Reviewerly/exHarmony huggingface dataset:
“Broadcast your gender.” A comparison of four text-based classification methods of German YouTube channels

Social media platforms provide a large array of behavioral data relevant to social scientific research. However, key information such as sociodemographic characteristics of agents are often missing. This paper aims to compare four methods of classifying social attributes from text. Specifically, we are interested in estimating the gender of German social media creators. By using the example of a random sample of 200 YouTube channels, we compare several classification methods, namely (1) a survey among university staff, (2) a name dictionary method with the World Gender Name Dictionary as a reference list, (3) an algorithmic approach using the website gender-api.com, and (4) a Multinomial Naïve Bayes (MNB) machine learning technique. These different methods identify gender attributes based on YouTube channel names and descriptions in German but are adaptable to other languages. Our contribution will evaluate the share of identifiable channels, accuracy and meaningfulness of classification, as well as limits and benefits of each approach. We aim to address methodological challenges connected to classifying gender attributes for YouTube channels as well as related to reinforcing stereotypes and ethical implications.
TWGH: A Tripartite Whale–Gray Wolf–Harmony Algorithm to Minimize Combinatorial Test Suite Problem

Today’s academics have a major hurdle in solving combinatorial problems in the actual world. It is nevertheless possible to use optimization techniques to find, design, and solve a genuine optimal solution to a particular problem, despite the limitations of the applied approach. A surge in interest in population-based optimization methodologies has spawned a plethora of new and improved approaches to a wide range of engineering problems. Optimizing test suites is a combinatorial testing challenge that has been demonstrated to be an extremely difficult combinatorial optimization limitation of the research. The authors have proposed an almost infallible method for selecting combinatorial test cases. It uses a hybrid whale–gray wolf optimization algorithm in conjunction with harmony search techniques. Test suite size was significantly reduced using the proposed approach, as shown by the analysis of the results. In order to assess the quality, speed, and scalability of TWGH, experiments were carried out on a set of well-known benchmarks. It was shown in tests that the proposed strategy has a good overall strong reputation test reduction size and could be used to improve performance. Compared with well-known optimization-based strategies, TWGH gives competitive results and supports high combinations (2 ≤ t ≤ 12).
Advanced Intelligent English Translation Based on Multisensor Data Fusion Optimization

English translation activity course is of great significance to cultivate students’ English translation level. In the context of multisensor data fusion, how to effectively carry out English translation activity course in colleges and universities has become an important topic. The educational value and intellectual property of advanced Intelligent English translation activity course are analyzed. From multisensor data fusion and the improvement of translation, translators psychological changes of the boot and prominent features, English translation activity and translation, the generality of the multisensor data fusion of multisensor data fusion personalization features this four aspects, which is under the background of the sensor data fusion of the practice for college English translation activity. Firstly, the theory of data fusion estimation is elaborated, and various data fusion structures in multisensor systems are summarized. Then, the data fusion estimation model based on Kalman filter is established, and the Kalman filtering algorithms of centralized, sequential, parallel, and joint structures are given, respectively. Simulation experiments are carried out on the algorithms. Experimental results show that the estimation accuracy of the system can be improved by multisensor data fusion. Then a rule-based lexical analyzer is designed. Combined with the system model, a rule-based lexical analyzer and a comprehensive dictionary, the linguistic knowledge source throughout the whole machine translation process, are researched and designed. A hashing algorithm for lexical retrieval is designed, and various rules and data structures related to the lexical analyzer are described in formal language. The analysis algorithms of morphological preprocessing, morphological analysis, unincluded word processing, phrase analysis, and part of speech tagging are introduced.
Analysis of Network Information Retrieval Method Based on Metadata Ontology

