Reviewerly/exHarmony huggingface dataset:
Artificial intelligence for Breast Cancer Detection

Millions of women globally are affected by breast cancer, which is a serious global health issue. Accurate diagnosis and early detection are essential for enhancing patient outcomes. The development of artificial intelligence (AI) has completely changed the way that breast cancer is diagnosed and treated. For the goal of diagnosing breast cancer, several AI techniques have been used, which include CAD systems, models based on deep learning and machine learning algorithms. To develop models that can precisely categorize and identify malignant lesions, separate harmless from tumors that are malignant and predict patient outcomes, these techniques make use of enormous databases of annotated pictures.AI algorithms can help with risk assessment by spotting high risk people who can benefit from specialized screenings or preventive measures. Despite these encouraging advancements, issues including quality of data, consistency, and ethical issues still exist. This research paper primarily focuses on the significance of AI in detecting breast cancer, the techniques used by AI and the fundamental ideas around it.
Billing and Payment Systems

The importance of e-business and its associated invoicing and payment systems cannot be understated as the world becomes more digital. These programs are required for online transactions since they let clients swiftly and conveniently pay for goods and services. E-businesses can also benefit from the efficiency and cost reductions of electronic payments. To ensure that these systems are secure, reliable, and accessible to all users, various challenges need to be overcome. This chapter will primarily concentrate on the invoicing and payment methods used in the e-business context. This chapter will go through the many invoicing and payment methods typically used in this field and the precautions that need to be taken to avoid fraud. The chapter will also look at the frequent obstacles people face while implementing these systems and offer solutions to those problems. By the end of this chapter, readers will have a better knowledge of the importance of effective invoicing and payment systems in e-business and the best ways to develop these systems.
Cybersecurity

Healthcare security involves the protection of the confidentiality, integrity and availability of health information and the systems used to treat patients. This increasingly includes the protection of patient safety as the industry’s reliance on IT and IoT systems necessitates that these systems be available to diagnose, monitor, and treat patients in today’s heavily interconnected and interoperative world of health services. Healthcare is continuously evolving, leveraging an ever-increasing number and variety of medical devices along with innovative IT and AI based systems to help drive improvements in patient outcomes and industry efficiency. To be successful, healthcare innovators need to navigate a web of national, state, and industry compliance regulations and most importantly include provisions for high levels of patient safety in their innovations, given ever-rising cyberattacks against the industry. Cybersecurity should not be seen as a cost of innovation or conducting business, but more of an enabler of new innovative and perhaps riskier services that help to drive adoption, satisfaction, and outcomes. Disruption can be good, but the minute that disruption faces cybersecurity concerns it ceases to become attractive, meaning that innovations face an uphill battle for approval and widescale adoption if they are not robust, resilient, and fully secure.
Is Bitcoin an emerging market? A market efficiency perspective

Abstract Despite recent studies focused on comparing the dynamics of market efficiency between Bitcoin and other traditional assets, there is a lack of knowledge about whether Bitcoin and emerging markets efficiency behave similarly. This paper aims to compare the market efficiency dynamics between Bitcoin and the emerging stock markets. In particular, this study indicates whether the dynamics of Bitcoin market efficiency mimic those of emerging stock markets. Thus, the paper's contribution emerges from the combination of Bitcoin and emerging markets in the field of dynamics of market efficiency. The dynamics of market efficiency are measured using the Hurst exponent in the rolling window. The study uses daily data for the MSCI Emerging Markets Index and the Bitcoin market over the period 2011–2022. Our results show that there is at most a moderate correlation between the dynamics of Bitcoin and emerging stock markets’ efficiency over the entire study period. The strongest correlations occur mainly in periods of high economic policy uncertainty in the largest Bitcoin mining countries. Therefore, the association between Bitcoin market efficiency and emerging stock markets’ efficiency may strengthen with an increase in economic policy uncertainty. These findings may be useful for investors and portfolio managers in constructing better investment strategies.
Privacy-Preserving Adaptive Remaining Useful Life Prediction via Source-Free Domain Adaption

Unsupervised domain adaptation (UDA) strives to transfer the learned knowledge to differently distributed datasets by utilizing both source and target data. Recently, an increasing number of UDA methods have been proposed for domain adaptive remaining useful lifetime (RUL) prediction. However, many industries value their privacy protection a lot. The confidentiality of degradation data in certain fields, such as aircraft engines or bearings, makes the source data inaccessible. To cope with this challenge, our work proposes a source-free domain adaption method to implement cross-domain RUL prediction. Especially, an adversarial architecture with one feature encoder and two RUL predictors is proposed. We first maximize the prediction discrepancy between the predictors to detect target samples that are far from the support of the source. Then the feature encoder is trained to minimize the discrepancy, which can generate features near the support. Besides, a weight regularization is utilized to replace the supervised training on the source domain. We evaluate our proposed approach on the commonly used C-MAPSS and FEMTO-ST datasets. Extensive experiment results demonstrate that our approach can significantly improve the prediction reliability on the target domain.
Light Residual Network for Human Activity Recognition using Wearable Sensor Data

