Reviewerly/exHarmony huggingface dataset:
A paced multi-stage block-wise approach for object detection in thermal images

The growing advocacy of thermal imagery in applications, such as autonomous vehicles, surveillance, and COVID-19 detection, necessitates accurate object detection frameworks for the thermal domain. Conventional methods could fall short, especially in situations with poor lighting, for instance, detection during night-time. In this paper, we propose a paced multi-stage block-wise framework for effectively detecting objects from thermal images. Our approach utilizes the pre-existing knowledge of deep neural network-based object detectors trained on large-scale natural image data to enhance performance in the thermal domain constructively. The employed, multi-stage approach drives our model to achieve higher accuracies. And the introduction of the pace parameter during domain adaption enables efficient training. Our experimental results demonstrate that the framework outperforms previous benchmarks on the FLIR ADAS dataset on the person, bicycle, and car categories. We have also illustrated further analysis of the framework, such as the effect of its components on accuracy and training efficiency, its generalizability to other thermal datasets, and its superior performance on night-time images in contrast to state-of-the-art RGB object detectors.
Review on Content-based Image Retrieval Models for Efficient Feature Extraction for Data Analysis

Recent advances in multimedia tools and the explosion of online image archives have prompted numerous studies on efficiently retrieving and managing visual data. All kinds of professionals, from journalists to designers to art historians, have the same problem: finding a certain photograph in a big collection. Image retrieval issues prompted academics to develop new methods for representing and indexing visual information. Content-Based Image Retrieval (CBIR) is a new approach to image retrieval relies on qualities intrinsic to the images themselves. A search is "content-based" looks at the image's actual content. Color, texture, shape, and spatial relationships are image content descriptors. Various real-world computer vision applications use multimedia content analysis and digital images make up the bulk of multimedia data. There has been an exponential increase in the complexity of multimedia items in recent years, particularly photos. Millions of images are published to various repositories such as Instagram, Facebook, and other social media. Computer vision researchers have difficulty finding a relevant image in an archive. The majority of search engines use traditional text-based algorithms rely on captions and metadata to find photos. CBIR, image classification, and image analysis have all seen an outbreak of retrieval activities in the recent two decades. Feature vectors are numerical values to represent high-level visuals in CBIR and image classification models. The image feature set and human visual comprehension have a large gap. This research addresses the gaps between picture feature extraction and human visual perception. This paper will discuss numerous recent advancements in CBIR and picture representation extensively. Analysis of various image retrieval and picture representation models, from low-level feature extraction to contemporary semantic deep learning approaches, has been analyzed. A comprehensive review is conducted on key CBIR and image representation principles and recommendations for additional study in the area.
Resource Consumption for Supporting Federated Learning in Wireless Networks

Federated learning (FL) has recently become one of the hottest focuses in wireless edge networks with the ever-increasing computing capability of user equipment (UE). In FL, UEs train local machine learning models and transmit them to an aggregator, where a global model is formed and then sent back to UEs. In wireless networks, local training and model transmission can be unsuccessful due to constrained computing resources, wireless channel impairments, bandwidth limitations, etc., which degrades FL performance in model accuracy and/or training time. Moreover, we need to quantify the benefits and cost of deploying edge intelligence, as model training and transmission consume certain amount of resources. Therefore, it is imperative to deeply understand the relationship between FL performance and multiple-dimensional resources. In this paper, we construct an analytical model to investigate the relationship between the FL model accuracy and consumed resources in FL empowered wireless edge networks. Based on the analytical model, we explicitly quantify the model accuracy, available computing resources and communication resources. Numerical results validate the effectiveness of our theoretical modeling and analysis, and demonstrate the trade-off between the communication and computing resources for achieving a certain model accuracy.
Effects of data and entity ablation on multitask learning models for biomedical entity recognition

Training domain-specific named entity recognition (NER) models requires high quality hand curated gold standard datasets which are time-consuming and expensive to create. Furthermore, the storage and memory required to deploy NLP models can be prohibitive when the number of tasks is large. In this work, we explore utilizing multi-task learning to reduce the amount of training data needed to train new domain-specific models. We evaluate our system across 22 distinct biomedical NER datasets and evaluate the extent to which transfer learning helps task performance using two forms of ablation.We found that multitasking models generally do not improve performance, but in many cases perform on par compared to single-task models. However, we show that in some cases, new unseen tasks can be trained as a single model using less data by starting with weights from a multitask model and improve performance.The software underlying this article are available in: https://github.com/NLPatVCU/multitasking_bert-1.
Using Singular Value Decomposition and Chaotic Maps for Selective Encryption of Video Feeds in Smart Traffic Management

