Reviewerly/exHarmony huggingface dataset:
Predictive anomaly detection for marine diesel engine based on echo state network and autoencoder

Marine diesel engine with high thermal efficiency and good economy has become the main power of ships. Anomaly detection is an important method to improve the operation reliability of marine diesel engine. Most anomaly detection research focuses on failures that have occurred, and few studies consider anomaly prediction. A predictive anomaly detection method based on echo state network (ESN) and deep autoencoder is proposed. Historical sample data is collected and used to train the prediction network ESN and the anomaly detection network deep auto-encoder. After training, the prediction network ESN is used to predict the sensor data sequence in the future. And the predicted sequence is input into the anomaly detection network deep auto-encoder to obtain the predictive anomaly detection result. The relative error and root mean square error of the proposed method are at least 0.089 and 1.002 lower than other methods, respectively. Compared with other anomaly detection methods, the proposed autoencoder method obtains the best precision, accurate, recall indicators. Experiments show that it is feasible to establish a predictive anomaly detection method. More experiments under different conditions need to be studied, and higher performance algorithms need to be developed in the future.
Scalable and Robust Photonic Integrated Unitary Converter Based on Multiplane Light Conversion

An optical unitary converter (OUC) that can convert a set of $N$ mutually orthogonal optical modes into another set of arbitrary $N$ orthogonal modes is expected to be a key device in diverse applications, including optical communication, deep learning, and quantum computing. While various types of OUC have been demonstrated on photonic integration platforms, the sensitivity against a slight deviation in waveguide dimensions has been the crucial issue in scaling $N$. Here, we demonstrate that an OUC based on the concept of multiplane light conversion (MPLC) shows outstanding robustness against waveguide deviations. Moreover, it becomes more and more insensitive to fabrication errors as we increase $N$, which is in clear contrast to the conventional OUC architecture, composed of $2\ifmmode\times\else\texttimes\fi{}2$ Mach-Zehnder interferometers. The physical origin behind this unique robustness and scalability is studied by considering a generalized OUC configuration. As a result, we reveal that the number of coupled modes in each stage plays an essential role in determining the sensitivity of the entire OUC. The maximal robustness is attained when all-to-all-coupled interferometers are employed, which are naturally implemented in a MPLC-based OUC.
FlexOS: towards flexible OS isolation

At design time, modern operating systems are locked in a specific safety and isolation strategy that mixes one or more hardware/software protection mechanisms (e.g. user/kernel separation); revisiting these choices after deployment requires a major refactoring effort. This rigid approach shows its limits given the wide variety of modern applications' safety/performance requirements, when new hardware isolation mechanisms are rolled out, or when existing ones break.
Scalable Neural Decoder for Topological Surface Codes

With the advent of noisy intermediate-scale quantum (NISQ) devices, practical quantum computing has seemingly come into reach. However, to go beyond proof-of-principle calculations, the current processing architectures will need to scale up to larger quantum circuits which will require fast and scalable algorithms for quantum error correction. Here, we present a neural network based decoder that, for a family of stabilizer codes subject to depolarizing noise and syndrome measurement errors, is scalable to tens of thousands of qubits (in contrast to other recent machine learning inspired decoders) and exhibits faster decoding times than the state-of-the-art union find decoder for a wide range of error rates (down to 1%). The key innovation is to autodecode error syndromes on small scales by shifting a preprocessing window over the underlying code, akin to a convolutional neural network in pattern recognition approaches. We show that such a preprocessing step allows to effectively reduce the error rate by up to 2 orders of magnitude in practical applications and, by detecting correlation effects, shifts the actual error threshold up to fifteen percent higher than the threshold of conventional error correction algorithms such as union find or minimum weight perfect matching, even in the presence of measurement errors. An in situ implementation of such a machine learning-assisted quantum error correction will be a decisive step to push the entanglement frontier beyond the NISQ horizon.
Liver Tumor Localization Based on YOLOv3 and 3D-Semantic Segmentation Using Deep Neural Networks

