Ensuring user-centered phishing detection is a significant challenge due to the difficulty in distinguishing threats. To address this, we propose a personalized tool - Holistic User-Centered Identification of Threats ...
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The apparent phenomena of commodity price fluctuations significantly affect the cost of living. Most current studies utilize datasets collected before the Russo-Ukrainian War and Covid-19. Moreover, many people are fo...
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ISBN:
(数字)9789819720279
ISBN:
(纸本)9789819720262
The apparent phenomena of commodity price fluctuations significantly affect the cost of living. Most current studies utilize datasets collected before the Russo-Ukrainian War and Covid-19. Moreover, many people are focusing on fund investment, exploring avenues such as commodity trading in addition to stocks and forex investments. However, most research for price prediction in commodities does not cover the periods of Covid-19 and the Russo-Ukrainian war. The aim of this project is to develop trading strategy models to predict whether to buy or sell a commodity, and to evaluate the potential rewards and profits. The dataset used contains daily historical prices of various types of commodities from the year 2000 until March 2022. Furthermore, a real-world dataset, specifically the gold trading dataset from Nasdaq, will be used to validate the performance of the best-performing trading models. The algorithms employed are reinforcement learning-based: Advantage Actor Critic (A2C) and Proximal Policy Optimization (PPO). Evaluation performance across six rounds of experiments has shown that the A2C model in a forex environment, using 80% of the dataset for training and 20% for testing, achieved the best results, with a Sharpe ratio of 0.63, a Sortino ratio of 1.0, an Omega ratio of 1.24, and a Calmar ratio of 0.55. The best-performing trading models in Objective 2 and Objective 3 are similar but employ different window sizes. Window size specifies the timesteps that will serve as reference points for the trading model to determine the next trade. Different datasets may require different window sizes, an issue that necessitates further refinement. This refinement is crucial as it involves tailoring the window size to align with the unique characteristics and volatility patterns of each dataset, thereby ensuring that the model's predictive accuracy is optimized for varied market conditions and historical trends. In conclusion, the best-performing trading model is the Advan
What makes a technology privacy-enhancing? In this study, we construct an explanation grounded in the technologies and practices that people report using to enhance their privacy. We conducted an online survey of priv...
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Sleep is a crucial bodily process that plays a vital role in maintaining overall health and well-being. When diagnosing and treating sleep disorders, the initial step is sleep staging. However, manual sleep staging by...
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Social engineering through online conversations can occur via phone calls, Skype, or Google Meet, among others. This paper presents a machine learning-based classifier for detecting scam conversations in various onlin...
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The present study introduces a novel approach to identifying the maturity of cacao fruits, utilizing YOLOv5s in tandem with the Convolutional Block Attention Module (CBAM). The efficacy of this method was evaluated on...
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Advanced Very High Resolution Radiometer(AVHRR)onboard National Oceanic and Atmospheric Administration(NOAA)satellites can provide over 40 years of global remote sensing observations,which can be used to retrieve long...
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Advanced Very High Resolution Radiometer(AVHRR)onboard National Oceanic and Atmospheric Administration(NOAA)satellites can provide over 40 years of global remote sensing observations,which can be used to retrieve long-term aerosol optical depth(AOD).This is of great significance to the study of global climate *** this paper,we proposed an algorithm to jointly calculate AOD and land surface properties from AVHRR *** assumptions that AOD doesn’t vary in adjacent space and earth surface property doesn’t vary in two days,the algorithm considered non-Lambertian surface reflection based on the shape of bidirectional reflectance distribution function(BRDF shape)and obtained AOD and surface property by optimal estimation(OE)*** algorithm has been applied to NOAA-7,9,11,14,16,18,and 19 satellites and AVHRR-retrieved AOD with 5×10 km over China(15°–60°N,70°–140°E)has been obtained from 1982 to *** of AVHRR-retrieved AOD against AErosol RObotic NETwork(AERONET)(in and around China)and China Aerosol Remote Sensing Network(CARSNET)AOD show good consistency with 62.62%points within the uncertainty ofΔτ=±(0.05+0.25τ)and root-mean-square error(RMSE)of *** comparison of the monthly mean AOD of multiple AOD datasets in the‘Beijing’,‘Dalanzadgad’,‘NCU_Taiwan’and‘Kanpur’stations shows that the results of the algorithm are *** yearly averaged AOD data also has similar agreements with MERRA-2(The Modern-Era Retrospective analysis for Research and Applications,Version 2)and AVHRRDB data(AVHRR‘Deep Blue’aerosol data set).The multi-year mean correlation coefficient is 0.70 and 0.61 and the percentages within the uncertainty are 80.01%and 67.29%compared with MERRA-2 AOD and AVHRRDB AOD respectively.
Recently, the Electric Vehicle (EV) market has experienced significant growth and is projected to expand exponentially with the advancement of technology. Major industries are increasingly adopting the concept of prod...
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Many publicly available datasets exist that can provide factual answers to a wide range of questions that benefit the public. Indeed, datasets created by governmental and nongovernmental organizations often have a man...
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Cyberbullying is a form of harassment or bullying that takes place online or through digital devices like smartphones,computers,or *** can occur through various channels,such as social media,text messages,online forum...
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Cyberbullying is a form of harassment or bullying that takes place online or through digital devices like smartphones,computers,or *** can occur through various channels,such as social media,text messages,online forums,or gaming *** involves using technology to intentionally harm,harass,or intimidate others and may take different forms,including exclusion,doxing,impersonation,harassment,and ***,due to the rapid growth of malicious internet users,this social phenomenon is becoming more frequent,and there is a huge need to address this ***,the main goal of the research proposed in this manuscript is to tackle this emerging challenge.A dataset of sexist harassment on Twitter,containing tweets about the harassment of people on a sexual basis,for natural language processing(NLP),is used for this *** algorithms are used to transform the text into a meaningful representation of numbers for machine learning(ML)input:Term frequency inverse document frequency(TF-IDF)and Bidirectional encoder representations from transformers(BERT).The well-known eXtreme gradient boosting(XGBoost)ML model is employed to classify whether certain tweets fall into the category of sexual-based harassment or ***,with the goal of reaching better performance,several XGBoost models were devised conducting hyperparameter tuning by *** this purpose,the recently emerging Coyote optimization algorithm(COA)was modified and adjusted to optimize the XGBoost ***,other cutting-edge metaheuristics approach for this challenge were also implemented,and rigid comparative analysis of the captured classification metrics(accuracy,Cohen kappa score,precision,recall,and F1-score)was ***,the best-generated model was interpreted by Shapley additive explanations(SHAP),and useful insights were gained about the behavioral patterns of people who perform social harassment.
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