Reviewerly/exHarmony huggingface dataset:
A comprehensive survey of covert communication techniques, limitations and future challenges

Data encryption aims to protect the confidentiality of data at storage, during transmission, or while in processing. However, it is not always the optimum choice as attackers know the existence of the ciphertext. Hence, they can exploit various weaknesses in the implementation of encryption algorithms and can thus decrypt or guess the related cryptographic primitives. Moreover, in the case of proprietary applications such as online social networks, users are at the mercy of the vendor's security measures. Therefore, users are vulnerable to various security and privacy threats. Contrary to this, covert communication techniques hide the existence of communication and thus achieve security through obscurity and hidden communication channels. Over the period, there has been a significant advancement in this field. However, existing literature fails to encompass all the aspects of covert communications in a single document. This survey thus endeavors to highlight the latest trends in covert communication techniques, related challenges, and future directions.
Lung cancer detection by using probabilistic majority voting and optimization techniques

Abstract The number of people dying from lung cancer in the world is increasing day by day. Therefore, early diagnosis of lung cancer holds a prominent position for the recovery of patients. Chest computed tomography scan images that are clinical adjuncts are used in the diagnosis of lung cancer. Herein, the proposed method automatically diagnoses lung cancer using machine learning algorithms, probabilistic majority voting, and optimization techniques. The paper uses images from Adenocarcinoma Left Lower Lobe, Large Cell Carcinoma Left Hilum, Normal, and Squamous Cell Carcinoma Left Hilum subjects. 80% and 20% of these images are used in the training and validation set, respectively, and there are 1000 images in the data set. For each image used in the study, image features are extracted using the Bag of Features method (2000 features). Linear Discriminant, Optimizable Support Vector Machine, and Optimizable K‐Nearest Neighbor (KNN) classifiers are trained using these features. Afterward, the new image label is estimated in two steps. First, if the two or three classifier outputs are the same, the image label is detected using the majority voting technique. Second, if the classifier outputs are not the same, the image label is determined according to the most successful classifier, Optimizable KNN. To better experience the majority voting technique used in the proposed method, its detailed theoretical framework is given in the study. Additionally, the Graphical User Interface application of the proposed method has been designed to save time and reduce the workload of expert radiologists. From the results, it is seen that the Accuracy (%), Error (%), Sensitivity (%), Specificity (%), Precision (%), F 1 Score (%), Matthews Correlation Coefficient, and Kappa of the proposed method are equal to 99.2837, 0.007163, 99.2571, 99.9139, 99.3486, 99.2816, 0.98738, and 0.94965 respectively.
Generalized Focal Loss: Towards Efficient Representation Learning for Dense Object Detection

Object detection is a fundamental computer vision task that simultaneously predicts the category and localization of the targets of interest. Recently one-stage (also termed "dense") detectors have gained much attention over two-stage ones due to their simple pipeline and friendly application to end devices. Dense object detectors basically formulate object detection as dense classification and localization (i.e., bounding box regression). The classification is usually optimized by Focal Loss and the box location is commonly learned under Dirac delta distribution. A recent trend for dense detectors is to introduce an individual prediction branch to estimate the quality of localization, which facilitates the classification to improve detection performance. This paper delves into the representations of the above three fundamental elements: quality estimation, classification and localization. Three problems are discovered in existing practices, including (1) the inconsistent usage of the quality estimation and classification between training and inference, (2) the inflexible Dirac delta distribution for localization, and (3) the deficient and implicit guidance for accurate quality estimation. To address these problems, we design new representations for these elements. Specifically, we merge the quality estimation into the class prediction vector to form a joint representation, use a vector to represent arbitrary distribution of box locations, and extract discriminant feature descriptors from the distribution vector for more reliable quality estimation. The improved representations eliminate the inconsistency risk and accurately depict the flexible distribution in real data, but contain continuous labels, which is beyond the scope of Focal Loss. We then propose Generalized Focal Loss (GFocal) that generalizes Focal Loss from its discrete form to the continuous version for successful optimization. Extensive experiments demonstrate the effectiveness of our method, without sacrificing the efficiency both in training and inference. Based on GFocal, we construct a considerably fast and lightweight detector termed NanoDet under mobile settings, which is 1.8 AP higher, 2x faster and 6x smaller than scaled YoloV4-Tiny.
Deep learning and natural language processing in computation for offensive language detection in online social networks by feature selection and ensemble classification techniques