This paper addresses the problem of Human Activity Recognition (HAR) of people wearing inertial sensors using data from the UCI-HAR dataset. We propose a light residual network which obtains an F1-Score of 97.6% that outperforms previous works, while drastically reducing the number of parameters by a factor of 15, and thus the training complexity. In addition, we propose a new benchmark based on leave-one(person)-out cross-validation to standardize and unify future classifications on the same dataset, and to increase reliability and fairness in the comparisons. In addition, we propose a common bechmark for the UCI-HAR dataset based on Leaving On (Participant) Out Cross-Validation (LOOCV) to justify the robustness of the architecture proposed and providing a fairer approach for comparisons.
Channel Attentional Correlation Filters Learning With Second-Order Difference for UAV Tracking

Unmanned aerial vehicle (UAV) visual tracking has been a hot research topic in the field of remote sensing. Many filter-based UAV trackers have achieved excellent performance. However, existing methods do not distinguish the importance of different feature channels with semantic information and background information, which may hinder the tracker’s ability to adapt to changing environments. To deal with this problem, we propose a channel attentional correlation filters learning model (CACF). Specifically, we introduce the fuzzy C-means algorithm to pre-classify the extracted features and then perform weight penalty to feature channels with different membership degrees. In addition, the filter can adapt more effectively to the background’s rapid changes during the UAV tracking process by learning the second-order difference between adjacent three frame features. Finally, the comparative experiments are conducted on three mainstream UAV datasets, including DTB70, UAV123@10fps, and UAVDT. The experimental results demonstrate the effectiveness of the proposed method. The tracking performance of CACF surpasses that of other state-of-the-art trackers.
Validity of Differential Characteristics of ARX Block Ciphers

In this paper, we examine the validity of the differential characteristics of ARX-based block ciphers. Recently, it was shown that some of the differential characteristics of block ciphers, SKINNY and GIFT, in the literature are not valid [1]. However, their work is limited to SPN-based block ciphers. We find out that some of the differential characteristics of block ciphers, SIMON, SPECK, and CHAM, that appeared in the literature are not valid. Our work indicates that not only the differential characteristics of SPN-based block ciphers, but also the differential characteristics of ARX-based block ciphers may be invalid.
Collision-Free Motion Generation Based on Stochastic Optimization and Composite Signed Distance Field Networks of Articulated Robot

Safe robot motion generation is critical for practical applications from manufacturing to homes. In this work, we proposed a stochastic optimization-based motion generation method to generate collision-free and time-optimal motion for the articulated robot represented by composite signed distance field (SDF) networks. First, we propose composite SDF networks to learn the SDF for articulated robots. The learned composite SDF networks combined with the kinematics of the robot allow for quick and accurate estimates of the minimum distance between the robot and obstacles in a batch fashion. Then, a stochastic optimization-based trajectory planning algorithm generates a spatial-optimized and collision-free trajectory offline with the learned composite SDF networks. This stochastic trajectory planner is formulated as a Bayesian Inference problem with a time-normalized Gaussian process prior and exponential likelihood function. The Gaussian process prior can enforce initial and goal position constraints in Configuration Space. Besides, it can encode the correlation of waypoints in time series. The likelihood function aims at encoding task-related cost terms, such as collision avoidance, trajectory length penalty, boundary avoidance, etc. The kernel updating strategies combined with model-predictive path integral (MPPI) is proposed to solve the maximum a posteriori inference problems. Lastly, we integrate the learned composite SDF networks into the trajectory planning algorithm and apply it to a Franka robot. The simulation and experiment results validate the effectiveness of the method.
A Fast Multi-Loss Learning Deep Neural Network for Automatic Modulation Classification

