Reviewerly/exHarmony huggingface dataset:
Unveiling Practices of Customer Service Content Curators of Conversational Agents

Conversational interfaces require two types of curation: data curation by data science workers and content curation by domain experts. Recent years have seen the possibilities for content curators to instruct conversational machines in the customer service domain (i.e., Machine Teaching). The activities of curating specialized data are time-consuming. These activities have a learning curve for the domain expert, and they rely on collaborators beyond the domain experts, including product owners, technology expert curators, management, marketing, and communication employees. However, recent research has looked at making this task easier for domain experts with a lack of knowledge in the Machine Learning system, and few papers have investigated the work practices and collaborations involved in this role. This paper aims to fill this gap, presenting and unveiling practices extracted from eleven semi-structured interviews and four design workshops with experts in Banking, Technical support, Humans Resources, Telecommunications, and Automotive sectors. First, we investigate the articulation work of the content curators and tech curators in training conversational machines. Second, we inspect the curatorial and collaboration strategies they use, which are not afforded by current conversational platforms. Third, we draw the design implications and possibilities to support individual and collaboration curating practices. We reflect on how those practices rely on self and collaboration with others for curation, trust, and data tracking and ownership.
Rethinking Local and Global Feature Representation for Dense Prediction

Although fully convolution networks (FCNs) have dominated dense prediction tasks (e.g., semantic segmentation, depth estimation and object detection) for decades, they are inherently limited in capturing long-range structured relationship with the layers of local kernels. While recent Transformer-based models have proven extremely successful in computer vision tasks by capturing global representation, they would deteriorate dense prediction results by over-smoothing the regions containing fine details (e.g., boundaries and small objects). To this end, we aim to provide an alternative perspective by rethinking local and global feature representation for the dense prediction task. Specifically, we deploy a Dual-Stream Convolution-Transformer architecture, called DSCT, by taking advantage of both the convolution and Transformer to learn a rich feature representation, combining with a task decoder to provide a powerful dense prediction model. DSCT extracts high resolution local feature representation from convolution layers and global feature representation from Transformer layers. With the local and global context modeled explicitly in every layer, the two streams can be combined with a decoder to perform task of semantic segmentation, monocular depth estimation or object detection. Extensive experiments show that DSCT can achieve superior performance on the three tasks above. For semantic segmentation, DSCT builds a new state of the art on Cityscapes validation set (83.31% mIoU) with only 80,000 training iterations and appealing performance (49.27% mIoU) on ADE20K validation set, outperforming most of the alternatives. For monocular depth estimation, our model achieves 2.423 RMSE on KITTI Eigen split, superior to most of the convolution or Transformer counterparts. For object detection, without using FPN, we can achieve 44.5% APb on COCO dataset when using Faster R-CNN, which is higher than Conformer.
X-Ray Breast Images Denoising Method Based on the Convolutional Autoencoder

Considering the potential risk of X-ray to patients, denoising of low-dose X-ray medical images is imperative. Inspired by deep learning, a convolutional autoencoder method for X-ray breast image denoising is proposed in this paper. First, image symmetry and flip are used to increase the number of images in the public dataset; second, the number of samples is increased further by image cropping segmentation, adding simulated noise, and producing the dataset. Finally, a convolutional autoencoder neural network model is constructed, and clean and noisy images are fed into it to complete the training. The results show that this method effectively removes noise while retaining image details in X-ray breast images, yielding higher peak signal-to-noise ratio and structural similarity index values than classical and novel denoising methods.
SlowFast Action Recognition Algorithm Based on Faster and More Accurate Detectors

Object detection algorithms play a crucial role in other vision tasks. This paper finds that the action recognition algorithm SlowFast’s detection algorithm FasterRCNN (Region Convolutional Neural Network) has disadvantages in terms of both detection accuracy and speed and the traditional IOU (Intersection over Union) localization loss is difficult to make the detection model converge to the minimum stability point. To solve the above problems, the article uses YOLOv3 (You Only Look Once), YOLOX, and CascadeRCNN to improve the detection accuracy and speed of the SlowFast. This paper proposes a new localization loss function that adopts the Lance and Williams distance as a new penalty term. The new loss function is more sensitive when the distance difference is smaller, and this property is very suitable for the late convergence of the detection model. The experiments were conducted on the VOC (Visual Object Classes) dataset and the COCO dataset. In the final videos test, YOLOv3 improved the detection speed by 10.5 s. CascadeRCNN improved by 3.1%AP compared to FasterRCNN in the COCO dataset. YOLOX’s performance on the COCO dataset is also mostly better than that of FasterRCNN. The new LIOU (Lance and Williams Distance Intersection over Union) localization loss function performs better than other loss functions in the VOC dataset. It can be seen that improving the detection algorithm of the SlowFast seems to be crucial and the proposed loss function is indeed effective.
An Optimized Probabilistic Roadmap Algorithm for Path Planning of Mobile Robots in Complex Environments with Narrow Channels

