Reviewerly/exHarmony huggingface dataset:
Lightweight Generative Joint Source-Channel Coding for Semantic Image Transmission with Compressed Conditional GANs

Deep learning-based Joint Source-Channel Coding (JSCC) has shown promising results in wireless image transmission by integrating deep generative models. However, the computational demands of these models pose a challenge for practical deployment. In this paper, we propose a novel approach called Lightweight conditional GAN-based Deep Joint Source-Channel Coding (LGJSCC) to address this challenge. LGJSCC introduces a compressed generator using a GAN compression method based on intermediate feature distillation, enabling efficient utilization of computational resources at the receiver side. To enhance the perceptual quality of the reconstructed images, we incorporate the Learned Perceptual Image Patch Similarity (LPIPS) loss along with pixel-wise distortion, adversarial loss, and distillation loss. Experimental results demonstrate that the compressed generator achieves a size reduction of only 4.45% of the original, with Multiply-Accumulate Operations (MAC) decreasing to 11.92% of the original, while maintaining comparable image reconstruction quality. This makes LGJSCC highly suitable for deployment on resource-constrained devices such as mobile and IoT devices.
A Novel ANS Coding with Low Computational Complexity

Asymmetric numeral systems (ANS) is a recent family of entropy coders, which provides the compression ratio of arithmetic coding with a processing speed similar to that of Huffman coding. It has been popular and incorporated into many compressors, including Facebook Zstandard, Apple LZFSE and Google Draco 3D. However, the encoding and decoding procedures of the ANS coding involve a large number of multiplication, division and modulo operations, resulting in a high computational demand. In this paper, we propose a novel ANS coding, which combines the truncated binary coding and the ANS coding to reduce computational complexity. The simulation results show that with an average loss of 1.1% in compression quality, the proposed method can fully avoid the usage of multiplication and division operations.
Semantic Information Extraction for Text Data with Probability Graph

In this paper, the problem of semantic information extraction for resource constrained text data transmission is studied. In the considered model, a sequence of text data need to be transmitted within a communication resource-constrained network, which only allows limited data transmission. Thus, at the transmitter, the original text data is extracted with natural language processing techniques. Then, the extracted semantic information is captured in a knowledge graph. An additional probability dimension is introduced in this graph to capture the importance of each information. This semantic information extraction problem is posed as an optimization framework whose goal is to extract most important semantic information for transmission. To find an optimal solution for this problem, a Floyd’s algorithm based solution coupled with an efficient sorting mechanism is proposed. Numerical results testify the effectiveness of the proposed algorithm with regards to two novel performance metrics including semantic uncertainty and semantic similarity.
Graph Convolutional Integration based Distributed Multi-View Learning in Urban Air Mobility

Urban Air Mobility (UAM) expands vehicles from the ground to the near-ground space, envisioned as a revolution for transportation systems. Comprehensive scene perception is the foundation for Autonomous Air Vehicles (AAV). However, AAV encounters a primary perception challenge: three-dimensional piloting makes the visual perception of AAVs easily obstructed by skyscrapers in urban. High perception learning requirements conflict with the view-limited visual information. To overcome the challenge, multi-view learning has been proposed to collect multi-view data to train the onboard deep learning model. But traditional multi-view learning is deployed on a single device operating centrally, which is difficult to deploy in dynamic environments. Accordingly, this paper proposes Graph Convolutional Network (GCN) based Distributed Multi-View learning (GCNDMV), taking account of GCN relation extractability to facilitate single-view representation learning integration. The proposed distributed multi-view learning framework allows distinct single-view representation learning integration. Moreover, due to the diversity gain of different single-view learning, various single-view representation learning of the GCN-DMV outperforms a homogeneous single-view representation learning of GCN-DMV in terms of recognition accuracy. Simulation experiments are conducted over a realistic multi-view dataset to verify the efficiency of the distributed multi-view learning framework.
Blockchain-Based 6G Distributed Network Framework

Currently, 5GC faces challenges such as high service discovery latency, a large number of direct connections between network functions, and high resource consumption. The future 6G network will be deployed in a distributed manner according to scenario requirements, forming a centralized and distributed network with the characteristics of multiparty participation. Therefore, distributed network technology will become increasingly important. To address these issues, this paper proposes a blockchain-based 6G distributed network framework and three key capabilities, including information on-chaining, information query, and service communication. Based on the proposed framework, each network joins the blockchain through corresponding distributed service nodes, which handles network-network, network- blockchain interactions, and blockchain-related transactions. In addition, network functions are connected to service proxies, and signaling between network functions is forwarded through service proxies. Based on this technical solution, distributed service registration, discovery and invocation tests are carried out at the current stage, verifying the feasibility of this technical solution.
Blockchain-based Collision Avoidance Access Protocol for UAV Swarm

