Context-Tree Weighting and Bayesian Context Trees: Asymptotic and Non-Asymptotic Justifications

Abstract: The Bayesian Context Trees (BCT) framework is a recently introduced, general collection of statistical and algorithmic tools for modelling, analysis and inference with discrete-valued time series. The foundation of this development is built in part on some well-known information-theoretic ideas and techniques, including Rissanen’s tree sources and Willems et al.’s context-tree weighting algorithm. This paper presents a collection of theoretical results that provide mathematical justifications and further insight into the BCT modelling framework and the associated practical tools. It is shown that the BCT prior predictive likelihood (the probability of a time series of observations averaged over all models and parameters) is both pointwise and minimax optimal, in agreement with the MDL principle and the BIC criterion. The posterior distribution is shown to be asymptotically consistent with probability one (over both models and parameters), and asymptotically Gaussian (over the parameters). And the posterior predictive distribution is also shown to be asymptotically consistent with probability one.


Defect enhancement method for ZrO2-bearing spherical surface representation based on complete defect stitching and self-defined background balancing algorithm

Abstract: To address the problems of difficulty in extracting complete defects and high interference of surface background in the process of ZrO2 bearing ball defect detection. A method of ZrO2 bearing ball surface representation defect enhancement based on complete defect stitching and self-defined background balance algorithm is proposed. To determine the location of complete defects, we propose a scanning and acquisition scheme for sub-aperture images and quickly stitch the edges of sub-aperture images. The image feature point gradient definition is optimized, and a less error Euclidean distance finding method is used for feature point matching. The stitched complete defect image is smooth and has high pixel accuracy. The background feature layer of the complete defect image is extracted for background gray leveling, and this process is introduced into the γ-value calculation process. The γ setting allows the image to be self-defined enhancement, outputting a fully defective image with a balanced background and outstanding defects. The average gradient is reduced by more than 90% after complete defect image enhancement, and the information entropy is preserved by about 40%. The defect edges of the image are intact, and the fine texture of the defect features is preserved.


Differentially Private Federated Clustering Over Non-IID Data

Abstract: In this paper, we investigate federated clustering (FedC) problem, that aims to accurately partition unlabeled data samples distributed over massive clients into finite clusters under the orchestration of a parameter server, meanwhile considering data privacy.Though it is an NP-hard optimization problem involving real variables denoting cluster centroids and binary variables denoting the cluster membership of each data sample, we judiciously reformulate the FedC problem into a non-convex optimization problem with only one convex constraint, accordingly yielding a soft clustering solution.Then a novel FedC algorithm using differential privacy (DP) technique, referred to as DP-FedC, is proposed in which partial clients participation and multiple local model updating steps are also considered.Furthermore, various attributes of the proposed DP-FedC are obtained through theoretical analyses of privacy protection and convergence rate, especially for the case of non-identically and independently distributed (non-i.i.d.) data, that ideally serve as the guidelines for the design of the proposed DP-FedC.Then some experimental results on two real datasets are provided to demonstrate the efficacy of the proposed DP-FedC together with its much superior performance over some state-of-the-art FedC algorithms, and the consistency with all the presented analytical results.


Performance- and Energy-Aware Gait-Based User Authentication With Intermittent Computation for IoT Devices

Abstract: Internet-of-Things (IoT) makes it possible to utilize a multitude of small and medium-scale devices allowing for increased functionality in flexible networks. However, user authentication on IoT devices is as important as it can be challenging. Due to limitations on the available energy, interface, and processing power, IoT devices can be the weakest link in their networks. Multiple authentication techniques have been developed to address these challenges. However, the existing techniques are limited in terms of performance, overheads, and efficiency. In contrast, our proposed authentication method uses a user’s walking gait as an input, because gait is unique to every user and can be collected using low-power inertial sensors found on all handheld devices. Our authentication method uses a lightweight Neural Network (NN) which is further complimented with early exits to further optimize computational cost(s). We also propose reinforcement learning that considers the energy consumption to dynamically determine which of the exits should be chosen to strike a balance between performance and computational and energy cost. Though effective, one of the challenges with IoT devices is their power supply such as dependence on batteries. Discharge of the battery or any other interruptions can lead to re-computations, which are expensive on already limited batteryoperated IoT devices. To address such challenges, especially for user authorization, we introduce intermittent computation to our proposed authentication framework. Intermittent computation can store the state of the NNs at checkpoints. In the case of power disruption, the execution will be resumed from the saved checkpoints instead of performing the whole execution. Most implementations of intermittent computation take place at the compiler level, which makes for a very efficient design, however, that also makes them hardware specific. Our method is implemented on software and is hardware-agnostic, allowing us to create checkpoints to save and retrieve the authentication framework state, in case of power interruption. The proposed authentication framework can authenticate users with up to more than 85% accuracy and can save up to 34% of computations due to the proposed intermittent computing.


Nighttime vehicle detection algorithm based on image translation technology1

Abstract: In order to address the problem of decreased accuracy in vehicle object detection models when facing low-light conditions in nighttime environments, this paper proposes a method to enhance the accuracy and precision of object detection by using the image translation technology based on the Generative Adversarial Network (GAN) in the field of computer vision, specifically the CycleGAN, from the perspective of improving the training set of object detection models. This is achieved by transforming the existing well-established daytime vehicle dataset into a nighttime vehicle dataset. The proposed method adopts a comparative experimental approach to obtain translation models with different degrees of fitting by changing the training set capacity, and selects the optimal model based on the evaluation of the effect. The translated dataset is then used to train the YOLO-v5-based object detection model, and the quality of the nighttime dataset is evaluated through the evaluation of annotation confidence and effectiveness. The research results indicate that utilizing the translated nighttime vehicle dataset for training the object detection model can increase the area under the PR curve and the peak F1 score by 10.4% and 9% respectively. This approach improves the annotation accuracy and precision of vehicle object detection models in nighttime environments without requiring additional labeling of vehicles in monitoring videos.


An efficient and robust gradient reinforcement learning: Deep comparative policy

Abstract: Recently, actor-critic architectures such as deep deterministic policy gradient (DDPG) are able to understand higher-level concepts for searching rich reward, and generate complex actions in continuous action space, and widely used in practical applications. However, when action space is limited and has dynamic hard margins, training DDPG can be problematic and inefficiency. Since real-world actuators always have margins and interferences, after initialization, the actor network is likely to be stuck at a local optimal point on action space margin: actor gradient orients to the outside of action space but actuators stop at the margin. If the hard margins are complex, dynamic and unknown to the DDPG agent, it is unable to use penalty functions to recover from local optimum. If we enlarge the random process for local exploration, the training could be in potential risk of failure. Therefore, simply relying on gradient of critic network to train the actor network is not a robust method in real environment. To solve this problem, in this paper we modify DDPG to deep comparative policy (DCP). Rather than leveraging critic-to-actor gradient, the core training process of DCP is regulated by a T-fold compare among random proposed adjacent actions. The performance of DDPG, DCP and related algorithms are tested and compared in two experiments. Our results show that, DCP is effective, efficient and qualified to perform all tasks that DDPG can perform. More importantly, DCP is less likely to be influenced by the action space margins, DCP can provide more safety in avoiding training failure and local optimum, and gain more robustness in applications with dynamic hard margins in the action space. Another advantage is that, complex penalty for margin touching detection is not required, the reward function can always be brief and short.


A Cross-Lingual Summarization method based on cross-lingual Fact-relationship Graph Generation

Abstract: The aim of cross-lingual summarization (CLS) is to condense the content of a document in one language into a summary in another language. In essence, a CLS model requires both translation and summarization capabilities, which presents a unique challenge, as the model must effectively tackle the difficulties associated with both tasks simultaneously (e.g., semantic alignment, information compression and factual inconsistency). Graph-based semantic representation can model important text information in a structured manner, which may alleviate these challenges. Therefore, in this paper, we propose a Cross-Lingual Summarization method based on cross-lingual Fact-relationship Graph Generation (FGGCLS). Specifically, we first construct fact-relationship graphs for source language documents and target language summaries. Then, we introduce a cross-lingual fact-relationship graph generation method, which converts the CLS problem into a cross-lingual fact-relationship graph generation problem. This approach simplifies semantic alignment and information compression through the generation of graphs and leads to improved fact consistency. Finally, the generated fact-relationship graph of the target language summary serves as a draft for generating the summary, which enhances the quality of the generated summary. We conduct systematic experiments on the Zh2EnSum and En2ZhSum datasets, and the results demonstrate that our method can effectively improve the performance of CLS and alleviate factual inconsistency.


Secure and Real-Time Traceable Data Sharing in Cloud-Assisted IoT

Abstract: Cloud-assisted Internet of Things (IoT) has become an increasingly popular paradigm to greatly improve the performance of IoT applications by delegating the cloud to manage the massive IoT data. How to achieve secure and real time traceable data sharing is crucial in this paradigm, especially, a large amount of sensitive data produced by IoT devices need to be stored or accessed to/from the clouds. This paper proposes a secure and traceable data sharing (STDS) scheme, which leverages the decentralized information flow control model to allow data owners to not only securely and efficiently share their data produced by IoT devices with data users, but also have the capability of tracking the data users’ identity with non-repudiation based on the hash chain technique. Subsequently, the high-level Petri net, satisfiability modulo theories library, and Z3 solver are used to formally analyze and verify STDS based on bounded model checking technique to prove the correctness and security STDS. The formal analysis results show that STDS fulfills its intended security goals. Finally, the performance evaluation results have demonstrated the efficiency of STDS.


