Reviewerly/exHarmony huggingface dataset:
A Large-scale Analysis of Athletes’ Cumulative Race Time in Running Events

Action recognition models and cumulative race time (CRT) are practical tools in sports analytics, providing insights into athlete performance, training, and strategy. Measuring CRT allows for identifying areas for improvement, such as specific sections of a racecourse or the effectiveness of different strategies. Human action recognition (HAR) algorithms can help to optimize performance, with machine learning and artificial intelligence providing real-time feedback to athletes. This paper presents a comparative study of HAR algorithms for CRT regression, examining two important factors: the frame rate and the regressor selection. Our results indicate that our proposal exhibits outstanding performance for short input footage, achieving a mean absolute error of 11 min when estimating CRT for runners that have been on the course for durations ranging from 8 to 20 h.
Extrinsic Calibration of Multiple Depth Cameras for 3D Face Reconstruction

This paper presents a reliable and robust approach for 3D face reconstruction using low-cost depth cameras. The solution is designed to withstand both camera noise and motion artifacts, making it potentially suitable for dealing with living subjects. Our method utilizes Iterative Closest Point (ICP) registration with a calibration pattern, allowing for scalable acquisition with multiple devices. Experiments are conducted adopting two low-cost depth cameras, and producing the 3D reconstruction of a dummy head to favour a metrological evaluation. The findings indicate that the suggested approach outperforms the alternative method of directly applying ICP to the facial point cloud. Additionally, the outcomes demonstrate that the low-cost solution deviates from the high-quality professional equipment by an average of 0.5 mm, showing the notable accuracy of the proposed method.
BHAC-MRI: Backdoor and Hybrid Attacks on MRI Brain Tumor Classification Using CNN

Deep learning (DL) models have demonstrated impressive performance in sensitive medical applications such as disease diagnosis. However, a backdoor attack embedded in the clean dataset without poisoning the labels poses a severe threat to the integrity of Artificial Intelligence (AI) technology. In literature, a lot of work has been done on backdoor attacks for medical applications, in which most of the authors assumed that the labels of the samples are also poisoned. This compromises the elusiveness of the backdoor attacks because poisoned samples can be identified by visual inspection by finding the mismatch between the labels of the samples. In this paper, an elusive backdoor attack is proposed, that makes the poisoned samples difficult to recognize. In the proposed approach a backdoor signal superimposed into a small portion of the clean dataset during training time is proposed. Moreover, this paper proposes a hybrid attack that further increases the Attack Success Rate (ASR). The proposed approach is evaluated over a Convolutional Neural Network (CNN)-based system for Magnetic Resonance Imaging (MRI) brain tumor classification, which demonstrated the effectiveness of the attacks, thus raising concern regarding using AI in sensitive applications.
Unveiling the Impact of Image Transformations on Deepfake Detection: An Experimental Analysis

With the recent explosion of interest in visual Generative AI, the field of deepfake detection has gained a lot of attention. In fact, deepfake detection might be the only measure to counter the potential proliferation of generated media in support of fake news and its consequences. While many of the available works limit the detection to a pure and direct classification of fake versus real, this does not translate well to a real-world scenario. Indeed, malevolent users can easily apply post-processing techniques to generated content, changing the underlying distribution of fake data. In this work, we provide an in-depth analysis of the robustness of a deepfake detection pipeline, considering different image augmentations, transformations, and other pre-processing steps. These transformations are only applied in the evaluation phase, thus simulating a practical situation in which the detector is not trained on all the possible augmentations that can be used by the attacker. In particular, we analyze the performance of a $$k$$ -NN and a linear probe detector on the COCOFake dataset, using image features extracted from pre-trained models, like CLIP and DINO. Our results demonstrate that while the CLIP visual backbone outperforms DINO in deepfake detection with no augmentation, its performance varies significantly in presence of any transformation, favoring the robustness of DINO.
Enhancing Open-Vocabulary Semantic Segmentation with Prototype Retrieval

