Reviewerly/exHarmony huggingface dataset:
A novel semantic segmentation method based on improved FCN for unmanned driving

Driverless semantic segmentation is one of the research hotspots in the field of computer vision in recent years. Therefore, this paper proposes an unmanned semantic segmentation method based on improved fully convolutional network. Based on the FCN model, this paper uses HRNet to replace the backbone network. HRNet adopts high-resolution feature fusion method to ensure the output of high-resolution feature maps. It is verified on the Cityscapes dataset. Through the comparison of ablation experiments, it is found that the proposed HRNet backbone network has an average increase of 21.54 % in mIoU compared with other backbone networks with FCN as the model, which proves the effectiveness of the proposed model. In addition, in order to prove the advanced nature of the method in this paper, four widely used models, UNet, PSPNet, DeepLabV3 and OCRNet, are selected for comparison. The average mIoU is increased by 11.77 %, which proves the advanced nature of the model in this paper.
Ultrasound based machine learning classification to match imaging properties of real tissues to artificial structures

Advancements in sensors, computing, and surgical data science have improved medical treatments, particularly minimally invasive procedures (MIPs), offering benefits like faster healing and reduced patient discomfort. However, challenges persist in terms of accuracy, safety, and awareness during MIPs, including difficulties in locating and tracking minimally invasive devices within the body. The research aims to address these challenges by developing sensor-based guidance methods and transformation algorithms, together with machine learning, achieving a mean F1 score of 0.8 in classifying ultrasound images of tested materials. This represents a step toward real-time tissue differentiation, ultimately offering safer and more accurate MIPs.Clinical relevance: This paper enhances MIPs by creating realistic interventional phantoms for training and device testing, reducing errors improving patient safety, and integrating machine learning classifications for real-time guidance.
Improved x-ray prohibited items detection algorithm for YOLOv7

An improved target detection algorithm for YOLOv7 is proposed to address the problems of numerous small targets and serious overlapping of items in X-ray prohibited items images. The ELAN in Head is replaced with the RepLKNet architecture, which uses a small number of large convolutional kernels to reduce memory consumption and improve the ability to capture shape bias thus enhancing image features; SPPF is introduced to optimise the multi-branch operation of the SPCSPC module to save computational resources and improve operational efficiency by exploiting its high processing speed; the SimAM attention mechanism is embedded in MP to give higher weights to highly characterized neurons are given higher weights to improve the network’s ability to capture key features; a new metric WiseIoU3 is introduced to replace IoU at the regression loss function of the detection head, and its gradient gain allocation strategy is applied to improve the overall detection performance of the model. The improved algorithm achieves 80.7% mAP on the EDS dataset, a 2.6% improvement over YOLOv7, with a significant reduction in the number of parameters and computation and a large improvement in FPS.
FGRL-Net: Fine-Grained Personalized Patient Representation Learning for Clinical Risk Prediction Based on EHRs

Personalized patient representation learning (PPRL) is a critical element in clinical risk prediction. It aims to obtain a complete portrait of each patient based on Electronic Health Records (EHR). Although existing works have achieved remarkable progress in healthcare prediction, there are still three major issues. First, feature correlation is crucial for risk prediction, but it has not yet been fully exploited by existing works. Second, variation pattern of dynamic feature contains useful information about patient's physical status, but adaptive pattern recognition is still a challenge. Third, existing works usually adopt a two-stage embedding process to process each dimension of the EHR data. However, some useful low-level information for PPRL will be lost. To address these issues, in this paper, we propose a fine-grained PPRL architecture named FG RL- N et for clinical risk prediction based on EHR. Specifically, we propose a Medical Feature Correlation Detection Module (FCM) to effectively learn the feature correlations for each patient and a Temporal Variation Pattern Recognition Module (TVM) to effectively detect the variation patterns of each dynamic feature. Moreover, we design a Fine-Grained Representation Mechanism (FGRM) to preserve the low-level information (from both feature and visit dimensions) useful for risk prediction. In addition, in the stage of data preprocessing, We utilize generic medical classification knowledge to classify numerical dynamic data. We conduct the in-hospital mortality experiment and the decompensation experiment on a real-world dataset. The experiment results show that the FGRL-Net outperforms state-of-the-art approaches. The source code is provided in github https://github.com/JackyChio/FGRL-Net.
Topology-Aware Debiased Self-Supervised Graph Learning for Recommendation

