Reviewerly/exHarmony huggingface dataset:
Bi-prism-based single-bilateral-telecentric-camera stereo-DIC for accurate underwater 3D deformation measurement: Implementation of a parametric model

Full-field surface shape and deformation measurements of submerged objects using stereo-digital image correlation (stereo-DIC) remains challenging due to the light ray refraction occurred on the interface of two different transparent media (e.g., air, glass and water). In this work, we proposed an underwater full-field 3D deformation measurement method based on the bi-prism-based single-bilateral-telecentric-camera (SBTC) stereo-DIC, which adopts a parametric model to establish the mathematical relationship between the image points and their corresponding object points by taking the refraction into account in the ray-tracing process. The key parameters (e.g., bi-prism/glass orientation and position) of the parametric model were calibrated by a one-step calibration. To verify the correctness and effectiveness of the proposed method, translation tests with a rotated bi-prism and underwater translation tests were performed. Experimental results indicate that the proposed parametric model can effectively handle the imperfect system configuration and the unavoidable light ray refraction that occurred on the optical path, thus allowing high-accuracy underwater deformation measurement. Compared with the existing underwater deformation measurement approaches, the proposed method can not only deliver displacement measurement with higher accuracy, but also is free of any assumptions on the configuration of the camera system or the refractive interface.
Named entity annotation schema for geological literature mining in the domain of porphyry copper deposits

Owing to the development of natural language processing and deep learning models, geological text data have become a vital resource for knowledge discovery and have attracted the attention of publishers, academic organizations, and domain scientists. However, the extraction of information from unstructured literature still remains a challenge, in which a fundamental issue is the categories and the type of discipline-specific information. This paper presents an effective workflow of building and applying ontologies in geoscience text mining, which includes a use case-driven method for building an ontology model of porphyry copper deposits, an entity annotation schema for text mining, and implementation of them to tackle real-world data. First, the Dexing porphyry copper deposit was selected as a case study to guide the construction of the ontology model. Text data in this study provided a series of entity instances. By analyzing both domain knowledge of mineral deposit models and the instance data, we built classes in the ontology. Second, with the established ontology, a named entity annotation schema comprising 21 entity tokens was designed to scale up the text mining tasks. Third, based on the annotation schema, a draft corpus with more than 200,000 words and a finely corrected corpus of 53,339 words were built for training a geological entity recognizer for porphyry copper deposits. The performance of the geological entity recognizer and the statistical distribution of entities in the corpus prove that the workflow presented in this study is effective for designing entity annotation schemas and facilitating large-scale text data mining in geoscience.
X-ray image analysis for osteoporosis diagnosis: From shallow to deep analysis

Recently, automated disease diagnosis based on medical images has become an integral component of digital pathology packages. Texture analysis is commonly used to address this issue, particularly in the context of estimating the osteoporosis progression in bone samples. Most research in this context uses handcrafted methods to directly extract bones image features despite the substantial correlation between sick and healthy bones, which explains the limited results. In this work, the handcrafted feature extraction method (e.g. HOG and/or LPQ) will be applied to a set of descriptors obtained from a deep analysis of bone texture images using Gabor's filter bank. In addition, the classifier automatically adjusts the Gabor filters settings, using the bat-inspired algorithm based optimization, to achieve deep analysis behavior and optimal performance. Using a typically osteoporosis database, our experimental results reveal a significant improvement over the state-of-the-art deep/handcrafted techniques, resulting in an excellent performance of 89.66% for osteoporosis diagnosis.
Blockchain for IoT-Based Healthcare using secure and privacy-preserving watermark

Patient medical data is the key data of an e-health application. Medical data frequently play an important role in disease analysis and are implicated in motivating values for educating diagnostic methods and modifying diagnostic results. Confidentiality of patient health documents is more important, and therefore researchers have considered many security measures, including access control, confidentiality, and integrity of these documents. To solve the security problem, this paper presents an advanced solution based on converting medical data into compact size, in the form of QR code before being deployed on our Blockchain. In this way, performing complex operations will be safely performed off-chain, such as collecting and preprocessing patient data, while submission of this data is done on an hourly basis. Chain. We recommend a data flow architecture that combines the Internet of Things (IoT) with blockchain (especially the Ethereum architecture), using smart contracts to access and store data while keeping Strong against many attacks. Our proposed system is effective in sharing and managing patient electronic health data. The tests involved implementing Ethereum chain configurations on PCs and two embedded IoT platforms, namely Raspberry Pi and PYNQ cards. Performance is evaluated in terms of security, execution time, memory, power and CPU consumption of various patient data scenarios.
Normalization free Siamese network for object tracking

