Reviewerly/exHarmony huggingface dataset:
Bimodal HAR-An efficient approach to human activity analysis and recognition using bimodal hybrid classifiers

Human activity recognition (HAR) is an emerging field that identifies human actions in different settings. This activity is recognized by sensors placed in the room or residence where we wish to observe human action. Real-world applications and automation employ activity recognition to detect anomalous behavior. For example, the anomalous behavior of patients such as walking while advised to rest in bed and falling elderly people need to be monitored carefully in hospitals as well as in home-based monitoring systems. Security, healthcare, human interaction, and computer vision use it. The activity is monitored through sensors and cameras. There is no general, explicit approach for inferring human activities from sensor data. Sensor data and heuristics present technological challenges. Several elements must be evaluated to build a reliable activity recognition system. Factors such as storage, connectivity, processing, energy efficiency, and system adaptability are important. Deep learning systems can better recognize human activities from earlier datasets. In this study, the hybrid One Dimensional Convolution Neural Network with Long Short Term Memory (LSTM) classifier is employed to improve the performance of HAR. It offers a method for automatically and data-adaptively removing reliable characteristics from raw data. This model proposes a two-way classification for abstract and individual activity monitoring. Human activities such as walking, sitting, walking downstairs, walking upstairs, laying, and standing along with mobile phone usage are considered in this study. We also compare state-of-the-art algorithms such as Support Vector Machine (SVM), K-Nearest Neighbour (KNN), Long Short Term Memory (LSTM), and Convolutional Neural Network (CNN). The UCI-HAR dataset is used for recognizing human activity in the proposed work. Features such as mean, median, and autoregressive coefficients are derived from the raw data and processed with principal component analysis to make them more reliable. The LSTM model accepts a series of activities, whereas the CNN accepts a single input. The CNN takes the single input data and each of the outputs is forwarded to the LSTM model, which classifies the activity. The Hybrid model achieves 97.89% accuracy with the new feature selection methods, whereas the CNN and LSTM individually produce 92.77% and 92.80% accuracy.
Analyzing the potential of virtual reality-supported training for industrial assembly tasks

Virtual Reality (VR) in industry offers opportunities to decrease design and production costs, maintain product quality and reduce the time needed to go from product concept to production. Due to its interactive nature, VR is also used for simulations of training sessions to communicate knowledge on assembly and maintenance processes. So far, there are hardly any scientific findings on the effectiveness of virtual learning and training for industrial applications. It is also unclear how training can be improved in virtual environments to maximize the learning outcome. This article investigates whether VR-supported training leads to an increase in learning success compared to traditional training-on-the-job accompanied by a tutor. Two variations of a VR tool for the training of a simplified assembly processes were developed. A laboratory study with 64 participants was conducted to get an insight into the importance of virtual instructions and their effect on learning success and perceived workload. The results indicate that small modifications in a VR training application have a significant effect on learning outcome. In particular, a lower level of visual guidance can cause a higher level of mental workload during the training, forcing the user to actively deal with the learning content. Based on these findings, the opportunities and risks of current VR training simulations are discussed and recommendations to increase the learning effect are formulated.
Sarcasm detection using news headlines dataset

Sarcasm has been an elusive concept for humans. Due to interesting linguistic properties, sarcasm detection has gained traction of the Natural Language Processing (NLP) research community in the past few years. However, the task of predicting sarcasm in a text remains a difficult one for machines as well, and there are limited insights into what makes a sentence sarcastic. Past studies in sarcasm detection either use large scale datasets collected using tag-based supervision or small scale manually annotated datasets. The former category of datasets are noisy in terms of labels and language, whereas the latter category of datasets do not have enough instances to train deep learning models reliably despite having high-quality labels. To overcome these shortcomings, we introduce a high-quality and relatively larger-scale dataset which is a collection of news headlines from a sarcastic news website and a real news website. We describe the unique aspects of our dataset and compare its various characteristics with other benchmark datasets in sarcasm detection domain. Furthermore, we produce insights into what constitute as sarcasm in a text using a Hybrid Neural Network architecture. First released in 2019, we dedicate a section on how the NLP research community has extensively relied upon our contributions to push the state of the art further in the sarcasm detection domain. Lastly, we make the dataset as well as framework implementation publicly available to facilitate continued research in this domain.
CRAS-YOLO: A Novel Multi-Category Vessel Detection and Classification Model Based on YOLOv5s Algorithm