In order to solve the problem that people can accurately search for the network information they need, the research on network information retrieval methods becomes more important. This article is mainly about the research of network information retrieval methods based on metadata ontology calculations. This article constructs an LDA three-layer Bayesian model with a three-layer structure of document, topic, and single order. The three-layer structure obeys a random polynomial distribution and can be calculated the joint distribution probability of all variables in the LDA model greatly increases the calculation efficiency. Using a cross-modal information retrieval method, it can mine the common data features between different modal data and analyze the semantic correlation between different modal data, improve the accuracy of search, and solve the existence of different modal data. There is a gap in the expression semantics between heterogeneous and different modal data. The experimental results in this paper show that the text feature extraction of the network information retrieval method based on the metadata ontology calculation has a good performance in terms of accuracy, and the accuracy of the extraction and clustering results is as high as about 90%. The improved CCA algorithm used is better than the traditional CCA and the accuracy is improved by 23%, which is 12% higher than the LDA-CCA algorithm.
Driving Maneuver Anomaly Detection Based on Deep Auto-Encoder and Geographical Partitioning

This paper presents GeoDMA , which processes the GPS data from multiple vehicles to detect anomalous driving maneuvers, such as rapid acceleration, sudden braking, and rapid swerving. First, an unsupervised deep auto-encoder is designed to learn a set of unique features from the normal historical GPS data of all drivers. We consider the temporal dependency of the driving data for individual drivers and the spatial correlation among different drivers. Second, to incorporate the peer dependency of drivers in local regions, we develop a geographical partitioning algorithm to partition a city into several sub-regions to do the driving anomaly detection. Specifically, we extend the vehicle-vehicle dependency to road-road dependency and formulate the geographical partitioning problem into an optimization problem. The objective of the optimization problem is to maximize the dependency of roads within each sub-region and minimize the dependency of roads between any two different sub-regions. Finally, we train a specific driving anomaly detection model for each sub-region and perform in-situ updating of these models by incremental training. We implement GeoDMA in Pytorch and evaluate its performance using a large real-world GPS trajectories. The experiment results demonstrate that GeoDMA achieves up to 8.5% higher detection accuracy than the baseline methods.
Sentiment analysis in cross-linguistic context: How can machine translation influence sentiment classification?

Abstract In recent years, there has been a lot of interest in cross-language sentiment classification, as the research in sentiment analysis has shifted focus from English to less resourceful languages. Cross-language sentiment classification attempts to leverage the automated machine translation (MT) capability utilizing the infrastructure of languages rich in linguistic resources, mainly English, to help build sentiment analysis systems for low-resource languages. In this study, we explore how MT influences cross-language sentiment classification. To this end, we perform three different experiments, obtaining promising results. In the first experiment, we automatically translate 4,000 positive and negative reviews from English into Greek and Italian, thus obtaining labeled sentiment datasets in these languages. Then, we train a Naive Bayes classifier and compare the performance with the source dataset. In the second experiment, the translated reviews are automatically translated back into the source language (English), aiming to compare the classification accuracy with the one obtained in the original dataset. In the final approach, the reviews are translated from the source (English) into Italian through an intermediate translation in Greek to examine whether the performance was further diminished compared with the approach of the first experiment.
Study of COVID-19 Monitoring System Based on Block Chain and Anonymity Techniques

Since the COVID-19 epidemic has been raging around the world, it has a great impact on people’s life and work; in addition to monitoring and controlling the epidemic situation, government departments are also facing the impact of economic downturn. The investigation of epidemic situation and the privacy of personal information often contradict each other. It is a headache for countries all over the world how to make both sides perfect. This study uses blockchain technology, combined with cryptography theory, proposes a set of epidemic monitoring information system, which has the function of anonymity. When hospitals or doctors release patient information, they do not need to worry about personal information leakage.
Boosting Innovation by Artificial Intelligence (AI): Effects and Potentials of AI in the Process of Innovating

Today enterprises must encounter more challenges than ever: besides dealing with global markets/competitors and an increasing flood of freely available information, the rising demand for individualized solutions, the ever-shorter life cycles of technologies and products, the digitalization pressure and the effects of Covid-19 are only a few more examples for challenges. All these challenges have indirect and direct impacts on the company’s innovation activities. To face the innovation tasks deriving from the above-mentioned challenges, Artificial Intelligence (AI) could be a key technology. Due to the increasingly developed capabilities of this technology and the already explored application opportunities, the implementation of AI in the innovation process (as cost-intensive activity) can be highly relevant. Therefore, this study conducts a desktop research of existing application scenarios of AI in innovation management and examines what different subsets of AI (such as learning or speech recognition) are existing. Moreover, it will examined which subsets of AI can additionally support innovation managers in different tasks of the single phases within the innovation process and how to apply these subsets of AI to these tasks. As a result, recommendations for the usage of specific subsets of AI for the individual tasks in innovation management will be given.
A Literature Review of Frameworks for Evaluating Potentially Disruptive Innovation

