Reviewerly/exHarmony huggingface dataset:
Temporal Machine Learning Analysis of Prior Mammograms for Breast Cancer Risk Prediction

The identification of women at risk for sporadic breast cancer remains a clinical challenge. We hypothesize that the temporal analysis of annual screening mammograms, using a long short-term memory (LSTM) network, could accurately identify women at risk of future breast cancer. Women with an imaging abnormality, which had been biopsy-confirmed to be cancer or benign, who also had antecedent imaging available were included in this case-control study. Sequences of antecedent mammograms were retrospectively collected under HIPAA-approved guidelines. Radiomic and deep-learning-based features were extracted on regions of interest placed posterior to the nipple in antecedent images. These features were input to LSTM recurrent networks to classify whether the future lesion would be malignant or benign. Classification performance was assessed using all available antecedent time-points and using a single antecedent time-point in the task of lesion classification. Classifiers incorporating multiple time-points with LSTM, based either on deep-learning-extracted features or on radiomic features, tended to perform statistically better than chance, whereas those using only a single time-point failed to show improved performance compared to chance, as judged by area under the receiver operating characteristic curves (AUC: 0.63 ± 0.05, 0.65 ± 0.05, 0.52 ± 0.06 and 0.54 ± 0.06, respectively). Lastly, similar classification performance was observed when using features extracted from the affected versus the contralateral breast in predicting future unilateral malignancy (AUC: 0.63 ± 0.05 vs. 0.59 ± 0.06 for deep-learning-extracted features; 0.65 ± 0.05 vs. 0.62 ± 0.06 for radiomic features). The results of this study suggest that the incorporation of temporal information into radiomic analyses may improve the overall classification performance through LSTM, as demonstrated by the improved discrimination of future lesions as malignant or benign. Further, our data suggest that a potential field effect, changes in the breast extending beyond the lesion itself, is present in both the affected and contralateral breasts in antecedent imaging, and, thus, the evaluation of either breast might inform on the future risk of breast cancer.
Experimental cheat-sensitive quantum weak coin flipping

As in modern communication networks, the security of quantum networks will rely on complex cryptographic tasks that are based on a handful of fundamental primitives. Weak coin flipping (WCF) is a significant such primitive which allows two mistrustful parties to agree on a random bit while they favor opposite outcomes. Remarkably, perfect information-theoretic security can be achieved in principle for quantum WCF. Here, we overcome conceptual and practical issues that have prevented the experimental demonstration of this primitive to date, and demonstrate how quantum resources can provide cheat sensitivity, whereby each party can detect a cheating opponent, and an honest party is never sanctioned. Such a property is not known to be classically achievable with information-theoretic security. Our experiment implements a refined, loss-tolerant version of a recently proposed theoretical protocol and exploits heralded single photons generated by spontaneous parametric down conversion, a carefully optimized linear optical interferometer including beam splitters with variable reflectivities and a fast optical switch for the verification step. High values of our protocol benchmarks are maintained for attenuation corresponding to several kilometers of telecom optical fiber.
Binary Fuzzy Codes and Some Properties of Hamming Distance of Fuzzy Codes

In this paper, we discussed binary fuzzy codes over a vector space by relating classical codes with the probability of a binary symmetric channel (BSC) for receiving a sent codeword correctly.We used the weight of error patterns between a received word and the possible sent codewords to define fuzzy words overdimensional vector space , and used it to define binary fuzzy codes.We also added some properties of Hamming distance of binary fuzzy codes, and the bounds of a Hamming distance of binary fuzzy codes for , where , and , are determined.Finding Hamming distance of binary fuzzy codes is used for decoding sent messages on a BSC.
The Metaverse evolution: Toward Future Digital Twin Campuses

The term ‘Metaverse’ has been used to refer to the next generation Internet (NextG). New, developing, and recently innovation technologies have enabled the incorporation of digital twins into education's metaverse. This is a shared virtual area that combines all virtual worlds over the Internet. This will enable users represented by digital avatars to interact and cooperate as if they were in the physical world. While the metaverse may seem futuristic, it is accelerating because of emerging technologies such as AI and Extended Reality. This paper explores the technologies utilised to build the metaverse and practical applications on improving the educational experience and offer value by optimising how students are taught. Thus, we shall study in detail eight enabling technologies that are important for the Metaverse ecosystem: Virtual, Augmented, and Mixed Reality. Autonomy of Avatar, Computer Agent, and Digital Twin. Data Storage, Data sharing and Data interoperability. This article will discuss prospective metaverse technology and its difficulties. Finally, we have looked at societal acceptance, privacy, and security challenges.
Research on Path Planning of Mobile Robot Based on Improved A-Star Algorithm

