Reviewerly/exHarmony huggingface dataset:
The influence of blockchains and internet of things on global value chain

Abstract Despite the increasing proliferation of deploying the internet of things (IoT) in the global value chain (GVC), several challenges might lead to a lack of trust among value chain partners, for example, technical challenges (i.e., confidentiality, authenticity, and privacy); and security challenges (i.e., counterfeiting, physical tampering, and data theft). In this study, we argue that blockchain technology (BT), when combined with the IoT ecosystem, will strengthen GVC and enhance value creation and capture among value chain partners. Therefore, we examine the impact of BT combined with the IoT ecosystem and how it can be utilized to enhance value creation and capture among value chain partners. We collected data through an online survey, and 265 U.K. Agri‐food retailers completed the survey. Our data were analyzed using structural equation modeling. Our finding reveals that BT enhances GVC by improving IoT scalability, security, and traceability combined with the IoT ecosystem. Moreover, the combination of BT and IoT strengthens GVC and creates more value for value chain partners, which serves as a competitive advantage. Finally, our research outlines the theoretical and practical contribution of combining BT and the IoT ecosystem.
Experiences in distance education and practical use of ICT during the COVID-19 epidemic of Slovenian primary school music teachers with different professional experiences

The aim of this study was to determine the influence of music teachers' professional experience on ICT use in Slovenian primary education before and after the proclamation of the COVID-19 epidemic. We collected data using an online questionnaire; eighty-three music teachers participated. We divided them into three groups with different professional experiences. The results show that music teachers with less professional experience used ICT more and had fewer problems than those with more professional experience. The creative ICT use of music teachers in primary schools today has a significant impact on the quality of learning objectives, motivation and efficiency.
MFFN: An Underwater Sensing Scene Image Enhancement Method Based on Multiscale Feature Fusion Network

Vision-guided autonomous underwater vehicles based on remote sensing play an important role in ocean missions. However, some problems exist in underwater visual perception, such as color distortion, low contrast, and fuzzy details, which restrict the applications of underwater visual tasks. Most of the state-of-the-art image enhancement methods are still limited in scene adaptability, recovery accuracy, and real-time processing. To solve these problems, we propose an underwater sensing scene image enhancement method called a multiscale feature fusion network (MFFN). To extract the multiscale feature, the measure merging the feature extraction module, the feature fusion module, and the attention reconstruction module is designed. This measure can also enhance the adaptability and visual effect of the scene. Moreover, we propose multiple objective functions for supervised training to match the nonlinear mapping. Based on the qualitative and quantitative evaluations, the proposed method produces competitive performance compared with some state-of-the-art methods, and the perception and statistical quality of underwater images are enhanced effectively.
Efficient corner detection based on corner enhancement filters

Multi-scale analysis based corner detection algorithms yield impressive performance, however, they are not efficient and not suitable for real-time computer vision tasks. The classical corner detection algorithms including FAST and Harris are computationally efficient, but their detection accuracy and repeatability are insufficient. This paper describes a novel fast corner detector with a simple architecture and high parallel computing characteristics. In order to simplify the corner detection architecture and improve its parallel computing performance, a new type of filter is proposed that can enhance corners and suppress edges and noise simultaneously. Then a novel efficient corner detector is proposed, which can be adapted to achieve real-time detection in hardware. Experimental results show that, with a very low computational cost and simple architecture, the proposed detector can achieve or even exceed the detection accuracy of multi-scale analysis based detectors. Its repeatability is similar to multi-scale analysis based detectors and clearly higher than other types of corner detectors. Therefore, it is potentially useful as an efficient corner detector for computer vision applications especially for portable real-time tasks.
Extremely Randomized Trees With Privacy Preservation for Distributed Structured Health Data

Artificial intelligence and machine learning have recently attracted considerable attention in the healthcare domain.The data used by machine learning algorithms in healthcare applications is often distributed over multiple sources, for instance, hospitals or patients' personal devices.One main difficulty lies in analyzing such data without compromising patients' privacy and personal data, which is a primary concern in healthcare applications.Therefore, in these applications, we are interested in running machine learning algorithms over distributed data without disclosing sensitive information about the data subjects.In this paper, we propose a distributed extremely randomized trees algorithm for learning from distributed data with privacy preservation.We present the implementation of our technique (which we refer to as k-PPD-ERT) on a cloud platform and demonstrate its performance based on medical data, including Heart Disease, Breast Cancer, and mental health datasets (Depresjon and Psykose datasets) associated with the Norwegian INTROducing Mental health through Adaptive Technology (INTROMAT) project.
Automated Deep Learning Empowered Breast Cancer Diagnosis Using Biomedical Mammogram Images

