Reviewerly/exHarmony huggingface dataset:
LPI Waveform Recognition Using Adaptive Feature Construction and Convolutional Neural Networks

Low probability of intercept (LPI) radar waveform recognition is one of the crucial functions in electronic intelligence systems. Advances in artificial intelligence promote the performance of the LPI waveform recognition with various signal features defined with analytical expressions. However, noisy LPI waveform recognition is still a challenge for traditional approaches even with noise elimination techniques, especially for the heavily contaminated noisy LPI signals. Recently, the adaptive analysis techniques, such as variational mode decomposition (VMD) and empirical mode decomposition (EMD) provide potential methods that explore the inherent features of signals and formulate adaptive features for signal recognition. In this article, we propose an adaptive feature construction framework that utilizes both the adaptive features (via VMD and EMD) and predefined analytical features (via Wigner–Ville distribution, Choi–William distribution, and wavelet analysis) to construct the fusion feature, which is further applied on the convolutional neural networks-based LPI waveform recognition system. The experimental results show that the proposed feature adaptive LPI network-based exploitation (FALPINE) approach achieves higher probability of correct classification than the state-of-the-art works, which demonstrates the superior performance of the proposed approach.
Reversible Data Hiding in Encrypted Images Based on Time-Varying Huffman Coding Table

Image privacy protection and management face many challenges, such as privacy disclosure, copyright dispute, and traceability difficulties, with the development of big data. Reversible data hiding in encrypted images (RDHEI) has been widely considered as an effective means to tackle these challenges. In this paper, a RDHEI based on time-varying Huffman coding table (TV-HCT) method is proposed to improve the security, embedding rate (ER) and efficiency. First, the initial HCT is generated according to the prediction errors of an image, which can improve compression performance. And then, the TV-HCT is obtained by scrambling equal-length codewords in the initial HCT using timestamps. This realizes the time variability of compression coding stream (CCS) of an image in that the image TV-HCT has large change space. Analysis shows that the average change space of TV-HCT in UCID is 3.97×10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">327</sup> , and the average ER of three databases is more than 0.44 bpp higher than the existing algorithms. Finally, the CCS is encrypted using the designed index class scrambling method to balance complexity and security. The proposed method not only strengthens the security against brute force attack and differential attack, but also improves ER and efficiency of the RDHEI technique. Experimental results and performance analysis demonstrate that the proposed algorithm outperforms the state-of-the-art RDHEI algorithms in terms of the security, ER and complexity.
Noise-Robust Fuzzy Classifier Designed With the Aid of Type-2 Fuzzy Clustering and Enhanced Learning

This paper introduces the design methodology of a noise-robust fuzzy classifier based on type-2 fuzzy clustering and enhanced learning methods. The design procedure for the noise-robust fuzzy classifier (NrFC) can be divided into two parts. First, interval type-2 fuzzy c-means clustering is applied to the hidden layer to minimize the effect of noise or outliers when training the model. Second, an enhanced learning method is employed to train the connection weights between the hidden and output layers. The proposed NrFC uses a cross-entropy error function as its cost function. The Softmax function represents a categorical distribution located at the output layer nodes. In addition, the connection weights of the output layer are adjusted through nonlinear least squares-based learning, and L2 norm-regularization is considered to avoid the degradation of the generalization ability caused by overfitting. The learning mechanism is realized by adding the L2 penalty term to the cross-entropy error function. It is used to cope with over-fitting and multicollinearity problems, which generally appear in conventional fuzzy neural networks. The design methodology of the NrFC is discussed and analyzed using several publicly available benchmark datasets. The performance of the proposed networks is quantified through comprehensive experiments and comparative analysis.
Topology-Aware Scheduling Framework for Microservice Applications in Cloud

Loosely coupled and highly cohesived microservices running in containers are becoming the new paradigm for application development. Compared with monolithic applications, applications built on microservices architecture can be deployed and scaled independently, which promises to simplify software development and operation. However, the dramatic increase in the scale of microservices and east-west network traffic in the data center have made the cluster management more complex. Not only does the scale of microservices cause a great deal of pressure on cluster management, but also cascading QoS violations present a substantial risk for SLOs (Service Level Objectives). In this paper, we propose a Microservice-Oriented Topology-Aware Scheduling Framework (MOTAS), which effectively utilizes the topologies of microservices and clusters to optimize the network overhead of microservice applications through a heuristic graph mapping algorithm. The proposed framework can also guarantee the cluster resource utilization. To deal with the dynamic environment of microservice, we propose a mechanism based on distributed trace analysis to detect and handle QoS violations in microservice applications. Through real-world experiments, the framework has been proved to be effective in ensuring cluster resource utilization, reducing application end-to-end latency, improving throughput, and handling QoS violations.
Privacy Preserving $n$-Party Scalar Product Protocol