The explosive growth of unmanned aerial vehicles (UAVs) pose more and more aerial and communication resources, making efficient spectrum access control a key bottleneck for UAV swarm applications. Conventionally, the absence of a centralized authority may cause lots of spectrum collisions among UAVs, which not only wastes the spectrum resources, but also causes long access delay. To address this challenge, we propose a blockchain-based access protocol that can effectively mitigate the access collisions among a distributed UAV swarm in high dynamic scenarios Specifically, we design a collision avoidance access mechanism by leveraging blockchain and hash access. We also analyze the performance of the proposed protocol and compare it with the slot multi-channel Aloha protocol. Simulation results demonstrate that the proposed protocol can effectively reduce the spectrum access collisions and is superior to slot multi-channel Aloha in access success rate, throughput, and average access delay.
Federated Learning-Based Privacy Protection for IoT-based Smart Healthcare Systems

In this paper, we propose a Federated Privacy-Enhanced Healthcare (FPEH) learning framework based on Gaussian differential privacy and Paillier homomorphic encryption to enhance data privacy protection for IoT-based smart healthcare systems. To address the data privacy issues of the clients in this scenario, we first apply a Gaussian differential privacy-based mechanism to add noise to the parameters in the client models. Secondly, a constant factor is initially introduced to enhance the randomness and unpredictability of the Gaussian noise. Furthermore, a gradient clipping mechanism is also adopted to ensure the effectiveness of the client model after adding noise. Finally, we employ Paillier homomorphic encryption to encrypt the noisy model parameters, further ensuring the security of the original data. We evaluate the proposed method by applying two datasets, namely MNIST and PneumoniaMNIST. Simulation results demonstrate that the proposed method can protect the privacy of the original data from being leaked and achieve a similar test accuracy level as the comparison algorithms.
Skip Connection Variant of Modified U-Net Architecture for Satellites Imagery

In this paper, we introduce an innovative model that builds upon the traditional U-Net architecture, specifically elevating the semantic segmentation performance for satellite imagery. Our architectural modification capitalizes on a concatenate block to effectively integrate the feature maps derived from each block. This strategic integration aids in mitigating the information loss from the extracted features and facilitates their equal distribution among numerous decoder blocks. The methodology underpins the capacity to augment semantic segmentation performance pertinent to satellite imagery, inherently marked by its intricate characteristics and wide-ranging features.
A Simple Deep Q-Network based Feature Selection Method

In this paper, a feature selection method based on a Deep Q-Network (DQN), a state generator module as well as a feature selection module is proposed. The state generator module generates a state vector based on the selected feature subset and the summary of dataset. On the other hand, the feature selection module selects features based on the Q-values and a predefined threshold. Our experimental results show that our proposed methods exhibit good performance in terms of training time.
DSFNet: Dual-Stream-Fusion Network for Farmland Parcel Mapping in High-Resolution Satellite Images

Cultivated land not only is the foundation and lifeline of food production, but also an important premise and basis for ensuring national food security. Accurate identification of farmland parcel is of great significance in agricultural production. This study proposed a dual-stream-fusion network (DSFNet), using a semantic segmentation network and a boundary detection network in parallel, for farmland parcel in high-resolution satellite images. We proposed a semantic segmentation module based on DeepLabV3+ network structure and a boundary detection module based on DexiNed network to extract richer farmland parcel features by fusing the semantic and boundary information. Then, a post-processing optimization scheme is designed based on morphology to improve the accuracy of farmland parcel. Moreover, the slicing and stitching of large remote sensing images are achieved by using the expansion prediction strategy to obtain the final high-precision result without obvious stitching trace. The experimental results illustrate the effectiveness of the proposed model for fine-scale farmland parcel mapping.
Cognitive and semantic construct for Digital Twin river basin geographic information fusion

