Reviewerly/exHarmony huggingface dataset:
MAPPING THE CHATTER: SPATIAL METAPHORS FOR DYNAMIC TOPIC MODELLING OF SOCIAL MEDIA

Abstract. Topic modelling is a branch of Natural Language Processing (NLP) that deals with the discovery of conversation topics in a given document corpus. In social media, this translates into aggregating social media posts, e.g. tweets, into topics of conversation and observing how these topics evolve over time (hence the “dynamic” adjective). Conveying the results of topic modelling can be challenging since the topics often do not lend themselves naturally to meaningful labelling. The volume of real world (global) social media also means that millions of topics can be ongoing at any given time and the relationships between them can involve hundreds of dimensions and relationships that continually emerge. The popularity of topics is itself subject to change over time and reflect the pulse of what is happening in society at large. In this paper, we propose a spatialization technique based on open-source software that reduces the intrinsic complexity of dynamic topic modelling results to familiar topographic objects, namely: ridges, valleys, and peaks. This offers new possibilities for understanding complex relationships that change over time whilst overcoming issues with traditional topic modelling visualisation approaches such as network graphs.
Assertiveness as the predictor of university students’ academic achievement

The purpose of this research is to evaluate the relationship between the academic success of university students and their assertiveness levels with student opinions. Descriptive analysis, one of the qualitative research methods, was used in the research. The study group of the research consists of 40 university students studying at various universities in Kazakhstan in the 2021–2022 academic year. The data of the study were collected with a semi-structured interview form developed by the researchers. As a result of the research, the vast majority of university students stated that their academic achievement and assertiveness levels are at a moderate level. In addition, the majority of university students participating in the research stated that there is a significant relationship between the level of assertiveness and the level of academic achievement. The majority of students stated that as the level of assertiveness increases, academic success will increase. In line with the results obtained from the research, it has become necessary to add courses related to assertiveness to the curriculum of the education given at universities to improve the assertiveness levels of university students.&#x0D; Keywords: Assertiveness, academic achievement, university students;
Why Data Scientists Prefer Glassbox Machine Learning

Recent research has shown that interpretable machine learning models can be just as accurate as blackbox learning methods on tabular datasets. In this tutorial we will walk you through leading open source tools for glassbox learning, and show how intelligible machine learning helps practitioners uncover flaws in their datasets, discover new science, and build models that are more fair and robust. We'll begin with an introduction to the science behind glassbox modeling, and walk through a series of case-studies that highlight the added value of interpretable methods in a variety of domains such as finance and healthcare without compromising accuracy. We'll also show how glassbox models can be used for state of the art differentially private learning, bias detection/mitigation, and how these models can be edited to remove undesirable effects with GAMChanger. We'll also discuss how to train interpretable models with deep neural nets.
Sports Deep Learning Method Based on Cognitive Human Behavior Recognition

An in-depth learning-based approach is designed to develop the ability to recognize human behavior on the move. We introduce 3D residual structures and create 3D residual models. In order to get the most out of the data relationship of several consecutive frames, this study introduces 3D techniques for assigning different values to the existing frames. Experiments show that both structures improve recognition performance. For the 3D residual model, 3D attention model, and 3D attention residual model, this study proposes two model fusion strategies: average and weighted. Among them, the weighted fusion is to give a higher fusion proportion to the high accuracy model by using the model weight calculation method designed in this study. The experimental results show that the additive fusion strategy based on feature contribution has an obvious improvement effect on the test results of the two benchmark datasets, with an increase of more than 2% points, including an increase of 2.69% on HMDB51. The effect of splicing and fusion strategy has also increased by more than 1% point, including 1.34% on UCF101 dataset and about 1.9% on HMDB51. It is proven that deep learning can effectively recognize human behavior in sports.
Dynamic Graph Segmentation for Deep Graph Neural Networks

We present Deep network Dynamic Graph Partitioning (DDGP), a novel algorithm for optimizing the division of large graphs for mixture of expert graph neural networks. Our work is motivated from the observation that real world graphs suffer from spatial concept drift, which is detrimental to neural network training. We answer the question of how we can divide a graph, with vertices in each subgraph sharing a similar distribution, so that an expert network trained over each subgraph may yield the best learning outcome. DDGP is a two pronged algorithm that consists of cluster merging, followed by cluster boundary refinement. We used the training performance of each expert model as feedback to iteratively refine partition boundaries among subgraphs. These partitions are distinct for each model and graph network. We provide theoretical proof of convergence for DDGP boundary refinement as a guarantee for model training stability. Finally, we demonstrate experimentally that DDGP outperforms state-of-the-art graph partitioning algorithms for a regression task on multiple large real world graphs, with GraphSage and Graph Attention as our expert models.
PULP-TrainLib: Enabling On-Device Training for RISC-V Multi-core MCUs Through Performance-Driven Autotuning