Privacy-preserving machine learning enables the training of models on decentralized datasets without the need to reveal the data, both on horizontal and vertically partitioned data. However, it relies on specialized techniques and algorithms to perform the necessary computations. The privacy preserving scalar product protocol, which enables the dot product of vectors without revealing them, is one popular example for its versatility. Unfortunately, the solutions currently proposed in the literature focus mainly on two-party scenarios, even though scenarios with a higher number of data parties are becoming more relevant. For example when performing analyses that require counting the number of samples which fulfill certain criteria defined across various sites, such as calculating the information gain at a node in a decision tree. In this paper we propose a generalization of the protocol for an arbitrary number of parties, based on an existing two-party method. Our proposed solution relies on a recursive resolution of smaller scalar products. After describing our proposed method, we discuss potential scalability issues. Finally, we describe the privacy guarantees and identify any concerns, as well as comparing the proposed method to the original solution in this aspect.
Corpus-Linguistic Analysis of Speech Communities on Anti-Gender Discourse in Slovene

This paper deals with a corpus-linguistic analysis of different text/media types in Slovene with the aim of finding out whether or not any of the communication channels covered by the corpora employed in our analysis serve as a means of unification against the concept of gender, thereby serving as a catalyst for creating and maintaining (new) speech communities.We aim to determine this by extracting the two most commonly misused concepts (terms) in Slovene, i.e. gender theory and gender ideology, and analysing their use in three different corpora of contemporary Slovene: the reference corpus Gigafida 2.0, the corpus of user-generated communication JANES, and the ELEXIS Slovenian Web 2020 corpus.Thus, we confirm that the media for user-generated communication, Twitter in particular, serve as a medium on which speech communities are being built, with their members sharing common views, activities, and beliefs.Hence, Twitter is used as a medium for maintaining and reproducing mutual ideology.
"Lethal Autonomous Weapon Systems – Emerging and Potentially Disruptive Technology"

"The renewal of the competition for the super power status has led to the revival of the emphasis on defense planning, by the great powers, on the capabilities intended for so-called high-end conventional, technological sophisticated warfare, which involves large-scale and high-intensity military actions. Weapons that can be linked to waging high-end warfare actually include the military uses of the lethal autonomous weapon systems (LAWS) and artificial intelligence (AI). The connection between robotics, and autonomous systems and AI has the potential to change the nature of warfare. Theoretically, a lethal weapon system should be considered autonomous if it is designed so that it can modify its integrated programming, does not take into account the objectives initially set and modifies its rules of engagement without human intervention. Human judgment will remain essential, but the line between what humans and machines do best will constantly be shifting in the coming years. The most critical dimension of autonomy is the task the machine is performing. There are significant expectations and challenges, opportunities, and risks associated with LAWS across the full range of military operations. LAWS will influence military operations in the future and will have at least an evolutionary – if not revolutionary – effect."
Learning Heterogeneous Graph Embedding with Metapath-Based Aggregation for Link Prediction

Along with the growth of graph neural networks (GNNs), many researchers have adopted metapath-based GNNs to handle complex heterogeneous graph embedding. The conventional definition of a metapath only distinguishes whether there is a connection between nodes in the network schema, where the type of edge is ignored. This leads to inaccurate node representation and subsequently results in suboptimal prediction performance. In heterogeneous graphs, a node can be connected by multiple types of edges. In fact, each type of edge represents one kind of scene. The intuition is that if the embedding of nodes is trained under different scenes, the complete representation of nodes can be obtained by organically combining them. In this paper, we propose a novel definition of a metapath whereby the edge type, i.e., the relation between nodes, is integrated into it. A heterogeneous graph can be considered as the compound of multiple relation subgraphs from the view of a novel metapath. In different subgraphs, the embeddings of a node are separately trained by encoding and aggregating the neighbors of the intrapaths, which are the instance levels of a novel metapath. Then, the final embedding of the node is obtained by the use of the attention mechanism which aggregates nodes from the interpaths, which is the semantic level of the novel metapaths. Link prediction is a downstream task by which to evaluate the effectiveness of the learned embeddings. We conduct extensive experiments on three real-world heterogeneous graph datasets for link prediction. The empirical results show that the proposed model outperforms the state-of-the-art baselines; in particular, when comparing it to the best baseline, the F1 metric is increased by 10.35% over an Alibaba dataset.
Similarity Fuzzy Semantic Networks and Inference. An Application to Analysis of Radical Discourse in Twitter

