Reviewerly/exHarmony huggingface dataset:
Generalized b-Symbol Weights of Linear Codes and b-Symbol MDS Codes

Generalized pair weights of linear codes are generalizations of minimum symbol-pair weights, which were introduced by Liu and Pan (2022) recently. Generalized pair weights can be used to characterize the ability of protecting information in the symbol-pair read wire-tap channels of type II. In this paper, we introduce the notion of generalized <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$b$ </tex-math></inline-formula> -symbol weights of linear codes over finite fields, which is a generalization of generalized Hamming weights and generalized pair weights. We obtain some basic properties and bounds of generalized <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$b$ </tex-math></inline-formula> -symbol weights which are called Singleton-like bounds for generalized <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$b$ </tex-math></inline-formula> -symbol weights. As examples, we calculate the generalized weight matrices for simplex codes and Hamming codes. We provide a necessary and sufficient condition for a linear code to be a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$b$ </tex-math></inline-formula> -symbol MDS code by using the generator matrix and the parity check matrix of this linear code. Finally, a necessary and sufficient condition of a linear isomorphism preserving <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$b$ </tex-math></inline-formula> -symbol weights between two linear codes is obtained. As a corollary, we get the classical MacWilliams extension theorem when <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$b=1$ </tex-math></inline-formula> .
Building Datacube for Maritime Applications on Discrete Global Grid System

Geospatial analysis often requires heterogeneous datasets of large volume or coverage which are not well suited to traditional data structures. Data cubes built upon Discrete Global Grid Systems (DGGS) seems to be an efficient way to manage big geospatial data. DGGS provide a uniform multiresolution spatial reference system and an efficient framework for building data cubes. The latter enables users to structure various data and provides analysis ready data for data processing, machine learning and visualization. In this study we introduce the methodology of data cube creation for marine research in Russian Arctic seas with H3 DGGS package and PostgreSQL using the R package “sf’ and Python module for H3 “h3-py”. Our experience is useful for organization and conducting large-regional geospatial analysis. Further work is identified to expand and develop methods of marine data cubes analysis.
Inverse diffraction grating problems as optimization tasks: from naïve to Bayes approach

The authors consider the inverse conical diffraction problem as an optimization problem. We apply several techniques: the genetic algorithm, the stochastic gradient descent method, the Bayesian approach, and the neural network approach. The boundary integral equation method is utilized to solve the direct problem. Using a range of numerical experiments, we demonstrate that the mixed Bayesian with stochastic gradient descent optimization technique allows one to obtain the solution to the inverse diffraction grating problem in the most convenient and fast way possible. The authors provide a detailed configuration description necessary for a successful optimization process.
Multi-model Lightweight Action Recognition with Group-Shuffle Graph Convolutional Network

Skeleton-based action recognition has attracted increasing attention in recent years. However, current skeleton-based action recognition models still exhausted huge parameters and computations to achieve superior accuracy. Despite effectiveness, the huge parameter/computation cost degrades the application of action recognition models on edge devices, such as mobile. How to obtain high accuracy while maintaining low computational/parameter efficiency remains a difficult yet significant challenge. In light of the above issues, we propose group-shuffle graph convolutional networks (GS-GCNs) for lightweight skeleton-based action recognition in videos. Specifically, GS-GCNs consist of two sequential modules: group-shuffle graph convolutional module (GSC) and depthwise-shuffle separable convolution module (DSC). GSC divides input features into several groups through feature channels, then shuffles the groups and sends each group into a discrete sub GCN to model relationships between each node in the skeleton. After that, DSC completes depthwise separable convolution on each group and shuffles each group. The final output is the concatenation of all group features. Essentially, through a shuffle-grouping strategy, GS-GCNs could significantly reduce the computational/parameter cost while obtaining competitive detection ability through an architecture of iterations. Extensive experiments show that GS-GCN achieves excellent performance on both NTU-RGB+D and NTU-RGB+D 120 datasets with an order of smaller model size than most previous works.
Unsupervised TCN-AE-Based Outlier Detection for Time Series With Seasonality and Trend for Cellular Networks

