Reviewerly/exHarmony huggingface dataset:
A Dual-Path Supplemental Information Learning Architecture for Breast Cancer Ki-67 Status Prediction in T2w MRI

In this paper, we propose a Dual-path Supplemental Information Learning Architecture (DSILA) for predicting breast cancer Ki-67 status based on T2-weighted (T2w) magnetic resonance imaging (MRI). DSILA consists of two components: 1) a transfer network with multi-scale feature selection strategy to obtain generic multi-scale features most relative to target, 2) a supplemental learning network with a large receptive field and channel-level attention to mine scenario-related semantic information. A regulation item – Aspect Overlap Loss (AOL), is further added to force the supplemental learning network to pay more attention to the regions overlooked by the transfer network. The experimental results tested on the collected T2w MRI breast cancer Ki-67 dataset show that DSILA outperforms state-of-the-art techniques among all adopted evaluation metrics, even achieving 0.85 in Area under the Receiver Operating Characteristic Curve (AUC).
Fast Personalized Human Activity Recognition on Heuristic Parameter Estimation

Personalized human activity recognition (HAR) on wearable sensor data is crucial for healthcare, industry and sport. Personalized HAR has two challenges: very few high-quality labeled personalized data and limited terminal computing resource. Most previous domain adaptation models ignore the transfer efficiency on the terminal, which is also one criterion of Artificial Intelligence of Things. Therefore, we propose a fast transfer HAR, FastTrans, to improve the efficiency and get a trade-off with recognition effectiveness. A fusion feature extraction module is designed to learn multi-scale features on the improved hybrid loss. The proposed heuristic parameter estimation method learns the approximate solutions of the classification weights in FastTrans by scanning the adaptation data only once. Besides, an efficient time series data augmentation is proposed as a plugin for dataset variety. The results on benchmark datasets show the dramatically competitive performance of FastTrans on transferring efficiency with close accuracy to other models.
M-Health System Framework for Diagnosing Inflammatory Breast Cancer with Fuzzy Logic

Inflammatory breast cancer (IBC) is an aggressive and fatal breast cancer. The American Joint Committee on Cancer defines the clinical symptoms of IBC as erythema, edema, peau d'orange that is over at least a third of the breast with a duration of the first symptom to the diagnosis of fewer than six months, and histopathologic diagnosis. Unfortunately, these signs are not present in many cases and make it challenging to diagnose IBC. In this work, we proposed a pioneering framework of an M-Health system for early diagnosis of IBC to improve survival rates and presented the supporting preliminary experimental results. Particular emphasis is given to the system framework and bilateral mammography images. Since IBC is a rare type of cancer, there is currently no public-domain mammography image dataset. Our work evaluated the system performance using the bilateral mammography images of six IBC and eight non-IBC breast cancer cases provided by the National Cancer Institute (NCI) - Cairo, Egypt. The proposed model extracts features and combines them in the feature bank to send to the Fuzzy logic Type 1 classifier for diagnosis. The system achieved promising performance with an accuracy of 92.3%, sensitivity of 83.3%, and specificity of 100%.
Software-Based Mass Customization of Artificial Neural Networks and its Benefits

Artificial intelligence (AI) is a rapidly growing field of research that aims to create decision-making computing systems Machine learning (ML) is a subclass of AI that processes vast quantities of data to train computational models. ML agents can identify patterns in large datasets and render predictions based on the data they were trained with. Deep learning (DL) is a subclass of ML that develops artificial neural networks with multiple hidden layers of artificial neurons to solve data-intensive problems. ML and DL research requires specialized knowledge and systems, keeping many organizations from using it or achieving the full benefits it could provide. In this paper, a user-friendly approach to building AI, ML and DL models is presented which allows users to build models using diagramming software, thus abstracting much of the complexity otherwise associated with building them, while also achieving benefits potentially only available via customization. This allows users to customize models at the artificial neuron level, allowing them to specify attributes such as individual neurons' activation functions and connectivity. This work aims to broaden the availability of ML/DL technologies and unlock the benefits of low-level customization for users of all sizes.
Enabling A Network AI Gym for Autonomous Cyber Agents

This work aims to enable autonomous agents for network cyber operations (CyOps) by applying reinforcement and deep reinforcement learning (RL/DRL). The required RL training environment is particularly challenging, as it must balance the need for high fidelity, best achieved through real network emulation, with the need for running large numbers of training episodes, best achieved using simulation. A unified training environment, namely the Cyber Gym for Intelligent Learning (CyGIL), is developed where an emulated CyGIL-E automatically generates a simulated CyGIL-S. From preliminary experimental results, CyGIL-S can train agents in minutes compared with the days required in CyGIL-E. The agents trained in CyGIL-S are transferrable directly to CyGIL-E, showing full decision proficiency in the emulated “real” network. Enabling offline RL, the CyGIL solution presents a promising direction towards sim-to-real in leveraging RL agents in real-world cyber networks.
Partial multi-label learning: exploration of binary ground-truth labels

