Reviewerly/exHarmony huggingface dataset:
Research on Modulation Recognition Method in Low SNR Based on LSTM

Abstract Modulation mode recognition of radio signal is a committed step between signal detection and signal demodulation. At present, quite a lot studies have fully proved that deep learning algorithms can effectively identify the modulation pattern of radio signals. However, the sudden decline of recognition accuracy under the condition of low signal-to-noise ratio needs to be continuously studied and solved. Inspired by the excellent performance of recurrent neural network in signal recognition, this article optimizes and improves the existing system methods, realizes the noise reduction processing of low signal-to-noise ratio signals, and further solves the problem of low recognition accuracy. Through a large number of experimental tests using RML public dataset, the effectiveness of this paper is verified. The results show that the accuracy of modulation pattern recognition of low signal-to-noise ratio signals reaches an average of 27.2%. At last, the paper analyzes the existing problems and optimization points, and looks forward to the further research of relevant contents in the future.
Designing for Microbreaks: Unpacking the Design Journey of Zenscape

This research aims to support positive wellbeing by encouraging people to take regular microbreaks from continuous on-screen work. We present Zenscape, an innovative tangible system that offers subtle reminders to engage in non-work related creative micro activities, through the stirring water action and sound. Zenscape integrates the ideologies of the Zen garden philosophy in its design and features a mini Zen garden consisting of sand and stones, and a water cup with a stirrer, all offering multiple micro activities for the user to get an escape from their work. Unlike time management apps, we do not target productivity, but rather focus on making the most of the microbreaks. We present a descriptive account of how we brought Zenscape into being through a conceptual framing of Diversion, Destination, Play and Return. We hope that our design reflections will inspire future systems on microbreaks beyond reminder and notification systems.
Detection of genuine multipartite entanglement based on principal basis matrix representations

Abstract We study the genuine multipartite entanglement in tripartite quantum systems by using the principal basis matrix representations of density matrices. Using the Schmidt decomposition and local unitary transformation, we first convert the general states to simpler forms and then construct some special matrices from the correlation tensors of the simplified density matrices. Based on the different linear combinations of these matrices, necessary conditions are presented to detect genuine multipartite entanglement of tripartite states. Detailed examples show that our method can detect more entangled states than previous ones.
An artificial intelligent cognitive approach for classification and recognition of white blood cells employing deep learning for medical applications

Cognitive computing aims to implement a unified computational theory similar to human thought, consisting of systems whose objective is to mimic human mental tasks based on the concepts of artificial intelligence and machine learning generating and understanding knowledge. Deep learning is an abstraction of the biological neural network, and can understood as a complex structure interconnected by simple processing elements (neurons), can perform operations as calculations in parallel, for data processing and representation of knowledge. Convolutional neural networks for image recognition through deep learning has been modeled to determine good performance with respect to digital image recognition, especially in medical areas, which is a classic problem of computational classification. In this context, an artificial intelligent cognitive approach was developed achieving an accuracy of 84.19%, which used Python employing Jupyter Notebook, with a dataset of 12,500 medical digital images of human blood smear fields of nonpathologic leukocytes.
Improved Sparrow Search Algorithm applied to path planning of mobile robot

Sparrow Search Algorithm(SSA) is a new swarm intelligent optimization algorithm that simulates the foraging behavior and anti-predation behavior of sparrows. It has the advantages of simple calculation, fast convergence, and few parameters that need to be adjusted. However, in the application of mobile robot path planning, there is insufficient population diversity in the later iterations and it’s easy to fall into a locally optimal solution. In order to solve these problems of the sparrow search algorithm in the path planning of mobile robots, an improved sparrow search algorithm is proposed. First, the algorithm uses opposition-based learning to optimize the initial population of sparrows, improves the quality of the initial solution, and enhances the local search ability of the algorithm. Second, it mixes the Metropolis criterion in the simulated annealing algorithm and makes the algorithm able to accept the new solution by judging whether to accept the new solution, so as Jumping out of the local optimum and strengthen the global search capability. Finally, the performance of the proposed improved algorithm is verified on 2D grid maps of different specifications built on the MATLAB platform. The simulation results show that the improved SSA has better optimization performance than the SSA and Particle Swarms Optimization and other traditional intelligent algorithms, and can effectively jump out of the local optimum and also plan a safe and feasible movement with the best cost and satisfying constraints in a stable and efficient manner.
Precise Target Geo-Location of Long-Range Oblique Reconnaissance System for UAVs