Timely identification of outliers occurring in key performance indicators (KPIs) of mobile cellular networks is crucial for prompt action to unexpected events. The KPIs of cellular networks typically exhibit seasonality and trend effects and these make the detection of outliers challenging. In this paper, an online unsupervised deep machine learning (DML) algorithm for outlier detection for time series with seasonality and trend is proposed. The proposed algorithm utilizes a neural network based on a temporal convolution network (TCN) and an autoencoder (AE) to reconstruct a time series that captures the normal behavior of the input data and then the reconstruction errors between the input and reconstructed time series at the output of the TCN-AE network are used for outlier detection. To train the TCN-AE network to learn the normal behavior of the input time series, we propose a two-step training process to overcome the presence of outliers in the training data. In addition, a novel loss function specially designed to address the seasonality and trend effects of the time series is proposed. Furthermore, a pre-processing technique is used to combat the adverse trend effect which might cause false alarms in outlier detection. The performance of the proposed TCN-AE-based outlier detection algorithm is evaluated using synthetic time series, Yahoo Webscope dataset and real time series of KPIs of mobile cellular networks. The results show that the proposed TCN-AE-based outlier detection algorithm achieves better detection accuracy in terms of F-score than other DML-based algorithms such as long short-term memory (LSTM)-AE-based algorithm and the convolutional neural network (CNN) based algorithm.
Performance Evaluation System Based on Multi-Indicators for Signal Recognition

Nowadays, the electromagnetic environment is becoming more and more complex, and it is increasingly difficult to accurately identify electromagnetic signals. At the same time, the quality of models and algorithms largely determines the final result of electromagnetic signal recognition, and a comprehensive and holistic evaluation system is needed to assess the quality of model or algorithm performance in the field of electromagnetic signal recognition. To address this phenomenon, this paper uses different machine learning models and AdaBoost algorithms to identify 100 classes of WiFi radiation source electromagnetic signals. In this paper, the classification performance, complexity and robustness of the models are evaluated comprehensively in three aspects respectively, and a multi-dimensional evaluation system for signal recognition is constructed, and the differences between different model algorithms in signal recognition effects are compared and analyzed, and the correlations between each evaluation index in different dimensions are explored. The experimental results show that different machine learning models and algorithms have different recognition effects on electromagnetic signals, among which the recognition accuracy of ResNet can reach more than 90%, but its computational complexity is high and easily affected by noise. DNN has poor recognition effect and the highest computational complexity, but it is not easily affected by noise. The AdaBoost algorithm does not necessarily improve the recognition classification accuracy of the underlying classifier. The evaluation system established in this paper is meaningful for assessing the performance of models and algorithms.
Universal Domain Adaptation without Source Data for Remote Sensing Image Scene Classification

Existing domain adaptation (DA) approaches are usually not well suited for practical DA scenarios of remote sensing image classification, since these methods (such as unsupervised DA) rely on rich prior knowledge about the relationship between label sets of source and target domains, and source data are usually not accessible in many cases due to the privacy or confidentiality issues.To this end, we propose a novel source data generation-based universal domain adaptation (SDG-UniDA) model, which includes two parts, i.e., the stage of source data generation and the stage of model adaptation.The first stage is to estimate the conditional distribution of source data from the pre-trained model using the knowledge of class-separability in the source domain and then to synthesize the source data.With this synthetic source data in hand, it becomes a universal DA task that requires no prior knowledge on the label sets.A novel transferable weight is proposed to distinguish the shared and private label sets to each domain, thereby promoting the adaptation in the automatically discovered shared label set and recognizing the "unknown" samples successfully.Empirical results show that SDG-UniDA is effective and practical in this challenging setting for remote sensing image scene classification.
Theoretical analysis of medical healthcare chatbot system

A Chatbot is a computer program which enables us to make communication through our text or voice and get reply through Artificial Intelligence (AI) technology. A chatbot can be used in different applications like gaming, customer service and e-commerce site. Chatbots are developed to interact with received messages automatically presenting the experience of human-to-human interaction in digital mode. Chatbots provide responses to messages containing keywords that are matched using machine learning technique to adapt the responses to fit the respective situations. A developing number of hospitals, private health centers, and medical clinics currently utilize Chatbots on their internet sites. These chatbots connect with the potential patients to visit the location of the medical centers, to help themselves discovering the right specialists for their treatment, to book appointments with the doctors, and to get access to the right treatment for cure. Using AI in a domain where human lives have the chances of becoming a question brings up and act as replacement to the issues of the need to assign a human staff to a task. This theoretical analysis AI based healthcare chatbot system will help hospitals to offer healthcare online support 24 x 7, answering intense as well as general queries appropriately.
Improving the Generalization of MAML in Few-Shot Classification via Bi-Level Constraint