Partial multi-label learning (PML) aims to accurately predict multi-labels for unknown instances with noise labels in training set. Accurate identification of ground-truth labels is essential to optimize performance. The accuracy of ground-truth labels affects label correlation capture, which guides classifier learning. However, current methods often rely on approximation of ground-truth labels through intermediate variables, rather than restoring binary ground-truth labels directly. To solve this problem, we propose a partial multi-label learning algorithm by exploring binary ground-truth labels (PML-EBGL). First, the candidate label matrix is decomposed into a ground-truth label matrix and a noise matrix. Then, the noise matrix is constrained using the l <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> norm for sparsity, and the binary ground-truth label matrix is used to explore label correlation, which is transferred to classifier by using the Laplacian term. Finally, the rotation matrix is added after the classifier, and the instance is projected onto the binary label matrix. Extensive experiments show that PML-EBGL outperforms state-of-the-art methods.
Counting and Locating Anything: Class-agnostic Few-shot Object Counting and Localization

We focus on a new task for class-agnostic object localization (CAOL), which not only counts objects of any class but also outputs their point locations. Given one or more examples of objects of interest, the CAOL model should output the count and point locations of objects of interest in the query image. We propose two solutions: (1) a post-processing method for class-agnostic object counting models based on density maps, called Maximum Search Strategy (MSS), which converts density maps into specific point coordinates without the need for a threshold. (2) a point-based Class-Agnostic Counting and Localization (CACL) model that directly predicts points for each object of interest, providing an end-to-end solution without the need for post-processing; While MSS obtains promising localization results as long as good quality density map is available, CACL achieves state-of-the-art results for both counting and localization tasks. Our experiments on the CARPK dataset demonstrate the strong generalization performance of CACL model.
DMRL-Net: Differentiable Multi-view Representation Learning Network

In recent years, deep multi-view representation learning has made considerable achievements due to its excellent nonlinear mapping capability. Yet its development is limited by the challenge of interpreting the underlying structure. For this reason, we propose a differentiable multi-view representation learning network to address the aforementioned issue, which is equipped with the interpretable working mechanism of sparse low-rank decomposition and outstanding representation ability of neural networks. The network is constructed by stacking multiple differentiable blocks that are explicitly reformulated from the optimization objective exhibiting interpretability. Benefiting from end-to-end optimization of deep networks, it can efficiently learn an interpretable deep representation of high-dimensional features from multi-view data. Extensive experimental results on several benchmark multi-view datasets demonstrate the effectiveness of the learned representation in comparison to several state-of-the-art algorithms.
Adaptively Hashing 3DLUTs for Lightweight Real-time Image Enhancement

Image enhancement is an essential and longstanding task in computer vision, in which the 3D lookup table (3DLUT) is widely used due to its powerful mapping capability and high time efficiency. However, the 3DLUT generally requires a large parameter amount since it caches the mapping results for all colors of the entire discrete color space with a 3D array. As a result, standard 3DLUT-based enhancement methods suffer from heavy memory footprints, limiting their practical applications. Based on the analyses of the inherent low grid utilization rate of 3DLUT, we propose HashLUT, an efficient hash form of the standard 3DLUT, and further build a lightweight real-time image enhancement network that adaptively learns HashLUTs and handles hash collisions end-to-end. Experiments on two benchmarks demonstrate that our model achieves comparable enhancement performance to the state-of-the-art methods with significantly fewer parameters. Source codes are available at https://github.com/Xian-Bei.
Identifying Impact Criteria for Software Requirement Verification and Validation Tools Selection

In the past few decades, the software requirement gathering phase as part of the software development life cycle has become of great interest to software project managers, developers, and clients. It has become a critical factor in project success and defining its quality. The requirement gathering phase is how developers gather the software projects' functional and non-functional requirements. Therefore, developers need to go through intensive testing activities to ensure that these requirements are correct and sufficient. In addition, project managers employ software tools for requirements verification and validation. However, there is a lack of research studies for identifying criteria affecting the selection of software tools used for requirements verification and validation. In this work, we focus on identifying some criteria to choose software tools for requirement verification and validation to serve the requirements gathering phases. The criteria can be utilized as a selection guide for IT employees and managers to select appropriate verification and validation tools.
Ada-SwinBERT: Adaptive Token Selection for Efficient Video Captioning with Online Self-Distillation