An open challenge in making Internet-of-Things sensor nodes “smart” and self-adaptive is to enable on-chip Deep Neural Network (DNN) training on Ultra-Low-Power (ULP) microcontroller units (MCUs). To this aim, we present a framework, based on PULP-TrainLib, to deploy DNN training tasks on RISC-V-based Parallel-ULP (PULP) MCUs. PULP-TrainLib is a library of parallel software DNN primitives enabling the execution of forward and backward steps on PULP MCUs. To optimize PULP-TrainLib’s kernels, we propose a strategy to automatically select and configure (autotune) the fastest among a set of tiling options and optimized floating-point matrix multiplication kernels, according to the tensor shapes of every DNN layer. Results on an 8-core RISC-V MCU show that our auto-tuned primitives improve MAC/clk by up to 2.4 $$\times $$ compared to “one-size-fits-all” matrix multiplication, achieving up to 4.39 MAC/clk - 36.6 $$\times $$ better than a commercial STM32L4 MCU executing the same DNN layer training workload. Furthermore, our strategy proves to be 30.7 $$\times $$ faster than AIfES, a state-of-the-art training library for MCUs, while training a complete TinyML model.
Asian hate speech detection on Twitter during COVID-19

Coronavirus disease 2019 (COVID-19) started in Wuhan, China, in late 2019, and after being utterly contagious in Asian countries, it rapidly spread to other countries. This disease caused governments worldwide to declare a public health crisis with severe measures taken to reduce the speed of the spread of the disease. This pandemic affected the lives of millions of people. Many citizens that lost their loved ones and jobs experienced a wide range of emotions, such as disbelief, shock, concerns about health, fear about food supplies, anxiety, and panic. All of the aforementioned phenomena led to the spread of racism and hate against Asians in western countries, especially in the United States. An analysis of official preliminary police data by the Center for the Study of Hate &amp;amp; Extremism at California State University shows that Anti-Asian hate crime in 16 of America's largest cities increased by 149% in 2020. In this study, we first chose a baseline of Americans' hate crimes against Asians on Twitter. Then we present an approach to balance the biased dataset and consequently improve the performance of tweet classification. We also have downloaded 10 million tweets through the Twitter API V-2. In this study, we have used a small portion of that, and we will use the entire dataset in the future study. In this article, three thousand tweets from our collected corpus are annotated by four annotators, including three Asian and one Asian-American. Using this data, we built predictive models of hate speech using various machine learning and deep learning methods. Our machine learning methods include Random Forest, K-nearest neighbors (KNN), Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), Logistic Regression, Decision Tree, and Naive Bayes. Our Deep Learning models include Basic Long-Term Short-Term Memory (LSTM), Bidirectional LSTM, Bidirectional LSTM with Drop out, Convolution, and Bidirectional Encoder Representations from Transformers (BERT). We also adjusted our dataset by filtering tweets that were ambiguous to the annotators based on low Fleiss Kappa agreement between annotators. Our final result showed that Logistic Regression achieved the best statistical machine learning performance with an F1 score of 0.72, while BERT achieved the best performance of the deep learning models, with an F1-Score of 0.85.
A Novel IoMT System for Pathological Diagnosis Based on Intelligent Mobile Scanner and Whole Slide Image Stitching Method

Digital pathology digitizes traditional pathological sections into whole slide images (WSIs), which greatly reduces the workload of pathologists and improves the efficiency of pathological diagnosis. However, conventional pathological scanners are usually bulky and expensive, limiting their popularity in remote and underdeveloped areas, and restricting the development and application of digital pathology in these areas. This paper proposes an efficient and robust Internet of Medical Things (IoMT) system for pathological diagnosis. For data acquisition, we developed an automatic portable pathological section scanner based on mobile Internet, Landing-Smart, which integrates four main components including a smartphone, a glass slide carrier, an electric controller and an optical imaging unit. Through the customized scan app, the smartphone uses its built-in camera to continually capture enlarged images of the field of view (FoV) in the section and uploads the images to the cloud server in real time via mobile internet, where the image processing and stitching method is implemented to generate the WSI of the pathological section. To validate the performance of the proposed system and Landing-Smart, we collect 209 cervical cytology smears and scan them into WSIs using conventional scanners and Landing-Smart, respectively. The double-blinded assessment results have illustrated that the WSIs obtained via Landing-Smart can be comparable to those obtained via general digital pathology scanners.
Corporate social responsibility and public diplomacy as formulas to reduce hate speech on social media in the fake news era

