Reviewerly/exHarmony huggingface dataset:
Infrared Image Super-Resolution via Generative Adversarial Network with Gradient Penalty Loss

Infrared thermal imaging technology has been gradually developed and widely applied in measurement and non-destructive testing. However, low-contrast blurred details and expensive acquisition equipment remain as barriers to its further practical applications and widespread adoption. In this paper, a novel framework comprising deep learning techniques is proposed to offer a relatively competitive and compatible solution of infrared image super-resolution. Firstly, radiance information from low-resolution imagery is detected and automatically translated to high-resolution through a Generative Adversarial Network (GAN) with Wasserstein distance. Secondly, a gradient penalty loss function is utilized for the discriminator to guide the generator to achieve reasonable and acceptable convergence. Through evaluation of three widely utilized infrared datasets, the proposed method demonstrates superior performance against the state-of-art method with more accurate Peak Signal-To-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM) respectively. The outcome of this study has implications for a real-application of deep learning based infrared non-destructive testing and measurement scenarios.
Parameter Estimation of LFM Signal Based on Improved Fractional Fourier Transform

Given the current computational complexity and the unsatisfactory anti-noise performance of the linear frequency modulated (LFM) signal parameter estimation method, this paper proposes a rather innovative and efficient method based on improved fractional Fourier transform (FrFT). This method first obtains the energy distribution of time-frequency domain (TFD) through short-time Fourier transform (STFT), which is used to determine the order search range of FrFT. On the other hand, incoherent accumulation is also employed to improve the anti-noise performance under low signal-to-noise-ratio (SNR) environments. Extensive computer simulations verified that the algorithm displays a good anti-noise performance while reducing the amount of calculation.
Spiral Strip

A visual graphic called Spiral Strip is presented consisting of color-coded segments arranged in a spiral. The width and length of each individual segment can be different, which can be used to convey additional information (besides the color coding). Although the primary motivation for developing the new graphic was related to climatic data, it can also be used with other types of data. Several examples based on climatic, population-size, and historical data are used to show the graphic’s visual appearance and main properties. The examples also demonstrate how the meaning of the graphic can be easy to grasp and intuitively understood by a casual observer. An easy-to-use Python code package for drawing the Spiral Strips was published in a public repository.
Sensor Data Prediction for Fixed-wing Drone Based on Online Sequential Learning

The special properties make the fixed-wing drones are widely used in the military area. Thus, the validity and the accuracy of the sensors and actuators are essential for fixed-wing drones. The sensors data prediction with a suitable learning algorithm is usually the basic step for data-driven approaches to increase the reliability of fixed-wing drones. Online learning algorithms could train the model from sensors data in real time, coinciding with the rich rough sensors data during the whole flight time. In addition, online learning algorithms based on Extreme Learning Machine, which is a light weight learning machine, show a great performance for time-series prediction. However, they have two drawbacks: overfitting models and less robust. Thus, we proposed a novel algorithm to promote the weights update strategy by updating the output weights based on statistical learning theory. And, the experiment of sensors data perdition shows the improved performance of the proposed algorithm experiment om compared with other online learning algorithms based on ELM.
Position encoding for heterogeneous graph neural networks

Many real-world networks are suitable to be modeled as heterogeneous graphs, which are made up of many sorts of nodes and links. When the heterogeneous map is a non-attribute graph or some features on the graph are missing, it will lead to poor performance of the previous models. In this paper, we hold that useful position features can be generated through the guidance of topological information on the graph and present a generic framework for Heterogeneous Graph Neural Networks(HGNNs), termed Position Encoding(PE). First of all, PE leverages existing node embedding methods to obtain the implicit semantics on a graph and generate low-dimensional node embedding. Secondly, for each task-related target node, PE generates corresponding sampling subgraphs, in which we use node embedding to calculate the relative positions and encode the positions into position features that can be used directly or as an additional feature. Then the set of subgraphs with position features can be easily combined with the desired Graph Neural Networks (GNNs) or HGNNs to learn the representation of target nodes. We evaluated our method on graph classification tasks over three commonly used heterogeneous graph datasets with two processing ways, and experimental results show the superiority of PE over baselines.
Recommender system based on adaptive threshold filtering GCN

We introduce the AT-GCN (Adaptive Threshold filtering Graph Convolutional Neural network model). AT-GCN is a recommendation model based on graph structure. Compared with the commonly used graph structure recommendation model, AT-GCN can effectively solve the problem of edge representation and information transfer, and improve the recommendation effect. In the experimental part, several groups of experiments were carried out on AT-GCN, and the above conclusions were finally verified by the experimental results.
Chinese book recommendation for elementary students: a comparison between ML and DL methods

