Reviewerly/exHarmony huggingface dataset:
Semi-supervised Medical Image Classification with Temporal Knowledge-Aware Regularization

Semi-supervised learning (SSL) for medical image classification has achieved exceptional success on efficiently exploiting knowledge from unlabeled data with limited labeled data. Nevertheless, recent SSL methods suffer from misleading hard-form pseudo labeling, exacerbating the confirmation bias issue due to rough training process. Moreover, the training schemes excessively depend on the quality of generated pseudo labels, which is vulnerable against the inferior ones. In this paper, we propose TEmporal knowledge-Aware Regularization (TEAR) for semi-supervised medical image classification. Instead of using hard pseudo labels to train models roughly, we design Adaptive Pseudo Labeling (AdaPL), a mild learning strategy that relaxes hard pseudo labels to soft-form ones and provides a cautious training. AdaPL is built on a novel theoretically derived loss estimator, which approximates the loss of unlabeled samples according to the temporal information across training iterations, to adaptively relax pseudo labels. To release the excessive dependency of biased pseudo labels, we take advantage of the temporal knowledge and propose Iterative Prototype Harmonizing (IPH) to encourage the model to learn discriminative representations in an unsupervised manner. The core principle of IPH is to maintain the harmonization of clustered prototypes across different iterations. Both AdaPL and IPH can be easily incorporated into prior pseudo labeling-based models to extract features from unlabeled medical data for accurate classification. Extensive experiments on three semi-supervised medical image datasets demonstrate that our method outperforms state-of-the-art approaches. The code is available at https://github.com/CityU-AIM-Group/TEAR .
Provably Secure Pseudo-Identity Three-Factor Authentication Protocol Based on Extended Chaotic-Maps for Lightweight Mobile Devices

Authentication and key agreement (AKA) play a major role in an open network environment to communicate between two or more participants securely. Authentication and key agreement protocols should protect sensitive data from a malevolent adversary by offering a several of services, such as user credential privacy and authentication when a user’s or server’s private key is revealed or a smart card is lost/stolen. Unfortunately, the majority of offered authentication and key agreement protocols suggested in the literature are vulnerable to many attacks. In this research, we present a pseudo-identity-based secure mutual authentication protocol that protects privacy. The suggested protocol resists replay attacks, de-synchronization attacks, eavesdropping attacks, denial of service attacks, insider attacks, known session-specific temporary information attacks, and man-in-the-middle attacks efficiently and also provides forward security. In order to offer privacy and prevent traceability, our protocol employs the pseudo-identity mechanism. We present a comprehensive security analysis, which includes formal verification with the Scyther tool; a BAN logic proof; and an informal discussion of various attacks, demonstrating that the proposed protocol is secure against all well-known attacks. Additionally, we give a performance study and a comparison to similar existing protocols.
Quantum Secret Permutating Protocol

In modern cryptography, distributing a private and unique index number to each participant is an important cryptographic task, which can be adopted to efficiently solve many complicated secure multiparty computations. In this paper, we define this cryptographic primitive, called Secret Permutating, in which every one of <inline-formula><tex-math notation="LaTeX">$n$</tex-math></inline-formula> participants can get a random but unique secret <inline-formula><tex-math notation="LaTeX">${k}_i \in \{ {1,2, \ldots,n} \}$</tex-math></inline-formula> . Furthermore, we focus on the unconditional security of Secret Permutating based on laws of quantum mechanics. Accordingly, by local Pauli operators and entanglement swapping of Bell states, we design novel quantum Secret Permutating protocols. What's more, to reduce the communicational complexity, we exploit the uniform, random and independent properties of quantum measurements to evenly divide all participants into many secret groups with the small approximate sizes. Finally, the analysis results and simulated experiments show that the proposed protocols have the unconditional security and the good feasibility.
Feature Re-calibration Based Multiple Instance Learning for Whole Slide Image Classification

Whole slide image (WSI) classification is a fundamental task for the diagnosis and treatment of diseases; but, curation of accurate labels is time-consuming and limits the application of fully-supervised methods. To address this, multiple instance learning (MIL) is a popular method that poses classification as a weakly supervised learning task with slide-level labels only. While current MIL methods apply variants of the attention mechanism to re-weight instance features with stronger models, scant attention is paid to the properties of the data distribution. In this work, we propose to re-calibrate the distribution of a WSI bag (instances) by using the statistics of the max-instance (critical) feature. We assume that in binary MIL, positive bags have larger feature magnitudes than negatives, thus we can enforce the model to maximize the discrepancy between bags with a metric feature loss that models positive bags as out-of-distribution. To achieve this, unlike existing MIL methods that use single-batch training modes, we propose balanced-batch sampling to effectively use the feature loss i.e., (+/−) bags simultaneously. Further, we employ a position encoding module (PEM) to model spatial/morphological information, and perform pooling by multi-head self-attention (PSMA) with a Transformer encoder. Experimental results on existing benchmark datasets show our approach is effective and improves over state-of-the-art MIL methods https://github.com/PhilipChicco/FRMIL .
Enthusiasts, Pragmatists, and Skeptics: Investigating Users’ Attitudes Towards Emotion- and Personality-Aware Voice Assistants across Cultures