Offensive communications have made their way into social media posts. Using computational algorithms to distinguish objectionable content is one of the most effective ways to deal with this problem. One of the most effective approaches to deal with this issue is to use computational methods to distinguish undesirable content. This research aims to tackle MOLD_DL (Multilingual Offensive Language Detection using deep learning) techniques and natural language processing used in feature selection and classification. Here the dataset has been collected from YouTube, Twitter and Facebook, which has been pre-processed for noise removal, filtering and removing the stop words and segmented. The feature selection has been carried out for segmented data using Fuzzy based convolutional neural network (FCNN). Then the extraction of selected features and classification has been carried out using ensemble architecture of Bi-LSTM model with Naïve Bayes architecture hybrid with Support Vector Machines (SVM). Evaluation of offensive language detection is classified automatically based on the emotions of the text. Here the experimental analysis has been carried out for YouTube, Twitter and Facebook datasets in terms of accuracy of 98%, precision of 95%, recall of 90%, F-1 score of 92.5% and RMSE of 45% with the confusion matrix in detecting offensive text of various languages.
Temporal knowledge graph representation learning with local and global evolutions

Temporal knowledge graph (TKG) representation learning aims to project entities and relations in TKGs to a low-dimensional vector space while preserving the evolutionary nature of TKGs. Most existing methods treat knowledge that happens at different times separately, which fails to explore how temporal knowledge graphs evolve over time. Actually, TKGs should evolve both on the local and global structures. The local structure evolution describes the formation process of the graph structure in a detailed manner, while the global structure evolution refers to the dynamic topology (e.g., community partition) of the graph, which is derived from the continuous formation process. Both are key factors for understanding the evolutionary nature of the TKGs. Unfortunately, little attention has been given to this area of research. In this paper, we propose a new TKG representation learning framework with local and global structure evolutions, named EvoExplore. Specifically, we define the local structure evolution as an establishment process of the relations between the entities, and propose a hierarchical-attention-based temporal point process to capture the formation process of the graph structure in a fine-grained manner. For global structure evolution, we propose a novel soft modularity parameterized by the entity representations to capture the dynamic community partition of the TKGs. Finally, we employ a multi-task loss function to jointly optimize the above two parts, which allows EvoExplore to learn the mutual influences of the local and global structure evolutions. Experimental results on three real-world datasets demonstrate the superiority of EvoExplore compared with the baseline methods. Code is available at https://github.com/zjs123/EvoExplore and https://github.com/zjs123/EvoExplore_MindSpore.
Explainable anomaly detection framework for predictive maintenance in manufacturing systems

To conduct preemptive essential maintenance, predictive maintenance detects the risk of unexpected shutdowns in a manufacturing system, thereby ensuring operational continuity. Traditional methods that heavily rely on the domain knowledge of expert engineers to detect any abnormal status in processing facilities are extremely time-consuming and domain-dependent. Conversely, recently studied data-driven approaches without much domain knowledge have yielded fairly good performance. However, most only identify whether the current status is normal or abnormal and do not offer any explanations or analyses. In this paper, we propose a real-time explainable anomaly detection framework for predictive maintenance in a manufacturing system. Various well-known anomaly detection algorithms are investigated to construct a framework suitable for shutdown prognosis. In addition, model interpretation techniques are also employed to provide a reasonable explanation for a detected shutdown. The experimental results on a real-world dataset derived from a chemical process show that the proposed framework could identify abnormal signs early and derive significant causes for each detected shutdown.
Segmentation only uses sparse annotations: Unified weakly and semi-supervised learning in medical images

Since segmentation labeling is usually time-consuming and annotating medical images requires professional expertise, it is laborious to obtain a large-scale, high-quality annotated segmentation dataset. We propose a novel weakly- and semi-supervised framework named SOUSA (Segmentation Only Uses Sparse Annotations), aiming at learning from a small set of sparse annotated data and a large amount of unlabeled data. The proposed framework contains a teacher model and a student model. The student model is weakly supervised by scribbles and a Geodesic distance map derived from scribbles. Meanwhile, a large amount of unlabeled data with various perturbations are fed to student and teacher models. The consistency of their output predictions is imposed by Mean Square Error (MSE) loss and a carefully designed Multi-angle Projection Reconstruction (MPR) loss. Extensive experiments are conducted to demonstrate the robustness and generalization ability of our proposed method. Results show that our method outperforms weakly- and semi-supervised state-of-the-art methods on multiple datasets. Furthermore, our method achieves a competitive performance with some fully supervised methods with dense annotation when the size of the dataset is limited.
Deep Learning Approach for Damage Classification Based on Acoustic Emission Data in Composite Materials