Traffic management in a smart city mainly relies on video feeds from various sources such as street cameras, car dash cams, traffic signal cameras, and so on. Ensuring the confidentiality of these video feeds during transmission is necessary. However, due to these devices’ poor processing power and memory capacity, the applicability of traditional encryption algorithms is not feasible. Therefore, a selective encryption system based on singular value decomposition (SVD) and chaotic maps is presented in this study. The proposed cryptosystem can be used in smart traffic management. We apply SVD to identify the most significant parts of each frame of the video feed for encryption. Chaotic systems were deployed to achieve high diffusion and confusion properties in the resulted cipher. Our results suggest that the computational overhead is significantly less than that of the traditional approaches with no compromise on the strength of the encryption.
A ‘Divide and Embed’ approach in a robust crypto-watermarking technique for enhancing the embedding capacity

Medical Image watermarking techniques have a major concern of addressing the issue of embedding capacity as the electronic health record (EHR) of a patient is large in size. Embedding large size watermark into medical images degrades the perceptual quality of the image which may lead to changes in morphological features of the tissues or organs. The main aim of the proposed work is to address the challenge of the trade-off between embedding capacity and image quality along with maintaining robustness. A ‘Divide and Embed’ approach is proposed to enhance the embedding capacity without affecting the image perceptibility and robustness. This approach is based on the availability of multiple medical images with a single patient’s record. The patient information used as a watermark is divided into several parts equal to the number of medical images available with a patient record and embedded separately using a region-based crypto-watermarking technique with a wavelet-based hybrid embedding process. The projected technique is tested by varying embedding capacity to check for both imperceptibility and robustness against various unintentional and intentional attacks for both CT scan and MR Images. The PSNR achieved is 77.9 dB for the higher embedding capacity of 56,548 pixels by maintaining the robustness with average BER to 0.0609 and average NC to 0.9406 after applying various attacks. Therefore, the proposed approach has enhanced the embedding capacity by maintaining the perceptual quality of the medical image as well as robustness is also consistent as compared to the other researcher’s work.
Fragile watermarking scheme for tamper localization in images using logistic map and singular value decomposition

This paper presents a fragile watermarking scheme for tamper localization using Singular Value Decomposition (SVD) and logistic map. The proposed scheme divides the image into blocks of size 2 × 2 pixels and generates an 8-bit watermark from each block. The watermark is computed by permuting the six Most Significant Bits (MSBs) of each pixel in the block using the logistic map, followed by SVD. To secure, the watermark thus generated is further encrypted using the logistic map. This encrypted watermark is embedded into 2 Least Significant Bits (LSBs) of each pixel to enable tamper detection and localization. The experimental results demonstrate that the proposed scheme can precisely locate tampered regions under copy-paste, content removal, text addition, noise addition, vector quantization, collage, content only, and constant feature attacks. Tamper localization accuracy is better or comparable to the state-of-the-art tamper localization algorithms.
Efficient Dynamic Searchable Encryption with Forward Privacy under the Decent Leakage

Dynamic searchable symmetric encryption (SSE) enables clients to update and search encrypted data stored on a server and provides efficient search operations instead of leakages of inconsequential information. The amount of permitted leakage is a crucial factor of dynamic SSE; more leakage allows us to design an efficient scheme, while leakage attacks tell us that the leakage has a real-world impact. Leakage-abuse attacks (NDSS 2012) and subsequent works suggest that dynamic SSE schemes should not unnecessarily reveal extra information during the search procedure, and in particular, file-injection attacks (USENIX Security 2016) showed that forward privacy, which restricts the leakage during the addition procedure, is a vital security notion for dynamic SSE. In this paper, we propose a new dynamic SSE scheme with a good balance of efficiency and security levels; our scheme achieves both high efficiency and forward-privacy and only requires the decent leakage, i.e., only allows the leakage of search and access patterns during search operations. Specifically, we first show there is still no such scheme by uncovering a flaw in the security proof of Etemad et al.'s scheme (PoPETs 2018) and showing that extra leakage is required to fix it. We then propose the first forward-private dynamic SSE scheme that only requires symmetric-key primitives and the standard, decent leakage to prove the security. Although the client's information is slightly larger than existing schemes, our experimental results show that our scheme is comparable to Etemad et al.'s scheme, which is the most-efficient-ever scheme with forward privacy, in terms of efficiency.
Multi-Path Siamese Convolution Network for Offline Handwritten Signature Verification

The existing methods are difficult to maintain the balance of high accuracy and low model capacity for writer-independent offline signature verification. Thus, this paper developed a novel deep learning model named Multi-Path Attention Siamese Convolution Network (MA-SCN), which consists of three modules: the generation module of different size inputs, ensuring the same size of the signature strokes and the different size of the image background; the feature extraction module based on the fusion attention mechanism, focusing on the stroke features; the verification module with optimal weight coefficients, improving the model's performance. The experimental results show that the setting of different factors such as the multi-branch structure, the fusion attention mechanism, and the weight coefficients is effective for improving the accuracy of offline signature verification on the three public datasets (CEDAR, Bengali, Hindi). Moreover, compared with the state-of-the-art offline signature verification model, our model has fewer parameters.
Deep Learning based Analysis on Code-Mixed Tamil Text for Sentiment Classification with Pre-Trained ULMFiT

