Miracle-dz/UniFAR-FTUs huggingface dataset:
Brazilian Automatic License Plate Recognition: On the Importance to Add Vehicle Detection Phase in a Deep Learning Approach

In recent years, surveillance cameras have found wide-ranging applications, particularly in automatic vehicle license plate recognition to enhance security. One of the major problems of performing vehicle license plate recognition is the wide variety of existing license plate standards around the world, only in Brazil there are two standards, in addition, automatic license plate recognition depends directly on the quality of the input image. The objective of this paper is to evaluate whether adding the vehicle detection step improves license plate recognition rates in a deep learning approach. The method was implemented using the YOLOv8s model in all stages, however, two different approaches were proposed, one with and the other without the vehicle detection stage, the database used was the UFPR-ALPR, which only presents plates of the old Brazilian standard of 3 letters and 4 numbers.
Unsupervised Models with LOF and PCA for Robust DDOS Attack Detection

The need for robust and efficient Distributed Denial of Service (DDoS) attack detection methods has become increasingly evident in today's networked world. Existing approaches, while commendable, often exhibit limitations that hinder their effectiveness. This paper introduces an innovative approach that leverages Local Outlier Factor (LOF) in conjunction with Principal Component Analysis (PCA) for unsupervised DDoS attack detection. Existing methods often fall short in addressing the evolving nature of DDoS attacks, struggling to maintain precision, accuracy, and recall rates. Our proposed model addresses these limitations by harnessing the power of LOF and PCA, offering a more adaptive and dynamic detection framework. LOF enables the identification of outliers in network traffic patterns, while PCA reduces the dimensionality of the data, enhancing the model's efficiency and speed. The advantages of our approach are manifold. It not only achieves superior performance in terms of precision, accuracy, recall, speed, and Area Under the ROC Curve (AUC) when compared to existing methods but also ensures a higher level of adaptability in detecting emerging DDoS attack vectors & sets. This adaptability is critical in today's ever-evolving threat landscape. The impact of this work extends beyond the realm of academia. In practical terms, our model offers network administrators a potent tool for safeguarding their infrastructures against DDoS attacks. By improving detection rates and reducing false positives, it contributes to the overall security posture of networks, ensuring uninterrupted services and enhanced user experience levels.
An Interdisciplinary Approach to Enhancing Cyber Threat Prediction Utilizing Forensic Cyberpsychology and Digital Forensics

The Cyber Forensics Behavioral Analysis (CFBA) model merges Cyber Behavioral Sciences and Digital Forensics to improve the prediction and effectiveness of cyber threats from Autonomous System Numbers (ASNs). Traditional cybersecurity strategies, focused mainly on technical aspects, must be revised for the complex cyber threat landscape. This research proposes an approach combining technical expertise with cybercriminal behavior insights. The study utilizes a mixed-methods approach and integrates various disciplines, including digital forensics, cybersecurity, computer science, and forensic psychology. Central to the model are four key concepts: forensic cyberpsychology, digital forensics, predictive modeling, and the Cyber Behavioral Analysis Metric (CBAM) and Score (CBS) for evaluating ASNs. The CFBA model addresses initial challenges in traditional cyber defense methods and emphasizes the need for an interdisciplinary, comprehensive approach. This research offers practical tools and frameworks for accurately predicting cyber threats, advocating for ongoing collaboration in the ever-evolving field of cybersecurity.
A Holistic Approach to Upwelling and Downwelling along the South-West Coast of India