Large-scale pre-trained vision-language models like CLIP exhibit impressive zero-shot capabilities in classification and retrieval tasks. However, their application to open-vocabulary semantic segmentation remains challenging due to the gap between the global features extracted by CLIP for whole-image recognition and the requirement for semantically detailed pixel-level features. Recent two-stage methods have attempted to overcome these challenges by generating mask proposals that are agnostic to specific classes, thereby facilitating the identification of regions within images, which are subsequently classified using CLIP. However, this introduces a significant domain shift between the masked and cropped proposals and the images on which CLIP was trained. Fine-tuning CLIP on a limited annotated dataset can alleviate this bias but may compromise its generalization to unseen classes. In this paper, we present a method to address the domain shift without relying on fine-tuning. Our proposed approach utilizes weakly supervised region prototypes acquired from image-caption pairs. We construct a visual vocabulary by associating the words in the captions with region proposals using CLIP embeddings. Then, we cluster these embeddings to obtain prototypes that embed the same domain shift observed in conventional two-step methods. During inference, these prototypes can be retrieved alongside textual prompts. Our region classification incorporates both textual similarity with the class noun and similarity with prototypes from our vocabulary. Our experiments show the effectiveness of using retrieval to enhance vision-language architectures for open-vocabulary semantic segmentation.
Investigation of Colors Categorized into Color Names on a Liquid Crystal Display to Enhance CAPTCHAs

This study focuses on the human ability to tolerate subtle differences in color and categorize similar colors into a single mass to enhance completely automated public turing test to tell computers and humans apart (CAPTCHA). First, an experiment was conducted to check whether the color selection condition could control the color categorization results. Consequently, it was clarified that when the selection condition was added, the color to be categorized changed, implying the possibility of controlling the categorized result. Subsequently, an investigation was conducted to clarify the range of liquid crystal display colors categorized as major color names. As a result, it was clarified that the hue angle size of the colors categorized by more than half of the participants was sometimes greater than the hue angle size between the primary and complementary colors. It was also clarified that among the categorized colors, there were categorized colors in the two color names. Finally, results from the first and second experiments were collated. It was confirmed that by using the categorized color range and selection conditions, it is possible to obtain an answer with a color name that is different from the original color name. These results suggest that the ability of humans to categorize similar colors into a single mass enhances CAPTCHA.
Enhancing Object Detection through Color Analysis

Abstract: Color detection is the process of detecting the names of the colors for the human eye, this is an easy process, but for the computer, it is not an easy task. Similarly, detecting the shape of an object is an easy task for the human eye. we can easily find what the object is, but for the computer, it is not easy it is a little bit harder than the human eye's vision. That's why we choose this project. We combined both the color and object detection same in this project, the purpose of combining the both of the detection is to make the identification of the specified object and color faster than the separate module The role of color information on object recognition in a literature review said that the large body of evidence showing positive effects of color information on object detection in studies a large number of visual recognition task, the color and object detection use the libraries of the python to detect the object and color of the specified input, the recognition of these things having the backend dataset. The color detection dataset has the certain amount of dataset which includes every color and their color hash code for the each form of color and for the object detection it has the separate dataset for the objects which has the shape, images of different kind of objects. The color and object detection work by combining to detect their specified work in their respective datasets, and we combine both works together to give the required output. This output may be useful information for any security threat, like stolen cars with a color and model, or a suspicious person with a special identification like a shirt or jacket color. This information might be useful for the police department. It also helps in the sorting of objects based on a three-color approach. It is a useful technology in paint and textile fields
Short-term power forecasting of fishing-solar complementary photovoltaic power station based on a data-driven model

A data-driven short-term power generation forecasting model has been proposed to address the problems of information redundancy and low forecasting accuracy for the previous model. Pearson correlation coefficient (PCC) was used to select the effective variables affecting photovoltaic (PV) power generation from the original data set. Radial basis function (RBF) neural network was used to train the existing data to fill the missing values, which ensured the authenticity and integrity of the data. Principal component analysis (PCA) was used to reduce the data dimension, and singular value decomposition (SVD) was used to reduce the matrix calculation of PCA. Then sine chaotic mapping was selected to optimize the sparrow search algorithm (SSA). Furthermore, the thresholds and weights of the back propagation (BP) neural network were further searched to construct the Sine-SSA-BP forecasting model. The original model was validated in a 100 MWp fishing-solar complementary PV power station with high relative humidity (RH). The results indicated this model had good adaptability to the high RH, especially in overcast and cloudy days. Under overcast, cloudy and sunny days, the mean error was reduced by 12.88%, 10.28% and 2.53% at maximum compared with BP, GA-BP and SSA-BP models respectively. The k-fold cross validation comprehensively and effectively verified the robustness and versatility of the prediction model. When k = 10, the minimum MAPE of the prediction model could reach 5.92 %, which effectively adapted to the continuous output prediction of PV power with strong volatility.
An imbalanced classification approach for establishment of cause-effect relationship between Heart-Failure and Pulmonary Embolism using Deep Reinforcement Learning

