Miracle-dz/UniFAR-FTUs huggingface dataset:
Accelerating Pattern Matching Using a Novel Multi-Pattern-Matching Algorithm on GPU

Nowadays, almost all network traffic is encrypted. Attackers hide themselves using this traffic and attack over encrypted channels. Inspections performed only on packet headers and metadata are insufficient for detecting cyberattacks over encrypted channels. Therefore, it is important to analyze packet contents in applications that require control over payloads, such as content filtering, intrusion detection systems (IDSs), data loss prevention systems (DLPs), and fraud detection. This technology, known as deep packet inspection (DPI), provides full control over the communication between two end stations by keenly analyzing the network traffic. This study proposes a multi-pattern-matching algorithm that reduces the memory space and time required in the DPI pattern matching compared to traditional automaton-based algorithms with its ability to process more than one packet payload character at once. The pattern-matching process in the DPI system created to evaluate the performance of the proposed algorithm (PA) is conducted on the graphics processing unit (GPU), which accelerates the processing of network packets with its parallel computing capability. This study compares the PA with the Aho-Corasick (AC) and Wu–Manber (WM) algorithms, which are widely used in the pattern-matching process, considering the memory space required and throughput obtained. Algorithm tables created with a dataset containing 500 patterns use 425 and 688 times less memory space than those of the AC and WM algorithms, respectively. In the pattern-matching process using these tables, the PA is 3.5 and 1.5 times more efficient than the AC and WM algorithms, respectively.
Dynamical Study of the Exoplanet Host Binary System HD 106515

Abstract HD 106515 AB (STF1619 AB) is a high common proper motion and common radial velocity binary star system composed of two G-type bright stars located at 35 pc and separated by about 7 arcsec. This system was observed by the Hipparcos satellite with a precision in distance and proper motion of 3 and 2%, respectively. The system includes a circumprimary planet of nearly 10 Jupiter masses and a semimajor axis of 4.59 AU, discovered using the radial velocity method. The observational arc of 21° shows a small curvature that evidences HD 106515 AB is a gravitationally bound system. This work determines the dynamical parameters for this system which reinforce the bound status of both stellar components. We determine orbital solutions from instantaneous position and velocity vectors. In addition, we provide a very preliminary orbital solution and a distribution of the orbital parameters, obtained from the line of sight (z). Our results show that HD 106515 AB presents an orbital period of about 4 800 years, a semimajor axis of 345 AU and an eccentricity of about 0.42. Finally, we use an N-body numerical code to perform simulations and reproduce the longer term octupole perturbations on the inner orbit.
Prediction of Undrained Lateral Capacity of Free-Head Rectangular Pile in Clay Using Finite Element Limit Analysis and Artificial Neural Network

: This paper is dedicated to investigating the undrained lateral capacity of rigid free-head rectangular/square piles in cohesive soil. The study utilizes the three-dimensional Finite Element Limit Analysis (FELA) framework to analyze and assess the pile's ability to withstand lateral loads. Both upper bound (UB) and lower bound (LB) FELA techniques are employed in the analysis process. The findings emphasize the importance of understanding the behavior and failure mechanisms exhibited by the surrounding soil around the pile. The analysis employs dimensionless approaches to obtain the normalized load factor, which represents the outcomes of the solution. The results obtained from the analysis demonstrate that the lateral load capacity of the soil is influenced by several key factors, including the pile's length-width ratio, the height-width ratio, the eccentricity of the lateral load, and the overburden stress where the discussion regarding the effects of all parameters is provided in the manuscript. The study also delves into the examination and understanding of the failure mechanisms exhibited by the soil in the context of lateral loaded piles. Based on the numerical outcome, the artificial neural network (ANN), which is one of soft-computing techniques, is utilized to establish a surrogate model for predicting the lateral capacity of rectangular piles. By considering these factors in the design process, engineers can make informed decisions that effectively optimize pile performance and ensure the long-term stability of structures.
Hydrodynamic performance optimization of marine propellers based on fluid-structure coupling

