Reviewerly/exHarmony huggingface dataset:
On exploring pose estimation as an auxiliary learning task for Visible–Infrared Person Re-identification

Visible–infrared person re-identification (VI-ReID) has been challenging due to the existence of large discrepancies between visible and infrared modalities. Most pioneering approaches reduce intra-modality variations and inter-modality discrepancies by learning modality-shared features. However, an explicit modality-shared cue, i.e., body keypoints, has not been fully exploited in VI-ReID. Additionally, existing feature learning paradigms imposed constraints on either global features or partitioned feature stripes, which neglect the prediction consistency of global and part features. To address the above problems, we exploit Pose Estimation as an auxiliary learning task to assist VI-ReID in an end-to-end framework. By jointly training these two tasks in a mutually beneficial manner, our model learns higher quality ID-related features. On top of it, the learnings of global features and local features are seamlessly synchronized by Hierarchical Feature Constraint (HFC), where the former supervises the latter using the knowledge distillation strategy. Experimental results on two benchmark VI-ReID datasets show that the proposed method consistently improves state-of-the-art methods by significant margins. Specifically, our method achieves nearly 20% mAP improvements against the state-of-the-art method on the RegDB dataset. Our intriguing findings highlight the usage of auxiliary task learning in VI-ReID. Our source code is available at https://github.com/yoqim/Pose_VIReID.
A breast cancer risk predication and classification model with ensemble learning and big data fusion

Breast cancer is a major health issue for women all over the world. Effective care and better patient outcomes depend on early identification and precise risk prediction. Ensemble learning algorithms and merging large data have recently emerged as potential ways to predict and classify breast cancer risk. This paper aims to investigate the feasibility of using ensemble learning and big data fusion for risk prediction and categorization of breast cancer. The key focus is building a reliable and precise model by combining the best features of various learning algorithms and a variety of vast and varied data sets. This research presents an Improved XGBoost Ensembling (I-XGBoost) technique with big data analytics to predict and diagnose breast cancer cells. The proposed work considers three critical identification phases to achieve greater accuracy: data pre-processing, feature extraction, and target role. The Wisconsin breast cancer diagnostic data was used for testing the proposed method. It is also compared in terms of performance with other classification methods like Decision Tree, Random Forest, Naive Bayes, K-Nearest Neighbors, Support Vector Machines, Adaboost, and XGBoost. This research aims to determine which characteristics are most beneficial in predicting cancer as malignant or benign. We show that I-XGBoost has an impressively high accuracy score of 99.84% using Spark’s Python Application Programming Interface (API). This research emphasizes the promise of ensemble learning and big data fusion for predicting and classifying breast cancer risks. By proposing a new method that draws on the best features of several models and uses the wealth of information available in big data, it adds to the current body of knowledge. Positive findings highlight the practical use of the suggested paradigm in clinical contexts.
A two-stage feature selection approach using hybrid quasi-opposition self-adaptive coati optimization algorithm for breast cancer classification

Breast cancer (BC) is one of the leading causes of high mortality rates among women. An early disease diagnosis is crucial in breast cancer's treatment for improving the survival rate of people who have been afflicted. Human error, incorrect diagnoses, and insufficient time make manual techniques for breast cancer diagnosis ineffective and inefficient. An automated breast cancer diagnosis system assists the medical expert in diagnosing the cancer at its earliest stage. When attempting to investigate all aspects (high dimensionality) of the medical records, the automated diagnosis system faces significant challenges. It reduces the success rate of the diagnosis system. This study presents a two-stage feature subset selection approach using quasi-opposition self-adaptive coati optimization algorithm for breast cancer classification. The proposed scheme's fitness function aims to maximize the success rate and minimize the feature selection ratio. We used the three benchmark datasets (Breast Cancer Wisconsin Diagnostic Dataset (WDBC), Breast Cancer Wisconsin Prognostic Dataset (WPBC), and Breast Cancer Wisconsin Original Dataset (WBCD)) to examine the proposed scheme's performance. The comparison analysis results show that the proposed scheme outperforms the other competitor schemes.
BLoG: Bootstrapped graph representation learning with local and global regularization for recommendation

