Reviewerly/exHarmony huggingface dataset:
Complexity reduction of ordinary kriging algorithm for 3D REM design

This work has investigated the feasibility of using cluster ordinary kriging (OK) interpolation to speed up indoor three-dimensional (3D) radio environment map (REM) generation time than the conventional OK-based REM generation time in two-dimensional (2D) and 3D ways mainly. The REM has been generated on the real-time received signal strength (RSS) dataset of an ultra-high frequency active television (TV) channel to explore TV grey space (TVGS) volume for cognitive access inside a six-storied office building. It can support future home networks for high-speed data-based applications. The optimal number of clusters has been found at K=4 on both 3D and 2D ways through distortion % deviation analysis. The maximum distortion % deviation = 88.74%, has been found at K=4. The exponential semivariogram model has been selected as the best-fit model after a thorough cross-validated comparative analysis of the clustered and non-clustered OK algorithms separately on residual squared error (RSE), root mean square error (RMSE) and the ratio of expected variance (EV) and kriging variance (KV) results. The RSE and RMSE are minimum, and the EV: KV ratio is in between 0.95 to 1.05 for the exponential model. Significant observation of the analysis of the results shows that cluster OK algorithms outperform conventional OK algorithms of corresponding domains on time complexity. A comparative study on RMSE, correlation coefficient (CC), and relative recovery error (RRE) show that cluster OK improves the accuracy. Cluster 2D and 3D OK-based detected TVGS volume is 13450 m3, 5.634% of the whole building volume.
Convolutional neural network architecture search based on fractal decomposition optimization algorithm

This paper presents a new approach to design the architecture and optimize the hyperparameters of a deep convolutional neural network (CNN) via of the Fractal Decomposition Algorithm (FDA). This optimization algorithm was recently proposed to solve continuous optimization problems. It is based on a geometric fractal decomposition that divides the search space while searching for the best solution possible. As FDA is effective in single-objective optimization, in this work we aim to prove that it can also be successfully applied to fine-tuning deep neural network architectures. Moreover, a new formulation based on bi-level optimization is proposed to separate the architecture search composed of discrete parameters from hyperparameters’ optimization. This is motivated by the fact that automating the construction of deep neural architecture has been an important focus over recent years as manual construction is considerably time-consuming, error-prone, and requires in-depth knowledge. To solve the bi-level problem thus formulated, a random search is performed aiming to create a set of candidate architectures. Then, the best ones are finetuned using FDA. CIFAR-10 and CIFAR-100 benchmarks were used to evaluate the performance of the proposed approach. The results obtained are among the state of the art in the corresponding class of networks (low number of parameters and chained-structured CNN architectures). The results are emphasized by the fact that the whole process was performed using low computing power with only 3 NVIDIA V100 GPUs. The source code is available at https://github.com/alc1218/Convolutional-Neural-Network-Architecture-Search-Based-on-Fractal-Decomposition-Optimization.
Explainable Artificial Intelligence for Bayesian Neural Networks: Toward Trustworthy Predictions of Ocean Dynamics

Abstract The trustworthiness of neural networks is often challenged because they lack the ability to express uncertainty and explain their skill. This can be problematic given the increasing use of neural networks in high stakes decision‐making such as in climate change applications. We address both issues by successfully implementing a Bayesian Neural Network (BNN), where parameters are distributions rather than deterministic, and applying novel implementations of explainable AI (XAI) techniques. The uncertainty analysis from the BNN provides a comprehensive overview of the prediction more suited to practitioners' needs than predictions from a classical neural network. Using a BNN means we can calculate the entropy (i.e., uncertainty) of the predictions and determine if the probability of an outcome is statistically significant. To enhance trustworthiness, we also spatially apply the two XAI techniques of Layer‐wise Relevance Propagation (LRP) and SHapley Additive exPlanation (SHAP) values. These XAI methods reveal the extent to which the BNN is suitable and/or trustworthy. Using two techniques gives a more holistic view of BNN skill and its uncertainty, as LRP considers neural network parameters, whereas SHAP considers changes to outputs. We verify these techniques using comparison with intuition from physical theory. The differences in explanation identify potential areas where new physical theory guided studies are needed.
Dynamic Scene Graph Generation via Temporal Prior Inference