Experience-based book recommendation systems suffer from blurred boundaries and unguaranteed quality. In China, there is no reasonable and credible book recommendation system for elementary school students based on big data and expert systems. The basis of book recommendation is text classification. In this paper, we first construct a hierarchy dictionary to encode books according to the textbook read by pupils in different grades. We then classify extra-curricular books into lower grade, upper grade, and non-elementary classes. Four machine learning methods and a deep learning algorithm are used to classify the text. The model metrics are evaluated by Accuracy, Recall, F1-score. Among them, both deep learning and machine learning have a good performance on binary classification tasks, with the accuracy of the LR method reaching 98.5%. This reflects the value of our construction of a hierarchical vocabulary. In addition, accuracy for the triple classification task generally achieves 75%.
Light-Weighted Deep Learning Model to Detect Fault in IoT-Based Industrial Equipment

Industry 4.0, with the widespread use of IoT, is a significant opportunity to improve the reliability of industrial equipment through problem detection. It is difficult to utilize a unified model to depict the working condition of devices in real-world industrial scenarios because of the complex and dynamic relationship between devices. The scope of this research is that it can detect equipment defects and deploys them in a natural production environment. The proposed research is describing an online detection method for system failures based on long short-term memory neural networks. In recent years, deep learning technology has taken over as the primary method for detecting faults. A neural network with a long short-term memory is used to develop an online defect detection model. Feature extraction from sensor data is done using the curve alignment method. Based on long-term memory neural networks, the fault detection model is built (LSTM). In the end, sliding window technology is used to identify and fix the problem: the model's online detection and update. The method's efficacy is demonstrated by experiments based on real data from power plant sensors.
An Overview of Multi-Sensor Fusion Methods with Partial Reliance on GNSS

Sensor Fusion deals with the amalgamation of multiple sensor data to provide a steady and reliable estimate of the pose: position and orientation of the system, generally a robot, relative to its environment. A good strategy for extracting sensor data and minimizing errors from the sensor needs to be adopted to address this challenge. Many algorithms have been employed to improve and solve this problem in recent times. Despite the tremendous expansion of work in this domain, a precise compilation and comparison of various methodologies have remained an unexplored subject. This paper presents the current state-of-the-art multi-sensor fusion methods, with a significant focus on partially Global Navigation Satellite System (GNSS) dependent techniques. We have investigated works with various architecture and classified them into two major categories: Loosely-coupled and Tightly-coupled. These methods are further differentiated based on the optimization used for minimizing error.
Payment Channels Under Network Congestion

Sending transactions on leading blockchains such as Ethereum can be slow and costly. A payment channel is a well-known scaling solution that minimizes transactions sent on the chain, and allows users to transact more efficiently. One of the guarantees of payment channels is that there is no counterparty risk, so an honest party is able to withdraw the amount of money that is reflected by the most recent transaction agreed by both parties. In this paper, we show that this guarantee can be violated when the network is under congestion. Regardless of whether or not the honest party is online, the malicious party can leverage high transaction fees to gain more money than they're supposed to. We present a novel construction of payment channels that helps mitigates these types of attacks.
Regador : APP for coffee water potential estimation

In coffee farming, reductions in water availability decrease productivity. Therefore, monitoring the water potential (Ψ), a measure that assesses whether a plant is under water stress, helps management. This article presents the design, development and evaluation of a georeferenced mobile application for coffee farming water potential in the south of Minas Gerais. This work was developed with the premises of Design Science. In particular, in the development of an IT artifact. The research was prescriptive and the artifact was generated as a proof of concept of the system. The analysis of the results was carried out with a quantitative approach. The evaluation of user satisfaction carried out with researchers and coffee growers obtained satisfactory results and validates the importance of the application developed in the decision-making process in the field. The impact is both in the technical and business spheres, since the article contemplates the methodology for developing an artifact that aims to bring scientific research and rural producers closer, as it materializes a model currently available only in specialized journals.
A damage tree modeling and vulnerability analysis method for typical building targets