Abstract Siamese‐based trackers have received global recognition for target tracking. However, these trackers employ batch‐normalized networks for feature extraction, which has been sensitive to batch size and hard to replicate on different hardware. Therefore, to meliorate tracking performance and effectively address this issue, the present work proposes a Normalization free Siamese (NfS) tracker by introducing normalization‐free networks in target tracking. The developed NfS has been trained end‐to‐end with large‐scale datasets such as COCO, TrackingNet, LaSOT, VID, DET, and GOT10k. Extensive experimentation has been carried out on six challenging benchmark datasets (OTB100, LaSOT, VOT2018, VOT2019, UAV123, and GOT10k), revealing that NfS ensures comparable performance with state‐of‐the‐art (SOTA) trackers on most of the benchmarks. It pushed the performance bar by a minimum of 2.88% and 2.37% on UAV123 for both precision and success scores. Also, it overshadows the compared trackers by a significant minimum margin of 11.88% and 8.14% on the LaSOT for similar metrics, demonstrating the higher discrimination capability of the NfS tracker for both natural and aerial target tracking tasks.
Capturing Symmetries of Quantum Optimization Algorithms Using Graph Neural Networks

Quantum optimization algorithms are some of the most promising algorithms expected to show a quantum advantage. When solving quadratic unconstrained binary optimization problems, quantum optimization algorithms usually provide an approximate solution. The solution quality, however, is not guaranteed to be good enough to warrant selecting it over the classical optimizer solution, as it depends on the problem instance. Here, we present an algorithm based on a graph neural network that can choose between a quantum optimizer and classical optimizer using performance prediction. In addition, we present an approach that predicts the optimal parameters of a variational quantum optimizer. We tested our approach with a specific quantum optimizer, the quantum approximate optimization algorithm, applied to the Max-Cut problem, which is an example of a quadratic unconstrained binary optimization problem. We observed qualitatively and quantitatively that graph neural networks are suited for a performance prediction of up to nine-vertex Max-Cut instances with a quantum approximate optimization algorithm with a depth of up to three. For the performance prediction task, the average difference between the actual quantum algorithm performance and the predicted performance is below 19.7% and, for the parameter prediction task, the solution using the predicted parameters is within 2.7% of the optimal parameter solution. Our method therefore has the capacity to find problems that are best suited for quantum solvers. The proposed method and the corresponding algorithm can be used for hybrid quantum algorithm selection.
Nonfungible tokens (NFTs) in dermatology and health care: A guide for health care professionals

Abstract A nonfungible token (NFT) represents an arbitrary piece of information, which can be created and exchanged on a blockchain, a type of public ledger secured by cryptographic algorithms. NFTs offer the possibility of leveraging blockchain technology‐related benefits, including verifiability, transparent execution, high availability and tamper‐resistance, for any tokenizable piece of information such as for example, clinical images in dermatology. In this narrative review, we explore current and potential future applications, as well as limitations of NFTs in health care. While health care applications for blockchains have been explored and implemented, NFTs have not yet been extensively studied in medical literature. Beyond the currently envisaged use in blood product tracking, consent management and an extension to electronic health records, the medical field and especially dermatology could benefit in other domains from this technology, including for clinical image safety, charge‐sensitive drug tracking and managing processes like referrals, lab orders, patient consent, legal claims such as malpractice, and improved availability and reliability of scientific data. The technology is limited by the sensitive nature of patient data, potentially counteracting the verifiability benefit, as well as accessibility for patients, and the technical skills required to create and maintain the infrastructure.
Meta-hallucinating prototype for few-shot learning promotion

