning network is easy to fall into local extreme points in supervised learning. If the training samples sufficiently cover future samples, the learned multi-layer weights can be well used to predict new test samples. This paper mainly studies the research and analysis of machine short text sentiment classification based on Bayesian network and deep neural network algorithm. It first introduces Bayesian network and deep neural network algorithms, and analyzes the comments of various social software such as Twitter, Weibo, and other popular emotional communication platforms. Using modeling technology popular reviews are designed to conduct classification research on unigrams, bigrams, parts of speech, dependency labels, and triplet dependencies. The results show that the range of its classification accuracy is the smallest as 0.8116 and the largest as 0.87. These values are obtained when the input nodes of the triple dependency feature are 12,000, and the reconstruction error range of the Boltzmann machine is limited between 7.3175 and 26.5429, and the average classification accuracy is 0.8301. The advantages of triplet dependency features for text representation in text sentiment classification tasks are illustrated. It shows that Bayesian and deep neural network show good advantages in short text emotion classification.
Collision and obstacle avoidance for agents based on velocity programming in unknown environments under kinematical constraints

Abstract This paper conducts research on obstacle and collision avoidance under kinematical constraints in unknown environments, while communication and simultaneous localization and mapping (SLAM) are unavailable to agents. Then a strategy based on mixed‐integer programming is proposed, in which velocity constraints are established with a modified Barrier function for obstacles that can be completely detected. As for obstacles that cannot be completely detected, a feasible set is built for the velocity programming based on the convex theory, and the contradicted constraints are addressed with the logic metric method. Besides, the actuator saturation is avoided by converting kinematical constraints into the restrictions on the magnitude, the restrictions on the direction, and the negative correlations between the components of the velocity respectively. Given that invalid nominal velocity leads to collisions, a virtual goal is estimated for nominal velocity improvement. In addition, the local extremum brought by empty programming space due to multiple constraints is repaired by fixing the velocity constraints. The feasibility of the proposed strategy is analyzed, and numerical simulations are provided to verify the effectiveness of the proposed strategy.