Reviewerly/exHarmony huggingface dataset:
Signal Augmentations Oriented to Modulation Recognition in the Realistic Scenarios

The recent years had witnessed a resurgence on neural network. Many hidden layers were stacked hierarchically to learn the high-level representations. Great performances were achieved by the learned representations. However, this kind of learning models were highly dependent on large amounts of signals with label information. In the realistic scenarios, it is very difficult and costly to collect the modulated signals with label information. Given small training samples, the fitting power of deep models were limited. To solve the problems, a new family of signal augmentation strategies, segment-wise generation and signal-wise generation are proposed. The former builds new signal by tuning a single signal, while the latter combines several different modulated signals together to produce new signal. Four kinds of techniques, segment shift in cyclic, segment correlation in random, pairwise signals combination, and multiple signals concatenation are presented. The aim is to simulate the unforeseen disturbances during signal sampling. The recognition performance under the realistic scenarios can be then improved. Multiple comparative studies were performed. The results demonstrated the effectiveness of proposed strategy in comparisons to the classical methods, as well as the deep learning algorithms.
SPRTracker: Learning Spatial-Temporal Pixel Aggregations for Multiple Object Tracking

Recently, multiple object tracking based on a single frame has achieved excellent performance. However, in crowded scenes, occlusion and motion blur will increase the difficulty of foreground object detection. In this letter, we propose a Spatial-temporal Pixel Resampling Tracker (SPRTracker) that introduces a novel cross-frame input framework to improve the anti-occlusion and anti-interference ability during tracking. We first propose a sampling mechanism in which Inter-frame Pixel Alignment (IPA) is designed to maintain spatial and temporal consistency in the propagation of pixel-level information between frames, aiming to improve the anti-interference ability during tracking target motion. Secondly, To improve the compensation effect of the gain information in the historical frame to the occluded target in the current frame, Similarity Reparameter Fusion (SRF) strategy is designed to fuse the features of the two frames. We evaluated the proposed method on two common benchmarks (MOT17 and MOT20), and the experimental results effectively demonstrated the superiority of our method.
Clustered Federated Learning with Model Integration for Non-IID Data in Wireless Networks

As a typical distributed learning paradigm, federated learning has enabled network edge intelligence by making full use of the local data and the computing resources at edge devices without privacy leakage. However, due to the non-IID characteristics of data samples and the unreliability of transmission circumstances, deployment of federated learning in edge networks cannot be well guaranteed. To tackle these challenges, in this paper, a clustered federated learning paradigm with model integration is proposed. First, the detailed framework of our paradigm is introduced. The key idea is to divide the users into multiple individual user clusters by managing the scale and participants of each cluster, and the distribution divergence can be mitigated via cluster-based federated learning. Then, all the learning models are ensembled by model integration to generalize on various target tasks. Second, an upper bound on the accuracy loss of our proposed paradigm is derived, which provides some insights for the impact of data distributions and channel qualities on model performance. To further improve the accuracy performance in wireless networks, a user clustering algorithm is sophisticatedly designed. Finally, the simulation results are provided to verify the significant performance gains of our proposed framework.
A Cross-Chain Interoperability Architecture for Smart City Environments

Blockchains have become quintessential for cutting-edge technology demands in the world. This trend will only continue to increase in the future. Looking at all modern technologies that seem to move towards decentralized architecture, blockchain and distributed ledger technology fit perfectly. In times of conventional database-orientated systems, modern frameworks made huge developments when application programming interfaces (APIs) and data were used cross-platform between centralized entities. A natural evolution for blockchain technology would be to enable communication and data exchange between blockchains (ie. both private and public). This could help not only digitize but could also set in a revolution for digital record-keeping that can be used for automation in the future. Our research work enables and tests interoperability between blockchains in different real-world scenarios. The research work also tries to understand various elements of a smart city environment and a few use-cases are discussed and experimented on.
How to Launch Jamming Attacks on Federated Learning in NextG Wireless Networks

We study how to launch jamming attacks on federated learning when performed over wireless channels in NextG systems. Federated learning has emerged as a decentralized learning framework, where clients can collectively train a global model at the server without sharing their training datasets. To support NextG wireless network applications such as spectrum sensing and user equipment identification, federated learning can be used to train machine learning models to classify wireless signals that are collected by clients (spectrum sensors) at different locations while keeping training datasets of clients confidential. Federated learning is known to be susceptible to insider threats by malicious clients. In this paper, we consider external threats in terms of the over-the-air jamming attacks on federated learning in a wireless network. We study three scenarios that an adversary can jam the local model updates transmitted from clients to the server (uplink attack), or jam the global model updates transmitted from the server to clients (downlink attack), or jam both. We impose an attack budget on the number of clients that can be attacked per federated learning round. These clients are selected based on their local model accuracies that are expected in the absence of an attack or ranked by observing the spectrum. This selection may be fixed or change over time. We show that this jamming attack leads to a major loss of performance for federated learning compared to benchmark attacks. These results raise new security concerns for federated learning when executed in NextG wireless networks subject to jamming attacks.
Semantic-Native Communication: A Simplicial Complex Perspective