Fiber-reinforced composites offer the benefits of high strength, high stiffness, lightweight, superior damping performance, and great design capability when compared to metal. The rigidity characteristics of the composite laminate in different directions may be adjusted to meet the requirements of the application by using appropriate materials and arranging the lay-up sequence. As a result, the purpose of this work is to explore the influence of lay-up type on propeller performance in terms of both hydrodynamic and structural performance. A transient fluid-structure interaction (FSI) algorithm based on the finite element method (FEM) combined with the computational fluid dynamics (CFD) technique is developed and used for the analysis of composite propellers. The hydrodynamic performance of the propeller is compared to that of a metallic material. Propeller propulsion efficiency, structural deformation, equivalent stress, and damage performance of different lay-up options under three different operating situations are compared. In addition, it is presented a parametric optimization approach to get the most appropriate lay-up program for composite blades with the best hydrodynamic properties and structural performance.
Impacts of contract farming on asset accumulation of malt barley farmers in Northwestern Ethiopia

Abstract Agriculture is the mainstay of the Ethiopian economy. Barley, including food and malt barley, is one of the major cereals produced by smallholder farmers. Though malt barley is the fastest-growing industry, demand is outpacing supply. As a result, Ethiopia is a net importer of raw malt barley. Recently, contract farming was implemented to address this issue in malt barley-potential areas. It has been one of the strategies utilized to enhance the commercialization of malt barley and replace imported malt barley, besides solving production and marketing challenges. The predicted results also include satisfying domestic malt barley demand, enhancing the welfare of smallholder farmers, and conserving the nation’s foreign exchange. Although not thoroughly investigated, the research area has a low rate of CF participation and a low malt barley yield. Furthermore, previous research has focused on the CF impact of income, food security, and yield indicators of welfare, but no attention has been devoted to the implications of asset accumulation, which is a greater long-term indicator of welfare. Given the issues and research gaps identified, this study seeks to evaluate the impact of CF on malt barley farmers’ asset accumulation in Northwestern Ethiopia. The data were collected from 398 samples in two districts, selected using multistage sampling techniques. The endogenous switching regression (ESR) model was used to account for bias coming from both observable and unobservable sources. For malt barley farmers, the average treatment effect on treated and untreated was 9652.7ETB and 7417.3ETB, respectively, and was significant at 1%. The base heterogeneities for CF participation and non-participation were 886ETB and −1349.4ETB, respectively. The average transitional heterogeneity was 2235.4ETB and statistically significant at 1%. Consequently, malt barley CF participation has a positive association with asset accumulation; this result supports transaction cost economic theory. Cooperative membership, off-farm employment, and age were all positively associated with the propensity to increase asset accumulation. The finding also reveals that land affected participants, whereas credit, household size, and farming experiences had a significant and positive effect on non-participants’ assets. This shows that the asset accumulation of the two groups was determined by common variables with different magnitudes and different variables. Therefore, the concerned bodies should consider these heterogeneities to strengthen and improve the CF participation of malt barley farmers as long as they are adjusted to local conditions. Furthermore, spillover effects of CF on other sectors (other crops and the environment) and non-participants should be considered to check whether it has negative or positive external effects.
When the Few Outweigh the Many: Illicit Content Recognition with Few-Shot Learning

The anonymity and untraceability benefits of the Dark web account for the exponentially-increased potential of its popularity while creating a suitable womb for many illicit activities, to date. Hence, in collaboration with cybersecurity and law enforcement agencies, research has provided approaches for recognizing and classifying illicit activities with most exploiting textual dark web markets' content recognition; few such approaches use images that originated from dark web content. This paper investigates this alternative technique for recognizing illegal activities from images. In particular, we investigate label-agnostic learning techniques like One-Shot and Few-Shot learning featuring the use Siamese neural networks, a state-of-the-art approach in the field. Our solution manages to handle small-scale datasets with promising accuracy. In particular, Siamese neural networks reach 90.9% on 20-Shot experiments over a 10-class dataset; this leads us to conclude that such models are a promising and cheaper alternative to the definition of automated law-enforcing machinery over the dark web.
Adaptive Weighted Combination Approach for Wind Power Forecast Based on Deep Deterministic Policy Gradient Method