Worldwide, more than 1.5 million deaths are occur due to liver cancer every year. The use of computed tomography (CT) for early detection of liver cancer could save millions of lives per year. There is also an urgent need for a computerized method to interpret, detect and analyze CT scans reliably, easily, and correctly. However, precise segmentation of minute tumors is a difficult task because of variation in the shape, intensity, size, low contrast of the tumor, and the adjacent tissues of the liver. To address these concerns, a model comprised of three parts: synthetic image generation, localization, and segmentation, is proposed. An optimized generative adversarial network (GAN) is utilized for generation of synthetic images. The generated images are localized by using the improved localization model, in which deep features are extracted from pre-trained Resnet-50 models and fed into a YOLOv3 detector as an input. The proposed modified model localizes and classifies the minute liver tumor with 0.99 mean average precision (mAp). The third part is segmentation, in which pre-trained Inceptionresnetv2 employed as a base-Network of Deeplabv3 and subsequently is trained on fine-tuned parameters with annotated ground masks. The experiments reflect that the proposed approach has achieved greater than 95% accuracy in the testing phase and it is proven that, in comparison to the recently published work in this domain, this research has localized and segmented the liver and minute liver tumor with more accuracy.
Multi-Manifold Deep Discriminative Cross-Modal Hashing for Medical Image Retrieval

Benefitting from the low storage cost and high retrieval efficiency, hash learning has become a widely used retrieval technology to approximate nearest neighbors. Within it, the cross-modal medical hashing has attracted an increasing attention in facilitating efficiently clinical decision. However, there are still two main challenges in weak multi-manifold structure perseveration across multiple modalities and weak discriminability of hash code. Specifically, existing cross-modal hashing methods focus on pairwise relations within two modalities, and ignore underlying multi-manifold structures across over 2 modalities. Then, there is little consideration about discriminability, i.e., any pair of hash codes should be different. In this paper, we propose a novel hashing method named multi-manifold deep discriminative cross-modal hashing (MDDCH) for large-scale medical image retrieval. The key point is multi-modal manifold similarity which integrates multiple sub-manifolds defined on heterogeneous data to preserve correlation among instances, and it can be measured by three-step connection on corresponding hetero-manifold. Then, we propose discriminative item to make each hash code encoded by hash functions be different, which improves discriminative performance of hash code. Besides, we introduce Gaussian-binary Restricted Boltzmann Machine to directly output hash codes without using any continuous relaxation. Experiments on three benchmark datasets (AIBL, Brain and SPLP) show that our proposed MDDCH achieves comparative performance to recent state-of-the-art hashing methods. Additionally, diagnostic evaluation from professional physicians shows that all the retrieved medical images describe the same object and illness as the queried image.
Scaling up SoccerNet with multi-view spatial localization and re-identification

Abstract Soccer videos are a rich playground for computer vision, involving many elements, such as players, lines, and specific objects. Hence, to capture the richness of this sport and allow for fine automated analyses, we release SoccerNet-v3, a major extension of the SoccerNet dataset, providing a wide variety of spatial annotations and cross-view correspondences. SoccerNet’s broadcast videos contain replays of important actions, allowing us to retrieve a same action from different viewpoints. We annotate those live and replay action frames showing same moments with exhaustive local information. Specifically, we label lines, goal parts, players, referees, teams, salient objects, jersey numbers, and we establish player correspondences between the views. This yields 1,324,732 annotations on 33,986 soccer images, making SoccerNet-v3 the largest dataset for multi-view soccer analysis. Derived tasks may benefit from these annotations, like camera calibration, player localization, team discrimination and multi-view re-identification, which can further sustain practical applications in augmented reality and soccer analytics. Finally, we provide Python codes to easily download our data and access our annotations.
Variational Deep Image Restoration