Real-world videos are composed of complex actions with inherent temporal continuity (eg "person-touching-bottle" is usually followed by "person-holding-bottle"). In this work, we propose a novel method to mine such temporal continuity for dynamic scene graph generation (DSGG), namely Temporal Prior Inference (TPI). As opposed to current DSGG methods, which individually capture the temporal dependence of each video by refining representations, we make the first attempt to explore the temporal continuity by extracting the entire co-occurrence patterns of action categories from a variety of videos in Action Genome (AG) dataset. Then, these inherent patterns are organized as Temporal Prior Knowledge (TPK) which serves as prior knowledge for models' learning and inference. Furthermore, given the prior knowledge, human-object relationships in current frames can be effectively inferred from adjacent frames via the robust Temporal Prior Inference algorithm with tiny computation cost. Specifically, to efficiently guide the generating of temporal-consistent dynamic scene graphs, we incorporate the temporal prior inference into a DSGG framework by introducing frame enhancement, continuity loss, and fast inference. The proposed model-agnostic strategies significantly boost the performances of existing state-of-the-art models on the Action Genome dataset, achieving 69.7 and 72.6 for [email protected] and [email protected] on PredCLS. In addition, the inference speed can be significantly reduced by 41% with an acceptable drop on [email protected] (69.7 to 66.8) by utilizing fast inference.
Application and Evaluation of Deep Neural Networks for Airborne Hyperspectral Remote Sensing Mineral Mapping: A Case Study of the Baiyanghe Uranium Deposit in Northwestern Xinjiang, China

Deep learning is a popular topic in machine learning and artificial intelligence research and has achieved remarkable results in various fields. In geological remote sensing, mineral mapping is an appealing application of hyperspectral remote sensing for geological surveyors. Whether deep learning can improve the mineral identification ability in hyperspectral remote sensing images, especially for the discrimination of spectrally similar and intimately mixed minerals, needs to be evaluated. In this study, shortwave airborne spectrographic imager (SASI) hyperspectral images of the Baiyanghe uranium deposit in Northwestern Xinjiang, China, were used as experimental data. Three deep neural network (DNN) models were designed: a fully connected neural network (FCNN), a one-dimensional convolutional neural network (1D CNN), and a one-dimensional and two-dimensional convolutional neural network (1D and 2D CNN). A sample dataset containing five minerals was constructed for model training and validation, which was divided into training, validation and test sets at a ratio of 6:2:2. The final test accuracies of the FCNN, 1D CNN, and 1D and 2D CNN were 91.24%, 93.67% and 94.77%, respectively. The three DNNs were used for mineral identification and mapping of SASI hyperspectral images of the Baiyanghe uranium mining area. The mapping results were compared with the mapping results of the support vector machine (SVM) and the mixture-tuned matched filtering (MTMF) method. Combined with the ground spectral data obtained by the spectrometer, spectral verification and interpretation were carried out on sections that the two kinds of methods identified differently. The verification results show that the mapping results of the 1D and 2D CNN were more accurate than those of the other methods. More importantly, for minerals with similar spectral characteristics, such as short-wavelength white mica and medium-wavelength white mica, the 1D and 2D CNN model had a more accurate discrimination effect than the other DNN models, indicating that the introduction of spatial information can improve the mineral identification ability in hyperspectral remote sensing images. In general, CNNs have good application prospects in geological mapping of hyperspectral remote sensing images and are worthy of further development in future work.
Analysis of distributed ledger technologies for industrial manufacturing

In recent years, industrial manufacturing has undergone massive technological changes that embrace digitalization and automation towards the vision of intelligent manufacturing plants. With the aim of maximizing efficiency and profitability in production, an important goal is to enable flexible manufacturing, both, for the customer (desiring more individualized products) and for the manufacturer (to adjust to market demands). Manufacturing-as-a-service can support this through manufacturing plants that are used by different tenants who utilize the machines in the plant, which are offered by different providers. To enable such pay-per-use business models, Distributed Ledger Technology (DLT) is a viable option to establish decentralized trust and traceability. Thus, in this paper, we study potential DLT technologies for efficient and intelligent integration of DLT-based solutions in manufacturing environments. We propose a general framework to adapt DLT in manufacturing, and then we introduce the use case of shared manufacturing, which we utilize to study the communication and computation efficiency of selected DLTs in resource-constrained wireless IoT networks.
Keyword-based faceted search interface for knowledge graph construction and exploration