With the explosive growth of online information, the significant application value of recommender systems has received considerable attention. Since user–item interactions can naturally fit into graph structure data, graph neural networks (GNNs), by virtue of their strong ability in graph representation learning, have become the new state-of-the-art approach to recommender systems. Recently, GNN-based contrastive self-supervised learning (SSL) methods have received careful attention due to their superiority over graph-based recommendation under the typical supervised learning paradigm. However, to achieve state-of-the-art performance, GNN-based recommendation with SSL often needs a huge amount of negative examples and the model's performance is heavily dependent on complex data augmentations. Also, the information interaction among various augmented views is often performed under a single perspective (e.g., structure/feature space or node/graph level). In this paper, we propose a novel bootstrapped graph representation learning with local and global regularization for recommendation, i.e., BLoG, which constructs positive/negative pairs based on the aggregated node features by referring to two alternate views of the original user–item graph structure. In particular, BLoG learns user–item representations by encoding two augmented versions of a user–item bipartite graph using two separate encoders: an online encoder and a target encoder. To facilitate the information interaction between these two distinct graph encoders, we introduce local and global regularization for recommendation, where a graph structural contrastive loss and a node-level semantic loss are defined for local regularization while a graph-level contrastive loss is used for global regularization. An alternative optimization approach is used to train the online encoder and the target encoder. Experimental studies on three benchmark datasets demonstrate that BLoG achieves better recommendation accuracy than several existing baselines.
High Dimensional Quantum Machine Learning With Small Quantum Computers

Quantum computers hold great promise to enhance machine learning, but their current qubit counts restrict the realisation of this promise. In an attempt to placate this limitation techniques can be applied for evaluating a quantum circuit using a machine with fewer qubits than the circuit naively requires. These techniques work by evaluating many smaller circuits on the smaller machine, that are then combined in a polynomial to replicate the output of the larger machine. This scheme requires more circuit evaluations than are practical for general circuits. However, we investigate the possibility that for certain applications many of these subcircuits are superfluous, and that a much smaller sum is sufficient to estimate the full circuit. We construct a machine learning model that may be capable of approximating the outputs of the larger circuit with much fewer circuit evaluations. We successfully apply our model to the task of digit recognition, using simulated quantum computers much smaller than the data dimension. The model is also applied to the task of approximating a random 10 qubit PQC with simulated access to a 5 qubit computer, even with only relatively modest number of circuits our model provides an accurate approximation of the 10 qubit PQCs output, superior to a neural network attempt. The developed method might be useful for implementing quantum models on larger data throughout the NISQ era.
Small Ship Detection of SAR Images Based on Optimized Feature Pyramid and Sample Augmentation

SAR images have become the latest high-resolution imaging equipment, which can monitor the Earth 24 hours a day. More and more deep learning technologies are applied to ship target detection, however, in complex environments, due to the small target of the ship, problems such as false detection and miss detection often occur. For this reason, SSPNet is proposed with several small-target-augmentation strategies to complete the detection of small ships on the sea. This network is an improvement of FPN. The model uses Context Attention Module (CAM), Scale Enhancement Module (SEM), and Scale Selection Module (SSM). CAM introduces the attention heat map, SEM uses the residual module to make the network pay more attention to specific scale targets, SSM introduces deep semantic features into shallow features. Weighted Negative Sampling (WNS) strategy is proposed to enable the network to select more representative samples. These modules make the network more suitable for small target detection. The results on SSDD dataset show that the model is superior to the existing object detection network, and the AP50 reaches 91.57%.
Improved A* Algorithm for Path Planning of Spherical Robot Considering Energy Consumption

Spherical robots have fully wrapped shells, which enables them to walk well on complex terrains, such as swamps, grasslands and deserts. At present, path planning algorithms for spherical robots mainly focus on finding the shortest path between the initial position and the target position. In this paper, an improved A* algorithm considering energy consumption is proposed for the path planning of spherical robots. The optimization objective of this algorithm is to minimize both the energy consumption and path length of a spherical robot. A heuristic function constructed with the energy consumption estimation model (ECEM) and the distance estimation model (DEM) is used to determine the path cost of the A* algorithm. ECEM and DCM are established based on the force analysis of the spherical robot and the improved Euclidean distance of the grid map, respectively. The effectiveness of the proposed algorithm is verified by simulation analysis based on a 3D grid map and a spherical robot moving with uniform velocity. The results show that compared with traditional path planning algorithms, the proposed algorithm can minimize the energy consumption and path length of the spherical robot as much as possible.
Leveraging Future Trajectory Prediction for Multi-Camera People Tracking

