Reviewerly/exHarmony huggingface dataset:
Effects of multiple post cure cycles on properties of composite carbon fibre and epoxy materials

Post-curing cycles have an effective impact on the thermal and mechanical properties of thermoset composite materials. Typically, post-cure cycles use heat to expose cured resin parts to high temperatures in an attempt to maximize their final properties. In most cases, these processes allow composite parts to achieve the highest possible strength and become stable. In industrial practice, however, it has been found that there are potential consequences of performing localized repairs on composite parts in an autoclave where the localized repair undergoes a normal curing process while the parent part undergoes an additional curing process. Thus, for the parent part, prolonged exposure to high temperatures can trigger a process of decomposition of the resin matrix, as could be the aging phenomena of composites in service. In such a scenario, two practical questions are worth asking: (1) to what extent can a resin matrix be post cured without risking degrading its thermal properties? (2) what are the residual mechanical and physical properties resulting from prolonged exposure of composite parts to elevated temperatures? In this work, an analysis was carried out to estimate the potential adverse effects on thermoset composite parts made of carbon fibre and epoxy resin materials commonly used in aerospace, in terms of thermal, physical and mechanical properties after a long exposure to high temperatures caused by multiple post cure cycles. Two types of prepreg laminates were used to make monolithic test panels using twill and plain weave architectures for the M21 and 8552 test panels respectively. Thermal tests were conducted to evaluate the evolution of the degree of cure, the glass transition temperature and the inter-laminar shear strength of post-cured panels, which were subjected to a different number of post cure cycles. The study found that multiple post cure cycles alter thermal properties, which in turn have adverse effects on physical and mechanical properties. The results demonstrated that the two types of composite materials respond differently to changes in properties. Twill weave panels quickly reach full cure after three stages while plain weave panels have undergone gradual curing. This difference in the curing process of the resin matrix clearly influenced the changes in the measurements of shear, surface hardness and compressive strengths.
CCR-Net: Consistent contrastive representation network for multi-view clustering

Recently, researchers have focused on utilizing given heterogeneous features to explore obvious discrimination information for clustering. Most of the current work exploits consistency using some fusion metrics, but the complementarity of multi-view features is not well leveraged. In this paper, we propose an efficient consistent contrastive representation network (CCR-Net) for multi-view clustering, which provides a generalized framework for multi-view learning tasks. First, the proposed model explores the complementarity by a designed contrastive fusion module to learn a shared fusion weight. Second, the proposed method utilizes a consistent representation module to ensure consistency and obtains a consistent graph. Furthermore, we also extend the proposed method to incomplete multi-view scenarios. The designed contrastive fusion module utilizes the complementarity of multiple views to fill in the missing view graphs. Moreover, the consistent feature representation module adds a maxpooling layer on CCR-Net to explore a shared local structure and extract a latent low-dimensional embedding. Finally, the proposed method presents end-to-end training and flexible task interfaces for multi-view learning. Comprehensive evaluations on challenging multi-view tasks demonstrate that the proposed method achieves outstanding performance.
Mobile Application-Based Sign Language Detector for Deaf People

In everyday life, we use hand gestures for several activities, like calling someone or showing directions to somebody, and don't even realise or consciously acknowledge them as a part of life. But for people with hearing disabilities, efficiently contributing to society is a much more important part of their everyday routine. They are their major medium of communication and an incredible way for human communication. Vision-based hand gesture recognition procedures have many demonstrated advantages compared with conventional gadgets. In this chapter, the authors discuss various available products to predict what a user is trying to convey through hand gestures using American Sign Language (ASL). While being able to help deaf people, this chapter also focuses on the proposed method or tool that can also be used for the practice of ASL by regular people to learn and practice sign language. This concept can also be implemented in schools easily to design ASL into their curriculum.
Visible security ink blended by Prussian blue analogues encapsulated gold (Au@PBA) nanoparticles for information hiding

Developing visible security ink with simple preparation, high optical stability and interference-free secret signals are critical to hiding security information. Here, two Prussian blue analogue encapsulated gold nanoparticles (Au@PBA NPs) with different triple-bond Raman emissions were synthesized to prepared novel red inkjet inks for security labels. And the ink formulation was well optimized to obtain suitable surface tension and viscosity. The labels contain public information in red as well as secret information that can only be acquired using Raman spectroscopy. With the visible public information on the labels, the secret information is not easy to be noticed. Also, the secret information written by the triple-bond Raman scattering is readily identifiable without interference. This method improves the security of secret information transmission, providing a new strategy for information hiding technology.
Automatic generation of short-answer questions in reading comprehension using NLP and KNN

