Reviewerly/exHarmony huggingface dataset:
A Gaussian mixture regression model based adaptive filter for non-Gaussian noise without a priori statistic

In many engineering systems, the distribution of measurement noise is unknown and non-Gaussian, such as skewed, multimodal, time-varying distributions and we can not obtain these prior statistics in advance. How to achieve state estimation via this kind of non-Gaussian measurement noises without prior statistic information is a challenging problem. In this paper, a novel Gaussian mixture regression model (GMRM) is proposed to model the unknown non-Gaussian measurement likelihood for Bayesian update to achieve nonlinear state estimation. Without any prior assumption or limitation of measurement noises’ statistics and distributions, the GMRM can still describe the measurement likelihood accurately based on a group of parameters which are adjusted by maximizing the evidence lower bound. Based on the optimized GMRM, a new variational Bayesian Gaussian mixture filter is proposed by using the variational Bayesian approach. To eliminate the influence of the initialization of the introduced parameters, a learning scheme is proposed to adaptively optimize their hyper-parameters based on the historical measurements. Finally, simulation examples are employed to illustrate the effectiveness of the filter.
Detecting Insider Attacks in Blockchain Networks

Blockchain technology has recently gained high popularity in data security, primarily to mitigate against data breach and manipulation. Since its inception in 2008, it has been applied in different areas mainly to maintain data integrity and consistency. Blockchain has been tailored to secure data due to its data immutability and distributive technology. Despite the high success rate in data security, the inability to identify compromised insider nodes is one of the significant problems encountered in blockchain architectures. A Blockchain network is made up of nodes that initiate, verify and validate transactions. If compromised, these nodes can manipulate submitted transactions, inject fake transactions, or retrieve unauthorized information that might eventually compromise the stored data’s integrity and consistency. This paper proposes a novel method of detecting these compromised blockchain nodes using a server-side authentication process and thwart their activities before getting updated in the blockchain ledger. In evaluating the proposed system, we perform four common insider attacks, which fall under the following three categories: (1) Those attacks targeting the Blockchain to bring it down. (2) the attacks that attempt to inject fake data into the database. (3) The attacks that attempt to hijack or retrieve unauthorized data. We described how we implement the attacks and how our architecture detects them before they impact the network. Finally, we displayed the attack detection time for each attack and compared our approach with other existing methods.
CTRL-C: Camera calibration TRansformer with Line-Classification

Single image camera calibration is the task of estimating the camera parameters from a single input image, such as the vanishing points, focal length, and horizon line. In this work, we propose Camera calibration TRansformer with Line-Classification (CTRL-C), an end-to-end neural network-based approach to single image camera calibration, which directly estimates the camera parameters from an image and a set of line segments. Our network adopts the transformer architecture to capture the global structure of an image with multi-modal inputs in an end-to-end manner. We also propose an auxiliary task of line classification to train the network to extract the global geometric information from lines effectively. Our experiments demonstrate that CTRL-C outperforms the previous state-of-the-art methods on the Google Street View and SUN360 benchmark datasets. Code is available at https://github.com/jwlee-vcl/CTRL-C.
Multiresolution Daubechies finite wavelet domain method for transient dynamic wave analysis in elastic solids

Abstract The multiresolution capability provided by the family of Daubechies wavelets is exploited to develop a new computational approach, termed as multiresolution finite wavelet domain method for the fast and hierarchical prediction of transient dynamic problems with focus on wave propagation in one‐ and two‐dimensional structural configurations. In the developed method, both scaling and wavelet functions are employed as basis functions for the approximation of displacements in the governing dynamic equations. Because of the orthogonal and the multiresolution properties of Daubechies wavelets, a two‐scale system of mass uncoupled dynamic equations, representing the coarse and the fine spatial wave component solutions is derived. Numerical results concerning wave propagation in one‐ and two‐dimensional elastic structures demonstrate substantial computational gains of the method in comparison with other well‐established numerical methods. Moreover, additional benefits of the involved coarse and fine solutions enabling the analysis and characterization of the resultant wave response are revealed and discussed.
Yield analysis and economic assessment for GIS-mapping of large scale solar PV potential and integration in Morocco