Semantic communication enables intelligent agents to extract meaning (or semantics) of information via interaction, to carry out collaborative tasks. In this paper, we study semantic communication from a topological space perspective, in which higher-order data semantics live in a simplicial complex. Specifically, a transmitter first maps its data into a k-order simplicial complex and then learns its high-order correlations. The simplicial structure and corresponding features are encoded into semantic embeddings in latent space for transmission. Subsequently, the receiver decodes the structure and infers the missing or distorted data. The transmitter and receiver collaboratively train a simplicial convolutional autoencoder to accomplish the semantic communication task. Experiments are carried out on a real dataset of Semantic Scholar Open Research Corpus, where one part of the semantic embedding is missing or distorted during communication. Numerical results show that the simplicial convolutional autoencoder enabled semantic communication effectively rebuilds the simplicial features and infer the missing data with 95% accuracy, while achieving stable performance under channel noise. In contrast, the conventional autoencoder enabled communication fails to infer any missing data. Moreover, our approach is shown to effectively infer the distorted data without prior simplicial structure knowledge at the receiver, by learning extracted semantic information during communications. Leveraging the topological nature of information, the proposed method is also shown to be more reliable and efficient compared to several baselines, notably at low signal-to-noise (SNR) levels.
Blind Asynchronous Over-the-Air Federated Edge Learning

Federated Edge Learning (FEEL) is a distributed machine learning technique where each device contributes to training a global inference model by independently performing local computations with their data. More recently, FEEL has been merged with over-the-air computation (OAC), where the global model is calculated over the air by leveraging the superposition of analog signals. However, when implementing FEEL with OAC, there is the challenge on how to precode the analog signals to overcome any time misalignment at the receiver. In this work, we propose a novel synchronization-free method to recover the parameters of the global model over the air without requiring any prior information about the time misalignments. For that, we construct a convex optimization based on the norm minimization problem to directly recover the global model by solving a convex semi-definite program. The performance of the proposed method is evaluated in terms of accuracy and convergence via numerical experiments. We show that our proposed algorithm is close to the ideal synchronized scenario by 10%, and performs $4\times$ better than the simple case where no recovering method is used.
Expression-based Security Framework for ATM Networks

In recent years, Automated Teller Machines (ATMs) are witnessed as the most widely used gadgets which required personal identification numbers (PINs) for authentication along with ATM cards. But PINs are suspected of many threats like a man-in-the-middle attack, spoofing, and eavesdropping, etc., which can affect secure and private information. In this paper, we present a novel secure authentication system for ATMs. In this paper, we replace ATM card with facial expression authentication and makes ATMs intelligent enough to recognize users and give them access to their accounts. Replacement of cards will solve the current problem of skimming data from cards with the use of skimmers.
Improving Boosted Generalized Additive Models with Random Forests: A Zoo Visitor Case Study for Smart Tourism

Smart Tourism for the Industry 4.0 and post Covid-19 challenge needs explainable AI Algorithms adapted for the Volatility, Uncertainty, Complexity and Ambiguity (VUCA) World with smart (physical components, algorithms, and IoT/mobile connectivity) elements. This paper shows how boosted generalized additives models (GAM) and random forest can be used in conjunction to improve the prediction and model explainability at the same time. This is achieved by using the predictions of the random forest as an outcome of the boosted GAM. Boosted GAMs can not only improve the explainability of random forest, but the random forest can also improve the predictability of boosted GAMs for modeling zoo visitors. This approach also has desirable regularization properties, such as model sparsity of the boosted GAMs. In addition, the current state of the art is provided and a detailed description with descriptive analysis of a case study for zoo visitors. The procedure with integrated XAI techniques, like variable importance measures and partial effects, is explained. In the future, the proposed concept can be implemented also for other industries or as a general method of XAI
Constrained Clustering: General Pairwise and Cardinality Constraints