This article examines the support from the disruptive innovation literature in recognizing potentially disruptive innovations. A literature review was carried out resulting in ten articles containing frameworks for evaluating disruptive innovation. Potentially disruptive innovations are by their nature hard to deal with. Being able to recognize them is a first step in dealing with them, but recent disruptive innovation literature describes the field as fragmented and misunderstood. The overview presented in this article can be used as a guide by academics and practitioners that intend to approach the field. Among the frameworks three types were observed: frameworks for classifying into disruptive or not, frameworks for classifying into types of disruption, and frameworks assessing disruptive potential. A comparison is made within and between framework types to illustrate similarities and differences between how they characterize types of disruption and which factors they consider for evaluating disruptive potential. The frameworks are applied to the IoT innovation of usage-based auto insurance. It was previously the subject of a case study, showing disruptive potential as a part of the digital transformation of insurance companies. Then the frameworks are evaluated based on the learnings from the case study.
RaComNet: High-Performance Deep Network for Waveform Recognition in Coexistence Radar-Communication Systems

In this paper, an efficient deep learning-based waveform recognition method is introduced for coexistence radar-communication systems in the presence of channel impairments. The method first leverages smooth pseudo Wigner-Ville distribution (SPWVD) to analyze signals in the time-frequency domain, which in turn provides a high-resolution visual representation of a signal with alleviation of cross-term interference. To effectively learn waveform patterns from a noisy-confusing dataset of transformed time-frequency images, we design a convolutional neural network (CNN), namely radar-communication waveform recognition network (RaComNet), which has several processing modules in cascade to extract representative features automatically. Each module is competently designed by incorporating residual connection and attention connection in a sophisticated structure to attain the following multifold advantages: feature diversity, gradient preservation, and feature refinement (i.e., strengthen relevant features and weaken irrelevant features), thus enhancing learning efficiency. Simulation results show that RaComNet is robust under impaired channel conditions and outperforms other existing CNN-based approaches in terms of accuracy.
Student Physical Violence Detection using Convolutional Neural Networks

Physical bullying has been an evident issue in the Philippines and Global Context. Thus, it was widely recognized as a major threat in the younger generation in almost every country in the world. It includes wide variety of acts and contexts. As worst reality, physical violence happens mostly in schools. As a result, it affects a person not only physically but also mentally that even leads to death. Nowadays, numerous research in action recognition has been done through Machine Learning to provide an assistance in predicting and recognizing actions using cameras. In this paper, the researcher utilized machine learning algorithms such as Convolutional Neural Networks to train and recognize actions of physical bullying such as Kicking, Punching, and Head Hitting. The findings revealed a positive result in detecting such actions using Convolutional Neural Networks. With such, it enables the prevention of further physical bullying occurrences in the future using CCTV Cameras.
Semi-supervised learning approaches for predicting South African political sentiment for local government elections

This study aims to understand the South African political context by analysing the sentiments shared on Twitter during the local government elections. An emphasis on the analysis was placed on understanding the discussions led around four predominant political parties – ANC, DA, EFF and ActionSA. A semi-supervised approach by means of a graph-based technique to label the vast accessible Twitter data for the classification of tweets into negative and positive sentiment was used. The tweets expressing negative sentiment were further analysed through latent topic extraction to uncover hidden topics of concern associated with each of the political parties. Our findings demonstrated that the general sentiment across South African Twitter users is negative towards all four predominant parties with the worst negative sentiment among users projected towards the current ruling party, ANC, relating to concerns centered around corruption, incompetence and loadshedding.
How to characterize the health of an Open Source Software project? A snowball literature review of an emerging practice