High-precision, real-time, and long-range target geo-location is crucial to UAV reconnaissance and target strikes. Traditional geo-location methods are highly dependent on the accuracies of GPS/INS and the target elevation, which restricts the target geo-location accuracy for LRORS. Moreover, due to the limitations of laser range and the common, real time methods of improving the accuracy, such as laser range finders, DEM and geographic reference data are inappropriate for long-range UAVs. To address the above problems, a set of work patterns and a novel geo-location method are proposed in this paper. The proposed method is not restricted by conditions such as the accuracy of GPS/INS, target elevation, and range finding instrumentation. Specifically, three steps are given, to perform as follows: First, calculate the rough geo-location of the target using the traditional method. Then, according to the rough geo-location, reimage the target. Due to errors in GPS/INS and target elevation, there will be a re-projection error between the actual points of the target and the calculated projection ones. Third, a weighted filtering algorithm is proposed to obtain the optimized target geo-location by processing the reprojection error. Repeat the above process until the target geo-location estimation converges on the true value. The geo-location accuracy is improved by the work pattern and the optimization algorithm. The proposed method was verified by simulation and a flight experiment. The results showed that the proposed method can improve the geo-location accuracy by 38.8 times and 22.5 times compared with traditional methods and DEM methods, respectively. The results indicate that our method is efficient and robust, and can achieve high-precision target geo-location, with an easy implementation.
Bayesian Optimization Allowing for Common Random Numbers

More Efficient Bayesian Optimization Through the Use of Common Random Numbers Bayesian optimization is a powerful tool for expensive stochastic black-box optimization problems, such as simulation-based optimization or hyperparameter tuning in machine learning systems. In “Bayesian Optimization Allowing for Common Random Numbers,” Pearce, Poloczek, and Branke show how explicitly modeling the random seed in the Gaussian process surrogate model allows Bayesian optimization to exploit the structure in the noise and benefit from variance reduction provided by common random numbers. The proposed knowledge gradient with common random numbers acquisition function iteratively determines a combination of input and random seed to evaluate the objective. It automatically trades off reusing old seeds to benefit from the variance reduction through common random numbers and querying new seeds to avoid bias because of a small number of seeds. The proposed algorithm is analyzed theoretically and empirically shows superior performance compared with previous approaches on various test problems.
Chatbot Application to Support Smart Agriculture in Thailand

A chatbot is a software developed to help reply to text or voice conversations automatically and quickly in real time. In the agriculture sector, the existing smart agriculture systems just use data from sensing and internet of things (IoT) technologies that exclude crop cultivation knowledge to support decision-making by farmers. To enhance this, the chatbot application can be an assistant to farmers to provide crop cultivation knowledge. Consequently, we propose the LINE chatbot application as an information and knowledge representation providing crop cultivation recommendations to farmers. It works with smart agriculture and recommendation systems. Our proposed LINE chatbot application consists of five main functions (start/stop menu, main page, drip irri gation page, mist irrigation page, and monitor page). Farmers will receive information for data monitoring to support their decision-making. Moreover, they can control the irrigation system via the LINE chatbot. Furthermore, farmers can ask questions relevant to the crop environment via a chat box. After implementing our proposed chatbot, farmers are very satisfied with the application, scoring a 96% satisfaction score. However, in terms of asking questions via chat box, this LINE chatbot application is a rule-based bot or script bot. Farmers have to type in the correct keywords as prescribed, otherwise they won't get a response from the chatbots. In the future, we will enhance the asking function of our LINE chatbot to be an intelligent bot.
The Role of Virtual Reality in Green Building Design

Abstract Green buildings have now become the basis of the ideal sustainable development. The application of the green building concept can be seen from three points of view, namely environmental, economic, and social aspects. Virtual Reality allows researchers to conduct realtime analytical studies of green building indicators and compare them with scenarios at the next design stage. This paper discusses the role of Virtual Reality in green building. The methodology in this paper uses a literature study research method, the data obtained are compiled, analysed, and concluded, to get a conclusion about the role of Virtual Reality in green building. From several literature studies, there are many things that can be done by Virtual Reality technology in applying the concept of green building.
Multi-UAV 3D Path Planning Based on Improved Particle Swarm Optimizer

The application of the multiple UAVs in the military and civilian fields has become more and more prominent. Path planning is one of the core problems in UAV mission planning system, and is actually a NP hard problem. An improved particle swarm optimizer called heterogeneous adaptive comprehensive learning and dynamic multi-swarm particle swarm optimizer (HACLDMS-PSO) has been proposed to solve this problem. This algorithm integrates three strategies: population dynamic adjustment strategy, perturbation mechanism, and adaptive learning probability mechanism. The population dynamic adjustment strategy increases the diversity of the population; the Levy flight and Cauchy mutation perturbation is used to encourage particles to jump out of the local optimal position; the adaptive learning probability mechanism is applied to promote the evolution of particles toward the global optimum. Compared with the other five algorithms, the proposed algorithm shows better accuracy, fast convergence and robustness.
Thrust Performance Improvement of PMSLM Based on Lasso Regression With Embedded Analytical Model

