Reviewerly/exHarmony huggingface dataset:
In-situ or side-by-side? A user study on augmented reality maintenance instructions in blind areas

In recent years, Augmented Reality (AR) has been effectively proposed as a tool to support workers in manual procedural tasks in industry, such as assembly and maintenance. It is very common for workers to deal with complete equipment in maintenance. Then, instructions may refer to components located in blind areas, i.e., visually occluded by part of the equipment. Displaying in-situ AR instructions in such blind areas requires handling occlusions; otherwise, side-by-side instructions can be exploited. In the literature, it is still unclear which solution better leverage workers’ performance in maintenance tasks accomplished in blind areas. Thus, we designed three AR presentation modes to convey maintenance instructions in a real machine: 3D in-situ rendered with X-ray technique; 3D side-by-side on a CAD model replicating the blind area; 2D side-by-side on a virtual mirror. We conducted a user study comparing these three presentation modes with a 2D drawing extracted from the original maintenance documentation of the machine. The performance of 42 participants was evaluated in terms of completion time, accuracy, and cognitive load. The results revealed that both in-situ and 3D side-by-side presentation modes perform better than 2D drawing. Specifically, the in-situ presentation mode outperforms the 3D side-by-side mode in terms of completion time. The side-by-side virtual mirror does not improve performance with respect to 2D drawing, then it needs to be redesigned for effective use in AR maintenance interfaces.
Digital divide, globalization and income inequality in sub-Saharan African countries: analysing cross-country heterogeneity

Purpose This study aims to analyse the impact of digital divide (DD) on income inequality in sub-Saharan Africa over the period 2004–2016. Design/methodology/approach In applying a finite mixture model (FMM) to a sample of 35 sub-Saharan African (SSA) countries, this study posits that DD affects income inequality differently. Findings The findings show that the effect of DD on income inequality varies across two distinct groups of countries, which differ according to their level of globalization. In addition, the study shows that most globalized countries are more inclined to be in the group where the effect of DD on income inequality is negative. The results are consistent with several robustness checks, including alternative measures of income inequality and additional control variables. Originality/value This study complements that extant literature by assessing linkages among the DD, globalization and income inequality in sub-Saharan African countries contingent on cross-country heterogeneity.
HOTTEST: Hate and Offensive content identification in Tamil using Transformers and Enhanced STemming

Offensive content or hate speech is defined as any form of communication that aims to annoy, harass, disturb, or anger an individual or community based on factors such as faith, ethnicity, appearance, or sexuality. Nowadays, offensive content posted in regional languages increased due to the popularity of social networks and other apps usage by common people. This work proposes a method to detect and identify hate speech or offensive content in Tamil. We have used the HASOC 2021 data set that contains YouTube comments in Tamil language and written in Tamil script. In this research work, an attempt is made to find suitable embedding techniques for Tamil text representation by applying TF-IDF and pre-trained transformer models like BERT, XLM-RoBERTa, IndicBERT, mBERT, TaMillion, and MuRIL. As Tamil is a morphologically rich language, a detailed analysis is made to study the performance of hate speech detection in Tamil by applying enhanced stemming algorithms. An extensive experimental study was performed with different classifiers such as logistic regression, SVM, stochastic Gradient Descent, decision tree, and ensemble learning models in combination with the above techniques. The results of this detailed experimental study show that stop word removal produces mixed results and does not guarantee improvement in the performance of the classifier to detect offensive content for Tamil data. However, the performance on stemmed data shows a significant improvement over un-stemmed data in Tamil texts. As the data is highly imbalanced, we also combined an oversampling/downsampling technique to analyze its role in designing the best offensive classifier for Tamil text. The highest performance was achieved by a combination of stemming the text data, embedding it with the multi-lingual model MuRIL and using a majority voting ensemble as the downstream classifier. We have achieved the F1-score of 84% and accuracy of 86% for detecting offensive content in Tamil YouTube comments.
Systematic Review of Advanced AI Methods for Improving Healthcare Data Quality in Post COVID-19 Era

At the beginning of the COVID-19 pandemic, there was significant hype about the potential impact of artificial intelligence (AI) tools in combatting COVID-19 on diagnosis, prognosis, or surveillance. However, AI tools have not yet been widely successful. One of the key reason is the COVID-19 pandemic has demanded faster real-time development of AI-driven clinical and health support tools, including rapid data collection, algorithm development, validation, and deployment. However, there was not enough time for proper data quality control. Learning from the hard lessons in COVID-19, we summarize the important health data quality challenges during COVID-19 pandemic such as lack of data standardization, missing data, tabulation errors, and noise and artifact. Then we conduct a systematic investigation of computational methods that address these issues, including emerging novel advanced AI data quality control methods that achieve better data quality outcomes and, in some cases, simplify or automate the data cleaning process. We hope this article can assist healthcare community to improve health data quality going forward with novel AI development.
Asymmetric cross-modal activation network for RGB-T salient object detection