Biomedical image processing is a hot research topic which helps to majorly assist the disease diagnostic process. At the same time, breast cancer becomes the deadliest disease among women and can be detected by the use of different imaging techniques. Digital mammograms can be used for the earlier identification and diagnostic of breast cancer to minimize the death rate. But the proper identification of breast cancer has mainly relied on the mammography findings and results to increased false positives. For resolving the issues of false positives of breast cancer diagnosis, this paper presents an automated deep learning based breast cancer diagnosis (ADL-BCD) model using digital mammograms. The goal of the ADL-BCD technique is to properly detect the existence of breast lesions using digital mammograms. The proposed model involves Gaussian filter based pre-processing and Tsallis entropy based image segmentation. In addition, Deep Convolutional Neural Network based Residual Network (ResNet 34) is applied for feature extraction purposes. Specifically, a hyper parameter tuning process using chimp optimization algorithm (COA) is applied to tune the parameters involved in ResNet 34 model. The wavelet neural network (WNN) is used for the classification of digital mammograms for the detection of breast cancer. The ADL-BCD method is evaluated using a benchmark dataset and the results are analyzed under several performance measures. The simulation outcome indicated that the ADL-BCD model outperforms the state of art methods in terms of different measures.
Using LSTM Neural Networks for Onsite Earthquake Early Warning

Abstract Onsite earthquake early warning (EEW) systems determine possible destructive S waves solely from initial P waves and issue alarms before heavy shaking begins. Onsite EEW plays a crucial role in filling in the blank of the blind zone near the epicenter, which often suffers the most from disastrous ground shaking. Previous studies suggest that the peak P-wave displacement amplitude (Pd) may serve as a possible indicator of destructive earthquakes. However, the attempt to use a single indicator with fixed thresholds suffers from inevitable errors because the diversity in travel paths and site effects for different stations introduces complex nonlinearities. In addition, the short warning time poses a threat to the validity of EEW. To conquer the aforementioned problems, this study presents a deep learning approach employing long short-term memory (LSTM) neural networks, which can produce a highly nonlinear neural network and derive an alert probability at every time step. The proposed LSTM neural network is then tested with two major earthquake events and one moderate earthquake event that occurred recently in Taiwan, yielding the results of a missed alarm rate of 0% and a false alarm rate of 2.01%. This study demonstrates promising outcomes in both missed alarms and false alarms reduction. Moreover, the proposed model provides an adequate warning time for emergency response.
The relationship between readability and scientific impact: Evidence from emerging technology discourses

This study examines how the readability of scientific discourses changes over time and to what extent readability can explain scientific impact in terms of citation counts. The basis are representative datasets of 135,502 abstracts from academic research papers pertaining to twelve technologies of different maturity. Using three different measures of readability, it is found that the language of the abstracts has become more complex over time. Across all technologies, less easily readable texts are more likely to receive at least one citation, while the effects are most pronounced for comparatively immature research streams. Among the more mature or larger discourses, the abstracts of the top 10% and 1% of the most often cited articles are significantly less readable. It remains open to what extent readability actually influences future citations and how much of the relationship is causal. If readability indeed drives citations, the results imply that scientists have an incentive to (artificially) reduce the readability of their abstracts in order to signal quality and competence to readers—both to get noticed at all and to attract more citations. This may mean a prisoner dilemma in academic (abstract) writing, where authors intentionally but unnecessarily complicate the way in which they communicate their work.
MDReg‐Net: Multi‐resolution diffeomorphic image registration using fully convolutional networks with deep self‐supervision

We present a diffeomorphic image registration algorithm to learn spatial transformations between pairs of images to be registered using fully convolutional networks (FCNs) under a self-supervised learning setting. Particularly, a deep neural network is trained to estimate diffeomorphic spatial transformations between pairs of images by maximizing an image-wise similarity metric between fixed and warped moving images, similar to those adopted in conventional image registration algorithms. The network is implemented in a multi-resolution image registration framework to optimize and learn spatial transformations at different image resolutions jointly and incrementally with deep self-supervision in order to better handle large deformation between images. A spatial Gaussian smoothing kernel is integrated with the FCNs to yield sufficiently smooth deformation fields for diffeomorphic image registration. The spatial transformations learned at coarser resolutions are utilized to warp the moving image, which is subsequently used as input to the network for learning incremental transformations at finer resolutions. This procedure proceeds recursively to the full image resolution and the accumulated transformations serve as the final transformation to warp the moving image at the finest resolution. Experimental results for registering high-resolution 3D structural brain magnetic resonance (MR) images have demonstrated that image registration networks trained by our method obtain robust, diffeomorphic image registration results within seconds with improved accuracy compared with state-of-the-art image registration algorithms.
Learning Personal Human Biases and Representations for Subjective Tasks in Natural Language Processing