In general, making evaluations requires a lot of time, especially in thinking about the questions and answers. Therefore, research on automatic question generation is carried out in the hope that it can be used as a tool to generate question and answer sentences, so as to save time in thinking about questions and answers. This research focuses on automatically generating short answer questions in the reading comprehension section using Natural Language Processing (NLP) and K-Nearest Neighborhood (KNN). The questions generated use article sources from news with reliable grammar. To maintain the quality of the questions produced, machine learning methods are also used, namely by conducting training on existing questions. The stages of this research in outline are simple sentence extraction, problem classification, generating question sentences, and finally comparing candidate questions with training data to determine eligibility. The results of the experiment carried out were for the Grammatical Correctness parameter to produce a percentage of 59.52%, for the Answer Existence parameter it yielded 95.24%, while for the Difficulty Index parameter it produced a percentage of 34.92%. So that the resulting average is 63.23%. So, this software deserves to be used as an alternative to automatically create reading comprehension questions.
An image-based visual servoing control method for UAVs based on fuzzy logic

Visual servoing is a method to achieve precise positioning and motion control of objects by visual feedback, and it is widely applied in the fields of robotics and unmanned aerial vehicles (UAVs) in recent years. This paper presents a novel image-based visual servoing (IBVS) control method for UAVs based on fuzzy logic to effectively solve the problem under field of view constraint and improve the control efficiency. In this paper, a fuzzy logic of servo gain is designed for the control input of visual servoing, which solves the problem of feature loss in IBVS and improves the efficiency. Meanwhile, a deep computing method based on known data is proposed to solve the unknown depth of Jacobian matrix, which makes the control easier to converge. The effectiveness of the proposed method is verified by the simulation of a quadrotor UAV equipped with a monocular camera.
DARE: Distill and Reinforce Ensemble Neural Networks for Climate-Domain Processing

Natural-language processing is well positioned to help stakeholders study the dynamics of ambiguous Climate Change-related (CC) information. Recently, deep neural networks have achieved good results on a variety of NLP tasks depending on high-quality training data and complex and exquisite frameworks. This raises two dilemmas: (1) the networks are highly reliant on powerful hardware devices and processing is time-consuming, which is not only inconducive to execution on edge devices but also leads to resource consumption. (2) Obtaining large-scale effective annotated data is difficult and laborious, especially when it comes to a special domain such as CC. In this paper, we propose a CC-domain-adapted BERT distillation and reinforcement ensemble (DARE) model for tackling the problems above. Specifically, we propose a novel data-augmentation strategy which is a Generator-Reinforced Selector collaboration network for countering the dilemma of CC-related data scarcity. Extensive experimental results demonstrate that our proposed method outperforms baselines with a maximum of 26.83% on SoTA and 50.65× inference time speed-up. Furthermore, as a remedy for the lack of CC-related analysis in the NLP community, we also provide some interpretable conclusions for this global concern.
Scheduling pre-operative assessment clinic with answer set programming

Abstract The problem of scheduling pre-operative assessment clinic (PAC) consists of assigning patients to a day for the exams needed before a surgical procedure, taking into account patients with different priority levels, due dates and operators availability. Realizing a satisfying schedule is of upmost importance for a hospital, since delay in PAC can cause delay in the subsequent phases, thus lowering patients’ satisfaction. In this paper, we propose a two-phase solution to the PAC problem: in the first phase, patients are assigned to a day taking into account a default list of exams; then, in the second phase, having the actual list of exams needed by each patient, we use the results of the first phase to assign a starting time to each exam. We first present a mathematical formulation for both problems. Further, we present a solution where modeling and solving are done via answer set programming. We then introduce a rescheduling solution that may come into play when the scheduling solution cannot be applied fully. Experiments employing synthetic benchmarks on both scheduling and rescheduling show that both solutions provide satisfying results in short time. We finally show the implementation and usage of a web application that allows to run our scheduling solution and analyze the results graphically in a transparent way.
Fast and Noise-Resilient Magnetic Field Mapping on a Low-Cost UAV Using Gaussian Process Regression