SNPF: Sensitiveness-Based Network Pruning Framework for Efficient Edge Computing

Abstract: Convolutional neural networks (CNNs) are used comprehensively in the field of the Internet of Things (IoTs), such as mobile phones, surveillance, and satellite. However, the deployment of CNNs is difficult because the structure of hand-designed networks is complicated. Therefore, we propose a sensitiveness based network pruning framework (SNPF) to reduce the size of original networks to save computation resources. SNPF will evaluate the importance of each convolutional layer by the reconstruction of inference accuracy when we add extra noise to the original model, and then remove filters in terms of the degree of sensitiveness for each layer. Compared with previous weight-norm based pruning methods such as “l1-norm”“, BatchNorm-Pruning”, and “Taylor-Pruning”, SNPF is robust to the update of parameters, which can avoid the inconsistency of evaluation for filters if the parameters of the pre-trained model are not fully optimized. Namely, SNPF can prune the network at the early training stage to save computation resources. We test our method on three prevalent models of VGG-16, ResNet-18, ResNet-50 and a customized Conv-4 with 4 convolutional layers. They are then tested on CIFAR-10, CIFAR-100, ImageNet, and MNIST, respectively. Impressively, we observe that even when the VGG-16 is only trained with 50 epochs, we can get the same evaluation of layer importance as the results when the model is fully trained. Additionally, we can also achieve comparable pruning results to previous weight-oriented methods on the other three models.


AttAuth: An Implicit Authentication Framework for Smartphone Users Using Multimodality Data

Abstract: Smartphones have become the most important devices for users to communicate and interact with different forms of media, and at the same time stored a large amount of sensitive and private data. The security and protection of such data has become increasingly critical. As the sensor technology rapid developed, the diversity of sensors on smartphones has greatly increased (e.g., motion sensor, touchscreen sensor), empowering the smartphones to provide continuous and implicit user authentication by capturing behavioral biometrics. Unfortunately, it remains a challenge to make full use of the data from the multi-modality sensors to provide accurate authentication. Towards this end, in this paper, we develop an implicit authentication framework AttAuth which explores organic integration of such multi-modality data to authenticate smartphone users through the usage session. Specifically, AttAuth first develops a series of data processing techniques to process the multi-channel motion sensor data and the discrete touchscreen data. Then, a temporal and channel-wise attention based recurrent neural network (TCA-RNN) is developed to build authentication model, which allows to jointly model continuously-monitored motion sensor data and irregularly-recorded discrete touchscreen data effectively. By generating a guidance vector based on touch events, TCA-RNN guides the temporal-wise attention mechanism on the processed multi-channel motion sensor data and outputs authentication results. We evaluate AttAuth on a real-world multi-modality smartphone usage dataset of 100 users. Extensive experiments demonstrate that AttAuth achieves the state-of-the-art authentication accuracy. Additional experiments are provided to examine the applicability of AttAuth in terms of running overheads and sensitivity to various application scenarios.


A Novel IoT-Enabled System for Real-Time Face Mask Recognition Based on Petri Nets

Abstract: Due to Coronavirus Disease 2019 (COVID-19), many countries have formulated pandemic prevention regulations, requiring the masses to wear a face mask before entering public places and taking public transportation. However, if the entrances of some places are manually controlled to check whether people are wearing a face mask or not, it becomes not only labor intensive, but also time consuming. Therefore, this paper aims to develop a face mask recognition system based on an edge computing platform. The traditional manual inspection control method is replaced by artificial intelligence (AI) technology to achieve automatic recognition and control. As an edge computing platform, Jetson Nano is an embedded system equipped with an AI platform, which can be used for object detection and image classification. Developed by Ultralytics LLC, the YOLOv5 model with PyTorch framework runs on the edge computing platform, featuring high speed, high precision, and small size. According to the model training results, the average precision (AP) reaches 95.41%, while the mean average precision (mAP) reaches 94.42%. The average single-class running time is 0.016 seconds, and the file size of training model is 3.8MB. The recognition distance is up to 8m, and the maximum face rotation angle is 90. In addition, a Petri net software tool, Workflow Petri Net Designer (WoPeD), with graphical features based on mathematical theories, is used to verify the mask recognition system; and ensures the system has acceptable precision and recall values.


An Efficient FPGA-based Depthwise Separable Convolutional Neural Network Accelerator with Hardware Pruning

Abstract: Convolutional neural networks (CNNs) have been widely deployed in computer vision tasks. However, the computation and resource intensive characteristics of CNN bring obstacles to its application on embedded systems. This article proposes an efficient inference accelerator on Field Programmable Gate Array (FPGA) for CNNs with depthwise separable convolutions. To improve the accelerator efficiency, we make four contributions: (1) an efficient convolution engine with multiple strategies for exploiting parallelism and a configurable adder tree are designed to support three types of convolution operations; (2) a dedicated architecture combined with input buffers is designed for the bottleneck network structure to reduce data transmission time; (3) a hardware padding scheme to eliminate invalid padding operations is proposed; and (4) a hardware-assisted pruning method is developed to support online tradeoff between model accuracy and power consumption. Experimental results show that for MobileNetV2 the accelerator achieves 10× and 6× energy efficiency improvement over the CPU and GPU implementation, and 302.3 frames per second and 181.8 GOPS performance that is the best among several existing single-engine accelerators on FPGAs. The proposed hardware-assisted pruning method can effectively reduce 59.7% power consumption at the accuracy loss within 5%.


Reference Tracking Optimization With Obstacle Avoidance via Task Prioritization for Automated Driving

Abstract: Obstacle avoidance is a fundamental operation for automated driving and its formulation traditionally originates from robotics and decision making control fields. Given the high complexity required to compute an obstacle-free trajectory, this operation is usually demanded to a lower frequency planning layer that provides then a trajectory reference to be followed by a higher frequency control layer. As a result, whenever replanning is needed (for example, due to a new detected obstacle), the control layer must wait for a new planned trajectory to be generated. In this paper, we propose a novel methodology to approach obstacle avoidance already in the control layer, which allows a prompter response. In particular, we show how obstacle avoidance and reference tracking can be integrated, thus with no need to switch among different controllers, based on a null-space based behavioral control approach, implemented in a (possibly nonlinear) model predictive control scheme. We demonstrate practical implementation of the proposed methodology employing two different vehicle dynamic models and in four different (urban and highway) scenarios. Furthermore, we provide a sensitivity analysis to understand how parameters choice affects the automated vehicle behavior.


PBFL: Privacy-Preserving and Byzantine-Robust Federated-Learning-Empowered Industry 4.0

Abstract: In Industry 4.0, Artificial Intelligence (AI) has been successfully applied in scenarios such as fault prediction, traffic analysis, and production decision-making. However, due to the sensitivity and security of data, privacy regulations prohibit the transfer and exchange of industrial data between entities, resulting in training data being fragmented into data silos that limit the accuracy of AI models. Federated learning (FL) can effectively break the data silo effect, but naive FL (FedAvg) is vulnerable to inference attacks from aggregators and Byzantine attacks from participants. To address these issues, we propose a privacy-preserving and Byzantine-robust federated learning scheme (PBFL) for Industry 4.0. Under the setting of an benign-majority participants, PBFL can always identify benign direction and magnitude of updates. Extensive experiments demonstrate that PBFL is more robust than state-of-the-art schemes, even with extreme proportion (49%) of malicious participants. Moreover, PBFL contains a series of well-optimized 2-Party computation (2PC) protocols, causing it reduces total runtime of the unoptimized implementation by around 3×~4× and 9×~10× for 32-bit and 64-bit circuits, respectively.


Adaptive Modulation and Retransmission Scheme for Semantic Communication Systems

Abstract: Traditional adaptive modulation scheme aims to maximize the spectral efficiency by selecting the appropriate modulation scheme under the premise of perfect bit-level data transmission. However, in task-oriented semantic communication systems, imperfect transmission can still lead to a good inference performance of semantic tasks due to the error correction capability of neural networks. In this paper, we propose novel adaptive modulation and retransmission schemes to maximize the spectral efficiency while guaranteeing the performance of semantic tasks. Specifically, we first introduce the robustness verification problem in semantic communication systems to analyze the robustness of neural network inference. We then formulate and solve the modulation scheme selection problem constrained by a robustness probability threshold. Consequently, a novel adaptive modulation scheme is developed to maximize the spectral efficiency while guaranteeing the goal of semantic communication. We also develop a retransmission scheme using existing combining techniques to further increase the data rate under harsh channel conditions. Extensive simulations are performed in unencoded and encoded semantic communication systems to validate the effectiveness of the proposed schemes.


Labels Are Culprits: Defending Gradient Attack on Privacy

Abstract: Federated learning (FL) is widely studied for local privacy protection, and it involves exchanging model parameters rather than raw data among clients. However, gradient attacks (GAs) make a malicious client or parameter server of FL infer the local data of other clients only based on the model parameters exchanged. In FL frameworks and processes, it is important to understand the features that provide heuristic information for inferring raw data, as well as how best to defend against GAs. The academic community is currently investigating this problem. In this study, we demonstrate that the labels of input samples play a key role in the success of GAs. We analyze the rank of the coefficient matrix of the non-homogeneous linear equation of gradients and input samples and propose an approach that performs special operations on the repetition and order of labels. The approach achieves a better defense effect against GAs without using a differential privacy (DP) framework. Our experimental results show that GAs fail (i.e., without leaking any valid information about local data) during the entire training process of a deep convolutional network in FL, and the accuracy of the network is less affected than that of DP. The code is available at https://github.com/zhaohuali/Label-based-Defense.