Video captioning aims at producing textual descriptions for the given video. Benefiting from the self-attention mechanism for capturing long-distance dependencies between video patches and language sentences, the fully Transformer-based models achieve promising performance recently. However, due to continuous temporal information, there exists a large amount of redundant and unimportant visual content. Indiscriminate use of video patches results in expensive computation and inefficient use of resources. To tackle this issue, we propose Ada-SwinBERT, a novel approach that adaptively selects salient video tokens to achieve a balance between efficiency and performance for video captioning. Moreover, we devise a training strategy with online self-distillation to make up for the information loss caused by discarding video tokens. Video-text alignment knowledge distilled from the teacher leads to a robust training process. By pruning 78.1% input tokens hierarchically, our approach greatly reduces 62.0% FLOPs compared with the base model while achieving competitive performance with SOTA methods.
A Variable-speed Silent Speech Recognition Method based on Surface Electromyography Signal

In the practical implementation of silent speech recognition based on surface electromyography (sEMG) signal, the change in the subjects' speech speed will affect the recognition performance. To mitigate this effect, a silent speech recognition method based on dynamic time warping (DTW) algorithm is proposed in this paper. Specifically, the facial sEMG signals of 22 Chinese words are collected, the energy threshold method is used to detect the active segment, the root mean square feature is extracted, and finally the DTW algorithm is used for recognition. The experimental results show that the method is robust to silent speech recognition tasks with different speech speeds. The average accuracy of using the DTW algorithm to classify uniform-speed words is 94.55%, and that of variable-speed words is 71.82%. In addition, the proposed method is suitable for few samples learning, which means it can quickly adapt to new tasks and individuals. These findings provide a novel way for the practical application of sEMG based silent speech recognition.
Robust Subgraph Augmentation for Graph Convolutional Networks with Few Labeled Nodes

Graph Neural Networks (GNNs) have garnered significant attention owing to their remarkable capacity to extract representations from non-Euclidean data. Despite their success, the performance of prevalent GNNs degrades significantly when structural noise is introduced into the graph data. Besides, the reliance of GNNs on a considerable volume of labeled nodes presents challenges in dealing with the low-data regime where labeled nodes are scarce. This paper aims to address a challenging scenario in which structural noise and the low-data regime occur simultaneously. Specifically, we exploit the valuable information from the local topologies, i.e. subgraphs, of a graph as the augmented supervision signals for the extensive amounts of unlabeled nodes. Moreover, to alleviate the effects of structural noise, we remove potential noisy edges linking dissimilar nodes, generating robust subgraphs for the downstream node classification tasks. We conduct a series of experimental studies on canonical node classification tasks using three graph datasets. The results of our experimental analyses demonstrate that our approach can achieve state-of-the-art performance under structural noises and the low-data regime.
BAProto: Boundary-Aware Prototype for High-quality Instance Segmentation

To date, the boundary quality remains unsatisfactory in instance segmentation, resulting in increasing attention to the mask refinement mechanism. Such a mechanism is supposed to be accurate, efficient and generic to the existing models. Yet, few methods met the three factors simultaneously. In this paper, to address this issue, we propose a Boundary-Aware Prototype (BAProto) for boundary refinement, which conducts pixel-wise prediction through the similarity of boundary representation and the specific prototype. Such a prototype is obtained by a memory unit for comprehensive learning. To our best knowledge, BAProto is the first approach that satisfies the above three factors at the same time. In particular, we elaborately design a three-phase segmentation loss, focusing on the learning of different regions to extract discriminating boundary representations for prototype establishment. Extensive experimental results show that BAProto is precise, efficient and model-agnostic on COCO and Cityscapes datasets.
Towards Boosting Black-Box Attack Via Sharpness-Aware

For black-box attacks, we utilize the transferability of adversarial examples to attack the unseen model successfully. However, existing attack algorithms are easily trapped into a sharp maximum, where a small perturbation changes its value significantly, leading to the failure of the attack. To tackle this issue, we propose a novel Sharpness-Aware Attack seeking the adversarial example with a flat maximum. Specifically, we sample several poor adversarial examples from the neighborhoods of the current point and alleviate the sharpness between them. We also sample anticipatory neighborhoods examples to make the attack algorithm converge quickly to an excellent starting point. Extensive experiments on the ImageNet dataset show the effectiveness of our method, combined with existing gradient-based attacks, our method yields an average attack success rate of 70.0% for nine defense models.
Simulation of Real-time Collision-Free Path Planning Method with Deep Policy Network in Human-Robot Interaction Scenario

