Reviewerly/exHarmony huggingface dataset:
How effective are current population-based metaheuristic algorithms for variance-based multi-level image thresholding?

Multi-level image thresholding is a common approach to image segmentation where an image is divided into several regions based on its histogram. Otsu's method is the most popular method for this purpose, and is based on seeking for threshold values that maximise the between-class variance. This requires an exhaustive search to find the optimal set of threshold values, making image thresholding a time-consuming process. This is especially the case with increasing numbers of thresholds since, due to the curse of dimensionality, the search space enlarges exponentially with the number of thresholds. Population-based metaheuristic algorithms are efficient and effective problem-independent methods to tackle hard optimisation problems. Over the years, a variety of such algorithms, often based on bio-inspired paradigms, have been proposed. In this paper, we formulate multi-level image thresholding as an optimisation problem and perform an extensive evaluation of 23 population-based metaheuristics, including both state-of-the-art and recently introduced algorithms, for this purpose. We benchmark the algorithms on a set of commonly used images and based on various measures, including objective function value, peak signal-to-noise ratio, feature similarity index, and structural similarity index. In addition, we carry out a stability analysis as well as a statistical analysis to judge if there are significant differences between algorithms. Our experimental results indicate that recently introduced algorithms do not necessarily achieve acceptable performance in multi-level image thresholding, while some established algorithms are demonstrated to work better.
Transfer learning from English to Slovak in speech recognition applications

The speed of research can be slowed down by limited access to SOTA computational resources. This paper compares two different speech recognition approaches, training from scratch and transfer learning. It is focused on the transfer learning technique known as fine-tuning in cross-language adaptation speech recognition tasks. The main idea of this paper is to prove the feasibility of adapting the foreign speech recognition model to the Slovak ASR model. We analyze the benefits of BPE tokenization in speech recognition, which we have not previously tested, and a particular pre-trained model in terms of training speed and model quality. In practice, the publicly available pre-trained English Librispeech100 model is used to create a transferred English-to-Slovak model on TUKE-BNewsSK corpus subset. In addition, the paper provides details on the ESPnet framework workflows involved in the creation of the proposed end-to-end fine-tuned model.
Geo-Distributed Multi-Tier Workload Migration Over Multi-Timescale Electricity Markets

Virtual machine (VM) migration enables cloud service providers (CSPs) to balance workload, perform zero-downtime maintenance, and reduce applications’ power consumption and response time. Migrating a VM consumes energy at the source, destination, and backbone networks, i.e., intermediate routers and switches, especially in a Geo-distributed setting. In this context, we propose a VM migration model called Low Energy Application Workload Migration ( <i>LEAWM</i> ) aimed at reducing the per-bit migration cost in migrating VMs over Geo-distributed clouds. With a Geo-distributed cloud connected through multiple Internet Service Providers (ISPs), we develop an approach to find out the migration path across ISPs leading to the most feasible destination. For this, we use the variation in the electricity price at the ISPs to decide the migration paths. However, reduced power consumption at the expense of higher migration time is intolerable for real-time applications. As finding an optimal relocation is <inline-formula><tex-math notation="LaTeX">$\mathcal {NP}$</tex-math></inline-formula> -Hard, we propose an <i>Ant Colony Optimization</i> (ACO) based bi-objective optimization technique to strike a balance between migration delay and migration power. A thorough simulation analysis of the proposed approach shows that the proposed model can reduce the migration time by 25%–30% and electricity cost by approximately 25% compared to the baseline.
VCD-FL: Verifiable, Collusion-Resistant, and Dynamic Federated Learning

Federated learning (FL) is essentially a distributed machine learning paradigm that enables the joint training of a global model by aggregating gradients from participating clients without exchanging raw data. However, a malicious aggregation server may deliberately return designed results without any operation to save computation overhead, or even launch privacy inference attacks using crafted gradients. There are only a few schemes focusing on verifiable FL, and yet they cannot achieve collusion-resistant verification. In this paper, we propose the first Verifiable, Collusion-resistant, and Dynamic FL (VCD-FL) to tackle this issue. Specifically, we first optimize Lagrange interpolation by gradient grouping and compression for achieving efficient verifiability of FL. To protect clients’ data privacy against collusion attacks, we propose a lightweight commitment scheme using irreversible gradient transformation. By integrating the proposed efficient verification mechanism with the novel commitment scheme, our VCD-FL can detect whether or not the aggregation server is involved in collusion attacks. Moreover, considering that clients might go offline due to some reason such as network anomaly and client crash, we adopt the secret sharing technique to eliminate the effect of federation dynamics on FL. To the best of our knowledge, this is the first work to achieve collusion-resistant verification and collusion attack detection with supporting the correctness, privacy, and dynamics. Finally, we theoretically prove the effectiveness of our VCD-FL, make comprehensive comparisons, and conduct a series of experiments on MNIST dataset with MLP and CNN models. The theoretical proof and experimental analysis demonstrate that our VCD-FL is computationally efficient, robust against collusion attacks, and able to support the dynamics of FL.
Depth Estimation of Traffic Scenes from Image Sequence Using Deep Learning