This paper presents a new variational inference framework for image restoration and a convolutional neural network (CNN) structure that can solve the restoration problems described by the proposed framework. Earlier CNN-based image restoration methods primarily focused on network architecture design or training strategy with non-blind scenarios where the degradation models are known or assumed. For a step closer to real-world applications, CNNs are also blindly trained with the whole dataset, including diverse degradations. However, the conditional distribution of a high-quality image given a diversely degraded one is too complicated to be learned by a single CNN. Therefore, there have also been some methods that provide additional prior information to train a CNN. Unlike previous approaches, we focus more on the objective of restoration based on the Bayesian perspective and how to reformulate the objective. Specifically, our method relaxes the original posterior inference problem to better manageable sub-problems and thus behaves like a divide-and-conquer scheme. As a result, the proposed framework boosts the performance of several restoration problems compared to the previous ones. Specifically, our method delivers state-of-the-art performance on Gaussian denoising, real-world noise reduction, blind image super-resolution, and JPEG compression artifacts reduction.
Double-Stream Position Learning Transformer Network for Image Captioning

Image captioning has made significant achievement through developing feature extractor and model architecture. Recently, the image region features extracted by object detector prevail in most existing models. However, region features are criticized for the lacking of background and full contextual information. This problem can be remedied by providing some complementary visual information from patch features. In this paper, we propose a Double-Stream Position Learning Transformer Network (DSPLTN) which exploits the advantages of region features and patch features. Specifically, the region-stream encoder utilizes a Transformer encoder with Relative Position Learning (RPL) module to enhance the representations of region features through modeling the relationships between regions and positions respectively. As for the patch-stream encoder, we introduce convolutional neural network into the vanilla Transformer encoder and propose a novel Convolutional Position Learning (CPL) module to encode the position relationships between patches. CPL improves the ability of relationship modeling by combining the position and visual content of patches. Incorporating CPL into the Transformer encoder can synthesize the benefits of convolution in local relation modeling and self-attention in global feature fusion, thereby compensating for the information loss caused by the flattening operation of 2D feature maps to 1D patches. Furthermore, an Adaptive Fusion Attention (AFA) mechanism is proposed to balance the contribution of enhanced region and patch features. Extensive experiments on MSCOCO demonstrate the effectiveness of the double-stream encoder and CPL, and show the superior performance of DSPLTN.
A Federated Generalized Linear Model for Privacy-Preserving Analysis

In the last few years, federated learning (FL) has emerged as a novel alternative for analyzing data spread across different parties without needing to centralize them. In order to increase the adoption of FL, there is a need to develop more algorithms that can be deployed under this novel privacy-preserving paradigm. In this paper, we present our federated generalized linear model (GLM) for horizontally partitioned data. It allows generating models of different families (linear, Poisson, logistic) without disclosing privacy-sensitive individual records. We describe its algorithm (which can be implemented in the user’s platform of choice) and compare the obtained federated models against their centralized counterpart, which were mathematically equivalent. We also validated their execution time with increasing numbers of records and involved parties. We show that our federated GLM is accurate enough to be used for the privacy-preserving analysis of horizontally partitioned data in real-life scenarios. Further development of this type of algorithm has the potential to make FL a much more common practice among researchers.
Learning about COVID-19: sources of information, public trust, and contact tracing during the pandemic

To assess the association between public attitudes, beliefs, and information seeking about the COVID-19 pandemic and willingness to participate in contact tracing in Michigan.Using data from the quarterly Michigan State of the State survey conducted in May 2020 (n = 1000), we conducted multiple regression analyses to identify factors associated with willingness to participate in COVID-19 contact tracing efforts.Perceived threat of the pandemic to personal health (B = 0.59, p = <.00, Ref = No threat) and general trust in the health system (B = 0.17, p < 0.001), were the strongest positive predictors of willingness to participate in contact tracing. Concern about misinformation was also positively associated with willingness to participate in contact tracing (B = 0.30, p < 0.001; Ref = No concern). Trust in information from public health institutions was positively associated with willingness to participate in contact tracing, although these institutions were not necessarily the main sources of information about COVID-19.Policy makers can enhance willingness to participate in public health efforts such as contact tracing during infectious disease outbreaks by helping the public appreciate the seriousness of the public health threat and communicating trustworthy information through accessible channels.
Localized Sparse Incomplete Multi-View Clustering