River basin is a natural area and system with complex process and interaction among hydrology and climate, human activity and ecologic environment. Integrated assessment and management is necessary for river basin under global change. Digital twin and geographic information technology provide useful methods for the integrated management problem. However, multidisciplinary models, multiple scales, multisource data, uncertainty and heterogeneity are potential challenge for the problem. This paper has proposed a framework of cognitive and semantic construct for digital twin river basin and geographic information fusion, and will provide a holism methodology for integrated assessment and management for river basin. An ontology framework for semantic digital twin river basin has been proposed based on the entities of natural phenomena and human activity, including object, field, process, state, event and property from static or dynamic viewpoint. A multi-sources information fusion framework for semantic digital twin river basin was proposed based on ontology and knowledge construct, from the three viewpoints: static feature model, dynamic process model and complex activity model.
An ontology-based approach for virtual constellation recommendation in remote sensing applications

Using virtual constellations to combine data from multiple satellites has become increasingly important for continuous monitoring of the Earth’s surface and environment. However, there is still a lack of a mechanism to effectively collect and promote these virtual constellations, limiting the applicability and reutilization of remote sensing data sources. This paper proposes an ontology-based approach including providing domain knowledge representation, data standardization, and semantic integration to manage and improve utilization of virtual constellations. Specifically, an ontology for virtual constellation recommendation is presented to represent and reason virtual constellation knowledge, including the composition of virtual constellations, related sensors, satellites, and remote sensing products. The ontology is developed using Protégé and validated through an application example of evapotranspiration. The model can recommend appropriate virtual constellations in specific application scenarios, supporting the reutilization, integration, and sharing of knowledge in the virtual constellation domain.
Repair of Holes Appearing in the Stereo Reconstruction Based on 3D Reprojection and DCGAN

With the development of digital 3D city construction, the use of multi-visual stereo method for 3D reconstruction of buildings has received a lot of attention and research. OpenMVG and OpenMVS are a common set of tools among them, due to their open-source code and high integrity. These tools complete the 3D reconstruction by recovering the structure from motion (SFM) of the target and then performing mesh reconstruction, texture mapping and other processes. However, due to incomplete shooting angles and occlusion problems, some holes exist on the surface of the reconstructed object, which affects the aesthetics and integrity of the result. To address this problem, this paper proposes a hole repair method based on 3D reprojection and DCGAN, to optimize the reconstruction results by integrating 3D structure repair and 2D texture repair. The holes contained in the reconstructed 3D target are filled by interpolation. For the obtained 2D imaging map containing holes, the texture values of the holes are recovered by introducing DCGAN generating adversarial networks, that are finally mapped to the 3D structure. The experimental test results indicate that the method can effectively restore the holes’ 3D information and texture speculation, which results in a more complete 3D reconstruction model of the building.
A Comparison of Energy Minimization Algorithms for Solving Max-Sat Problem with Probabilistic Ising Machines

Ising machines are one of the most promising unconventional computing paradigms in the field of combinatorial optimization. Several ways of employing the Ising model were devised in the latest years and, among them, probabilistic computing with p-bits stands out for its high hardware compatibility and remarkable performance. One of the key elements of the solving process of a given instance of a problem is the energy minimization algorithm used. In this work, classical annealing (CA), parallel tempering (PT), and simulated quantum annealing (SQA) are compared over the same instance of a maximum satisfiability problem. The results show that, for a high number of replicas, SQA performs better than the other two algorithms. Conversely, with contained number of replicas, CA and PT are comparable in performance between each other and both are superior to SQA. Those results call for the development of platforms/protocols where to compare and potentially combine those annealing approaches.
QuChain: On the Security and Privacy of Peer-to-Peer System using Blind Quantum Computation

Quantum threats to blockchain technology have become a pressing concern since the advent of quantum computing has the potential to undermine system security and trust. These advances might jeopardize the cryptographic techniques that are currently employed to protect and verify authenticity in blockchain systems. This article discusses the significance of quantum computing as a threat to blockchain systems, as well as the potential of adopting blind quantum computation to mitigate threats. The tremendous blind quantum computation approach for blockchain technology enables the classical processing of encrypted data to protect records in a quantum-resistant manner while disclosing no information about the data itself.
Quantized Simulated Bifurcation for the Ising Model

Developed from the Ising model, Ising machines are promising to be efficient domain-specific accelerators for solving combinatorial optimization problems (COPs). A simulated bifurcation (SB) algorithm enables an Ising machine to achieve massive parallelism in the simulation of Hamiltonian dynamics. Although SB significantly accelerates the search, more resources are required due to the use of continuous variables for the position of oscillators to obtain discrete spin states, compared to conventional simulated annealing. This article proposes ternary and multiple-value quantized SB (qSB) algorithms by discretizing the position variables used for the hardware-consuming multiply-accumulate (MAC) operations in SB. These quantization schemes do not only reduce the computational complexity, but also improve the solution quality for COPs. Specifically, the ternary qSB with dynamic threshold settings converts the MAC into addition and subtraction. To improve the precision in number representation when solving large-scale COPs, a uniform quantization scheme is applied to provide multiple-valued quantization. Alternatively, a logarithmic qSB leverages the evolution characteristics of position variables and implements multiplication by using simple shift operations. We demonstrate that using the proposed qSB improves the solution quality in a long search and accelerates energy convergence in a short search for solving COPs tackled by up to 2000 fully connected spins.
FedCime: An Efficient Federated Learning Approach For Clients in Mobile Edge Computing