Autonomous cars can accurately perceive the deployment of traffic scenes and the distance between visual objects in the scenarios through understanding the depth. Therefore, the depth estimation of scenes is a crucial step in the obstacle avoidance and pedestrian protection from autonomous vehicles. In this paper, a method for stereo depth estimation based on image sequences is introduced. In this project, we improve the performance of deep learning-based model by combining depth hints algorithm and MobileNetV2 encoder to enhance the loss function and increases computing speed. To the best of our knowledge, this is the first time MobileNetV2 is applied to depth estimation based on KITTI dataset.
End-To-End Deep-Learning-Based Tamil Handwritten Document Recognition and Classification Model

Overview: Handwriting recognition (HR) involves converting handwritten text into machine-readable text. Tamil handwritten document recognition remains a challenging process in various text real-world applications owing to the differences in the sizes, styles and orientation angles of Tamil alphabets. Prior studies concentrated only on character-level segmentation, and each character was subsequently classified. The recently developed machine learning (ML) and deep learning (DL) approaches can be utilized for Tamil handwritten character recognition (HCR). Objective: This paper attempts to present an end-to-end DL-based Tamil handwritten document recognition (ETEDL-THDR) model. Methods: Segmentation is used, first at the word level and then at the line level. ETEDL-THDR text recognition can be accomplished using two modules: line segmentation and line recognition. Initially, the text ETEDL-THDR model targets improving input image quality using the median filtering (MF) technique. To create meaningful regions, more line and character segmentation activities are performed. A deep convolutional neural network (DCNN) based MobileNet approach is also applied to derive feature vectors. Finally, the water strider optimization (WSO) algorithm with a bidirectional gated recurrent unit (BiGRU) model is used to identify the Tamil characters. Results: Extensive experimental analyses of the text ETEDL-THDR model have been carried out, and the results show that the text ETEDL-THDR model performs better than more recent methodologies, with a maximum accuracy of 98.48%, a precision of 98.38%, a sensitivity of 97.98%, specificity of 98.27% and text F-measure of 98.35%. Conclusion: The comparison results show that the proposed model can recognize Tamil handwritten documents in real time.
A Wasserstein GAN for Joint Learning of Inpainting and Spatial Optimisation

Image inpainting is a restoration method that reconstructs missing image parts. However, a carefully selected mask of known pixels that yield a high quality inpainting can also act as a sparse image representation. This challenging spatial optimisation problem is essential for practical applications such as compression. So far, it has been almost exclusively addressed by model-based approaches. First attempts with neural networks seem promising, but are tailored towards specific inpainting operators or require postprocessing. To address this issue, we propose the first generative adversarial network (GAN) for spatial inpainting data optimisation. In contrast to previous approaches, it allows joint training of an inpainting generator and a corresponding mask optimisation network. With a Wasserstein distance, we ensure that our inpainting results accurately reflect the statistics of natural images. This yields significant improvements in visual quality and speed over conventional stochastic models. It also outperforms current spatial optimisation networks.
LSA: Understanding the Threat of Link-based Scapegoating Attack in Network Tomography

Network tomography is a powerful and convenient tool to infer the internal states of a network via end-to-end path measurements. However, this paper shows that when identifiability is not satisfied at all links (which is general in applications for cost saving purpose), a special kind of attack, i.e., <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">link-based scapegoating attack (LSA)</i> that can greatly degrade the network performance while misleading the network tomography to identify some normal links as problematic links can be launched. To fully understand the threat from LSA, this paper in particular investigates the conditions and efficient methods to launch LSA from an attacker's point of view. Specifically, an efficient algorithm to figure out the potential scapegoat links is first proposed, and the conditions to launch LSA are presented. Then a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">minimum-cost scapegoating attack (MCSA)</i> problem is proposed, which studies how to manipulate the least-cost link sets to launch LSA. A weighted set cover model and a greedy approximation algorithm are designed to solve <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MCSA</i> problem, which is approached by a linear programming method with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$H_{K}$</tex-math></inline-formula> approximation ratio. Evaluation on both synthetic and real network topologies shows the wide existence of LSA threats, the feasibility of the proposed LSA conditions, and the attacking strategies.
Cryptography in Distributed Ledger Technologies from a Layered Perspective: A State of the Art