Building target is an important target on the battlefield. In the process of system simulation, it is necessary to analyze the vulnerability of various typical building targets, this paper presents a damage tree modeling and vulnerability analysis method for typical building targets, including target composition and functional characteristic analysis, system structure functional damage tree analysis, target vulnerability model establishment, vulnerability characteristic analysis and so on, which can be used for damage modeling and vulnerability analysis of related typical building targets.
Human Motion Generation Using Variational Recurrent Neural Network

research-article Share on Human Motion Generation Using Variational Recurrent Neural Network Authors: Makoto Murakami Department of Information Sciences and Arts, Toyo University, Japan Department of Information Sciences and Arts, Toyo University, JapanSearch about this author , Takahiro Ikezawa Department of Information Sciences and Arts, Toyo University, Japan Department of Information Sciences and Arts, Toyo University, JapanSearch about this author Authors Info & Claims ICDSP '22: Proceedings of the 6th International Conference on Digital Signal ProcessingFebruary 2022 Pages 104–109https://doi.org/10.1145/3529570.3529588Online:29 June 2022Publication History 0citation0DownloadsMetricsTotal Citations0Total Downloads0Last 12 Months0Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Inference and Prediction in Big Data Using Sparse Gaussian Process Method

Gaussian process is one of computationally expensive algorithm for large datasets and lack of the flexibility to model different datasets is a common problem for modeling it. We introduce sparse Gaussian regression with the combination of designed kernels to solve the computational complexity of a traditional Gaussian process by taking pseudo input from large datasets and developing a model with better accuracy which enables Gaussian process application. We design a better combination of the kernel that can catch up with most of our data points. We demonstrate the approach on a large weather dataset and sales record dataset. Both are open source big datasets available online. Numerous experiments and comparisons with traditional Gaussian process methods using both large datasets demonstrate the efficiency and accuracy of sparse Gaussian processes.
LMPTs: Eliminating Storage Bottlenecks for Processing Blockchain Transactions

We present the Layered Merkle Patricia Trie (LMPT), a performant storage data structure for processing transactions in high-throughput systems when com-pared to traditional Merkle Patricia Tries used in Ethereum clients. LMPTs keep smaller intermediary tries in memory to alleviate read and write amplification from high-latency disk storage. As an additional feat, they also allow for the I/O and transaction verifier threads to be scheduled in parallel and independently. LMPTs can ultimately reduce significant I/O traffic that happens on the critical path of transaction processing. Empirical results presented here confirm that LMPTs can process up to × 6 more transactions per second on real-life workloads when compared to existing Ethereum clients.
Application of the deep learning approach for identification and face recognition in real-time video

The use of facial recognition technology has increased strongly during the past period by many countries in many areas, and this technology has become the most secure tool at the level of countries, institutions, or on a personal level, as this technology is considered one of the modern systems for identifying people in digital images or frames Video. These systems do not need any dependencies such as key and card but only a facial image is needed. Many methods have been created and developed in order to perform face detection. One of the most popular methods is the ViolaJones Haar Cascade Classifier(V-J). But this method has several limitations in front of the following factors: across scale, non-frontal face, and low light intensity. In this article, we propose a classifier based on the Histogram of the Oriented Gradients (HOG) algorithm through deep learning that solves the previous limits. Experimental results are provided to demonstrate the robustness and accuracy of our system
The Experiences of EFL Teachers Teaching English Skills and Contents in the Midst of Covid-19 Pandemic

The major purpose of this research was to obtain the voices of EFL senior teachers regarding the use of the WhatsApp- group tool. The researchers applied a qualitative method design by practicing two instruments: semi-structured interview and indirect observation. The case study design was undertaken by engaging two EFL senior teachers who actively implemented the WhatsApp group tool in the learning process in the midst of the outbreak of COVID-19. Four designs of qualitative frameworks were convenience aspect (ease of use) and usefulness aspect (Interaction and motivation), educational aspect, and technical aspect was simultaneously utilized to draw their experience in teaching English skills and content courses. The result of research discussions declared that the EFL teachers claimed the different voices toward the utilizing WhatsApp-group tool. Grammar lecturers claimed that this digital tool is a learning management system, integrating the tools for educational aims, and perceiving advantages in boosting the effectiveness and efficiency of the teaching and learning process. In addition, Grammar lecturers as educators emphasized to their learners that the young generation does not only learn the academic aspects but also build a good attitude, critically-thinking, social caring, and high tolerance the society. The learning strategy is well-known as the maternity reflective method. In contrast, the Prose lecturer claimed several obstacles; junk notifications, flooding irrelevant chats in the WhatsApp forum, poor signal, and consuming extra time. Furthermore, he added that interactions in the WhatsApp-group tool as supportive learning cannot boost learners to be active due to some factors. This evidence became a significant challenge in applying WhatsApp as a management teaching media. Thus, the pedagogical implication of utilizing WhatsApp- group L tool in EFL classroom should be carefully tackled to minimize the overuse of this mobile application
How the History of Changing Environments Affects Traits of Evolvable Robot Populations