Incomplete multi-view clustering, which aims to solve the clustering problem on the incomplete multi-view data with partial view missing, has received more and more attention in recent years. Although numerous methods have been developed, most of the methods either cannot flexibly handle the incomplete multi-view data with arbitrary missing views or do not consider the negative factor of information imbalance among views. Moreover, some methods do not fully explore the local structure of all incomplete views. To tackle these problems, this paper proposes a simple but effective method, named localized sparse incomplete multi-view clustering (LSIMVC). Different from the existing methods, LSIMVC intends to learn a sparse and structured consensus latent representation from the incomplete multi-view data by optimizing a sparse regularized and novel graph embedded multi-view matrix factorization model. Specifically, in such a novel model based on the matrix factorization, a l1 norm based sparse constraint is introduced to obtain the sparse low-dimensional individual representations and the sparse consensus representation. Moreover, a novel local graph embedding term is introduced to learn the structured consensus representation. Different from the existing works, our local graph embedding term aggregates the graph embedding task and consensus representation learning task into a concise term. Furthermore, to reduce the imbalance factor of incomplete multi-view learning, an adaptive weighted learning scheme is introduced to LSIMVC. Finally, an efficient optimization strategy is given to solve the optimization problem of our proposed model. Comprehensive experimental results performed on six incomplete multi-view databases verify that the performance of our LSIMVC is superior to the state-of-the-art IMC approaches. The code is available in https://github.com/justsmart/LSIMVC.
Quantifying and Mitigating Popularity Bias in Conversational Recommender Systems

Conversational recommender systems (CRS) have shown great success in accurately capturing a user's current and detailed preference through the multi-round interaction cycle while effectively guiding users to a more personalized recommendation. Perhaps surprisingly, conversational recommender systems can be plagued by popularity bias, much like traditional recommender systems. In this paper, we systematically study the problem of popularity bias in CRSs. We demonstrate the existence of popularity bias in existing state-of-the-art CRSs from an exposure rate, a success rate, and a conversational utility perspective, and propose a suite of popularity bias metrics designed specifically for the CRS setting. We then introduce a debiasing framework with three unique features: (i) Popularity-Aware Focused Learning to reduce the popularity-distorting impact on preference prediction; (ii) Cold-Start Item Embedding Reconstruction via Attribute Mapping, to improve the modeling of cold-start items; and (iii) Dual-Policy Learning, to better guide the CRS when dealing with either popular or unpopular items. Through extensive experiments on two frequently used CRS datasets, we find the proposed model-agnostic debiasing framework not only mitigates the popularity bias in state-of-the-art CRSs but also improves the overall recommendation performance.
Solve the Puzzle of Instance Segmentation in Videos: A Weakly Supervised Framework With Spatio-Temporal Collaboration

Instance segmentation in videos, which aims to segment and track multiple objects in video frames, has garnered a flurry of research attention in recent years. In this paper, we present a novel weakly supervised framework with Spatio-Temporal Collaboration for instance Segmentation in videos, namely STC-Seg. Concretely, STC-Seg demonstrates four contributions. First, we leverage the complementary representations from unsupervised depth estimation and optical flow to produce effective pseudo-labels for training deep networks and predicting high-quality instance masks. Second, to enhance the mask generation, we devise a puzzle loss, which enables end-to-end training using box-level annotations. Third, our tracking module jointly utilizes bounding-box diagonal points with spatio-temporal discrepancy to model movements, which largely improves the robustness to different object appearances. Finally, our framework is flexible and enables image-level instance segmentation methods to operate the video-level task. We conduct an extensive set of experiments on the KITTI MOTS and YT-VIS datasets. Experimental results demonstrate that our method achieves strong performance and even outperforms fully supervised TrackR-CNN and MaskTrack R-CNN. We believe that STC-Seg can be a valuable addition to the community, as it reflects the tip of an iceberg about the innovative opportunities in the weakly supervised paradigm for instance segmentation in videos.
An Efficient Strategy for Blood Diseases Detection Based on Grey Wolf Optimization as Feature Selection and Machine Learning Techniques