Federated learning (FL) enables collaborative training of a global model using localized data from multiple devices. However, in resource-constrained mobile edge computing (MEC) environments, non-independent and identically distributed (non-IID) data generated by these devices poses challenges for traditional FL algorithms like Federated Averaging (FedAvg), leading to decreased accuracy of the global model. In addition, dynamic mobile networks with intermittent connectivity, dropouts, and high migration rates hinder the communication of model updates to the central server. To address these challenges, we present FedCime, a novel tier-based FL approach that selects high-utility mobile clients likely to complete training to replace migrating clients during the round of training. Our evaluation shows that FedCime is scalable and significantly improves training performance in terms of accuracy and computational efficiency compared to state-of-the-art FL algorithms.
On the Harmful Effects of Active Network Probing

Active network probing, commonly known as a speed test, is the prevalent network speed measurement and diagnostic method. Speed tests primarily measure achievable throughput by conducting bulk downloads that saturate the bottleneck link. However, the impact of speed tests on user Quality of Experience (QoE) has not been thoroughly explored. In this paper, we investigate the effects of active network probing on user QoE during two common activities: file downloading and video streaming, focusing on key QoE metrics such as download time, video bitrate, and buffering. Our analysis reveals that the standard speed test significantly extends download times (by up to 88% in WiFi and 46% in cellular networks) and adversely affects various video QoE metrics, particularly bitrate, resulting in an average bitrate reduction ranging from 46% to 60%. Moreover, we assess the outcomes of typical speed test scenarios, such as single and double tests, and establish that both variants impair QoE, with double tests causing greater disruptions. Our findings offer a comprehensive insight into the ramifications of active network probing on user applications and emphasize the necessity for approaches to alleviate its detrimental effects on QoE.
TRIM: crossTalk-awaRe qubIt Mapping for multiprogrammed quantum systems

The challenge of mapping logical qubits to physical qubits in quantum systems has been addressed in prior proposals that optimize the Probability of Successful Trial (PST) by considering the coherence and gate error rates. However, these proposals do not account for crosstalk errors, which occur when active qubits interact during execution. The reason for this is that crosstalk only appears after the initial mapping, while previous strategies allocate qubits based on program and quantum system characteristics using one-step mapping methods. Scheduling-based solutions have been created to address this problem by inserting barriers between gates to reduce crosstalk, but at the expense of increased execution time and coherence error rates, ultimately decreasing overall accuracy. This paper presents and evaluates TRIM, a novel strategy that characterizes crosstalk and eliminates it in an iterative fashion using a multi-step greedy search method, which can be applied to any qubit mapping to reduce crosstalk while keeping execution time and coherence errors in check. Evaluations of TRIM using multiple workloads show PST improvements of 7.3% for single-programmed execution and 7.7% for multiprogramming scenarios, while reducing or keeping the number of gates, compared to a state-of-the-art mapping scheme. Additionally, TRIM achieves 5.4% and 3.3% PST improvements for single-programmed and multiprogrammed executions, respectively, compared to a state-of-the-art scheduling strategy.
SecureGAN: Secure Three-Party GAN Training

Generating Adversarial Network (GAN) is a prominent unsupervised learning method that utilizes two competing neural networks to generate realistic data, which has been widely employed in image synthesis and data augmentation. Outsourcing GAN training to cloud servers can significantly reduce the computation load on local devices. Furthermore, in outsourcing settings, training data can be gathered from multiple users, leading to larger amounts of data and, as a result, improved training accuracy. However, outsourcing is associated with privacy risks, as training data often contains sensitive information. To address this problem, we propose SecureGAN, a privacy-preserving framework for GAN that aims to protect the privacy of the training input and output. We implement secure protocols based on replicated secret sharing technology to protect the privacy of the linear and nonlinear layers. We conduct experiments using the MP-SPDZ framework, and the results demonstrate the effectiveness of the proposed protocols.