In this work, we study constrained clustering, where constraints are utilized to guide the clustering process. In existing works, two categories of constraints have been widely explored, namely pairwise and cardinality constraints. Pairwise constraints enforce the cluster labels of two instances to be the same (must-link constraints) or different (cannot-link constraints). Cardinality constraints encourage cluster sizes to satisfy a user-specified distribution. However, most existing constrained clustering models can only utilize one category of constraints at a time. In this paper, we enforce the above two categories into a unified clustering model starting with the integer program formulation of the standard K-means. As these two categories provide useful information at different levels, utilizing both of them is expected to allow for better clustering performance. However, the optimization is difficult due to the binary and quadratic constraints in the proposed unified formulation. To alleviate this difficulty, we utilize two techniques: equivalently replacing the binary constraints by the intersection of two continuous constraints; the other is transforming the quadratic constraints into bi-linear constraints by introducing extra variables. Then we derive an equivalent continuous reformulation with simple constraints, which can be efficiently solved by Alternating Direction Method of Multipliers (ADMM) algorithm. Extensive experiments on both synthetic and real data demonstrate: 1) when utilizing a single category of constraint, the proposed model is superior to or competitive with state-of-the-art constrained clustering models, and 2) when utilizing both categories of constraints jointly, the proposed model shows better performance than the case of the single category. The experimental results show that the proposed method exploits the constraints to achieve perfect clustering performance with improved clustering to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2-5$ </tex-math></inline-formula> % in classical clustering metrics, e.g., Adjusted Random Index (ARI), Mirkin’s Index (MI), and Huber’s Index (HI), outerperfomring all compared-againts methods across the board. Moreover, we show that our method is robust to initialization.
Adversarial Attack and Defense on Natural Language Processing in Deep Learning: A Survey and Perspective

Natural language processing (NLP) presently has become a new paradigm and enables a variety of applications such as text classification, information retrieval, and natural language generation by leveraging deep learning techniques. However, recent studies have shown that the NLP models based on deep neural network are susceptible to maliciously designed adversarial examples. Therefore, the main challenges lie in improving the robustness and ensuring the security of the system. In this paper, we first introduce common NLP tasks and quality measures on adversarial example. Next, we present a comprehensive review on literature in terms of both adversarial attack and defense methods, based upon the granularity and type. Our work is also the first of its kind to provide a brief overview of the adversarial examples on Chinese texts as a result of the language difference. Finally, we summarize this study by providing directions for future research.
Toward Lightweight Cryptography: A Survey

The main problem in Internet of Things (IoT) security is how to find lightweight cryptosystems that are suitable for devices with limited capabilities. In this paper, a comprehensive literature survey that discusses the most prominent encryption algorithms used in device security in general and IoT devices in specific has been conducted. Many studies related to this field have been discussed to identify the most technical requirements of lightweight encryption systems to be compatible with variances in IoT devices. Also, we explored the results of security and performance of the AES algorithm in an attempt to study the algorithm performance for keeping an acceptable security level which makes it more adaptable to IoT devices as a lightweight encryption system.
UIBVFED-Mask: A Dataset for Comparing Facial Expressions with and without Face Masks

After the COVID-19 pandemic the use of face masks has become a common practice in many situations. Partial occlusion of the face due to the use of masks poses new challenges for facial expression recognition because of the loss of significant facial information. Consequently, the identification and classification of facial expressions can be negatively affected when using neural networks in particular. This paper presents a new dataset of virtual characters, with and without face masks, with identical geometric information and spatial location. This novelty will certainly allow researchers a better refinement on lost information due to the occlusion of the mask.
Multi-Hider Reversible Data Hiding Using a Weighted Color Transfer and Modulus Operation

This paper proposes a multi-hider dual-image reversible data-hiding algorithm. Generating dual stego images, the proposed algorithm provides a high embedding capacity, exhibits high image quality, offers reversibility to restore the stego images, and suggests seven levels of secret message extraction. The message embedding in our algorithm contains two phases. In Phase-1, we embed an n-ary secret message vector followed by an m-ary secret message in each pixel using two distinct secret keys when rendering a new image from the source and target images using an optimal weighted color-transfer process. This phase produces a tentative stego image which exhibits a new color appearance, different from the input source image. In Phase-2, we introduce a weighted modulus operation to embed a k-ary secret message vector into a dual-pixel constructed from the tentative stego image. The message concealment is elaborately designed so that the hidden secret messages are intact. This phase produces dual stego images which carry three distinct secret messages. Using legitimate secret keys, an authorized receiver can extract one or parts of secret messages and recover the tentative stego color-transferred image without arousing any suspicion. The experimental results and analysis confirm that our scheme can resist RS and PVD steganalytic attacks. In addition, our algorithm provides both high embedding capacity and better image quality, outperforming its counterparts. To the best of our knowledge, our scheme is the first in the relevant literature that provides multi-hider reversible data hiding, thereby offering various levels of message extraction for secure data communication.
Combustion Optimization Under Deep Peak Shaving Based on DYNA-A3C With Dynamic Weight