In this paper, we propose a new path planning algorithm based on the probabilistic roadmaps method (PRM), in order to effectively solve the autonomous path planning of mobile robots in complex environments with multiple narrow channels. The improved PRM algorithm mainly improves the density and distribution of sampling points in the narrow channel, through a combination of the learning process of the PRM algorithm and the APF algorithm. We also shortened the required time and path length by optimizing the query process. The first key technology to improve the PRM algorithm involves optimizing the number and distribution of free points and collision-free lines in the free workspace. To ensure full visibility of the narrow channel, we extend the obstacles through the diagonal distance of the mobile robot while ignoring the safety distance. Considering the safety distance during movement, we re-classify the all sampling points obtained by the quasi-random sampling principle into three categories: free points, obstacle points, and adjacent points. Next, we transform obstacle points into the free points of the narrow channel by combining the APF algorithm and the characteristics of the narrow channel, increasing the density of sampling points in the narrow space. Then, we include potential energy judgment into the construction process of collision-free lines shortening the required time and reduce collisions with obstacles. Optimizing the query process of the PRM algorithm is the second key technology. To reduce the required time in the query process, we adapt the bidirectional A* algorithm to query these local paths and obtain an effective path to the target point. We also combine the path pruning technology with the potential energy function to obtain a short path without collisions. Finally, the experimental results demonstrate that the new PRM path planning technology can improve the density of free points in narrow spaces and achieve an optimized, collision-free path in complex environments with multiple narrow channels.
A Toolkit for Co-Designing towards Community-Based Active Ageing: Lessons Learned during Development

It is increasingly recognized that community-based interventions for active ageing are more lasting and effective, yet the tools and methods for developing these interventions are lacking. This study investigates how to co-design community-based active ageing with older adults via the development of a toolkit to support this goal. Rapid reviews were conducted to understand (i) the effective behavioural change techniques for older adults, (ii) how to co-design with older adults for community-based interventions, and (iii) how to design tools for behaviour change that are easy to use. These reviews served as the foundation for developing a toolkit to support the co-design of community-based active ageing, which was evaluated during an interdisciplinary hackathon with older adults. Quantitative data from the surveys suggested that the confidence levels of students in developing interventions for health behaviour change and in co-designing with older adults increased after the hackathon, and the enjoyment of participating in the hackathon and of using the toolkit were statistically significant factors influencing this increase. Qualitative data from interviews and observations revealed how the toolkit was (un)used by the participants and what aspects of the toolkit can be improved. We encourage future researchers and practitioners to apply and adapt our research findings to the communities of older adults that they are working with.
Block Chain Technology and Its Future Scope

Block chain is one of the emerging technologies currently in the industry and the market. It is because of its decentralized and auditable properties. The major usage of this technology is to protect the data breach by its high-layered protective environment and its endless system. In this paper, we have discussed about the current applications of this technology, how securely it works, and its current market growth.
Special Issue “Application of Fuzzy Control in Computational Intelligence”

Due to the fitted structure of fuzzy logic, neural networks and evolutionary computing, neuro-fuzzy systems, evolutionary-fuzzy systems, and evolutionary neural systems, we can study computational intelligence [...]
CAR-Tourist: An Integrity-Preserved Collaborative Augmented Reality Framework-Tourism as a Use-Case

The unprecedented growth in Augmented Reality (AR) has captured the focus of researchers and the industrial sector. The development of AR applications and their implementation in various domains is broadening. One of the advancements in the field of AR is Collaborative AR, which provides ample opportunities for the members of a team to work on a particular project remotely. The various activities carried out remotely, in a collaborative fashion, are based on the active interaction and transmission of data and applications across a communication channel that constitutes a mesh of frequently interacting applications, thus providing a real feeling of working together physically in the purportedly same demographic area. However, in the integration of different roles, remotely working in collaborative AR has a great chance of being intruded upon and manipulated. Consequently, the intrusion may explore novel vulnerabilities to various sensitive collaborative projects. One of the security concerns for collaborative and interconnected remote applications is to have pristine environments, where the participants of the collaborative AR can reliably trust each other during the execution of the various processes. This paper presents an integrity-aware CAR-Tourist (Collaborative Augmented reality for Tourism) framework wherein the unauthorized user’s access is denied and the remote participants of the network are provided with a secure environment through the state-of-the-art Blockchain architecture. This study further provides a use-case implementation of a tourism application. Each tourist has the chance to hire a remote guide for collaborative guidance over a blockchain-trusted network. Moreover, the proposed framework is lightweight, as the only necessary communication between the tourist and guide is recorded in the blockchain network. Each user has to register on a permission blockchain to be allowed to perform certain activities on our proposed CAR-Tourist framework. The decentralized Blockchain approach provides a consensus mechanism based on which not every participant is free to intrude on ongoing communication. Thus, through the proposed framework, all the participants in the collaborative Augmented Reality will have the essential trust of working remotely without external intrusion.
Multi-Objective Optimization of Sugarcane Milling System Operations Based on a Deep Data-Driven Model

