Reviewerly/exHarmony huggingface dataset:
Knowledge grounded medical dialogue generation using augmented graphs

Smart healthcare systems that make use of abundant health data can improve access to healthcare services, reduce medical costs and provide consistently high-quality patient care. Medical dialogue systems that generate medically appropriate and human-like conversations have been developed using various pre-trained language models and a large-scale medical knowledge base based on Unified Medical Language System (UMLS). However, most of the knowledge-grounded dialogue models only use local structure in the observed triples, which suffer from knowledge graph incompleteness and hence cannot incorporate any information from dialogue history while creating entity embeddings. As a result, the performance of such models decreases significantly. To address this problem, we propose a general method to embed the triples in each graph into large-scalable models and thereby generate clinically correct responses based on the conversation history using the recently recently released MedDialog(EN) dataset. Given a set of triples, we first mask the head entities from the triples overlapping with the patient's utterance and then compute the cross-entropy loss against the triples' respective tail entities while predicting the masked entity. This process results in a representation of the medical concepts from a graph capable of learning contextual information from dialogues, which ultimately aids in leading to the gold response. We also fine-tune the proposed Masked Entity Dialogue (MED) model on smaller corpora which contain dialogues focusing only on the Covid-19 disease named as the Covid Dataset. In addition, since UMLS and other existing medical graphs lack data-specific medical information, we re-curate and perform plausible augmentation of knowledge graphs using our newly created Medical Entity Prediction (MEP) model. Empirical results on the MedDialog(EN) and Covid Dataset demonstrate that our proposed model outperforms the state-of-the-art methods in terms of both automatic and human evaluation metrics.
Automated CNN Architectural Design: A Simple and Efficient Methodology for Computer Vision Tasks

Convolutional neural networks (CNN) have transformed the field of computer vision by enabling the automatic extraction of features, obviating the need for manual feature engineering. Despite their success, identifying an optimal architecture for a particular task can be a time-consuming and challenging process due to the vast space of possible network designs. To address this, we propose a novel neural architecture search (NAS) framework that utilizes the clonal selection algorithm (CSA) to automatically design high-quality CNN architectures for image classification problems. Our approach uses an integer vector representation to encode CNN architectures and hyperparameters, combined with a truncated Gaussian mutation scheme that enables efficient exploration of the search space. We evaluated the proposed method on six challenging EMNIST benchmark datasets for handwritten digit recognition, and our results demonstrate that it outperforms nearly all existing approaches. In addition, our approach produces state-of-the-art performance while having fewer trainable parameters than other methods, making it low-cost, simple, and reusable for application to multiple datasets.
Toward Adaptive Human–Robot Collaboration for the Inclusion of People with Disabilities in Manual Labor Tasks

While human–robot collaboration is already integrated in industrial and service robotics applications, it is only used with able-bodied workers. However, collaboration through assistive robots is a major driver toward the inclusion of people with disabilities, which was demonstrated in recent research projects. Currently, inclusive robot workplaces have to be customized toward the work process and the individual needs of the person. Within, robots act along a fixed schedule and are not able to adapt to changes within the process or the needs of the interacting person. Hence, such workplaces are expensive and unappealing for companies of the first labor market, and do not realize the full potential of the technology. In this work, we propose a generalized approach toward the inclusion of people with disabilities with collaborative robots. To this end, we propose a system that analyzes the in situ capabilities of a person using a two-stage reasoning approach. The methodology is based on an ontology that allows the matchmaking of individual capabilities with process requirements. Capabilities are modeled in two time frames, through which fast (e.g., fatigue) and slow effects (e.g., worsening of illness) become distinguishable. The matchmaking is used in task allocation to establish high-level control over the assistive system. By this approach, inclusive workplaces become autonomously adaptive to the in situ capabilities of the individual person, without the need for customization. Therefore, collaborative workplaces become not only inclusive, but a contributor toward a labor market for all.
Design and Implementation of Two Immersive Audio and Video Communication Systems Based on Virtual Reality

