Reviewerly/exHarmony huggingface dataset:
Physical model and image translation fused network for single-image dehazing

The visibility and contrast of images captured in adverse weather such as haze or fog degrade dramatically, which further hinders the accomplishment of high-level computer vision tasks such as object detection and semantic segmentation in these conditions. Many methods have been proposed to solve image dehazing problem by using image translation networks or physical model embedding in CNNs. However, the physical model cannot effectively describe the hazy generation process in complex scenes and estimating the model parameters with only a hazy image is an ill-posed problem. Image translation-based methods may lead to artefacts or colour shifts in the recovered results without the guidance or constraints of physical model information. In this paper, an end-to-end physical model and image translation fused network is proposed to generate realistic haze-free images. Since the transmission map can express the haze distribution in the scene, the proposed method adopts an encoder with a multiscale residual block to extract hazy image features, and two separate decoders to recover a clear image and to estimate the transmission map. The multiscale features of the transmission map and image translation are fused to guide the decode processes with a conditional attention feature fusion block, which is composed of sequential channelwise and spatialwise attention. Moreover, a multitask and multiscale deep supervision mechanism is adopted to enhance the feature fusion and recover more image details. The algorithm can efficiently fuse the physical model information and the hazy image translation to address the problem existent in the methods only based on physical model embedding or direct image translation. Experimental results on the visual quality enhancement of hazy images and semantic segmentation tasks in hazy scenes demonstrate that our model can efficiently recover haze-free images, while performing on par with state-of-the-art methods.
Implementation of Secure and Privacy-aware AI Hardware using Distributed Federated Learning

In modern devices, such as smartphones and IoT, AI hardware implements different ML models to train massive amounts of data for various applications. However, based on the sensitivity of this data, privacy and security concerns, or both, may restrict users from accessing the data storage to conduct the ML training using conventional methods. Federated learning (FL) is consequently emerged to maintain training data distribution among smart mobile devices while aggregating locally processed updates. In addition to improving the model training performance as the number of clients rises, FL also creates a privacy-preserved shared data model. FL execution, however, can be time-consuming. This study proposes a parallelized approach to enhance the performance and privacy of the FL algorithm (FedAvg). In this regard, the FedAvg algorithm is expanded to our proposed model, distributed FedAvg (D-FedAvg), which enables several clients to collaborate concurrently and train a single learning model. To evaluate the performance of the proposed model, we investigated the impact of various numbers of clients and training rounds on the result. To validate our findings, we conducted extensive experiments using MNIST datasets trained using two federated learning models, MLP and CNN. The results show our D-FedAvg can maintain data privacy and dramatically enhance the execution time of FL compared to traditional FedAvg. According to the study, the FL framework's time complexity increases as the number of clients and rounds increases, but parallelization allows it to operate 2–3 times faster than usual.
Generating Lower-Cost Garbled Circuits: Logic Synthesis Can Help

Garbled circuit (GC)-based frameworks are the cornerstone of advanced secure multi-party computation (MPC) protocols in various domains. These applications, such as secure network inference, require both scalability and real-time computation. However, the data communication among parties required by GC is currently a bottleneck of its runtime performance. Most existing works focus on minimizing the number of ANDs in logic networks over the basis {AND, XOR, NOT}, represented by XOR-AND graphs (XAG). AND is the only logic primitive among the three that contributes to providing the necessary multiplicative complexity (MC) of the desired logic function but causes communication costs. Inspired by the garbling gadget technique, we conduct a thorough study on the plausibility of adopting XAGs as the underneath logic representation to generate low-cost GCs and make two proposals: (1) merging small-fanin-size ANDs in XAGs, and (2) adopting OneHot gate, rather than AND, as the logic primitive to express MC, in order to reduce garbling costs. The first proposal optimizes GCs within a shorter runtime, whereas the second reduces garbling costs more. To validate our ideas, we propose a XAG-targeted merging algorithm and a logic synthesis flow for XOR-OneHot graphs (X1G). Compared to best-known results, our XAG- and X1Gtargeted implementations achieve reductions in garbling cost by up to 25.27% and 35.48% respectively.
On Using Perceptual Loss within the U-Net Architecture for the Semantic Inpainting of Textile Artefacts with Traditional Motifs