In this paper, the technical and economic potential of large-scale integration of photovoltaic (PV) power plants in Morocco is assessed. For this purpose, environmental and technical constraints were first evaluated and gathered into a land exclusion map to identify the unsuitable areas for PV deployment. Subsequently, using accurate and high resolution data as well as Geographic Information System (GIS) tools, calculation of the technical potential for three scenarios -based on the plants efficiency- has been conducted. Afterwards, the economic potential -LCOE- was derived. The results indicate that 87.52% of the total area is considered suitable for PV installations. The total technical potential stood at 49,643 TWh/year for the third scenario with a decrease of 5% between the three alternatives. As for the LCOE, the cheapest electricity price is about 0.0331 $/kWh in the Souss-Massa region. These results are with high importance since they highlight the significant large-scale PV potential of Morocco, which can serve as a reference for investors in selecting optimal sites from a technical and economic standpoint.
Real-Time Eye Tracking for Bare and Sunglasses-Wearing Faces for Augmented Reality 3D Head-Up Displays

Eye pupil tracking is important for augmented reality (AR) three-dimensional (3D) head-up displays (HUDs). Accurate and fast eye tracking is still challenging due to multiple driving conditions with eye occlusions, such as wearing sunglasses. In this paper, we propose a system for commercial use that can handle practical driving conditions. Our system classifies human faces into bare faces and sunglasses faces, which are treated differently. For bare faces, our eye tracker regresses the pupil area in a coarse-to-fine manner based on a revised Supervised Descent Method based eye-nose alignment. For sunglasses faces, because the eyes are occluded, our eye tracker uses whole face alignment with a revised Practical Facial Landmark Detector for pupil center tracking. Furthermore, we propose a structural inference-based re-weight network to predict eye position from non-occluded areas, such as the nose and mouth. The proposed re-weight sub-network revises the importance of different feature map positions and predicts the occluded eye positions by non-occluded parts. The proposed eye tracker is robust via a tracker-checker and a small model size. Experiments show that our method achieves high accuracy and speed, approximately 1.5 and 6.5 mm error for bare and sunglasses faces, respectively, at less than 10 ms on a 2.0GHz CPU. The evaluation dataset was captured indoors and outdoors to reflect multiple sunlight conditions. Our proposed method, combined with AR 3D HUDs, shows promising results for commercialization with low crosstalk 3D images.
Superpixels With Content-Adaptive Criteria

Superpixels are widely used in computer vision applications. Most of the existing superpixel methods use established criteria to indiscriminately process all pixels, resulting in superpixel boundary adherence and regularity being unnecessarily inter-inhibitive. This study builds upon a previous work by proposing a new segmentation strategy that classifies image content into meaningful areas containing object boundaries and meaningless parts that include color-homogeneous and texture-rich regions. Based on this classification, we design two distinct criteria to process the pixels in different environments to achieve highly accurate superpixels in content-meaningful areas and keep the regularity of the superpixels in content-meaningless regions. Additionally, we add a group of weights when adopting the color feature, successfully reducing the undersegmentation error. The superior accuracy and the moderate compactness achieved by the proposed method in comparative experiments with several state-of-the-art methods indicate that the content-adaptive criteria efficiently reduce the compromise between boundary adherence and compactness.
Unsupervised fault detection and recovery for intelligent robotic rollators

Fault diagnosis is a key safety component in robotic assistive technologies. Although conventional model-based methods for sensor fault diagnosis in mobile robots have been well established, they face challenges due to model parameter changes and uncertainties. On the other hand, data-driven approaches becomes more appealing in order to take advantage from available historical data in the era of Big Data. To provide a new generic unsupervised solution to the fault detection and recovery, we explicitly include kinematic relations and temporal finite differences from measured sensor signals into training a multi-task deep neural network. To evaluate the proposed fault diagnosis and recovery framework, experiments have been conducted on a robotic rollator platform. Experiments under several conditions confirm that the proposed approach, which leverages machine learning-enhanced algorithms, exhibits reliable performance. Outperforming other baselines and state-of-the-art diagnosis algorithms, the framework presents a promising solution to sensor fault recovery challenges in assistive devices.
Microseismic First-Arrival Picking Using Fine-Tuning Feature Pyramid Networks

Microseismic event picking is one of the key steps in seismic processing and imaging. Manually picking is a widely used way to pick the microseismic events, which is time-consuming. The standard short-term average/long-term average (STA/LTA) is a traditional method to pick the microseismic first arrivals, which would lead to inaccurate first-arrival picks in case of low signal-to-noise ratio (SNR). We developed a workflow to automatically pick the microseismic first arrivals by using the feature pyramid networks (FPNs). To train the proposed model, we first randomly select part of the microseismic traces and manually pick the time index of the first arrivals. Next, we segment every selected trace into two parts based on the time index of the manual picking and then assign each part a label. Afterward, we train the proposed fine-tuning FPN model by using the training data and the corresponding labels. It should be noticed that we proposed a loss function, named the point-aware loss, for solving the microseismic first-arrival picking issue. Finally, we predict the microseismic first arrivals by using the well-trained fine-tuning FPN model. The numerical examples demonstrate that our proposed model successfully identifies the microseismic first arrivals. The microseismic first arrivals predicted by using our proposed model are more robust and more accurate than those obtained by using the STA/LTA and the encoder–decoder network.
Do digital competence frameworks align with preparing beginning teachers for digitally infused contexts? An evaluation from a New Zealand perspective