Abstract An attempt has been made to develop a holistic understanding of upwelling and downwelling along the south-west coast of India. The main objective was to elucidate the roles of different forcings involved in the vertical motion along this coast. The south-west coast of India was characterized by upwelling during the south-west monsoon (May to September) and by downwelling during the north-east monsoon and winter (November to February). The average vertical velocity calculated along the south-west coast from the vertical shift of the 26 °C isotherm is 0.57 m/day during upwelling and 0.698 m/day during downwelling. It was concluded that upwelling along the south-west coast of India is driven by offshore Ekman transport due to the alongshore wind, Ekman pumping, horizontal divergence of currents and by the propagation of coastally trapped waves. Whereas downwelling along the coast is driven only by convergence of currents and the propagation of coastally trapped Kelvin waves. Along the west coast of India, the downwelling-favorable Kelvin waves come from the equator and upwelling-favorable waves come from the Gulf of Mannar region.
UNDERSTANDING OF FIRE DISTRIBUTION IN THE SOUTH SUMATRA PEAT AREA DURING THE LAST TWO DECADES

Peat fire is an annual catastrophic event in Indonesia, particularly in South Sumatra region during the dry season. Several previous studies have suggested that anthropogenic forces (land cover) contribute to peat fires. Analysis of the hotspots distribution on land cover types is needed to address peat fires in the futures. This study is aimed to evaluate the distribution of hotspots on the land cover type of the South Sumatra peat area during the last two decades (1997-2016). Remote sensing technique with visual image interpretation method using multitemporal satellite images is used to identify the variations of land cover in the study area. The results showed that the peat area of South Sumatra in 2013 dominated by ferns/ shrub (58%) and only about 10% covered by peat swamp forest (PSF). The largest hotspot distribution during the observation periods was spread in land cover types of Ferns/ shrub (34.808 hotspots) and Industrial Plantation (7.223 hotspots). Note that the densest hotspot located in the industrial plantation with an average of 37 hotspots/ 100 km2 / year. Meanwhile, only a small percentage of the hotspots located in PSF and occurred consistently over the last five years of observation periods. Based on results, the South Sumatra peatland is very vulnerable to burn during the dry season in the future. The government as policy maker must protect the existence of PSF and keep the ferns/ shrub from the fires and subsequent land cover changes (Industrial plantation or small holder area).
Seismic Imaging of Lithosphere Structure and Upper Mantle Deformation Beneath East‐Central China and Their Tectonic Implications

We conduct a nonlinear joint inversion of receiver functions and surface waves to obtain the lithosphere structure in east‐central China. We also use SKS splitting measurements to evaluate upper mantle deformation. The velocity model reveals that, to the east of the North‐South Gravity Lineament, the crust and lithosphere are significantly thinned. Three extensive crustal/lithospheric thinning subregions are identified. This indicates that the lithospheric modification in east‐central China is nonuniform due to heterogeneity of lithospheric strength. Extensive crustal and lithospheric thinning probably occurs in some weak zones such as basin‐range junction belts and large faults. The structure beneath the Dabie orogenic belt is complex due to the collision between the North and South China Blocks during the Late Paleozoic‐Triassic. The Dabie orogenic belt is delineated by a thick crust with a midcrust low‐velocity zone and a two‐directional convergence in the lithospheric scale. The crust and uppermost mantle across the Tanlu fault exhibit a strong seismic velocity contrast, which suggests deep penetration of this lithospheric‐scale fault. Most of our splitting measurements show nearly E‐W trending fast polarization. The similar present‐day lithosphere structure and upper mantle deformation may imply that the eastern North China Craton and South China Block have been dominated by a common dynamic process since the Late Mesozoic. The westward subduction of the Philippine plate and the long‐range effects of the collision between the Indian and Eurasia plates during the Cenozoic may have also contributed to the present velocity structure and stress field of eastern China.
How Does Interactivity Shape Users’ Continuance Intention of Intelligent Voice Assistants? Evidence from SEM and fsQCA