To solve the problem that both analytical model (AM) and finite element model are difficult to give consideration to the calculation accuracy and efficiency at the same time in motor optimization work, a novel Lasso regression with the embedded AM, called EAM-LR, is proposed to quickly and accurately calculate the thrust performance of the permanent magnet synchronous linear motor (PMSLM) in this article. First, the thrust performances of PMSLM are analyzed by the AM to determine the variation range of structural design parameters. Based on the variation range, a finite-element sample database is established. Then, combined with the finite-element sample database, the analytical mapping functions derived from AM, are integrated into Lasso regression to establish EAM-LR. The accuracy of EAM-LR was verified by comparison with AM, traditional lasso regression, and well-known extreme learning machine. Finally, combined with the EAM-LR, the chaotic golden section algorithm is introduced to search the optimal structure parameters of PMSLM, and the control simulation and prototype experiment prove the effectiveness of the proposed method.
A New Adaptive Filter to Remove Impulsive Noise in Color Images

In this paper, a new adaptive vector directional distance filter (AVDDF) is proposed. The AVDDF filter is developed to remove the ‘salt and papper’ impulsive noise in color images. This algorithm consists in first step to detect pixels that are likely to have been contaminated with noise by using a threshold value. In second step, after recognizing the corrupted pixels, the smallest angular‐magnitude distance is used to replace the noisy pixels. The proposed filter is tested with several standard color images which are contaminated with various levels of ‘salt and pepper’ impulsive noise (3%, 5%, 10%, 20% and 30%). The performance of the proposed filter is measured with peak signal‐to‐noise ratio (PSNR) and normalized color difference (NCD). The simulation results show that the proposed filter permits to ameliorate the performance of the classic vector directional distance filter (VDDF). Indeed, it provides an improvement by an average of 5% and 26% in the PSNR and NCD relative to the VDDF filter, respectively, with a small increase in the execution time by 6%. Besides, the AVDDF filter allows also an enhancement by an average of 2% and 14% in the PSNR and NCD relative to adaptive vector median filter (AVMF), respectively. © 2022 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.
MuSCF-Net: Multi-scale, Multi-Channel Feature Network Using Resnet-based Attention Mechanism for Breast Histopathological Image Classification

Breast cancer (BC) is the major cause of fatality among women in developing as well as under developing countries. Identifying the subtype of malignant (cancer) lesions can help to give proper treatment to cancer patients. The computer-aided diagnosis (CAD) is an automated and more accurate method for histopathological image classification. With the recent development in computer vision and deep learning, convolution neural networks (CNN) achieved enormous success in image classification and is widely used in medical image processing. To recognize the subtype of cancer automatically of the whole slide images (WSI), which is computationally impossible. The training on image patch from the images enables to design low-complexity CNN for feature extraction. The proposed Multi-Scale, Multi-Channel feature network (MuSCF-Net) using Resnet-Based Attention mechanism for breast histopathological image classification follows the knowledge sharing strategy by sharing learned features at each stream across the network and the attention mechanism. The proposed module achieved accuracy for different magnification factors (MF) (X40, X100, X200, and X400) but the superior accuracy, i.e., 97.57% for multi-class and 98.95% for binary at X200 MF. Performance measures like Accuracy, Recall, Precision, and Sensitivity, etc. parameters used for proposed module to classify histopathological breast images. We observed that MuSCF-Net model was better than existing models like VGG16, Xception, and ResNet152.
Utilizing Reinforcement Learning to Continuously Improve a Primitive-Based Motion Planner

This paper describes how the performance of motion primitive-based planning algorithms can be improved using reinforcement learning. Specifically, we describe and evaluate a framework that autonomously improves the performance of a primitive-based motion planner. The improvement process consists of three phases: exploration, extraction, and reward updates. This process can be iterated continuously to provide successive improvement. The exploration step generates new trajectories, and the extraction step identifies new primitives from these trajectories. These primitives are then used to update rewards for continued exploration. This framework required novel shaping rewards, development of a primitive extraction algorithm, and modification of the Hybrid A* algorithm. The framework is tested on a navigation task using a nonlinear F-16 model. The framework autonomously added 91 motion primitives to the primitive library and reduced average path cost by 21.6 s, or 35.75% of the original cost. The learned primitives are applied to an obstacle field navigation task, which was not used in training, and reduced path cost by 16.3 s, or 24.1%. Additionally, two heuristics for the modified Hybrid A* algorithm are designed to improve effective branching factor.
Impact-Oriented Dialogue for Culturally Safe Adolescent Sexual and Reproductive Health in Bauchi State, Nigeria: Protocol for a Codesigned Pragmatic Cluster Randomized Controlled Trial

