Reviewerly/exHarmony huggingface dataset:
Human Verification over Activity Analysis via Deep Data Mining

Human verification and activity analysis (HVAA) are primarily employed to observe, track, and monitor human motion patterns using redgreen-blue (RGB) images and videos.Interpreting human interaction using RGB images is one of the most complex machine learning tasks in recent times.Numerous models rely on various parameters, such as the detection rate, position, and direction of human body components in RGB images.This paper presents robust human activity analysis for event recognition via the extraction of contextual intelligence-based features.To use human interaction image sequences as input data, we first perform a few denoising steps.Then, human-to-human analyses are employed to deliver more precise results.This phase follows feature engineering techniques, including diverse feature selection.Next, we used the graph mining method for feature optimization and AdaBoost for classification.We tested our proposed HVAA model on two benchmark datasets.The testing of the proposed HVAA system exhibited a mean accuracy of 92.15% for the Sport Videos in the Wild (SVW) dataset.The second benchmark dataset, UT-interaction, had a mean accuracy of 92.83%.Therefore, these results demonstrated a better recognition rate and outperformed other novel techniques in body part tracking and event detection.The proposed HVAA system can be utilized in numerous real-world applications including, healthcare, surveillance, task monitoring, atomic actions, gesture and posture analysis.
Zero Watermarking Algorithm for Medical Image Based on Resnet50-DCT

Medical images are used as a diagnostic tool, so protecting their confidentiality has long been a topic of study.From this, we propose a Resnet50-DCT-based zero watermarking algorithm for use with medical images.To begin, we use Resnet50, a pre-training network, to draw out the deep features of medical images.Then the deep features are transformed by DCT transform and the perceptual hash function is used to generate the feature vector.The original watermark is chaotic scrambled to get the encrypted watermark, and the watermark information is embedded into the original medical image by XOR operation, and the logical key vector is obtained and saved at the same time.Similarly, the same feature extraction method is used to extract the deep features of the medical image to be tested and generate the feature vector.Later, the XOR operation is carried out between the feature vector and the logical key vector, and the encrypted watermark is extracted and decrypted to get the restored watermark; the normalized correlation coefficient (NC) of the original watermark and the restored watermark is calculated to determine the ownership and watermark information of the medical image to be tested.After calculation, most of the NC values are greater than 0.50.The experimental results demonstrate the algorithm's robustness, invisibility, and security, as well as its ability to accurately extract watermark information.The algorithm also shows good resistance to conventional attacks and geometric attacks.
Opportunities and Challenges in Educational Chatbots

The rapid technological developments have revolutionised approaches toward learning. The adoption of eLearning technologies such as chatbots has been increasing in the past few years, as there are various opportunities that can be identified to integrate educational chatbots with online learning process. For example, chatbots in education can provide various services such as personal tutoring, personal support, assessment and evaluation, etc. Iissues in remote learning—such as real-time assistance, feedback, and support—can be addressed by deploying educational chatbots. Yet, there are various challenges associated with chatbot technologies in education, e.g., novelty effect, cognitive load, the readiness of students and teachers, etc. This study reviews the various opportunities and challenges associated with educational chatbots in learning. These findings would help future researchers and designers to identify the core functionality and design aspects of educational chatbots, and also aids future research by the recommendations of research propositions.
Antecedents and Role of Trust in Chatbot Use Intentions: An Indian Perspective

The digital age has changed the way businesses are run today. Technology is not just a priviledge but also a necessity. The recent pandemic has given important lessons to business to be proactive and advanced in technology. Customers occupy the centrestage in any business and giving them solutions promptly for their queries can leave a positive impression and lead to long term customer enagagment. For this, a trained team of employees are required who can give their services incessantly. However, the rising employee retention costs have impacted the profit margins of organisations and more human intervention becomes a hurdle in standardization of processes. Therefore, organisations are roping in artificial intelligence to be more efficient and cost effective. Chatbots are artificial intelligence softwares that have enabled organisations to give answers to customer queries online. The study intends to examine the significant factors in determining customers’ intentions to use chatbots. This paper aims to understand the role of user experience, performance expectancy, effort expectancy, and trust in customer chatbot use intentions from the Indian point of view. A structured questionnaire was utilized to gather data for testing the proposed model, which was conceptualized based on extant literature on technology acceptance and consumer behavior. A survey response of 354 respondents was taken. In order to test the constructs, the collected data was analyzed through AMOS 21. The research findings depicted the positive impact of user experience, trust performance expectancy, and effort expectancy on customer intention to use chatbots, which influences actual usage. This paper empirically demonstrates the relationship among various variables affecting customer intentions to use chatbots. Since the paper uses data collected from a sample not randomly selected, it may regulate the generalization of the results. This study intends to add to the current research gap in the existing literature about customer intention to use chatbots, mainly in the Indian context. The research examined how positive user experience, performance, effort expectancy, and trust affect customer intentions to take support from chatbots.
Deep-Reinforcement-Learning-Based IoT Sensor Data Cleaning Framework for Enhanced Data Analytics