Purpose With the rapid expansion in the use of intelligent voice assistants (IVAs) in people’s daily lives, how to improve users’ continuous intention is crucial for the sustainable development of intelligent voice technology. Utilizing the stimulus-organism-response (S-O-R) framework, we propose a theoretical model to examine how three dimensions of interactivity (ie, two-way communication, responsiveness, perceived control) impact individuals’ affective reactions (ie, psychological ownership, subjective well-being) and continuance intention of IVAs and how that effect differs technology readiness. Methods To validate the proposed model, 412 valid samples were collected in China and underwent analysis using a comprehensive approach that incorporated partial least squares-structural equation modeling (PLS-SEM) and fuzzy-set qualitative comparative analysis (fsQCA). Results The findings from the PLS-SEM analysis indicate that three dimensions of interactivity have significant impacts on affective reactions to varying degrees, thus affecting users’ continuance intention. Among these dimensions, responsiveness is the strongest predictor of affective reactions. Additionally, the impact of subjective well-being on continuance intention is stronger when users with high technology readiness. Finally, the results from fsQCA support the PLS-SEM findings and provide three configurations with different combinations of antecedents that sufficiently explain high continuance intention. Conclusion Our findings reveal the internal mechanisms through which the three dimensions of interactivity impact users’ continued usage of IVAs. This study is among the first to examine the effects of dimensions of interactivity on behavioral intentions, utilizing both symmetric (PLS-SEM) and asymmetric (fsQCA) methodologies to identify the most significant dimensions of interactivity and determine sufficient combinations of dimensions to predict users’ intention to continue using IVAs. These findings offer valuable and fresh insights for both theoretical understanding and practical application.
On the Sensitivity of Hillslope Runoff and Channel Transmission Losses in Arid Piedmont Slopes

Channel transmission losses alter the streamflow response of arid and semiarid watersheds and promote focused groundwater recharge. This process has been primarily studied in dryland channels draining large areas that are displaced away from hillslope runoff generation. In contrast, small watersheds on arid piedmont slopes allow the investigation of interactive hillslope and channel processes that control the partitioning between surface and subsurface flows. In this study, we utilize high‐resolution, long‐term measurements of water balance components in an instrumented watershed of the Chihuahuan Desert to set up, parameterize, and test a process‐based, distributed hydrologic model modified to account for channel losses. A transient method for capturing capillary effects in channels results in simulations with a reliable representation of the watershed energy balance, soil moisture dynamics, hillslope infiltration, channel transmission (or percolation) losses, and streamflow yield over the study period. The simulation also reproduces a conceptual model of hillslope infiltration‐excess runoff generation linked to downstream channel percolation losses that depend on the rainfall event size. Model‐derived thresholds were obtained for the amount of hillslope runoff (6 mm) and rainfall (12.5 mm) necessary for streamflow yield, such that 40% of percolation occurs for small events that do not reach the outlet. Using a set of scenarios, we identify that hillslope infiltration controls the rainfall threshold necessary to initiate percolation, while channel infiltration affects the partitioning into percolation and streamflow yield. Thus, the connectivity along hillslope‐channel pathways is deemed an essential control on the streamflow generation and groundwater recharge in arid regions with complex terrain.
Prediction of Soil Expansivity with Direct and Indirect Method in Banten Region, Indonesia

Expansive soils are widespread in Indonesia, particularly in Banten Province, causing extensive road damages. The study aimed to predict the soil expansivity of soil subgrade along the damaged road segments in Malingping, Cikeusik-Cibaliung, and Labuan districts. Indirect and direct expansivity predictions were employed to determine soil swelling potential. Indirect expansivity prediction was conducted based on various criteria using the Atterberg limit and grain size distribution. Direct expansivity prediction was conducted using an oedometer apparatus to measure the expansion index according to ASTM D-4829. A dynamic cone penetration test was also conducted to measure CBR values. Various indirect methods showed inconclusive results of swelling potentials, low to very high. The direct method provided more conclusive results, Expansivity Index = 100-167 and swelling values = 9%-18%, thus, categorized as expansive clay (high to very high swelling potential). CBR values were 1.49%-5% which is lower than minimum provisioned CBR (6%).
Local variation in the shape of superimposed bed forms as a function of local bathymetry