Acute Lymphoblastic Leukemia (ALL) is a cancer that infects the blood cells causing the development of lymphocytes in large numbers. Diagnostic tests are costly and very time-consuming. It is important to diagnose ALL using Peripheral Blood Smear (PBS) images, especially in the initial screening cases. Several issues affect the examination process such as diagnostic error, symptoms, and nonspecific nature signs of ALL. Therefore, the objective of this study is to enforce machine-learning classifiers in the detection of Acute Lymphoblastic Leukemia as benign or malignant after using the grey wolf optimization algorithm in feature selection. The images have been enhanced by using an adaptive threshold to improve the contrast and remove errors. The model is based on grey wolf optimization technology which has been developed for feature reduction. Finally, acute lymphoblastic leukemia has been classified into benign and malignant using K-nearest neighbors (KNN), support vector machine (SVM), naïve Bayes (NB), and random forest (RF) classifiers. The best accuracy, sensitivity, and specificity of this model were 99.69%, 99.5%, and 99%, respectively, after using the grey wolf optimization algorithm in feature selection. To ensure the effectiveness of the proposed model, comparative results with other classification techniques have been included.
Combination of GIS and Multivariate Analysis to Assess the Soil Heavy Metal Contamination in Some Arid Zones

Recent decades have witnessed a danger to food security as well as to human health because of pollutants’ negative impact on crop quality. An accurate estimate of the heavy metal concentrations in Egypt’s north Nile Delta is required to lower the high concentration levels of heavy metal in the soil as a means to develop a remediation strategy that stabilizes heavy metals in contaminated soil. Using a geo-accumulation index (I-geo), contamination factor (CF), Improved Nemerow’s Pollution Index (Pn), and Potential Ecological Risk Index (PERI), supported by GIS; principal component analysis (PCA), and cluster analysis, six heavy metals (As, Co, Cu, Ni, V, and Zn) were analyzed from 15 soil profile layers (61 soil samples) to determine the extent of the soil contamination in the area studied. The findings demonstrate the widespread I-geo contamination of As, Co, Cu, Ni, V, and Zn in different layers. The ranges for the I-geo values were from −8.2 to 5.3; 4.11 to 1.8; 6.4 to 1.9; −9.7 to 2.8; −6.3 to 2.9; and from −12.5 to 2.4 for As, Co, Cu, Ni, V, and Zn, respectively. I-geo categorization therefore ranged from uncontaminated to strongly/extremely contaminated. The CF values varied from 0.01 to 60.6; 0.09 to 5.17; 0.02 to 10.51; 0 to 10.51; 0.02 to 7.12; and 0 to 7.68 for As, Co, Cu, Ni, V, and Zn, respectively. In decreasing sequence, the CFs are arranged as follows: CF (As), CF (Ni), CF (Zn), CF (V), CF (Cu), and CF (Co). Most of the research region (71.9%) consisted of a class of moderately to heavily polluted areas. Additionally, a large portion of the study region (49.17%) has a very high risk of contamination, as per the results of the PERI index. The use of a correlation matrix, cluster analysis, and principal component analysis(PCA) to evaluate the variability in the soil’s chemical content revealed the impact from anthropogenic activities on the heavy metal concentration levels in the study area’s soil. The current findings reflect the poor quality of management in the research region, which led to the increase in the concentration of heavy metals in the soil. Decision-makers could use the outcomes from the spatial distribution maps for contaminants and their levels as a basis for creating heavy metal mitigation strategies.