Reviewerly/exHarmony huggingface dataset:
Transformation of blockchain and opportunities for education 4.0

There are many ways in which education 4.0 can continue to develop rapidly and coexist with the development of increasingly advanced technology to bond with each other to be balanced, one of which is using blockchain technology that is integrated in the education sector for various purposes. The main direction in developing global integration in education includes the creating a single educational space and optimization of the interaction between education and stakeholder relationships. The blockchain method implemented in education 4.0 was not widely used because initially, blockchain was only known for the financial sector. Blockchain is comprehensive and appropriate for this era, as blockchain offers technology, trust, and transparency by replacing the previous system with a new system. A particular problem is a need for innovative research to provide new insights into blockchain transformation inf education and application opportunities that can be accepted and used optimally. Researchers used the vast mind method and literature study. The goal is to inform the added value of blockchain that is applied in education as needed, the renewal of research and opportunities for implementing blockchain in education 4.0. The blockchain technology that can be used in education, for example, is archiving, learning, certification and other.
A novel keyframe extraction method for video classification using deep neural networks

Abstract Combining convolutional neural networks (CNNs) and recurrent neural networks (RNNs) produces a powerful architecture for video classification problems as spatial–temporal information can be processed simultaneously and effectively. Using transfer learning, this paper presents a comparative study to investigate how temporal information can be utilized to improve the performance of video classification when CNNs and RNNs are combined in various architectures. To enhance the performance of the identified architecture for effective combination of CNN and RNN, a novel action template-based keyframe extraction method is proposed by identifying the informative region of each frame and selecting keyframes based on the similarity between those regions. Extensive experiments on KTH and UCF-101 datasets with ConvLSTM-based video classifiers have been conducted. Experimental results are evaluated using one-way analysis of variance, which reveals the effectiveness of the proposed keyframe extraction method in the sense that it can significantly improve video classification accuracy.
Sentiment Classification of Crowdsourcing Participants’ Reviews Text Based on LDA Topic Model

The review text received by crowdsourcing participants contains valuable knowledge, opinions, and preferences, which is an important basis for employers to make trading decisions, and crowdsourcing participants to improve service level and quality. However, there are two kinds of emotional polarity in the review text, the attention paid to sentiment classification of review text with fuzzy emotional boundaries is insufficient. This paper proposes a supervised text sentiment classification method with Latent Dirichlet Allocation (LDA) to improve the classification performance of review text with fuzzy sentiment boundaries. Taking the review text of crowdsourcing participants on the Zhubajie platform as the data set, using N-gram, Word2vec, and TF-IDF algorithms to extract text features. The LDA topic model is applied to expand the number of text features and extract eight topics that affect employers’ sentiment tendencies. Text classifiers are constructed based on Support Vector Machine (SVM), Random Forest (RF), Gradient Boosting Decision Tree (GDBT), and Extreme Gradient Boosting (XGBoost) algorithms, and the effectiveness of the sentiment classification methods are verified by ten-fold cross-validation and confusion matrix. Experimental results show that using the LDA topic model to extend the features of review text can effectively alleviate the problem that the classifier is difficult to distinguish the sentiment categories of different emotion polarity words coexisting text, and enhance the ability of emotion boundary fuzzy text classification. Based on TF-IDF and LDA to extract and expand text features, the GBDT text sentiment classifier with the accuracy of 0.881; the F1-measure of the second, third, fourth, and fifth categories samples are 0.462, 0.571, 0.278, and 0.647 respectively, which is better than SVM, RF, and XGBoost classifiers and has the best classification performance.
Improving Generalization of Deep Learning Models for Diagnostic Pathology by Increasing Variability in Training Data: Experiments on Osteosarcoma Subtypes

Artificial intelligence has an emerging progress in diagnostic pathology. A large number of studies of applying deep learning models to histopathological images have been published in recent years. While many studies claim high accuracies, they may fall into the pitfalls of overfitting and lack of generalization due to the high variability of the histopathological images.Use the model training of osteosarcoma as an example to illustrate the pitfalls of overfitting and how the addition of model input variability can help improve model performance.We use the publicly available osteosarcoma dataset to retrain a previously published classification model for osteosarcoma. We partition the same set of images into the training and testing datasets differently than the original study: the test dataset consists of images from one patient while the training dataset consists images of all other patients. We also show the influence of training data variability on model performance by collecting a minimal dataset of 10 osteosarcoma subtypes as well as benign tissues and benign bone tumors of differentiation.The performance of the re-trained model on the test set using the new partition schema declines dramatically, indicating a lack of model generalization and overfitting. We show the additions of more and moresubtypes into the training data step by step under the same model schema yield a series of coherent models with increasing performances.In conclusion, we bring forward data preprocessing and collection tactics for histopathological images of high variability to avoid the pitfalls of overfitting and build deep learning models of higher generalization abilities.
Testing multiple linear regression systems with metamorphic testing