In recommendation, graph-based Collaborative Filtering (CF) methods mitigate the data sparsity by introducing Graph Contrastive Learning (GCL). However, the random negative sampling strategy in these GCL-based CF models neglects the semantic structure of users (items), which not only introduces false negatives (negatives that are similar to anchor user (item)) but also ignores the potential positive samples. To tackle the above issues, we propose Topology-aware Debiased Self-supervised Graph Learning (TDSGL) for recommendation, which constructs contrastive pairs according to the semantic similarity between users (items). Specifically, since the original user-item interaction data commendably reflects the purchasing intent of users and certain characteristics of items, we calculate the semantic similarity between users (items) on interaction data. Then, given a user (item), we construct its negative pairs by selecting users (items) which embed different semantic structures to ensure the semantic difference between the given user (item) and its negatives. Moreover, for a user (item), we design a feature extraction module that converts other semantically similar users (items) into an auxiliary positive sample to acquire a more informative representation. Experimental results show that the proposed model outperforms the state-of-the-art models significantly on three public datasets. Our model implementation codes are available at https://github.com/malajikuai/TDSGL.
Continual Learning via Manifold Expansion Replay

In continual learning, the learner learns multiple tasks in sequence, with data being acquired only once for each task. Catastrophic forgetting is a major challenge to continual learning. To reduce forgetting, some existing rehearsal-based methods use episodic memory to replay samples of previous tasks. However, in the process of knowledge integration when learning a new task, this strategy also suffers from catastrophic forgetting due to an imbalance between old and new knowledge. To address this problem, we propose a novel replay strategy called Manifold Expansion Replay (MaER). We argue that expanding the implicit manifold of the knowledge representation in the episodic memory helps to improve the robustness and expressiveness of the model. To this end, we propose a greedy strategy to keep increasing the diameter of the implicit manifold represented by the knowledge in the buffer during memory management. In addition, we introduce Wasserstein distance instead of cross entropy as distillation loss to preserve previous knowledge. With extensive experimental validation on MNIST, CIFAR10, CIFAR100, and TinyImageNet, we show that the proposed method significantly improves the accuracy in continual learning setup, outperforming the state of the arts.
Unmasking Deception: A Comparative Study of Tree-Based and Transformer-Based Models for Fake Review Detection on Yelp

The increasing prevalence of fake online reviews jeopardizes firms' profits, consumers' well-being, and the trustworthiness of e-commerce ecosystems. We face the significant challenge of accurately detecting fake reviews. In this paper, we undertake a comprehensive investigation of traditional and state-of-the-art machine learning models in classification, based on textual features, to detect fake online reviews. We attempt to examine existing and noteworthy models for fake online review detection, in terms of the effectiveness of textual features, the efficiency of sampling methods, and their performance of detection. Adopting a quantitative and data-driven approach, we scrutinize both tree-based and transformer-based detection models. Our comparative studies evidence that transformer-based models (specifically BERT and GPT-3) outperform tree-based models (i.e., Random Forest and XGBoost), in terms of accuracy, precision, and recall metrics. We use real data from online reviews on Yelp.com for implementation. The results demonstrate that our proposed approach can identify fraudulent reviews effectively and efficiently. Synthesizing ChatGPT-3, tree-based, and transformer-based models for fake online review detection is rather new but promising, this paper highlights their potential for better detection of fake online reviews.
Multi-View Image Rectification for UAV-Captured Image Sequences

The image sequences captured by Unmanned Aerial Vehicles (UAVs) can be applied to many computer vision tasks. However, due to the instability of UAV flight, the captured image sequences will deviate from the preset trajectory and pose, which reduce the quality of subsequent applications such as panoramic image stitching. In this paper, a novel method is proposed to rectify UAV-captured image sequences by transforming the images to a regular trajectory with the uniform pose. First, to minimize the total transformation deviation, virtual regular camera trajectory is derived by minimizing the global error of coordinates between actual and virtual camera trajectories. Then, camera-pose-relevant local homography is proposed by inserting the camera pose into local homography to transform the images to the derived virtual trajectory with the uniform pose and correct translation parallax. The experimental results demonstrate the effectiveness of the proposed rectification algorithm from both theoretical and application levels.
DQMix-BERT: Distillation-aware Quantization with Mixed Precision for BERT Compression