Time Estimation for a New Block Generation in Blockchain-Enabled Internet of Things

Abstract: The Internet of Things (IoT) has emerged with Distributed Ledger Technology (DLT) to address existing scalability challenges and improve the trustworthiness of machine-tomachine communication. Among the numerous potential benefits of combining IoT and DLT, Blockchain, a subset of DLT, is a crucial enabler to accelerate secure IoT adoption. Appending a new block to a blockchain, especially in a blockchain-based IoT ecosystem, requires more delay than expected. This delay is one of several issues limiting the broader adoption of blockchain within the IoT domain. To assess this delay, we develop a new comprehensive model to estimate the time required to generate a new block in a blockchain-enabled IoT system. To this end, we develop sub-computation models and compare time consumption associated with the block generation process by conducting an extensive analysis of the following selected IoT layers: device layer, cluster head layer, fog/edge layer, and cloud layer. Our study identifies potential time-consuming steps in adding a new block to a network. Our results demonstrate that the type of blockchain framework and data encryption algorithms could affect the block generation time and that Avalanche, Conflux, Algorand, Polkadot Hyperledger Fabric outperforms Ethereum in terms of block generation time in IoT networks. On the other hand, the blockchain framework does not play a significant role in block generation time for smaller data packets. We also observed the benefit of using 256-bit ECC (elliptic curve cryptography) encryption and the fog layer in IoT networks to enhance the scalability of the block generation process. All in all, our results indicate that the total block generation time varies depending on the selected IoT framework, data encryption algorithm, blockchain type, and key functions of the layers. However, we found that time delays associated with queuing or block size are negligible relative to the other key components of block generation time.


Conversational recommender based on graph sparsification and multi-hop attention

Abstract: Conversational recommender systems provide users with item recommendations via interactive dialogues. Existing methods using graph neural networks have been proven to be an adequate representation of the learning framework for knowledge graphs. However, the knowledge graph involved in the dialogue context is vast and noisy, especially the noise graph nodes, which restrict the primary node’s aggregation to neighbor nodes. In addition, although the recurrent neural network can encode the local structure of word sequences in a dialogue context, it may still be challenging to remember long-term dependencies. To tackle these problems, we propose a sparse multi-hop conversational recommender model named SMCR, which accurately identifies important edges through matching items, thus reducing the computational complexity of sparse graphs. Specifically, we design a multi-hop attention network to encode dialogue context, which can quickly encode the long dialogue sequences to capture the long-term dependencies. Furthermore, we utilize a variational auto-encoder to learn topic information for capturing syntactic dependencies. Extensive experiments on the travel dialogue dataset show significant improvements in our proposed model over the state-of-the-art methods in evaluating recommendation and dialogue generation.


Reparameterizing and dynamically quantizing image features for image generation

Abstract: For autoregressive image generation, vector-quantized VAEs (VQ-VAEs) quantize image features with discrete codebook entries and reconstruct images from quantized features. However, they treat each codebook entry separately, which causes losses of image details. In this paper, we propose to reparameterize image features with weight vectors to treat all codebook entries as an entity, and present a novel dynamically vector quantized VAE (DVQ-VAE) to quantize reparameterized image features. Specifically, each image feature corresponds to a weight vector and we sum weighted codebook entries to obtain values of image features. In this way, image features can incorporate information from different codebook entries. Additionally, a novel continuous weight regularization loss is proposed to improve the reconstruction of image details. Our method achieves competitive results with prior state-of-the-art works for image generation and extensive experiments are conducted to take a deep insight into our DVQ-VAE.


Facial expression-enhanced recommendation for virtual fitting rooms

Abstract: With the development of Augmented Reality (AR) technology in the retail industry, virtual fitting room (VFR) are considered promising enhancement of e-commerce by providing users with an immersive environment to try on new products, especially fashion products. While allowing users having more vivid impression of products, virtual fitting rooms also offer sellers more channels to collect information on user preferences, which can be used to enhance recommender systems. This study proposes to leverage facial expression recognition technology together with fine-grained human-computer interactions in virtual fitting rooms to personalize product recommendations. This paper proposes a recommendation algorithm based on confidence setting, negative feedback sampling, and matrix factorization to model user behaviors in virtual fitting rooms. We conduct an experiment on 81 subjects to evaluate the proposed method. Experimental results show the proposed method outperforms existing methods using traditional behavior information. Our study provides a strong support to the value of AR in enhancing e-commerce.


A High-Performance, Conflict-Free Memory-Access Architecture for Modular Polynomial Multiplication

Abstract: In this paper, we present the HiCoP architecture, a high-performance, conflict-free memory access, modular polynomial multiplication design that accelerates the number theoretic transform (NTT), inverse NTT (INTT), and modular polynomial multiplications. To optimize hardware costs, the HiCoP architecture utilizes a high-radix reconfigurable butterfly unit (RBU) that can be dynamically configured to perform NTT, INTT, and point-wise multiplications, alongside an area-efficient Montgomery modular multiplier (MMM) tailored for NTT-friendly modulus. Moreover, by integrating pre-processing, post-processing, and Montgomery domain transformations into NTT and INTT operations, we effectively minimize the cycle count for modular polynomial multiplication. Additionally, we propose a novel conflict-free memory access algorithm that simplifies the control logic and eliminates the need for ping-pong memory in the HiCoP architecture. Experimental results of modular polynomial multiplications demonstrate significant performance gains for the HiCoP architecture implemented on the Xilinx Virtex-7 field-programmable gate array (FPGA) platform, with up to 8.75×, 4.15×, 10.57×, and 8.50× improvements in throughput-to-hardware-cost ratio for LUT count, FF count, BRAM count, and DSP count, respectively.


Augmented Reality Navigation System for Biliary Interventional Procedures With Dynamic Respiratory Motion Correction

Abstract: Biliary interventional procedures require physicians to track the interventional instrument tip (Tip) precisely with X-ray image. However, Tip positioning relies heavily on the physicians' experience due to the limitations of X-ray imaging and the respiratory interference, which leads to biliary damage, prolonged operation time, and increased X-ray radiation.We construct an augmented reality (AR) navigation system for biliary interventional procedures. It includes system calibration, respiratory motion correction and fusion navigation. Firstly, the magnetic and 3D computed tomography (CT) coordinates are aligned through system calibration. Secondly, a respiratory motion correction method based on manifold regularization is proposed to correct the misalignment of the two coordinates caused by respiratory motion. Thirdly, the virtual biliary, liver and Tip from CT are overlapped to the corresponding position of the patient for dynamic virtual-real fusion.Our system is respectively evaluated and achieved an average alignment error of 0.75 ± 0.17 mm and 2.79 ± 0.46 mm on phantoms and patients. The navigation experiments conducted on phantoms achieve an average Tip positioning error of 0.98 ± 0.15 mm and an average fusion error of 1.67 ± 0.34 mm after correction.Our system can automatically register the Tip to the corresponding location in CT, and dynamically overlap the 3D virtual model onto patients to provide accurate and intuitive AR navigation.This study demonstrates the clinical potential of our system by assisting physicians during biliary interventional procedures. Our system enables dynamic visualization of virtual model on patients, reducing the reliance on contrast agents and X-ray usage.


Multi-view fusion for universal translation quality estimation

Abstract: Machine translation quality estimation (QE) aims to evaluate the result of translation without reference. Despite the progress it has made, state-of-the-art QE models are proven to be biased. More specifically, they over-rely on spurious statistical features while ignoring the bilingual semantic adequacy, leading to performance degradation. Besides, existing approaches require large amounts of annotation data, restricting their applications in new domains and languages. In this work, we propose a universal framework for quality estimation based on multi-view fusion. We first introduce noise to the target side of the parallel sentence pair, either by pre-trained language model or by large language model. After that, with the clean parallel pairs and the noised pairs as different views, the QE model is trained to distinguish the clean pairs from the noised ones. Our method can improve the accuracy and generalizability in supervised scenario, and can solely perform estimation in zero-shot scenario. We perform experiments on WMT QE evaluation datasets under different scenarios, verifying the effectiveness of our method. We also make an in-depth investigation of the bias of QE model.


SCH: Symmetric Consistent Hashing for cross-modal retrieval

Abstract: When handling large-scale multimodal data, hashing-based retrieval methods have gained significant attention due to their advantages in terms of low storage consumption and quick query speed. Despite the good performance of supervised hashing techniques, there are still certain limitations that need to be addressed. Firstly, they ignore valuable information such as reconstruction residuals when employing matrix factorization to learn the low-dimensional space. Secondly, most methods map heterogeneous data to the same feature space, which is unreasonable because different modalities possess distinct dimensions and distributions. Lastly, some methods relax constraints in order to optimize discrete hash codes, leading to considerable quantization errors. To solve the above problems, a novel two-step hashing-based technology called Symmetric Consistent Hashing (SCH) is proposed. Specifically, in the first step, two separate latent semantic spaces are learned by leveraging Principal Component Analysis to mitigate principal energy loss and Locality Preserving Projections to preserve local similarity. Then, the latent spaces of different modalities are aligned to facilitate the sharing of high-level semantic information by exploiting modality consistency. Next, hash codes are learned from distinct latent spaces, which are embedded in the second step for learning hash functions through Kernel Logistic Regression. To validate the effectiveness of SCH, extensive experiments on three publicly available benchmark datasets are accomplished. The results demonstrate the superiority of SCH over state-of-the-art hashing baselines, thus confirming the potency of the proposed approach.


