Reviewerly/exHarmony huggingface dataset:
Application of Autonomous Driving Algorithm Based on HSV and Yolov5 in Micro Intelligent Vehicles

Authors:
raw_author_name: Xiaozhe Yang
raw_affiliation_string: The Faculty of Intelligent Manufacturing, Nanning University, Nanning, 530299, China
raw_author_name: Huiting Lu
raw_affiliation_string: The Faculty of Intelligent Manufacturing, Nanning University, Nanning, 530299, China
raw_author_name: Yongzhu Long
raw_affiliation_string: The Faculty of Intelligent Manufacturing, Nanning University, Nanning, 530299, China

Abstract: Artificial intelligence driving technology has become the main direction of technological progress in intelligent vehicles. To address the issue of road recognition and traffic sign recognition response in the field of intelligent vehicle autonomous driving, a vehicle autonomous driving algorithm based on HSV road detection and yolov5 traffic sign recognition is proposed and applied to micro intelligent vehicles. The hardware of the mini smart car is controlled by NVIDIA Jetson AGX Orin, and the software method is embedded in the Ubuntu system. The yolov5 neural network is used to solve the classification and location problems of traffic signs. The results of experimental testing prove that micro intelligent vehicles can drive according to regulations on simulated roads and accurately recognize road signs, and can make corresponding actions accurately. The research results provide certain reference value for the application and development of vehicle autonomous driving.
Single-loss hash image retrieval method based on improved visual transformer

Authors:
raw_author_name: Zhijie Wang
raw_affiliation_string: Donghua University

Abstract: Deep hashing methods have gained popularity in image retrieval due to their advantages such as low storage requirements and high efficiency. However, existing deep hashing methods for large-scale image retrieval tasks suffer from issues including low discriminative power of binary hash codes, difficult optimization of losses, and low retrieval accuracy. This paper proposes a single-loss hash image retrieval method based on an improved visual transformer to address these issues. The proposed method utilizes a pre-trained Vision Transformer (ViT) on ImageNet as the backbone network, augmented with a hash coding layer to extract image features more comprehensively. Additionally, we design a single learning objective loss function that addresses the discriminative power of hash codes and quantization errors, thereby eliminating the complexity of adjusting various loss weights. Experimental evaluations on ImageNet100, NUS-WIDE, CIFAR10, and MS-COCO datasets demonstrate the superior performance of the proposed method compared to contemporary methods, indicating its adaptability to diverse data.
A Machine Learning Approach to Improve the Usability of Severe Thunderstorm Wind Reports

Authors:
raw_author_name: Subrata Pal
raw_affiliation_string: Iowa State University, Ames, Iowa
raw_author_name: Somak Dutta
raw_affiliation_string: Iowa State University, Ames, Iowa
raw_author_name: Ranjan Maitra
raw_affiliation_string: Iowa State University, Ames, Iowa
raw_author_name: Jennifer Newman
raw_affiliation_string: Iowa State University, Ames, Iowa

Abstract: Abstract Many concerns are known to exist with thunderstorm wind reports in the National Center for Environmental Information Storm Events Database , including the overestimation of wind speed, changes in report frequency due to population density, and differences in reporting due to damage tracers. These concerns are especially pronounced with reports that are not associated with a wind speed measurement, but are estimated, which make up almost 90% of the database. We have used machine learning to predict the probability that a severe wind report was caused by severe intensity wind, or wind ≥ 50 kt. A total of six machine learning models were trained on 11 years of measured thunderstorm wind reports, along with meteorological parameters, population density, and elevation. Objective skill metrics such as the area under the ROC curve (AUC), Brier score, and reliability curves suggest that the best performing model is the stacked generalized linear model, which has an AUC around 0.9 and a Brier score around 0.1. The outputs from these models have many potential uses such as forecast verification and quality control for implementation in forecast tools. Our tool was evaluated favorably at the Hazardous Weather Testbed Spring Forecasting Experiments in 2020, 2021, and 2022.
Software Effort Estimation using Machine Learning Algorithms

