Reviewerly/exHarmony huggingface dataset:
Radar-Based 3D Skeleton Estimation Enhanced with Joint Temporal-Spatial Constraints

Radar-based human pose estimation faces the challenge of anatomically incorrect pose estimation within frames and unstable pose estimation results across multiple frames, primarily due to the sensitivity of radar signals to the radial component of motion. To overcome this, we propose a two-stream model that incorporates spatio-temporal constraints into learning network. Spatial stream network captures the physical connections among joints, ensuring anatomical consistency within frames. Meanwhile, temporal stream network focuses on learning action-specific synergies in motion, capturing the temporal dependencies among joints. The fusion of two streams enables accurate and stable 3D skeleton estimation. Experiments demonstrate the effectiveness of our method.
Path Planning of Mobile Robot Based on Improved Artificial Potential Field Method

As a classical path planning algorithm for mobile robots, artificial potential field method still has some flaws in some application scenarios. In this paper, we improve the artificial potential field method from two aspects to deal with these problems. The hyperbolic tangent function of the distance from the target to the robot is used to improve the repulsive force field to tackle the problem of the unreachable target point near obstacles. A behavior-based strategy based on collision cone is proposed to cope with the local minima problem. Simulation results prove that the improved method is effective.
Deep Reinforcement Learning-Based Multi-objective 3D Path Planning for Vehicles

A path planning method based on deep reinforcement learning is proposed to attain a good trade-off between distance and energy consumption in 3D environments. This method achieves optimal path planning through downsampling the high-resolution 3D map to a smaller version while preserving terrain information, followed by a deep Q-network trained via imitation learning and a well-designed reward function to explore an optimal route on the downsized map. Ultimately, a path-enhanced algorithm is employed to recover the planned path onto the original high-resolution map. The simulation experiment shows that this novel approach provides a promising solution for multi-objective 3D path planning problem.
A frequency, coverage, and dispersion analysis of the academic collocation list in university student writing

Abstract This study investigates whether cross-disciplinary academic collocation lists are of equal importance to EAP (English for academic purposes) learners across different disciplines. We target Ackermann and Chen’s (Ackermann, Kirsten &amp; Yu-Hua Chen. 2013. Developing the Academic Collocation List (ACL)–A corpus-driven and expert-judged approach. Journal of English for Academic Purposes 12(4). 235–247) Academic Collocation List (ACL) and conduct a frequency, coverage, and dispersion analysis of its 2,464 pairs in the British Academic Written English (BAWE) corpus. Using BAWE data, we also examine whether university students increase use of academic collocations across years. Generally, our results indicate that cover-all academic collocation lists, like academic word lists, show uneven frequency distributions across disciplines; the ACL covers considerably more word combinations in soft than hard sciences. According to our dispersion analysis, furthermore, an important cause for such skewed distributions is that a substantial part of the ACL (i.e., almost 25 %) is in actuality discipline-specific rather than generic collocations. In terms of collocation development, we do observe steady increases across years. Interestingly, university students throughout levels not only use more academic collocations, but display a stronger tendency to raise the proportion of discipline-specific collocations in writing. This study is novel as it systematically examines distributions and developments of academic collocations, and is pedagogically valuable in that it collects 483 ACL entries which are suitable targets for EAP learning.
Do I Have Your Attention: A Large Scale Engagement Prediction Dataset and Baselines

The degree of concentration, enthusiasm, optimism, and passion displayed by individual(s) while interacting with a machine is referred to as ‘user engagement’. Engagement comprises of behavioral, cognitive, and affect related cues [17]. To create engagement prediction systems that can work in real-world conditions, it is quintessential to learn from rich, diverse datasets. To this end, a large scale multi-faceted engagement in the wild dataset EngageNet is proposed. 31 hours duration data of 127 participants representing different illumination conditions are recorded. Thorough experiments are performed exploring the applicability of different features, action units, eye gaze, head pose, and MARLIN. Data from user interactions (question-answer) are analyzed to understand the relationship between effective learning and user engagement. To further validate the rich nature of the dataset, evaluation is also performed on the EngageWild dataset. The experiments show the usefulness of the proposed dataset. The code, models, and dataset link are publicly available at https://github.com/engagenet/engagenet_baselines.
Multimodal Conversational Agents for People with Neurodevelopmental Disorders

Neurodevelopmental Disorders (NDD) involve developmental deficits in cognition, social interaction, and communication. Despite growing interest, gaps persist in understanding usability, effectiveness, and perceptions of such agents. We organize a workshop focusing on the use of conversational agents with multi-modal capabilities for therapeutic interventions in NDD. The workshop brings together researchers and practitioners to discuss design, evaluation, and ethical considerations. Anticipated outcomes include identifying challenges, sharing advancements, fostering collaboration, and charting future research directions.
Identifying Interlocutors' Behaviors and its Timings Involved with Impression Formation from Head-Movement Features and Linguistic Features