Voice Assistants (VAs) are becoming a regular part of our daily life. They are embedded in our smartphones or smart home devices. Just as natural language processing has improved the conversation with VAs, ongoing work in speech emotion recognition also suggests that VAs will soon become emotion- and personality-aware. However, the social implications, ethical borders and the users' general attitude towards such VAs remain underexplored. In this paper, we investigate users' attitudes towards and preferences for emotionally aware VAs in three different cultures. We conducted an online questionnaire with N = 364 participants in Germany, China, and Egypt to identify differences and similarities in attitudes. Using a cluster analysis, we identified three different basic user types (Enthusiasts, Pragmatists, and Skeptics), which exist in all cultures. We contribute characteristic properties of these user types and highlight how future VAs should support customizable interactions to enhance user experience across cultures.
Unsupervised Recognition of Informative Features via Tensor Network Machine Learning and Quantum Entanglement Variations

Given an image of a white shoe drawn on a blackboard, how are the white pixels deemed (say by human minds) to be informative for recognizing the shoe without any labeling information on the pixels? Here we investigate such a ``white shoe'' recognition problem from the perspective of tensor network (TN) machine learning and quantum entanglement. Utilizing a generative TN that captures the probability distribution of the features as quantum amplitudes, we propose an unsupervised recognition scheme of informative features with the variations of entanglement entropy (EE) caused by designed measurements. In this way, a given sample, where the values of its features are statistically meaningless, is mapped to the variations of EE that statistically characterize the gain of information. We show that the EE variations identify the features that are critical to recognize this specific sample, and the EE itself reveals the information distribution of the probabilities represented by the TN model. The signs of the variations further reveal the entanglement structures among the features. We test the validity of our scheme on a toy dataset of strip images, the MNIST dataset of hand-drawn digits, the fashion-MNIST dataset of the pictures of fashion articles, and the images of brain cells. Our scheme opens the avenue to the quantum-inspired and interpreted unsupervised learning, which can be applied to, e.g., image segmentation and object detection.
Unsupervised Cross-Domain Feature Extraction for Single Blood Cell Image Classification

Diagnosing hematological malignancies requires identification and classification of white blood cells in peripheral blood smears. Domain shifts caused by different lab procedures, staining, illumination, and microscope settings hamper the re-usability of recently developed machine learning methods on data collected from different sites. Here, we propose a cross-domain adapted autoencoder to extract features in an unsupervised manner on three different datasets of single white blood cells scanned from peripheral blood smears. The autoencoder is based on an R-CNN architecture allowing it to focus on the relevant white blood cell and eliminate artifacts in the image. To evaluate the quality of the extracted features we use a simple random forest to classify single cells. We show that thanks to the rich features extracted by the autoencoder trained on only one of the datasets, the random forest classifier performs satisfactorily on the unseen datasets, and outperforms published oracle networks in the cross-domain task. Our results suggest the possibility of employing this unsupervised approach in more complicated diagnosis and prognosis tasks without the need to add expensive expert labels to unseen data.
Transformer Based Multi-view Network for Mammographic Image Classification

Most of the existing multi-view mammographic image analysis methods adopt a simple fusion strategy: features concatenation, which is widely used in many features fusion methods. However, concatenation based methods can't extract cross view information very effectively because different views are likely to be unaligned. Recently, many researchers have attempted to introduce attention mechanism related methods into the field of multi-view mammography analysis. But these attention mechanism based methods still partly rely on convolution, so they can't take full advantages of attention mechanism. To take full advantage of multi-view information, we propose a novel pure transformer based multi-view network to solve the question of mammographic image classification. In our primary network, we use a transformer based backbone network to extract image features, a "cross view attention block" structure to fuse multi-view information, and a "classification token" to gather all useful information to make the final prediction. Besides, we compare the performance when fusing multi-view information at different stages of the backbone network using a novel designed "(shifted) window based cross view attention block" structure and compare the results when fusing different views' information. The results on DDSM dataset show that our networks can effectively use multi-view information to make judgments and outperform the concatenation and convolution based methods.
Research on Cobot Action Decision-Making Method Based on Intuitionistic Fuzzy Set and Game Theory