An effective way for few-shot learning (FSL) is to establish a metric space where the distance between a query and the prototype of each class is computed for classification, and the key lies on hallucinating the appropriate prototypes for each class of the given FSL task. Most existing prototypical approaches hallucinate the class-wise prototype based on the given support samples with an equal contribution assumption, i.e., each support sample contributes equally to the corresponding prototype. However, due to the limited-data regime as well as the strict assumption, the hallucinated prototypes often deviate from the ideal ones that are determined by the sample distribution of each unseen class, and thus causing poor generalization performance. To mitigate this problem, we present a prototype meta-hallucination approach which shows two aspects of advantages. On one hand, instead of directly inferring the complicated sample distribution, it meta-learns to establish a difference distribution based generative model that infers the distribution of inter-sample difference and synthesizes new labeled samples through fusing the sampled inter-sample difference and each given support sample. This empowers us to augment the support set with more content-diverse samples and is beneficial to reduce the bias in prototype hallucination. On the other hand, we argue that each support sample may contribute no-equally to the ideal prototype that it belongs to and their relations vary with class characteristics. Following this, our approach meta-learns to dynamically re-weight all support samples in prototype hallucination, which makes it flexible to locate the ideal prototype for each unseen class based on its characteristics. Experiments on four FSL benchmark datasets show that our approach can effectively improve the performance of the prototypical baseline and outperform several state-of-the-art competitors with a clear margin.
How augmented reality can improve e-commerce website quality through interactivity and vividness: the moderating role of need for touch

Purpose This paper aims to propose a conceptual model to examine the effect of an augmented reality (AR)–based product display (vs a picture-based product display) on interactivity, vividness, website quality and consumer responses. In addition, the moderating role of the need for touch (NFT) in the effect of AR on media features is identified. Design/methodology/approach Hypotheses are tested using a one-factor between-subjects design for both a student sample (Study 1, N = 120) and a nonstudent sample (Study 2, N = 272). Data are analyzed using a series of analyses of variance, multivariate analyses of covariance and structural equation modeling. Findings Study 1 shows that an AR-based product display generates greater website quality, interactivity and vividness than a picture-based product display. Moreover, an AR-based product display improves interactivity and vividness only for high-NFT consumers; however, no significant difference emerged for low-NFT consumers. Study 2 replicates and extends our findings by identifying the specific processes that consumers go through when evaluating a website. Originality/value The current research advances the understanding of how product presentation technologies can attract customers with different haptic orientations and provides practical implications for online retailers interested in improving their customers' e-commerce experience.
I-SPLIT: Deep Network Interpretability for Split Computing

This work makes a substantial step in the field of split computing, i.e., how to split a deep neural network to host its early part on an embedded device and the rest on a server. So far, potential split locations have been identified exploiting uniquely architectural aspects, i.e., based on the layer sizes. Under this paradigm, the efficacy of the split in terms of accuracy can be evaluated only after having performed the split and retrained the entire pipeline, making an exhaustive evaluation of all the plausible splitting points prohibitive in terms of time. Here we show that not only the architecture of the layers does matter, but the importance of the neurons contained therein too. A neuron is important if its gradient with respect to the correct class decision is high. It follows that a split should be applied right after a layer with a high density of important neurons, in order to preserve the information flowing until then. Upon this idea, we propose Interpretable Split (I-SPLIT): a procedure that identifies the most suitable splitting points by providing a reliable prediction on how well this split will perform in terms of classification accuracy, beforehand of its effective implementation. As a further major contribution of I-SPLIT, we show that the best choice for the splitting point on a multiclass categorization problem depends also on which specific classes the network has to deal with. Exhaustive experiments have been carried out on two networks, VGG16 and ResNet-50, and three datasets, Tiny-Imagenet-200, notMNIST, and Chest X-Ray Pneumonia. The source code is available at https://github.com/vips4/I-Split.
Better Lattice Quantizers Constructed From Complex Integers

This paper investigates low-dimensional quantizers from the perspective of complex lattices. We adopt Eisenstein integers and Gaussian integers to define checkerboard lattices <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal {E}_{m}$ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal {G}_{m}$ </tex-math></inline-formula> . By explicitly linking their lattice bases to various forms of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal {E}_{m}$ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal {G}_{m}$ </tex-math></inline-formula> cosets, we discover the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal {E}_{m,2}^{+}$ </tex-math></inline-formula> lattices, based on which we report the best known lattice quantizers in dimensions 14, 15, 18, 19, 22 and 23. Fast quantization algorithms of the generalized checkerboard lattices are proposed to enable evaluating the normalized second moment (NSM) through Monte Carlo integration.
Progressive Deep Image Compression for Hybrid Contexts of Image Classification and Reconstruction

