Reviewerly/exHarmony huggingface dataset:
Lightweight Monocular Depth Estimation through Guided Decoding

We present a lightweight encoder-decoder architecture for monocular depth estimation, specifically designed for embedded platforms. Our main contribution is the Guided Upsampling Block (GUB) for building the decoder of our model. Motivated by the concept of guided image filtering, GUB relies on the image to guide the decoder on upsampling the feature representation and the depth map reconstruction, achieving high resolution results with fine-grained details. Based on multiple GUBs, our model outperforms the related methods on the NYU Depth V2 dataset in terms of accuracy while delivering up to 35.1 fps on the NVIDIA Jetson Nano and up to 144.5 fps on the NVIDIA Xavier NX. Similarly, on the KITTI dataset, inference is possible with up to 23.7 fps on the Jetson Nano and 102.9 fps on the Xavier NX. Our code and models are made publicly available <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sup> https://github.com/mic-rud/GuidedDecoding.
Scheduling Techniques for Liver Segmentation: ReduceLRonPlateau vs OneCycleLR

Machine learning and computer vision techniques have influenced many fields including the biomedical one. The aim of this paper is to investigate the important concept of schedulers in manipulating the learning rate (LR), for the liver segmentation task, throughout the training process, focusing on the newly devised OneCycleLR against the ReduceLRonPlateau. A dataset, published in 2018 and produced by the Medical Segmentation Decathlon Challenge organizers, called Task 8 Hepatic Vessel (MSDC-T8) has been used for testing and validation. The reported results that have the same number of maximum epochs (75), and are the average of 5-fold cross-validation, indicate that ReduceLRonPlateau converges faster while maintaining a similar or even better loss score on the validation set when compared to OneCycleLR. The epoch at which the peak LR occurs perhaps should be made early for the OneCycleLR such that the super-convergence feature can be observed. Moreover, the overall results outperform the state-of-the-art results from the researchers who published the liver masks for this dataset. To conclude, both schedulers are suitable for medical segmentation challenges, especially the MSDC-T8 dataset, and can be used confidently in rapidly converging the validation loss with a minimal number of epochs.
More Than Accuracy: A Composite Learning Framework for Interval Type-2 Fuzzy Logic Systems

In this article, we propose a novel composite learning framework for interval type-2 (IT2) fuzzy logic systems (FLSs) to train regression models with a high accuracy performance and capable of representing uncertainty. In this context, we identify three challenges, first, the uncertainty handling capability, second, the construction of the composite loss, and third, a learning algorithm that overcomes the training complexity while taking into account the definitions of IT2-FLSs. This article presents a systematic solution to these problems by exploiting the type-reduced set of IT2-FLS via fusing quantile regression and deep learning (DL) with IT2-FLS. The uncertainty processing capability of IT2-FLS depends on employed center-of-sets calculation methods, while its representation capability is defined via the structure of its antecedent and consequent membership functions. Thus, we present various parametric IT2-FLSs and define the learnable parameters of all IT2-FLSs alongside their constraints to be satisfied during training. To construct the loss function, we define a multiobjective loss and then convert it into a constrained composite loss composed of the log-cosh loss for accuracy purposes and a tilted loss for uncertainty representation, which explicitly uses the type-reduced set. We also present a DL approach to train IT2-FLS via unconstrained optimizers. In this context, we present parameterization tricks for converting the constraint optimization problem of IT2-FLSs into an unconstrained one without violating the definitions of fuzzy sets. Finally, we provide comprehensive comparative results for hyperparameter sensitivity analysis and an inter/intramodel comparison on various benchmark datasets.
Hybrid Classical-Quantum Optimization Techniques for Solving Mixed-Integer Programming Problems in Production Scheduling

Quantum computing (QC) holds great promise to open up a new era of computing and has been receiving significant attention recently.To overcome the performance limitations of near-term QC, utilizing the current quantum computers to complement classical techniques for solving real-world problems is of utmost importance.In this paper, we develop QC-based solution strategies that exploit quantum annealing and classical optimization techniques for solving large-scale scheduling problems in manufacturing systems.The applications of the proposed algorithms are illustrated through two case studies in production scheduling.First, we present a hybrid QC-based solution approach for the job-shop scheduling problem.Second, we propose a hybrid QC-based parametric method for the multi-purpose batch scheduling problem with a fractional objective.The proposed hybrid algorithms can tackle optimization problems formulated as mixed-integer linear and mixed-integer fractional programs, respectively, and provide feasibility guarantees.Performance comparison between state-of-the-art exact and heuristic solvers and the proposed QC-based hybrid solution techniques is presented for both job-shop and batch scheduling problems.Unlike conventional classical solution techniques, the proposed hybrid frameworks harness quantum annealing to supplement established deterministic optimization algorithms and demonstrate performance efficiency over standard off-the-shelf optimization solvers.
Quantum Collective Learning and Many-to-Many Matching Game in the Metaverse for Connected and Autonomous Vehicles