Sentiment Analysis is the process of getting people&#x2019;s ideas on what they think or feel about a particular issue or product or a person and classifying the information expressed about an issue in a positive, negative or a neutral manner. This extracted information can be found very much useful in determining popularity of a person or a product and they are very much useful in ecommerce in suggesting products to buy and in social media like YouTube in suggesting videos to view. Mostly people express their views and share their thoughts in their mother Tongue along with usage of mixed language words is common nowadays. Hence Sentiment analysis of codemixed language plays a major role. There is only little work available in Tamil mixed Sentiment analysis. In order to know the opinion of people who speak Tamil in an effective manner an effective algorithm is needed. Since there are many algorithms available in machine learning and deep learning, this work aims to find sentiment in code mixed words. Deep learning based Bi-LSTM model with ULMFiT Embedding gives more promising results for code-mixed language than other existing algorithms.
Information leakage due to operative errors in card-based protocols

Card-based protocols provide secure multi-party computation using a deck of physical cards via a series of operations, such as shuffling and turning over cards, that are executed by humans. Although almost all existing protocols have been designed to be perfectly secure, operative errors by humans can lead to information leakage. In this paper, we propose a technique for analyzing information leakage due to operative errors in card-based protocols. In particular, we introduce a concept, which we call a “probability trace,” and by enhancing the KWH diagram proposed by Koch, Walzer, and Härtel, we propose a diagrammatic representation for displaying the amount of information leaked. By applying our diagram to a card-based protocol with an operative error, we can precisely reveal the leakage of players' inputs from the protocol output. In addition, we discuss how to detect human errors when people execute the existing six-card AND protocol.
Performance Evaluation of NFT Trading Platform Based on Hyperledger Fabric Blockchain

This paper proposes a NFT (Non-Fungible Token) trading platform over Hyperledger Fabric blockchain, proceeding with performance evaluation to confirm innovatory speed improvement and power conservation. The growing public interest for NFTs in recent years has played a major role in expanding the market size. However, as a result of expansion, massive overload to the Ethereum network occurred, leading to high gas costs for trading NFT assets, which is rather considered a burdensome amount. To overcome this problem, we introduce a NFT trading platform on Hyperledger Fabric with reference to ERC-721 standard of Ethereum. ERC-721 standard is implemented by Hyperledger Fabric chaincode, which is similar to Ethereum smart contract, where all features of the current protocol are implemented. Parallel processing of Hyperledger fabric enables fast NFT trade management with minimal cost since no gas cost is required with Hyperledger Fabric Raft consensus protocol. It is applicable to fields other than the most actively traded works of art. By providing an optimized trading environment, we expect further development of the NFT trading industry.
SAR-DRDNet: A SAR image despeckling network with detail recovery

Synthetic aperture radar (SAR) image is affected by inherent speckle, greatly hindering its interpretation and subsequent applications. In SAR images despeckling tasks, it is still challenging to remove speckle while preserving spatial details. Traditional methods generally require a complicated design of relationships between the speckled and noise-free SAR images. The growing popularity of deep learning algorithms exhibits great potential in resolving this limitation. However, the existing deep learning-based despeckling methods need further improvement in speckle suppression and details preservation. Therefore, we propose an end-to-end SAR images despeckling residual network with detail recovery, named SAR-DRDNet, which is mainly composed of non-local blocks (NLBs) and detail recovery blocks (DRBs). NLB takes full advantage of the global information of the SAR image to suppress speckle. DRB is further employed to recover lost details in the process of NLBs, taking into account the multi-scale contextual information of pixels and has a larger receptive field without reducing the resolution of feature maps. We validate the proposed SAR-DRDNet on the simulated and real SAR data. Both quantitative and qualitative comparisons demonstrate the superiority of the proposed SAR-DRDNet over the many mainstream methods, evidenced by its capability to achieve a great balance between speckle suppression and textural details preservation.
Do economic crises cause trading in Bitcoin?