Motivation: Society's dependence on Open Source Software (OSS) and the communities that maintain the OSS is ever-growing. So are the potential risks of, e.g., vulnerabilities being introduced in projects not actively maintained. By assessing an OSS project's capability to stay viable and maintained over time without interruption or weakening, i.e., the OSS health, users can consider the risk implied by using the OSS as is, and if necessary, decide whether to help improve the health or choose another option. However, such assessment is complex as OSS health covers a wide range of sub-topics, and existing support is limited. Aim: We aim to create an overview of characteristics that affect the health of an OSS project and enable the assessment thereof. Method: We conduct a snowball literature review based on a start set of 9 papers, and identify 146 relevant papers over two iterations of forward and backward snowballing. Health characteristics are elicited and coded using structured and axial coding into a framework structure. Results: The final framework consists of 107 health characteristics divided among 15 themes. Characteristics address the socio-technical spectrum of the community of actors maintaining the OSS project, the software and other deliverables being maintained, and the orchestration facilitating the maintenance. Characteristics are further divided based on the level of abstraction they address, i.e., the OSS project-level specifically, or the project's overarching ecosystem of related OSS projects. Conclusion: The framework provides an overview of the wide span of health characteristics that may need to be considered when evaluating OSS health and can serve as a foundation both for research and practice.
Classification and identification level ambiguity in error annotation

The vast majority of corpus annotation projects goes through a piloting phase in which the annotation scheme is gradually shaped through iterative annotation cycles until its final version is produced and applied to the collected data. The differences in annotators’ choices are usually recorded and reflected by the ‘Inter-annotator Agreement’ (IAA) that serves as a proxy to understand and resolve the raised issues. However, little has been reported on how to formulate a systematic approach to: (i) tracing the source of the differences in the annotators’ choices and (ii) provide attainable solutions that would considerably increase IAA. In this paper, the ‘Greek Learner Corpus II’ (GLCII) -the largest online greek learner corpus will serve as a basis to shed light on two commonly met types of ambiguity in error annotation that are closely related to target languages in which syncretism is ubiquitous in grammar (e.g., Greek and Romanian): a classification level and an identification level ambiguity.
A swarm optimizer with modified feasible-based mechanism for optimum structure in steel industry

This study proposes a swarm optimizer with a modified feasible-based mechanism approach for finding an optimum design for steel frames. The proposed optimization approach addresses the problem of stagnation possibility in the traditional particle swarm optimization in which none of the particles tries to explore a position better than the previous best position for multiple numbers of iterations. This method is based on accelerated particle swarm optimization and big bang–big crunch optimization algorithms. In addition, a modified feasible-based mechanism is used to correct the particle’s position. The new method’s performance is evaluated by solving two structural problems to minimize the weight of steel frames. The results show that the optimized designs obtained by the proposed algorithm are better than those found by the competing algorithms from the literature.
Prediction of College Students’ Sports Performance Based on Improved BP Neural Network

Sports performance prediction has gradually become a research hotspot in various colleges and universities, and colleges and universities pay more and more attention to the development of college students' comprehensive quality. Aiming at the problems of low accuracy and slow convergence of the existing college students' sports performance prediction models, a method of college students' sports performance prediction based on improved BP neural network is proposed. First, preprocess the student's sports performance data, then use the BP neural network to train the data samples, optimize the selection of weights and thresholds in the neural network through the DE algorithm, and establish an optimal college student's sports performance prediction model, and then based on cloud computing, the platform implements and runs the sports performance prediction model, which speeds up the prediction of sports performance. The results show that the model can improve the accuracy of college students' sports performance prediction, provide more reliable prediction results, and provide valuable information for sports training.
Learning with Augmented Reality Headsets? Experiences of a Use Case in Vocational Education

For over a decade augmented reality (AR) technology has been discussed as educational tool. Latest technical advancements such as voice control, hand-tracking and moving virtual objects in augmented interaction using AR head-mounted-displays (HMDs) widen the opportunities for educational use even further. Still, the question remains how AR headsets can contribute to learning. As an example, we will present a use case in vocational education in which students shall practice the reading of hydraulic diagrams using advanced AR headsets. We developed a first prototype and examine its feasibility for a learning concept. In particular, we tested a) use of hand-tracking to select and move technical components, and b) use of voice commands and the gaze cursor to highlight the technical components by their names. Based on the experiences, we will analyse the strengths, weaknesses, opportunities and threats of our learning concept in a SWOT analysis. Finally, advantages and disadvantages of implementing advanced AR headsets in vocational education practice will be discussed.