Gaze estimation with semi-supervised eye landmark detection as an auxiliary task

Abstract: The changes in gaze are often reflected in the movements of eye landmarks, highlighting the relevance of eye landmark learning for accurate gaze estimation. To leverage eye landmarks, we propose a gaze estimation framework that incorporates eye landmark detection as an auxiliary task. However, obtaining eye landmark annotations for real-world gaze datasets is challenging. To address this, we exploit synthetic data, which provides precise eye landmark labels, by jointly training an eye landmark detector using labeled synthetic data and unlabeled real-world data in a semi-supervised manner. To reduce the influence of discrepancy between synthetic and real-world data, we improve the generalization ability of the landmark detector by performing a self-supervised learning strategy on a large scale of unlabeled real-world images. The proposed method outperforms other state-of-the-art gaze estimation methods on three gaze datasets, indicating the effectiveness of leveraging eye landmark detection as an auxiliary task to enhance gaze estimation performance.


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Abstract: For B∈Fq[T] of degree 2n≥2, consider the number of ways of writing B=E2+γF2, where γ∈Fq⁎ is fixed, and E,F∈Fq[T] with degE=n and degF=m<n. We denote this restricted sum-of-squares function by Sγ;m(B). We obtain an exact formula for the variance of Sγ;m(B) over short intervals in Fq[T] where q is an odd prime power. Interestingly, when m+1≤n≤2m, the variance vanishes on some (not all) short intervals, which is in contrast to the standard function that counts representations as a sum of two squares. We use the method of additive characters and Hankel matrices that we previously used for the variance and correlations of the divisor function. In Section 2, we give a short overview of our approach. Section 3 gives new results relating to values of quadratic forms, over Fq, that are defined from Hankel matrices.


DFLNet: Deep Federated Learning Network With Privacy Preserving for Vehicular LoRa Nodes Fingerprinting

Abstract: The rapid advancement of the Internet of Things (IoT) and intelligent vehicle networks has led to the widespread deployment of massive long-range (LoRa) nodes. These nodes play a crucial role in facilitating the sharing of critical information towards various vehicles. However, a significant challenge that persists is the secure authentication of LoRa. Recently, radio frequency fingerprinting (RFF) emerges as a robust potential solution, offering both rapid and scalable identification capabilities. By harnessing machine learning (ML) techniques, the inherent hardware-level, unique, and resilient features of RFF can be throughly explored. Given the inherent non-identical (heterogeneity) of LoRa networks and the non-stationary characteristics of chirp signals, conventional approaches prove inadequate in effectively capturing the RFF features. To surmount these challenges, a novel scheme termed as “deep federated learning network (DFLNet)” is proposed. DFLNet facilitates distributed RFF learning while ensuring privacy preservation within diverse LoRa networks. Addressing the heterogeneity of datasets, we incorporate an adaptive optimizer that contributes to the efficient convergence improvement. The DFLNet adeptly manages computational demands and privacy concerns through the utilization of mobile edge computing (MEC) servers, thus enabling distributed learning. Empirical evaluations substantiate the efficacy of DFLNet, attaining an impressive identification accuracy of up to approximately 96.7% utilizing merely around 5000 samples per client, while concurrently ensuring a robust privacy preservation.


AGRIC: Artificial-Intelligence-Based Green Routing for Industrial Cyber–Physical System Pertaining to Extreme Environment

Abstract: Industrial Cyber-Physical Systems (ICPS) can play a crucial role in damage assessment during extreme conditions by leveraging their integration of physical infrastructure, sensing capabilities, and advanced analytics. However, due to the wireless sensing devices that are made to operate in ICPS, there is a dire need to address the green routing (energy-efficient) challenges through an optimized solution. In recent times, Artificial Intelligence (AI) has had a significant impact on Wireless Sensor Networks (WSNs) designed to operate as ICPS components. In this research work, we present AGRIC: AI-based Green Routing for ICPS. While following the cluster-based routing, the election of Cluster Head (CH) is executed using our proposed AI-inspired Extended Spotted Hyena Levy Flight Optimization (ESHLFO) algorithm. Furthermore, to address the energy hole problem, four energy-unlimited data collection nodes are used around the periphery of the network. The results of the experiment demonstrate the fact AGRIC delivers network longevity and supreme performance in the context of stability time, throughput, and energy left over in the network as important performance indicators.


Graph-Based Data Publication via Differentially Structural Inference

Abstract: Due to the rapid development of Internet of Things, a large number of data are collected and published. Nevertheless, the process of data publication entails the risk of data privacy disclosure. Most of the related works can be largely classified into data publication based on anonymity, differential privacy, and graph. However, these existing works either cannot provide theoretically provable privacy protection, or only considered one kind of data attribute and thus cannot guarantee the desirable data utility. To this end, we propose a graph-based data publication scheme via differentially structural inference that can provide theoretically provable differential privacy for individuals, and maintain desirable data utility in many practical applications rather than a certain kind of statistics or data mining results. The main idea is to map the dataset to be published into a data graph, and further use the hierarchical random graph model in statistics to encode the structure of the data graph into dendrograms. Then, we use the Markov Chain Monte Carlo to infer an optimal dendrogram, and moreover design threshold strategy to differentially disturb the optimal dendrogram. Finally, we generate the sanitized data graph based on the disturbed optimal dendrogram, and further map the sanitized data graph to the sanitized dataset to be published. Thereafter, we theoretically prove the performance boundaries of both the privacy preservation and the data utility guarantees provided in our work. Furthermore, the extensive experimental results on two real-world datasets demonstrate that the proposed scheme is superior to the existing work and Baseline, guaranteeing the data utility and preserving the data privacy in many practical applications.


Matching Game for Multi-Task Federated Learning in Internet of Vehicles

Abstract: To overcome the inherent defects of massive data uploading and processing in traditional machine learning, federated learning is emerged as a promising tool given that it enables to implement privacy-preserved distributed machine learning in Internet of Vehicles (IoV). However, the performance of the federated learning suffers from several challenges, especially ineffective execution of delay-sensitive tasks triggered simultaneously by moving vehicles. To minimize the total execution delay of multiple tasks, we propose a multi-task federated learning framework which improves task completion rate and enables each task to be completed in time. Moreover, we also aim to improve the network utility of the IoV. The algorithm of joint optimization algorithm is proposed to achieve a stable partition of vehicle coalitions based on the block coordinate descent (BCD) method, the matching game-based coalition method, and gradient projection method. The performance of the proposed multi-task federated learning is evaluated through numerical simulations in terms of total latency, network utility, and accuracy of federated learning tasks. The results show that our proposed multi-task federated learning framework and algorithm guarantees the completion of multiple delay-sensitive tasks effectively while improving vehicular network utility.


Quantum remote control utilizing multiple degrees of freedom

Abstract: We present protocols for remote implementation of general operators between two nodes, which are considered: the first where an arbitrary single-qubit operation acts on photon state in the polarization degree of freedom, and the second with photon state in the temporal degree of freedom. By employing simultaneous entanglement in two independent degrees of freedom, both protocols are fulfilled near deterministically in two steps. In the first step, the effective cross-Kerr interaction and X-quadrature measurements of the coherent states are main tools for achieving the goal. Together with the available existing optical elements and common single-photon detectors, the construction circuit of the second step is simple and feasible with current technologies. Our protocols are beneficial to long-distance quantum communication and quantum metrology.


Improved error tolerance of programmable photonic integrated circuits for MNIST handwritten digit classification

Abstract: Programmable photonic integrated circuits (PPICs) can realize linear optical unitary transformation between arbitrary multi-port input and multi-port output for optical computing and signal processing. With the scale increase of PPICs, the accumulation of manufacturing errors will lead to overall performance deviations. A symmetrical Mach-Zehnder interferometer (MZI) circuit with a shorter optical path and lower propagation loss has been proposed recently to replace the traditional asymmetrical MZI circuit. However, no systematic research on the error compensation of symmetrical MZI circuits has been reported yet. In this paper, the error tolerance characteristics of symmetrical MZI circuits and asymmetrical MZI circuits are analyzed and compared in detail. An effective strategy to compensate the 3 dB beam splitter error of a symmetrical MZI circuit is proposed. A deep learning task of the Modified National Institute of Standards and Technology (MNIST) handwritten digit dataset classification was carried out with two hidden layers, which are implemented by rectangular symmetrical MZI circuit structures. The recognition accuracy can be significantly improved with this error compensation method.