The Internet of things (IoT) combines different sources of collected data which are processed and analyzed to support smart city applications. Machine learning and deep learning algorithms play a vital role in edge intelligence by minimizing the amount of irrelevant data collected from multiple sources to facilitate these smart city applications. However, the data collected by IoT sensors can often be noisy, redundant, and even empty, which can negatively impact the performance of these algorithms. To address this issue, it is essential to develop effective methods for detecting and eliminating irrelevant data to improve the performance of intelligent IoT applications. One approach to achieving this goal is using data cleaning techniques, which can help identify and remove noisy, redundant, or empty data from the collected sensor data. This paper proposes a deep reinforcement learning (deep RL) framework for IoT sensor data cleaning. The proposed system utilizes a deep Q-network (DQN) agent to classify sensor data into three categories: empty, garbage, and normal. The DQN agent receives input from three received signal strength (RSS) values, indicating the current and two previous sensor data points, and receives reward feedback based on its predicted actions. Our experiments demonstrate that the proposed system outperforms a common time-series-based fully connected neural network (FCDQN) solution, with an accuracy of around 96% after the exploration mode. The use of deep RL for IoT sensor data cleaning is significant because it has the potential to improve the performance of intelligent IoT applications by eliminating irrelevant and harmful data.
ShipGAN: Generative Adversarial Network based simulation-to-real image translation for ships

Recent advances in robotics and autonomous systems (RAS) have significantly improved the autonomy level of unmanned surface vehicles (USVs) and made them capable of undertaking demanding tasks in various environments. During the operation of USVs, apart from normal situations, it is those unexpected scenes, such as busy waterways or navigation in dust/nighttime, impose most dangers to USVs as these scenes are rarely seen during training. Such a rare occurrence also makes the manual collection and recording of these scenes into dataset difficult, expensive and inefficient, with the majority of existing public available datasets not able to fully cover them. One of many plausible solutions is to purposely generate these data using computer vision techniques with the assistance from high-fidelity simulations that can create various desirable motions/scenarios. However, the stylistic difference between the simulation images and the natural images would cause a domain shift problem. Hence, there is a need for designing a method that can transfer the data distribution and styles of the simulation images into the realistic domain. This paper proposes and evaluates a novel solution to fill this gap using a Generative Adversarial Network (GAN) based model, ShipGAN, to translate the simulation images into realistic images. Experiments were carried out to investigate the feasibility of generating realistic images using GAN-based image translation models. The synthetic realistic images from the simulation images were demonstrated to be reliable by the object detection and image segmentation algorithms trained with natural images.
Metadata as a Methodological Commons: From Aboutness Description to Cognitive Modeling

ABSTRACT Metadata is data about data, which is generated mainly for resources organization and description, facilitating finding, identifying, selecting and obtaining information①. With the advancement of technologies, the acquisition of metadata has gradually become a critical step in data modeling and function operation, which leads to the formation of its methodological commons. A series of general operations has been developed to achieve structured description, semantic encoding and machine-understandable information, including entity definition, relation description, object analysis, attribute extraction, ontology modeling, data cleaning, disambiguation, alignment, mapping, relating, enriching, importing, exporting, service implementation, registry and discovery, monitoring etc. Those operations are not only necessary elements in semantic technologies (including linked data) and knowledge graph technology, but has also developed into the common operation and primary strategy in building independent and knowledge-based information systems. In this paper, a series of metadata-related methods are collectively referred to as ‘metadata methodological commons’, which has a lot of best practices reflected in the various standard specifications of the Semantic Web. In the future construction of a multi-modal metaverse based on Web 3.0, it shall play an important role, for example, in building digital twins through adopting knowledge models, or supporting the modeling of the entire virtual world, etc. Manual-based description and coding obviously cannot adapted to the UGC (User Generated Contents) and AIGC (AI Generated Contents)-based content production in the metaverse era. The automatic processing of semantic formalization must be considered as a sure way to adapt metadata methodological commons to meet the future needs of AI era.
Stream Sediment Indicator Mineral Signatures of the Casino Porphyry Cu-Au-Mo Deposit, Yukon, Canada