Many tasks in natural language processing like offensive, toxic, or emotional text classification are subjective by nature. Humans tend to perceive textual content in their own individual way. Existing methods commonly rely on the agreed output values, the same for all consumers. Here, we propose personalized solutions to subjective tasks. Our four new deep learning models take into account not only the content but also the specificity of a given human. The models represent different approaches to learning the representation and processing data about text readers. The experiments were carried out on four datasets: Wikipedia discussion texts labelled with attack, aggression, and toxicity, as well as opinions annotated with ten numerical emotional categories. Emotional data was considered as multivariate regression (multitask), whereas Wikipedia data as independent classifications. All our models based on human biases and their representations significantly improve the prediction quality in subjective tasks evaluated from the individual’s perspective.
A 2.5D semantic segmentation of the pancreas using attention guided dual context embedded U-Net

Automatic segmentation of the pancreas from medical images is important for clinical assessment of pancreas-related diseases. However, pancreatic segmentation based on computer tomography (CT) images is time-consuming and prone to errors because of the variances in shape and texture. Since various studies based on 2D/3D convolution neural networks (CNNs) have achieved encouraging performance for medical image segmentation, the 2D methods enjoy low inference time but suffer from a lack of 3D information. 3D methods are superior in performance for difficult targets requiring contextual information, but encounter the issue of high computational cost. Thus, we proposed a 2.5D segmentation method for pancreatic segmentation to balance utilizing contextual information and the high computational cost. This represents the 3D structural relationship among contiguous slices in a special representation. In the preprocessing stage, light-weight 3D voxels and the corresponding label mapping method were designed to explicitly express the differences in the target structure in contiguous slices. This would enable the network to learn spatial relationships directly. A 2D CNN embedded multi-attention mechanism and dual-context feature fusion method were designed to describe 3D information through 2D operations. In the post-processing stage, a fusion method was used to refine the segmentation results. The proposed method was evaluated on an abdominal contrast-enhanced CT dataset. Results showed Dice was 87.19%. Compared to the corresponding 2D and 3D methods, the proposed 2.5D method improved Dice by 1.14% and 2.80%, and was 60 times faster than the 3D method by using 0.1 times of the trainable parameters. Moreover, evaluations were performed on the NIH Pancreas-CT dataset, and the proposed 2.5D method achieved better segmentation performance than state-of-the-art methods.
DroneSegNet: Robust Aerial Semantic Segmentation for UAV-Based IoT Applications

Unmanned Aerial Vehicles (UAVs) are the promising “Flying IoT” devices of the future, which can be equipped with various sensors and cognitive capabilities to perform numerous tasks related to remote sensing, search and rescue operations, object tracking, segmentation of roads and buildings, surveillance, etc. However, these AI-driven tasks require heavy computation and may lead to suboptimal performance with embedded processors on a power-constrained battery-operated drone. This work proposes a novel deep learning approach for performing robust semantic segmentation of aerial scenes captured by UAVs. In our setup, the power-constrained drone is used only for data collection, while the computationally intensive tasks are offloaded to a GPU cloud server. Our architecture performs robust semantic segmentation by learning the segmentation maps from jointly utilizing of aerial scenes along with the respective “elevation maps” in a semi-supervised approach. We propose a three-tier deep learning architecture, wherein the first module aims at preliminary feature extraction from aerial scenes using a backbone feature extractor. The second module captures the spatial dependency between the aerial scenes and their respective elevation maps to obtain better semantic information, which is achieved by a bi-directional LSTM. The third module is aimed at enhancing the performance of semantic segmentation through a semi-supervised approach with an encoder to generate segmentation maps and a decoder to reconstruct feature maps. This semi-supervised feature learning ensures robust extraction along with scalability. The proposed architecture was validated on real-world aerial datasets and achieves state-of-the-art results for aerial image segmentation.
Outlier detection via multiclass deep autoencoding Gaussian mixture model for building chiller diagnosis