Regression is one of the most commonly used statistical techniques. However, testing regression systems is a great challenge because of the absence of test oracle in general. In this paper, we show that Metamorphic Testing is an effective approach to test multiple linear regression systems. In doing so, we identify intrinsic mathematical properties of linear regression, and then propose 11 Metamorphic Relations to be used for testing. Their effectiveness is examined using mutation analysis with a range of different regression programs. We further look at how the testing could be adopted in a more effective way. Our work is applicable to examine the reliability of predictive systems based on regression that has been widely used in economics, engineering and science, as well as of the regression calculation manipulated by statistical users.
Demonstration of dynamical control of three-level open systems with a superconducting qutrit

Abstract We propose a method for the dynamical control in three-level open systems and realize it in the experiment with a superconducting qutrit. Our work demonstrates that in the Markovian environment for a relatively long time (3 μ s), the systemic populations or coherence can still strictly follow the preset evolution paths. This is the first experiment for precisely controlling the Markovian dynamics of three-level open systems, providing a solid foundation for the future realization of dynamical control in multiple open systems. An instant application of the technique demonstrated in this experiment is to stabilize the energy of quantum batteries.
Research on the Mental Health of College Students Based on Fuzzy Clustering Algorithm

The mental health of young college students has always been a social concern. Strengthening the supervision of college students’ mental health problems is an important research content. In this regard, this paper proposes to apply fuzzy cluster analysis to the health analysis of college students and explore college students through fuzzy clustering. Explore the potential relationship between the factors that affect the health of college students, and this will provide a reference for the early prevention and intervention of college students’ mental health problems. In view of this, an improved fuzzy clustering method based on the firefly algorithm is proposed. First, the Chebyshev diagram is introduced into the firefly algorithm to initialize the population distribution. Then, an adaptive step size method is proposed to balance exploration and development capabilities. Finally, in the local search process, a Gaussian perturbation strategy is added to the optimal individual in each iteration to make it jump out of the local optimal. The process has good optimization capabilities and is easy to obtain the global optimal value. It can be used as the initial center of the fuzzy C-means clustering algorithm for clustering, which can effectively enhance the robustness of the algorithm and improve the global optimization ability. In order to evaluate the effectiveness of the algorithm, comparative experiments were carried out on four datasets, and the experimental results show that the algorithm is better than the comparison algorithm in clustering accuracy and robustness.
Leveraging Two Types of Global Graph for Sequential Fashion Recommendation

Sequential fashion recommendation is of great significance in online fashion shopping, which accounts for an increasing portion of either fashion retailing or online e-commerce. The key to building an effective sequential fashion recommendation model lies in capturing two types of patterns: the personal fashion preference of users and the transitional relationships between adjacent items. The two types of patterns are usually related to user-item interaction and item-item transition modeling respectively. However, due to the large sets of users and items as well as the sparse historical interactions, it is difficult to train an effective and efficient sequential fashion recommendation model. To tackle these problems, we propose to leverage two types of global graph, i.e., the user-item interaction graph and item-item transition graph, to obtain enhanced user and item representations by incorporating higher-order connections over the graphs. In addition, we adopt the graph kernel of LightGCN for the information propagation in both graphs and propose a new design for item-item transition graph. Extensive experiments on two established sequential fashion recommendation datasets validate the effectiveness and efficiency of our approach.
Speedy Image Crowd Counting by Light Weight Convolutional Neural Network