Purpose Analyse the presence of hate speech in society, placing special emphasis on social media. In this sense, the authors strive to build a formula to moderate this type of content, in which platforms and public institutions cooperate, from the fields of corporate social responsibility and public diplomacy, respectively. Design/methodology/approach To this aim, it is important to focus efforts on the creation of counter-narratives; the establishment of content moderation guidelines, which are not necessarily imposed by unilateral legislation; the promotion of suitable scenarios for the involvement of civil society; transparency on the part of social media companies; and supranational cooperation that is as transnational as possible. To exemplify the implementation of initiatives against hate speech, two cases are analysed that are paradigmatic for assuming two effective approaches to the formula indicated by the authors. Findings The authors analyse, in the case of the European Union, its “Code of conduct to counteract illegal online hate speech”, which included the involvement of different social media companies. And in the case of Canada, the authors discuss the implementation of the bill to include a definition of hate speech and the establishment of specific sanctions for this in the Canadian Human Rights Act and the Canadian Penal Code. Originality/value The case of the European Union was a way of seeking consensus with social media companies without legislation, while the case of Canada involved greater legislative and penalisation. Two ways of seeking the same goal: curbing hate speech.
Metaheuristics-Based Optimization of a Robust GAPID Adaptive Control Applied to a DC Motor-Driven Rotating Beam with Variable Load

This work aims to analyze two metaheuristics optimization techniques, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), with six variations each, and compare them regarding their convergence, quality, and dispersion of solutions. The optimization target is the Gaussian Adaptive PID control (GAPID) to find the best parameters to achieve enhanced performance and robustness to load variations related to the traditional PID. The adaptive rule of GAPID is based on a Gaussian function that has as adjustment parameters its concavity and the lower and upper bound of the gains. It is a smooth function with smooth derivatives. As a result, it helps avoid problems related to abrupt increases transition, commonly found in other adaptive methods. Because there is no mathematical methodology to set these parameters, this work used bio-inspired optimization algorithms. The test plant is a DC motor with a beam with a variable load. Results obtained by load and gain sweep tests prove the GAPID presents fast responses with very low overshoot and good robustness to load changes, with minimal variations, which is impossible to achieve when using the linear PID.
Negative Gradient Differential Biogeography-based Optimization for Mobile Robot Path Planning

Mobile robot path planning (MRPP) plays an irreplaceable role in the process of intelligent robots and practical artificial intelligence. The traditional global path planning methods have some shortcomings, such as difficulty in digging into environmental information and finding the optimal path effectively. To solve the above problems, this paper proposes a negative gradient differential biogeography-based optimization (NG-DBBO), which has strong local search ability and global optimization ability. Firstly, we present a differential migration approach to increase the population diversity in the iterative process of NG-DBBO, which can realize the information sharing between feature solutions effectively. Then a negative gradient descent strategy based on negative gradient descent is introduced to improve the learning rate, which not only enhances initial global search ability, but also avoids premature convergence. Noteworthily, the convergence of the algorithm is analyzed for single-peak and multi-peak problems respectively. After that, NG-DBBO is combined with the cubic spline interpolation to realize MRPP by the defined coding method and fitness function. The simulation experiments are used to demonstrate the availability of our method, which consist of two parts. In the first part, we select 23 benchmark functions to verify the accuracy and convergence speed of the NG-DBBO algorithm. The practicability of path planning in different environments is demonstrated in the second part.
Design of Piano Intelligent Teaching System Based on Neural Network Algorithm

In recent years, with the improvement of economic level, music art has become a major focus of extracurricular teaching. Students of all ages are included. However, the high cost of piano teaching and the unique one-to-one teaching method of teachers and students lead to the lack of piano education resources. Learning piano has become a luxury activity. Therefore, using computer multimedia software to teach piano has become a feasible way to alleviate the current contradiction. For piano teaching, the main difficulties are the differences between teachers and students (i.e., the data change at both ends due to the network), the instability of the network system, and the neural network algorithm can solve these difficulties. Based on this point, this work aims to introduce neural network algorithm into piano teaching intelligent system. This paper first introduces the theoretical basis of the neural network algorithm, then expounds the algorithm flow and general framework of the algorithm in speech recognition, and explains the split explanation in combination with five aspects: preprocessing, character extraction, acoustic model, linguistic model, and decoding. Then, it introduces the system design of intelligent piano teaching and describes the general system requirements and product architecture. Finally, the intelligent piano teaching system is tested and applied to prove the effectiveness of the system. I hope this intelligent piano teaching system can provide more convenience for piano teaching.
A Multimodal Model for College English Teaching Using Text and Image Feature Extraction