Pulmonary Embolism (PE) — a non-cardiac cause of cardiac arrest is a strenuous job to perform as it is non-specific in presentation and shares various overlapping features with various other clinical disorders like Myocardial Infarction, Pneumonia, etc. This paper delineates a method to identify Pulmonary Embolism (PE) as a reason for Heart Failure using Deep Reinforcement Learning (DRL) algorithm. The methodology has been partitioned into two phases. Phase I acts towards the creation of a novel dataset as there was no data available for this particular problem statement. Phase II deals with the scope and application of the DRL algorithm on the above-invented dataset. The dataset formed is imbalanced in its essence. To effectively tackle the imbalanced dataset challenge, a cutting-edge imbalanced classification algorithm rooted in the power of Deep Reinforcement Learning (DRL) has been embraced. The classification model has been drawn up as a Sequential Decision-Making procedure and is resolved by making use of the DRL algorithm viz Double Deep Q-Network (DDQN). A classification action is performed by an agent on a single sample at each time step. Classification actions are assessed by the environment, based on the assessment, a reward is given to an agent. In order to make the agent more responsive towards the minority class samples, rewards belonging to the minority class are higher. Under the supervision of this reward system, the agent finally learns the optimal classification policy for this imbalanced dataset problem. Comparative analysis of the DDQN algorithm with other Deep Reinforcement Learning algorithms, including Dueling DQN, and Proximal Policy Optimization (PPO) algorithms has been performed in order to arrive at the best learning technique for the dataset. Furthermore, the DDQN algorithm has been evaluated against a selection of Machine Learning (ML) algorithms. From the experimentation, it is demonstrated that the DDQN model outperformed other approaches with an accuracy of 99.99%, G-mean 0.9999, Recall 1.0000, and Specificity 0.9999.
BOLD-net: Brightness enhancement for old images using deep curve estimation and attention modules

In this paper, a novel deep-learning network for the brightness enhancement of old images is proposed. Although the task for brightness enhancement of old images is similar to the low-light enhancement problem of modern images, the causes of darkness and image characteristics are significantly different. Unlike modern images, bright degradation in old images is due to low-quality camera sensors, unsophisticated techniques that capture the images, and the harsh environment where the photos/films were stored. Though existing low-light enhancement networks show good reconstruction capabilities for modern images, they result in overexposed, color-distorted outputs if applied to old images. To resolve these, we propose a novel deep-learning network with a combination of two convolutional neural networks, which estimates curve maps to adjust the dynamic range, and an attention-guided illumination map to adjust the illumination of the given input image. In addition, channel attention blocks in the illumination map estimation network are further performed to reduce image noise and prevent over- or underexposure. The networks perform in parallel to enhance the brightness of the image and preserve inherent colors and texture details. The proposed method was evaluated on the peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and learned perceptual image patch similarity (LPIPS) and compared with the state-of-the-art methods on a dataset containing old video frames from different decades. Experimental results show that the proposed method outperforms state-of-the-art methods in terms of visual quality for brightness enhancement on old photo and video datasets.
Meteorological data layout and task scheduling in a multi-cloud environment

The meteorological cloud mainly provides computing ability and meteorological datasets for meteorological model tasks. If the location of the required dataset and the execution location of the task are different, this will consume a large amount of time and bandwidth to transfer the data for the task. Meteorological data layout allocates meteorological datasets to various clouds. Because meteorological datasets are required by multiple meteorological model tasks and multiple times, the data layout is very important in the meteorological clouds. This paper focuses on how to layout out the meteorological datasets based on the association (internal meteorological datasets and between meteorological model tasks) and schedule resources for meteorological model tasks in the meteorological cloud. First, to find the association in the meteorological datasets and meteorological models, we use Apriori algorithm to mine frequent itemsets between datasets used by different meteorological models, and then we use the result to help layout meteorological data. After that, we present a heuristic algorithm for scheduling meteorological tasks. Finally, simulation comparison shows that the meteorological data layout method has a lowest value in the number of involved clouds for every task, the average size of transmitted datasets from other clouds, and the average time of transmitted datasets between clouds. We also prove that the scheduling method based on the data layout increases the number of completed tasks before their deadlines and reduces the average execution time.
On the Ding and Helleseth's 7th open problem about optimal ternary cyclic codes

Cyclic codes are a subclass of linear codes and have applications in consumer electronics, data storage systems and communication systems as they have efficient encoding and decoding algorithms. In 2013, Ding, et al. presented nine open problems about optimal ternary cyclic codes. The first two and the 6th problems were completely solved, and the third and last three problems were partially solved. In this paper, we focus on the 7th problem. By determining the root set of some special polynomials over finite fields, we give an incomplete answer and then present a counterexample. Furthermore, we construct two new classes of optimal ternary cyclic codes with respect to the Sphere Packing Bound by studying some special polynomials over finite fields.
Fast progressive polygonal approximations for online strokes