A prediction-explanation framework is proposed to identify when and what behaviors are involved in forming interlocutors’ impressions in group discussions. We targeted the self-reported scores of 16 impressions, including enjoyment and concentration. To that end, we formulate the problem as discovering behavioral features that contributed to the impression prediction and determining the timings that the behaviors frequently occurred. To solve this problem, this paper proposes a two-fold framework consisting of the prediction part followed by the explanation part. The former prediction part employs random forest regressors using functional head-movement features and BERT-based linguistic features, which can capture various aspects of interactive conversational behaviors. The later part measures the levels of features’ contribution to the prediction using a SHAP analysis and introduces a novel idea of temporal decomposition of features’ contributions over time. The influential behaviors and their timings are identified from local maximums of the temporal distribution of features’ contributions. Targeting 17-group 4-female discussions, the predictability and explainability of the proposed framework are confirmed by some case studies and quantitative evaluations of the detected timings.
The crime-as-communication approach: Challenging the idea of online routine activities by taking communication seriously

In mediated environments, a simple but important understanding holds true: there is plenty of communication without crime, yet no crime without communication. Put differently, the criminal act in technology-mediated environments is always concurrently a communicative act. Nevertheless, descriptions of online activities and their role in studies of cybercrime often only amount to what can be considered lazy signifiers: online banking is not a single activity, but a series of requests for information made to, and responses received from, servers through the use of one’s networked device. The user’s interpretations of the content form the basis for deciding on the next action(-as-communication). Available theoretical approaches, notably the routine activities approach, lack this type of specificity in considering as a meaningful whole something that is better understood as its constitutive elements – messages in communicative exchanges. Hence, the current theoretical status quo hinders rather than contributes to cybercrime-related thought and research. Rather than fitting a ‘terrestrial’ understanding of action onto acts of cybercrime, the crime-as-communication approach instead builds a communication-based foundation for understanding action in technology-mediated environments. By making explicit the communication processes that constitute cybercrime, crime-as-communication offers a principled way of analysing mediated crime specifically, and action in mediated environments more broadly. This approach has practical implications for research and policy-making concerning economic crimes like fraud and intellectual property violations, interpersonal crimes such as harassing pursuit and criminal threats, computer offences like unauthorised access and offences against peace and public order, including incitement to hatred and disinformation or propaganda for war.
Increasing Mutual Cooperation Values Using the Scramble Method in Class X Students of SMA Negeri 1 Palu

This class action research was carried out in class . with the aim of improving students' mutual cooperation skills during the learning process using the scramble type cooperative model, this type of research is descriptive quantitative, in the initial test students get a score of 2.65 or 66%, during the learning process it is seen that students still need to increase in collaboration and also communication/discussion of the problems being faced, in the implementation of the first cycle students see an increase in students who are able to discuss and exchange opinions to agree on joint decisions. The results of the student mutual cooperation skills questionnaire increased to 3.16 or 79%, the shortcoming in the first cycle was that the coordination of group members in compiling keywords still needed to be improved, in the second cycle changes were made to student activities in compiling keywords where the group leader was appointed to coordinate group members in the process of compiling keywords, the results of the second cycle questionnaire showed an increase to 3.54 or 88%.
BERT models for Brazilian Portuguese: Pretraining, evaluation and tokenization analysis

Recent advances in language representation using neural networks have made it viable to transfer the learned internal states of large pretrained language models (LMs) to downstream natural language processing (NLP) tasks. This transfer learning approach improves the overall performance on many tasks and is highly beneficial when labeled data is scarce, making pretrained LMs valuable resources specially for languages with few annotated training examples. In this work, we train BERT (Bidirectional Encoder Representations from Transformers) models for Brazilian Portuguese, which we nickname BERTimbau. We evaluate our models on three downstream NLP tasks: sentence textual similarity, recognizing textual entailment, and named entity recognition. Our models improve the state-of-the-art in all of these tasks, outperforming Multilingual BERT and confirming the effectiveness of large pretrained LMs for Portuguese. We release our models to the community hoping to provide strong baselines for future NLP research: https://github.com/neuralmind-ai/portuguese-bert.
SmartHealth: An intelligent framework to secure IoMT service applications using machine learning