Purpose Massive amounts of data, manifesting in various forms, are being produced on the Web every minute and becoming the new standard. Exploring these information sources distributed in different Web segments in a unified way is becoming a core task for a variety of users’ and companies’ scenarios. However, knowledge creation and exploration from distributed Web data sources is a challenging task. Several data integration conflicts need to be resolved and the knowledge needs to be visualized in an intuitive manner. The purpose of this paper is to extend the authors’ previous integration works to address semantic knowledge exploration of enterprise data combined with heterogeneous social and linked Web data sources. Design/methodology/approach The authors synthesize information in the form of a knowledge graph to resolve interoperability conflicts at integration time. They begin by describing KGMap, a mapping model for leveraging knowledge graphs to bridge heterogeneous relational, social and linked web data sources. The mapping model relies on semantic similarity measures to connect the knowledge graph schema with the sources' metadata elements. Then, based on KGMap, this paper proposes KeyFSI, a keyword-based semantic search engine. KeyFSI provides a responsive faceted navigating Web user interface designed to facilitate the exploration and visualization of embedded data behind the knowledge graph. The authors implemented their approach for a business enterprise data exploration scenario where inputs are retrieved on the fly from a local customer relationship management database combined with the DBpedia endpoint and the Facebook Web application programming interface (API). Findings The authors conducted an empirical study to test the effectiveness of their approach using different similarity measures. The observed results showed better efficiency when using a semantic similarity measure. In addition, a usability evaluation was conducted to compare KeyFSI features with recent knowledge exploration systems. The obtained results demonstrate the added value and usability of the contributed approach. Originality/value Most state-of-the-art interfaces allow users to browse one Web segment at a time. The originality of this paper lies in proposing a cost-effective virtual on-demand knowledge creation approach, a method that enables organizations to explore valuable knowledge across multiple Web segments simultaneously. In addition, the responsive components implemented in KeyFSI allow the interface to adequately handle the uncertainty imposed by the nature of Web information, thereby providing a better user experience.
Smart technology vs. embarrassed human: The inhibiting effect of anticipated technology embarrassment

The travel and tourism segment has recently seen some of the most considerable growth in interactive kiosks because of the COVID-19 pandemic. Consequently, it is important for companies to understand how customers feel when they are using these kiosks. This research answers the call for research of automation in tourism as a social phenomenon (Tussyadiah, 2020) by investigating the role of a social emotion – anticipated technology embarrassment. This research identifies anticipated technology embarrassment as a negative emotion that may hinder interactive kiosks' usage. Moreover, this study suggests that specific queue design and queue distractor can effectively reduce anticipated technology embarrassment. Two observational field studies and three lab experiments confirm our hypotheses.
Continual learning of longitudinal health records

Continual learning denotes machine learning methods which can adapt to new environments while retaining and reusing knowledge gained from past experiences. Such methods address two issues encountered by models in non-stationary environments: ungeneralisability to new data, and the catastrophic forgetting of previous knowledge during retraining. This is a pervasive problem in clinical settings where patient data exhibits covariate shift not only between populations, but also continuously over time. However, while continual learning methods have seen nascent success in the imaging domain, they have been little applied to the multi-variate sequential data characteristic of patient recordings. Here we evaluate a variety of continual learning methods on longitudinal ICU data in a series of representative healthcare scenarios. We find that while several methods mitigate short-term forgetting, domain shift remains a challenging problem over a large series of tasks, with only replay based methods achieving stable long-term performance.
Distantly supervised relation extraction with KB-enhanced reconstructed latent iterative graph networks

We investigate distantly supervised relation extraction with knowledge-guided latent graphs and an iterative graph learner. For the relation extraction tasks, we assume that the input sentences contain latent graphs with useful structural information between mentions and relations of the distantly supervised data. We first embed input sentences with default initialized graphs to utilize this information. We also used the pre-trained Knowledge Bases (KB) to guide the latent space of a Variational Graph Auto-Encoders (VGAE) module. Then the VGAE applies the re-parameterization mechanism to reconstruct the initial graph, injecting extra knowledge into the initial latent graph and reducing its unsteadiness. Subsequently, we optimize the latent graph structures and their corresponding node embeddings simultaneously by the iterative graph learner, and the better latent graphs can improve the downstream relation extraction task. Experiment results on three datasets (NYT10, WIKIDISTANT and GIDS) show that latent graph learning helps our model perform better than previous works.
STAD-GAN: Unsupervised Anomaly Detection on Multivariate Time Series with Self-training Generative Adversarial Networks

Anomaly detection on multivariate time series (MTS) is an important research topic in data mining, which has a wide range of applications in information technology, financial management, manufacturing system, and so on. However, the state-of-the-art unsupervised deep learning models for MTS anomaly detection are vulnerable to noise and have poor performance on the training data containing anomalies. In this article, we propose a novel Self-Training based Anomaly Detection with Generative Adversarial Network (GAN) model called STAD-GAN to address the practical challenge. The STAD-GAN model consists of a generator-discriminator structure for adversarial learning and a neural network classifier for anomaly classification. The generator is learned to capture the normal data distribution, and the discriminator is learned to amplify the reconstruction error of abnormal data for better recognition. The proposed model is optimized with a self-training teacher-student framework, where a teacher model generates reliable high-quality pseudo-labels to train a student model iteratively with a refined dataset so that the performance of the anomaly classifier can be gradually improved. Extensive experiments based on six open MTS datasets show that STAD-GAN is robust to noise and achieves significant performance improvement compared to the state-of-the-art.
Stain normalization using score-based diffusion model through stain separation and overlapped moving window patch strategies