This paper presents a fast and progressive polygonal approximation algorithm for online strokes. A stroke is defined as a sequence of points between a pen-down and a pen-up. The proposed method generates polygonal approximations progressively as the user inputs the stroke. The proposed algorithm is suitable for real time shape modeling and retrieval. The number of operations used in the proposed algorithm is bounded by O(n), where n is the number of points in a stroke. Detailed experimental results show that the proposed method is not only fast, but also accurate enough compared to other reported algorithms.
Video anomaly detection based on cross-frame prediction mechanism and spatio-temporal memory-enhanced pseudo-3D encoder

Intelligent video anomaly detection (VAD) methods play a crucial role in conserving human resources, reducing the financial burden on governments, and promptly and accurately identifying abnormal behaviors. Frame prediction, which performs VAD by reasonably predicting normal video and distorting predicted anomalous video, is a popular and efficient method. Although Auto-Encoders (AE) show excellent performance in video frame prediction methods, the ability of these methods to use temporal information and poorly reconstruct anomalous videos is insufficient. To improve the shortcomings of AE in VAD, we propose a VAD method based on the cross-frame prediction mechanism and the spatio-temporal memory-enhanced pseudo-3D encoder. This method significantly enhances the recognition capability of abnormal activities in surveillance videos. Firstly, we use the prediction mechanism that uses multiple past frames with intervals to predict future frames. It can broaden the extra information and limit the memory consumption. Then we design the pseudo-3D encoder to encode the spatio-temporal information in videos, avoiding the problems that the 2D encoder cannot obtain the temporal dimensional information and the 3D encoder has complicated structure and overmuch parameters. Finally, we design the spatio-temporal memory block with three loss functions to store the spatio-temporal information of normal videos, which can expand the predicted differences between normal and abnormal examples. Experiments on UCSD Ped2, CUHK Avenue and ShanghaiTech datasets experimentally show that the proposed method achieves 99.4%, 90.5% and 74.3% of the AUC values. Our method shows excellent performance among single-stage semi-supervised anomaly detection methods.
A structured approach for resilience-oriented human performance assessment and prediction in offshore safety-critical operations

Resilience engineering is in the ascendant across diverse disciplines and research fields. Researchers and engineers work to create resilient systems and emphasize human elements for providing resilience. It prompts a new paradigm for viewing human performance in socio-technical systems, where variability is regarded as a measure of the potential for resilient human performance. This paper proposes a structured approach for resilience-oriented human performance assessment and prediction in offshore safety-critical operations. In the structured framework, a context-based Bayesian network model is developed to evaluate the internal and external variability of operator functions. For this purpose, a taxonomy of variability shaping factors is developed for operations at sea involving human-automation collaboration. Further , this study proposes integrating the functional resonance analysis method (FRAM) and fuzzy cognitive maps (FCM) to model the dynamic interaction of operator functions and simulate the coupling of operator variability in performing safety-critical operations. The integration augments both methods to tackle human performance modeling from both qualitative and quantitative aspects. A case study is presented in the context of offshore dynamic positioning operations to demonstrate the applicability of the approach. The results confirm that it provides a useful tool for managing operator variability and resilient performance to improve human-related safety.
Experimental Direct Measurement of the Relative Entropy of Coherence

Quantum coherence is the most distinguished feature of quantum mechanics, which characterizes the superposition properties of quantum states. It plays a critical role in various fields, ranging from quantum information technology to quantum biology. Although various coherence quantifiers have been proposed since the resource theory of coherence was established, there are a lack of experimental methods to estimate them efficiently, which restricts the applications of coherence. Relative entropy of coherence is one of the main quantifiers of coherence, and is frequently used in quantum information science. Such nonlinear properties of quantum states are usually calculated from full descriptions of the quantum state, although they are not related to all parameters that specify the state. Here, we experimentally measure the relative entropy of coherence for the arbitrary qubit states directly in the photonic system without using standard state tomography. In the experiment, we directly measure the von Neumann entropy of the quantum states through interference, thus obtaining the relative entropy of coherence, and finding that the experimental results are in good agreement with the theory. Our work provides a nice alternative experimental scheme for measuring the relative entropy of coherence.
Simulation model of energy exchange in an intelligent decision-making system based on a multi-agent neurocognitive architecture

The paper proposes a simulation model of energy exchange in an intelligent decision-making system based on a multi-agent neurocognitive architecture that has a neurofunctional and structural similarity to the neural groups of the brain. An invariant of multi-agent neurocognitive architecture is presented. An algorithm for energy exchange between agneurons and actors at different levels of the intelligent agent invariant has been developed. Further work will be to test the presented architecture in the simulation program being developed.