Accurate wind power forecast (WPF) is critical for ensuring secure and economic operation of a power system, and combination forecasting approaches for WPF have been proved effective on attaining accurate forecasting results. However, the weights in a combination forecasting model are usually predetermined based on the global performance on the training set, limiting the adaptability of the model to different scenarios. To further enhance the accuracy and effectiveness of the combination forecasting approaches, this article proposes an adaptive weighted combination forecasting approach based on the deep deterministic policy gradient (DDPG) so as to consider the local behavior accompanied by the change of external environment. Three sub-models are first selected considering the equal-likelihood and dispersion indices to construct a combination model. Then, the DDPG agent is trained to act as a weight generator by interacting with the environment and to maximize the reward. Thus, the DDPG agent can perceive the environmental changes online and dynamically weight the sub-models to attain accurate forecasting results. Case studies demonstrate that the forecasting accuracy of the proposed approach is better than that of all sub-models and several benchmark combination forecasting approaches.
The Role of Technostress on Educators' Work Performance at Universities in the Special Capital Region of Jakarta

The COVID-19 pandemic forced the education system to adapt a new teaching system from offline to online. However, the ability of lecturers is often unable to keep up with rapid technological developments and the lack of resources provided by institutions, causes lecturers to feel stressed during the teaching and learning process. This study aims to determine the effect of technostress on lecturer performance using the Person-Organization Fit theory approach. The results showed that at the organizational level, the discrepancy between abilities and demands (AD-O) (B= -.39; p.01) had a significant negative and positive effect on lecturer needs with supplies (NS-O) (B=. 24;p.05) with performance. At the individual level, the mismatch between needs and supply (NS-T) (B=-.51; p.01), the absence of social support from colleagues (PPF) (B=-.32; p.01) had an effect significant negative. The findings of this study indicate that both the organizational level and peer support (AD-O and PPF) as well as the individual level (NS-T) play an important role in predicting a decrease in lecturer performance, while the mismatch between ability demands at the individual level has no effect on performance. The implications of this research will be discussed further in the discussion.
Dynamical response of equatorial waves in variational data assimilation

A faithful depiction of the tropical atmosphere requires three-dimensional sets of observations. Despite the increasing amount of observations presently available, these will hardly ever encompass the entire atmosphere and, in addition, observations have errors. Additional (background) information will always be required to complete the picture. Valuable added information comes from the physical laws governing the flow, usually mediated via a numerical weather prediction (NWP) model. These models are, however, never going to be error-free, why a reliable estimate of their errors poses a real challenge since the whole truth will never be within our grasp. The present thesis addresses the question of improving the analysis procedures for NWP in the tropics. Improvements are sought by addressing the following issues:- the efficiency of the internal model adjustment, - the potential of the reliable background-error information, as compared to observations,- the impact of a new, space-borne line-of-sight wind measurements, and- the usefulness of multivariate relationships for data assimilation in the tropics.Most NWP assimilation schemes are effectively univariate near the equator. In this thesis, a multivariate formulation of the variational data assimilation in the tropics has been developed. The proposed background-error model supports the mass-wind coupling based on convectively-coupled equatorial waves. The resulting assimilation model produces balanced analysis increments and hereby increases the efficiency of all types of observations.Idealized adjustment and multivariate analysis experiments highlight the importance of direct wind measurements in the tropics. In particular, the presented results confirm the superiority of wind observations compared to mass data, in spite of the exact multivariate relationships available from the background information. The internal model adjustment is also more efficient for wind observations than for mass data. In accordance with these findings, new satellite wind observations are expected to contribute towards the improvement of NWP and climate modeling in the tropics. Although incomplete, the new wind-field information has the potential to reduce uncertainties in the tropical dynamical fields, if used together with the existing satellite mass-field measurements.The results obtained by applying the new background-error representation to the tropical short-range forecast errors of a state-of-art NWP model suggest that achieving useful tropical multivariate relationships may be feasible within an operational NWP environment.
Multivariate regression trees as an ‘ explainable machine learning ’ approach to exploring relationships between hydroclimatic characteristics and agricultural and hydrological drought severity