Unified Representation Learning for Multi-View Clustering by Between/Within View Deep Majorization

Abstract: Multi-view data are characterized by a variety of features that are semantically coherent, with different views usually complementing each other. Multi-view clustering seeks to learn a unified representation that is comprehensive yet discriminative to partition the samples. However, existing research ignored the possible redundancy of latent feature in each view, then yielded a unified task-oriented feature based on the correlation of these multi-view latent features without explicit consideration of the influence of the unified feature. Such a way failed to accurately measure the association among views. To fully take the association into account, we propose a deep multi-view Unified Representation Learning network, named by deep-URL, using measurement between multi-view data to realize desirable unified representation. Specifically, deep-URL employs a multi-view encoder-decoder framework that maps multi-scale data into a same latent space to obtain view-specific latent representation. Within each view, the view-specific features are pruned to eliminate the redundant information in the latent space by minimizing the within-view mutual information. Between the multiple views, a unified informative representation is derived to accurately estimate the nonlinear associations between views via maximizing conditional mutual information of the view-specific features. The deep unified representation is further elaborated to achieve the full reconstruction of each view to ensure its reliability. The proposed network is trained by between/within view deep representation majorization. It achieves redundant representation removal within each view, unified features integration across views, and universal reconstruction via decoding. Extensive experiments on five real datasets with largely varied sample sizes demonstrate that the deep-URL achieved superior clustering performance by comparing with fourteen baseline methods.


Distributed Bayesian target tracking with reduced communication: Likelihood consensus 2.0

Abstract: The likelihood consensus (LC) enables Bayesian target tracking in a decentralized sensor network with possibly nonlinear and non-Gaussian sensor characteristics. Here, we propose an evolved LC methodology—dubbed “LC 2.0”—with significantly reduced intersensor communication. LC 2.0 uses multiple refinements of the original LC including a sparsity-promoting calculation of expansion coefficients, the use of a B-spline dictionary, a distributed adaptive calculation of the relevant state-space region, and efficient binary representations. We consider the use of the proposed LC 2.0 within a distributed particle filter and within a distributed particle-based probabilistic data association filter. Our simulation results demonstrate that a reduction of intersensor communication by a factor of about 190 can be obtained without compromising the tracking performance.


Enhanced D-CNN architecture and centroid-based algorithm for real-time vehicle tracking and accident detection from surveillance videos

Abstract: In this new era of intelligence and automation, it is important to develop intelligent software to analyse traffic data and detect abnormal activities occurring in the public. Information from GPS, Surveillance cameras, traffic management systems etc will be helpful for the researchers to develop such algorithms. In this research work, we propose a method to detect traffic accidents and used a deep convolutional neural network (D-CNN) and Centroid based vehicle tracking algorithm for vehicle detection. Overlapping bounding boxes and speed of the vehicle are considered for collision detection. The vehicle is tracked using a centroid tracking algorithm to find acceleration, speed and trajectory values of each vehicle in the continuous frames. The trajectory and angle change after the collision can be used to classify the accidents. The result shows a detection accuracy of 99% in such a way outperforms the other latest methods. The results from the proposed method can be used in several accident reconstruction softwares like PC crash, ARPro etc.


GAF-MAE: A Self-Supervised Automatic Modulation Classification Method Based on Gramian Angular Field and Masked Autoencoder

Abstract: With the development of deep learning (DL), several fields have ushered in leapfrog development, such as image classification and natural language processing. Combing the powerful feature extraction tool, DL-based Automatic Modulation Classification (AMC) emerges. However, most DL-based AMC methods require massive labeled samples for training, which is difficult for non-cooperative scenarios. In this paper, we propose a novel self-supervised AMC method called GAF-MAE. Gramian Angular Field (GAF) is applied for domain transformation from time series to images first, then a Masked Autoencoder (MAE) is built for self-supervised reconstruction tasks through unlabeled samples. After self-supervised pretrain, a small number of labeled samples are used for downstream classification finetuning. We conduct a comprehensive evaluation of a public dataset RadioML.2016.10a. The simulation results show that GAF-MAE can achieve a relatively high average accuracy of 54.85% even if the label proportion is 5% and 61.38% when the label proportion reaches 100%, which outperforms other well-known models.


Towards constrained optimization of cloud applications: A hybrid approach

Abstract: A database system is one of the most common cloud applications where elasticity allows to dynamically allocate resources in response to changing workload demands. In such systems, users usually configure resources based on empirical decisions, for example by boosting computational resources with the expectation of improving database throughput. The latter also suggests that users manually scale resources and tune the database configurations offline to meet the application demands. However, this approach could rapidly increase infrastructure expenditures while this is also time consuming. In this paper, we propose SONA, a framework to support constrained performance optimization of cloud applications using a hybrid approach of artificial neural networks and genetic algorithms. The proposed framework monitors the source system to identify the optimal configurations that maximize application performance based on genuine workload executions. As a result, the experimental analysis presents a novel dataset collected from TPC-C runs on MySQL server. The optimization process is being constrained to satisfy user and application requirements including the cloud infrastructure expenditures, the cost-performance ratio, the baseline performance and the average percentage of idle CPU resources. Furthermore, SONA uses a cloned containerized environment that replicates the main application to avoid system overhead during the optimization process. Our results demonstrate the effectiveness of SONA framework to optimize application performance of OLTP applications deployed on cloud.


Controllable Style Transfer via Test-time Training of Implicit Neural Representation

Abstract: Existing CNN-based style transfer frameworks have suffered from inaccurate control of pixel-wise stylization. As the CNN operation is designed based on kernel-wise operation, such a design unavoidably makes pixels affect each other. To mitigate this problem, we propose a controllable style transfer framework that leverages Implicit Neural Representation to encode each pixel respectively and optimize each style and content pair via test-time training. Unlike previous CNN-based style transfer frameworks, this formulation naturally enables accurate pixel-wise stylization control. In addition, to give explicit controllability on the degree of stylization, we define two vectors that represent the content and style respectively, enabling control by interpolating these vectors. We further demonstrate that, after being test-time trained once, our framework can show a various range of applications by precisely controlling the stylized images pixel-wise and freely adjusting image resolution and degree of stylization without further optimization or training.


Information geometry based extreme low-bit neural network for point cloud

Abstract: Deep learning has significantly advanced three-dimensional computer vision applied to point clouds. Nevertheless, the substantial consumption of time, storage, and energy substantially limits its deployment on edge devices with constrained resources. Extremely low bit quantization has received wide attention due to its extremely high compression ratio, but the problem of a significant drop in accuracy cannot be ignored. To alleviate the obvious accuracy degradation for extreme low-bit quantization, this paper proposes a novel compression framework for binary and ternary neural networks applied to point clouds, which introduces information geometry to model quantization to compensate the severe feature manifold distortion. It applies differential geometry on manifolds to study the implicit information of the point cloud feature data. Based on the theoretical analysis from the novel perspective of information geometry, two optimization modules are proposed to alleviate severe geometry distortions on differential manifolds. The first module, scaling recovery, provides layer-wise scaling parameters to reduce geometric distortion caused by quantization. The second module, Pooling Recovery, is specially designed to alleviate more severe pooling geometry distortions in point clouds. These two modules benefit both binary and ternary neural networks with ignored overheads. For ternary quantization, optimizations on convolution weights and gradients are additionally introduced. The proposed self-adaptive gradient estimation provides a more accurate approximation to the non-differential ternary staircase function. Convolution weight optimization is implemented on an information-geometry optimized model to achieve even higher accuracy and less memory consumption. Experimental results validate that the proposed models significantly outperform state-of-the-art methods and demonstrate better scalability. Overall, this compression framework has the potential to facilitate the deployment of deep learning models on edge devices with limited resources, opening up new opportunities for applications of three-dimensional computer vision.


Blind quality-based pairwise ranking of contrast changed color images using deep networks

Abstract: Next-generation multimedia networks are expected to provide systems and applications with top Quality of Experience (QoE) to users. To this end, robust quality evaluation metrics are critical. Unfortunately, most current research focuses only on modeling and evaluating mainly distortions across the pipeline of multimedia networks. While distortions are important, it is also as important to consider the effects of enhancement and other manipulations of multimedia content, especially images and videos. In contrast to most existing works dedicated to evaluating image/video quality in its traditional context, very few research efforts have been devoted to Image Quality Enhancement Assessment (IQEA) and more specifically, Contrast Enhancement Evaluation (CEE). Our contribution fills this gap by proposing a pairwise ranking scheme for estimating and evaluating the perceptual quality of image contrast change (contrast enhancement and/or contrast-distorted images) process. We propose a novel Deep Learning-based Blind Quality pairwise Ranking scheme for Contrast-Changed (Deep-BQRCC) images. This method provides an automatic pairwise ranking of a set of contrast-changed images. The proposed framework is based on using a pair of Convolutional Neural Networks (CNN) together with a saliency-based attention model and a color-difference visual map. Extensive experiments were conducted to validate the effectiveness of the proposed workflow through an ablation analysis. Different combinations of CNN models and pooling strategies were analyzed. The proposed Deep-BQRCC approach was evaluated over three dedicated publicly available datasets. The experimental results showed an increase in performance within a range of 3–10% compared to state-of-the-art IQEA measures.


Fast anchor graph preserving projections

Abstract: The existing graph-based dimensionality reduction algorithms need to learn an adjacency matrix or construct it in advance, therefore the time complexity of the graph-based dimensionality reduction algorithms is not less than O(n2d), where n denotes the number of samples, d denotes the number of dimensions. Moreover, the existing dimensionality reduction algorithms do not consider the cluster information in the original space, resulting in the weakening or even loss of valuable information after dimensionality reduction. To address the above problems, we propose Fast Anchor Graph Preserving Projections (FAGPP), which learns the projection matrix, the anchors and the membership matrix at the same time. Especially, FAGPP has a built-in Principal Component Analysis (PCA) item, which makes our model not only deal with the cluster information of data, but also deal with the global information of data. The time complexity of FAGPP is O(nmd), where m denotes the number of the anchors and m is much less than n. We propose a novel iterative algorithm to solve the proposed model and the convergence of the algorithm is proved theoretically. The experimental results on a large number of high-dimensional benchmark image data sets demonstrate the efficiency of FAGPP. The data sets and the source code are available from https://github.com/511lab/FAGPP.


