Reviewerly/exHarmony huggingface dataset:
A Machine Learning Approach to Improve the Usability of Severe Thunderstorm Wind Reports

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

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

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

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

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

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

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

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?

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

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

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

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.
Aging with robots: a brief review on eldercare automation

Abstract Robotic solutions designed to cater to the needs of the elderly, commonly known as eldercare robots or nursing robots, hold the promise of delivering intelligent elderly care, alleviating societal caregiving pressures, and reducing financial burdens on nations. Scholars across various disciplines have delved into the realm of eldercare robotics from different perspectives. Four types of robots at the design level are currently used in the elderly care industry: anthropomorphic, zoomorphic, cartoon, and mechanical-functional. They can play such roles as assistants, companions, and even surrogate pets, providing support for the elderly’s daily routines, cognitive enhancement, and emotional well-being. Acceptance of eldercare robots hinges on three key factors: technical attributes, user-specific characteristics, and the surrounding social environment. The utilization of eldercare robots has the potential to positively impact various aspects of the elderly population, such as their physiological health, cognitive abilities, psychological well-being, and social interactions. However, it can also lead to social isolation, reduced autonomy, increased sense of objectification, blurred responsibility attribution, and tendencies towards deceptive and childish behavior. Additionally, eldercare robots also influence healthcare professionals in terms of workload, working conditions, job satisfaction and sense of purpose, both positively and negatively. This paper examines these research findings within the context of theories in communication, technology ethics, and medical ethics, shedding light on the multifaceted landscape of eldercare robotics.
A dependence graph pattern mining method for processor performance analysis

As the complexity of processor microarchitecture and applications increases, obtaining performance optimization knowledge, such as critical dependent chains, becomes more challenging. To tackle this issue, this paper employs pattern mining methods to analyze the critical path of processor micro-execution dependence graphs. We propose a high average utility pattern mining algorithm called Dependence Graph Miner (DG-Miner) based on the characteristics of dependence graphs. DG-Miner overcomes the limitations of current pattern mining algorithms for dependence graph pattern mining by offering support for variable utility, candidate generation using endpoint matching, the adjustable upper bound, and the concise pattern judgment mechanism. Experiments reveal that, compared with existing upper bound candidate generation methods, the adjustable upper bound reduces the number of candidate patterns by 28.14% and the running time by 27% on average. The concise pattern judgment mechanism enhances the conciseness of mining results by 16.31% and reduces the running time by 39.82%. Furthermore, DG-Miner aids in identifying critical dependent chains, critical program regions, and performance exceptions.
Modeling arithmetic systems of elliptic curve cryptography using Microsoft Excel VBA

Purpose. This study aims to develop a new teaching module to illustrate the arithmetic systems of Elliptic Curve Cryptography, a powerful yet simple algorithm for information security, by exploring the capability of the Visual Basic Applications of Microsoft Excel in user friendly way. Methodology. The research is performed using research and development approach, which is divided into five steps utilizing VBA features of Microsoft Excel. It starts with modeling arithmetic in Microsoft Excel spreadsheet, then testing the validity through calculation and setup of the actual arithmetic of Elliptic Curve Cryptography using VBA Excel, before performing the test of the VBA application and finally visualizes the results in graphical mode. Findings. Novel teaching software based on of Microsoft Excel Visual Basic Applications is produced that is able to simulate arithmetic system behind Elliptic Curve Cryptography in an easy way for students. Originality. To the best of the authors’ knowledge, this is the first simulation based on Excel VBA to illustrate the arithmetic systems of Elliptic Curve Cryptography for teaching purposes. Practical value. In general, mastering cryptography will need a steep learning curve; however, using Microsoft Excel as a simulation platform will accelerate learning. The main practical value is the ease of Microsoft Excel, which will turn cryptography learning which was commonly very difficult for student to become easier and user friendly.
Petrography and Heavy Minerals as Tools for Provenance Identification of the Injana Sandstone (Upper Miocene), Al-Habbaniyah City, Anbar Governorate, Iraq

This study deals with the petrography and heavy mineral analysis of the Injana sandstone at Al-Habbaniyah City. Thirteen samples are collected from the Injana Formation. A petrographic study is achieved on ten samples while all samples are analyzed for heavy minerals. The petrographic analysis showed that the common components of the Injana sandstone are rock fragments, especially sedimentary fragments. Quartz grains (monocrystalline and polycrystalline) and feldspars (k-feldspar and plagioclase), bounded by matrix and carbonate cement. The suggested provenance for the Injana Formation is mainly sedimentary and igneous sources and the less dominant metamorphic source. These sandstones are considered to be mineralogically submature-immature and classified as litharenites. Heavy minerals analysis showed two sets of minerals including opaque and transparent minerals, the opaque minerals are significant components of the heavy mineral assemblages, then chlorite, epidotes, amphiboles, garnet, pyroxenes (ortho and clino), zircon, tourmaline, biotite, muscovite, rutile, and others. This assemblage implies that the dense minerals are typically found in basic igneous and metamorphic rocks, with less frequent occurrences in acidic igneous and reworked sediments. According to QLF and QmFLt classifications, the tectonic provenance of the Injana sandstone is described as recycled orogen. The MF-MT-GM classification showed that the sandstone of Injana is derived primarily from active continental margins. The result of maturity MI and ZTR revealed low values of maturity, the MI value ranges between 0.6 to 1.5 with an average of 0.9 and ZTR Maturity ranges between 0.7% and 9% with an average of 3.9%, these low values of maturity indicate sub-mature to immature sandstone.
Applying the world's advanced models of cloud-based e-learning training to foreign language teaching at Vietnam universities today

E-learning is a common inevitable trend of the world education in the context of globalization, especially after the Covid-19 epidemic had broken out and has not yet completely ended. Currently, cloud-based training is growing more and more strongly in the background of the 4.0 technology revolution. Recognizing the importance of that issue, this paper studies some typical cloud-based e-learning training models in the world, and applies them to foreign language teaching at Vietnamese universities nowadays.
Systematic Design of a Natural Sciences Collections Digitisation Dashboard

This paper describes the design and build of a pilot Natural Sciences Collections Digitisation Dashboard (CDD). The CDD will become a key service for the Distributed System of Scientific Collections Research Infrastructure (DiSSCo) and aims to improve the discoverability of natural science collections (NSCs) held in European institutions, both digitised and undigitised. Furthermore, it will serve as a dynamic visual assessment tool for strategic decision-making, including the prioritisation of digitisation. The CDD pilot includes high-level information from nine European NSCs, covering the number of objects, taxonomic scope, storage type, chronostratigraphy (Earth Science Collections), geographical region and level of detail in digitisation. This information is structured through a standardised Collection Classification Scheme, which uses high-level categorisation to describe physical natural science collections.