Progressive deep image compression (DIC) with hybrid contexts is an under-investigated problem that aims to jointly maximize the utility of a compressed image for multiple contexts or tasks under variable rates. In this paper, we consider the contexts of image reconstruction and classification. We propose a DIC framework, called residual-enhanced mask-based progressive generative coding (RMPGC), designed for explicit control of the performance within the rate-distortion-classification-perception (RDCP) trade-off. Three independent mechanisms are introduced to yield a semantically structured latent representation that can support parameterized control of rate and context adaptation. Experimental results show that the proposed RMPGC outperforms a benchmark DIC scheme using the same generative adversarial nets (GANs) backbone in all six metrics related to classification, distortion, and perception. Moreover, RMPGC is a flexible framework that can be applied to different neural network backbones. Some typical implementations are given and shown to outperform the classic BPG codec and four state-of-the-art DIC schemes in classification and perception metrics, with a slight degradation in distortion metrics. Our proposal of a nonlinear-neural-coded and richly structured latent space makes the proposed DIC scheme well suited for image compression in wireless communications, multi-user broadcasting, and multi-tasking applications.
Moralized language predicts hate speech on social media

Hate speech on social media threatens the mental health of its victims and poses severe safety risks to modern societies. Yet, the mechanisms underlying its proliferation, though critical, have remained largely unresolved. In this work, we hypothesize that moralized language predicts the proliferation of hate speech on social media. To test this hypothesis, we collected three datasets consisting of N = 691,234 social media posts and ∼35.5 million corresponding replies from Twitter that have been authored by societal leaders across three domains (politics, news media, and activism). Subsequently, we used textual analysis and machine learning to analyze whether moralized language carried in source tweets is linked to differences in the prevalence of hate speech in the corresponding replies. Across all three datasets, we consistently observed that higher frequencies of moral and moral-emotional words predict a higher likelihood of receiving hate speech. On average, each additional moral word was associated with between 10.76% and 16.48% higher odds of receiving hate speech. Likewise, each additional moral-emotional word increased the odds of receiving hate speech by between 9.35 and 20.63%. Furthermore, moralized language was a robust out-of-sample predictor of hate speech. These results shed new light on the antecedents of hate speech and may help to inform measures to curb its spread on social media.
Game theory based compatible incentive mechanism design for non-cryptocurrency blockchain systems

With the success of Bitcoin, nowadays, both academia and industry have been focusing on the expansion of blockchain in non-cryptocurrency applications to facilitate the development of Industry 4.0. The incentive mechanism in blockchain, which encourages miners to participate in validation processes, is important to the maintenance of the system. However, the techniques, which are widely applied in existing blockchain systems, are not suitable for non-cryptocurrency applications with business models and have become a major issue that undermines the adoption of blockchain. In this paper, we propose a compatible incentive mechanism for non-cryptocurrency applications to encourage validators' actions to align with consensus algorithm. The compatible incentive mechanism works with a consensus algorithm to make the blockchain technology more adaptable in industry applications. The proposed design matches with the voting-based Practical Byzantine Fault Tolerance (PBFT) consensus algorithm which is adopted by most non-cryptocurrency applications. Game theory-based analysis is conducted to identify the problems caused by participants' misbehaviours. Meanwhile, a mechanism design based on game theory analysis is adopted to develop our incentive mechanism which satisfies the compatibility requirement. Both mathematical proof and simulation experiments are used to evaluate our mechanism design. The results demonstrate that our compatible incentive mechanism encourages the desired honest behaviour among validators so that the blockchain can be maintained to align with the consensus algorithm.
A blockchain-based compact audit-enabled deduplication in decentralized storage

Secure deduplication and data auditing are significant in improving the resource utilization and data integrity protection of data outsourcing services. However, existing audit-enabled deduplication schemes cannot implement block-level deduplication over audit tags since they contain file information, and have to bear the consequent storage burden. Also, they suffer from the low coupling problem caused by the difference in optimal data chunking between deduplication and auditing. In this paper, we propose a blockchain-based compact audit-enabled deduplication scheme in decentralized storage. Specifically, we introduce the concept of N-ary tag commitment tree (NTCT), which integrates the information of all data blocks into a file authenticator to support integrity verification and enable block-level deduplication over audit tags. On this basis, we propose a blockchain-based compact audit-enabled deduplication protocol, which adopts an aggregatable vector commitment to generate audit tags, thereby overcoming the low coupling problem between deduplication and auditing. Notably, data users can outsource the task of tag generation to storage servers. Meanwhile, the capabilities of duplication detection, proof of ownership (PoW), and integrity verification are integrated into audit tags to reduce tag redundancy. Furthermore, we present an update protocol to allow data users to achieve a lightweight data update without uploading the entire new data. Finally, security analysis and performance evaluation demonstrate that our scheme is practical.