(2021). Do digital competence frameworks align with preparing beginning teachers for digitally infused contexts? An evaluation from a New Zealand perspective. European Journal of Teacher Education. Ahead of Print.
Early and Late Fusion of Emojis and Text to Enhance Opinion Mining

Opinion mining has gained increasing importance to draw insights from social media content to support decision making. Despite the explosive growth of efforts on linguistic analysis to detect and track people’s opinions, more specifically when dealing with aspect-level opinion mining, the results are still away from generalization to real-world applications. Nowadays, emojis are getting excessively popular in social media communication as a complementary way to quickly express opinions and ideas in a visual manner. Two emoji-related issues are highlighted: ambiguity and misinterpretation of emojis’ sentiment and tendency of persons to adopt emojis more in positive cases. This paper aims at investigating to what extent the usage of emojis can contribute to the automated detection of sentiment polarity of text messages with focus on Twitter posts in the Arabic language, a widely spoken language but has complex morphology and limited reliable resources for sentiment analysis. For this purpose, after an extensive review of the state-of-the-art of emojis-related work, a dataset is composed and several feature extraction methods are applied for both text and emojis modalities. Moreover, various early and late fusion techniques are proposed to combine both modalities at different levels including feature, score, decision and hybrid. The experimental results revealed that emojis features can significantly improve the classification results, especially when integrated with text at the score level.
Harshness-aware sentiment mining framework for product review

Sentiment mining has been a helpful mechanism that targets to understand the market feedback on certain commodities by utilizing user comments. In general, the process of yielding each comment is essentially associated with his/her criteria for rating (i.e., the degree of harshness) , which makes users provide biased comments. For instance, for a tolerant user, although the user is extremely dissatisfied with the product, harshness still makes her yield a neutral comment which cannot indicate the product quality. Existing work straightforwardly removes the comments of harsh users and those of tolerant ones, which is not the best strategy. To this end, we propose a harshness-aware sentiment analysis framework for product review. First, we depict the process of providing comments from users as a probabilistic graphical model in which the harshness is incorporated. Second, we employ a Bayesian-based inference for sentiment mining. Extensive experimental evaluations have shown that the results of the proposed method are more consistent with the expert evaluations than those of the state-of-the-art methods, and even outperform the method which infers the final evaluations with the ground truth of comments without considering users’ harshness.
Flying Through a Narrow Gap Using End-to-End Deep Reinforcement Learning Augmented With Curriculum Learning and Sim2Real

Traversing through a tilted narrow gap is previously an intractable task for reinforcement learning mainly due to two challenges. First, searching feasible trajectories is not trivial because the goal behind the gap is difficult to reach. Second, the error tolerance after Sim2Real is low due to the relatively high speed in comparison to the gap's narrow dimensions. This problem is aggravated by the intractability of collecting real-world data due to the risk of collision damage. In this brief, we propose an end-to-end reinforcement learning framework that solves this task successfully by addressing both problems. To search for dynamically feasible flight trajectories, we use a curriculum learning to guide the agent toward the sparse reward behind the obstacle. To tackle the Sim2Real problem, we propose a Sim2Real framework that can transfer control commands to a real quadrotor without using real flight data. To the best of our knowledge, our brief is the first work that accomplishes successful gap traversing task purely using deep reinforcement learning.
Review on power-production modeling of hybrid wind and PV power parks

The interest for co-located wind and solar photovoltaic (PV) parks, also known as hybrid power parks (HPPs), is increasing both in industry and in the scientific community. Co-locating wind and PV can lead to synergies in power production, infrastructure, and land usage, which may lower the overall plant cost compared to single technology systems. This review paper summarizes the existing research on power output modeling related to utility-scale HPPs and identifies knowledge-gaps. The main literature shows that there is a need for improved modeling methodologies accounting for the variability of the combined power production. There is potential for immediate improvement by combining state-of-the-art models that have been developed in separate fields and harmonizing the vocabulary across the different research fields. The study also shows that the total number of peer reviewed studies on utility-scale HPPs is limited and further research, in particular comparative studies, is needed to give a comprehensive view of the benefits and challenges of combining technologies. Other areas such as physical design, control strategies, market participation, and quantification of the possible synergies for physical implementation of HPPs also need to be studied further.
Sentimental Analysis Applications and Approaches during COVID-19: A Survey