The extraction of sugarcane juice is the first step of sugar production. The optimal values of process indicators and the set values of operating parameters in this process are still determined by workers' experience, preventing adaptive adjustment of the production process. To address this issue, a multi-objective optimization framework based on a deep data-driven model is proposed to optimize the operation of sugarcane milling systems. First, the sugarcane milling process is abstracted as the interaction of material flow, energy flow, and information flow (MF-EF-IF) by introducing synergetic theory, and each flow's order parameters and state parameters are obtained. Subsequently, the state parameters of the subsystems are taken as inputs, and the order parameters-including the grinding capacity, electric consumption per ton of sugarcane, and sucrose extraction-are produced as outputs. A collaborative optimization model of the MF-EF-IF of the milling system is established by using a deep kernel extreme learning machine (DK-ELM). The established milling system model is applied for an improved multi-objective chicken swarm optimization (IMOCSO) algorithm to obtain the optimal values of the order parameters. Finally, the milling process is described as a Markov decision process (MDP) with the optimal values of the order parameters as the control objectives, and an improved deep deterministic policy gradient (DDPG) algorithm is employed to achieve the adaptive optimization of the operating parameters under different working conditions of the milling system. Computational experiments indicate that enhanced performance is achieved, with an increase of 3.2 t per hour in grinding capacity, a reduction of 660 W per ton in sugarcane electric consumption, and an increase of 0.03% in the sucrose extraction.
A survey of field programmable gate array (FPGA)-based graph convolutional neural network accelerators: challenges and opportunities

Graph convolutional networks (GCNs) based on convolutional operations have been developed recently to extract high-level representations from graph data. They have shown advantages in many critical applications, such as recommendation system, natural language processing, and prediction of chemical reactivity. The problem for the GCN is that its target applications generally pose stringent constraints on latency and energy efficiency. Several studies have demonstrated that field programmable gate array (FPGA)-based GCNs accelerators, which balance high performance and low power consumption, can continue to achieve orders-of-magnitude improvements in the inference of GCNs models. However, there still are many challenges in customizing FPGA-based accelerators for GCNs. It is necessary to sort out the current solutions to these challenges for further research. For this purpose, we first summarize the four challenges in FPGA-based GCNs accelerators. Then we introduce the process of the typical GNN algorithm and several examples of representative GCNs. Next, we review the FPGA-based GCNs accelerators in recent years and introduce their design details according to different challenges. Moreover, we compare the key metrics of these accelerators, including resource utilization, performance, and power consumption. Finally, we anticipate the future challenges and directions for FPGA-based GCNs accelerators: algorithm and hardware co-design, efficient task scheduling, higher generality, and faster development.
Spatiotemporal Characteristics and Influencing Factors of Sunshine Duration in China from 1970 to 2019

In order to alleviate global warming and the energy crisis, it is of great significance to develop and utilize solar energy resources. Sunshine duration (SD) is considered to be the best substitute for solar radiation and a key factor in evaluating solar energy resources. Therefore, the spatial and temporal characteristics of SD and the reasons for its changes have received extensive attention and discussion. Based on the data of 415 meteorological stations from 1970 to 2019, this paper uses linear trend analysis, Mann–Kendall mutation analysis, the Hurst index, empirical orthogonal decomposition, correlation analysis and partial correlation analysis to analyze the spatiotemporal characteristics of SD and its relationship with influencing factors. The results show that the annual SD in China shows a downward trend, with a climate trend rate of −37.93 h/10a, and a significant decline from 1982 to 2019. The seasonal SD shows a downward trend, and the downward trend is most obvious in summer. The annual and seasonal SD will still show a downward trend in the future. The spatial distribution of SD not only has an overall consistent distribution but also takes the Yellow River from Ningxia to Shandong as the boundary, showing a north–south opposite distribution. Annual SD has a significant positive correlation, a significant negative correlation, a positive correlation and a negative correlation with wind speed, precipitation, temperature and relative humidity, respectively, and it is most closely related to wind speed and precipitation. In addition, the change in SD may also be related to human activities.
Don’t Take it Personally: Resistance to Individually Targeted Recommendations from Conversational Recommender Agents