Due to the complex functioning of Smart Healthcare Systems (SHS), many security concerns have been raised in the past. It provisions the attackers to hamper the working of SHS in a variety of ways, e.g., injection of false data to replace vital signs, tampering of medical devices to prevent informing critical situations, etc. In this work, a novel ML-based framework, i.e., SmartHealth is proposed to secure IoMT devices in SHS. SmartHealth watches the vital signs gathered through different IoMT to analyze the change in various body activities to differentiate between normal activities and dangerous security attacks. The performance of the SmartHealth is also analyzed for three different dangerous attacks. During performance analysis, it has been observed that SmartHealth can identify wicked activities in IoMT 92% times accurately with an F1-score of 90%.
Planetary Flight Obstacle Avoidance Guidance Method Based on ES and DQN

Obstacle avoidance constitutes a critical challenge in planetary exploration flights. Traditional approaches struggle to meet safety requirements due to the vast distance from Earth and the substantial measurement and control delay errors involved. To address these issues, this study proposes a novel hybrid obstacle avoidance guidance method that combines Evolution-Strategies (ES) and Deep Q-Network (DQN) algorithms. This approach enables autonomous obstacle avoidance flights in complex and unpredictable environments. By leveraging a data-driven approach, the method utilizes DQN for intelligent decision-making during obstacle avoidance flights, while ES is employed for obstacle avoidance guidance. Extensive simulations demonstrate that the proposed method achieves autonomous obstacle avoidance flights in complex and unfamiliar environments, exhibiting remarkable robustness and adaptability.
Intelligent Path Planning for Home Service Robots Based on Improved RRT Algorithm

In order to improve the safety and efficiency of home service robots operating in complex environments, we need to carry out effective and proper motion planning for the robot’s path. Facing the problems of random growth direction, a large number of redundant nodes in random search, and poor smoothness in the RRT algorithm, this paper proposes an improved RRT path planning algorithm for home service robots. By introducing the idea of the artificial potential field method, the growth direction of new nodes is constrained, and the original fixed step size is changed to dynamic step size, the APF-RRT algorithm based on dynamic step size is proposed to speed up the search efficiency, and by simplifying the path and combining the idea of Floyd’s algorithm to smooth the path, a safety threshold is added when removing redundant points and inserting new nodes to keep the robot away from obstacles. The path length is shortened, the redundant nodes are reduced, and the smoothness of the robot is improved. MATLAB simulation and physical verification results show that the improved algorithm can reduce the path length, decrease the number of nodes, and improve the smoothness, which verifies the effectiveness and superiority of the algorithm.
Improving Unmanned Panoramic Perception Algorithm of DAFPN-YOLO

On the basis of YOLO, combined with multi-task learning to realize the tasks of traffic object detection, drivable area segmentation and lane line segmentation in unmanned driving, the unmanned panoramic perception algorithm based on DAFPN-YOLO is proposed. Then, dynamic attention is used to achieve scale perception, space perception and task perception to improve the model’s performance in three tasks: traffic object detection, driveable area segmentation and lane line segmentation. The multi-task loss function is adjusted, and FocalLoss is introduced to improve the model’s performance in the face of category-unbalanced data. The accuracy of vehicle perception algorithm for driverless cars is improved.
Adding a 'Group Advisory Layer' to Your Use of Generative AI Tools Through Structured Prompting: Using Personas for Advisory Boards, Task Forces, Mastermind Groups, and Other Collections of Personas to Assist in Evaluations, Assessments, Recommendations, Decision-making, and much more (Including Law-related Examples)

Imagine a world where expert advice is at your fingertips, instantly available, tailored just for you. Think of a tool that’s always ready to give expert advice, without the need for complex coding or tech skills. The Group Advisory Layer Method (G-A-L Method™) revolutionizes decision-making by merging traditional principles of mastermind groups and advisory boards with the cutting-edge capabilities of generative AI. Traditional advisory boards, often hindered by logistics and time constraints, meet their match as the G-A-L Method offers on-demand, diverse, and tailored insights, all without the real-world hassle.It’s like having a virtual team you can chat with any time, made up of tireless AI-created ‘personas’ that act like real people. Instead of juggling schedules or waiting for feedback, you get quick and practical tips from this always-on expert team. The G-A-L Method pioneers dynamic group interactions using personas to give you practical, just-in-time expert advice. What’s more, it makes sure real people (like you) are involved where they add the most value.With the G-A-L Method, you’re not just listening to machines – you’re teaming up with them. This white paper is an invitation to unlock the untapped potential of these generative AI tools in a practical, structured way to move your efforts forward. We are poised at the brink of a transformative era where informed decisions can be made rapidly and confidently. The G-A-L Method is more than a technique—it’s a game-changer.
Örtülü Yüzlerin Tanınmasında Haar Cascade ve MongoDB Entegrasyonuyla Geliştirilen Yüz Tanıma Sisteminin (YTS) Performans Değerlendirmesi