It is impressive when one gets to see a hundreds or thousands years old artefact exhibited in the museum, whose appearance seems to have been untouched by centuries. Its restoration had been in the hands of a multidisciplinary team of experts and it had undergone a series of complex procedures. To this end, computational approaches that can support in deciding the most visually appropriate inpainting for very degraded historical items would be helpful as a second objective opinion for the restorers. The present paper thus attempts to put forward a U-Net approach with a perceptual loss for the semantic inpainting of traditional Romanian vests. Images taken of pieces from the collection of the Oltenia Museum in Craiova, along with such images with garments from the Internet, have been given to the deep learning model. The resulting numerical error for inpainting the corrupted parts is adequately low, however the visual similarity still has to be improved by considering further possibilities for finer tuning.
Backwards from zero: How the U.S. public evaluates the use of zero-day vulnerabilities in cybersecurity

Zero-day vulnerabilities are software and hardware flaws that are unknown to computer vendors. As powerful means of carrying out cyber intrusions, such vulnerabilities present a dilemma for governments. Actors that develop or procure such vulnerabilities may retain them for future use; alternatively, agencies possessing such vulnerabilities may disclose the flaws to affected vendors so they can be patched, thereby denying vulnerabilities not only to adversaries but also themselves. Previous research has explored the ethics and implications of this dilemma, but no study has investigated public opinion regarding zero-day exploits. We present results from a survey experiment testing whether conditions identified as important in the literature influence respondents’ support for disclosing or stockpiling zero-day vulnerabilities. Our results show that respondents overwhelmingly support disclosure, a conclusion only weakly affected by the likelihood that an adversary will independently discover the vulnerability. Our findings suggest a gap between public preferences and current U.S. policy.
PLS-IoT Enhancement Against Eavesdropping via Spatially Distributed Constellation Obfuscation

Wireless communication has become a ubiquitous facet of modern-day life. With the advent of Internet of Things (IoT) and massive machine-type connectivity, the number of devices is expected to increase even more. However, increased dependence on wireless connectivity is marred with risks related to user privacy and data confidentiality, especially for low-power IoT devices. Therefore, it is imperative to provide an extremely reliable secure algorithm with minimal complexity and computational expense in IoT paradigm. With that in mind, this letter presents asymmetric multi-level physical layer security (PLS) scheme, in which each transmitted symbol undergoes two types of distortion: channel-based phase distortion and multi-reception amplitude randomization. The proposed scheme offers a substantial security advantage for legitimate links, while also streamlining receiver design. Numerical results indicate that the scheme effectively confounds eavesdroppers without compromising the bit error rate of the intended receiver. Additionally, the proposed scheme provides another level of security by concealing the modulation information, requiring the receiver to detect the modulation scheme before detecting transmitted symbols.
Blockchain-Enabled Fine-Grained Searchable Encryption With Cloud–Edge Computing for Electronic Health Records Sharing

The integration of Internet of Things (IoT) with cloud–edge computing in cyber–physical systems has revolutionized the way healthcare enterprises manage electronic health records (EHRs). With more healthcare enterprises outsourcing encrypted EHRs to the cloud, searchable encryption (SE) is utilized to retrieve encrypted data, especially attribute-based SE (ABSE) can achieve fine-grained access control. However, ABSE usually requires a lot of computation, which imposes a serious burden on resource-limited devices. Moreover, ensuring fairness in data access is crucial in the healthcare domain, where both data users and owners may have conflicting interests. In order to overcome these problems, this article proposes an SE scheme with fine-grained access control for cloud-based EHRs sharing assisted by blockchain. It transfers computing tasks to edge servers and enables users to control who has access to their EHRs. The adoption of blockchain and smart contracts guarantees data integrity and transaction fairness. Moreover, a consensus algorithm is designed for the higher efficiency of the proposed scheme. Finally, security analysis proves that the proposed scheme resists adaptive chosen keyword attacks (CKAs). Performance analysis further confirms that it has more functionalities and is efficient for smart healthcare.
Integrated deep learning paradigm for document-based sentiment analysis

An integrated deep learning paradigm for the analysis of document-based sentiments is presented in this article. Generally, sentiment analysis has enormous applications in the real world, particularly in e-commerce and/or cloud computing-oriented businesses. Integrated deep learning paradigms for document-based sentiment analysis seek to efficiently categorize the polarity of contextual sentiments into positive, negative, and neutral to aid organizations in making informed decisions. Nonetheless, the sparsity of text and disambiguation of natural languages make it relatively difficult for existing methods to provide precise identification and extraction when subjected to document-based data. As a result, this study introduces BERT-MultiLayered Convolutional Neural Network (B-MLCNN) as a computationally viable integrated deep learning paradigm. The B-MLCNN considers the overall textual review as a single document and classifies the available sentiments. First, the BERT pre-trained language model handles the feature vector representation and captures any global features. Further, the multi-layered convolutional neural network (MLCNN) with different kernel dimensions handles feature extraction. A softmax function produces classification results. The experimental setup with B-MLCNN based on IMDB movie reviews, 2002 movie reviews, 2004 movie reviews, and Amazon review datasets achieved accuracies of 95%, 88%, 95%, and 95% respectively, which promises to be efficient to deploy in practical applications.
Few-shot object segmentation with a new feature aggregation module

