Reviewerly/exHarmony huggingface dataset:
Deformation measurement in Al thin films at elevated temperatures by digital image correlation with speckles prepared by femtosecond laser

Deformation measurement in thin films at elevated temperatures is critical for assessing the reliability and performance of microelectronic devices. Digital image correlation (DIC) is a full-field, noncontact deformation measurement technique. However, the traditional DIC approach requires spraying paints for speckles, which is not suitable for high-temperature applications. The aim of this work is to propose a fully customizable speckle preparation method by pulsed laser that can deliver ideal speckle pattern, good contrast and consistency and controllable damage to sample for deformation measurement at elevated temperatures by DIC. Al thin films deposited on Si substrates were used for speckle preparation by FLs at different powers (10 mW, 8 mW, 6 mW and 4 mW). Serial images of speckles were captured as the temperature increased from room temperature to 325 °C, followed by data analysis. A finite element model was built to evaluate the deviation of measured strains in Al thin film from ideal values. To evaluate the damage of laser to sample surface during speckle preparation, a two-temperature model (TTM) was used to evaluate the ideal ablation depth at different laser fluences. Scanning electron microscopy and white light interferometry were also used to examine the surface damage and depth of holes. Based on our findings, speckles prepared by laser irradiation at 8 mW have the best speckle quality and measurement accuracy. The depth of speckles increased linearly with increasing laser power, ranging from 11.8 to 32.9 nm. The ideal depth calculated by the TTM was slightly lower than the experimental results, which can be attributed to the parameters of the model deviating from the actual sample (such as the reflectance of Al). DIC with speckles prepared by an FL can provide good measurement accuracy at elevated temperatures while causing minimal damage to the sample.
Logical Data Model for Intelligent Video Surveillance Systems

The article presents a logical data model for intelligent video surveillance systems. New opportunities for creating data models focused on the widespread use of machine learning methods are considered. New methods for constructing logical models and their application in intelligent video surveillance systems are proposed. A smartphone application has been developed. The effectiveness of using the proposed logical models for data processing in fog and cloud environments has been proved. Specific examples are given.
Dual attention-guided and learnable spatial transformation data augmentation multi-modal unsupervised medical image segmentation

Unsupervised domain adaptation multi-modal medical image segmentation method is used for joint training to realize the segmentation of different modal medical images at the same time. Since the domain shift of different modal images and the limited labeled medical images, the accuracy of these methods needs to be further improved. In this work, we present a novel unsupervised domain adaptation method, named as Dual Attention-guided and Learnable spatial transformation data Augmentation multi-modal unsupervised medical image segmentation (DALA). Firstly, this paper mainly introduces the position and channel Dual Attention Mechanism (Dual Attent-M) into the low-level encoder to improve the feature extraction ability of the network and enhance the domain adaptation training of the network. Secondly, a learnable Spatial Transformation data Augmentation method (Spatial Tran-Aug) is further proposed to learn the spatial mapping relationship between the source image and the target image to synthesize high-quality data for training. Experiments on the Multi-Modality Whole Heart Segmentation (MMWHS) dataset show that compared with the multi-modal segmentation methods such as PnP-AdaNet, SynSeg-Net, AdaOutput, CyCADA, Prior SIF and SIFA, the proposed method DALA can achieve better segmentation results, and the average DICE predicted by CT and MR is increased to 78.2% and 67.9%, the mean ASSD decreased to 4.4 and 4.7.
Hypergraph Collaborative Network on Vertices and Hyperedges

In many practical datasets, such as co-citation and co-authorship, relationships across the samples are more complex than pair-wise. Hypergraphs provide a flexible and natural representation for such complex correlations and thus obtain increasing attention in the machine learning and data mining communities. Existing deep learning-based hypergraph approaches seek to learn the latent vertex representations based on either vertices or hyperedges from previous layers and focus on reducing the cross-entropy error over labeled vertices to obtain a classifier. In this paper, we propose a novel model called Hypergraph Collaborative Network (HCoN), which takes the information from both previous vertices and hyperedges into consideration to achieve informative latent representations and further introduces the hypergraph reconstruction error as a regularizer to learn an effective classifier. We evaluate the proposed method on two cases, i.e., semi-supervised vertex and hyperedge classifications. We carry out the experiments on several benchmark datasets and compare our method with several state-of-the-art approaches. Experimental results demonstrate that the performance of the proposed method is better than that of the baseline methods.
A machine learning model for separating epithelial and stromal regions in oral cavity squamous cell carcinomas using H&amp;E-stained histology images: A multi-center, retrospective study