RGB-thermal salient object detection (RGB-T SOD) has unique advantages in terms of handling challenging scenes with cluttered backgrounds, low illumination, and low contrast. However, because they do not consider the significant differences between different imaging mechanisms and inherent characteristics of thermal images, existing RGB-T SOD methods are generally unable to handle diverse feature fusion demands and may yield unsatisfactory performance. To overcome this problem and achieve more effective RGB-T SOD, we propose an asymmetric cross-modal activation network to exploit the interactions of modality-specific features based on an asymmetric feature fusion strategy. Specifically, a two-stream asymmetric feature aggregation encoder module is proposed to fuse multimodality features adaptively and extract complementary information. The self-attention of multimodality features is leveraged to guide cross-modal interactions, which can propagate long-range contextual dependencies and extract effective saliency cues. Furthermore, a multitask decoder is proposed to achieve SOD and thermal image reconstruction in a unified framework. Salient objects can be located and segmented accurately based on reconstructed high-resolution feature representations. Extensive experiments on public RGB-T and RGB-D SOD datasets demonstrate the superiority of the proposed network and ablation experiments highlight the effectiveness of each component. Our code and saliency maps are available at: www.github.com/xanxuso/ACMANet.
Recent trends in human activity recognition – A comparative study

Identification of human actions from video has gathered much attention in past few years. Most of the computer vision tasks such as Health Care Activity Detection, Suspicious Activity detection, Human Computer Interactions etc. are based on the principle of activity detection. Automatic labelling of activity from videos frames is known as activity detection. With the introduction of deep networks, the process of activity detection is clustered into two groups known as hand-crafted feature based approach and automatic feature extraction approach. This paper focuses on various approaches used in recent literature based on traditional and automatic approach. Moreover, hierarchy for different approaches under them such as space based, motion based, genetic based, fuzzy based, dictionary based are discussed. With introduction of Convolutional Neural Networks and Recurrent Neural Networks, automatic learning capability from input modality makes them first choice to be implemented for activity recognition. In this paper various approaches have been analyzed according to methodology, accuracy, classifier and datasets.
RD-YOLO: An Effective and Efficient Object Detector for Roadside Perception System

In recent years, intelligent driving technology based on vehicle-road cooperation has gradually become a research hotspot in the field of intelligent transportation. There are many studies regarding vehicle perception, but fewer studies regarding roadside perception. As sensors are installed at different heights, the roadside object scale varies violently, which burdens the optimization of networks. Moreover, there is a large amount of overlapping and occlusion in complex road environments, which leads to a great challenge of object distinction. To solve the two problems raised above, we propose RD-YOLO. Based on YOLOv5s, we reconstructed the feature fusion layer to increase effective feature extraction and improve the detection capability of small targets. Then, we replaced the original pyramid network with a generalized feature pyramid network (GFPN) to improve the adaptability of the network to different scale features. We also integrated a coordinate attention (CA) mechanism to find attention regions in scenarios with dense objects. Finally, we replaced the original Loss with Focal-EIOU Loss to improve the speed of the bounding box regression and the positioning accuracy of the anchor box. Compared to the YOLOv5s, the RD-YOLO improves the mean average precision (mAP) by 5.5% on the Rope3D dataset and 2.9% on the UA-DETRAC dataset. Meanwhile, by modifying the feature fusion layer, the weight of RD-YOLO is decreased by 55.9% while the detection speed is almost unchanged. Nevertheless, the proposed algorithm is capable of real-time detection at faster than 71.9 frames/s (FPS) and achieves higher accuracy than the previous approaches with a similar FPS.
ExpGCN: Review-aware Graph Convolution Network for explainable recommendation

Existing works in recommender system have widely explored extracting reviews as explanations beyond user-item interactions, and formulated the explanation generation as a ranking task to enhance item recommendation performance. To associate explanations with users and items, graph neural networks (GNN) are usually employed to learn node representations on the heterogeneous user-item-explanation interaction graph. However, modeling heterogeneous graph convolution poses limitations in both message passing styles and computational efficiency, resulting in sub-optimal recommendation performance. To address the limitations, we propose an Explanation-aware Graph Convolution Network (ExpGCN). In particular, the heterogeneous interaction graph is divided to subgraphs regard to the edge types in ExpGCN. By aggregating information from distinct subgraphs, ExpGCN is capable of generating node representations for explanation ranking task and item recommendation task respectively. Task-oriented graph convolution can not only reduce the complexity of heterogeneous node aggregation, but also alleviate the performance degeneration caused by the conflicts between task learning objectives, which has been neglected in current studies. Extensive experiments on four public datasets show that ExpGCN significantly outperforms state-of-the-art baselines with high efficiency, demonstrating the effectiveness of ExpGCN in explainable recommendations.
The technical and economic potential of urban rooftop photovoltaic systems for power generation in Guangzhou, China