Authors:
raw_author_name: Kruti Lavingia
raw_affiliation_string: Nirma University, India
raw_author_name: Raj Patel
raw_affiliation_string: Institute of Technology, Nirma University, India
raw_author_name: Vivek Patel
raw_affiliation_string: Institute of technology, Nirma University, India
raw_author_name: Ami Lavingia
raw_affiliation_string: Sal College of Engineering, India

Abstract: Effort estimation is a crucial aspect of software development, as it helps project managers plan, control, and schedule the development of software systems. This research study compares various machine learning techniques for estimating effort in software development, focusing on the most widely used and recent methods. The paper begins by highlighting the significance of effort estimation and its associated difficulties. It then presents a comprehensive overview of the different categories of effort estimation techniques, including algorithmic, model-based, and expert-based methods. The study concludes by comparing methods for a given software development project. Random Forest Regression algorithm performs well on the given dataset tested along with various Regression algorithms, including Support Vector, Linear, and Decision Tree Regression. Additionally, the research identifies areas for future investigation in software effort estimation, including the requirement for more accurate and reliable methods and the need to address the inherent complexity and uncertainty in software development projects. This paper provides a comprehensive examination of the current state-of-the-art in software effort estimation, serving as a resource for researchers in the field of software engineering.
Tunable photon-photon correlations in waveguide QED systems with giant atoms

Authors:
raw_author_name: Wenju Gu
raw_affiliation_string: School of Physics and Optoelectronic Engineering, Yangtze University, Jingzhou 434023, China
raw_author_name: Lei Chen
raw_affiliation_string: School of Physics and Optoelectronic Engineering, Yangtze University, Jingzhou 434023, China
raw_author_name: Zhen Yi
raw_affiliation_string: School of Physics and Optoelectronic Engineering, Yangtze University, Jingzhou 434023, China
raw_author_name: Gao-xiang Li
raw_affiliation_string: Department of Physics, Huazhong Normal University, Wuhan 430079, China

Abstract: We investigate the scattering processes of two photons in a one-dimensional waveguide coupled to two giant atoms. By adjusting the accumulated phase shifts between the coupling points, we are able to effectively manipulate the characteristics of these scattering photons. Utilizing the Lippmann-Schwinger formalism, we derive analytical expressions for the wave functions describing two-photon interaction in separate, braided, and nested configurations. Based on these wave functions, we also obtain analytical expressions for the incoherent power spectra and second-order correlation functions. In contrast to small atoms, the incoherent spectrum, which is defined by the correlation of the bound state, can exhibit more tunability due to the phase shifts. Additionally, the second-order correlation functions in the transmission and reflection fields could be tuned to exhibit either bunching or antibunching upon resonant driving. These unique features offered by the giant atoms in waveguide QED could benefit the generation of nonclassical itinerant photons in quantum networks.
One-step parity measurement of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>N</mml:mi></mml:math> cat-state qubits via reverse engineering and optimal control

Authors:
raw_author_name: Yi-Hao Kang
raw_affiliation_string: Department of Physics, Fuzhou University, Fuzhou 350108, China; Department of Physics, Harbin Institute of Technology, Harbin 150001, China; Fujian Key Laboratory of Quantum Information and Quantum Optics, Fuzhou University, Fuzhou 350108, China
raw_author_name: Ye-Hong Chen
raw_affiliation_string: Department of Physics, Fuzhou University, Fuzhou 350108, China; Fujian Key Laboratory of Quantum Information and Quantum Optics, Fuzhou University, Fuzhou 350108, China; Theoretical Quantum Physics Laboratory, Cluster for Pioneering Research, RIKEN, Wako-shi, Saitama 351-0198, Japan
raw_author_name: Yang Liu
raw_affiliation_string: Department of Physics, Fuzhou University, Fuzhou 350108, China; Fujian Key Laboratory of Quantum Information and Quantum Optics, Fuzhou University, Fuzhou 350108, China
raw_author_name: Cheng Zhang
raw_affiliation_string: Department of Physics, Fuzhou University, Fuzhou 350108, China; Fujian Key Laboratory of Quantum Information and Quantum Optics, Fuzhou University, Fuzhou 350108, China
raw_author_name: Yu Wang
raw_affiliation_string: School of Physics, Hangzhou Normal University, Hangzhou 311121, China
raw_author_name: Jie Song
raw_affiliation_string: Department of Physics, Harbin Institute of Technology, Harbin 150001, China
raw_author_name: Yan Xia
raw_affiliation_string: Department of Physics, Fuzhou University, Fuzhou 350108, China; Fujian Key Laboratory of Quantum Information and Quantum Optics, Fuzhou University, Fuzhou 350108, China