Conversational recommender agents are artificially intelligent recommender systems that provide users with individually-tailored recommendations by targeting individual needs and communicating in a flowing dialogue. These are widely available online, communicating with users while demonstrating human-like (anthropomorphic) social cues. Nevertheless, little is known about the effect of their anthropomorphic cues on users’ resistance to the system and recommendations. Accordingly, this study examined the extent to which conversational recommender agents’ anthropomorphic cues and the type of recommendations provided (user-initiated and system-initiated) influenced users’ perceptions of control, trustworthiness, and the risk of using the platform. The study assessed how these perceptions, in turn, influence users’ adherence to the recommendations. An online experiment was conducted among users with conversational recommender agents and web recommender platforms that provided user-initiated or system-initiated restaurant recommendations. The results entail that user-initiated recommendations, compared to system-initiated, are less likely to affect users’ resistance to the system and are more likely to affect their adherence to the recommendations provided. Furthermore, the study’s findings suggest that these effects are amplified for conversational recommender agents, demonstrating anthropomorphic cues, in contrast to traditional systems as web recommender platforms.
Exploration of EFL Freshman Law Students’ Attitudes on Telegram Messenger Usability in a Legal Terminology Course

In recent years, there has been an increasing tendency for educational institutions and EFL instructors to integrate social media platforms in English language learning environments. The hypothesis is that social media platforms, including Facebook, Twitter, and Telegram, have positive impacts on the development of reading, writing, and communication skills, as well as the vocabulary development of EFL students. Despite the prolific literature on the effectiveness and usability of social media platforms, including Telegram, in EFL contexts, very little research has been done on the impact of Telegram Messenger on language development in general and the vocabulary development of ESP students. Thus, this study explores EFL law students' views of the usability of Telegram Messenger in a Telegram-supported legal terminology course. This quantitative study applies convenience sampling and adapts Kusuma and Suwartono’s (2021) survey questionnaire to collect data. The findings reveal a multi-faceted and very clear picture of Telegram Messenger as an informal form of virtual learning media. The findings confirm that Telegram Messenger is a very beneficial and effective tool, as demonstrated in the legal terminology course. Students express satisfaction with Telegram Messenger due to its easy accessibility and flexible use. They have positive attitudes concerning the educational applications of Telegram Messenger, especially in establishing learner–learner and lecturer–learner types of interaction and keeping them updated with lectures and examination-related issues. Telegram Messenger assists students in catching up with the lectures they cannot attend due to unfavorable circumstances.
Classification and Object Detection of 360° Omnidirectional Images Based on Continuity-Distortion Processing and Attention Mechanism

The use of 360° omnidirectional images has occurred widely in areas where comprehensive visual information is required due to their large visual field coverage. However, many extant convolutional neural networks based on 360° omnidirectional images have not performed well in computer vision tasks. This occurs because 360° omnidirectional images are processed into plane images by equirectangular projection, which generates discontinuities at the edges and can result in serious distortion. At present, most methods to alleviate these problems are based on multi-projection and resampling, which can result in huge computational overhead. Therefore, a novel edge continuity distortion-aware block (ECDAB) for 360° omnidirectional images is proposed here, which prevents the discontinuity of edges and distortion by recombining and segmenting features. To further improve the performance of the network, a novel convolutional row-column attention block (CRCAB) is also proposed. CRCAB captures row-to-row and column-to-column dependencies to aggregate global information, enabling stronger representation of the extracted features. Moreover, to reduce the memory overhead of CRCAB, we propose an improved convolutional row-column attention block (ICRCAB), which can adjust the number of vectors in the row-column direction. Finally, to verify the effectiveness of the proposed networks, we conducted experiments on both traditional images and 360° omnidirectional image datasets. The experimental results demonstrated that better performance than for the baseline model was obtained by the network using ECDAB or CRCAB.
Biomimetic Perception, Cognition, and Control: From Nature to Robots [From the Guest Editors]

A wide range of technological developments are inspired by biological individuals and advanced synthetic materials, cognitive sensors, control algorithms, artificial intelligence technology, and intelligent systems. One of the major challenges is to create a comprehensive study by integrating different techniques into robotic systems so that the performance of robots can be improved and applied to more complex and diverse scenarios. The articles in this issue focus on the state of the art in biomimetic perception, cognition, and control research and aim to explore related technical avenues in the multimodal bioinformation perception framework, intelligent cognition and learning, robotic systems and control, and new biomimetic sensors.