Cryptography is the practice of secure communication to protect data's confidentiality, integrity, and authenticity. In the context of blockchains, cryptography is used to secure transactions, protect users’ privacy, and ensure the integrity and security of the network. Nevertheless, this later is subject to many attacks, such as post-quantum attacks. In this paper, we survey and extract how cryptography is used in blockchains from a layered perspective: data, Consensus, and Execution to Application layers. We summarize our findings and results using a table containing the functions of cryptographic protocols, families, functions, primitives, and schemes and how they are utilized for each studied blockchain platform. We discuss each cryptographic technique's related DCEA layer in the forecited table. Therefore, this work helps assess current cryptographic techniques used in current Distributed Ledger Technologies or design and develop new hardened ones by choosing post-quantum-related cryptographic techniques for each layer.
<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si14.svg"><mml:mrow><mml:mo>(</mml:mo><mml:mi>t</mml:mi><mml:mo>,</mml:mo><mml:mi>k</mml:mi><mml:mo>,</mml:mo><mml:mi>n</mml:mi><mml:mo>)</mml:mo></mml:mrow></mml:math> Regional Secret Image Sharing over Finite Fields

Secret image sharing (SIS), a tool capable of distributing a private image among certain number of shareholders, ensures both authorized recovery and unauthorized forbidding of the same. However, usually, in SIS every pixel is treated with equal weight. This is a hindrance in case of partial query to the original secret image, where usual SIS might over-inform the querier. To prevent this over-spilling of information, we introduce, in this work, the notion of Regional Secret Sharing, where access to a special region is not possible in general, unless an essential participant submits his shadow. We formally define (t,k,n)-Regional secret image sharing ((t,k,n)-RSIS) and provide a construction for the same using polynomial based secret image sharing. Our construction which is done over a finite field is completely lossless. We also formally prove the protocol's behaviour in different cases of authorized access and unauthorized access. Moreover, we support our constructions with sufficient experimental results. This is the first usage of the notion of region in secret image sharing. Our construction is information theoretically secure, meaning we do not assume any computational assumptions for the adversary, in the semi-honest model and it does not require any preprocessing unlike most SIS schemes, making the scheme efficient as well.
Enhancing some separability criteria in many-body quantum systems

Abstract Quantum measurements based on mutually unbiased bases, mutually unbiased measurements and general symmetric informationally complete positive operator valued measurements, which are commonly used in quantum information and quantum computation. In this paper, by a broad class of informationally complete symmetric measurements and correlation matrices, some separability criteria are proposed for different quantum systems that are more robust and comprehensive than the corresponding criteria previously introduced L M Lai, T Li, S M Fei, and Z X Wang, (2018 Quantum Inf. Process. , 17 , 314); J W Shang, A Asadian, and H J Zhu, (2018 Phys. Rev. A 98 , 022309). Furthermore, we extend the separability criteria of tripartite quantum systems to many-body systems. The proposed entanglement criteria may further enrich the relationship between quantum entanglement and measurement.
An efficient lightweight network for single image super-resolution

The outstanding performance of convolutional neural networks (CNNs) shown in single image super-resolution (SISR) strongly depends on network's depth, which hampers its application in low-power computing devices. In this paper, a lightweight and efficient network (LESR) is proposed for SISR by constructing the shallow feature extraction block (SFBlock), the cascaded sparse mask blocks (SMBlocks) and the feature fusion block (FFBlock). The SFBlock efficiently extracts global informative features from the original low resolution image using sparse self-attention, SMBlock skips the redundant computation in extracted features, and more meaningful information is distilled for the sequential reconstruction block by the FFBlock. In addition, a recently proposed activation function called ACON-C is used to replace the ReLU function to ease the training difficulty. Extensive experiments show that our proposed network performs better than most advanced lightweight SISR algorithms with comparable parameters and less FLOPs on benchmark database for ×2/3/4 SISR.
Systematic Literature Review on Fuzzy Hybrid Methods in Photovoltaic Solar Energy: Opportunities, Challenges, and Guidance for Implementation