Arbitrary-scale Super-resolution via Deep Learning: A Comprehensive Survey

Abstract: Super-resolution (SR) is an essential class of low-level vision tasks, which aims to improve the resolution of images or videos in computer vision. In recent years, significant progress has been made in image and video super-resolution techniques based on deep learning. Nevertheless, most of the methods only consider SR with a few integer scale factors, which limits the application of the SR techniques to real-world problems. Recently, the methods to achieve arbitrary-scale super-resolution via a single model have attracted much attention. However, there is no work to thoroughly analyze the arbitrary-scale methods based on deep learning. In this work, we present a comprehensive and systematic review of 45 existing deep learning-based methods for arbitrary-scale image and video SR. We first classify the existing SR methods according to the resolved scale factors. Furthermore, we propose an in-depth taxonomy for state-of-the-art methods based on the core problem of how to achieve arbitrary-scale super-resolution, i.e., how to perform arbitrary-scale upsampling. Moreover, the performance of existing arbitrary-scale SR methods is compared, and their advantages and limitations are analyzed. We also provide some guidance for the selection of these methods in different real-world applications. Finally, we briefly discuss the future directions of arbitrary-scale super-resolution, which shows some inspirations for the progress of subsequent works on arbitrary-scale image and video super-resolution tasks. The repository of this work is available at https://github.com/Weepingchestnut/Arbitrary-Scale-SR.


A scalable dynamic ensemble selection using fuzzy hyperboxes

Abstract: Dynamic ensemble selection (DES) systems work by estimating the level of competence of each classifier from a pool of classifiers and selecting the most competent ones for the classification of a given test instance during inference time. The majority of dynamic ensemble selection (DES) methods evaluate the competence of classifiers using the K-Nearest Neighbors to the unknown query sample. However, KNN is very sensitive to local data distribution and needs to store all data in memory. Moreover, it performs several computations for each individual query sample. Thus, relying on the KNN technique hampers the use of DES approaches for large-scale problems and situations where data distributions are non-uniform. This article introduces a novel DES framework called FH-DES, which employs fuzzy hyperboxes to generate a competence map or incompetence map for each classifier. The competence map is generated from correctly classified samples to indicate the competence level of the classifier at each data point in the feature space, whereas the incompetence map, which shows regions where the classifier has low accuracy, is generated from misclassified samples. In this way, we can assess the competence or incompetence level of the classifier just by using the map without having to process previous samples. This feature results in a more accurate dynamic selection system with lower computational complexity compared to other dynamic selection methods. Moreover, we introduce several hyperbox expansion and contraction strategies that add incremental learning capability to the framework while keeping the computational cost low. Experimental results demonstrate that FH-DES achieves high classification accuracy with lower complexity than state-of-the-art dynamic selection methods. The source code for FH-DES is available at https://github.com/redavtalab/FH-DES.


GP-BART: A novel Bayesian additive regression trees approach using Gaussian processes

Abstract: The Bayesian additive regression trees (BART) model is an ensemble method extensively and successfully used in regression tasks due to its consistently strong predictive performance and its ability to quantify uncertainty. BART combines "weak" tree models through a set of shrinkage priors, whereby each tree explains a small portion of the variability in the data. However, the lack of smoothness and the absence of an explicit covariance structure over the observations in standard BART can yield poor performance in cases where such assumptions would be necessary. The Gaussian processes Bayesian additive regression trees (GP-BART) model is an extension of BART which addresses this limitation by assuming Gaussian process (GP) priors for the predictions of each terminal node among all trees. The model's effectiveness is demonstrated through applications to simulated and real-world data, surpassing the performance of traditional modeling approaches in various scenarios.


ProFi: Scalable and Efficient Website Fingerprinting

Abstract: Website Fingerprinting (WFP) attacks infer the websites or webpages a user is visiting from encrypted traffic. To date, it remains uncertain if WFP can attack many users from a central location in an online scenario. We close this gap with PROFI, a WFP attack that detects websites based on the initial TLS connection from the client to the server using at most the connection’s first 30 packets. PROFI achieves a precision and recall of 86.51 % and 85.35 % in a closed-world, and 68.90 % and 78.71 % in an open-world scenario, which is competitive to state-of-the-art (SoA) WFP attacks, while taking a fraction of the time of SoA attacks to classify a webpage. Further, we implement PROFI as a micro service-based prototype and evaluate the attack in an online scenario with real traffic traces. We show that PROFI can monitor up to 100 websites at 10 G, corresponding to up to 424 webpages per second. We also show that PROFI has the potential to interfere with a victim’s webpage access.


Dynamic gradient reactivation for backward compatible person re-identification

Abstract: We study the backward compatible problem for person re-identification (Re-ID), which aims to constrain the features of an updated new model to be comparable with the existing features from the old model in galleries. Most of the existing works adopt distillation-based methods, which focus on pushing new features to imitate the distribution of the old ones. However, the distillation-based methods are intrinsically sub-optimal since it forces the new feature space to imitate the inferior old feature space. To address this issue, we propose the Ranking-based Backward Compatible Learning (RBCL), which directly optimizes the ranking metric between new features and old features. Different from previous methods, RBCL only pushes the new features to find best-ranking positions in the old feature space instead of strictly alignment, and is in line with the ultimate goal of backward retrieval. However, the sharp sigmoid function used to make the ranking metric differentiable also incurs the gradient vanish issue, therefore stems the ranking refinement during the later period of training. To address this issue, we propose the Dynamic Gradient Reactivation (DGR), which can reactivate the suppressed gradients by adding dynamically computed constants during the forward step. To further help target the best-ranking positions, we include the Neighbor Context Agents (NCAs) to approximate the entire old feature space during training. Unlike previous works that mainly test on the in-domain settings, we make the early attempt to introduce the cross-domain settings (including both supervised and unsupervised) for the backward compatible person Re-ID task, which are more challenging yet meaningful. The experimental results on all five settings show that the proposed RBCL outperforms previous state-of-the-art methods by large margins.


Event-Based Deep Reinforcement Learning for Quantum Control

Abstract: Deep reinforcement learning (DRL) has been widely used in many quantum control tasks, in which rewards are used to guide the agent in acquiring an optimal control strategy. However, designing suitable rewards is usually challenging, which seriously affects the DRL's application performance, especially for complex quantum control tasks. In this article, we propose a simple and general reward design method by constructing some events, where events are defined by fidelity intervals. In the proposed event-based DRL (EDRL), events are used to describe the evolution of a quantum system. Rewards are designed according to several defined events rather than a large number of specific evolved quantum states, thereby resulting in a reduction in dimensions to be considered. With designed event-attributed rewards, the agent can capture the main evolution characteristics of a quantum system, and then obtain an effective control strategy. The effectiveness of the proposed EDRL method is verified by three quantum systems: i.e., 1) the one-qubit closed quantum system, 2) the two-qubit closed quantum system, and 3) the two-level open quantum system. Numerical comparisons demonstrate that the proposed EDRL outperforms the traditional DRL algorithms (deep Q-network and proximal policy optimization) with some representative reward methods for solving quantum control problems.


Design of English viewing, listening, and speaking mobile teaching software based on an interactive digital media algorithm

Abstract: China has now embraced the information era, which has had a significant impact on everyday life, employment, and educational practices. Information technology has also had a significant impact on the growth of the education sector, resulting in a fast-paced and resource-rich setting for student interaction. Through the network platform, various interactive software can improve students’ learning methods, especially language teaching software. English audio-visual speaking is software for training English language listening and speaking, which can carry out relevant oral activities and topic discussions according to the imported materials. As a result, you can assist pupils in using the vocabulary and knowledge associated with the subject, which will increase their interest in learning. English teachers can fully prepare for speaking and listening tasks in the classroom by using audio-visual speaking. At the same time, through the learning of TV and movie trailers, English audio-visual speaking can provide readers with background knowledge, which is ready for readers to fully understand the language and content in the video materials. Based on information technology, this paper constructs English audio-visual and oral mobile teaching software, which depends on interactive digital media algorithms. Through the mobile teaching software for English audio-visual speaking, students can form good English listening and reading habits, which will provide important help for English language learning.First, this essay examines the value and benefits of mobile applications for providing English instruction orally and visually, which might help to illustrate the need for software development. The research then suggests various algorithms for English that are related to audio, visual, and oral input that can detect, assess, and correct students’ learning mistakes. Finally, this work develops the fundamental methodology of the audio-visual and verbal mobile software for instruction in English.


Economic and Energy-Efficient Wireless Federated Learning Based on Stackelberg Game

Abstract: Wireless federated learning (FL) is a distributed learning framework, in which mobile devices (MDs) collaboratively train a model while protecting data privacy. Considering selfishness of MDs, they are unwilling to sacrifice their resources to assist model training without any reward, which may limit performance of FL. In this paper, we propose an incentive mechanism between a base station (BS) and several mobile devices (MDs) modeled as a Stackelberg game. During this game, BS first decides the total reward that can be distributed to MDs, and then each MD determines its number of local iterations according to its utility function. In addition, we derive the closed-form expressions of the optimal reward function of the BS and the number of local iterations of the MDs. Finally, the numerical results validate the efficacy of our proposed scheme.