Due to the impact of the COVID-19 pandemic in recent years, remote communication has become increasingly common, which has also spawned many online solutions. Compared with an in-person scenario, the feeling of immersion and participation is lacking in these solutions, and the effect is thus not ideal. In this study, we focus on two typical virtual reality (VR) application scenarios with immersive audio and video experience: VR conferencing and panoramic live broadcast. We begin by introducing the core principles of traditional video conferencing, followed by the existing research results of VR conferencing along with the similarities, differences, pros, and cons of each solution. Then, we outline our view about what elements a virtual conferencing room should have. After that, a simple implementation scheme for VR conferencing is provided. Regarding panoramic video, we introduce the steps to produce and transmit a panoramic live broadcast and analyze several current mainstream encoding optimization schemes. By comparing traditional video streams, the various development bottlenecks of panoramic live broadcast are identified and summarized. A simple implementation of a panoramic live broadcast is presented in this paper. To conclude, the main points are illustrated along with the possible future directions of the two systems. The simple implementation of two immersive systems provides a research and application reference for VR audio and video transmission, which can guide subsequent relevant research studies.
Experimental Evaluation of Collision Avoidance Techniques for Collaborative Robots

This paper presents the implementation of an obstacle avoidance algorithm on the UR5e collaborative robot. The algorithm, previously developed and verified in simulation, allows one to modify in real time the trajectory of the manipulator with three different modalities to avoid obstacles. Some test cases with fixed or dynamic obstacles affecting the robot’s motion were first simulated and then experimented on. The paper describes the hardware/software architecture of the robotic system: an external controller is realized by a standard PC that communicates with the robot controller by a TCP/IP protocol; algorithms and data processing are executed by Python/Matlab software that guarantees a duty cycle of at least 100 Hz. The error analysis between simulated and real data allows one to conclude that the developed algorithms revealed to be effectively applied to a real robotic system, showing behavior similar to what is expected by simulations.
Learning to extract from multiple perspectives for neural keyphrase extraction

Keyphrase extraction is a fundamental task in natural language processing that aims at extracting a set of important phrases from a source document. Generally, when people understand documents and extract relevant information, they consider multiple perspectives, which helps to reduce misunderstandings and errors in extractions. However, most existing keyphrase extraction approaches focus on only one or two perspectives, resulting in inaccurate extractions. To address this issue, we propose a new neural keyphrase extraction model, called Multiple Perspectives Neural Keyphrase Extraction (MPNKE), which learns representations and estimates the importance of candidate phrases from multiple perspectives, similar to how a human would approach the task. Extensive experimental results on several benchmark keyphrase extraction datasets demonstrate that MPNKE outperforms existing state-of-the-art models in most cases.
Natural Language Processing in Marketing

The increasing importance and proliferation of text data provide a unique opportunity and novel lens to study human communication across a myriad of business and marketing applications. For example, consumers compare and review products online, individuals interact with their voice assistants to search, shop, and express their needs, investors seek to extract signals from firms' press releases to improve their investment decisions, and firms analyze sales call transcripts to increase customer satisfaction and conversions. However, extracting meaningful information from unstructured text data is a nontrivial task. In this chapter, we review established natural language processing (NLP) methods for traditional tasks (e.g., LDA for topic modeling and lexicons for sentiment analysis and writing style extraction) and provide an outlook into the future of NLP in marketing, covering recent embedding-based approaches, pretrained language models, and transfer learning for novel tasks such as automated text generation and multi-modal representation learning. These emerging approaches allow the field to improve its ability to perform certain tasks that we have been using for more than a decade (e.g., text classification). But more importantly, they unlock entirely new types of tasks that bring about novel research opportunities (e.g., text summarization, and generative question answering). We conclude with a roadmap and research agenda for promising NLP applications in marketing and provide supplementary code examples to help interested scholars to explore opportunities related to NLP in marketing.
Multi-Object Tracking: Decoupling Features to Solve the Contradictory Dilemma of Feature Requirements

Multi-object tracking achieves the acquisition of target location information and identity information through two subtasks, detection and re-identification (ReID). The existing commonly used one-shot framework has speed advantages, but the two subtasks have different feature requirements, which leads to competitive learning in the training and thus weakens the feature quality. We propose a feature decoupling based multi-object tracking framework FDTrack for contradictory feature requirements. Through the mutual inhibition of the two subtasks, the features of the backbone network are decoupled. Then the decoupled features are self-constrained to enhance effective features. Considering the instability of the target state and the different confidence of the detections, a more reasonable association strategy is employed to maximize the matchings between detections, thus recovering low-confidence targets. FDTrack is extensively tested on the MOT17 and MOT20 benchmarks. The experimental results show that FDTrack surpasses the previous state-of-the-art (SOTA) methods and has good anti-interference and real-time performance. Moreover, our proposed modules have good portability and can be applied in other one-shot trackers to achieve performance improvement.
Hand Gesture Recognition Using Densely Connected Deep Residual Network and Channel Attention Module for Mobile Robot Control