In HVAC systems, the application of fault detection and diagnosis (FDD) for chillers has brought many benefits to building utilities, i.e., indoor thermal comfort, energy saving, and energy consumption management. Although recent studies have achieved great progress on chiller fault diagnostic problem, some serious issues have been raised in these studies and need to be resolved comprehensively. In that context, two problems are listed and studied in this paper namely outliers detection and the insufficiency of labeled data. To deal with outliers mixed in chiller data, this paper proposes a supervised multiclass deep autoencoding Gaussian mixture model (S-DAGMM) algorithm which is an ensemble model of individual unsupervised DAGMMs. This mechanism helps S-DAGMM to detect ambiguous outliers in both training and testing data. In addition, this paper proposes to use DAGMM to pretrain a deep neural network (DNN). Since DAGMM can learn the data distribution from the unlabeled data, it can prevent DNN from getting stuck on local optima. Comprehensive experimental results in this study have proved the outstanding performance of the proposed approach, compared with state-of-the-art rival methods.
Key.Net: Keypoint Detection by Handcrafted and Learned CNN Filters Revisited

We introduce a novel approach for keypoint detection that combines handcrafted and learned CNN filters within a shallow multi-scale architecture. Handcrafted filters provide anchor structures for learned filters, which localize, score, and rank repeatable features. Scale-space representation is used within the network to extract keypoints at different levels. We design a loss function to detect robust features that exist across a range of scales and to maximize the repeatability score. Our Key.Net model is trained on data synthetically created from ImageNet and evaluated on HPatches and other benchmarks. Results show that our approach outperforms state-of-the-art detectors in terms of repeatability, matching performance, and complexity. Key.Net implementations in TensorFlow and PyTorch are available online.
CoCNN: Co-occurrence CNN for recommendation

Under the assumption that items are independent and identically distributed, most existing CF methods learn representation from user–item pairs, but ignore the connections among items, leading to limited performance. Considering the challenge of recommendation, we propose a novel neural network, CoCNN, which combines a Co-occurrence pattern and CNN for CF with implicit feedback. The key idea of the co-occurrence pattern is that some items always appear between pairs on a user’s favorite list. In CoCNN, co-occurrence relationships act as a bridge in user–item pairs and item–item pairs, which are not observed directly. To model user–item and item–item information simultaneously, we propose a multi-task neural network to share the knowledge of the two tasks. Finally, experimental results demonstrate that CoCNN successfully captures more useful information, and therefore can be used as a simple and effective tool for recommendation. Our projects are available online at https://github.com/XiuzeZhou/CoCNN.
A Collective Anomaly Detection Technique to Detect Crypto Wallet Frauds on Bitcoin Network

The popularity and remarkable attractiveness of cryptocurrencies, especially Bitcoin, absorb countless enthusiasts every day. Although Blockchain technology prevents fraudulent behavior, it cannot detect fraud on its own. There are always unimaginable ways to commit fraud, and the need to use anomaly detection methods to identify abnormal and fraudulent behaviors has become a necessity. The main purpose of this study is to use the Blockchain technology of symmetry and asymmetry in computer and engineering science to present a new method for detecting anomalies in Bitcoin with more appropriate efficiency. In this study, a collective anomaly approach was used. Instead of detecting the anomaly of individual addresses and wallets, the anomaly of users was examined. In addition to using the collective anomaly detection method, the trimmed_Kmeans algorithm was used for clustering. The results of this study show the anomalies are more visible among users who had multiple wallets. The proposed method revealed 14 users who had committed fraud, including 26 addresses in 9 cases, whereas previous works detected a maximum of 7 addresses in 5 cases of fraud. The suggested approach, in addition to reducing the processing overhead for extracting features, detect more abnormal users and anomaly behavior.
A semi-supervised deep learning based video anomaly detection framework using RGB-D for surveillance of real-world critical environments

Video surveillance has become very important in the current era of smart cities. Large amounts of surveillance cameras are deployed at public and private places for surveillance of infrastructural property and public safety. These surveillance cameras generate huge amount of video data and it is impractical for a human observer to continuously monitor these long-hour videos manually and detect any unwanted or anomalous event. This paper presents a multi-modal semi-supervised deep learning based CNN-BiLSTM autoencoder framework to detect anomalous events in critical surveillance environments like Bank-ATMs. The significance of the framework is that it only requires weakly labelled normal video samples for training. We leverage the power of transfer learning by extracting important video features using a compact pretrained CNN to significantly reduce the computational complexity of training and detection. Moreover, due to the unavailability of any dataset for ATM surveillance in the public domain, we also contributed a unique RGB + D dataset for surveillance of ATMs. The proposed framework is tested on the collected RGB + D dataset and other real-world benchmark video anomaly datasets: Avenue and UCFCrime2Local. Results show that the proposed framework gives competitive results with other state-of-the-art methods and can be applied to both indoor and outdoor environments for detection of anomalies in real-world surveillance sites.