The success of convolutional neural network for object segmentation depends on a large amount of training data and high-quality samples. But annotating such high-quality training data for pixel-wise segmentation is labor-intensive. To reduce the massive labor work, few-shot learning has been introduced to segment objects, which uses a few samples for training without compromising the performance. However, the current few-shot models are biased towards the seen classes rather than being class-irrelevant due to lack of global context prior attention. Therefore, this study aims at proposing a few-shot object segmentation model with a new feature aggregation module. Specifically, the proposed work develops a detail-aware module to enhance the discrimination of details with diversified attributes. To enhance the semantics of each pixel, we propose a global attention module to aggregate detailed features containing semantic information. Furthermore, to improve the performance of the proposed model, the model uses support samples that represents class-specific prototype obtained by respective category prototype block. Next, the proposed model predicts label of each pixel of query sample by estimating the distance between the pixel and prototypes. Experiments on standard datasets demonstrate significance of the proposed model over SOTA in terms of segmentation with a few training samples.
<scp>sm</scp><i>ProbLog: Stable Model Semantics in ProbLog for Probabilistic Argumentation</i>

Abstract Argumentation problems are concerned with determining the acceptability of a set of arguments from their relational structure. When the available information is uncertain, probabilistic argumentation frameworks provide modeling tools to account for it. The first contribution of this paper is a novel interpretation of probabilistic argumentation frameworks as probabilistic logic programs. Probabilistic logic programs are logic programs in which some of the facts are annotated with probabilities. We show that the programs representing probabilistic argumentation frameworks do not satisfy a common assumption in probabilistic logic programming (PLP) semantics, which is, that probabilistic facts fully capture the uncertainty in the domain under investigation. The second contribution of this paper is then a novel PLP semantics for programs where a choice of probabilistic facts does not uniquely determine the truth assignment of the logical atoms. The third contribution of this paper is the implementation of a PLP system supporting this semantics: sm ProbLog. sm ProbLog is a novel PLP framework based on the PLP language ProbLog. sm ProbLog supports many inference and learning tasks typical of PLP, which, together with our first contribution, provide novel reasoning tools for probabilistic argumentation. We evaluate our approach with experiments analyzing the computational cost of the proposed algorithms and their application to a dataset of argumentation problems.
Self Attention Networks in Speaker Recognition

Recently, there has been a significant surge of interest in Self-Attention Networks (SANs) based on the Transformer architecture. This can be attributed to their notable ability for parallelization and their impressive performance across various Natural Language Processing applications. On the other hand, the utilization of large-scale, multi-purpose language models trained through self-supervision is progressively more prevalent, for tasks like speech recognition. In this context, the pre-trained model, which has been trained on extensive speech data, can be fine-tuned for particular downstream tasks like speaker verification. These massive models typically rely on SANs as their foundational architecture. Therefore, studying the potential capabilities and training challenges of such models is of utmost importance for the future generation of speaker verification systems. In this direction, we propose a speaker embedding extractor based on SANs to obtain a discriminative speaker representation given non-fixed length speech utterances. With the advancements suggested in this work, we could achieve up to 41% relative performance improvement in terms of EER compared to the naive SAN which was proposed in our previous work. Moreover, we empirically show the training instability in such architectures in terms of rank collapse and further investigate the potential solutions to alleviate this shortcoming.
Underwater Object Detection Algorithm Based on Adding Channel and Spatial Fusion Attention Mechanism

Underwater target detection is the foundation and guarantee for the autonomous operation of underwater vehicles and is one of the key technologies in marine exploration. Due to the complex and special underwater environment, the detection effect is poor, and the detection precision is not high. In this paper, YOLOv5 (You Only Look Once v5) is used as the overall structural framework of the target detection algorithm, and improvement is made on the basis of its detection precision in the underwater environment. Specifically, an attention mechanism (Channel and Spatial Fusion Attention, CSFA) that fuses the channel attention and spatial attention is proposed and added to the YOLOv5 network framework, enabling the network to focus on both the prominent features of the detected object and the spatial information of the detected object. The proposed method was tested on the underwater target detection dataset provided by the China Underwater Robot Professional Competition. The experimental detection precision (P) reached 85%, the recall (R) reached 82.2%, and the mean average precision (mAP) reached 87.5%. The effectiveness of the proposed method was verified, and its underwater target detection performance was better than that of ordinary models.
SiamPRA: An Effective Network for UAV Visual Tracking