In order to solve A series of problems such as long search path time, excessive number of corners and uneven planning path based on the traditional A-Star algorithm, an improved A-Star algorithm is proposed in this paper. To achieve global path optimization and corner optimization. Matlab platform was used to simulate the path planning algorithm. By introducing and changing the proportionality of heuristic functions, the evaluation function was optimized to improve the search efficiency. Then the path is smoothed by using the cubic B-spline curve to eliminate the corner cusps of the path and improve the smoothness of the path under the premise of ensuring the global path optimal. The experimental data prove the operability of the improved algorithm the improved search efficiency is significantly improved, and a number of corners is effectively reduced and the path tends to be smooth under the premise that the path length is close.
Causal Unstructured Pruning in Linear Networks Using Effective Information

Excessive number of parameters in today’s (deep) neural networks demands tremendous computational resources and slows down training speed. The problem also makes it difficult to deploy these neural network models on capability constrained devices such as mobile devices. To address this challenge, we propose an unstructured pruning method that measures the causal structure of neural networks based on effective information (EI). It introduces an intervention to the input and computes the mutual information between the interference and its corresponding output, within a single linear layer measuring the importance of each weight. In the experiments, we found that the sparsity of EI pruning can reach more than 90%. Only 10% of non-zero parameters in the linear layers were needed compared to the benchmark methods without pruning, while ensuring similar level of accuracy and stable training performance in iterative pruning. In addition, as the invariance of the causal structure of the network is exploited, the network after pruning using EI is highly generalizable and interpretable than other methods.
Ultra-low Dose CT Image Denoising based on Conditional Denoising Diffusion Probabilistic model

Due to repeated examinations of lung nodules by Standard Dose Computed Tomography (SDCT), patients suffer from an increased risk of further cancer deterioration caused by the accumulated X-ray dose. Although radiologist have attempted using Ultra-low dose CT images instead of SDCT for diagnosis, the reduction of CT dose decreases the final reconstructed image quality and seriously hinders diagnosis. To compensate for the reduced image quality, we presents a novel noise reduction approach, conditional Denoising Diffusion Probabilistic Model (c-DDPM), by exploiting the advantages of Diffusion Probabilistic Models (DDPM). c-DDPM applies a 2.5D feature fusion strategy to account for CT spatial details, and constrains the denoising procession, by combining the loss function l <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> and l <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ssim</inf> . We evaluate c-DDPM and a state-of-the-art method CycleGAN, the commercial IMR method and iDose on an actual patients dataset with a total of 170 patients. Objective assessment shows that c-DDPM can suppress the isolated artifacts and generate more compelling ULDCT images with PSNR (35.19±0.73) and SSIM (0.85±0.03). The subjective evaluation performed by radiologists also demonstrates that our approach can effectively improve perceptual image quality, achieving an overall image quality score of 4/5 or above in 88.4% of cases and an image noise score of 4/5 or above in 100% of the cases. Finally, we provides comprehensive empirical evidence showing that in the lung nodule detection task, ULDCT images denoised through c-DDPM my be detected 11% more valid nodules than of CycleGAN.
An Enhanced Haar Cascade Face Detection Schema for Gender Recognition

Gender identification software is becoming increasingly popular in the age of machine-to-machine interactions, as the need for person-aligned, effective, and ethical systems become more apparent. In this age of machine-to-machine interactions, automated gender identification software is becoming more popular. The bulk of the systems for gender identification that were examined used either textual or audiovisual data as input to determine their gender. Many different approaches to automatically detect gender based on traits gathered from people's bodies and/or behaviors have been suggested, and there are simply too many to include here. The accuracy of automated gender identification, on the other hand, has always been a topic of worry or a source of criticism. Initial face identification is accomplished by the use of the Haar Cascade face detection method, which is based on the Viola-J ones face recognition algorithm. Facial characteristics such as the eyes, mouth, and nose are then identified. Second, using the Viola-Jones face detection algorithm, the faces and facial characteristics such as the eyes, mouth, and nose are recognized, and the results are shown. In order to increase the accuracy of gender identification, adaptive filters are employed to reduce the amount of noise present before the detection process is carried out. The facial qualities that have been gathered are utilized as input or test data for the neural network, which is subsequently trained on these characteristics. It is intended that the neural network acquires the features of the genders and then functions as a classifier when it comes to detecting them. Instead of saying it another way, a neural network known as Keras is used for feature extraction and gender identification, which is free and open-source software.
Generating Muslim Name using Character-Level Language Model in Deep Learning