Navigating a mobile robot in an indoor or outdoor environment is an interesting research area for human–robot collaboration (HRC). In such a scenario, hand gesture offers some unique abilities for HRC to provide nonverbal communication between the user and the robot. This article proposes a novel real-time hand gesture recognition (HGR) technique for mobile robot control application. A compact convolutional neural network (CNN)-based HGR system, denoted as densely connected residual channel attention module (DRCAM), is proposed to recognize the vision-based hand gestures effectively. Since fingers are the most vital sign for HGR, an attention mechanism using multiscale representation is proposed, which emphasizes finger information more effectively. The proposed CNN model employs the cascading structure of residual blocks with a multiscale channel attention module to learn low- to high-level information of hand gestures. In addition, the cascading structures are connected through dense connectivity, which strengthens the feature propagation and facilitates feature reuse. Experiments are conducted on an ingenuously developed dataset and a publicly available American sign language finger-spelling (ASL-FS) benchmarked dataset to evaluate the performance of the proposed technique. The experimental result illustrates that the proposed DRCAM network outperforms the state-of-the-art methods in terms of mean accuracy (MA) using the leave-one-subject-out cross-validation (LOO CV) test. Finally, the training model is used to develop a software-based user interface system for the control of a mobile robot in a real-time environment.
SUES-200: A Multi-Height Multi-Scene Cross-View Image Benchmark Across Drone and Satellite

Cross-view image matching aims to match images of the same target scene acquired from different platforms. With the rapid development of drone technology, cross-view matching by neural network models has been a widely accepted choice for drone position or navigation. However, existing public datasets do not include images obtained by drones at different heights, and the types of scenes are relatively homogeneous, which yields issues in assessing a model’s capability to adapt to complex and changing scenes. In this end, we present a new cross-view dataset called SUES-200 to address these issues. SUES-200 contains 24120 images acquired by the drone at four different heights and corresponding satellite view images of the same target scene. To the best of our knowledge, SUES-200 is the first public dataset that considers the differences generated in aerial photography captured by drones flying at different heights. In addition, we developed an evaluation for efficient training, testing and evaluation of cross-view matching models, under which we comprehensively analyze the performance of nine architectures. Then, we propose a robust baseline model for use with SUES-200. Experimental results show that SUES-200 can help the model to learn highly discriminative features of the height of the drone.
Research on Global Ship Path Planning Method Based on Improved Ant Colony Algorithm

To solve the global path planning problem of the ship in the static and dynamic environment, we propose an improved ant colony algorithm to plan the ship’s navigation path. We use the artificial potential field method to compute the force direction of the ship at the initial iteration stage. The attraction potential field function is modified to improve the iteration efficiency of the hybrid ant colony algorithm. We design the pseudo-random state transition rule and improve the convergence of the hybrid algorithm by strengthening the selection of good paths. When updating the pheromone, we consider the path’s length, safety, and smoothness to plan a safer navigation path. The simulation results show that the improved ant colony algorithm has a faster convergence speed than the original ant colony algorithm. The optimal solution quality is higher, which can realize global ship path planning in static and dynamic environments.
Online Harassment: Assessing Harms and Remedies

Online harassment refers to a wide range of harmful behaviors, including hate speech, insults, doxxing, and non-consensual image sharing. Social media platforms have developed complex processes to try to detect and manage content that may violate community guidelines; however, less work has examined the types of harms associated with online harassment or preferred remedies to that harassment. We conducted three online surveys with US adult Internet users measuring perceived harms and preferred remedies associated with online harassment. Study 1 found greater perceived harm associated with non-consensual photo sharing, doxxing, and reputational damage compared to other types of harassment. Study 2 found greater perceived harm with repeated harassment compared to one-time harassment, but no difference between individual and group harassment. Study 3 found variance in remedy preferences by harassment type; for example, banning users is rated highly in general, but is rated lower for non-consensual photo sharing and doxxing compared to harassing family and friends and damaging reputation. Our findings highlight that remedies should be responsive to harassment type and potential for harm. Remedies are also not necessarily correlated with harassment severity—expanding remedies may allow for more contextually appropriate and effective responses to harassment.
Detection of 3D Human Posture Based on Improved Mediapipe