Abstract: In this paper, a one-step protocol is proposed for the parity measurement of $N$ cat-state qubits which are encoded on the cat states of the modes in superconducting Kerr-nonlinear cavities. The parity measurement is performed with the help of an auxiliary qutrit. Especially, the auxiliary qutrit can (cannot) be excited to the higher-energy levels when the cat-state qubits are in the even- (odd-) parity state. By designing the Rabi frequency of the classical fields via reverse engineering and optimal control, the qutrit is driven to an excited dressed state in the even-parity case, which is robust to the systematic errors of the qutrit-cavity coupling strengths. Accordingly, the parity of the cat-state qubits can be distinguished with high accuracy by measuring the final population of the ground state of the auxiliary qutrit. Numerical simulations also show that the protocol is insensitive to the systematic errors of the classical fields, the inhomogeneity of the coupling strengths, intercavity cross talk, unwanted qutrit transitions, and decoherence. Therefore, the protocol may provide an effective approach for parity measurement of multiple cat-state qubits.
Artificial Intelligence Based Virtual Assistant for Vision Impaired Person

Authors:
raw_author_name: Navneet Kumar

Abstract: Abstract: This project is a brilliant example of how technology can be harnessed for a profoundly positive purpose, enhancing the quality of life for those who may face challenges in certain aspects of daily living. It's about creating a tool that complements and supports individuals rather than substituting human interaction or assistance. That sounds like an incredible initiative! Combining technologies like object detection through YOLO algorithms, speech synthesis via text-to-speech, and incorporating them into smart glasses could significantly enhance the independence and mobility of visually impaired individuals. These glasses could provide real-time information about their surroundings, enabling them to navigate and interact more confidently with their environment.
A Stretchable Breathable Washable All-Textile Smart Wearable Keyboard

Authors:
raw_author_name: Haojie Wang
raw_affiliation_string: Wenzhou University, Chashan town, Wenzhou, 325035, CHINA
raw_author_name: Xizhang Chen
raw_affiliation_string: Wenzhou University, Chashan town, Wenzhou, 325035, CHINA

Abstract: Abstract The development of comfortable and lightweight wearable devices is an emerging technological goal in human-computer interaction. Conductive textiles attract a great deal of interest as versatile materials for wearable devices. It can naturally integrate into our daily clothing, making wearable devices more comfortable and convenient. However, developing all-textile wearable devices remains an important challenge. And the wiring of wearable devices in practical applications is also a thorny issue. In this study, a novel wearable smart keyboard is proposed. Benefit from the ingenious structural design of double-faced effect functional textiles and 3D spacer textiles, the smart keyboard achieves all-textile structure and the wires can also be hidden inside the 3D spacer textile. The smart keyboard is stretchable, breathable, washable and can be integrated with daily clothing by sewing. In addition to the functions of traditional keyboards, smart keyboards also have the ability to detect tapping force which lays the foundation for future intelligent applications, such as tracking the tapping force of typewriting to enhance security.The application examples of integrating the smart keyboard into lab-coat for typewriting are demonstrated. This study shows that the novel smart keyboard has great potential in the fields of wearable devices and human-computer interface.&amp;#xD;
The Design and Development of Hands-on Activities for Digital Forensics Education

Authors:
raw_author_name: Xinli Wang
raw_affiliation_string: School of Computing Grand Valley State University
raw_author_name: Vijay Bhuse
raw_affiliation_string: School of Computing Grand Valley State University
raw_author_name: Sara Sutton
raw_affiliation_string: School of Computing Grand Valley State University