Tissue slides from Oral cavity squamous cell carcinoma (OC-SCC), particularly the epithelial regions, hold morphologic features that are both diagnostic and prognostic. Yet, previously developed approaches for automated epithelium segmentation in OC-SCC have not been independently tested in a multi-center setting. In this study, we aimed to investigate the effectiveness and applicability of a convolutional neural network (CNN) model to perform epithelial segmentation using digitized H&E-stained diagnostic slides from OC-SCC patients in a multi-center setting. A CNN model was developed to segment the epithelial regions of digitized slides (n = 810), retrospectively collected from five different centers. Deep learning models were trained and validated using well-annotated tissue microarray (TMA) images (n = 212) at various magnifications. The best performing model was locked down and used for independent testing with a total of 478 whole-slide images (WSIs). Manually annotated epithelial regions were used as the reference standard for evaluation. We also compared the model generated results with IHC-stained epithelium (n = 120) as the reference. The locked-down CNN model trained on the TMA image training cohorts with 10x magnification achieved the best segmentation performance. The locked-down model performed consistently and yielded Pixel Accuracy, Recall Rate, Precision Rate, and Dice Coefficient that ranged from 95.8% to 96.6%, 79.1% to 93.8%, 85.7% to 89.3%, and 82.3% to 89.0%, respectively for the three independent testing WSI cohorts. The automated model achieved a consistently accurate performance for automated epithelial region segmentation compared to manual annotations. This model could be integrated into a computer-aided diagnosis or prognosis system.
Memory-efficient DNN training on mobile devices

On-device deep neural network (DNN) training holds the potential to enable a rich set of privacy-aware and infrastructure-independent personalized mobile applications. However, despite advancements in mobile hardware, locally training a complex DNN is still a nontrivial task given its resource demands. In this work, we show that the limited memory resources on mobile devices are the main constraint and propose Sage as a framework for efficiently optimizing memory resources for on-device DNN training. Specifically, Sage configures a flexible computation graph for DNN gradient evaluation and reduces the memory footprint of the graph using operator- and graph-level optimizations. In run-time, Sage employs a hybrid of gradient checkpointing and micro-batching techniques to dynamically adjust its memory use to the available system memory budget. Using implementation on off-the-shelf smartphones, we show that Sage enables local training of complex DNN models by reducing memory use by more than 20-fold compared to a baseline approach. We also show that Sage successfully adapts to run-time memory budget variations, and evaluate its energy consumption to show Sage's practical applicability.
Blockchain for Health IoT: A privacy‐preserving data sharing system

Abstract Health Internet of Things (Health IoT) has been limited by isolated information and a lack of security. As the combination of blockchain and Health IoT could potentially address these two limitations, it has attracted significant interest. However, blockchain‐based systems often fail to balance data sharing and privacy protection. Therefore, we proposed a Health IoT‐based privacy‐preserving data sharing blockchain system. We designed a privacy‐preserving method based on the content extraction signature scheme to enable patients to establish fine‐grained privacy protection. We designed a Byzantine fault‐tolerant leader election mechanism that enhances the security of the Raft algorithm while providing efficiency in the data sharing. Furthermore, we designed a summary contract to ensure efficient data retrieval. The proposed mechanism was evaluated in terms of the efficiency and security. The simulation and analysis results demonstrate that our scheme offers a secure and effective technique for achieving privacy‐preserving and efficient sharing of IoT medical data.
Using existing metadata standards and tools for a digital language archive: a balancing act

Purpose Building a digital language archive requires a number of steps to ensure collecting, describing, preserving and providing access to language data in effective and efficient ways. The purpose of this paper is to describe how the Computational Resource for South Asian Languages (CoRSAL) group has partnered with the University of North Texas (UNT) Digital Library to build a series of interconnected digital collections that leverage existing UNT technical and metadata infrastructure to provide access to data from and for various language communities. Design/methodology/approach This paper introduces the reader to the background of this project and discusses some of the areas important for representing language materials where both University of North Texas Libraries (UNTL) metadata and CoRSAL metadata practices were adapted to better fit the needs of intended audiences. Findings We discuss a workflow for standardized language representation (the Language field), defining roles for persons related to the item (Creator and Contributor fields) and including subject representation for language materials (Subjects and Keywords fields). Practical implications Although further work is needed to improve language data representation in the CoRSAL digital language archive, the model adopted by the team and lessons learned could benefit others in the language archiving community. Originality/value This paper is a significantly extended version of the presentation made at the 1st International Workshop on Digital Language Archives in 2021.
Anomalous load profile detection in power systems using wavelet transform and robust regression