Abstract Case studies around porphyry Cu deposits in the glaciated regions of the Canadian Cordillera have identified the indicator mineral signatures of these deposits in till samples and demonstrated that these are useful methods for porphyry Cu exploration. This study applies the same indicator methods to stream sediment samples around the Casino calc-alkaline porphyry Cu-Au-Mo deposit in the unglaciated terrain of west-central Yukon, Canada. It is one of the largest porphyry Cu-Au-Mo deposits in Canada and is hosted in Late Cretaceous quartz monzonite and associated breccias. Bulk (8–16 kg) coarse-grained stream sediment samples were collected in creeks around the deposit, nearby porphyry Cu occurrences and background areas. The Casino deposit has an obvious indicator mineral signature in the &amp;lt;2-mm heavy (&amp;gt;3.2-specific gravity [SG]) and mid-density (2.8- to 3.2-SG) fractions of stream sediments that is detectable at least 18 km downstream and includes gold, chalcopyrite, tourmaline, molybdenite, sphalerite, jarosite, goethite, and pyrite. In contrast, not all of these mineralogically anomalous samples have corresponding anomalous geochemical signatures in the fine fraction. The porphyry indicator minerals identified in this study are similar to those reported for glaciated terrain with the addition of molybdenite and secondary minerals. Indicator mineral methods provide physical evidence of nearby mineralization and can be chemically analyzed to provide insights about the nature of the mineralizing system. Government and exploration surveys could benefit from the addition of indicator mineral methods to routine stream sediment sampling protocols in unglaciated regions of the Yukon and elsewhere globally where porphyry Cu exploration is conducted.
Object-Ratio-Preserving Video Retargeting Framework based on Segmentation and Inpainting

The recent development of video-based content platforms led the easy access to videos decades ago. However, some past videos have a old screen ratio. If an image with this ratio is executed on a display with a wider screen ratio, the image is excessively stretched horizontally or creates a black box, which prevents efficient viewing of content. In this paper, we propose a method for retargeting the old ratio video frames to a wider ratio while maintaining the original ratio of important objects in content using deep learning-based semantic segmentation and inpainting techniques. Our research shows that proposed method can make a retargeted frames visually natural.
Detecting Arbitrary Keypoints on Limbs and Skis with Sparse Partly Correct Segmentation Masks

Analyses based on the body posture are crucial for top-class athletes in many sports disciplines. If at all, coaches label only the most important keypoints, since manual annotations are very costly. This paper proposes a method to detect arbitrary keypoints on the limbs and skis of professional ski jumpers that requires a few, only partly correct segmentation masks during training. Our model is based on the Vision Transformer architecture with a special design for the input tokens to query for the desired keypoints. Since we use segmentation masks only to generate ground truth labels for the freely selectable keypoints, partly correct segmentation masks are sufficient for our training procedure. Hence, there is no need for costly hand-annotated segmentation masks. We analyze different training techniques for freely selected and standard keypoints, including pseudo labels, and show in our experiments that only a few partly correct segmentation masks are sufficient for learning to detect arbitrary keypoints on limbs and skis.
CAD2Render: A Modular Toolkit for GPU-accelerated Photorealistic Synthetic Data Generation for the Manufacturing Industry

The use of computer vision for product and assembly quality control is becoming ubiquitous in the manufacturing industry. Lately, it is apparent that machine learning based solutions are outperforming classical computer vision algorithms in terms of performance and robustness. However, a main drawback is that they require sufficiently large and labeled training datasets, which are often not available or too tedious and too time consuming to acquire. This is especially true for low-volume and high-variance manufacturing. Fortunately, in this industry, CAD models of the manufactured or assembled products are available. This paper introduces CAD2Render, a GPU-accelerated synthetic data generator based on the Unity High Definition Render Pipeline (HDRP). CAD2Render is designed to add variations in a modular fashion, making it possible for high customizable data generation, tailored to the needs of the industrial use case at hand. Although CAD2Render is specifically designed for manufacturing use cases, it can be used for other domains as well. We validate CAD2Render by demonstrating state of the art performance in two industrial relevant setups. We demonstrate that the data generated by our approach can be used to train object detection and pose estimation models with a high enough accuracy to direct a robot. The code for CAD2Render is available at https://github.com/EDM-Research/CAD2Render.
Subjective and Objective Video Quality Assessment of High Dynamic Range Sports Content