The social media and electronic media has a vast amount of user-generated data such as people' comment and reviews about different product, diseases, government policies etc. Sentimental analysis is the emerging field in text mining where people's feeling and emotions are extracted using different techniques. COVID-19 has declared as pandemic and effected people's lives all over the globe. It caused the feelings of fear, anxiety, anger, depression and many other psychological issues. In this survey paper, the sentimental analysis applications and methods which are used for COVID-19 research are briefly presented. The comparison of thirty primary studies shows that Naive Bayes and SVM are the widely used algorithms of sentimental analysis for COVID-19 research. The applications of sentimental analysis during COVID includes the analysis of people's sentiments specially students, reopening sentiments, analysis of restaurants reviews and analysis of vaccine sentiments.
Hierarchical Edge Refinement Network for Saliency Detection

At present, most saliency detection methods are based on fully convolutional neural networks (FCNs). However, FCNs usually blur the edges of salient objects. Due to that, the multiple convolution and pooling operations of the FCNs will limit the spatial resolution of the feature maps. To alleviate this issue and obtain accurate edges, we propose a hierarchical edge refinement network (HERNet) for accurate saliency detection. In detail, the HERNet is mainly composed of a saliency prediction network and an edge preserving network. Firstly, the saliency prediction network is used to roughly detect the regions of salient objects and is based on a modified U-Net structure. Then, the edge preserving network is used to accurately detect the edges of salient objects, and this network is mainly composed of the atrous spatial pyramid pooling (ASPP) module. Different from the previous indiscriminate supervision strategy, we adopt a new one-to-one hierarchical supervision strategy to supervise the different outputs of the entire network. Experimental results on five traditional benchmark datasets demonstrate that the proposed HERNet performs well compared with the state-of-the-art methods.
Artificial Intelligence Algorithm with ICD Coding Technology Guided by the Embedded Electronic Medical Record System in Medical Record Information Management

The study aims to explore the application of international classification of diseases (ICD) coding technology and embedded electronic medical record (EMR) system. The study established an EMR information knowledge system and collected the data of patient medical records and disease diagnostic codes on the front pages of 8 clinical departments of endocrinology, oncology, obstetrics and gynecology, ophthalmology, orthopedics, neurosurgery, and cardiovascular medicine for statistical analysis. Natural language processing-bidirectional recurrent neural network (NLP-BIRNN) algorithm was used to optimize medical records. The results showed that the coder was not clear about the basic rules of main diagnosis selection and the classification of disease coding and did not code according to the main diagnosis principles. The disease was not coded according to different conditions or specific classification, the code of postoperative complications was inaccurate, the disease diagnosis was incomplete, and the code selection was too general. The solutions adopted were as follows: communication and knowledge training should be strengthened for coders and medical personnel. BIRNN was compared with the convolutional neural network (CNN) and recurrent neural network (RNN) in accuracy, symptom accuracy, and symptom recall, and it suggested that the proposed BIRNN has higher value. Pathological language reading under artificial intelligence algorithm provides some convenience for disease diagnosis and treatment.
EtherClue: Digital investigation of attacks on Ethereum smart contracts

Programming errors in Ethereum smart contracts can result in catastrophic financial losses from stolen cryptocurrency. While vulnerability detectors can prevent vulnerable contracts from being deployed, this does not mean that such contracts will not be deployed. Once a vulnerable contract is instantiated on the blockchain and becomes the target of attacks, the identification of exploit transactions becomes indispensable in assessing whether it has been actually exploited and identifying which malicious or subverted accounts were involved. In this work, we study the problem of post-factum investigation of Ethereum attacks using Indicators of Compromise (IoCs) specially crafted for use in the blockchain. IoC definitions need to capture the side-effects of successful exploitation in the context of the Ethereum blockchain. Therefore, we define a model for smart contract execution, comprising multiple abstraction levels that mirror the multiple views of code execution on a blockchain. Subsequently, we compare IoCs defined across the different levels in terms of their effectiveness and practicality through EtherClue, a prototype tool for investigating Ethereum security incidents. Our results illustrate that coarse-grained IoCs defined over blocks of transactions can detect exploit transactions with less computation; however, they are contract-specific and suffer from false negatives. On the other hand, fine-grained IoCs defined over virtual machine instructions can avoid these pitfalls at the expense of increased computation which are nevertheless applicable for practical use.