Abstract: It has been widely admitted by researchers and educators that hands-on activities are a core component in digital forensics education to help students gain practical skills that are needed in real-world forensic investigations. However, it is not clear in existing works about what kinds of hands-on activities are recommended to be integrated into a digital forensics course and how to design and develop them. In our teaching practice, hands-on activities for a digital forensics course are designed in three categories: 1) activities that assist students in learning how to use common digital forensics tools; 2) activities that help students gain in-depth understanding of the basic concepts and fundamental knowledge that are presented in class lectures; 3) activities that promote students the development of mindsets and data analytical skills that are needed for a digital forensic investigator. Various formats are employed to develop these hands-on exercises in different categories. The educational objectives and student learning outcomes map well to the CAE-CD (Centers of Academic Excellence - Cyber Defense) outcomes by completing their forensic knowledge units. In this paper, we share our idea and experience to design and implement such hands-on assignments in each category for meeting specific educational objectives. Sample exercises are briefly described to explain our idea in each category. Open source tools and data sets are introduced for references. Experiences, lessons, and sample feedback from students are discussed. Our results will provide a point of reference for those who teach digital forensics courses at a college or university, or are developing a digital forensic curriculum.
Matting Algorithm with Improved Portrait Details for Images with Complex Backgrounds

Authors:
raw_author_name: Dan Zhang
raw_affiliation_string: School of Computer Science, Qinghai Normal University, Xining 810000, China; The State Key Laboratory of Tibetan Intelligent Information Processing and Application, Qinghai Normal University, Hutai, Xining 810008, China
raw_author_name: Sheng-Ling Geng
raw_affiliation_string: Academy of Plateau Science and Sustainability, People’s Government of Qinghai Province & Beijing Normal University, Haihu, Xining 810004, China; School of Computer Science, Qinghai Normal University, Xining 810000, China; The State Key Laboratory of Tibetan Intelligent Information Processing and Application, Qinghai Normal University, Hutai, Xining 810008, China
raw_author_name: Ming-Quan Zhou
raw_affiliation_string: Academy of Plateau Science and Sustainability, People’s Government of Qinghai Province & Beijing Normal University, Haihu, Xining 810004, China; School of Computer Science, Qinghai Normal University, Xining 810000, China; The State Key Laboratory of Tibetan Intelligent Information Processing and Application, Qinghai Normal University, Hutai, Xining 810008, China

Abstract: With the continuous development of virtual reality, digital image applications, the required complex scene video proliferates. For this reason, portrait matting has become a popular topic. In this paper, a new matting algorithm with improved portrait details for images with complex backgrounds (MORLIPO) is proposed. This work combines the background restoration module (BRM) and the fine-grained matting module (FGMatting) to achieve high-detail matting for images with complex backgrounds. We recover the background by inputting a single image or video, which serves as a priori and aids in generating a more accurate alpha matte. The main framework uses the image matting model MODNet, the MobileNetV2 lightweight network, and the background restoration module, which can both preserve the background information of the current image and provide a more accurate prediction of the alpha matte of the current frame for the video image. It also provides the background prior of the previous frame to predict the alpha matte of the current frame more accurately. The fine-grained matting module is designed to extract fine-grained details of the foreground and retain the features, while combining with the semantic module to achieve more accurate matting. Our design allows training on a single NVIDIA 3090 GPU in an end-to-end manner and experiments on publicly available data sets. Experimental validation shows that our method performs well on both visual effects and objective evaluation metrics.
How Interpretable Is “Interpretable” Machine Learning?