Automatic modulation classification (AMC) enables significant applications in both the military and civilian domains. Inspired by the great success of deep learning (DL), a dual-stream neural network using in-phase/quadrature (I/Q) and amplitude/phase (A/P) data has made a superior performance. However, this dual-stream model is oversized (large number of parameters) and time-consuming (high inference time). To deal with that, a novel lightweight single stream neural network made up of group convolutional layer and transformer encoder layer is proposed. Specifically, the group convolutional layer divides the input tensor into different groups for convolution, which needs fewer parameters and has lower computational complexity than commonly used convolutional layers. For the transformer encoder layer, it can generate all time steps’ outputs in parallel by using multi-head attention scheme, which is impossible for the commonly used recurrent-like layers (i.e., the recurrent-like layers output all time steps one by one.). Furthermore, a novel class center distance expansion loss incorporating the cross entropy loss is proposed for model training, which can enlarge the distance of different class centers Finally, the overlapping risk of different class features is reduced. As a result, the inference time of the proposed FastMLDNN is <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$13\times $ </tex-math></inline-formula> faster than MLDNN, and the model size is near <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${}\frac {1}{6}$ </tex-math></inline-formula> as MLDNN, which only sacrifices 0.12% in accuracy. The source code has been released in the github: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/Singingkettle/ChangShuoRadioRecognition/tree/main/configs/fastmldnn</uri> .
A Semi-Automatic Oriental Ink Painting Framework for Robotic Drawing From 3D Models

Creating visually pleasing stylized ink paintings from 3D models is a challenge in robotic manipulation. We propose a semi-automatic framework that can extract expressive strokes from 3D models and draw them in oriental ink painting styles by using a robotic arm. The framework consists of a simulation stage and a robotic drawing stage. In the simulation stage, geometrical contours were automatically extracted from a certain viewpoint and a neural network was employed to create simplified contours. Then, expressive digital strokes were generated after interactive editing according to user's aesthetic understanding. In the robotic drawing stage, an optimization method was presented for drawing smooth and physically consistent strokes to the digital strokes, and two oriental ink painting styles termed as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Noutan</i> (shade) and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Kasure</i> (scratchiness) were applied to the strokes by robotic control of a brush's translation, dipping and scraping. Unlike existing methods that concentrate on generating paintings from 2D images, our framework has the advantage of rendering stylized ink paintings from 3D models by using a consumer-grade robotic arm. We evaluate the proposed framework by taking three standard models and a user-defined model as examples. The results show that our framework is able to draw visually pleasing oriental ink paintings with expressive strokes.
Rapid Mixed Reality Prototyping for Novel Interaction Devices: Evaluating a Transparent Handheld Display

This paper proposes and evaluates a rapid mixed reality prototyping process for novel interaction devices. The process consists of five steps: ideation & visualization, CAD modeling, physical construction, MR augmentation, and comparative evaluation. It is exemplarily tested by developing a physical prototype of a transparent handheld display device and evaluating it against an opaque version ( $$N=16$$ ). Results show that rapid MR prototyping can detect certain UX differences without the need to actually realize the final device. The transparent version had significantly higher hedonic quality. However, no obvious advantage of transparency could be identified regarding task completion. It turned out that the tablet-based AR solution had limited ability to mediate the technological concept and led to ergonomic problems in the experimental setup. However, the proposed process is flexible enough to allow the use of high-quality MR augmentation solutions such as immersive head-mounted display AR, which could overcome the identified limitations.
Breast Cancer Detection Using Deep Learning: An Investigation Using the DDSM Dataset and a Customized AlexNet and Support Vector Machine

The most lethal and devastating form of cancer, breast cancer, is often first detected when a lump appears in the breast. The cause can be attributed to a typical proliferation of cells in the mammary glands. Early breast cancer detection improves survival. Breast cancer screening and early detection are commonly carried out using imaging techniques such as mammography and ultrasound. Convolutional neural networks (CNNs) can identify breast cancer on mammograms. Layers of artificial neurons detect patterns and properties in images to help identify abnormalities more accurately. CNNs may be trained on large datasets to improve accuracy and handle more complex visual information than traditional methods. We introduced a unique approach termed BreastNet-SVM with the objective of automating the identification and categorization of breast cancer in mammograms. This study uses a nine-layer model with two fully connected layers to retrieve data features. Furthermore, we utilized support vector machines (SVM) for classification purposes. To conduct this experiment, we used a well-known benchmark dataset (DDSM). It is shown that the suggested model has a 99.16% accuracy rate, a 97.13% sensitivity rate, and a 99.30% specificity rate. The top approaches for detecting breast cancer were compared to the recommended BreastNet-SVM model. In terms of accuracy, the proposed BreastNet-SVM model fared better in experimental results on a DDSM dataset.
Motion Planning as Online Learning: A Multi-Armed Bandit Approach to Kinodynamic Sampling-Based Planning