Multi-category vessel detection and classification based on satellite imagery attract a lot of attention due to their significant applications in the military and civilian domains.In this study, we generated a new Artificial-SAR-Vessel dataset based on the combination of the FUSAR-Ship dataset and the SimpleCopyPaste method.We further proposed a novel multi-category vessel detection called CRAS-YOLO which consisted of a convolutional block attention module (CBAM), receptive fields block (RFB), and adaptively spatial feature fusion (ASFF) based on YOLOv5s.The proposed CRAS-YOLO improved the feature pyramid network based on the path aggregation network (PANet), which integrates the RFB feature enhancement module and ASFF feature fusion strategy to obtain richer feature information and realize the adaptive fusion of multi-scale features (RA-PANet).At the same time, a CBAM is added to the backbone to accurately locate the vessel location and improve detection capability.The results confirmed that the proposed CRAS-YOLO model reached a precision, recall rate, and mean average precision (mAP) (0.5) of up to 90.4%, 88.6%, and 92.1% respectively.The proposed model also outperformed previous studies' results in another Sar Ship Detection (SSDD) dataset with precision, recall, and mAP scores of up to 97.3%, 95.5%, and 98.7% respectively.
CrimeNet: Neural Structured Learning using Vision Transformer for violence detection

The state of the art in violence detection in videos has improved in recent years thanks to deep learning models, but it is still below 90% of average precision in the most complex datasets, which may pose a problem of frequent false alarms in video surveillance environments and may cause security guards to disable the artificial intelligence system. In this study, we propose a new neural network based on Vision Transformer (ViT) and Neural Structured Learning (NSL) with adversarial training. This network, called CrimeNet, outperforms previous works by a large margin and reduces practically to zero the false positives. Our tests on the four most challenging violence-related datasets (binary and multi-class) show the effectiveness of CrimeNet, improving the state of the art from 9.4 to 22.17 percentage points in ROC AUC depending on the dataset. In addition, we present a generalisation study on our model by training and testing it on different datasets. The obtained results show that CrimeNet improves over competing methods with a gain of between 12.39 and 25.22 percentage points, showing remarkable robustness.
Improving pseudo labels with intra-class similarity for unsupervised domain adaptation

Unsupervised domain adaptation (UDA) transfers knowledge from a label-rich source domain to a different but related fully-unlabeled target domain. To address the problem of domain shift, more and more UDA methods adopt pseudo labels of the target samples to improve the generalization ability on the target domain. However, inaccurate pseudo labels of the target samples may yield suboptimal performance with error accumulation during the optimization process. Moreover, once the pseudo labels are generated, how to remedy the generated pseudo labels is far from explored. In this paper, we propose a novel approach to improve the accuracy of the pseudo labels in the target domain. It first generates coarse pseudo labels by a conventional UDA method. Then, it iteratively exploits the intra-class similarity of the target samples for improving the generated coarse pseudo labels, and aligns the source and target domains with the improved pseudo labels. The accuracy improvement of the pseudo labels is made by first deleting dissimilar samples, and then using spanning trees to eliminate the samples with the wrong pseudo labels in the intra-class samples. We have applied the proposed approach to several conventional UDA methods as an additional term. Experimental results demonstrate that the proposed method can boost the accuracy of the pseudo labels and further lead to more discriminative and domain invariant features than the conventional baselines.
Central bank digital currency: A systematic literature review using text mining approach

Central bank digital currency (CBDC) is seen as a possible next step in the evolution of money, offering a more stable unit of account, a more efficient medium of exchange, and a safer way to store value. However, since it began to get significant attention from academics and practitioners a few years ago, many concerns about how a central bank may build an efficient CBDC and how it would impact a country's current financial system still remain unanswered satisfactorily. Based on the combination of text mining and systematic review methods, this work presents a thorough literature assessment of 191 academic papers on CBDC in order to identify major research issues and knowledge gaps that may be addressed in the future. We find seven primary research themes linked to CBDC including (1) Central bank, (2) CBDC and other digital currency, (3) CBDC and money markets, (4) CBDC and monetary policy, (5) CBDC design and technologies, (6) CBDC and payment system, and (7) CBDC and financial stability and regulatory. The finding helps provide both overall and in-depth views of the current state of research in digital fiat currency topics, as well as drawing some important implications and suggestions on directions for the future research.
Graph Lifelong Learning: A Survey

Graph learning is a popular approach for perfor ming machine learning on graph-structured data. It has revolutionized the machine learning ability to model graph data to address downstream tasks. Its application is wide due to the availability of graph data ranging from all types of networks to information systems. Most graph learning methods assume that the graph is static and its complete structure is known during training. This limits their applicability since they cannot be applied to problems where the underlying graph grows over time and/or new tasks emerge incrementally. Such applications require a lifelong learning approach that can learn the graph continuously and accommodate new information whilst retaining previously learned knowledge. Lifelong learning methods that enable continuous learning in regular domains like images and text cannot be directly applied to continuously evolving graph data, due to its irregular structure. As a result, graph lifelong learning is gaining attention from the research community. This survey paper provides a comprehensive overview of recent advancements in graph lifelong learning, including the categorization of existing methods, and the discussions of potential applications and open research problems.
Upper Body Pose Estimation Using a Visual–Inertial Sensor System With Automatic Sensor-to-Segment Calibration