High Dynamic Range (HDR) video streaming has be-come more popular because of the faithful color and bright-ness presentation. However, the live streaming of HDR, especially of sports content, has unique challenges, as it was usually encoded and distributed in real-time without the post-production workflow. A set of unique problems that occurs only in live streaming, e.g. resolution and frame rate crossover, intra-frame pulsing video quality defects, complex relationship between rate-control mode and video quality, are more salient when the videos are streamed in HDR format. These issues are typically ignored by other subjective databases, disregard the fact that they have a sig-nificant impact on the perceived quality of the videos. In this paper, we present a large-scale HDR video quality dataset for sports content that includes the above mentioned important issues in live streaming, and a method of merging multi-ple datasets using anchor videos. We also benchmarked ex-isting video quality metrics on the new dataset, particularly over the novel scopes included in the database, to evaluate the effectiveness and efficiency of the existing models. We found that despite the strong overall performance over the entire database, most of the tested models perform poorly when predicting human preference for various encoding pa-rameters, such as frame rate and adaptive quantization.
BAPose: Bottom-Up Pose Estimation with Disentangled Waterfall Representations

We propose BAPose, a novel bottom-up approach that achieves state-of-the-art results for multi-person pose estimation. Our end-to-end trainable framework leverages a disentangled multi-scale waterfall architecture and incorporates adaptive convolutions to infer keypoints more precisely in crowded scenes with occlusions. The multiscale representations, obtained by the disentangled water-fall module in BAPose, leverage the efficiency of progres-sive filtering in the cascade architecture, while maintaining multi-scale fields-of- view comparable to spatial pyra-mid configurations. Our results on the challenging COCO and CrowdPose datasets demonstrate that BAPose is an efficient and robust framework for multi-person pose estimation, significantly improving state-of-the-art accuracy. Human Pose Estimation, Multi-Scale Representations
Knowledge distillation-enhanced multitask framework for recommendation

With the ever-growing amount of online information, recommender systems (RSs) act as information filtering tools and are widely used in various e-commerce platforms. Recommender methods generally adopt only one type of behavior data for single-task learning. Multitask learning is commonly used to simultaneously address multiple tasks utilizing as much information as possible. We propose a knowledge distillation-enhanced shared-bottom model for recommender multitask learning. The postview click-through rate, postview conversion rate and average transaction value are learned together to determine the final gross merchandise volume. First, a mixture of experts with gate networks is used as a shared bottom to learn task-specific representations for all tasks, while a tree distillation framework is designed as an expert for better feature selection. Then, we design a neural factorization machine as a task-specific prediction network to estimate individual goals. Finally, an objective function is designed based on task outputs to optimize the gross merchandise volume. Our proposed framework can enhance recommender preferences while avoiding generating a large number of parameters. This model serves the e-commerce scenario, with the following specific use scenario: building personalized GMV predictions for small scenarios on e-commerce platforms at low cost, which is a 0 to 1 process that is often highly rewarding. Ultimately, we propose to improve the reliability of e-commerce scenarios.
Synthehicle: Multi-Vehicle Multi-Camera Tracking in Virtual Cities

Smart City applications such as intelligent traffic routing, accident prevention or vehicle surveillance rely on computer vision methods for exact vehicle localization and tracking. Privacy issues make collecting real data difficult, and labeling data is a time-consuming and costly process. Due to the scarcity of accurately labeled data, detecting and tracking vehicles in 3D from multiple cameras proves challenging to explore. We present a massive synthetic dataset for multiple vehicle tracking and segmentation in multiple overlapping and non-overlapping camera views. Unlike existing datasets, which only provide tracking ground truth for 2D bounding boxes, our dataset additionally contains perfect labels for 3D bounding boxes in camera- and world coordinates, depth estimation, and instance, semantic and panoptic segmentation. The dataset consists of 17 hours of labeled video material, recorded from 340 cameras in 64 diverse day, rain, dawn, and night scenes, making it the most extensive dataset for multi-target multi-camera tracking so far. We provide baselines for detection, vehicle re-identification, and single- and multi-camera tracking. Code and data are publicly available. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> Code and data: https://github.com/fubel/synthehicle
Exploiting Temporal Context for Tiny Object Detection

In surveillance applications, the detection of tiny, low-resolution objects remains a challenging task. Most deep learning object detection methods rely on appearance features extracted from still images and struggle to accurately detect tiny objects. In this paper, we address the problem of tiny object detection for real-time surveillance applications, by exploiting the temporal context available in video sequences recorded from static cameras. We present a spatiotemporal deep learning model based on YOLOv5 that exploits temporal context by processing sequences of frames at once. The model drastically improves the identification of tiny moving objects in the aerial surveillance and person detection domains, without degrading the detection of stationary objects. Additionally, a two-stream architecture that uses frame-difference as explicit motion information was proposed, further improving the detection of moving objects down to <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$4\times 4$</tex> pixels in size. Our approaches outperform previous work on the public WPAFB WAMI dataset, as well as surpassing previous work on an embedded NVIDIA Jetson Nano deployment in both accuracy and inference speed. We conclude that the addition of temporal context to deep learning object detectors is an effective approach to drastically improve the detection of tiny moving objects in static videos.