Collaborative Human Recognition With Lightweight Models in Drone-Based Search and Rescue Operations

Abstract: Due to the flexibility of drones, it is promising to use them in Search and Rescue (SAR) operations for intelligently searching for lost humans over large areas. However, the limited computing resources of drones pose challenges when deploying deep neural network models. To address this problem, we design a lightweight human recognition model for drones, combining the Ghost Module and the Mobilenetv3 Block. The Ghost Module generates more feature maps with fewer parameters, and the squeeze-and-excitation (SE) attention module in the Mobilenetv3 Block greatly improves recognition accuracy. Furthermore, recognizing that relying solely on the human recognition model offers limited assistance in SAR operations, we propose a collaborative recognition mode between the drone and the rescue command center (RCC). In this collaborative recognition mode, we design an offloading model for deployment on the drone. The offloading model learns from the middle layer perception features of the lightweight recognition model and selectively offloads the vision taken by the drone to the RCC. The recognition results provided by the RCC are used to update the parameters of the offloading model. We introduce a reinforcement learning algorithm, a dual-buffer-based proximal policy optimization algorithm (DBPPO), to train the offloading model with the goal of maximizing accuracy and recall while minimizing the offloading ratio. Specifically, we incorporate an additional data buffer for training the Actor network in the PPO algorithm in a supervised manner, with supervised training interspersed throughout the PPO training process. Eventually, experiments comparing different methods demonstrate the effectiveness of the lightweight recognition model and the collaborative recognition mode in SAR operations.


GDB: Gated Convolutions-based Document Binarization

Abstract: Document binarization is a crucial pre-processing step for various document analysis tasks. However, existing methods fail to accurately capture stroke edges, primarily due to the inherent limitations of vanilla convolutions and the absence of adequate boundary-related supervision during stroke edge extraction. In this paper, we formulate text extraction as the learning of gating values and propose an end-to-end network architecture based on gated convolutions, named GDB, to address the problem of imprecise stroke edge extraction. The gated convolutions enable the selective extraction of stroke feature with different attention. Our proposed framework comprises two stages. Firstly, a coarse sub-network with an extra edge branch is trained to enhance the precision of feature maps by incorporating a priori mask and edge information. Secondly, a refinement sub-network is cascaded to enhance the output of the first stage using gated convolutions based on the sharp edges. To effectively incorporate global information, GDB also integrates a parallelized multi-scale operation that combines local and global features. We conduct comprehensive experiments on ten Document Image Binarization Contest (DIBCO) datasets from 2009 to 2019 and Document Deblurring Datasets. Experimental results show that our proposed methods outperform the state-of-the-art methods across all metrics on average. Extensive ablation studys demonstrate the efficacy of key components. Available codes: https://github.com/Royalvice/GDB.


ReFuSeAct: Representation fusion using self-supervised learning for activity recognition in next generation networks

Abstract: Over the years, wearable sensors have gained a lot of attention from the research community due to their non-invasive nature, adoption of sensors by general public, and their applicability in healthcare services. With the advancements in communication networks, machine learning methods, and wearable sensor deployment, it is essential to design a method that could accurately classify human activities while reducing the dependence on annotated data. Traditional machine learning approaches require large-scale annotated data in order to provide a reasonable recognition performance. Recently, self-supervised learning methods are proposed but they are either limited to single sensor devices or fail to model intra-modal correlations within the self-supervised learning paradigm. In this work, we propose Representation Fusion using Self-supervised learning for Activity Recognition (ReFuSeAct) framework that uses modality-specific encoders, attention encoders, and decision-level fusion strategies to address the aforementioned limitations. The self-supervised learning paradigm ensures that the method achieves better performance even with less amount of annotated data. The architecture proposed for modality-specific encoder ensures that extraction of representative features that could help in improving recognition performance. The feature-level fusion performed using the proposed attention encoders enhances the quality of representative features that could be used in supervised learning phase. Finally, the decision-level fusion strategy enhances the activity recognition accuracy in comparison to the single deep learning classifier. Our experimental analysis shows that the proposed approach records 9.1% improvement over semi-supervised learning baselines and more than 2% improvement in comparison to existing self-supervised learning approaches.


Low-light image enhancement using gamma correction prior in mixed color spaces

Abstract: In this paper, we propose an efficient and fast low-light image enhancement method using an atmospheric scattering model based on an inverted low-light image. The transmission map is derived as a function of two saturations of the original image in the two color spaces. Due to the difficulty in estimating the saturation of the original image, the transmission map is converted into a function of the average and maximum values of the original image. These two values are estimated from a given low-light image using the gamma correction prior. In addition, a pixel-adaptive gamma value determination algorithm is proposed to prevent under- or over-enhancement. The proposed algorithm is fast because it does not require the training or refinement process. The simulation results show that the proposed low-light enhancement scheme outperforms state-of-the-art approaches regarding both computational simplicity and enhancement efficiency. The code is available on https://github.com/TripleJ2543.


A study of error correction method based on selective focus for curved rail profile measurement

Abstract: Rail profile detection is an important means of railway operation and maintenance. Currently, structured light profile measurement technology is mainly used for vehicle-mounted rail profile measurement. The body attitude of the vehicle will change as it passes by a curve, and the laser plane of the rail profile measurement device would be non-perpendicular to the rail in the local longitudinal direction, resulting in errors in rail profile measurement. To address this issue, an error correction method based on selective focus for curved rail profile measurement is proposed. The virtual 3D rail is generated with the section image of a single-beam laser and projected onto the image plane, which is then combined with the projection data of virtual 3D rail and the actual rail from the rail image to build the optimization function. The optimal auxiliary plane perpendicular to the rail's longitudinal direction is then determined. Finally, error correction is achieved by projecting the measured data onto the optimal auxiliary plane. The effectiveness and accuracy of the proposed method are validated through experiments, demonstrating that regardless of the laser plane's relative pitch or yaw motion with respect to the rail, the corrected profile wear measurement error was under 0.10 mm, satisfying the requirement for rail wear measurement accuracy. In this study, the plane perpendicular to the longitudinal direction of the rail was obtained through selective focus, thus providing a new insight into future work on rail profile measurement error correction.


SemiAMR: Semi-Supervised Automatic Modulation Recognition With Corrected Pseudo-Label and Consistency Regularization

Abstract: In modern communication systems, automatic modulation recognition (AMR) has broad application prospects but a large amount of training data is usually required. Due to the high cost of adding annotations to signals, it is of great significance to study AMR under the condition of few labeled samples. In this paper, an end-to-end semi-supervised radio signal recognition algorithm is proposed. In view of the poor anti-noise performance of the original IQ data, short-time Fourier transform (STFT) is introduced to mine the stable modulation characteristics. In order to overcome the noise problem caused by pseudo-labels, a mixed sample data augmentation (MSDA) method PFMix is proposed, and the consistency constraint of the model disturbance based on mean teacher (MT) framework and data disturbance based on PFMix is designed. Meanwhile, in order to provide pseudo-labels with high confidence, entropy regularization is constructed by taking the prediction variance of MT framework as correction factor. The experimental results using the open source RML2016.10A dataset show the superior performance of the proposed method. The average recognition accuracy with 1% labeled data is 88.82% at 0 dB, which is a significant improvement compared with state of the art in semi-supervised AMR.


Efficient low-rank quaternion matrix completion under the learnable transforms for color image recovery

Abstract: Recent low-rank quaternion matrix completion (LRQMC) approaches have been extensively studied to recover missing data of color images. However, these methods need to frequently compute the quaternion singular value decompositions (QSVD) of the quaternion matrix, making them unsuitable for large-scale data. In this paper, we suggest an efficient LRQMC model based on the learnable transforms for color image recovery. The key idea is to project the large-scale quaternion matrix to a small-scale quaternion matrix via the semi-orthogonal transforms along each mode, which significantly reduces the computational cost of QSVD. We then apply a nonconvex approximation of rank (i.e., weighted Schatten p-norm) onto the small-scale quaternion matrix to achieve a better quaternion rank estimation. The alternating direction method of multipliers scheme is developed to solve the proposed model, and the weak convergence property of the algorithm is discussed. Experimental results on color images demonstrate that our method is considerably faster than state-of-art approaches while achieving comparative recovery performance.


SBAC: Substitution cipher access control based on blockchain for protecting personal data in metaverse

Abstract: The evolution of the metaverse necessitates inventive solutions that can uphold data security. In this paper, we present a novel approach that seamlessly integrates blockchain and substitution cipher techniques to enhance the security landscape of the metaverse. The proposed approach encompasses the generation of the rule table for substitution encryption and decryption, as well as a methodology for data encryption and decryption utilizing the rule table. Furthermore, we conducted a comprehensive evaluation of the proposed approach's security robustness, especially focusing on its resistance against potential security attacks, including the brute-force attack. Moreover, a series of performance experiments were meticulously conducted to comprehensively gauge the effectiveness and efficiency of the proposed technique. The findings revealed that the proposed approach outperforms asymmetric key algorithms in terms of encryption elapsed time. Additionally, the decryption process of the proposed approach is notably faster than both symmetric and asymmetric key algorithms. The results of security analysis and performance evaluations underscore the viability and efficiency of the proposed approach, positioning it as a promising solution for secure and swift data management within the metaverse environment.