Upper body kinematics is essential for motor function assessment and robot-assisted rehabilitation training. Wearable sensor systems, such as inertial measurement units (IMUs), provide affordable solutions to replace laboratory-based motion capture systems for use in daily life. However, the sensor-to-segment calibration often relies on predefined posture or movements, which is hard to perform accurately, particularly for patients with a limited range of motion. A visual–inertial sensor system is presented, which includes three sensor modules attached to the trunk, upper arm, and forearm. Each module has an IMU and an ArUco marker, which can be captured by a camera and the driftless orientation of the modules is computed from visual–inertial fusion. The sensor-to-segment transformations are calibrated from a period of arbitrary arm movements in either a 2-D plane or 3-D space, simulating the training process assisted by end-effector robots. Experiments were conducted to validate the feasibility and evaluate the accuracy of the proposed method. The estimated shoulder and elbow joint angles correlated well ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$&gt;$ </tex-math></inline-formula> 0.986) with the ground truth from the optical motion capture (OMC) system. The joint angles presented low root-mean-square errors (RMSEs) ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$&lt; 4^{\circ }$ </tex-math></inline-formula> ) except for the forearm pronation–supination angle (9.34°), which relied on manual alignment. The sensor system provides a simple and easy-to-use solution for movement assessment during robot-assisted training.
A Hybrid Gravitational Emulation Local Search-Based Algorithm for Task Scheduling in Cloud Computing

The flexibility of cloud computing to provide a dynamic and adaptable infrastructure in the context of information technology and service quality has made it one of the most challenging issues in the computer industry. Task scheduling is a major challenge in cloud computing. Scheduling tasks so that they may be processed by the most effective cloud network resources has been identified as a critical challenge for maximizing cloud computing’s performance. Due to the complexity of the issue and the size of the search space, random search techniques are often used to find a solution. Several algorithms have been offered as possible solutions to this issue. In this study, we employ a combination of the genetic algorithm (GA) and the gravitational emulation local search (GELS) algorithm to overcome the task scheduling issue in cloud computing. GA and the particle swarm optimization (PSO) algorithms are compared to the suggested algorithm to demonstrate its efficacy. The suggested algorithm outperforms the GA and PSO, as shown by the experiments.
Lossy Image Compression with Quantized Hierarchical VAEs

Recent work has shown a strong theoretical connection between variational autoencoders (VAEs) and the rate distortion theory. Motivated by this, we consider the problem of lossy image compression from the perspective of generative modeling. Starting from ResNet VAEs, which are originally designed for data (image) distribution modeling, we redesign their latent variable model using a quantization-aware posterior and prior, enabling easy quantization and entropy coding for image compression. Along with improved neural network blocks, we present a powerful and efficient class of lossy image coders, outperforming previous methods on natural image (lossy) compression. Our model compresses images in a coarse-to-fine fashion and supports parallel encoding and decoding, leading to fast execution on GPUs. Code is made available online.
CFL-Net: Image Forgery Localization Using Contrastive Learning

Conventional forgery localizing methods usually rely on different forgery footprints such as JPEG artifacts, edge inconsistency, camera noise, etc., with cross-entropy loss to locate manipulated regions. However, these methods have the disadvantage of over-fitting and focusing on only a few specific forgery footprints. On the other hand, real-life manipulated images are generated via a wide variety of forgery operations and thus, leave behind a wide variety of forgery footprints. Therefore, we need a more general approach for image forgery localization that can work well on a variety of forgery conditions. A key assumption in underlying forged region localization is that there remains a difference of feature distribution between untampered and manipulated regions in each forged image sample, irrespective of the forgery type. In this paper, we aim to leverage this difference of feature distribution to aid in image forgery localization. Specifically, we use contrastive loss to learn mapping into a feature space where the features between un-tampered and manipulated regions are well-separated for each image. Also, our method has the advantage of localizing manipulated region without requiring any prior knowledge or assumption about the forgery type. We demonstrate that our work outperforms several existing methods on three benchmark image manipulation datasets. Code is available at https://github.com/niloy193/CFLNet
Uncertainty-aware Label Distribution Learning for Facial Expression Recognition