With the adoption of carbon peak and carbon neutrality targets, countries and cities must spend more on renewable energy to replace polluting and emitting traditional fossil energy sources. Distributed rooftop photovoltaic (PV) cells, in comparison to hydropower and wind generation, use only space and radiation resources and are the least restricted by geography and climate, making them a significant choice for communities looking to create green electricity. Using Guangzhou, a city in southern China, as an example, we offer four installation scenarios based on rooftop area data and research on relevant characteristics and analyze the technical and economic potential of PV power generation on the rooftops of urban buildings. The following conclusions are reached: the rooftop area in Guangzhou suitable for PV installation is 391.7 km2, with a maximum potential power generation capacity of 44.06–72.12 billion kWh per year, which could lower Guangzhou’s greenhouse gas emissions from the power industry by 70.12–100%. The optimal economics are reached with a 20° installation tilted angle and monocrystalline silicon PV panel material, with a 6-year payback period.
The vendor-agnostic EMPAIA platform for integrating AI applications into digital pathology infrastructures

Automated image analysis and artificial intelligence (AI) are becoming increasingly common in digital pathology software. While various proprietary pathology systems exist, there are no fully vendor-agnostic integration approaches for AI apps. This makes it difficult for vendors of AI solutions to integrate their products into the multitude of non-standard software systems in pathology. The EMPAIA Consortium is developing an open and decentralized platform allowing AI-based apps of different vendors to be integrated with existing clinical IT infrastructures. For this purpose, we defined, analyzed, and prioritized relevant use cases and identified requirements for an open platform to support these use cases. We then designed the platform architecture described here to meet these requirements based on web technologies. For all platform services open source reference implementations are available, that are used by developers of AI apps as an integration target. Developers of compatible clinical systems can either use and integrate components of the reference implementation or directly implement the interfaces as per specification, allowing apps to run in their clinical environment. Pathology laboratories can use both on-premises and cloud deployments of the platform. Apps can be obtained via a central marketplace so that pathologists can use them in their daily workflow. An adoption of this platform will enable interoperability among different existing digital pathology software systems. This reduces integration efforts for software vendors, while users will benefit from a wider variety of tools and a quicker availability of new and innovative methods. Ultimately, the platform will reduce barriers to market entry for AI vendors and provide pathologists with access to advanced AI tools.
Integrating Real-World Distractions into Virtual Reality

With the proliferation of consumer-level virtual reality (VR) devices, users started experiencing VR in less controlled environments, such as in social gatherings and public areas. While the current VR hardware provides an increasingly immersive experience, it ignores stimuli originating from the physical surroundings that distract users from the VR experience. To block distractions from the outside world, many users wear noise-canceling headphones. However, this is insufficient to block loud or transient sounds (e.g., drilling or hammering) and, especially, multi-modal distractions (e.g., air drafts, temperature shifts from an A/C, construction vibrations, or food smells). To tackle this, we explore a new concept, where we directly integrate the distracting stimuli from the user's physical surroundings into their virtual reality experience to enhance presence. Using our approach, an otherwise distracting wind gust can be directly mapped to the sway of trees in a VR experience that already contains trees. Using our novel approach, we demonstrate how to integrate a range of distractive stimuli into the VR experience, such as haptics (temperature, vibrations, touch), sounds, and smells. To validate our approach, we conducted three user studies and a technical evaluation. First, to validate our key principle, we conducted a controlled study where participants were exposed to distractions while playing a VR game. We found that our approach improved users’ sense of presence, compared to wearing noise-canceling headphones. From these results, we engineered a sensing module that detects a set of simple distractive signals (e.g., sounds, winds, and temperature shifts). We validated our hardware in a technical evaluation and in an out-of-lab study where participants played VR games in an uncontrolled environment. Moreover, to gather the perspective of VR content creators that might one day utilize a system inspired by our findings, we invited game designers to use our approach and collected their feedback and VR designs. Finally, we present design considerations for mapping distracting external stimuli and discuss ethical considerations of integrating real-world stimuli into virtual reality.
A horizontal and vertical crossover cuckoo search: optimizing performance for the engineering problems