In image/video analysis, crowds are actively researched, and their numbers are counted. In the last two decades, many crowd counting algorithms have been developed for a wide range of applications in crisis management systems, large-scale events, workplace safety, and other areas. The precision of neural network research for estimating points is outstanding in computer vision domain. However, the degree of uncertainty in the estimate is rarely indicated. Point estimate is beneficial for measuring uncertainty since it can improve the quality of decisions and predictions. The proposed framework integrates Light weight CNN (LW-CNN) for implementing crowd computing in any public place for delivering higher accuracy in counting. Further, the proposed framework has been trained through various scene analysis such as the full and partial vision of heads in counting. Based on the various scaling sets in the proposed neural network framework, it can easily categorize the partial vision of heads count and it is being counted accurately than other pre-trained neural network models. The proposed framework provides higher accuracy in estimating the headcounts in public places during COVID-19 by consuming less amount of time.
AI invading the workplace: negative emotions towards the organizational use of personal virtual assistants

Abstract Personal virtual assistants (PVAs) based on artificial intelligence are frequently used in private contexts but have yet to find their way into the workplace. Regardless of their potential value for organizations, the relentless implementation of PVAs at the workplace is likely to run into employee resistance. To understand what motivates such resistance, it is necessary to investigate the primary motivators of human behavior, namely emotions. This paper uncovers emotions related to organizational PVA use, primarily focusing on threat emotions. To achieve our goal, we conducted an in-depth qualitative study, collecting data from 45 employees in focus-group discussions and individual interviews. We identified and categorized emotions according to the framework for classifying emotions Beaudry and Pinsonneault (2010) designed. Our results show that loss emotions, such as dissatisfaction and frustration, as well as deterrence emotions, such as fear and worry, constitute valuable cornerstones for the boundaries of organizational PVA use.
Digital intelligence: A must-have for project managers

The Fourth Industrial Revolution (4IR) requires a fundamental change in the way we live, work and relate to one another. It requires a new type of intelligence to deal with this fundamental change. This new type of intelligence requires new competencies. Yet, it is not crystal clear what these new competencies should be, especially those relating to the discipline of project management. Project leaders are required to implement and manage 4IR-related projects but do not necessarily have the required competencies. This is especially the case in a developing country such as South Africa. The study explored the digital competencies that are deemed important by project managers in managing projects in the digital economy. Q-methodology was used to determine potential perspectives, and three confirming perspectives and one emergent perspective were identified. The main competence deemed important is online communication and collaboration, whereas cybersecurity is deemed unimportant. This study is the first of its kind and provides insight into the digital intelligence of project leaders. It contributes to the current debate around the future digital competencies and skills that are required. It also provides guidelines for organisations on how to plan and structure training and to upskill their current project leaders. A concerted effort needs to be in place to upskill project leaders. It cannot be left to fate.
A methodological review of qualitative research syntheses in CALL: The state-of-the-art

Qualitative research in computer-assisted language learning (CALL) has received more attention recently, most evidently from the publications of two special issues on qualitative research in CALL by CALICO Journal (2015) and Language Learning & Technology (2018). The inherent strength of qualitative studies in CALL is that they celebrate depth of interpretation by focusing on specific contexts and a small number of participants. Yet this strength can sometimes be perceived as a limitation when transferability and applicability of findings are considered. However, this potential shortcoming can be overcome when findings of multiple studies are meaningfully synthesised, for example, in a qualitative research synthesis (QRS). QRS is a relatively new form of research synthesis in CALL which systematically summarises qualitative findings guided by research questions and a predetermined research protocol. To report current methodological practices and identify areas of strengths and weaknesses, this exploratory review analyses the methodological sections of 16 QRS in CALL using a recent, CALL-based QRS methodological framework comprising seven stages (Chong & Plonsky, 2021). Findings reported include implementation of the seven methodological stages, and convergent and divergent methodological practices. Suggestions, especially about qualitative data extraction and synthesis, are made in light of the findings.
A Taxonomical Review of Multiplexer Designs for Electronic Circuits &amp; Devices

This research paper reviews and briefly discusses about the multiplexers and demultiplexers. This research paper aims to explore the history of multiplexers, types of multiplexers, applications and the real-time use cases of multiplexers. Furthermore, it also includes a brief introduction on the different multiplexing techniques employed in analog and digital electronics, ongoing research studies and future research scope for multiplexers.
Distributed quantum computing with QMPI