Despite significant progress over the past few years, ambiguity is still a key challenge in Facial Expression Recognition (FER). It can lead to noisy and inconsistent annotation, which hinders the performance of deep learning models in real-world scenarios. In this paper, we propose a new uncertainty-aware label distribution learning method to improve the robustness of deep models against uncertainty and ambiguity. We leverage neighborhood information in the valence-arousal space to adaptively construct emotiona distributions for training samples. We also consider the uncertainty of provided labels when incorporating them into the label distributions. Our method can be easily integrated into a deep network to obtain more training supervision and improve recognition accuracy. Intensive experiments on several datasets under various noisy and ambiguous settings show that our method achieves competitive results and outperforms recent state-of-the-art approaches. Our code and models are available at https://github.com/minhnhatvt/label-distribution-learning-fer-tf.
Fast Online Video Super-Resolution with Deformable Attention Pyramid

Video super-resolution (VSR) has many applications that pose strict causal, real-time, and latency constraints, including video streaming and TV. We address the VSR problem under these settings, which poses additional important challenges since information from future frames is unavailable. Importantly, designing efficient, yet effective frame alignment and fusion modules remain central problems. In this work, we propose a recurrent VSR architecture based on a deformable attention pyramid (DAP). Our DAP aligns and integrates information from the recurrent state into the current frame prediction. To circumvent the computational cost of traditional attention-based methods, we only attend to a limited number of spatial locations, which are dynamically predicted by the DAP. Comprehensive experiments and analysis of the proposed key innovations show the effectiveness of our approach. We significantly reduce processing time and computational complexity in comparison to state-of-the-art methods, while maintaining a high performance. We surpass state-of-the-art method EDVR-M on two standard benchmarks with a speed-up of over 3×.
ConfMix: Unsupervised Domain Adaptation for Object Detection via Confidence-based Mixing

Unsupervised Domain Adaptation (UDA) for object detection aims to adapt a model trained on a source domain to detect instances from a new target domain for which annotations are not available. Different from traditional approaches, we propose ConfMix, the first method that introduces a sample mixing strategy based on region-level detection confidence for adaptive object detector learning. We mix the local region of the target sample that corresponds to the most confident pseudo detections with a source image, and apply an additional consistency loss term to gradually adapt towards the target data distribution. In order to robustly define a confidence score for a region, we exploit the confidence score per pseudo detection that accounts for both the detector-dependent confidence and the bounding box uncertainty. Moreover, we propose a novel pseudo labelling scheme that progressively filters the pseudo target detections using the confidence metric that varies from a loose to strict manner along the training. We perform extensive experiments with three datasets, achieving state-of-the-art performance in two of them and approaching the supervised target model performance in the other. Code is available at https://github.com/giuliomattolin/ConfMix.
Consumer resistance to service robots at the hotel front desk: A mixed-methods research

The diffusion of service robots, powered by artificial intelligence (AI) technology, at the hotel front desk is facing challenges. Drawing on innovation resistance literature, this study investigates consumer resistance to hotel front-desk service robots by developing a service robot resistance model. A mixed-methods design was used involving qualitative interviews and quantitative surveys. The results identified eight barriers to using service robots that can be categorised as functional, social interaction, and aesthetic barriers. Consumer resistance occurs in response to the risk of malfunction, complexity, usage barrier, lack of warmth, and unattractive appearance, whereas flexibility barrier, communication barrier and stiff kinesics have no statistically significant effect. This study contributes to innovation resistance research and provides guidance for the adoption and diffusion of service robots in the hospitality and tourism industry.
Segmentalized mRMR Features and Cost-Sensitive ELM With Fixed Inputs for Fault Diagnosis of High-Speed Railway Turnouts

Turnouts are crucial to the safety of high-speed railways. Due to the intensive use and complex environment, breakdowns caused by different faults occur frequently in practice. Considering that the operation of turnouts is a multi-stage process during which each stage has its specific health characteristics, this paper proposes segmentalized maximal-relevancy and minimal-redundancy (mRMR) for feature extraction from each stage separately. Based on mathematical analysis of the turnout mechanism, the electric power curve is segmented into four stages, from which time-domain analysis and mRMR are combined to extract valid features corresponding to different movements respectively. Then, a novel classifier named cost-sensitive Extreme Learning Machine with fixed inputs (cf-ELM) is proposed for fault classification. We modify the inputs of ELM and define a new formula to limit the input weights and biases for the sake of stability of the network structure. Besides, a cost-sensitive optimization method is also presented in this classifier to embed the failure degree and data proportion into cost calculation rules to deal with data imbalance. To verify our proposed method, real data collected from a turnout of Beijing-Shanghai high-speed railway is used. It is proven by comparisons that the accuracy of our method has achieved 100% with fast running speed and also outperforms traditional methods in terms of stability and generalization remarkably.