This study presents a comprehensive approach to mapping local magnetic field anomalies with robustness to magnetic noise from an unmanned aerial vehicle (UAV). The UAV collects magnetic field measurements, which are used to generate a local magnetic field map through Gaussian process regression (GPR). The research identifies two categories of magnetic noise originating from the UAV's electronics, adversely affecting map precision. First, this paper delineates a zero-mean noise arising from high-frequency motor commands issued by the UAV's flight controller. To mitigate this noise, the study proposes adjusting a specific gain in the vehicle's PID controller. Next, our research reveals that the UAV generates a time-varying magnetic bias that fluctuates throughout experimental trials. To address this issue, a novel compromise mapping technique is introduced, enabling the map to learn these time-varying biases with data collected from multiple flights. The compromise map circumvents excessive computational demands without sacrificing mapping accuracy by constraining the number of prediction points used for regression. A comparative analysis of the magnetic field maps' accuracy and the spatial density of observations employed in map construction is then conducted. This examination serves as a guideline for best practices when designing trajectories for local magnetic field mapping. Furthermore, the study presents a novel consistency metric intended to determine whether predictions from a GPR magnetic field map should be retained or discarded during state estimation. Empirical evidence from over 120 flight tests substantiates the efficacy of the proposed methodologies. The data are made publicly accessible to facilitate future research endeavors.
Compression-resistant backdoor attack against deep neural networks

In recent years, many backdoor attacks based on training data poisoning have been proposed. However, in practice, those backdoor attacks are vulnerable to image compressions. When backdoor instances are compressed, the feature of specific backdoor trigger will be destroyed, which could result in the backdoor attack performance deteriorating. In this paper, we propose a compression-resistant backdoor attack based on feature consistency training. To the best of our knowledge, this is the first backdoor attack that is robust to image compressions. First, both backdoor images and their compressed versions are input into the deep neural network (DNN) for training. Then, the feature of each image is extracted by internal layers of the DNN. Next, the feature difference between backdoor images and their compressed versions are minimized. As a result, the DNN treats the feature of compressed images as the feature of backdoor images in feature space. After training, the backdoor attack against DNN is robust to image compression. Furthermore, we consider three different image compressions (i.e., JPEG, JPEG2000, WEBP) in feature consistency training, so that the backdoor attack is robust to multiple image compression algorithms. Experimental results demonstrate the effectiveness and robustness of the proposed backdoor attack. When the backdoor instances are compressed, the attack success rate of common backdoor attack is lower than 10%, while the attack success rate of our compression-resistant backdoor is greater than 97%. The compression-resistant attack is still robust even when the backdoor images are compressed with low compression quality. In addition, extensive experiments have demonstrated that, our compression-resistant backdoor attack has the generalization ability to resist image compression which is not used in the training process.
Wearable Sensor-Based Human Activity Recognition System Employing Bi-LSTM Algorithm

Abstract Human activity recognition (HAR) systems employing wearable sensors are a promising area of research for tracking human activity. Recently, wearable devices such as smartwatches and sensors have been developed for activity recognition and monitoring. These systems aim to obtain the subject’s state within his or her environment by exploiting heterogeneous sensors attached to the body. With the development of deep learning, new strategies have emerged to facilitate and solve the HAR problems. In this work, a deep multilayer bidirectional long-short memory (Bi-LSTM) architecture has been implemented to detect human activities. Instead of training a single model as in traditional LSTM methods, two models are presented in the Bi-LSTM scheme, one for learning the input data sequence and the other for learning the reverse sequence. Finally, a new novel postprocessing approach has been proposed based on windowing and voting in the last step to improve the average F1 score. Comprehensive investigations on the three publicly available datasets consisting of a different set of activities were used to evaluate the performance of the proposed framework. The empirical results of this paper on AReM, Mhealth and PAMAP2 datasets attained 95.46, 95.79 and 93.41% average F1 score, respectively. The results also revealed that selecting the window size and implementing the appropriate voting method has a significant effect on improving the average percentage of the F1 score.
Addition machines, automatic functions and open problems of Floyd and Knuth

Floyd and Knuth investigated in 1990 register machines which can add, subtract and compare integers as primitive operations. They asked whether their current bound on the number of registers for multiplying and dividing fast (running in time linear in the size of the input) can be improved and whether one can output fast the powers of two summing up to a positive integer in subquadratic time. Both questions are answered positively. Furthermore, it is shown that every function computed by only one register is automatic and that the automatic functions with one input can be computed with four registers in linear time; automatic functions with a larger number of inputs can be computed with 5 registers in linear time. There is a nonautomatic function with one input which can be computed with two registers in linear time.
The unitary dependence theory for characterizing quantum circuits and states

Abstract Most existing quantum algorithms are discovered accidentally or adapted from classical algorithms, and there is the need for a systematic theory to understand and design quantum circuits. Here we develop a unitary dependence theory to characterize the behaviors of quantum circuits and states in terms of how quantum gates manipulate qubits and determine their measurement probabilities. Compared to the conventional entanglement description of quantum circuits and states, the unitary dependence picture offers more practical information on the measurement and manipulation of qubits, easier generalization to many-qubit systems, and better robustness upon partitioning of the system. The unitary dependence theory can be applied to systematically understand existing quantum circuits and design new quantum algorithms.
Performance Comparison of Various Neural Networks for Speech Recognition