In this paper we introduce a new Knowledge Representation model, the Similarity Fuzzy Semantic Networks. It is an extension of Fuzzy Semantic Networks that incorporates reasoning by similarity through a Similarity Inference Rule. Moreover, we show as it can be effectively applied to a trending and complex problem like the analysis of radical discourse in Twitter.
Exploiting the Levels of System Specification for Modeling of Mind

Our objective is to show how the hierarchy of system specifications and morphisms affords a framework that supports modeling and simulation of mind/brain in a coherent manner. Such a framework provides a credible path for simulation/implementation of cognitive behavior at the level of neurons and neuronal compositions. Here we characterize this methodology and use it to extend the set of DEVS building blocks and architectural patterns within complex decision trees and advanced classifier implementations as DEVS coupled models. Such a methodology offers insight into the way real brains realize cognitive behaviors and can improve upon current hardware architecture to support simulation/implementation of neuromorphic designs that comply with physiologically based brain network properties and constraints.
Forecasting with Visibility Using Privacy Preserving Federated Learning

In the fluctuating and unstable supply chain environment, accurate demand forecasting is especially important. To improve the prediction accuracy, one possible way is to improve supply chain visibility by sharing information and knowledge among the supply chain entities. However, there is a potential risk that the raw data may be leaked to the competitors, affecting the business opportunities. To avoid information leakage, a secure demand forecasting with supply chain visibility is necessary. This paper proposes a Federated Learning based approach to predict demand for supplier with supply chain visibility while protecting the data privacy of other entities within the supply chain. To evaluate performance of forecasting accuracy, we designed a supply chain simulation model to generate data. From the experimental results, our proposed method outperforms the other demand forecasting methods without visibility and achieves a similar performance to the method with full visibility.
Interaction-feedback network for multi-task daily activity forecasting

The category and occurrence time of daily activity are two basic tasks of daily activity forecasting in a smart home. Multi-task learning has been employed for daily activity forecasting. However, the category and occurrence time of daily activity are weakly correlated to each other, and the loss functions of the two tasks are difficult to achieve at the same time in multi-task learning. In this paper, a daily activity forecasting approach based on an interaction-feedback network is presented. The proposed method combines the multi-head attention mechanism, convolutional neural networks (CNNs), and bidirectional long short-term (Bi-LSTM) memory units to generate a parallel multi-task forecasting model. In addition, a feedback mechanism is introduced to supervise the learning of the two involved tasks. Public datasets are utilized to evaluate the proposed method. Experimental findings show its effectiveness for multi-task daily activity forecasting in terms of precision, recall, F-score, MAE, RMSE, and R2.
A dual channel decomposition and remapping fusion model for low illumination images with a wide field of view

In this paper, we investigate the problem of low illumination image enhancement under wide field of view condition. Our contribution consists of two parts: Firstly, we propose an adaptive two-parameter luminance remapping enhancement function (A2BREF) based on biological visual properties. Secondly, we propose a simple and effective noise suppression method for composite weighted map. In this paper, an image decomposition method based on the total variational energy is used to decompose the input image into structure and detail components. We apply luminance enhancement to the structure component and noise suppression to the detail component. The two components are processed in parallel without interfering with each other. First, we transform the structure component from RGB space to HSV space, and A2BREF combines the global and local luminance information of the input image to perform an adaptive luminance remapping of the V component. Then, the mapping result and the initial V component are fused with PCA in a specific order to enhance the image brightness. Secondly, the detail component contains a lot of noise and detail information of the input image. In this paper, we extract two weight maps from different stages of the detail component by means of improved adaptive Gaussian filters, and the two weight maps are combined to obtain the final weight map. After multiplying the weight maps with the initial detail components and stretching the contrast, the ideal detail components are obtained. Finally, the processed structure and detail components are combined to obtain the final result. The experimental results show that the proposed method has advantages over the contrast algorithm in terms of both subjective visual and objective metrics.
Online measurement of granular velocity of rotary drums by a fast PIV deep network FPN-FlowNet