The accuracy of artificial intelligence (AI) models is crucial for connected and autonomous vehicles (CAVs). However, in reality, model training under less frequent weather faces the problem of insufficient sampling. Also, in the real world, weather, sunlight, etc., can only change with the speed of the real-time clock, so the traditional sampling process is very slow. Moreover, currently, collective learning, which can make up the limited experience and computing power of a single vehicle, is always introduced to cases where the data from participants have the same structure, wasting massive heterogeneous data from vehicles of different brands. Therefore, in this paper, we propose a quantum collective learning and many-to-many matching game-based scheme in the metaverse for CAVs. The environment is simulated in the metaverse, which has its own time clock system, thereby expanding sample size and speeding up the sampling process. And we quantify the quality of intelligence in collective learning from the perspective of feature diversity. It is the cornerstone of collective learning between heterogeneous vehicles, facilitating maximum utilization of data with different structures. Then, we formulate the distributed vehicles selection problem as a many-to-many matching game and use Gale–Shapely algorithm to solve it. Also, we formulate the spectrum resource allocation problem as a discrete Markov decision process (MDP) and adopt a quantum-inspired reinforcement learning (QRL) algorithm to find the optimal policy to achieve the high revenue of the system. In simulations, the performance of the proposed scheme is compared with existing methods.
Deep CNN Model for Multimodal Medical Image Denoising

In the literature, numerous techniques have been employed to decrease noise in medical image modalities, including X-Ray (XR), Ultrasonic (Us), Computed Tomography (CT), Magnetic Resonance Imaging (MRI), and Positron Emission Tomography (PET). These techniques are organized into two main classes: the Multiple Image (MI) and the Single Image (SI) techniques. In the MI techniques, images usually obtained for the same area scanned from different points of view are used. A single image is used in the entire procedure in the SI techniques. SI denoising techniques can be carried out both in a transform or spatial domain. This paper is concerned with single-image noise reduction techniques because we deal with single medical images. The most well-known spatial domain noise reduction techniques, including Gaussian filter, Kuan filter, Frost filter, Lee filter, Gabor filter, Median filter, Homomorphic filter, Speckle reducing anisotropic diffusion (SRAD), Nonlocal-Means (NL-Means), and Total Variation (TV), are studied. Also, the transform domain noise reduction techniques, including wavelet-based and Curvelet-based techniques, and some hybridization techniques are investigated. Finally, a deep (Convolutional Neural Network) CNN-based denoising model is proposed to eliminate Gaussian and Speckle noises in different medical image modalities. This model utilizes the Batch Normalization (BN) and the ReLU as a basic structure. As a result, it attained a considerable improvement over the traditional techniques. The previously mentioned techniques are evaluated and compared by calculating qualitative visual inspection and quantitative parameters like Peak Signal-to-Noise Ratio (PSNR), Correlation Coefficient (Cr), and system complexity to determine the optimum denoising algorithm to be applied universally. Based on the quality metrics, it is demonstrated that the proposed deep CNN-based denoising model is efficient and has superior denoising performance over the traditional denoising techniques.
Uncertainty Guided Multi-View Stereo Network for Depth Estimation

Deep learning has greatly promoted the development of multi-view stereo in recent years. However, how to measure the reliability of the estimated depth map for practical applications and make reasonable depth hypothesis sampling for the cost volume building in the coarse-to-fine architecture are still unresolved crucial problems. To this end, an Uncertainty Guided multi-view Network (UGNet) is proposed in this paper. In order to enable the network to perceive the uncertainty, an uncertainty-aware loss function is introduced, which not only can infer uncertainty implicitly in an unsupervised manner but also can reduce the bad impact of high uncertainty regions and the erroneous labels in the training set during training. Moreover, an uncertainty-based depth hypothesis sampling strategy is further proposed to adaptively determine the depth search range of each pixel for finer stages, which helps to generate more rational depth intervals compared with other methods and build more compact cost volumes without redundancy. Experimental results on DTU dataset, BlendedMVS dataset, Tanks and Temples dataset and ETH3D high-res benchmark show that our method achieves promising reconstruction results compared with other state-of-the-art methods.
PAIDDE: A Permutation-Archive Information Directed Differential Evolution Algorithm