In modern power systems, a clean database of historical electric loads is crucial for system analysis, planning studies, load forecasting, and decision making. To this end, the clean database should be free of anomalies while preserving the power consumption behavior of special events like holidays. In recent years, model-based anomaly detection methods have been successfully used for anomaly detection and correction in electrical load. In these methods, the collected load data are first compared with the predicted load obtained from a forecasting model; if the difference is greater than a specific limit, the collected load data are replaced with the predicted value. One of the disadvantages of these methods is the consideration of load profiles of the days with higher levels of difficulty in load forecasting, such as holidays, as anomalous load profiles and replacing them with predicted values, which results in fading some important characteristics of the load series. Therefore in this paper, a two-stage anomaly detection method is proposed to alleviate this problem. In the first stage, the wavelet transform is used to identify suspected load profiles. In the second stage, a robust regression method is used to further inspect the suspected load profiles and decide whether they are anomalies or not. By performing experiments on three datasets (a utility of Iran, New York City, and a North American utility) and comparing the results with four other methods, it’s shown that the proposed method decreases false detection of anomalies while accurately correcting anomalous load profiles. Results of load forecasting with the Iranian dataset corrected by the proposed method showed an average of 2.28% and 1.58% error on the holidays and total days, respectively, which is less than the other four methods.
On Producing Accurate Rating Predictions in Sparse Collaborative Filtering Datasets

The typical goal of a collaborative filtering algorithm is the minimisation of the deviation between rating predictions and factual user ratings so that the recommender system offers suggestions for appropriate items, achieving a higher prediction value. The datasets on which collaborative filtering algorithms are applied vary in terms of sparsity, i.e., regarding the percentage of empty cells in the user–item rating matrices. Sparsity is an important factor affecting rating prediction accuracy, since research has proven that collaborative filtering over sparse datasets exhibits a lower accuracy. The present work aims to explore, in a broader context, the factors related to rating prediction accuracy in sparse collaborative filtering datasets, indicating that recommending the items that simply achieve higher prediction values than others, without considering other factors, in some cases, can reduce recommendation accuracy and negatively affect the recommender system’s success. An extensive evaluation is conducted using sparse collaborative filtering datasets. It is found that the number of near neighbours used for the prediction formulation, the rating average of the user for whom the prediction is generated and the rating average of the item concerning the prediction can indicate, in many cases, whether the rating prediction produced is reliable or not.
3D Networking and Collaborative Environment for Online Education

This paper elucidates an approach to develop an online 3D meeting application on the concept of the metaverse. In spite of many existing online meeting platforms, this application will open the world to a new digital era, based on virtual reality and web3.0. Holographic based approach to education will not only ensure intuitive learning but will also bridge the gap of remote monitoring, generally not found in existing 2D meeting platforms. This application will be a multiplayer networking platform for students and teachers to collaborate. Not only in the education sector, it has use cases in medical training, military meetings, corporate affairs, and sports events. It will provide features of hosting meetings, conducting talks, white-boarding, and multi-player projectors.
Heterogeneous graph neural network for attribute completion

Heterogeneous graphs consist of multiple types of nodes and edges, and contain comprehensive information and rich semantics, which can properly model real-world complex systems. However, the attribute values of nodes are often incomplete with many missing attributes, as the cost of collecting node attributes is prohibitively expensive or even impossible (e.g., sensitive personal information). While a handful of graph neural network (GNN) models are developed for attribute completion in heterogeneous networks, most of them either ignore the use of similarity between nodes in feature space, or overlook the different importance of different-order neighbor nodes for attribute completion, resulting in poor performance. In this paper, we propose a general Attribute Completion framework for HEterogeneous Networks (AC-HEN), which is composed of feature aggregation, structure aggregation, and multi-view embedding fusion modules. Specifically, AC-HEN leverages feature aggregation and structure aggregation to obtain multi-view embeddings considering neighbor aggregation in both feature space and network structural space, which distinguishes different contributions of different neighbor nodes by conducting weighted aggregation. Then AC-HEN uses the multi-view embeddings to complete the missing attributes via an embedding fusion module in a weak supervised learning paradigm. Extensive experiments on three real-world heterogeneous network datasets demonstrate the superiority of AC-HEN against state-of-the-art baselines in both attribute completion and node classification. The source code is available at: https://github.com/Code-husky/AC-HEN.
Educational data advocates: emerging forms of teacher agency in postdigital classrooms