Damage detection and the classification of carbon fiber-reinforced composites using non-destructive testing (NDT) techniques are of great importance. This paper applies an acoustic emission (AE) technique to obtain AE data from three tensile damage tests determining fiber breakage, matrix cracking, and delamination. This article proposes a deep learning approach that combines a state-of-the-art deep learning technique for time series classification: the InceptionTime model with acoustic emission data for damage classification in composite materials. Raw AE time series and frequency-domain sequence data are used as the input for the InceptionTime network, and both obtain very high classification performances, achieving high accuracy scores of about 99%. The InceptionTime network produces better training, validation, and test accuracy with the raw AE time series data than it does with the frequency-domain sequence data. Simultaneously, the InceptionTime model network shows its potential in dealing with data imbalances.
E2E Fidelity Aware Routing and Purification for Throughput Maximization in Quantum Networks

This paper studies reliable teleportation of quantum bits (called qubits) in a quantum data network with multiple sources (S) and destinations (D) as well as repeaters. To teleport qubits for a SD pair reliably, not only an entanglement path for the SD pair, but also appropriate purification of the links along the path is required to ensure that the end-to-end (E2E) fidelity of the established entanglement connections is high enough.This is the first work on quantifying the E2E fidelity, and also using this E2E fidelity to determine critical links to achieve the most resource efficient purification. A novel approach called E2E Fidelity aware Routing and Purification (EFiRAP) is proposed to maximize network throughput, i.e., the number of entanglement connections among multiple SD pairs, with each connection having an E2E fidelity above a given required threshold. EFiRAP accomplishes this goal by first preparing multiple candidate entanglement paths and determining optimal purification schemes, and then selecting the final set of entanglement paths that can maximize network throughput under the given quantum resource constraints. Existing works only ensured the fidelity of individual links, rather than the E2E fidelity is above a given threshold. Extensive simulations show that the proposed EFiRAP can enhance network throughput by about 50% when compared with the state-of-the-art approach.
Mathematical Modeling of Credit Scoring System Based on the Monge-Kantorovich Problem

In this article the issues of further improvement of the credit system in order to ensure the implementation of the Decree of the President of the Republic of Uzbekistan dated May 12, 2020 No 5992 "On the Strategy of Banking Reform in the Republic of Uzbekistan for 2020-2025" studied. Problems of information security in the credit system and their solutions, issues of secure storage of personal data of borrowers through blockchain technology, as well as effective modeling of lending and borrower relations, lending institutions on the basis of established criteria recalculation model and quantitative analysis of the assessment, the application of the Monge-Kantorovich problem in the development of the credit scoring model, the process of forming a credit scoring model based on the model applied to the Monge-Kantorovich transport problem. The credit scoring model based on the Monge-Kantorovich problem is similar to the Bayesian network-based credit scoring model, and calculations are performed using elements of probability theory. Borrowers were evaluated using optimal evaluation criteria. As a result, the process of deciding whether or not to grant a loan to a borrower has been completed. The results obtained were analyzed and a credit scoring model based on the Monge-Kantorovich problem was proposed.
BM-Net: CNN-Based MobileNet-V3 and Bilinear Structure for Breast Cancer Detection in Whole Slide Images

Breast cancer is one of the most common types of cancer and is the leading cause of cancer-related death. Diagnosis of breast cancer is based on the evaluation of pathology slides. In the era of digital pathology, these slides can be converted into digital whole slide images (WSIs) for further analysis. However, due to their sheer size, digital WSIs diagnoses are time consuming and challenging. In this study, we present a lightweight architecture that consists of a bilinear structure and MobileNet-V3 network, bilinear MobileNet-V3 (BM-Net), to analyze breast cancer WSIs. We utilized the WSI dataset from the ICIAR2018 Grand Challenge on Breast Cancer Histology Images (BACH) competition, which contains four classes: normal, benign, in situ carcinoma, and invasive carcinoma. We adopted data augmentation techniques to increase diversity and utilized focal loss to remove class imbalance. We achieved high performance, with 0.88 accuracy in patch classification and an average 0.71 score, which surpassed state-of-the-art models. Our BM-Net shows great potential in detecting cancer in WSIs and is a promising clinical tool.
A Unified Understanding of Deep NLP Models for Text Classification

The rapid development of deep natural language processing (NLP) models for text classification has led to an urgent need for a unified understanding of these models proposed individually. Existing methods cannot meet the need for understanding different models in one framework due to the lack of a unified measure for explaining both low-level (e.g., words) and high-level (e.g., phrases) features. We have developed a visual analysis tool, DeepNLPVis, to enable a unified understanding of NLP models for text classification. The key idea is a mutual information-based measure, which provides quantitative explanations on how each layer of a model maintains the information of input words in a sample. We model the intra- and inter-word information at each layer measuring the importance of a word to the final prediction as well as the relationships between words, such as the formation of phrases. A multi-level visualization, which consists of a corpus-level, a sample-level, and a word-level visualization, supports the analysis from the overall training set to individual samples. Two case studies on classification tasks and comparison between models demonstrate that DeepNLPVis can help users effectively identify potential problems caused by samples and model architectures and then make informed improvements.
Data Driven Model Estimation for Aerial Vehicles: A Perspective Analysis