Abstract Speech recognition is a method where an audio signal is translated into text, words, or commands and also tells how the speech is recognized. Recently, many deep learning models have been adopted for automatic speech recognition and proved more effective than traditional machine learning methods like Artificial Neural Networks(ANN). This work examines the efficient learning architectures of features by different deep neural networks. In this paper, five neural network models, namely, CNN, LSTM, Bi-LSTM, GRU, and CONV-LSTM, for the comparative study. We trained the networks using Audio MNIST dataset for three different iterations and evaluated them based on performance metrics. Experimentally, CNN and Conv-LSTM network model consistently offers the best performance based on MFCC Features.
Classification of WBC cell classification using fully connected convolution neural network

Abstract White blood cells (WBCs) are cells that is key factor of the immune systems which is help to our body fight off contagions and other diseases. In order to enhance the diagnosis of various diseases in medical field by using image processing techniques from the blood cells. In that, Leukemia is associated with one type of cancer of the blood and bone marrow. It is look like spongy tissue inside the bones where blood cells are made. In this paper, a fully connected. Convolution neural network is used to segmented and classification of blood cell microscope WBC images for healthy and unhealthy conditions. The performance of the classifier was analyzed. The accuracy sensitivity specificity and pression are 96.84%, 96.26%,97.35% and 96.39% respectively.
Mobile-Based Weather Forecasting and Visualization Using Augmented Reality

The aim is to design a 3D augmented reality (AR) weather visualization to investigate future tragedy and daily forecasting. Imagine a world where all physical objects could be made, destroyed, customized, and used in endless ways. Augmented reality (AR) and virtual reality (VR) can create such a place. The background for the study is thus: to analyze, learn implementing and building of an AR Weather application using the OpenWeatherMap Weather API. OpenWeatherMap provides access to local and global weather data via application programming interface (API). OpenWeatherMap serves millions of customers around the world by providing a wide selection of weather-related goods with varying degrees of detail and precision. The research question is if smartphone- and tablet-based 3D AR weather visualizations can be effective tools to enhance current weather forecasting techniques. The methods used in this chapter were making use of AR weather App using unity, AR Foundation, and OpenWeatherMap to stimulate the visualization.
Model View Controller Frameworks for Mobile View Website

Websites have evolved over the past few years utilizing a variety of programming languages and frameworks. There were other developers working on them concurrently. Developers deploy their projects utilizing a number of design patterns to handle these sophisticated web apps, which makes the code simpler and easier to work with. They are well known for using the model view controller approach. Model, view, and controller, or MVC, are the three components that make up an application in accordance with the MVC architectural pattern. Each of these parts is made to address particular facets of application development. This chapter focuses on MVC for mobile device website viewing. The various sections of this chapter address the introduction to MVC, MVC components, the architecture pattern, several techniques used to implement the pattern in the project, and an example of MVC architecture. Towards the end of the chapter, the advantages and disadvantages of MVC are examined.
Keep an eye on faces: Robust face detection with heatmap-Assisted spatial attention and scale-Aware layer attention

Modern anchor-based face detectors learn discriminative features using large-capacity networks and extensive anchor settings. In spite of their promising results, they are not without problems. First, most anchors extract redundant features from the background. As a consequence, the performance improvements are achieved at the expense of a disproportionate computational complexity. Second, the predicted face boxes are only distinguished by a classifier supervised by pre-defined positive, negative and ignored anchors. This strategy may ignore potential contributions from cohorts of anchors labeled negative/ignored during inference simply because of their inferior initialisation, although they can regress well to a target. In other words, true positives and representative features may get filtered out by unreliable confidence scores. To deal with the first concern and achieve more efficient face detection, we propose a Heatmap-assisted Spatial Attention (HSA) module and a Scale-aware Layer Attention (SLA) module to extract informative features using lower computational costs. To be specific, SLA incorporates the information from all the feature pyramid layers, weighted adaptively to remove redundant layers. HSA predicts a reshaped Gaussian heatmap and employs it to facilitate a spatial feature selection by better highlighting facial areas. For more reliable decision-making, we merge the predicted heatmap scores and classification results by voting. Since our heatmap scores are based on the distance to the face centres, they are able to retain all the well-regressed anchors. The experiments obtained on several well-known benchmarks demonstrate the merits of the proposed method.