Adolescents (10-19 years) are a big segment of the Nigerian population, and they face serious risks to their health and well-being. Maternal mortality is very high in Nigeria, and rates of pregnancy and maternal deaths are high among female adolescents. Rates of HIV infection are rising among adolescents, gender violence and sexual abuse are common, and knowledge about sexual and reproductive health risks is low. Adolescent sexual and reproductive health (ASRH) indicators are worse in the north of the country.In Bauchi State, northern Nigeria, the project will document the nature and extent of ASRH outcomes and risks, discuss the findings and codesign solutions with local stakeholders, and measure the short-term impact of the discussions and proposed solutions.The participatory research project is a sequential mixed-methods codesign of a pragmatic cluster randomized controlled trial. Focus groups of local stakeholders (female and male adolescents, parents, traditional and religious leaders, service providers, and planners) will identify local priority ASRH concerns. The same stakeholder groups will map their knowledge of factors causing these concerns using the fuzzy cognitive mapping (FCM) technique. Findings from the maps and a scoping review will inform the contextualization of survey instruments to collect information about ASRH from female and male adolescents and parents in households and from local service providers. The survey will take place in 60 Bauchi communities. Adolescents will cocreate materials to share the findings from the maps and survey. In 30 communities, randomly allocated, the project will engage adolescents and other stakeholders in households, communities, and services to discuss the evidence and to design and implement culturally acceptable actions to improve ASRH. A follow-up survey in communities with and without the intervention will measure the short-term impact of these discussions and actions. We will also evaluate the intervention process and use narrative techniques to assess its impact qualitatively.Focus groups to explore ASRH concerns of stakeholders began in October 2021. Baseline data collection in the household survey is expected to take place in mid-2022. The study was approved by the Bauchi State Health Research Ethics Committee, approval number NREC/03/11/19B/2021/03 (March 1, 2021), and by the Faculty of Medicine and Health Sciences Institutional Review Board McGill University (September 13, 2021).Evidence about factors related to ASRH outcomes in Nigeria and implementation and testing of a dialogic intervention to improve these outcomes will fill a gap in the literature. The project will document and test the effectiveness of a participatory approach to ASRH intervention research.ISRCTN Registry ISRCTN18295275; https://www.isrctn.com/ISRCTN18295275.DERR1-10.2196/36060.
A CNN Based Encrypted Network Traffic Classifier

Internet encryption ensures security by improving privacy between sender and receiver. The unstructured form of encrypted data creates a problem of poor traffic classification for security systems. Recent developments using Artificial Intelligence to address this problem left issues like model simplicity, complexity, imbalanced dataset etc, unaddressed. Overfitting, underfitting and ultimately poor classification are outcomes of poorly designed models. This paper applies deep learning to the problem of traffic classification. An eleven layered Convolutional Neural Network (CNN) is designed and trained with a range of images generated from the metadata of encrypted traffic. At its core, the design is simple and deals with overfitting. The proposed model is assessed with the standard metrics, accuracy, precision, recall and score, then compared to a baseline model. The model is trained and tested for seven classification problems, using three encryption types (https, vpn, tor). For all classification tasks, the model achieved accuracies ranging from 91% - 99%, which is an indication of optimum generalization strength. Our model outperformed the baseline model which had accuracies ranging from 67.6% - 99%, an indication of poor generalization strength.
Visualization Method for the Spreading Curve of COVID-19 in Universities using GNN

As the reopening of the university after the spread of COVID-19 on campus and we simulate and visualize the initial states spreading of COVID-19. In this research, we analyze and forecast the COVID-19 spreading curve of the resumption of in-person classes at university by the graph structure with the spread weight of edges based on each student's relation. Our approach is based on the effectiveness of three distancing strategies designed to keep the curve flat and aid make the spread of the COVID-19 controllable. By detecting the possibility of student relation based on three strategies, we can analyze the COVID-19 spreading curve by Graph Neural Network (GNN) and SIR model. In this article, we discuss two types of Open Group and Closed Group on university campuses and analyze face-to-face lectures, indoor social activities, and campus cafeterias. To verify the effectiveness of our two types of group, we simulated with the random infection curve by graph neural network model. At last, we visualized the COVID-19 spreading process and the results of diffusion prediction.