Although traditional face recognition systems (FRS) can detect with a certain success rate whether a mask is worn, they may fail due to the fact that most of the faces of the people who wear masks are covered. The difficulties arising from the fact that a significant part of the faces of individuals wearing masks are covered limits the performance of existing FRSs. In this research, it is aimed to integrate the Haar Cascade method with the MongoDB database in real time using the OpenCV library for mask-wearing face recognition and to demonstrate its performance with extensive experiments. In the experiments, the data set created within the scope of this study from realistic face images, in which most of the masked faces are covered, was used. Our research has shown that the accuracy of face recognition is 85% for masked faces, 61% for unmasked faces, and 41% when half of the face is covered by a different object. It is considered that this study will contribute to the literature in terms of providing a more effective and applicable mask detection solution by combining the Haar Cascade method with real-time database management integration.
Task-Related Pretraining with Whole Word Masking for Chinese Coherence Evaluation

This paper presents an approach for evaluating coherence in Chinese middle school student essays, addressing the challenges of time-consuming and inconsistent essay assessment. Previous approaches focused on linguistic features, but coherence, crucial for essay organization, has received less attention. Recent works utilized neural networks, such as CNN, LSTM, and transformers, achieving good performance with labeled data. However, labeling coherence manually is costly and time-consuming. To address this, we propose a method that pretrains RoBERTa with whole word masking (WWM) on a low-resource dataset of middle school essays, followed by finetuning for coherence evaluation. The WWM pretraining is unsupervised and captures general characteristics of the essays, adding little cost to the low-resource setting. Experimental results on Chinese essays demonstrate that this strategy improves coherence evaluation compared to naive finetuning on limited data. We also explore variants of their method, including pseudo labeling and additional neural networks, providing insights into potential performance trade-offs. The contributions of this work include the collection and curation of a substantial dataset, the proposal of a cost-effective pretraining method, and the exploration of alternative approaches for future research.
Overview of the NLPCC 2023 Shared Task 10: Learn to Watch TV: Multimodal Dialogue Understanding and Response Generation

In this paper, we present an overview of NLPCC 2023 Shared Task 10, Multimodal Dialogue Understanding and Response Generation, which includes four sub-tasks: dialogue scene identification, dialogue session identification, dialogue response retrieval, and dialogue response generation. A bilingual multi-modal dialogue dataset consisting of 100M utterances was made public for the shared task. This dataset contains 119K dialogue scene boundaries and 62K dialogue session boundaries annotated manually. This paper presents details of this shared task, dataset, evaluation metric and evaluation results.
TCSA: A Text-Guided Cross-View Medical Semantic Alignment Framework for Adaptive Multi-view Visual Representation Learning

Recently, in the medical domain, visual-language (VL) representation learning has demonstrated potential effectiveness in diverse medical downstream tasks. However, existing works typically pre-trained on the one-to-one corresponding medical image-text pairs, disregarding fluctuation in the quantity of views corresponding to reports (e.g., chest X-rays typically involve 1 to 3 projection views). This limitation results in sub-optimal performance in scenarios with varying quantities of views (e.g., arbitrary multi-view classification). To address this issue, we propose a novel Text-guided Cross-view Semantic Alignment (TCSA) framework for adaptive multi-view visual representation learning. For arbitrary number of multiple views, TCSA learns view-specific private latent sub-spaces and then maps them to a scale-invariant common latent sub-space, enabling individual treatment of arbitrary view type and normalization of arbitrary quantity of views to a consistent scale in the common sub-space. In the private sub-spaces, TCSA leverages word context as guidance to match semantic corresponding sub-regions across multiple views via cross-modal attention, facilitating alignment of different types of views in the private sub-space. This promotes the combination of information from arbitrary multiple views in the common sub-space. To the best of our knowledge, TCSA is the first VL framework for arbitrary multi-view visual representation learning. We report the results of TCSA on multiple external datasets and tasks. Compared with the state of the art frameworks, TCSA achieves competitive results and generalize well to unseen data.
Overview of the NLPCC 2023 Shared Task: Chinese Spelling Check

This paper provides an overview of the Chinese Spelling Check shared task 8, held at NLPCC 2023. The task aims to correct spelling errors in Chinese sentences, including homophonic errors, visually similar errors, and other types of errors found in Chinese news articles. We present the task’s description, previous work related to Chinese Spelling Check, statistics on the provided dataset, evaluation results, and a summary of the submitted approaches. The dataset consists of 1k instances for development and 11k instances for evaluation, collected from publicly available news articles and books. The errors in the dataset were manually identified and labeled. A total of 47 teams registered for the task, with 12 teams submitting their results. The evaluation metric used is the F1 score, with the highest achieved score being 0.5888. The submitted approaches employ various techniques to enhance performance, each focusing on different aspects of the problem. For more information, please visit our official website.