Hematoxylin and eosin (H&E) staining is the gold standard modality for diagnosis in medicine. However, the dosage ratio of hematoxylin to eosin in H&E staining has not been standardized yet. Additionally, H&E stains fade out at various speeds. Therefore, the staining quality could differ among each image, and stain normalization is a critical preprocessing approach for training deep learning (DL) models, especially in long-term and/or multicenter digital pathology studies. However, conventional methods for stain normalization have some significant drawbacks, such as collapsing in the structure and/or texture of tissue. In addition, conventional methods must require a reference patch or slide. Meanwhile, DL-based methods have a risk of overfitting and/or grid artifacts. We developed a score-based diffusion model of colorization for stain normalization. However, mistransfer, in which the model confuses hematoxylin with eosin, can occur using a score-based diffusion model due to its high diversity nature. To overcome this mistransfer, we propose a stain separation method using sparse non-negative matrix factorization (SNMF), which can decompose pathology slide into Hematoxylin and Eosin to normalize each stain component. Furthermore, inpainting with overlapped moving window patches was used to prevent grid artifacts of whole slide image normalization. Our method can normalize the whole slide pathology images through this stain normalization pipeline with decent performance.
Towards more efficient methods for solving regular-expression heavy string constraints

Widespread use of string solvers in the formal analysis of string-heavy programs has led to a growing demand for more efficient and reliable techniques which can be applied in this context. Designing practical algorithms for the (generally undecidable) satisfiability problem for systems of string constraints requires a deep understanding of the structure of constraints present in the targeted cases. In this paper, we first investigate relevant benchmarks containing regular expression membership predicates, extract a series of first order logic theories, and prove the decidability, respectively undecidability, of their satisfiability problem. Further, building on the theoretical results, we present a novel length-aware solving algorithm for the quantifier-free first-order theory over regular expression membership predicates and linear arithmetic over string length. We implement and evaluate this algorithm and related heuristics in the Z3 theorem prover. Besides the theoretical aspects leading to this algorithm, a crucial insight that underpins it is that real-world regex and string formulas contain a wealth of information about upper and lower bounds on lengths of strings, and such information can be used very effectively to simplify operations on automata representing regular expressions. Additionally, we present several novel general heuristics, such as the prefix/suffix method, that can be used to make a variety of regex solving algorithms more efficient in practice. We showcase the power of our algorithm and heuristics via an extensive empirical evaluation over a large and diverse benchmark of 57256 regex-heavy instances, almost 75% of which are derived from industrial applications or contributed by other solver developers. Our solver outperforms five other state-of-the-art string solvers over this benchmark.
Blockchain in Healthcare: A Decentralized Platform for Digital Health Passport of COVID-19 Based on Vaccination and Immunity Certificates

COVID-19 has become a very transmissible disease that has had a worldwide impact, resulting in a huge number of infections and fatalities. Testing is critical to the pandemic's successful response because it helps detect illnesses and so attenuate (isolate/cure) them and now vaccination is a life-safer innovation against the pandemic which helps to make the immunity system stronger and fight against this infection. Patient-sensitive information, on the other hand, is now held in a centralized or third-party storage paradigm, according to COVID-19. One of the most difficult aspects of using a centralized storage strategy is maintaining patient privacy and system transparency. The application of blockchain technology to support health initiatives that can minimize the spread of COVID-19 infections in the context of accessibility of the system and for verification of digital passports. Only by combining blockchain technology with advanced cryptographic algorithms can a secure and privacy-preserving solution to COVID-19 be provided. In this article, we investigate the issue and propose a blockchain-based solution incorporating conscience identity, encryption, and decentralized storage via interplanetary file systems (IPFS). For COVID-19 test takers and vaccination takers, our solution includes digital health passports (DHP) as a certification of test or vaccination. We explain smart contracts constructed and tested with Ethereum to preserve a DHP for test and vaccine takers, allowing for a prompt and trustworthy response from the necessary medical authorities. We use an immutable trustworthy blockchain to minimize medical facility response times, relieve the transmission of incorrect information, and stop the illness from spreading via DHP. We give a detailed explanation of the proposed solution's system model, development, and assessment in terms of cost and security. Finally, we put the suggested framework to the test by deploying a smart contract prototype on the Ethereum TESTNET network in a Windows environment. The study's findings revealed that the suggested method is effective and feasible.