Reviewerly/exHarmony huggingface dataset:
Integrating explainability into deep learning-based models for white blood cells classification

White blood cells (WBCs) are crucial constituents of the blood that protect the human body against infections and viruses. The classification of WBCs in a blood smear image is used to diagnose a range of haematological disorders. The manual identification of WBCs can result in potential errors, necessitating the need for automated systems that can assist in classification. Recently, various deep learning models, such as DenseNet121, Xception, MobileNetV2, ResNet50, and VGG16, have been used to classify WBCs. However, the available classification models are black boxes because their decisions are difficult for humans to understand without further exploration. The interpretability and explainability of these models are essential, as their decisions can have severe consequences for patients. In this paper, we integrate an explainable AI (XAI) technique called local interpretable model-agnostic explanations (LIME) with the DenseNet121 classification model for WBC classification. Interpretable results would allow the users to understand and verify the model's predictions, enhancing their confidence in the automated diagnosis.
Unleashing the Transferability Power of Unsupervised Pre-Training for Emotion Recognition in Masked and Unmasked Facial Images

Facial expressions are an essential part of nonverbal communication and major indicators of human emotions.Effective automatic Facial Emotion Recognition (FER) systems can facilitate comprehension of an individual's intention, and prospective behaviors in Human-Computer and Human-Robot Interaction.However, FER faces an enduring challenge, commonly encountered in real-life, of partial occlusions caused by objects such as sunglasses and hands.With the onset of the COVID-19 pandemic, facial masks become a major obstruction for FER systems.The utilization of facial masks exacerbates the occlusion issue since these cover a significant portion of a person's face, including the highly informative mouth area from which positive and negative emotions can be differentiated.Conversely, the efficacy of FER is largely contingent upon the supervised learning paradigm, which necessitates costly and laborious data annotation.Our study centers on utilizing the reconstruction capability of a Convolutional Residual Autoencoder to differentiate between positive and negative emotions.The proposed approach employs unsupervised feature learning and takes as inputs facial images of individuals with masks and without masks.Our study puts particular emphasis on the transferability of the proposed approach to different domains in comparison to current state-of-the-art fully supervised methods.The comprehensive experimental evaluation demonstrates the superior transferability of the proposed approach, highlighting the effectiveness of the unsupervised feature learning pipeline.Despite outperforming more complex methods in some scenarios, the proposed approach is characterized by relatively low computational expense.The source code of the proposed approach, along with the facial images created for this study, will be publicly accessible following the acceptance of this paper.
Wavelet Approximation-Aware Residual Network for Single Image Deraining

It has been made great progress on single image deraining based on deep convolutional neural networks (CNNs). In most existing deep deraining methods, CNNs aim to learn a direct mapping from rainy images to clean rain-less images, and their architectures are becoming more and more complex. However, due to the limitation of mixing rain with object edges and background, it is difficult to separate rain and object/background, and the edge details of the image cannot be effectively recovered in the reconstruction process. To address this problem, we propose a novel wavelet approximation-aware residual network (WAAR), wherein rain is effectively removed from both low-frequency structures and high-frequency details at each level separately, especially in low-frequency sub-images at each level. After wavelet transform, we propose novel approximation aware (AAM) and approximation level blending (ALB) mechanisms to further aid the low-frequency networks at each level recover the structure and texture of low-frequency sub-images recursively, while the high frequency network can effectively eliminate rain streaks through block connection and achieve different degrees of edge detail enhancement by adjusting hyperparameters. In addition, we also introduce block connection to enrich the high-frequency details in the high-frequency network, which is favorable for obtaining potential interdependencies between high- and low-frequency features. Experimental results indicate that the proposed WAAR exhibits strong performance in reconstructing clean and rain-free images, recovering real and undistorted texture structures, and enhancing image edges in comparison with the state-of-the-art approaches on synthetic and real image datasets. It shows the effectiveness of our method, especially on image edges and texture details.
Vaser: Optimizing 360-Degree Live Video Ingest via Viewport-Aware Neural Enhancement

As a revolutionary technique, 360-degree live video streaming provides users with an immersive and realistic experience for a live event. However, due to the limited upload bandwidth of broadcasters, the ingest of 360-degree live video streams has become a bottleneck that seriously lowers the overall utility of viewers. In this paper, we first conduct a series of measurement studies to unveil the characteristics of 360-degree video viewers and the effectiveness of neural enhancement under various settings. Based on the obtained insights, we further propose a novel neural-enhanced 360-degree live video ingest framework called Vaser. A key innovation of Vaser is to take viewport information into account when conducting neural enhancement and tile uploading. Vaser can significantly reduce the upload bandwidth demand for 360-degree live video ingest without sacrificing user experiences. We also propose an enhanced complexity-based patch selection algorithm to improve the efficiency of SR model training. To better utilize the upload bandwidth, we exploit deep reinforcement learning (DRL) to determine the bitrate of upload streams and the frequency of SR model updates. Last, we implement the whole framework and validate its effectiveness using real network traces. The experimental results show that our proposed framework can reduce the upload bandwidth consumption by 40%-55.6% on average with an upscale ratio of 2 and improve the overall utility of viewers by 1.15x-3.61x compared to other baseline approaches.
Query-Efficient Decision-Based Black-Box Patch Attack