Abstract As science and technology advance, more engineering-type problems emerge. Technology development has likewise led to an increase in the complexity of optimization problems, and the need for new optimization techniques has increased. The swarm intelligence optimization algorithm is popular among researchers as a flexible, gradient-independent optimization method. The cuckoo search (CS) algorithm in the population intelligence algorithm has been widely used in various fields as a classical optimization algorithm. However, the current CS algorithm can no longer satisfy the performance requirements of the algorithm for current optimization problems. Therefore, in this paper, an improved CS algorithm based on a crossover optimizer (CC) and decentralized foraging (F) strategy is proposed to improve the search ability and the ability to jump out of the local optimum of the CS algorithm (CCFCS). Then, in order to verify the performance of the algorithm, this paper demonstrates the performance of CCFCS from six perspectives: core parameter setting, balance analysis of search and exploitation, the impact of introduced strategies, the impact of population dimension, and comparison with classical algorithms and similar improved algorithms. Finally, the optimization effect of CCFCS on real engineering problems is tested by five classic cases of engineering optimization. According to the experimental results, CCFCS has faster convergence and higher solution quality in the algorithm performance test and maintains the same excellent performance in engineering applications.
Improved Residual Network based on norm-preservation for visual recognition

Residual Network (ResNet) achieves deeper and wider networks with high-performance gains, representing a powerful convolutional neural network architecture. In this paper, we propose architectural refinements to ResNet that address the information flow through several layers of the network, including the input stem, downsampling block, projection shortcut, and identity blocks. We will show that our collective refinements facilitate stable backpropagation by preserving the norm of the error gradient within the residual blocks, which can reduce the optimization difficulties of training very deep networks. Our proposed modifications enhance the learning dynamics, resulting in high accuracy and inference performance by enforcing norm-preservation throughout the network training. The effectiveness of our method is verified by extensive experimental results on five computer vision tasks, including image classification (ImageNet and CIFAR-100), video classification (Kinetics-400), multi-label image recognition (MS-COCO), object detection and semantic segmentation (PASCAL VOC). We also empirically show consistent improvements in generalization performance when applying our modifications over different networks to provide new insights and inspire new architectures. The source code is publicly available at: https://github.com/bharatmahaur/LeNo.
An evolutionary-state-based selection strategy for enhancing differential evolution algorithm

The survivor selection operation is one of the factors affecting the performance of differential evolution (DE) algorithms. However, in single-objective DE algorithms, researchers seldom address the improvement of survivor selection operations. Most DE algorithms use greedy selection operations. In this research, we propose an evolutionary-state-based selection (ESS) strategy for single-objective DE, which constructs a probability model based on evolutionary states to solve real-parameter optimization problems. In the survivor selection process, it is no longer true that only the winner can survive to the next generation; the loser also has a certain probability of surviving to the next generation, and this probability is related to the evolutionary states. Based on ESS, a new DE variant called evolutionary-state-based differential evolution (ESDE) is proposed. To verify the ESDE and ESS performance, ESDE was compared with eight state-of-the-art DE variants, and ablation experiments were performed on ESDE to analyze the impact of ESS on the ESDE performance. The experimental results show that ESDE is significantly better than the other eight DE variants, and ESS could significantly improve the DE performance.
FineAction: A Fine-Grained Video Dataset for Temporal Action Localization

Temporal action localization (TAL) is an important and challenging problem in video understanding. However, most existing TAL benchmarks are built upon the coarse granularity of action classes, which exhibits two major limitations in this task. First, coarse-level actions can make the localization models overfit in high-level context information, and ignore the atomic action details in the video. Second, the coarse action classes often lead to the ambiguous annotations of temporal boundaries, which are inappropriate for temporal action localization. To tackle these problems, we develop a novel large-scale and fine-grained video dataset, coined as FineAction, for temporal action localization. In total, FineAction contains 103K temporal instances of 106 action categories, annotated in 17K untrimmed videos. Compared to the existing TAL datasets, our FineAction takes distinct characteristics of fine action classes with rich diversity, dense annotations of multiple instances, and co-occurring actions of different classes, which introduces new opportunities and challenges for temporal action localization. To benchmark FineAction, we systematically investigate the performance of several popular temporal localization methods on it, and deeply analyze the influence of fine-grained instances in temporal action localization. As a minor contribution, we present a simple baseline approach for handling the fine-grained action detection, which achieves an mAP of 13.17% on our FineAction. We believe that FineAction can advance research of temporal action localization and beyond. The dataset is available at https://deeperaction.github.io/datasets/fineaction.