Purpose The paper provides new evidence for Bitcoin’s safe-haven property by examining the relationship between currency price, return and Bitcoin trading volume. Design/methodology/approach A unique dataset from a person-to-person (p2p) exchange is used to investigate association between Bitcoin trading volume and currency prices. Currency returns are used to identify local economic crises, the 8 crisis affected currencies are Venezuela Bolivar (VES), Iranian Rial (IRR), Ukrainian Hryvnia (UAH), Argentine Peso (ARS), Egyptian Pound (EGP), Nigerian Naira (NGN), Turkish Lira (TRY) and Kazakhstani Tenge (KZT). Findings The paper demonstrates that local economic crises are positively associated with increased Bitcoin trading. There is a negative association between trading volume and currency value (and return), suggesting low currency price and currency depreciation are accompanied with increased Bitcoin trading. The results not only hold for the crisis affected currencies but also currencies of advanced economies. Granger causality test also reinforces the negative association results. Originality/value The finding indicates some forms of flight-to-safety have occurred during local market crises when capital flight from domestic markets to Bitcoin, strengthening Bitcoin’s hedging asset status. However, total global trading volume declines after the start of the COVID pandemic, suggesting that Bitcoin is still regarded as a speculative asset. Overall, the findings show that Bitcoin is a hedging asset to protect against local currency depreciation, but not a safe-haven asset for the global crisis.
A Fuzzy Grouping Genetic Algorithm for Solving a Real-World Virtual Machine Placement Problem in a Healthcare-Cloud

Due to the large-scale development of cloud computing, data center electricity energy costs have increased rapidly. Energy saving has become a major research direction of virtual machine placement problems. At the same time, the multi-dimensional resources on the cloud should be used in a balanced manner in order to avoid resources waste. In this context, this paper addresses a real-world virtual machine placement problem arising in a Healthcare-Cloud (H-Cloud) of hospitals chain in Saudi Arabia, considering server power consumption and resource utilization. As a part of optimizing both objectives, user service quality has to be taken into account. In fact, user quality of service (QoS) is also considered by measuring the Service-Level Agreement (SLA) violation rate. This problem is modeled as a multi-objective virtual machine placement problem with the objective of minimizing power consumption, resource utilization, and SLA violation rate. To solve this challenging problem, a fuzzy grouping genetic algorithm (FGGA) is proposed. Considering that multiple optimization objectives may have different degrees of influence on the problem, the fitness function of the proposed algorithm is calculated with fuzzy logic-based function. The experimental results show the effectiveness of the proposed algorithm.
Three phases of quantum annealing: Fast, slow, and very slow

Currently, existing quantum annealers have proven themselves as viable technology for the first practical applications in the noisy-intermediate-scale-quantum era. However, to fully exploit their capabilities, a comprehensive characterization of their finite-time excitations is instrumental. To this end, we develop a phase diagram for driven Ising chains, from which the scaling behavior of the excess work can be read off as a function of process duration and system size. "Fast" processes are well described by the Kibble-Zurek mechanism; "slow" processes are governed by effective Landau-Zener dynamics; and "very slow" processes can be approximated with adiabatic perturbation theory.
A robust handwritten recognition system for learning on different data restriction scenarios

Handwritten Text Recognition (HTR) systems have gained interest in fields of academic research and commercial applications. Deep learning techniques, and more precisely Convolutional Neural Networks (CNNs), have enabled many recent successes in the computer vision community. However, due to high computational costs, applying CNNs to many real applications is challenging since the specific training data is restricted in many cases. Therefore, in this paper, we present a Gated-CNN-BGRU optical model capable of dealing with this complex challenge. The proposed model was evaluated on five well-known datasets in HTR (Bentham, IAM, RIMES, Saint Gall, and Washington). Additionally, we redefine the training and validation partitions for each dataset, progressively varying the percentage of data between both partitions to create a total of 50 scenarios with different data volumes. The experiment validates and shows that the proposed model presents statistically significant results, surpassing the current models by an average of 2.96 and 8.91 percentage points in character and word recognition accuracy. In the most complex scenario of using 49 images for training, we achieved character and word precision of 87.25% and 71.54% respectively. That means an improvement of 78.32 and 53.54 percentage points, respectively, of the state-of-the-art optical models.
DFC-D: A dynamic weight-based multiple features combination for real-time moving object detection

Abstract Real-time moving object detection is an emerging method in Industry 5.0, that is applied in video surveillance, video coding, human-computer interaction, IoT, robotics, smart home, smart environment, edge and fog computing, cloud computing, and so on. One of the main issues is accurate moving object detection in real-time in a video with challenging background scenes. Numerous existing approaches used multiple features simultaneously to address the problem but did not consider any adaptive/dynamic weight factor to combine these feature spaces. Being inspired by these observations, we propose a background subtraction-based real-time moving object detection method, called DFC-D. This proposal determines an adaptive/dynamic weight factor to provide a weighted fusion of non-smoothing color/gray intensity and non-smoothing gradient magnitude. Moreover, the color-gradient background difference and segmentation noise are employed to modify thresholds and background samples. Our proposed solution achieves the best trade-off between detection accuracy and algorithmic complexity on the benchmark datasets while comparing with the state-of-the-art approaches.