The visual navigation system is an important module in intelligent unmanned aerial vehicle (UAV) systems as it helps to guide them autonomously by tracking visual targets. In recent years, tracking algorithms based on Siamese networks have demonstrated outstanding performance. However, their application to UAV systems has been challenging due to the limited resources available in such systems.This paper proposes a simple and efficient tracking network called the Siamese Pruned ResNet Attention (SiamPRA) network and applied to embedded platforms that can be deployed on UAVs. SiamPRA is base on the SiamFC network and incorporates ResNet-24 as its backbone. It also utilizes the spatial-channel attention mechanism, thereby achieving higher accuracy while reducing the number of computations. Further, sparse training and pruning are used to reduce the size of the model while maintaining high precision. Experimental results on the challenging benchmarks VOT2018, UAV123 and OTB100 show that SiamPRA has a higher accuracy and lower complexity than other tracking networks.
Real-Time Seismic Intensity Measurements Prediction for Earthquake Early Warning: A Systematic Literature Review

With the gradual development of and improvement in earthquake early warning systems (EEWS), more accurate real-time seismic intensity measurements (IMs) methods are needed to assess the impact range of earthquake intensities. Although traditional point source warning systems have made some progress in terms of predicting earthquake source parameters, they are still inadequate at assessing the accuracy of IMs predictions. In this paper, we aim to explore the current state of the field by reviewing real-time seismic IMs methods. First, we analyze different views on the ultimate earthquake magnitude and rupture initiation behavior. Then, we summarize the progress of IMs predictions as they relate to regional and field warnings. The applications of finite faults and simulated seismic wave fields in IMs predictions are analyzed. Finally, the methods used to evaluate IMs are discussed in terms of the accuracy of the IMs measured by different algorithms and the cost of alerts. The trend of IMs prediction methods in real time is diversified, and the integration of various types of warning algorithms and of various configurations of seismic station equipment in an integrated earthquake warning network is an important development trend for future EEWS construction.
Reconstruction of Compressed Hyperspectral Image Using SqueezeNet Coupled Dense Attentional Net

This study addresses image denoising alongside the compression and reconstruction of hyperspectral images (HSIs) using deep learning techniques, since the research community is striving to produce effective results to utilize hyperspectral data. Here, the SqueezeNet architecture is trained with a Gaussian noise model to predict and discriminate noisy pixels of HSI to obtain a clean image as output. The denoised image is further processed by the tunable spectral filter (TSF), which is a dual-level prediction filter to produce a compressed image. Subsequently, the compressed image is analyzed through a dense attentional net (DAN) model for reconstruction by reverse dual-level prediction operation. All the proposed mechanisms are employed in Python and evaluated using a Ben-Gurion University-Interdisciplinary Computational Vision Laboratory (BGU-ICVL) dataset. The results of SqueezeNet architecture applied to the dataset produced the denoised output with a Peak Signal to Noise Ratio (PSNR) value of 45.43 dB. The TSF implemented to the denoised images provided compression with a Mean Square Error (MSE) value of 8.334. Subsequently, the DAN model executed and produced reconstructed images with a Structural Similarity Index Measure (SSIM) value of 0.9964 dB. The study proved that each stage of the proposed approach resulted in a quality output, and the developed model is more effective to further utilize the HSI. This model can be well utilized using HSI data for mineral exploration.
Literature Review of the Recent Trends and Applications in Various Fuzzy Rule-Based Systems

Fuzzy rule based systems (FRBSs) is a rule-based system which uses linguistic fuzzy variables as antecedents and consequent to represent human understandable knowledge. They have been applied to various applications and areas throughout the soft computing literature. However, FRBSs suffers from many drawbacks such as uncertainty representation, high number of rules, interpretability loss, high computational time for learning etc. To overcome these issues with FRBSs, there exists many extensions of FRBSs. This paper presents an overview and literature review of recent trends on various types and prominent areas of fuzzy systems (FRBSs) namely genetic fuzzy system (GFS), hierarchical fuzzy system (HFS), neuro fuzzy system (NFS), evolving fuzzy system (eFS), FRBSs for big data, FRBSs for imbalanced data, interpretability in FRBSs and FRBSs which use cluster centroids as fuzzy rules. The review is for years 2010-2021. This paper also highlights important contributions, publication statistics and current trends in the field. The paper also addresses several open research areas which need further attention from the FRBSs research community.