Artificial intelligence-based surveillance system, one of the essential systems for smart cities, plays a critical role in ensuring the safety and well-being of individuals. In this paper, we propose a real-time, low-computation cost Multi-Camera Multi-Target (MCMT) tracking system for people, leveraging deep-learning-based trajectory prediction with spatial-temporal information and social information. By predicting people’s future trajectories, our algorithm effectively handles object occlusion problems and maintains accurate tracking while keeping computational costs low. Our approach addresses object occlusion without relying on computationally expensive re-identification, and improves MCMT tracking performance using graph-based tracklet representation, and spectral clustering. As a result, our proposed approach is tested on the 2023 AI City Challenge Track 1 test dataset, automatically generated on the NVIDIA Omiverse Platform, our method achieves an IDF1 score of 0.6171 and real-time performance at 27.6 FPS. Code and pre-trained models are publicly available at https://github.com/yuntaeJ/SCIT-MCMT-Tracking.
WSRD: A Novel Benchmark for High Resolution Image Shadow Removal

Shadow removal is an important computer vision task, whose aim is to successfully detect the shadow affected area appearing through light occlussion, followed by a photorealistic restoration of the affected image contents, textures, and details. After decades of research, a multitude of hand-crafted restoration techniques were proposed, following different observations on shadow formation models, with scenes altered in particular conditions. However, the increased popularity of deep learning based solutions enabled a significant step forward for the shadow removal solutions, both in terms of reconstruction fidelity and perceptual properties. However, the publicly available datasets remain focused around a particularly low complexity setup, with a low variety of light occluders and affected backgrounds, and with limited representation for more complex light interactions and complex shadow patterns. In this work, we propose WSRD, a novel benchmark for high resolution image shadow removal, characterized by a large variety in terms or represented objects, backgrounds and light occluders. We study more complex interactions, combining self shadows with externally casted shadows, to further extend the study of the phenomenon, its factors and effects. To prove WSRD as a relevant benchmark, we propose DNSR, a novel shadow removal method, comparing the results on WSRD with the performance level observed on other well-established benchmarks like ISTD and ISTD+. We validate our approach comparing with existing state-of-the-art (SOTA) methods, improving both in reconstruction fidelity and perceptual properties, setting a new SOTA for the field.
Benchmarking the Robustness of LiDAR-Camera Fusion for 3D Object Detection

To achieve autonomous driving, developing 3D detection fusion methods, which aim to fuse the camera and LiDAR information, has draw great research interest in recent years. As a common practice, people rely on large-scale datasets to fairly compare the performance of different methods. While these datasets have been carefully cleaned to ideally minimize any potential noise, we observe that they cannot truly reflect the data seen on a real autonomous vehicle, whose data tends to be noisy due to various reasons. This hinders the ability to simply estimate the robust performance under realistic noisy settings. To this end, we collect a series of real-world cases with noisy data distribution, and systematically formulate a robustness benchmark toolkit. It can simulate these cases on any clean dataset, which has the camera and LiDAR input modality. We showcase the effectiveness of our toolkit by establishing two novel robustness benchmarks on widely-adopted datasets, nuScenes and Waymo, then holistically evaluate the state-of-the-art fusion methods. We discover that: i) most fusion methods, when solely developed on these data, tend to fail inevitably when there is a disruption to the LiDAR input; ii) the improvement of the camera input is significantly inferior to the LiDAR one. We publish the robust fusion dataset, benchmark, detailed documents and instructions on https://anonymous-benchmark.github.io/robust-benchmark-website.
Variational Quantum Simulators Based on Waveguide QED

Waveguide QED simulators are analog quantum simulators made by quantum emitters interacting with one-dimensional photonic band gap materials. One of their remarkable features is that they can be used to engineer tunable-range emitter interactions. Here, we demonstrate how these interactions can be a resource to develop more efficient variational quantum algorithms for certain problems. In particular, we illustrate their power in creating wave function Ansätze that capture accurately the ground state of quantum critical spin models (XXZ and Ising) with fewer gates and optimization parameters than other variational Ansätze based on nearest-neighbor or infinite-range entangling gates. Finally, we study the potential advantages of these waveguide Ansätze in the presence of noise. Overall, these results evidence the potential of using the interaction range as a variational parameter and place waveguide QED simulators as a promising platform for variational quantum algorithms.
Securing Secrets in Cyber-Physical Systems: A Cutting-Edge Privacy Approach with Consortium Blockchain