Deep neural networks (DNNs) have been showed to be highly vulnerable to imperceptible adversarial perturbations. As a complementary type of adversary, patch attacks that introduce perceptible perturbations to the images have attracted the interest of researchers. Existing patch attacks rely on the architecture of the model or the probabilities of predictions and perform poorly in the decision-based setting, which can still construct a perturbation with the minimal information exposed – the top-1 predicted label. In this work, we first explore the decision-based patch attack. To enhance the attack efficiency, we model the patches using paired key-points and use targeted images as the initialization of patches, and parameter optimizations are all performed on the integer domain. Then, we propose a differential evolutionary algorithm named DevoPatch for query-efficient decision-based patch attacks. Experiments demonstrate that DevoPatch outperforms the state-of-the-art black-box patch attacks in terms of patch area and attack success rate within a given query budget on image classification and face verification. Additionally, we conduct the vulnerability evaluation of ViT and MLP on image classification in the decision-based patch attack setting for the first time. Using DevoPatch, we can evaluate the robustness of models to black-box patch attacks. We believe this method could inspire the design and deployment of robust vision models based on various DNN architectures in the future.
A research on cross‐chain and interoperation methods of fusion protocol

Abstract At present, cross‐chain technology and cross‐chain system pay more attention to cross‐chain security and performance, and have shown great improvement in security, scalability and efficiency. However, the generality of cross‐chain technology in different scenarios is poor, and it may not fit in different business fields. In order to solve the generality problem of cross‐chain technology, this paper sorts out and analyzes the research of blockchain cross‐chain protocol, and proposes a fusion protocol cross‐chain method based on relay‐chain technology. This paper will introduce the cross‐chain framework and cross‐chain transaction process of fusion protocol. Cross‐chain framework includes blockchain cross‐chain interaction architecture, cross‐chain interaction governance mechanism and cross‐chain privacy protection scheme. The transaction process is divided into three different stages. Finally, combined with cross‐chain key technologies and landing application requirements, the future research direction of blockchain cross‐chain technology is given. The fusion protocol cross‐chain method based on relay‐chain technology proposed here is a groundbreaking innovation in the field of blockchain cross‐chain technology. By integrating the advantages of different blockchain systems and protocols, the fusion protocol is able to achieve a high degree of generality and compatibility, making it suitable for various business scenarios and applications.
The Co-Design of an Embodied Conversational Agent to Help Stroke Survivors Manage Their Recovery

Whilst the use of digital interventions to assist patients with self-management involving embodied conversational agents (ECA) is emerging, the use of such agents to support stroke rehabilitation and recovery is rare. This iTakeCharge project takes inspiration from the evidence-based narrative style self-management intervention for stroke recovery, the ‘Take Charge’ intervention, which has been shown to contribute to significant improvements in disability and quality of life after stroke. We worked with the developers and deliverers of the ‘Take Charge’ intervention tool, clinical stroke researchers and stroke survivors, to adapt the ‘Take Charge’ intervention tool to be delivered by an ECA (i.e., the Taking Charge Intelligent Agent (TaCIA)). TaCIA was co-designed using a three-phased approach: Stage 1: Phase I with the developers and Phase II with people who delivered the original Take Charge intervention to stroke survivors (i.e., facilitators); and Stage 2: Phase III with stroke survivors. This paper reports the results from each of these phases including an evaluation of the resulting ECA. Stage 1: Phase I, where TaCIA V.1 was evaluated by the Take Charge developers, did not build a good working alliance, provide adequate options, or deliver the intended Take Charge outcomes. In particular, the use of answer options and the coaching aspects of TaCIA V.1 were felt to conflict with the intention that Take Charge facilitators would not influence the responses of the patient. In response, in Stage 1: Phase II, TaCIA V.2 incorporated an experiment to determine the value of providing answer options versus free text responses. Take Charge facilitators agreed that allowing an open response concurrently with providing answer options was optimal and determined that working alliance and usability were satisfactory. Finally, in Stage 2: Phase III, TaCIA V.3 was evaluated with eight stroke survivors and was generally well accepted and considered useful. Increased user control, clarification of TaCIA’s role, and other improvements to improve accessibility were suggested. The article concludes with limitations and recommendations for future changes based on stroke survivor feedback.
A Three-Stage Uyghur Recognition Model Combining the Attention Mechanism and Different Convolutional Recurrent Networks