Few-shot classification (FSC), which aims to identify novel classes in the presence of a few labeled samples, has drawn vast attention in recent years. One of the representative few-shot classification methods is model-agnostic meta-learning (MAML), which focuses on learning an initialization that can quickly adapt to novel categories with a few annotated samples. However, due to insufficient samples, MAML can easily fall into the dilemma of overfitting. Most existing MAML-based methods either improve the inner-loop update rule to achieve better generalization or constrain the outer-loop optimization to learn a more desirable initialization, without considering improving the two optimization processes jointly, resulting in unsatisfactory performance. In this paper, we propose a bi-level constrained MAML (BLC-MAML) method for few-shot classification. Specifically, in the inner-loop optimization, we introduce a supervised contrastive loss to constrain the adaptation procedure, which can effectively increase the intra-class aggregation and inter-class separability, thus improving the generalization of the adapted model. In the case of the outer loop, we propose a cross-task metric (CTM) loss to constrain the adapted model to perform well on the different few-shot task. The CTM loss can enforce the adapted model to learn more discriminative and generalized feature representations, further boosting the generalization of the learned initialization. By simultaneously constraining the bi-level optimization procedure, the proposed BLC-MAML can learn an initialization with better generalization. Extensive experiments on several FSC benchmarks show that our method can effectively improve the performance of MAML under both the within-domain and cross-domain settings, and also perform favorably against the state-of-the-art FSC algorithms.
CycDA: Unsupervised Cycle Domain Adaptation to Learn from Image to Video

Although action recognition has achieved impressive results over recent years, both collection and annotation of video training data are still time-consuming and cost intensive. Therefore, image-to-video adaptation has been proposed to exploit labeling-free web image source for adapting on unlabeled target videos. This poses two major challenges: (1) spatial domain shift between web images and video frames; (2) modality gap between image and video data. To address these challenges, we propose Cycle Domain Adaptation (CycDA), a cycle-based approach for unsupervised image-to-video domain adaptation. We leverage the joint spatial information in images and videos on the one hand and, on the other hand, train an independent spatio-temporal model to bridge the modality gap. We alternate between the spatial and spatio-temporal learning with knowledge transfer between the two in each cycle. We evaluate our approach on benchmark datasets for image-to-video as well as for mixed-source domain adaptation achieving state-of-the-art results and demonstrating the benefits of our cyclic adaptation.
A Multivariate KPIs Anomaly Detection Framework With Dynamic Balancing Loss Training

Anomaly detection on multivariate KPIs (Key Performance Indicators, such as CPU utilization, sockets status, and HTTP requests per second) is of utmost importance to the systems’ reliability. Unsupervised methods have been of considerable interests and have significantly progressed due to their superior effectiveness. However, the state-of-art unsupervised anomaly detection methods still suffer from high false or missed alarm rates. To this end, in this paper, we propose MM, a practical <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M</b> ultivariate KPIs anomaly detection framework following the principles of <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M</b> ulti-task learning with the proposed dynamic balancing loss function. To capture KPIs’ characteristics to the most extent, we simultaneously train multiple sequential autoencoders with different connections based on a designed semi-Random Connection Recurrent Neural Network (sRC-RNN). These autoencoders can be treated as different reconstruction tasks while training. Furthermore, we propose a dynamic loss function to adaptively balance the tasks’ weights. Extensive experiments show that MM outperforms the state-of-art unsupervised multivariate KPIs anomaly detection algorithms and achieves an average F1-score of 0.95 on two public machine-level KPIs datasets and 0.96 on an internal container-level KPIs dataset.
Defuse: A Data Annotator and Model Builder for Software Defect Prediction

We propose a language-agnostic tool for software defect prediction, called DEFUSE. The tool automatically collects and classifies failure data, enables the correction of those classifications, and builds machine learning models to detect defects based on those data. We instantiated the tool in the scope of Infrastructure-as-Code, the DevOps practice enabling management and provisioning of infrastructure through the definition of machine-readable files. We present its architecture and provide examples of its application.Demo video: https://youtu.be/37mmLdCX3jU.
Cross-platform testing of quantum computing platforms

Quantum computing has been attracting the attention of both applied research and companies. Continuous progress on fundamental hardware technology promises to bring us more reliable and large-scale quantum computers on which to run the next generation of quantum algorithms. These programs are compiled and executed on dedicated platforms, and similarly to classical programs, a large effort is required to test these platforms and create a robust software toolchain.
Multi-Attention Cascade Model Based on Multi-Head Structure for Image-Text Retrieval