Transformer-based architecture models like BERT have performed excellently for various Natural Language Processing (NLP) tasks. However, these models are usually computationally expensive with a large number of parameters. As a result, deploying them in edge devices has become a challenging task. The existing compression work on lower-precision quantization still has a severe accuracy decrease and rarely focuses on the information hidden in the different modules of the model. In this paper, we propose a distillation-aware quantization with mixed precision method combined with quantization and knowledge distillation. We achieve the ultra-low mixed precision quantization with the different sensitivity of different modules of BERT. Moreover, we leverage knowledge distillation to reduce the model accuracy degradation. We extensively test our method on four GLUE tasks. It shows that DQMix-BERT outperforms the other BERT compression methods and even achieves comparable performance to the original BERT model while achieving ~8x compression.
Interval-Valued Data Based Local Mean K-Nearest Neighbors Classifier

The K-Nearest Neighbor (KNN) algorithm is a straightforward and efficient classification technique that exhibits strong performance when addressing fixed data challenges. However, data information is occasionally provided as intervals. To enhance classification performance while dealing with interval-valued data, this paper suggests a novel KNN-based classifier known as the interval-valued data based local mean k-nearest neighbors (ILMKNN) rule. Rather than calculating the direct distances between the variables, the proposed ILMKNN computes the Euclidean distances between the lower and higher bounds of the interval-valued variables. The experiment findings on six UCI data sets show that our suggested classifier performs well in comparison to the interval-valued data-based k-nearest neighbors (IKNN) classifier and is somewhat resistant to diverse selections of k.
DualYOLO: Remote Small Target Detection with Dual Detection Heads Based on Multi-scale Feature Fusion

In recent years, accurate and real-time long-range small target detection has become a popular and challenging task, particularly in time-sensitive scenarios such as unmanned aerial vehicle (UAV) scene analysis and military reconnaissance. Most existing solutions rely on deep CNNs to learn strong feature representations of objects isolated from the background to detect small objects with minimal visual features in images. However, this approach incurs significant computational overhead. In this paper, we propose DualYOLO, a fast and accurate long-range small object detection method that combines multi-level multi-scale feature fusion (MLMFF) and concat channel attention (CatCA). Specifically, in order to prevent small targets from becoming more and more blurred after multilayer convolution operations, DualYOLO fuses the features of different layers in the backbone network to obtain small target features with strong semantics and high detail. Furthermore, we use a new loss function to address the sensitivity of IoU to small object position deviations, thereby improving detection accuracy. In terms of data preprocessing, we utilize an image slicing strategy to process the dataset. The experimental results show that DualYOLO achieves 82.2% accuracy (in terms of mAP@.5) on the VEDAI dataset processed using slices, with a performance more than 2% higher than that of large models (e.g., YOLOv5x,YOLOR and YOLOv7).
Redistillation of Radio Frequency Knowledge for RFF Imbalanced Sample Recognition

Radio Frequency Fingerprint (RFF) technology is an effective means to defend against cheating and counterfeiting attacks in wireless communication. However, to move from a theoretical algorithm to a practical application, the challenges of imbalanced data samples and environmental noise must be addressed for Radio Frequency (RF) identification technology. Although noise reduction can restore the signal to some extent, the recognition performance of RFF technology is affected when the dataset is imbalanced. While many RF identification algorithms focus on identification performance under a low Signal-to-Noise Ratio (SNR), performance degradation caused by data imbalance is a pressing problem that requires attention. Directly applying re-sampling algorithms in imbalanced dataset processing can lead to data overlap and neural network over-fitting. To address these issues, this paper proposes a “Redistillation of Radio Frequency Knowledge” (RRFK) algorithm combined with knowledge distillation (KD). The experimental results show that the proposed algorithm can achieve good recognition performance in both stepped and long-tail imbalanced data sets.
Composable Image Coding for Machine via Task-oriented Internal Adaptor and External Prior

Traditional image coding standards are typically optimized with a focus on human perception, which conflicts with the fact that most of the images are now analyzed by machines. To enable a variety of downstream intelligent tasks, contemporary approaches either utilize traditional codecs for image compression which are then used for task analysis, or develop a unified feature compression paradigm with deep learning techniques. However, they might suffer from accumulative errors and poor compatibility/generalization due to the conflict between standardized codecs and diverse machine tasks. We argue that a favorable image coding for machine (ICM) framework should have highly efficient adaptation capability, and take the ultimate task goals into account. Oriented at this, we propose a composable ICM solution dubbed Com-ICM, which develops plug-and-play lightweight internal adaptors injected into the codec architecture for efficient task transfer, and leverages off-the-shelf (large) models to provide external prior information for further task-oriented semantics learning. The internal adaptors (from the architectural aspect) and external priors (from the precondition aspect) complement each other, resulting in a mutually beneficial effect. We evaluate Com-ICM on diverse vision benchmarks, including image classification, object detection, and semantic segmentation, demonstrating its effectiveness and superiority. We are also actively submitting Com-ICM as a technical proposal to the international organization for standardization.
Research on Deep Reinforcement Learning of Unmanned Multi-sensor Fusion Based on Adaptive Reward Mechanism