Unmanned Aerial Vehicles (UAVs) are important tool for various applications, including enhancing target detection accuracy in various surface-to-air and air-to-air missions. To ensure mission success of these UAVs, a robust control system is needed, which further requires well-characterized dynamic system model. This paper aims to present a consolidated framework for the estimation of an experimental UAV utilizing flight data. An elaborate estimation mechanism is proposed utilizing various model structures, such as Autoregressive Exogenous (ARX), Autoregressive Moving Average exogenous (ARMAX), Box Jenkin’s (BJ), Output Error (OE), and state-space and non-linear Autoregressive Exogenous. A perspective analysis and comparison are made to identify the salient aspects of each model structure. Model configuration with best characteristics is then identified based upon model quality parameters such as residual analysis, final prediction error, and fit percentages. Extensive validation to evaluate the performance of the developed model is then performed utilizing the flight dynamics data collected. Results indicate the model’s viability as the model can accurately predict the system performance at a wide range of operating conditions. Through this, to the best of our knowledge, we present for the first time a model prediction analysis, which utilizes comprehensive flight dynamics data instead of simulation work.
Multimodal Federated Learning on IoT Data

Federated learning is proposed as an alternative to centralized machine learning since its client-server structure provides better privacy protection and scalability in real-world applications. In many applications, such as smart homes with Internet-of-Things (IoT) devices, local data on clients are generated from different modalities such as sensory, visual, and audio data. Existing federated learning systems only work on local data from a single modality, which limits the scalability of the systems. In this paper, we propose a multimodal and semi-supervised federated learning framework that trains autoencoders to extract shared or correlated representations from different local data modalities on clients. In addition, we propose a multimodal FedAvg algorithm to aggregate local autoencoders trained on different data modalities. We use the learned global autoencoder for a downstream classification task with the help of auxiliary labelled data on the server. We empirically evaluate our framework on different modalities including sensory data, depth camera videos, and RGB camera videos. Our experimental results demonstrate that introducing data from multiple modalities into federated learning can improve its classification performance. In addition, we can use labelled data from only one modality for supervised learning on the server and apply the learned model to testing data from other modalities to achieve decent <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$F_{1}$</tex> scores (e.g., with the best performance being higher than 60%), especially when combining contributions from both unimodal clients and multimodal clients.
Multi-granularity episodic contrastive learning for few-shot learning

Few-shot learning (FSL) aims at fast adaptation to novel classes with few training samples. Among FSL methods, meta-learning and transfer learning-based methods are the most powerful ones. However, most of them rely to some extent on cross-entropy loss, which leads to representations that are overly concerned with the classes already seen, and in turn leads to sub-optimal generalization on novel classes. In this study, we are inspired by meta-learning and transfer learning-based methods and believe good feature representations are vital for FSL. To this end, we propose a new multi-granularity episodic contrastive learning method (MGECL) that introduces contrastive learning into the episode training process. In particular, by enforcing our proposed contrastive loss on both class and instance granularities, the model is able to extract category-independent discriminative patterns and learn richer and more transferable feature representations. Extensive experiments demonstrate that our proposed method achieves state-of-the-art performance on three popular few-shot benchmarks. Our code is available at https://github.com/z1358/MGECL_PR.
Voice bots on the frontline: Voice-based interfaces enhance flow-like consumer experiences &amp; boost service outcomes

Abstract Voice-based interfaces provide new opportunities for firms to interact with consumers along the customer journey. The current work demonstrates across four studies that voice-based (as opposed to text-based) interfaces promote more flow-like user experiences, resulting in more positively-valenced service experiences, and ultimately more favorable behavioral firm outcomes (i.e., contract renewal, conversion rates, and consumer sentiment). Moreover, we also provide evidence for two important boundary conditions that reduce such flow-like user experiences in voice-based interfaces (i.e., semantic disfluency and the amount of conversational turns). The findings of this research highlight how fundamental theories of human communication can be harnessed to create more experiential service experiences with positive downstream consequences for consumers and firms. These findings have important practical implications for firms that aim at leveraging the potential of voice-based interfaces to improve consumers’ service experiences and the theory-driven “conversational design” of voice-based interfaces.