Based on the continuous development of motion capture technology for ordinary video images, unmarked optical motion capture has become the fastest human posture recognition technology.Compared with other technical products, Google's 3D human body recognition framework-Mediapipe is the most mature representative in this field.However, Mediapipe also has many defects in the detection of 3D human posture.In this paper, firstly, to solve the problem of inaccurate detection of human posture by Mediapipe, the accuracy of 2D human posture detection is improved through the speed threshold correction method for each joint; According to the problem that the monocular camera can not detect the depth Z value in the human posture data accurately, the Z value of the joint point is corrected for the human tilt angle through statistics; Then, according to the inaccurate recognition of Z value caused by large body posture, the accurate correction of Z value of human posture under different body posture is realized by normalizing the simulation proportion of each body limb; Finally, in order to solve the problem of jitter, lag problem and periodic noise in multiple frames caused by the speed change of human joints, one euro filtering and mean filtering of joint data are carried out.This paper verifies that the accuracy of 3D human posture detection based on the improved Mediapipe is more than 90% through the multi-pose recognition test for people of different heights, weights, ages and gender.
A novel phase unwrapping method for binocular structured light 3D reconstruction based on deep learning

Fringe projection has been prevalently adopted in the field of three-dimensional(3D) reconstruction. However, it is still a challenge to achieve high-precision and efficient measurement. To achieve real-time and efficient 3D shape measurement of the objects, we must extract the 3D coordinate data of the object from as few fringe patterns as possible. In the paper, we develop a new technique combining composite fringe coding and stair phase coding to solve the high-quality absolute phase and realize binocular 3D morphology reconstruction based on deep learning. This method only needs to project a composite fringe coding pattern to acquire the wrapped phase and a stair phase coding pattern to obtain the fringe order. Then we can recover the unwrapped phase maps of the captured fringe patterns. According to the calibration parameters of the binocular camera, the absolute phase is stereo matched to get the disparity map, and then converted into 3D spatial coordinates of the object. The influence of defocus and noise on the deviation between the wrapped phase and fringe order is solved by staggered phase unwrapping method. We demonstrated the validity and robustness of the novel method by experiments, which provides a promising research direction for 3D shape measurement based on binocular structured light.
Persuasive Language in ELT-Related Ads on Social Media

In line with the concept of mobile learning in English Language Teaching (ELT), the aim of this research is to explore how Iranian ELT practitioners take advantage of social media to propose supportive and impactful language learning programs by adhering to persuasive linguistic devices. The research design is nonexperimental and explorative. ELT-related commercial videos and pictures were identified on social media platforms, including Instagram, Facebook, TikTok, and YouTube. We delved into the syntactic and pragmatic features of the data on ELT-related ads to identify the persuasive techniques and strategies these ads resort to for attracting language learners to online classes and services. To analyze the data, the widely-used and acknowledged Cialdini's (The psychology of persuasion, Quill William Morrow, 1984) principles of persuasion are employed. The results manifested that 'reciprocity' and 'scarcity' were the most used persuasive strategies, while 'commitment and consistency' and 'consensus' were the least favorable persuasion principles in these ads. The analysis of the Iranian ELT-related ads indicated that the language used within this context is purposeful and strategic. A contextual investigation of the ELT-related ads on social media can meaningfully contribute to social practices underlying English language pedagogy and digital literacy.
Interpreting learning models in manufacturing processes: Towards explainable AI methods to improve trust in classifier predictions

Smart manufacturing processes, building upon machine learning (ML) models could potentially reduce the pre-production testing and validation time for new processes. Beyond calculating accurate and reliable models, one critical challenge would be for users of these models (plant operators, engineers and technicians) to trust these models' outputs. We propose to apply explainable AI methods to create trustworthy AI-based manufacturing systems. Consequently, these systems will be enriched with capabilities to explain their reasoning processes and outputs (e.g., predictions) automatically. This paper applies explainable AI methods to two problems in manufacturing: ultrasonic weld (USW) quality prediction and body-in-white (BIW) dimensional variability reduction. Class activation maps were computed to explain the effect of input signals and their patterns on the quality predictions of an ultrasonic weld yield by a neural network (good or bad). Contrastive gradient based saliency maps were also created to assess the robustness of this classifier. Furthermore, we explain a given connectionist network that predicts the dimensional quality of body-in-white framer points based on deviations in underbody points. Explaining these predictions help engineers understand which underbody points have more influence on deviations in the framer points. These two applications highlight the importance of explainable AI methods in the modern manufacturing industry.