Based on the problems of low learning efficiency, poor stability and weak generalization ability in the unmanned driving decision-making algorithm, this paper proposes two solutions: For the problem of low generalization ability caused by single sensor input data, this paper propose a multi-sensor fusion deep reinforcement learning algorithm, which reduces information redundancy through differential design and realizes the feature complementarity of each sensor. For the problems of the traditional reward function design method, this paper design a Multi-objective adaptive Markov Decision Processes reward function (MAMDP), which realizes the adaptive and real-time dynamic evaluation of the decision system. The experimental results show that the convergence, efficiency and stability of this Adaptive reward Deep Deterministic Policy Gradient algorithm (ADDPG) have been significantly improved, which effectively improves the accuracy and stability of driverless decision-making behavior.
A Mixed Reality System for Interaction with Heterogeneous Robotic Systems

The growing spread of robots for service and industrial purposes calls for versatile, intuitive and portable interaction approaches. In particular, in industrial environments, operators should be able to interact with robots in a fast, effective, and possibly effortless manner. To this end, reality enhancement techniques have been used to achieve efficient management and simplify interactions, in particular in manufacturing and logistics processes. Building upon this, in this paper we propose a system based on mixed reality that allows a ubiquitous interface for heterogeneous robotic systems in dynamic scenarios, where users are involved in different tasks and need to interact with different robots. By means of mixed reality, users can interact with a robot through manipulation of its virtual replica, which is always colocated with the user and is extracted when interaction is needed. The system has been tested in a simulated intralogistics setting, where different robots are present and require sporadic intervention by human operators, who are involved in other tasks. In our setting we consider the presence of drones and AGVs with different levels of autonomy, calling for different user interventions. The proposed approach has been validated in virtual reality, considering quantitative and qualitative assessment of performance and user's feedback.
RGB-D Image-Based Deep Neural Network Body Shape Classifier

Leveraging the untapped potential of depth information in RGB-D images, this study introduces a deep neural network classifier for advanced body shape classification. Going beyond traditional RGB image analysis, our method innovatively employs multi-task learning, simultaneously performing body shape classification, posture estimation, and body part segmentation to achieve superior accuracy. This approach promises to revolutionize personalization avenues in healthcare, fashion, and entertainment industries, establishing a new benchmark in body shape analysis.
Evaluating Automated Negotiations

Automated Negotiation (AN) is a process for reaching agreement between agents representing self-interested parties. Several negotiation protocols and strategies have been proposed over the years. In this paper, we argue that evaluating a negotiation session is not straightforward and depends on several contextual factors. The paper proposes a multifaceted evaluation criteria for negotiation sessions that can be adjusted to the evaluation context. The proposed evaluation criteria are then applied to several negotiation scenarios with state-of-the-art negotiation algorithms.
Leveraging Feature Interaction for Modeling User-Item Interaction in Recommender Systems

Challenges in recent recommender systems include how to model high-order feature interaction and how to exploit user-item interaction, particularly for neural network-based recommender systems. While previous approaches have focused only on one aspect, this paper attempts to address both simultaneously by extracting augmented embeddings for users and items with feature interaction and modeling user-item interaction using graph neural networks. Real-world experimental results show that the proposed method outperforms state-of-the-art methods considering one type of interaction.
Learning end-to-end depth maps compression with conditional quality-controllable autoencoder

3D video is leading to the emergence of new technologies, such as virtual, augmented, and mixed realities, which find applications in several fields mainly healthcare, education, and industry. Even for the Internet of Things (IoT), the future lies in 3D vision and depth for machines such as autonomous cars, robots, and drones to have a deep perception like humans. Texture-dedicated compression methods are not efficient for depth maps due to features distinctions between depth and texture images. To tackle this issue, we propose a learning variable-rate depth map compression model with conditional quality-controllable autoencoder. Specifically, the encoder extracts deep features from depth maps through an advanced Convolutional Neural Network (CNN) model, trained using a combination of grayscale texture and depth images. This latter incorporates an initial layer of predefined wedgelet filters, succeeded by a VGG 19 pretrained model. To effectively differentiate between depth maps and grayscale textures within the training dataset, an image style classification technique using the learnt deep correlation Features is employed. Our end-to-end compression network ensures better performances over main candidate methods and depth-oriented 3D-HEVC compression standard.