The introduction of state-of-the-art measurement techniques has lead to large amounts of accurate bathymetric data. Multi-beam swath systems measure bands of seabed on high spatial resolutions. Knaapen et al. (2005) for example achieve a 0.3m resolution on 25 meters depth. When combined met high precision Ernstsen (2006), this gives impressi ve detailed and accurate bed levels of the sea bed. So far however, these data are not analyzed to their f ull potential. Approaches are missing to extract the es sential information from the data. This gap in knowledge is very obvious in the analysis of bed forms on the sea bed. Often, they are used only to visualize the subject, or analyzed by hand to deriv e information that could be gathered with single-beam echo sounders. Recently, progress has been made. Knaapen et al (2005), Knaapen (2005) and Ernstsen (2006a), all determine bed form migration for multiple crests, r elating it to the height and wavelength of the bed forms. The approach proposed by Knaapen (2005) allows this analysis to be applied to very large amounts of bed form crests. This paper presents an improved and extended analysis technique that gives an improved height estimate and gives additional in formation on the shape and orientation of the bed forms. The sandy beds of shallow seas are covered by a wide range of rhythmic bed forms. These patterns range from the very small ripples that can be found at the beach near the water line, to the largest ti dal sand banks that are well known because of their ris k for ships. In between, a range of wavy patterns exi t, which all vary in wavelength, height and orientatio n (Table 1). We will apply the proposed data analysis technique to mega-ripples, sand waves and sand banks. This approach leads to the amounts of information on the bed patterns that can be used to determine statistical information on the properties o f the bed forms in the field. The interpretation of t hese statistics results in some challenging findings. Section 1 describes the different steps in the approach to analyze bed forms. Section 2 presents the data to which the analysis approach is being applie d. The results are given in section 3 and discussed in section 4. Finally, section 5 summarises the conclu sion from the research. Local variation in the shape of superimposed bed fo rms as a function of local bathymetry
Greenhouse Gas Emission Reduction Architecture in Computer Science: A Systematic Review

Computer Science Architecture (CSA), encompassing data, application, technology, and business architecture, is a vital tool for addressing climate change challenges. It aims to reduce greenhouse gas emissions from sectors such as stationary energy, transportation, industry, product use, waste, and land use. CSA principles extend to advanced technologies like artificial intelligence (AI), the Internet of Things (IoT), Machine Learning (ML), data centers, blockchain, multi-agent systems, sensors, and smart grids. This review explores CSA’s role in mitigating greenhouse gas emissions for a sustainable environment. It analyzes implications, indicators, and methodologies for data, application, technology, and business architecture, highlighting their direct impact on creating an environmentally friendly technological landscape. The study also delves into emerging trends and suggestions for future research, contributing to the discourse on leveraging technology for a greener future.
Computational Investigation of the Influence of Combustion Chamber Characteristics on a Heavy-Duty Ammonia Diesel Dual Fuel Engine

In response to increasingly stringent emissions regulations and the depletion of conventional fuel sources, integrating carbon-free fuels into the transport sector has become imperative. While hydrogen (H2) presents significant technical challenges, ammonia (NH3) could present a better alternative offering ease of transport, storage, and distribution, with both ecological and economic advantages. However, ammonia substitution leads to high emissions of unburned NH3, particularly at high loads. Combustion chamber retrofitting has proven to be an effective approach to remedy this problem. In order to overcome the problems associated with the difficult combustion of ammonia in engines, this study aims to investigate the effect of the piston bowl shape of an ammonia/diesel dual fuel engine on the combustion process. The primary objective is to determine the optimal configuration that offers superior engine performance under high load conditions and with high ammonia rates. In this study, a multi-objective optimization approach is used to control the creation of geometries and the swirl rate under the CONVERGETM 3.1 code. To maximize indicated thermal efficiency and demonstrate the influence of hydrogen enrichment on ammonia combustion in ammonia/diesel dual fuel engines, a synergistic approach incorporating hydrogen enrichment of the primary fuel was implemented. Notably, the optimum configuration, featuring an 85% energy contribution from ammonia, outperforms others in terms of combustion efficiency and pollutant reduction. It achieves over 43% reduction in unburned NH3 emissions and a substantial 31% improvement in indicated thermal efficiency.
Productive Development Policies in Costa Rica: Market Failures, Government Failures, and Policy Outcomes