In the era of interconnected and intelligent cyber-physical systems, preserving privacy has become a paramount concern. This paper aims a groundbreaking proof-of-concept (PoC) design that leverages consortium blockchain technology to address privacy challenges in cyber-physical systems (CPSs). The proposed design introduces a novel approach to safeguarding sensitive information and ensuring data integrity while maintaining a high level of trust among stakeholders. By harnessing the power of consortium blockchain, the design establishes a decentralized and tamper-resistant framework for privacy preservation. However, ensuring the security and privacy of sensitive information within CPSs poses significant challenges. This paper proposes a cutting-edge privacy approach that leverages consortium blockchain technology to secure secrets in CPSs. Consortium blockchain, with its permissioned nature, provides a trusted framework for governing the network and validating transactions. By employing consortium blockchain, secrets in CPSs can be securely stored, shared, and accessed by authorized entities only, mitigating the risks of unauthorized access and data breaches. The proposed approach offers enhanced security, privacy preservation, increased trust and accountability, as well as interoperability and scalability. This paper aims to address the limitations of traditional security mechanisms in CPSs and harness the potential of consortium blockchain to revolutionize the management of secrets, contributing to the advancement of CPS security and privacy. The effectiveness of the design is demonstrated through extensive simulations and performance evaluations. The results indicate that the proposed approach offers significant advancements in privacy protection, paving the way for secure and trustworthy cyber-physical systems in various domains.
Performance of a photovoltaic-thermoelectric generator panel in combination with various solar tracking systems

Photovoltaic cells have been widely used to harvest solar energy resources. However, their efficiency still needs to be improved as they are rarely designed to consider the sun's heat and movement. No literature has been reported on the performance of the combined photovoltaic and thermoelectric generators with different solar tracking systems. In this study, we investigated the performance of photovoltaic and combined photovoltaic-thermoelectric generator systems installed on three panels consisting of fixed, 1-axis, and 2-axis solar trackers. Light-dependent resistors were mounted on the panels to receive solar radiation. A microcontroller receives the signal and drives the motor on the vertical and horizontal axis to follow the sun's movement according to its azimuth and altitude angle. Thermocouples were used to record the temperature of the photovoltaic and thermoelectric generator surfaces. The experiment was conducted in Magetan City, East Java, Indonesia, from 05:00 to 18:00 Western Indonesia Time. The result shows that the solar tracker can drive the panel's axis to follow the sun's motion after receiving the resistance signal from the monitored azimuth and altitude angles. The observed solar radiation using the fixed, 1-axis, and 2-axis panels is 5.639, 6.858, and 7.523 kWh/m2/d, respectively. Installing the thermoelectric generator and heat sink reduces all panels' photovoltaic temperature by 3%. The maximum power of the photovoltaic-thermoelectric generator system on the fixed, 1-axis, and 2-axis panels increases by 0.77 W, 1.19 W, and 1.65 W, respectively, which comes from the photovoltaic power by 84% and the thermoelectric generator power by 16%. The maximum efficiency of the photovoltaic-thermoelectric generator system on the fixed, 1-axis, and 2-axis panels is 10.57%, 12.53%, and 13.99%, respectively, which increases at approximately 3% from the standalone photovoltaic efficiency. The present study's result improves the photovoltaic system's design by incorporating a thermal conversion and solar tracker.
GLENet: Boosting 3D Object Detectors with Generative Label Uncertainty Estimation

The inherent ambiguity in ground-truth annotations of 3D bounding boxes, caused by occlusions, signal missing, or manual annotation errors, can confuse deep 3D object detectors during training, thus deteriorating detection accuracy. However, existing methods overlook such issues to some extent and treat the labels ass deterministic. In this paper, we formulate the label uncertainty problem as the diversity of potentially plausible bounding boxes of objects. Then, we propose GLENet, a generative framework adapted from conditional variational autoencoders, to model the one-to-many relationship between a typical 3D object and its potential ground-truth bounding boxes with latent variables. The label uncertainty generated by GLENet is a plug-and-play module and can be conveniently integrated into existing deep 3D detectors to build probabilistic detectors and supervise the learning of the localization uncertainty. Besides, we propose an uncertainty-aware quality estimator architecture in probabilistic detectors to guide the training of the IoU-branch with predicted localization uncertainty. We incorporate the proposed methods into various popular base 3D detectors and demonstrate significant and consistent performance gains on both KITTI and Waymo benchmark datasets. Especially, the proposed GLENet-VR outperforms all published LiDAR-based approaches by a large margin and achieves the top rank among single-modal methods on the challenging KITTI test set. The source code and pre-trained models are publicly available at https://github.com/Eaphan/GLENet .