Deep learning aided prostate cancer detection for early diagnosis & treatment using MR with TRUS images

Abstract: Although difficult, robust and reliable synchronization of multimodal medical pictures has several practical uses. For instance, in MR-TRUS fusing guided prostate treatments, picture registration between the two modalities is essential. However, due to the significant variety in image appearance and correlation, MR-TRUS picture registration remains a challenging issue. In this research, we suggest employing deep convolutional neural networks (CNN) i.e. three dimensional CNN U-NET (3D-Conv-Net) to develop a resemblance measure for MR-TRUS registration. Finally, for the second-order optimal of the taught measure, we apply a composite optimisation method that searches the solution space for an appropriate starting point. We also use a multi-stage process to improve the optimisation metric.


Blockchain assisted node validation approach for security provisioning in WSN environment

Abstract: Wireless sensor networks (WSNs) are a new technology that helps with a variety of practical uses, involving healthcare and monitoring the environment. In recent years, security has been considered as important topic in WSN since it is vulnerable to several security threats. Recent works uses cryptographic techniques to ensure security in WSN. In existing works, the security methodologies require high resources but still assure low level security. To resolve this issue, this paper proposes a node validation method which is lightweight as well as assures high level security. The main idea behind this work is to integrate Blockchain technology with WSN environment. We presented a novel Blockchain-assisted Node Validation (BlockNode) methodology for ensuring high level security. To maintain energy efficiency, the network is segregated into multiple clusters by Valid Cluster Formation (VCF) approach. In each cluster, optimum CH is selected by using type-II fuzzy algorithm. The VCF approach only allows the valid nodes which are authorized by Blockchain validation. Then, the data transmission is secured by Jacobian Curve Encryption (JCE) algorithm. For optimal route selection, Energy-aware Reinforcement Learning (ERL) algorithm is proposed. Overall, the proposed work high level security with minimum resource consumption. The experimental results obtained from NS-3.25 simulation tool confirms that the proposed work achieves better performance in security level, encryption & decryption time, delay, energy consumption, delivery ratio and throughput.


GAN-Based Siamese Neuron Network for Modulation Classification Against White-Box Adversarial Attacks

Abstract: With the extensive application of modulation classification in the field of wireless communication, various methods of modulation classification have been proposed to improve the classification performance, where the deep learning (DL)-based modulation classification technology is gradually popular. However, recent studies have discovered that the DL-based modulation classification methods are vulnerable to white-box adversarial attacks, which can significantly degrade the classification performance by injecting imperceptible perturbations into clean signals. In this paper, to defend against white-box adversarial attack, we propose a generative adversarial network (GAN)-based Siamese neuron network (GSNN) for accomplishing modulation classification, where a generator is trained to generate perturbations that can easily fool the discriminator and the discriminator tries to classify correctly both original and adversarial samples generated by the generator. The discriminator acted by a Siamese network is designed to measure the similarity of sample pair based on Euclidean distance, transferring the classification problem into pairwise comparison problem. After the training of GSNN is completed, we adopt the distance-based NCM classifier to accomplish the classification task. We verify the defense performance of our proposed GSNN on both the public dataset and simulated dataset. Simulation results illustrate that our proposed method is superior to the common SoftMax classifier.


Underwater Visibility Enhancement IoT System in Extreme Environment

Abstract: Imagery captured in extreme underwater environments often presents unique challenges, including blurred details, color distortion, and reduced contrast. These discrepancies largely emanate from the intricate interplay of light absorption and scattering within the aquatic medium. Predominant restoration techniques, rather simplistically, apply a static attenuation coefficient, neglecting the dynamic nuances of underwater conditions, leading to an inconsistent restoration outcome. To counter these impediments, we introduce an avant-garde Underwater Internet of Things (Underwater IoT) system, underpinned by a scene-depth fusion paradigm. Our methodology astutely accounts for the spectral decay of light underwater to infer a more refined attenuation coefficient tailored to the specific scene. This system, employing a quadtree decomposition for precise localization coupled with depth mapping, facilitates an astute estimation of prevailing luminescence. This depth map, once synthesized and refined, aids in gauging the precise attenuation dynamics of the aqueous milieu, culminating in a more precise transmission map derivation. Segueing from this, we employ an inverse model to refurbish the original image. Experimental results highlight our system’s prowess in counteracting issues like muddied details and chromatic anomalies while concurrently amplifying contrast. In juxtaposition with a spectrum of existing methodologies, our innovation outshines in terms of finesse and accuracy, underscoring its unparalleled efficacy in the challenging underwater conditions.


Binary Sequences With a Low Correlation via Cyclotomic Function Fields of Odd Characteristic

Abstract: Sequences with a low correlation have very important applications in communications, cryptography, and compressed sensing. In the literature, many efforts have been made to construct good sequences with various lengths, where binary sequences attract great attention. As a result, various constructions of good binary sequences have been proposed. However, most of the known constructions made use of the multiplicative cyclic group structure of finite field F pn for a prime <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p and a positive integer <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n . In fact, all <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">pn + 1 rational places including the place at infinity of the rational function field over F pn can form a cyclic structure under an automorphism of order <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">pn + 1. In this paper, we make use of this cyclic structure to provide an explicit construction of binary sequences with a low correlation of length <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">pn + 1 via cyclotomic function fields over Fpn for any odd prime <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p . Each family of binary sequences has size <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">pn -2 and its correlation is upper bounded by 4+⎣2 · <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">pn/2 ⎦. To the best of our knowledge, this is the first construction of binary sequences with a low correlation of length <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">pn +1 for odd prime <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p . Moreover, our sequences can be constructed explicitly and have competitive parameters.


Event-based diffractive neural network chip for dynamic action recognition

Abstract: Dynamic action recognition has promising applications in human–computer interaction, information encryption, and high-speed image processing. However, it is challenging for a high-dynamic scene due to the huge amount of data and the computation costs. Here, we have proposed an event-based diffractive neural network (EBDNN) chip to recognize human dynamic actions. An event camera with high dynamic range and high temporal resolution is used for dynamic action capture. Based on the approach of compressing dimensions, a series of static images can be obtained by superimposing the event frames of the intervals, which serve as the train dataset of a diffractive neural network with photon wave properties. Compared to a frame camera with the same temporal resolution, the required data volume is only 0.0048% of that, which greatly shortens the training time of the diffractive neural network on the computer and saves computing resources. Notably, the diffractive neural network was trained to perform image classification tasks at the speed of light with almost no energy consumption, and the diffractive devices, which includes 400 × 400 neurons, is printed based on the two-photon polymerization technology. In addition, the experimental accuracy was as high as 99.17%. Our work opens up a new direction for photoelectrically integrated intelligent device, paving the way for the development of real-time monitoring systems and other machine vision tasks, such as autonomous driving and video surveillance.


3D Skeleton-Based Human Motion Prediction Using Dynamic Multi-Scale Spatiotemporal Graph Recurrent Neural Networks

Abstract: A dynamic multi-scale spatiotemporal graph recurrent neural network (DMST-GRNN) model has been introduced, which is leveraged to use human motion prediction on a 3D skeleton-based human activity dataset. It offers a multi-scale approach to spatial & temporal graphs using multi-scale graph convolution units (MGCUs) to describe the human body's semantic interconnection. The proposed DMST-GRNN is an encoder-decoder framework where a series of MGCUs are used as encoders to learn spatiotemporal features and a novel graph-gated recurrent unit lite (GGRU-L) for the decoder to predict human pose. Extensive experiments have been carried out with two datasets, Human3.6M and CMU Mocap, where both short and long videos were considered to validate the performance of the proposed model. The DMST-GRNN model outperforms the existing baseline on the Human3.6M datasets by 11.95% and 7.74% of average mean angle errors (avg MAE) for short and long-term motion prediction, respectively. Similarly, CMU Mocap datasets, the DMST-GRNN model predicts future posture more accurately than the previous best approaches by 2.77% and 5.51% of average mean angle errors (avg MAE) for short and long-term motion prediction, respectively. A comparison analysis was also presented with other measures like mean angle error, prediction loss and standard deviation. A separate discussion has been included to analyze the effect of different multiscale on spatial and temporal graphs, along with the impact of MGCU unit counts.


Shift-Sum Decoding of Non-Binary Cyclic Codes

Abstract: This paper proposes a novel shift-sum decoding method for non-binary cyclic codes, which only requires finite field operations but yields advanced decoding performance. Using the cyclically different minimum-weight dual codewords (MWDCs) and their proper shifts, a frequency matrix can be obtained as a reliability metric for identifying the error positions and magnitudes. By analyzing the statistical distributions of the matrix entries, the rationale for the shift-sum decoding’s advanced error-correction capability is revealed. Based on this decoding method, a hard-decision iterative shift-sum (HISS) decoding algorithm is first proposed. It can correct errors beyond half of the code’s minimum Hamming distance. By further utilizing the reliability information obtained from the channel, a soft-decision iterative shift-sum (SISS) decoding algorithm is then proposed to improve the decoding performance. Both the HISS and the SISS algorithms are realized only with polynomial multiplications and numerical comparisons, which are hardware-friendly. To further improve the error-correction performance, the HISS and SISS algorithms can be integrated in a Chase decoding mechanism for handling the test-vectors. Simulation results on Reed-Solomon (RS) and non-binary BCH (NB-BCH) codes show that the proposed algorithms yield a competent decoding and complexity performances in comparison with the existing decoding algorithms.