Kinodynamic motion planners allow robots to perform complex manipulation tasks under dynamics constraints or with black-box models. However, they struggle to find high-quality solutions, especially when a steering function is unavailable. This letter presents a novel approach that adaptively biases the sampling distribution to improve the planner's performance. The key contribution is to formulate the sampling bias problem as a non-stationary multi-armed bandit problem, where the arms of the bandit correspond to sets of possible transitions. High-reward regions are identified by clustering transitions from sequential runs of kinodynamic RRT and a bandit algorithm decides what region to sample at each timestep. The letter demonstrates the approach on several simulated examples as well as a 7-degree-of-freedom manipulation task with dynamics uncertainty, suggesting that the approach finds better solutions faster and leads to a higher success rate in execution.
MFAN: A Multi-Projection Fusion Attention Network for No-Reference and Full-Reference Panoramic Image Quality Assessment

Panoramic images can provide viewers with a 360° perspective of the scene, and create an immersive experience to viewers, which makes it plays an important role in virtual reality (VR) applications. However, different from traditional 2D images, panoramic images are projected into 2D plane to process, e.g., compression, and viewed in VR devices, which makes their quality assessment be more challenging. In this paper, we propose a Multi-projection Fusion Attention Network (MFAN) to improve the accuracy of panoramic image quality assessment (PIQA). In particular, we propose to extract features from 2D plane images generated from multiple projection methods to overcome the distortions caused by a single projection. Furthermore, we design an attention module to improve the efficiency of features in PIQA task. In addition, our model can be utilized in both full refer-ence and no reference scenarios. Extensive experimental results show that our proposed MFAN can significantly outperform the existing IQA methods.
Second-Order Unsupervised Feature Selection via Knowledge Contrastive Distillation

Unsupervised feature selection aims to select a subset from the original features that are most useful for the downstream tasks without external guidance information. While most unsupervised feature selection methods focus on ranking features based on the intrinsic properties of data, most of them do not pay much attention to the relationships between features, which often leads to redundancy among the selected features. In this paper, we propose a two-stage <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</u> econd- <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">O</u> rder unsupervised <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">F</u> eature selection via knowledge contrastive dis <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">T</u> illation (SOFT) model that incorporates the second-order covariance matrix with the first-order data matrix for unsupervised feature selection. In the first stage, we learn a sparse attention matrix that can represent second-order relations between features by contrastively distilling the intrinsic structure. In the second stage, we build a relational graph based on the learned attention matrix and perform graph segmentation. To this end, we conduct feature selection by only selecting one feature from each cluster to decrease the feature redundancy. Experimental results on 12 public datasets show that SOFT outperforms classical and recent state-of-the-art methods, which demonstrates the effectiveness of our proposed method. Moreover, we also provide rich in-depth experiments to further explore several key factors of SOFT.
Multi-label Generalized Zero-Shot Learning Using Identifiable Variational Autoencoders

Multi-label Zero-Shot Learning (ZSL) is an extension of traditional single-label ZSL, where the objective is to accurately classify images containing multiple unseen classes that are not available during training. Current techniques depends on attention mechanisms and Generative Adversarial Networks (GAN) to address multi-label ZSL and Generalized Zero-Shot Learning (GZSL) challenge. However, generating features for both multi-label ZSL and GZSL in the context of disentangled representation learning remains unexplored. In this paper, we propose an identifiable Variational Autoencoder (iVAE) based generative framework for multi-label ZSL and GZSL. The main idea of our proposed approach is to learn disentangled representations for generating semantically consistent multi-label features using an attribute-level feature fusion technique. We perform comprehensive experiments on two benchmark datasets, NUS-WIDE and MS COCO, for both multi-label ZSL and GZSL. Furthermore, disentangled representation learning for both multi-label ZSL and GZSL on standard datasets achieves commendable performance as compared to existing methods.
Data Association Aware POMDP Planning With Hypothesis Pruning Performance Guarantees

Autonomous agents that operate in the real world must often deal with partial observability, which is commonly modeled as partially observable Markov decision processes (POMDPs). However, traditional POMDP models rely on the assumption of complete knowledge of the observation source, known as fully observable data association. To address this limitation, we propose a planning algorithm that maintains multiple data association hypotheses, represented as a belief mixture, where each component corresponds to a different data association hypothesis. However, this method can lead to an exponential growth in the number of hypotheses, resulting in significant computational overhead. To overcome this challenge, we introduce a pruning-based approach for planning with ambiguous data associations. Our key contribution is to derive bounds between the value function based on the complete set of hypotheses and the value function based on a pruned-subset of the hypotheses, enabling us to establish a trade-off between computational efficiency and performance. We demonstrate how these bounds can both be used to certify any pruning heuristic in retrospect and propose a novel approach to determine which hypotheses to prune in order to ensure a predefined limit on the loss. We evaluate our approach in simulated environments and demonstrate its efficacy in handling multi-modal belief hypotheses with ambiguous data associations.