Collision avoidance is essential for manipulators working in human-robot interaction scenarios where humans and robots collaborate to ensure human safety and working efficiency. However, traditional path planning methods like RRT are not well-suited for the dynamic and real-time requirement because of the human intervention where the human arms move continuously and randomly. Therefore, we propose a deep policy network based on reinforcement learning to realize real-time collision-free trajectory planning. First, we use a state-of-art human pose estimation network for robot perception. Second, a reinforcement learning-based path plan method is designed to realize the real-time obstacle avoidance. Also, We create the simulation environment based on the human-robot interaction scenarios and the training results show that our method can realize real-time obstacle avoidance path planning in human-robot interaction scenarios by offline training and online deploying. To see the simulation results, please visit the project video at https://www.bilibili.com/video/BVlbW4ylq70Q.
Multi-template Tracker Driven by Cache Manager Algorithm, Towards Multi-distractor Scenarios

Although the Siamese tracker has evolved rapidly in recent years, lacking discrimination has always been its biggest disadvantage, compared to the discriminative correlation filters (DCF) tracker. It is because mainstream Siamese trackers take only the initial frame as the template. In this paper, we present a new multi-template transformer-based tracker to harness the tracking target’s temporal features and distinguish them from several distractors. It introduces the cache algorithm to adaptively store templates and dynamically recommend the most appropriate templates for the tracker. Moreover, a multi-template integration method is proposed here, by designing a transformer block with learnable template embedding, to reduce the impact of drifts in dynamic templates. We conduct quantitative experiments on multi-distractor scenarios and general scenarios. The results show the proposed tracker outperforms state-of-the-art trackers in multi-distractor scenarios, and can still keep real-time speed. Codes and models are available on GitHub <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
Edge-aware Neural Implicit Surface Reconstruction

Recently, neural implicit 3D reconstruction in indoor scenarios has achieved impressive performance. Utilizing the volume rendering method and neural implicit representation to learn 3D scenes, such per-scene optimization methods could reconstruct pretty complete models but also suffer from missing details and overly-smoothed reconstructions. In this paper, we propose a novel edge-aware neural implicit surface reconstruction method, named Ea-NeuS, to learn high-quality 3D models with fine details. Specifically, we use the edge of objects to locate the important areas, and propose a simple yet effective edge-guided ray-sampling strategy to learn the 3D models. The aforementioned edge information further guides the normal prior supervision, which helps reduce inaccurate optimization in detailed regions. We additionally use the visibility-aware sparse points to pilot the 3D points sampling along the rays and perform explicit supervision. As a result, our method achieves superior performance compared with existing methods on various scenes.
Contrastive Fusion Representation: Mitigating Adversarial Attacks on VQA Models

Visual Question Answering (VQA) is the vision-language task of answering text-based questions presented in an image and has been advanced by the remarkable success of multimodal deep networks. Similar to unimodal networks, multimodal VQA models are also vulnerable to adversarial examples, which raises severe threats to the corresponding applications. Although several adversarial training methods have been proposed, most of them focus on improving the generalization ability of VQA models on clean samples instead of mitigating the adversarial attacks. In this paper, we systemically analyze the core structure of multimodal VQA networks and propose a novel adversarial training algorithm to mitigate adversarial attacks on VQA models. Specifically, our key component is a regularization term with our carefully designed Contrastive Fusion Representation (CFR), which can reduce the sensitivity of VQA models to adversarial perturbations of both the vision and language inputs. We further enhance the adversarial training with augmented CFRs. Comprehensive experimental results show that our method can mitigate adversarial attacks as well as preserve the generalization ability on clean samples under various system settings and outperforms other defense methods.
A Novel Hybrid Multi-resolution Image Registration Algorithm with Curvelet Trasnform and Artificial Neural Networks

The purpose of image registration is to transform multiple images of the same subject taken from different points of view, times, depths, or sensors into one coordinate system. In this study, a novel hybrid image registration approach is developed based on curvet transform, discrete cosine transform, and artificial neural networks. Curvelet transform is an emerging multi-resolution analysis method that can effectively represent objects with highly anisotropic elements such as lines and curves. The proposed algorithm combines the orientation selective property of curvelet transform and the “energy compaction” property of discrete cosine transform together to provide a more efficient way to extract features of curvilinear structures from images. Besides, the learning ability and nonlinear mapping ability of artificial neural network provide a flexible and intelligent tool for data fusion on feature matching and parameter estimation. The performances of the proposed approaches are studied and compared with other methods on medical magnetic resonance images (MRI) via computer simulations.
Spatial Resolution Enhancement for Halftone Images Using Convolutional Neural Networks

To print a grayscale image on a printer with current technology (laser or inkjet), it is necessary to convert it into a binary image called halftone, so that when a human being looks at it from a distance, the binary image visually resembles the original image. In this work, we present a technique to increase the spatial resolution of halftone images based on convolutional neural networks (CNNs). To our knowledge, this is the first work that seeks to solve this problem using CNNs. We use our algorithm to increase the resolution of binary halftones and show that our algorithm is considerably better the previous decision-tree based method. We also use our algorithm to increase the resolution of scanned old comics pages, and show that our algorithm is better than conventional resampling methods.