Practical applications of quantum computers require millions of physical qubits and it will be challenging for individual quantum processors to reach such qubit numbers. It is therefore timely to investigate the resource requirements of quantum algorithms in a distributed setting, where multiple quantum processors are interconnected by a coherent network. We introduce an extension of the Message Passing Interface (MPI) to enable high-performance implementations of distributed quantum algorithms. In turn, these implementations can be used for testing, debugging, and resource estimation. In addition to a prototype implementation of quantum MPI, we present a performance model for distributed quantum computing, SENDQ. The model is inspired by the classical LogP model, making it useful to inform algorithmic decisions when programming distributed quantum computers. Specifically, we consider several optimizations of two quantum algorithms for problems in physics and chemistry, and we detail their effects on performance in the SENDQ model.
SOLVER: Scene-Object Interrelated Visual Emotion Reasoning Network

Visual Emotion Analysis (VEA) aims at finding out how people feel emotionally towards different visual stimuli, which has attracted great attention recently with the prevalence of sharing images on social networks. Since human emotion involves a highly complex and abstract cognitive process, it is difficult to infer visual emotions directly from holistic or regional features in affective images. It has been demonstrated in psychology that visual emotions are evoked by the interactions between objects as well as the interactions between objects and scenes within an image. Inspired by this, we propose a novel Scene-Object interreLated Visual Emotion Reasoning network (SOLVER) to predict emotions from images. To mine the emotional relationships between distinct objects, we first build up an Emotion Graph based on semantic concepts and visual features. Then, we conduct reasoning on the Emotion Graph using Graph Convolutional Network (GCN), yielding emotion-enhanced object features. We also design a Scene-Object Fusion Module to integrate scenes and objects, which exploits scene features to guide the fusion process of object features with the proposed scene-based attention mechanism. Extensive experiments and comparisons are conducted on eight public visual emotion datasets, and the results demonstrate that the proposed SOLVER consistently outperforms the state-of-the-art methods by a large margin. Ablation studies verify the effectiveness of our method and visualizations prove its interpretability, which also bring new insight to explore the mysteries in VEA. Notably, we further discuss SOLVER on three other potential datasets with extended experiments, where we validate the robustness of our method and notice some limitations of it.
UDO

UDO is a versatile tool for offline tuning of database systems for specific workloads. UDO can consider a variety of tuning choices, reaching from picking transaction code variants over index selections up to database system parameter tuning. UDO uses reinforcement learning to converge to near-optimal configurations, creating and evaluating different configurations via actual query executions (instead of relying on simplifying cost models). To cater to different parameter types, UDO distinguishes heavy parameters (which are expensive to change, e.g. physical design parameters) from light parameters. Specifically for optimizing heavy parameters, UDO uses reinforcement learning algorithms that allow delaying the point at which the reward feedback becomes available. This gives us the freedom to optimize the point in time and the order in which different configurations are created and evaluated (by benchmarking a workload sample). UDO uses a cost-based planner to minimize reconfiguration overheads. For instance, it aims to amortize the creation of expensive data structures by consecutively evaluating configurations using them. We evaluate UDO on Postgres as well as MySQL and on TPC-H as well as TPC-C, optimizing a variety of light and heavy parameters concurrently.
Optimising the selection of samples for robust lidar camera calibration

We propose a robust calibration pipeline that optimises the selection of calibration samples for the estimation of calibration parameters that fit the entire scene. We minimise user error by automating the data selection process according to a metric, called Variability of Quality (VOQ) that gives a score to each calibration set of samples. We show that this VOQ score is correlated with the estimated calibration parameter's ability to generalise well to the entire scene, thereby overcoming the overfitting problems of existing calibration algorithms. Our approach has the benefits of simplifying the calibration process for practitioners of any calibration expertise level and providing an objective measure of the quality for our calibration pipeline's input and output data. We additionally use a novel method of assessing the accuracy of the calibration parameters. It involves computing reprojection errors for the entire scene to ensure that the parameters are well fitted to all features in the scene. Our proposed calibration pipeline takes 90s, and obtains an average reprojection error of 1-1.2cm, with standard deviation of 0.4-0.5cm over 46 poses evenly distributed in a scene. This process has been validated by experimentation on a high resolution, software definable lidar, Baraja Spectrum-Scan™; and a low, fixed resolution lidar, Velodyne VLP-16. We have shown that despite the vast differences in lidar technologies, our proposed approach manages to estimate robust calibration parameters for both. Our code and data set used for this paper are made available at https://github.com/acfr/cam_lidar_calibration.