The environment is one of the key factors in the emergence of intelligent creatures, but it has received little attention within the Evolutionary Robotics literature. This article investigates the effects of changing environments on morphological and behavioral traits of evolvable robots. In particular, we extend a previous study by evolving robot populations under diverse changing-environment setups, varying the magnitude, frequency, duration, and dynamics of the changes. The results show that long-lasting effects of early generations occur not only when transitioning from easy to hard conditions, but also when going from hard to easy conditions. Furthermore, we demonstrate how the impact of environmental scaffolding is dependent on the nature of the environmental changes involved.
Identifying Xenophobia in Twitter Posts Using Support Vector Machine with TF/IDF Strategy

Context: Xenophobia is the fear of foreign groups. Nevertheless, it is understood that this phenomenon emcompasses something much broader, as it brings to light not only fear, but also rejection or hostility towards different ethnic groups. Although it is not a contemporary problem, recent factors such as economic and humanitarian crises have shown that the problem is growing. Problem: Twitter is one of the most used social networks for data mining studies, due to its large number of posts. These singularities make the platform conducive to the proliferation of hate speech. Solution: The present research aims to develop a tweet classifier system for xenophobic messages. IS theory: This work was conceived under the aegis of Organizational Learning Theory. In particular, the Support Vector Machines strategy was used together with the TF-IDF statistical technique, in order to engineer a predictive model for learning potential patterns within the collected data. Method: The research conducted in this study is quantitative, organized through the following methodological procedures: (i) data collection, (ii) controlled laboratory experiments, and (iii) construction of the classifier. Summary of Results: Among the results for the developed classifier, the one with the best performance was the SVM with Kernel Sigmoid, with an accuracy of 90%. Thus, the research results are encouraging for the identification of xenophobia in social media. Contribution and Impact in the IS area: As contributions, in addition to the classification system, we also have the creation of a database on Xenophobia, something that, as far as is known, does not exist in the Brazilian context.
An Automated Tool to Assess Air Space Size in Histopathology Images of Lung Tissue

The mean linear intercept (MLI) score is a useful and common approach for quantifying lung structure in histopathological images. In this paper, we describe a computer tool to determine the MLI score in a fully automated manner. A multiresolution semantic segmentation approach is used to segment the various structures within whole slide images (WSIs) of the lungs. Next, multiple field-of-view (FOV) images from the original WSI and masks are extracted. The extracted FOVs are screened, rejecting images that contain bronchi or blood vessels within the region of interest and accepting those that remain. The accepted FOVs are then used to calculate the MLI score using an intersection counting approach. The automated tool was tested using 20 WSIs from mice that were exposed to one of four conditions that affected their lung structure. The root-mean-squared deviation between the MLI score of our proposed method and a human rater was 5.73 (standard deviation 5.65), and there was a very strong correlation (r=0.9931). The proposed automated tool provides an efficient, accurate, and accessible method that could replace current manual and semi-automated techniques.
Unsupervised robust discriminative subspace representation based on discriminative approximate isometric embedding

Subspace learning has shown a tremendous potential in the fields of machine learning and computer vision due to its effectiveness. Subspace representation is a key subspace learning method that encodes subspace membership information. To effectively encode the subspace memberships of data, some structured prior constraints are imposed on the subspace representation, such as low-rank, sparse, and so on. To handle various noises, existing methods tend to separate a specific type of noise using a specific scheme to obtain robust subspace representation. When encountering diversified noises, their subspace-preserving property may not be guaranteed. To address this issue, we propose a novel unsupervised robust discriminative subspace representation to mitigate the impacts of diversified noises via discriminative approximate isometric embedding, rather than directly separating noises from the high-dimensional space, as done like the existing methods. To ensure the performance of our approach, we provide a crucial theorem, termed as noisy Johnson-Lindenstrauss theorem. Meanwhile, Laplacian rank constraint is imposed on the discriminative subspace representation to uncover the ground truth subspace memberships of noisy data and improve the graph connectivity of subspaces. Extensive experiments on several benchmark datasets and two large-scale datasets validate the effectiveness and robustness of our approach with respect to diversified noises.