The rapid development of the internet and multimedia technology in recent years has continued to push foreign language education in the direction of modern education. Multimodal education is becoming more and more important in the field of English education as an advanced educational concept in the field of language education. As a result, many English teachers have begun to emphasize the use of multimodal teaching theory in their classrooms. This paper investigates a multimodal model that incorporates text and image features, based on multimodal discourse theory, systemic functional linguistics theory, and foreign language teaching theory. This paper develops a multimodal model that can search for images and texts from various perspectives. We use an image feature bias term in the log-bilinear natural language model to influence the probability of predicting the next word based on the context, resulting in a multimodal model. The experimental results show that the proposed model, as an image-text relationship evaluation index system, has a slower search speed than other models but better search accuracy.
Research on Fast Recognition of Vulnerable Traffic Participants in Intelligent Connected Vehicles on Edge Computing

Real-time and fast recognition of all kinds of traffic participants in intelligent driving has always been a major difficulty in the research of internet of vehicles. With the advent of edge computing, we try to deploy an image recognition algorithm directly to the intelligent vehicles. However, the original image recognition algorithm is difficult to be directly deployed on the vehicles due to limited edge device resources. Based on this, a fast recognition model of vulnerable traffic participants based on depthwise separable convolutional neural network (DSCYOLO) is proposed in this paper. The algorithm can significantly reduce the convolutional parameter quantity and computing load, making it suitable for deployment on the vehicle-mounted edge embedded devices. In order to validate the effectiveness of the proposed method, its simulation results are compared with the main target detection models Faster R-CNN, SSD and YOLOv3. The results show that the recognition time of the proposed model is reduced by 80.28%, 66.80% and 86.74%, respectively, on the basis of a relatively high recognition precision. The model can realize real-time detection and fast recognition of vulnerable traffic participants, so as to avoid a large number of traffic accidents. It has significant social and economic benefits.
Bounding Quantum Capacities via Partial Orders and Complementarity

Quantum capacities are fundamental quantities that are notoriously hard to compute and can exhibit surprising properties such as superadditivity. Thus, a vast amount of literature is devoted to finding tight and computable bounds on these capacities. We add a new viewpoint by giving operationally motivated bounds on several capacities, including the quantum capacity and private capacity of a quantum channel and the one-way distillable entanglement and private key of a quantum state. These bounds are generally phrased in terms of capacity quantities involving the complementary channel or state. As a tool to obtain these bounds, we discuss partial orders on quantum channels and states, such as the less noisy and the more capable order. Our bounds help to further understand the interplay between different capacities, as they give operational limitations on superadditivity and the difference between capacities in terms of the information-theoretic properties of the complementary channel or state. They can also be used as a new approach towards numerically bounding capacities, as discussed with some examples.
Research opportunities in the application of blockchain in video games: A scoping review

The study of blockchain (BC) technologies has been applied to a wide breadth of research fields in recent years, chasing the realization of its full potential. Among them, video games have become a very eager adopter of blockchain in many of its different facets: cryptocurrencies, Non-Fungible Tokens (NFTs), and smart contracts. This paper presents the results of a scoping review in order to answer the research question "What is the current state of academic research in blockchain applied to video games?". After a thorough search in a diverse set of scientific databases, a total of 39 fitting publications were methodically retrieved, studied and categorized. The results draw a clearer picture of the current state-of-the art, as well as the identification of relevant trends and opportunities in this topic.
The Construction of Adaptive Learning for Sports Based on Aerobics Trajectory Recognition Model

Perceiving the movement track of aerobics is a key element of learning aerobics, but the current aerobics movement is not very professional, the ability to identify the movement track is weak, and improper movement in the movement process is easy to cause physical injury. In order to improve the safety of athletes in bodybuilding training, this paper uses Kinect to hold the coach’s body contour, determine the standard level of coaches’ sports, and combine the characteristics for aerobics training, so as to improve the sports level of coaches, through data acquisition, data processing, and feature extraction to assist sports learning, as well as human posture recognition. The calculation and recognition of human skeleton joints are completed by two algorithms, which improve the human motion recognition algorithm. The aerobics data collected by Kinect device is specified and digitized, which enhances the robustness of the system and improves the performance of the algorithm and the accuracy of the motion data.