Authors:
raw_author_name: Ho Chit Siu
raw_affiliation_string: MIT Lincoln Laboratory
raw_author_name: Kevin Leahy
raw_affiliation_string: MIT Lincoln Laboratory; now at Worcester Polytechnic Institute
raw_author_name: Makai Mann
raw_affiliation_string: MIT Lincoln Laboratory; now at Anduril Industries

Abstract: The modern machine learning (ML) revolution has led to ML systems deployed at scale for the first time, substantially impacting society beyond computer science experts. It is critical that these systems are integrated appropriately with minimal negative side effects, and an important component of this goal is identifying whether these tools are interpretable by humans. Formal methods is a computer science discipline that may address this problem via mathematically grounded techniques for specifying and verifying the desired operation of software and hardware systems. In the ML context, ML systems may be designed to output a set of formal specifications that describe precisely what they learned, which can then be translated into a set of natural language rules. A widespread belief in the formal methods community is that the use of their approaches in machine learning lends interpretability to systems, making their reasoning understandable by humans. We evaluate the claim that formal specifications are interpretable by humans via an experiment with sixty-two participants. We describe the format of the study, provide statistical analysis of the results, and recommend modifications to researchersâ claims and ergonomic improvements to data presentation and validation training.
Deep learning assisted solar forecasting for battery swapping stations

Authors:
raw_author_name: Dipanjan Bose
raw_affiliation_string: Electrical Engineering Department, Indian Institute of Engineering Science and Technology, Shibpur, Howrah, India
raw_author_name: Chandan Kumar Chanda
raw_affiliation_string: Electrical Engineering Department, Indian Institute of Engineering Science and Technology, Shibpur, Howrah, India

Abstract: For the development of renewable energy generation as a viable alternative to fossil fuel-based generation, solar power has received widespread acceptance. An increase in solar power usage can mitigate the impact of climate change, energy demand, and cost-effective dispatch. To use solar power efficiently and ensure grid consistency, reliable and accurate forecasting of information becomes very crucial. Hence, the main purpose of this work is to get the best suitable solar irradiance forecasting model and to implement the forecasted solar power to the designed hybrid battery swapping stations. Each year, new techniques and approaches are used to improve the model accuracy with the critical aim of lowering the variability of predictions. This paper takes a close look at different deep-learning methods to predict solar irradiance for a certain period of time. The real-time time series data is used, and for analyzing the time series data, the statistical ARIMA model, LSTM-based RNN technique, and Dual Attention-based Recurrent Neural network have been used. These models are implemented using Jupyter Notebook with Python programming language. To analyze the efficiency and evaluate the performance of real-time data of the models, a comparative study has been done on the different error metrics. This paper reveals that the LSTM model is providing the best solar power forecasting with the least error metrics like MSE of .0091, MAE of .0525, RMSE of .0953, and MSLE of .0047.
High-level noise load identification based on Chebyshev polynomial piecewise fitting

Authors:
raw_author_name: Yu Cui
raw_affiliation_string: Northeast Petroleum University (China)
raw_author_name: Wei Gao
raw_affiliation_string: Northeast Petroleum University (China)

Abstract: When the telemetry data noise level is high, the traditional integral moving average processing model has a general effect on noise reduction. In order to improve the noise reduction ability and obtain higher load identification accuracy, a new noise reduction model is constructed based on the Chebyshev orthogonal polynomial and the idea of piecewise overlap. The impact load is simulated by half sine wave. Under the condition of 15 % noise, the error values of load identification results are 1.94 % and 0.76 % respectively after the treatment of integral sliding noise reduction model and Chebyshev noise reduction model. In contrast, the new Chebyshev noise reduction model under high-level noise conditions has better accuracy and wider application.
Webshell combination detection method based on Naïve Bayes and decision tree

Authors:
raw_author_name: Fei Ren
raw_affiliation_string: State Information Center (China)

Abstract: The paper aims to enhance the recall rate of Webshell detection methods based on machine learning algorithms. It covers a broad research scope on Webshells, with a focus on Webshells as the research object. The research employs a combination of the Naive Bayes and Decision Tree methods, extracting and analyzing static features of Webshells. Significant conclusions regarding the improvement of recall rates are drawn from this analysis. Experimental validation conducted on publicly available datasets demonstrates that the proposed method significantly increases the recall rate of the Webshell detection model while maintaining a low false-positive rate. This improvement holds crucial significance for enhancing the security of web applications and servers, offering a feasible research approach in the field of Webshell detection.