Evolutionary algorithms have show great successes in various real-world applications ranging in molecule to astronomy.As a mainstream evolutionary algorithm, differential evolution (DE) possesses the characteristics of simple algorithmic structure, easy implement, and efficient search performance.Nevertheless, it still suffers from the issues of local optimal trapping and premature of evolution problems.In this study, we innovatively improve the performance of DE by incorporating a full utilization of information feedback, which includes the population's holistic information and the direction of differential vectors.The proposed permutation-archive information directed differential evolution (PAIDDE) algorithm is verified on a set of 29 benchmark numerical functions and 22 real-world optimization problems.Extensive experimental and statistical results show that PAIDDE can significantly outperform other 12 state-of-the-art algorithms in terms of solution qualities.Additionally, the computational complexity, solution distribution, convergence speed, search dynamics, and population diversity of PAIDDE are systematically analyzed.The source code of PAIDDE can be found at https://toyamaailab.github.io/sourcedata.html.
A Hybrid Architecture for Federated and Centralized Learning

Many of the machine learning tasks rely on centralized learning (CL), which requires the transmission of local datasets from the clients to a parameter server (PS) entailing huge communication overhead. To overcome this, federated learning (FL) has been suggested as a promising tool, wherein the clients send only the model updates to the PS instead of the whole dataset. However, FL demands powerful computational resources from the clients. In practice, not all the clients have sufficient computational resources to participate in training. To address this common scenario, we propose a more efficient approach called hybrid federated and centralized learning (HFCL), wherein only the clients with sufficient resources employ FL, while the remaining ones send their datasets to the PS, which computes the model on behalf of them. Then, the model parameters are aggregated at the PS. To improve the efficiency of dataset transmission, we propose two different techniques: i) increased computation-per-client and ii) sequential data transmission. Notably, the HFCL frameworks outperform FL with up to 20% improvement in the learning accuracy when only half of the clients perform FL while having 50% less communication overhead than CL since all the clients collaborate on the learning process with their datasets.
Few-Shot Scene Classification of Optical Remote Sensing Images Leveraging Calibrated Pretext Tasks

Small data holds big AI potential. As one of the promising small data AI approaches, few-shot learning has the goal to learn a model efficiently that can recognize novel classes with extremely limited training samples. Therefore it is critical to accumulate useful prior knowledge obtained from large-scale base class dataset. To realize few-shot scene classification of optical remote sensing images, we start from a baseline model that trains all base classes using a standard cross-entropy loss leveraging two auxiliary objectives to capture intrinsical characteristics across the semantic classes. Specifically, rotation prediction learns to recognize the 2D rotation of an input to guide the learning of class-transferable knowledge, and contrastive learning aims to pull together the positive pairs while pushing apart the negative pairs to promote intra-class consistency and inter-class inconsistency. We jointly optimize such two pretext tasks and semantic class prediction task in an end-to-end manner. To further overcome the overfitting issue, we introduce a regularization technique, adversarial model perturbation, to calibrate the pretext tasks so as to enhance the generalization ability. Extensive experiments on public remote sensing benchmarks including NWPU-RESISC45, AID, and WHU-RS-19 demonstrate that our method works effectively and achieves best performance that significantly outperforms many state-of-the-art approaches.
Security Assurance Model of Software Development for Global Software Development Vendors

The number of security attacks and the impact has grown considerably in the recent several years. As a result, new emerging software development models are required that assist in developing software that is secure by default. This article reviews the most widely used security software models. It proposes a new Security Assurance Model (SAM) for Software Development that is adaptable to all contemporary scenarios, emphasizing global software development (GSD) vendor companies. The SAM of Software Development was developed after studying 11 well-known development models and analyzing results obtained from a systematic literature review (SLR) and questionnaire survey. The SAM of Software Development consists of seven security assurance levels: Governance and Security Threat Analysis, Secure Requirement Analysis, Secure Design, Secure Coding, Secure Testing and Review, Secure Deployment, and Security Improvement. The security assurance levels of SAM of software development consist of 46 critical software security risks (CSSRs) and 388 practices for addressing these risks. The proposed SAM of Software Development was assessed based on a tool created by Motorola, which is used to evaluate the present state of a company&#x2019;s software processes and find areas for improvement. We conducted 3 case studies on software development companies, using data from real software projects to examine the results of a practical experiment in each company. The results of the case studies indicate that the proposed SAM of Software Development helps measure the security assurance level of an organization. In addition, it can potentially serve as a framework for researchers to develop new software security measures.
An Anonymous IoT-Based E-Health Monitoring System Using Blockchain Technology