This paper analyzes five Productive Development Policies (PDPs) implemented in Costa Rica, finding that they are not optimally addressing market failures. Moreover, government failures rather than market failures represent the main justification for PDPs. Even in the presence of market failures, the policy instruments applied are not necessarily the most economically efficient but rather the most politically feasible options. In addition, the lack of policy evaluation and monitoring prevents adjustments and corrections of such policies. Addressing the arguments for policy intervention and incorporating the results of evaluation into policy design and reform are necessary conditions for success. In spite of positive policy outcomes, limitations to enhance competitiveness and create the conditions for productivity growth are still present. An umbrella approach in the case of those PDPs that reinforce each other is necessary for productivity growth.
Forecasting Rapid Drought Intensification Using the Climate Forecast System (CFS)

In this study, a statistical method is developed to generate probabilistic forecasts of U.S. Drought Monitor (USDM)‐depicted drought intensification over two‐, four‐, and six‐week time periods using recent observations and forecast model output from the Climate Forecasting System (CFS). The predictors used include weekly anomalies in precipitation, potential evapotranspiration, dew point depression, and soil moisture computed over different time lags. A comparison between the baseline skill obtained using recent observations only and the skill obtained by adding CFS forecast fields as predictors shows that the inclusion of CFS model output leads to only a very modest increase in skill (about 14% increase in variance explained over the central and eastern United States). An analysis of this result reveals that the small increase in skill is due to limited skill in the CFS forecasts themselves, rather than to a time delay in the USDM response to conditions on the ground. Perfect model experiments also show that not all forecast lead times are equally important. For example, in the upper Midwest and western United States, the first two weeks account for at least two thirds of the total realizable skill for a four‐week forecast.
ResearchOnline@ND ResearchOnline@ND

Background . Studies attempting to elucidate an association between homocysteine and symptom progression in Parkinson’s disease (PD) have had largely discrepant ﬁndings. This study aimed to investigate elevated serum homocysteine levels and symptom progression in a cohort of PD patients. Methods . Serum homocysteine, folate
The 2009 Recovery Act and the Expected Inflation Channel of Government Spending∗

There exist sticky price models in which the output response to a government spending change can be large if the central bank is nonresponsive to inflation. According to this “expected inflation channel,” government spending drives up expected inflation, which in turn, reduces the real interest rate and leads to an increase in private consumption. This paper examines whether the channel was important in the post-WWII U.S., with particular attention to the 2009 Recovery Act period. First, we show that a model calibrated to have a large output multiplier requires a large response of expected inflation to a government spending shock. Next, we show that this large response is inconsistent with structural vector autoregression evidence from the Federal Reserve’s passive policy period (1959-1979). Then, we study expected inflation measures during the Recovery Act period in conjunction with a panel of professional forecaster surveys, a cross-country comparison of bond yields and fiscal policy news announcements. We show that the expected inflation response was too small to engender a large output multiplier.
Research Division Federal Reserve Bank of St . Louis Working Paper Series Advances in Forecast Evaluation

This paper surveys recent developments in the evaluation of point forecasts. Taking Wests (2006) survey as a starting point, we briey cover the state of the literature as of the time of Wests writing. We then focus on recent developments, including advancements in the evaluation of forecasts at the population level (based on true, unknown model coe¢ cients), the evaluation of forecasts in the nite sample (based on estimated model coe¢ cients), and the evaluation of conditional versus unconditional forecasts. We present original results in a few subject areas: the optimization of power in determining the split of a sample into in-sample and out-of-sample portions; whether the accuracy of inference in evaluation of multi-step forecasts can be improved with judicious choice of HAC estimator (it can); and the extension of Wests (1996) theory results for population-level, unconditional forecast evaluation to the case of conditional forecast evaluation. JEL Nos.: C53, C12, C52