The measurement of velocity field of granules with respect to time in one rotary drum is of great significance in the study of mixing process. However, the existing measurement methods are not suitable to be used in online processing in real applications for the following reasons: limited spatial resolution, brightness sensitivity and long time-consumption. In this work, a hybrid framework containing an unsupervised deep network FPN-FlowNet and one particle-center detection model is proposed. FPN-FlowNet is built by introducing the FPN model for 4-level environment-robust feature extraction and weighted fusion for dense velocity field calculation; Particle center detection model is designed based on graph-cut and another well-trained deep model VGG16-LUnet to accurately obtain the particle positions. Extensive experiments including the actual granular flows in rotary drum along with the corresponding software simulation results demonstrate that our approach achieves competitive performance over the state-of-the-art method and satisfactory speed for online processing.
Region Anomaly Detection via Spatial and Semantic Attributed Graph in Human Monitoring

This paper proposes a graph-based deep framework for detecting anomalous image regions in human monitoring. The most relevant previous methods, which adopt deep models to obtain salient regions with captions, focus on discovering anomalous single regions and anomalous region pairs. However, they cannot detect an anomaly involving more than two regions and have deficiencies in capturing interactions among humans and objects scattered in multiple regions. For instance, the region of a man making a phone call is normal when it is located close to a kitchen sink and a soap bottle, as they are in a resting area, but abnormal when close to a bookshelf and a notebook PC, as they are in a working area. To overcome this limitation, we propose a spatial and semantic attributed graph and develop a Spatial and Semantic Graph Auto-Encoder (SSGAE). Specifically, the proposed graph models the “context” of a region in an image by considering other regions with spatial relations, e.g., a man sitting on a chair is adjacent to a white desk, as well as other region captions with high semantic similarities, e.g., “a man in a kitchen” is semantically similar to “a white chair in the kitchen”. In this way, a region and its context are represented by a node and its neighbors, respectively, in the spatial and semantic attributed graph. Subsequently, SSGAE is devised to reconstruct the proposed graph to detect abnormal nodes. Extensive experimental results indicate that the AUC scores of SSGAE improve from 0.79 to 0.83, 0.83 to 0.87, and 0.91 to 0.93 compared with the best baselines on three real-world datasets.
Localization of Ontology Concepts in Deep Convolutional Neural Networks

With the proliferation of deep artificial neural networks, techniques allowing end users to understand why a network came to a certain conclusion are becoming increasingly important. The lack of such understanding is becoming a limiting factor in applying deep neural networks in critical tasks, where the price of error is high. Recently it has been shown that internal representations built by a deep neural network can sometimes be aligned with concepts of a domain ontology, related to the network target. This opens an opportunity of explaining the results of a deep neural network in human terms (defined in the ontology). The paper presents the results of several experiments aimed at understanding what layers of a neural network are most perspective for the alignment with given ontology concept (characterized by its relations with the network target). The experiments were performed with several datasets (XTRAINS, SCDB) and several network architectures (including custom convolutional neural network architecture, ResNet, MobileNetV2). For these dataset-neural architecture pairs we built "concept localization maps" showing how informative is the output of each layer for predicting that given sample corresponds to a certain concept. The results of the experiments show that the concepts that are "closer" to the target concept (definition-wise) are typically better expressed (or, localized) in the last layers. Besides, the concept expression typically follows a roughly unimodal shape. We believe that these results can be used for building effective algorithms for concept extraction and improve the ontology-based explanation techniques for deep neural networks.
Application of an ontological model for the analysis of UAV cybersecurity

This article investigates the risks, information and cyber-security threats that arose because of the rapid growth in popularity of unmanned aerial vehicles (UAVs), paying special attention to existing and future vulnerabilities. Conceptual approaches are proposed for detecting and categorizing cyber threats associated with UAVs, including the use of UAVs as targets and vectors of a cyberattack. These approaches have been applied to real-world threat scenarios to validate and illustrate the types of attacks that are currently possible. It also presents industry trends and the implications of these trends for cybersecurity. The analysis is based on the application of an ontological approach in assessing the vector of attacks on UAVs.
Automated methods for conducting an argumented dialogue with the user

Human-computer interaction in the modern world has become a necessity, but the lack of user confidence in automatic systems creates great difficulties in their implementation. The problem is that in each case it is often impossible to understand the reasons for the decision. Therefore, the user has to try to find the arguments for this solution proposed by the system. The purpose of the work is to create an intelligent assistant capable of arguing his decisions, thereby strengthening the user's confidence in the correctness of his advice. The development of an intelligent assistant is based on a three-level semantic user model and an ontological domain model. The developed software package is designed to work as a virtual digital assistant in various fields. The implemented methods are based on the application of partial models and ontological homomorphisms. The system allows the user to search for the desired solution using the theory of argumentation and case-based reasoning, receiving a reasoned answer with a clear and transparent argumentation tree.