The Internet of Things (IoT) has made it possible for health institutions to have remote diagnosis, reliable, preventive, and real-time decision-making. However, the anonymity and privacy of patients are not considered in IoT. Therefore, this article proposes a blockchain-based anonymous system, known as GarliMediChain, for providing anonymity and privacy during COVID-19 information sharing. In GarliMediChain, garlic routing and blockchain are integrated to provide low-latency communication, privacy, anonymity, trust, and security. Also, COVID-19 information is encrypted multiple times before transmitting to a series of nodes in the network. To ensure that COVID-19 information is successfully shared, a blockchain-based coalition system is proposed. The coalition system enables health institutions to share information while maximizing their payoffs. In addition, each institution uses the proposed fictitious play to study the strategies of others in order to update its belief by selecting the best responses from them. Furthermore, simulation results show that the proposed system is resistant to security-related attacks and is robust, efficient, and adaptive. From the results, the proposed proof-of-epidemiology-of-interest consensus protocol has 15.93% less computational cost than 26.30% of proof-of-work and 57.77% proof-of-authority consensus protocol, respectively. Nonetheless, the proposed GarliMediChain system promotes global collaborations by combining existing anonymity and trust solutions with the support of blockchain technology.
CSNTSteg: Color Spacing Normalization Text Steganography Model to Improve Capacity and Invisibility of Hidden Data

The rapid growth of online communication has increased the demand for secure communication.Most government entities, healthcare providers, the legal sector, financial and banking, and other industries are vulnerable to information security issues.Text steganography is one way to protect secure communication by hiding secret messages in the cover text.Hiding a high amount of secret information without raising the attacker's suspicion is the main challenge in steganography.This paper proposes the Color and Spacing Normalization stego (CSNTSteg) model to resolve the low capacity and invisibility problem on text steganography.CSNTSteg consists of two stages: the pre-embedding stage, which achieves high capacity by utilizing RGB coding and character spacing.It is designed to increase the number of bits per location and usable characters.Besides, it applies the Huffman coding technique to compress the secret message to add more capacity enhancement.The second stage is color and spacing normalization, which accomplishes high invisibility by normalizing the RGB coding and character spacing of the cover and stego text.CSNTSteg overcomes the color differences issue between the cover and stego texts regardless of the color of the cover text.To assess the quality of CSNTSteg, the experimental results are compared with existing works.CSNTSteg shows superior capacity over the existing studies with a percentage of 98.85%.CSNTSteg also achieves high invisibility by reducing the color differences with a percentage of 4.7% and 5.07% for black and colored cover text, respectively.Furthermore, CSNTSteg improves robustness by 94.22% by reducing the distortion in stego text.Overall, the CSNTSteg model embeds a high capacity of secret data while maintaining invisibility and security, offering a new perspective on text steganography to protect against visual and statistical attack issues.
Hierarchical Graph Augmented Deep Collaborative Dictionary Learning for Classification

Recently, deep dictionary learning (DDL) has aroused attention due to its abilities of learning multiple different dictionaries and extracting multi-level abstract feature representations for samples. It has been applied to many intelligent recognition tasks, such as vehicle detection, traffic sign recognition and driver monitoring. Nevertheless, the off-the-shelf DDL-based methods ignore the essential structural information of data in multi-layer dictionary learning. The learned hierarchical data representations are less discriminative. To address this issue, we develop a new DDL framework, called the hierarchical graph augmented deep collaborative dictionary learning (HGDCDL). Firstly, we propose a new deep collaborative dictionary learning (DCDL) that applies collaborative representation to the deepest-level representation learning. Most importantly, equipped with a simple yet effective hierarchal graph construction mechanism, our HGDCDL uses the structure of data to regularize dictionary learning, and generates more informative dictionaries and discriminative representations at different levels. Extensive experiments show that our HGDCDL performs significantly better than the state-of-the-art shallow and deep representation learning methods for classification.
Developing metrics to assess the effectiveness of cybersecurity awareness program

Abstract Cybersecurity awareness (CSA) is not just about knowing, but also transforming things learned into practice. It is a continuous process that needs to be adjusted in subsequent iterations to improve its usability as well as sustainability. This is possible only if a CSA program is reviewed and evaluated timely. Review and evaluation of an awareness program offer an insight into the program's effectiveness on the audience and organization, an invaluable piece of information for the continuous improvement of the program. Further, it provides the information required by the management and sponsor to decide on whether to invest in the program or not. Despite these advantages, there does not exist a common understanding of what factors to measure and how to measure them during the evaluation process. As a result, we have proposed evaluation metrics for the purpose. In order to do so, we performed a literature review of 32 papers mainly to extract the following data: (i) what factors did the paper measure, and (ii) how did it measure the factors? Next, we adapted the European Literacy Policy Network's four indicators (i.e. impact, sustainability, accessibility, and monitoring) for awareness evaluation to make it appropriate for evaluating a CSA program. We believe that measuring all four indicators will contribute to making the evaluation process systematic, complete, and replicable. More importantly, it will help to produce more inclusive, accurate, and usable results for the future enhancement of the program.