Image-text retrieval aims to find semantically relevant results from another modality when given an image or a sentence as a query. It remains challenging because the non-semantic information in the current image representation affects the alignment of the two-modal feature. In addition, there is a lack of topic information combined with textual features in the text representation. To solve these problems, we propose a novel Multi-Attention Cascade Model (MACM) for cross-modal image-text retrieval. Specifically, we first design a Multi-Attention Cascade Transformer Encoder (MACTE) to infer less semantically relevant information in images, which cascades the self-attention mechanism, the channel attention mechanism and the spatial attention mechanism based on the multi-head structure. Therefore, it can filter unimportant regions in the image in different dimensions and highlight important regions. Then, to solve the problem of losing topic information due to too long sentences, we design a Central Word Enhancer (CWE), which uses Gate Recurrent Unit and Transformer Encoder to extract multi-granularity text features jointly capture long-distance dependencies, thereby effectively preventing the deviation of finding central words which means topic information. Finally, we introduce a graph structure as an inter-modality interaction module to represent the features of a single modality respectively. Experiments on Flickr30k and MSCOCO datasets demonstrate the effectiveness of our method, which achieves the state-of-the-art result for text-image retrieval. The Recall@1 on Flickr30k by our method improves image-to-text retrieval by 5% and text to image retrieval by 5.1%. The Recall@1 on MSCOCO improves image-to-text retrieval by 2.5% and text to image retrieval by 2.8%.
Source Printer Identification with Microscopic Printing using Deep Learning

Due to the advent of digital technologies, it has become much easier to falsify printed documents for malicious purposes. Therefore, it is essential to research and develop efficient algorithms to distinguish authentic printed documents from fakes. A source printer identification technique is one of the methods to trace the source of the documents, thereby protecting the reliability and integrity of printed documents. This paper proposes a method to identify the source printers using Deep Learning approach. Particularly, we utilise a Convolutional Neural Network for features extraction from printed microscopic patterns of different printing sources. These extracted features are fed into a multi-class Support Vector Machine and Random Forest classification for the source identification. The actual printed dot patterns from common printing technologies such as conventional offset, waterless offset, electrophotographic are used to evaluate the effectiveness of our proposed approach. This study highlights the fact that printed patterns in gray images and features based on Convolutional Neural Network extraction help to reserve much more distinguishable characteristics of printing sources. The hybrid classification algorithms outperform the previous studies using hand-extracted features, with more than 99 % of samples correctly classified in the test set.
Glomeruli Identification in Renal Biopsy using Deep Learning Approaches

Deep learning algorithms are having a significant influence on the health sector. The digitization of biopsy data in the form of Whole Slide Images (WSIs) has enabled the automation of illness prediction and detection in the human body. One use of deep learning in healthcare is the identification of Glomeruli. Glomeruli are blood vessel clusters that enter the kidneys and act as blood filters. Each kidney has around one million Glomeruli. Glomerulosclerosis is a sign of a number of chronic and end-stage renal disorders. Glomerulosclerosis is a type of renal damage induced by scarring of the Glomeruli, which is referred to be sclerotic in nature. Typically, glomerular disorders cause glomerular damage by allowing protein and red blood cells to seep into urine. It is a sign of a number of chronic and end-stage renal disorders. Identification of sclerotic Glomeruli helps in assessing the functioning of the kidneys and can aid nephropathology research. The manual identification and classification of Glomeruli from microscopy images requires highly trained experts and is manual, labor intensive and nonstandardized. The counting of Glomeruli is a tedious and time consuming process. Hence, there is a need for automation in this field which can be addressed with the help of state of the art segmentation and classification algorithms. This paper proposes a UNet based segmentation model followed by an Inception V3 architecture for the classification of Glomeruli into sclerosed and normal. Comparison with other UNet architectures is carried out and the results show that our system has performed better achieving an overall accuracy of about 96%.
Neural Networks in Imandra: Matrix Representation as a Verification Choice

The demand for formal verification tools for neural networks has increased as neural networks have been deployed in a growing number of safety-critical applications. Matrices are a data structure essential to formalising neural networks. Functional programming languages encourage diverse approaches to matrix definitions. This feature has already been successfully exploited in different applications. The question we ask is whether, and how, these ideas can be applied in neural network verification. A functional programming language Imandra combines the syntax of a functional programming language and the power of an automated theorem prover. Using these two key features of Imandra, we explore how different implementations of matrices can influence automation of neural network verification.