this paper proposes novel deep-learning models that can generate Muslim names. A recurrent neural network (RNN) approach is used as the machine learning model. Generate new names, using the character-level language model. This technique has learned the different name patterns and generates new names. In order to achieve this objective, thousands of Muslim names have been collected from popular domains and then trimmed for the data set. After processing the whole dataset, 1000 pure and most common names are selected for a dataset. The deployed system is using some important functions provided by neural network libraries. Here, the argument also reveals that if a huge data set is used to employ this deep learning technique, more accurate results are expected. These outcomes will be better when compared with the traditional machine learning algorithms. The efficacy of the proposed model is proved by the similarity of artificial names with real names.
Deep learning based DeepFake video detection

Recent advances in DeepFake face-swapping technology have made it simple to create fake videos that appear remarkably real. Since it has been employed in numerous instances for deceit, extortion, and the falsification of facts, its widespread use has generated a huge social, security, and political risk. Its use on websites and social media has become more widespread. Detecting this crime is becoming more and more important due to the potential harm false videos may inflict on a global scale. This research offers a method for building a deep learning model that really can tell the difference between authentic and false videos. The article describes how to create new models based on the VGG16 neural network, a previously created neural network that does image categorization, using transfer learning in the computer vision field. Deep learning is still becoming better at both producing and spotting DeepFakes. DeepFake detection algorithms are developed using dated public datasets, and as a result, they may become obsolete with time. and require continual updating. The research findings are encouraging, and our results reached an accuracy rate of over 90%.
Recipe Recommendation Based on Information Propagation of Knowledge Graphs

During these years, the popularity of the Internet, services such as recipe sharing, food reviews and take-out have become increasingly common in life. In the scenario of using this kind of life service, people face the problem of information overload, so people are more inclined to accept system suggestions. Recipe recommendation is pushed to the forefront. A good recipe recommendation algorithm can greatly increase the happiness index of people's lives. However, existing research in the field of recipe recommendation generally ignores the relationship between recipe attributes (such as ignoring lamb souvlaki and Korean BBQ lamb chops have the same ingredients, craftsmanship, taste, etc.) resulting in incomplete learning of user and recipe representations, which leads to a situation of poor recommendation performance. To solve this problem, this paper presents a new recipe recommendation method based on the Information Propagation mechanism of Knowledge Graph (InPKG). Specifically, this method analyzes the higher-order structural features and relational characteristics of entities through the knowledge graph higher-order propagation properties, and uses a message aggregator to aggregate the obtained higher-order characteristics to obtain a richer representation of entities. The experimental results show that InPKG has advantages over existing classical recipe recommendation models and has good interpretability.
SLRFormer: Continuous Sign Language Recognition Based on Vision Transformer

Human-Robot interaction (HRI) usually focuses on the interaction between normal people and robots, ignoring the needs of deaf-mute people. Deaf-mute individuals utilize sign language to communicate their thoughts and emotions. Therefore, continuous sign language recognition (CSLR) can be introduced to the robot for communicating with deaf-mute people. However, the mainstream CSLR, which consists of two main modules, i.e., visual feature extraction and contextual modeling, has several problems. Visual features are usually extracted frame-by-frame and lack global contextual information, which results in a crucial impact on subsequent context modeling. In addition, we discovered a substantial degree of redundancy in the sign language data, which can significantly slow down model training and exacerbate the problem of model overfitting. To solve these problems, in this paper, we propose a novel vision transformer-based sign language recognition network combined with the off-frame extraction (KFE) module for accurate end-to-end recognition of input video sequences. Two CSLR benchmarks, TJUT-SLRT and USTC-CSL, have been the subject of our experiments. The outcomes of our experiments illustrate the efficacy of our method.
Maximizing Mutual Information Across Feature and Topology Views for Representing Graphs

Recently, maximizing mutual information has emerged as a powerful tool for unsupervised graph representation learning. Existing methods are typically effective in capturing graph information from the topology view but consistently ignore the node feature view. To circumvent this problem, we propose a novel method by exploiting mutual information maximization across feature and topology views. Specifically, we first construct the feature graph to capture the underlying structure of nodes in feature spaces by measuring the distance between pairs of nodes. Then we use a cross-view representation learning module to capture both local and global information content across feature and topology views on graphs. To model the information shared by the feature and topology spaces, we develop a common representation learning module by using mutual information maximization and reconstruction loss minimization. Here, minimizing reconstruction loss forces the model to learn the shared information of feature and topology spaces. To explicitly encourage diversity between graph representations from the same view, we also introduce a disagreement regularization to enlarge the distance between representations from the same view. Experiments on synthetic and real-world datasets demonstrate the effectiveness of integrating feature and topology views. In particular, compared with the previous supervised methods, the proposed method achieves comparable or even better performance under the unsupervised representation and linear evaluation protocol.
An Efficient Architecture for Imputing Distributed Data Sets of IoT Networks