The bounded rational properties of humans in human-robot collaboration (HRC) is a fundamental reason for collisions in proximity HRC. As HRC scenarios in manufacturing become increasingly popular, robot action decision-making needs to consider such properties of humans. In previous studies, humans are usually considered as rational agents whose behaviors are predictable and planned, but humans are susceptible to distractions caused by external disturbances, and different cognitive processes of the task can produce unpredictable behaviors. To better simulate human bounded rational behavior, based on an intuitionistic fuzzy (IF) multi-attribute decision algorithm, we propose a cobot action decision-making method that integrates human intention, safety, and efficiency, to produce a human-like decision. The proposed decision-making method integrates the bounded rational behavior of humans and establishes the IF set of human action intention for efficiency and comfort using cumulative prospect theory(CPT) and IF set. The proposed method lights on the human decision-making mechanism for proximity HRC actions. We use IF set to calculate the score and accuracy values of two Nash equilibria of a static chicken game, which can predict human action intentions with collision risk and provide optimal action decisions for the robot simultaneously. We generated 10,000 sets of data using the Monte Carlo method and validated the effectiveness of our proposed method by comparing it with MDP and POMDP methods. The results showed that the decision-making method could effectively perform the task of making action decisions for the robot. Simulation experiments and Turing test results show that our proposed method can predict a human’s subjective action decision intention in a situation with potential collision risk with 87.53% accuracy. At the same time, the experimental participants believe that the robot can get 4.83 out of 5 satisfaction points for an action decision.
A Fine-Grained Attention Model for High Accuracy Operational Robot Guidance

Deep learning enhanced Internet of Things (IoT) is advancing the transformation toward smart manufacturing. Intelligent robot guidance is one of the most potential deep learning + IoT applications in the manufacturing industry. However, low costs, efficient computing, and extremely high localization accuracy are mandatory requirements for vision robot guidance, particularly in operational factories. Therefore, in this work, a low-cost edge computing-based IoT system is developed based on an innovative fine-grained attention model (FGAM). FGAM integrates a deep-learning-based attention model to detect the region of interest (ROI) and an optimized conventional computer vision model to perform fine-grained localization concentrating on the ROI. Trained with only 100 images collected from real production line, the proposed FGAM has shown superior performance over multiple benchmark models when validated using operational data. Eventually, the FGAM-based edge computing system has been deployed on a welding robot in a real-world factory for mass production. After the assembly of about 6000 products, the deployed system has achieved averaged overall process and transmission time down to 200 ms and overall localization accuracy up to 99.998%.
Fast FF-to-FFPE Whole Slide Image Translation via Laplacian Pyramid and Contrastive Learning

Formalin-Fixed Paraffin-Embedded (FFPE) and Fresh Frozen (FF) are two major types of histopathological Whole Slide Images (WSIs). FFPE provides high-quality images, however the acquisition process usually takes 12 to 48 h, while FF with relatively low-quality images takes less than 15 min to acquire. In this work, we focus on the task of translating FF to FFPE style (FF2FFPE), to synthesize FFPE-style images from FF images. However, WSIs with giga-pixels impose heavy constraints on computation and time resources. To address these issues, we propose the fastFF2FFPE for translating FF into FFPE-style efficiently. Specifically, we decompose FF images into low- and high-frequency components based on the Laplacian Pyramid, wherein the low-frequency component at low resolution is transformed into FFPE-style with low computational cost, and the high-frequency component is used for providing details. We further employ contrastive learning to encourage similarities between original and output patches. We conduct FF2FFPE translation experiments on The Cancer Genome Atlas (TCGA) Glioblastoma Multiforme (GBM) and Lung Squamous Cell Carcinoma (LUSC) datasets, and verify the efficacy of our model on Microsatellite Instability prediction in gastrointestinal cancer. The code and models are released at https://github.com/hellodfan/fastFF2FFPE .
Domain Adaptive Mitochondria Segmentation via Enforcing Inter-Section Consistency

Deep learning-based methods for mitochondria segmentation require sufficient annotations on Electron Microscopy (EM) volumes, which are often expensive and time-consuming to collect. Recently, Unsupervised Domain Adaptation (UDA) has been proposed to avoid annotating on target EM volumes by exploiting annotated source EM volumes. However, existing UDA methods for mitochondria segmentation only address the intra-section gap between source and target volumes but ignore the inter-section gap between them, which restricts the generalization capability of the learned model on target volumes. In this paper, for the first time, we propose a domain adaptive mitochondria segmentation method via enforcing inter-section consistency. The key idea is to learn an inter-section residual on the segmentation results of adjacent sections using a CNN. The inter-section residuals predicted from source and target volumes are then aligned via adversarial learning. Meanwhile, guided by the learned inter-section residual, we can generate pseudo labels to supervise the segmentation of adjacent sections inside the target volume, which further enforces inter-section consistency. Extensive experiments demonstrate the superiority of our proposed method on four representative and diverse EM datasets. Code is available at https://github.com/weih527/DA-ISC .
The Dice Loss in the Context of Missing or Empty Labels: Introducing $$\varPhi $$ and $$\epsilon $$