.Commercial platforms are being increasingly used in classrooms as teaching and learning tools. Beyond shifts in practice to accommodate this, the teacher becomes a tool of the platform – generating and collecting data for commercial entities. It is time to move from commercial platforms working through teachers to influence education, towards working with teachers in the ethical use of educational data. The purpose of this paper is twofold. Firstly, we draw on semi-structured interviews to present an exploratory typology of teacher influencer behaviour. Secondly, we argue that there is potential for teacher influencers to act as advocates. We introduce the conceptual notion of an ‘educational data advocate’. We call for Teacher Influencers to be recognised as a source of expertise to understand the implications of the use of data-driven educational technologies. By doing so, a new pedagogical economy that promotes ethics and rights alongside educational and commercial outcomes may be generated.
Optimal linear-optical noiseless quantum amplifiers driven by auxiliary multiphoton Fock states

Quantum scissors refer to interferometric schemes that allow combined teleportation and amplification of quantum states. Here, the author demonstrates that quantum scissors without feedforward can be optimized to have higher success probability than previously shown, and that quantum scissors can themselves be outperformed by generalized interferometric couplings.
Geology, mineralogy and pyrite trace elements constraints on gold mineralization mechanism at the giant Dayingezhuang gold deposit, Jiaodong Peninsula, China

Identifying the mechanism of gold deposition is important to reveal ore-forming processes and genesis. The giant Dayingezhuang gold deposit, with 283 tons of proven gold resources, is located in the northwestern of the Jiaodong gold province, China. Pyrite is the dominant sulfide minerals and visible gold dominates Au resources. Five types of pyrite were identified at the Dayingezhuang gold deposit: (1) Py-1a is euhedral grain with Co– and Ni-rich content; (2) Py-1b is subhedral to anhedral grain with intermediate rich in Co, Ni, As and minor Te; (3) Py-2a is rich in As and crystalizes with a mass of mineral inclusions; (4) Py-2b is also rich in As but glossy; and (5) Py-3 is mainly euhedral grain and crystalizes with much more galena. Laser ablation spectra commonly shows Au and Ag-Bi-Pb spikes in Py-1b, Py-2a, Py-2b and Py-3, reflecting the occurrence of nanoparticles in pyrite. LA-ICP-MS mapping analysis shows elemental zoning of Co, Ni and As, which is considered as the element depletion (Co and Ni) of pre-existing Py-1a and enrichment (As) from ore-forming fluid via coupled dissolution-reprecipitation (CDR) mechanism. Au in pyrite is totally below 1 ppm (average 0.154 ppm and median 0.264 ppm) and without arresting variation in different pyrites, and mass balance indecates that invisible gold mobilized from pyrite may not interpret the formation of world-class gold deposit in the Jiaodong gold province. Most of the gold grains distribute in the microfractures of pyrites which is caused by brittle deformation. Fracture of rocks and minerals significantly improve the efficiency of water–rock interaction and result in sudden drop of fluid pressure. A probable model is that a fluid with extremely different physicochemical conditions and high As-Au-Cu-Pb-Bi-Te content give rise to Au mineralization, and visible gold precipitate in a gold-supersaturated hydrothermal fluid which is attributed to sulfidation and fluid phase separation via hydrofracturing. In addition, similar occurrences of gold and silver (individual minerals in microfractures of pyrites and spikes on laser ablation spectra of pyrite) and paragenetic sequence indicate that gold and silver mineralization belong to single geological event.
Accuracy comparison of various remote sensing data in lithological classification based on random forest algorithm

This study compares the capability of Landsat-8, Sentinel-2, and ASTER multispectral data in mapping lithological units in the Bukadaban Peak, China, by evaluating the performance of the Random Forest (RF) classifier. Moreover, the study assesses the importance of remote sensing original bands and the bands derived from enhancement techniques such as Principal Component Analysis (PCA) and Minimum Noise Fraction (MNF) to the classification process. Results revealed that RF selected PC8, PC9, PC12, and MNF1 as the most important features in the Sentinel-2 dataset. Several MNF bands of ASTER were more important than the original and PC bands. The original bands were the strongest predictors in Landsat-8 dataset, while PC2, PC5, and MNF5 were relatively significant. Sentinal-2 and ASTER datasets achieved very similar classification accuracy but outperformed Landsat-8 dataset. ASTER dataset yielded the highest overall accuracy 81.8%, which is 0.18% higher than Sentinel-2 and 3.86% higher than Landsat-8.