Uyghur text recognition faces several challenges in the field due to the scarcity of publicly available datasets and the intricate nature of the script characterized by strong ligatures and unique attributes. In this study, we propose a unified three-stage model for Uyghur language recognition. The model is developed using a self-constructed Uyghur text dataset, enabling evaluation of previous Uyghur text recognition modules as well as exploration of novel module combinations previously unapplied to Uyghur text recognition, including Convolutional Recurrent Neural Networks (CRNNs), Gated Recurrent Convolutional Neural Networks (GRCNNs), ConvNeXt, and attention mechanisms. Through a comprehensive analysis of the accuracy, time, normalized edit distance, and memory requirements of different module combinations on a consistent training and evaluation dataset, we identify the most suitable text recognition structure for Uyghur text. Subsequently, utilizing the proposed approach, we train the model weights and achieve optimal recognition of Uyghur text using the ConvNeXt+Bidirectional LSTM+attention mechanism structure, achieving a notable accuracy of 90.21%. These findings demonstrate the strong generalization and high precision exhibited by Uyghur text recognition based on the proposed model, thus establishing its potential practical applications in Uyghur text recognition.
Weight Optimization of Discrete Truss Structures Using Quantum-Based HS Algorithm

Recently, a new field that combines metaheuristic algorithms and quantum computing has been created and is being applied to optimization problems in various fields. However, the application of quantum computing-based metaheuristic algorithms to the optimization of structural engineering is insufficient. Therefore, in this paper, we tried to optimize the weight of the truss structure using the QbHS (quantum-based harmony search) algorithm, which combines quantum computing and conventional HS (harmony search) algorithms. First, the convergence performance according to the parameter change of the QbHS algorithm was compared. The parameters selected for the comparison of convergence performance are QHMS, QHMCR, QPAR, ϵ, and θr. The selected parameters were compared using six benchmark functions, and the range for deriving the optimal convergence performance was found. In addition, weight optimization was performed by applying it to a truss structure with a discrete cross-sectional area. The QbHS algorithm derived a lower weight than the QEA (quantum-inspired evolutionary algorithm) and confirmed that the convergence performance was better. A new algorithm that combines quantum computing and metaheuristic algorithms is required for application to various engineering problems, and this effort is essential for the expansion of future algorithm development.
Semantic segmentation of remote sensing image based on Contextual U-Net

Semantic segmentation of remote sensing images is a challenging and critical task. The complexity of the remote sensing environment often poses difficulties in accurately capturing object boundaries. To address this challenge, we propose a Contextual U-Net (CU-Net) architecture for semantic segmentation of remote sensing images, which incorporates three collaborative improvements. Firstly, a Boundary Feature Extraction (BFE) module is introduced to fuse semantic feature information from the backbone network with boundary feature information, thereby enhancing the accuracy of edge segmentation in remote sensing images. Secondly, we propose an Adaptive Feature Selection (AFS) module that highlights representative semantic channels for irregular objects, enabling long-distance dependence capture between pixels in the irregular region of the boundary and pixels inside the objects. Thirdly, a Recursive Feature Fusion (RFF) module is introduced to effectively aggregate hierarchical features through adaptive inter-layer feature guidance, facilitating accurate capture of image edges and textures. We collected high-quality remote sensing data through UAVs, comprising 4509 images across 6 different categories. Extensive experiments demonstrate that the proposed CU-Net architecture outperforms state-of-the-art methods.
A Comprehensive Analysis of Real-Time Car Safety Belt Detection Using the YOLOv7 Algorithm

Using a safety belt is crucial for preventing severe injuries and fatalities during vehicle accidents. In this paper, we propose a real-time vehicle occupant safety belt detection system based on the YOLOv7 (You Only Look Once version seven) object detection algorithm. The proposed approach aims to automatically detect whether the occupants of a vehicle have buckled their safety belts or not as soon as they are detected within the vehicle. A dataset for this purpose was collected and annotated for validation and testing. By leveraging the efficiency and accuracy of YOLOv7, we achieve near-instantaneous analysis of video streams, making our system suitable for deployment in various surveillance and automotive safety applications. This paper outlines a comprehensive methodology for training the YOLOv7 model using the labelImg tool to annotate the dataset with images showing vehicle occupants. It also discusses the challenges of detecting seat belts and evaluates the system’s performance on a real-world dataset. The evaluation focuses on distinguishing the status of a safety belt between two classes: “buckled” and “unbuckled”. The results demonstrate a high level of accuracy, with a mean average precision (mAP) of 99.6% and an F1 score of 98%, indicating the system’s effectiveness in identifying the safety belt status.
Blockchain in supply chain management: a feature-function framework for future research