Albeit the Dice loss is one of the dominant loss functions in medical image segmentation, most research omits a closer look at its derivative, i.e. the real motor of the optimization when using gradient descent. In this paper, we highlight the peculiar action of the Dice loss in the presence of missing or empty labels. First, we formulate a theoretical basis that gives a general description of the Dice loss and its derivative. It turns out that the choice of the reduction dimensions $$\varPhi $$ and the smoothing term $$\epsilon $$ is non-trivial and greatly influences its behavior. We find and propose heuristic combinations of $$\varPhi $$ and $$\epsilon $$ that work in a segmentation setting with either missing or empty labels. Second, we empirically validate these findings in a binary and multiclass segmentation setting using two publicly available datasets. We confirm that the choice of $$\varPhi $$ and $$\epsilon $$ is indeed pivotal. With $$\varPhi $$ chosen such that the reductions happen over a single batch (and class) element and with a negligible $$\epsilon $$ , the Dice loss deals with missing labels naturally and performs similarly compared to recent adaptations specific for missing labels. With $$\varPhi $$ chosen such that the reductions happen over multiple batch elements or with a heuristic value for $$\epsilon $$ , the Dice loss handles empty labels correctly. We believe that this work highlights some essential perspectives and hope that it encourages researchers to better describe their exact implementation of the Dice loss in future work.
Unraveling the airline value creation network with fuzzy cognitive maps

The creation of value is a critical factor that determines the competitive capacity of firms and their ability to survive. Notwithstanding its importance, value creation usually becomes a fuzzy concept that is difficult to grasp, especially when increasingly complex elements of reality are incorporated into its analysis. Building on the narrative of the firm as a complex adaptive system, this article provides empirical evidence showing value creation as emergent behavior resulting from hyper-connected value repositories. Several implications for scholars and practitioners are discussed. For scholars, expert knowledge is provided from 28 airline industry executives around the world together with a method based on fuzzy cognitive maps that allows modeling and simulating value creation in airlines through 26 value repositories and 77 interdependencies. For practitioners, the article raises awareness of the need to incorporate a complexity mindset into value creation analysis and smooths the transition towards a value performance management tool.
Modality-Adaptive Feature Interaction for Brain Tumor Segmentation with Missing Modalities

Multi-modal Magnetic Resonance Imaging (MRI) plays a crucial role in brain tumor segmentation. However, missing modality is a common phenomenon in clinical practice, leading to performance degradation in tumor segmentation. Considering that there exist complementary information among modalities, feature interaction among modalities is important for tumor segmentation. In this work, we propose Modality-adaptive Feature Interaction (MFI) with multi-modal code to adaptively interact features among modalities in different modality missing situations. MFI is a simple yet effective unit, based on graph structure and attention mechanism, to learn and interact complementary features between graph nodes (modalities). Meanwhile, the proposed multi-modal code, indicating whether each modality is missing or not, guides MFI to learn adaptive complementary information between nodes in different missing situations. Applying MFI with multi-modal code in different stages of a U-shaped architecture, we design a novel network U-Net-MFI to interact multi-modal features hierarchically and adaptively for brain tumor segmentation with missing modality(ies). Experiments show that our model outperforms the current state-of-the-art methods for brain tumor segmentation with missing modalities.
Adaptive maximum correntropy based robust CKF with variational Bayesian for covariance estimation

To address the interference of outliers on the estimation of state and measurement noise covariance matrix, an adaptive maximum correntropy cubature Kalman filter with variational Bayesian approximation over a sliding window is proposed. The multiple kernel size is adjusted for different noise within a reasonable range based on the squared Mahalanobis distance of innovation, which overcomes the excessive convergence problem in the adjustment process. The correntropy matrix is established using the adaptive multiple kernel size to achieve measurement-specific outliers processing. Then the measurement noise covariance matrix is updated as inverse Wishart distribution exploiting the posterior smoothing-based variational Bayesian approximations with correntropy matrix, suppressing the disturbance of measurement outliers to the modification of the measurement noise covariance matrix. Finally, the target tracking simulation and cooperative positioning experiment demonstrate that the proposed method can effectively achieve the robust state estimation with accurate modification of MNCM in the presence of outliers.