The combustion process of boilers under deep peak shaving is a multivariate process which has complex characteristics such as super multivariability, being nonlinear, and large delay. It is difficult to handle complex data and calculate appropriate distributed results. To this end, this study applies the A3C method based on the dynamic weight Dyna structure to the boiler combustion system. This method trains and optimizes the boiler combustion system by establishing a data center and designing appropriate states and reward values, and the simulation results show that this method can be used to optimize the boiler combustion system. It can effectively reduce NO X emissions and improve the boiler combustion efficiency.
TSS-Net: two-stage with sample selection and semi-supervised net for deep learning with noisy labels

The significant success of Deep Neural Networks (DNNs) relies on the availability of annotated large-scale datasets. However, it is time-consuming and expensive to obtain the available annotated datasets of huge size, which hinders the development of DNNs. In this paper, a novel two-stage framework is proposed for learning with noisy labels, called Two-Stage Sample selection and Semi-supervised learning Network (TSS-Net). It combines sample selection with semi-supervised learning. The first stage divides the noisy samples from the clean samples using cyclic training. The second stage uses the noisy samples as unlabeled data and the clean samples as labelled data for semi-supervised learning. Unlike previous approaches, TSS-Net does not require specifically designed robust loss functions and complex networks. It achieves decoupling of the two stages, which means that each stage can be replaced with a superior method to achieve better results, and this improves the inclusiveness of the network. Our experiments are conducted on several benchmark datasets in different settings. The experimental results demonstrate that TSS-Net outperforms many state-of-the-art methods.
Mixed Reality or Simply Mobile? A Case Study on Enabling Less Skilled Workers to Perform Routine Maintenance Tasks

The availability of skilled workers has become a severe issue for production companies in developed countries like Germany in recent years. Specialists for maintenance tasks are in strong demand and their workforce should be steered towards the most challenging tasks in maintenance. In this case study we analyze, whether less skilled or not specifically instructed workers can perform routine maintenance tasks without being directly supported by experts. In our setup ten participants try to handle a maintenance routine checklist in three different ways: paper-based printouts, on a mobile tablet, and with a Microsoft HoloLens 2 Mixed Reality (MR) device. The results show that workers fail to complete the task with a mere paper-based approach, but are able to succeed both with the mobile tablet and MR application. Quantitative time measurements and qualitative analyses indicate that the mobile tablet app was easier to learn, did not impair the worker as much as the HoloLens and worked more stable. However, the HoloLens solution showed advantages as hands-free and spatial guiding device, which could be important aspects when better MR hardware will be available and MR interaction principles become common knowledge.
Quantum Internet: from Medium Access Control to Entanglement Access Control

Multipartite entanglement plays a crucial role for the design of the Quantum Internet, due to its potentiality of significantly increasing the network performance. In this paper, we design an entanglement access control protocol for multipartite state, which exhibits several attractive features. Specifically, the designed protocol is able to jointly extract in a distributed way an EPR pair from the original multipartite entangled state shared by the set of network nodes, and to univocally determines the identities of the transmitter node and the receiver node in charge of using the extracted EPR pair. Furthermore, the protocol avoids to delegate the signaling arising with entanglement access control to the classical network, with the exception of the unavoidable classical communications needed for EPR extraction and qubit teleportation. Finally, the protocol supports the anonymity of the entanglement accessing nodes.
Mitigating Attacks on Artificial Intelligence-based Spectrum Sensing for Cellular Network Signals

Cellular networks (LTE, 5G, and beyond) are dramatically growing with high demand from consumers and more promising than the other wireless networks with advanced telecommunication technologies. The main goal of these networks is to connect billions of devices, systems, and users with high-speed data transmission, high cell capacity, and low latency, as well as to support a wide range of new applications, such as virtual reality, metaverse, telehealth, online education, autonomous and flying vehicles, advanced manufacturing, and many more. To achieve these goals, spectrum sensing has been paid more attention, along with new approaches using artificial intelligence (AI) methods for spectrum management in cellular networks. This paper provides a vulnerability analysis of spectrum sensing approaches using AI-based semantic segmentation models for identifying cellular network signals under adversarial attacks with and without defensive distillation methods. The results showed that mitigation methods can significantly reduce the vulnerabilities of AI-based spectrum sensing models against adversarial attacks.