Purpose Blockchain technology offers numerous venues for supply chain applications and research. However, the connections between specific blockchain features and future applications have been unclear to date in its evolution. The purpose of this study is to fill this void. Design/methodology/approach The authors advance the understanding of blockchain in supply chain management by providing a new research framework built on unique blockchain features as applied across core supply chain functions. Findings This study’s framework is a feature-function matrix that integrates four overarching supply chain functions (i.e. supplier management, logistics, production processes and customer management) with nine blockchain features (i.e. traceability/provenance, accessibility, visibility, immutability, distributed/shared ledger, validity, peer-to-peer transacting, pseudonymity and programmability). This study’s feature-function framework is supported by a structured, systematic review of reviews using PRISMA methods. The authors use the framework to present a future blockchain research agenda in supply chain management. Originality/value The authors provide a new blockchain feature/supply chain function framework and provide a structured path for future research.
Use of Metrological Characteristics in Ultrasound Imaging and Artificial Intelligence Techniques for Disease Prediction in Soft Tissue Organs

The global population nowadays is severely affected by various diseases of the soft tissue organs caused mainly because of some infection, heredity, change of lifestyle, etc. The timely detection and accurate diagnosis of these health conditions are of utmost importance in order to improve the chances of recovery and survival. Many medical imaging modalities have proven to effectively diagnose these diseases and their progression in a noninvasive way. Out of all the available modalities, ultrasound is the preferred mode of examination for imaging soft tissue organs for disease prediction because of its ease of use, low cost, portability, and lack of ionizing radiation. The radiologists manually study these scans for making an accurate diagnosis of the underlying condition. However, ultrasound imaging is highly operator-dependent and its effectiveness is adversely affected because of the presence of speckle noise. Therefore, to overcome these issues and for an efficient disease diagnosis, different computer-aided diagnostic (CAD) systems have been developed by researchers using artificial intelligence techniques along with the metrological characteristics of the diseased part as visible on the ultrasound scan of the organ under study. The main purpose of the present chapter is to shed light on the different types of diseases that affect the soft tissue organs like kidney, liver, thyroid, breast and their sonographic appearances, and characteristics. The chapter further describes methodologies developed in recent existing literature (year 2017 onwards) for the classification of diseases using ultrasound images of these organs through a CAD system using state-of-the-art deep learning and machine learning methods. The chapter also gives an insight into designing an efficient CAD system for the classification of breast tumors. The authors in the chapter have used nonsubsampled contourlet transform (NSCT) for multiresolution analysis of the original tumor images. From the subimages obtained using NSCT, extraction of texture features has been carried out using gray level co-occurrence matrix (GLCM), whereas shape features have been computed from the preprocessed tumor images. The computed feature set (texture + shape) has been used for classifying breast tumors using an adaptive neuro-fuzzy classifier with linguistic hedges (ANFC-LH) classifier based on the optimal features selected on the basis of the hedge values associated with the fuzzy rules.
The Qupit Stabiliser ZX-travaganza: Simplified Axioms, Normal Forms and Graph-Theoretic Simplification

We present a smorgasbord of results on the stabiliser ZX-calculus for odd prime-dimensional qudits (i.e.qupits).We derive a simplified rule set that closely resembles the original rules of qubit ZX-calculus.Using these rules, we demonstrate analogues of the spider-removing local complementation and pivoting rules.This allows for efficient reduction of diagrams to the affine with phases normal form.We also demonstrate a reduction to a unique form, providing an alternative and simpler proof of completeness.Furthermore, we introduce a different reduction to the graph state with local Cliffords normal form, which leads to a novel layered decomposition for qupit Clifford unitaries.Additionally, we propose a new approach to handle scalars formally, closely reflecting their practical usage.Finally, we have implemented many of these findings in DiZX, a new open-source Python library for qudit ZX-diagrammatic reasoning.
Floquetifying the Colour Code

Floquet codes are a recently discovered type of quantum error correction code.They can be thought of as generalising stabilizer codes and subsystem codes, by allowing the logical Pauli operators of the code to vary dynamically over time.In this work, we use the ZX-calculus to create new Floquet codes that are in a definable sense equivalent to known stabilizer codes.In particular, we find a Floquet code that is equivalent to the colour code, but has the advantage that all measurements required to implement it are of weight one or two.Notably, the qubits can even be laid out on a square lattice.This circumvents current difficulties with implementing the colour code fault-tolerantly, while preserving its advantages over other well-studied codes, and could furthermore allow one to benefit from extra features exclusive to Floquet codes.On a higher level, as in Ref. [BLN + 23], this work shines a light on the relationship between 'static' stabilizer and subsystem codes and 'dynamic' Floquet codes; at first glance the latter seems a significant generalisation of the former, but in the case of the codes that we find here, the difference is essentially just a few basic ZX-diagram deformations.