In the era of the Internet of Things (IoT), spatially distributed IoT devices collect and store data in a distributed fashion for computational efficiency. However, in IoT networks, due to the fragile device, harsh deployment environment, and unreliable transmission, the possibility of missing data is increasing, which may significantly affect subsequent data processing. Traditional approaches to impute missing data in IoT distributed data sets bring huge communication overheads. In this article, we develop an efficient architecture for distributed IoT data imputation based on a designed multidiscriminator conditional generative adversarial network. The architecture intelligently learns the characteristics of the distributed data sets to accurately impute missing values. Our experiments are performed using three data sets under two different data missing mechanisms. The experimental results demonstrate that using three data sets, the proposed imputation technique can drastically reduce the imputation error by up to 88.66%, 94.27%, and 95.53% at the premise of low transmission cost, respectively, compared to five state-of-the-art methods.
Dual-Arm Mobile Manipulation Planning of a Long Deformable Object in Industrial Installation

In this letter, we present a planning method considering regrasp and mobility for the manipulation problem of a long deformable object in an industrial scenario. The object is carried through designated rollers by a dual-arm robot in the presence of environmental constraints. The problem is formulated by modeling the deformable object as a set of line segments and categorizing obstacles into different types. A hierarchical 2.5D manipulation planner is proposed, which first plans the ideal path of a deformable object in a 2D plane and obtains its topological representation. Next, dual-arm regrasps are determined by utilizing the deformation models, and collision-free paths are planned to meet topological constraints for the grippers. The feasibility of the proposed method is experimentally validated using a humanoid robot, by integrating the planner with a loco-manipulation planning framework and QP-based whole-body motion controller.
The Use of SNN for Ultralow-Power RF Fingerprinting Identification With Attention Mechanisms in VDES-SAT

data exchange system (VDES) is explored as a navigational safety guarantor for ships worldwide. As the number of jammers and spoofers targeting VDES is on the rise, its security authentication emerges as an urgent issue. Radio frequency fingerprinting identification (RFFI) as a noncryptographic physical-layer security solution can effectively defend against increasingly sophisticated attacks. Nevertheless, the power consumption of frequent RFFI is not negligible for VDES satellite components with energy constraints. To this end, spiking neural networks (SNNs) are first employed to build an ultralow-power RFFI system for VDES, where the optimized spiking neurons can yield high accuracy with very few spikes. Moreover, multiple RF fingerprinting features are fused to highly integrate the information of the VDES signal in different dimensions, and the attention mechanism is utilized to further improve RFFI performance. Numerical results show that our SNN-based RFFI system can yield identification accuracy up to 92.59% at a signal-to-noise ratio (SNR) of 25 dB on VDES data sets and reduces power consumption by 64% compared to artificial neural networks (ANNs) of comparable accuracy on 45-nm CMOS process.
A Transformer-Based Contrastive Semi-Supervised Learning Framework for Automatic Modulation Recognition

The application of deep learning improves the processing speed and the accuracy of automatic modulation recognition (AMR). As a result, it realizes intelligent spectrum management and electronic reconnaissance. However, deep learning-aided AMR usually requires a large number of labeled samples to obtain a reliable neural network model. In practical applications, due to economic costs and privacy constraints, there is a small number of labeled samples but a large number of unlabeled samples. This paper proposes a Transformer-based contrastive semi-supervised learning framework for AMR. First, self-supervised contrastive pre-training of the Transformer-based encoder is completed using unlabeled samples, and data augmentation is realized through time warping. Then, the pre-trained encoder and a randomly initialized classifier are fine-tuned using labeled samples, and hierarchical learning rates are employed to ensure classification accuracy. Considering the problems of applying Transformer to AMR, a convolutional transformer deep neural network is proposed, which involves convolutional embedding, attention bias, and attention pooling. In experiments, the feasibility of the framework is analyzed through linear evaluation of the framework components on the RML2016.10a dataset. Also, the proposed framework is compared with existing semi-supervised methods on RML2016.10a and RML2016.10b datasets to verify its superiority and stability.