The application of fuzzy hybrid methods has significantly increased in recent years across various sectors. However, the application of fuzzy hybrid methods for modeling systems or processes, such as fuzzy machine learning, fuzzy simulation, and fuzzy decision-making, has been relatively limited in the energy sector. Moreover, compared to standard methods, the benefits of fuzzy-hybrid methods for capturing complex problems are not adequately explored for the solar energy sector, which is one of the most important renewable energy sources in electric grids. This paper investigates the application of fuzzy hybrid systems in the solar energy sector compared to other sectors through a systematic review of journal articles published from 2012 to 2022. Selection criteria for choosing an appropriate method in each investigated fuzzy hybrid method are also presented and discussed. This study contributes to the existing literature in the solar energy domain by providing a state-of-the-art review of existing fuzzy hybrid techniques to (1) demonstrate their capability for capturing complex problems while overcoming limitations inherent in standard modeling methods, (2) recommend criteria for selecting an appropriate fuzzy hybrid technique for applications in solar energy research, and (3) assess the applicability of fuzzy hybrid techniques for solving practical problems in the solar energy sector.
Recent application of artificial intelligence on histopathologic image-based prediction of gene mutation in solid cancers

Evaluation of genetic mutations in cancers is important because distinct mutational profiles help determine individualized drug therapy. However, molecular analyses are not routinely performed in all cancers because they are expensive, time-consuming and not universally available. Artificial intelligence (AI) has shown the potential to determine a wide range of genetic mutations on histologic image analysis. Here, we assessed the status of mutation prediction AI models on histologic images by a systematic review.A literature search using the MEDLINE, Embase and Cochrane databases was conducted in August 2021. The articles were shortlisted by titles and abstracts. After a full-text review, publication trends, study characteristic analysis and comparison of performance metrics were performed.Twenty-four studies were found mostly from developed countries, and their number is increasing. The major targets were gastrointestinal, genitourinary, gynecological, lung and head and neck cancers. Most studies used the Cancer Genome Atlas, with a few using an in-house dataset. The area under the curve of some of the cancer driver gene mutations in particular organs was satisfactory, such as 0.92 of BRAF in thyroid cancers and 0.79 of EGFR in lung cancers, whereas the average of all gene mutations was 0.64, which is still suboptimal.AI has the potential to predict gene mutations on histologic images with appropriate caution. Further validation with larger datasets is still required before AI models can be used in clinical practice to predict gene mutations.
Patient-centric soulbound NFT framework for electronic health record (EHR)

Abstract Interest in leveraging blockchain technology to boost healthcare and e-health solutions has lately increased. Blockchain has proven to have enormous promise in a range of e-health industries because of its decentralized and reliable nature, including the secure exchange of electronic health records (EHRs) and database access management among numerous medical entities. A unique paradigm known as the “patient-centric approach” places the patient at the center of the healthcare system and gives them complete control over who has access to and can share their personal health information. Strong confidentiality and safety requirements are necessary for health information. Additionally, other concerns must be resolved, such as secrecy, interoperability, scalability, cost-effectiveness, and timeliness. This paper offers a patient-centric privacy-preserving framework for an efficient and safe medical record to address these problems. Based on three parameters transaction cost, execution time, and gas cost. Three blockchain platforms are compared by using the smart contract to find out the suitable platform for the implementation of this framework. Blockchain platforms served as a benchmark for the performance assessment of a designed framework. Although blockchain will not fix every issue in healthcare organizations, it will undoubtedly assist in dramatically reducing some of the most critical ones.
Twitter-based analysis of anti-refugee discourses in Türkiye

As the number of refugees in Türkiye continues to grow, the constructed discourse of welcoming refugees with open arms has weakened, transforming the image of refugees from ‘guests’ to ‘threats’. Social media platforms are used to spread hate speech, discrimination, racism, and otherization. Drawing on the Corpus-assisted Critical Discourse Analysis approach, the present research investigates how language is used to produce online discourses in tweets about refugees in the aftermath of a major social triggering event in Türkiye. This mixed-methods study incorporated both a corpus analysis of tweets posted on Twitter over a specific period, as well as a critical discourse analysis of a randomly selected subset of 100 tweets. The findings revealed that the prevalence of discontent and frustration of the host population against refugees has turned into a discourse of discrimination, hate, and racism. Moreover, a negative portrayal of refugees was created and maintained not only through lexical choices, but also characteristics attributed to them, as well as assumptions and prejudices about them.
A multimodal fusion enabled ensemble approach for human activity recognition in smart homes

How to deal with multi-modality data from different types of devices is a challenging issue for accurate recognition of human activities in a smart environment. In this paper, we propose a multimodal fusion enabled ensemble approach. Firstly, useful features collected from Bluetooth beacons, binary sensors, and smart floor are extracted and presented by fuzzy logic based-method with variable-size temporal windows. Secondly, a group of support vector machine classifiers are used to perform the classification task. Finally, a weighted ensemble method is used to obtain the final prediction. Especially, by applying the geometric framework, we are able to obtain the optimal weights for the ensemble. The proposed approach is evaluated on the UJAmI dataset. The experimental results demonstrate the efficacy and robustness of the proposed method.