The typical causes of droughts are lower precipitation and/or higher than normal evaporation in a region. The region’s characteristics and anthropogenic interventions may enhance or alleviate these events. Evaluating the multiple factors that influence droughts is complex and requires innovative approaches. To address this complexity, this study employs a combination of modelling and machine learning tools to assess the relationship between hydroclimatic characteristics and the 15 severity of agricultural and hydrological droughts. The Soil Water Assessment Tool is used for hydrological modelling. Model outputs, soil moisture and streamflow are used to calculate the drought indicators for the subsequent drought analysis. Other simulated hydroclimatic parameters are treated as hydroclimatic drivers of droughts. A machine learning technique, the multivariate regression tree approach, is then applied to identify the hydroclimatic characteristics that govern agricultural and hydrological drought severity. The case study is the Cesar River basin (Colombia). 20 Our research indicates that multiple parameters influence the Cesar River basin’s exposure to agricultural and hydrological droughts. Accordingly, the basin can be divided into three distinct areas. First is the upper part of the river valley. Due to precipitation shortfalls and high potential evapotranspiration, this region is very susceptible to agricultural and hydrological droughts. The second area is the middle part of the river valley. This area is likewise very susceptible to agricultural and 25 hydrological droughts; however, severe drought conditions are brought on by inadequate rainfall partitioning and an unbalanced water cycle that favours water loss through percolation and evapotranspiration. Third, the Zapatosa marsh and the Serrania del Perijá foothills present moderate exposure to agricultural and hydrological droughts. Mild drought conditions appear to be related to the capacity of the subbasins to retain water, which lowers evapotranspiration losses and promotes percolation. Our results show that the presented methodology, in combining hydrologic modelling and machine learning
Can Virtual Influencers Replace Human Influencers in Live-Streaming E-Commerce? An Exploratory Study from Practitioners’ and Consumers’ Perspectives

Abstract We conducted expert interviews and a consumer experiment to investigate practitioners’ and consumers’ attitudes toward and adoption of virtual influencers to substitute human influencers in live-streaming e-commerce (LSC). Results of consumer data confirmed practitioner insights that virtual (versus human) influencers generated less positive attitudes and lower levels of perceived warmth, trust, usefulness, and dialogue. Contrary to practitioner insights, consumers did not perceive virtual influencers as more enjoyable, easier to use, more distant, and more likely to generate purchase intention than human influencers. Furthermore, influencer warmth (usefulness) is positively related to influencer attitude (purchase intention). Implications and limitations are discussed.
Measuring the Relationship Between Economic Crises and Investment Portfolios - A Sample Study of Iraqi Banks

Purpose: The study aimed to measure the closing price indicators for the Iraqi bank sector during the period (2010-2020) and using the PMG / ARDL index, which is an acronym for pooled mean group / Autoregressive distributed lag, i.e. self-regression to slow down. Distributed tablet data, to measure the relationship between the components of the economic crises of the dependent variable (closing price) of the Iraqi banks, the study sample, and the independent variables each of (gross domestic product, market value, global oil prices and economic crises). Theoretical framework: The Iraqi economy is affected by economic crises along with the performance of investment portfolios in Iraqi banks. Methodology of the study: To highlight the different conclusions taken by the banks that contribute In its success: Barony's co-integration test, according to the four formulas, shows that there is long-term integration between the dependent variable (closing price) and the independent variables (market value, gross domestic product, crude oil prices) during the quarterly period 2010-2020, which means that the PMG method can be applied. /ARDL. Findings: This study showed that there is an adverse effect from the gross domestic product to the closing prices of the banking sector, and this can be justified by the increase in the gross domestic product associated with the increase in oil revenues. Because the oil sector contributes to 60% of GDP in Iraq. Practical & Social implications: Study main necessary comes from paying attention to the development of investment portfolios in Iraq by following solid policies in implementing the necessary procedures for investing in portfolios, raising awareness of the culture of investment portfolios within investment communities or investment companies, and establishing the idea of establishing a fund to guarantee investment portfolios in a symbiotic manner that reduces the risks of fluctuations in oil revenues resulting from Crises. Originality of study: Comes from that decision makers can reach solutions and treatments to mitigate their severity, and address the imbalance in the stock market in a way that achieves an improvement in the contribution formed by the rest sectors in the total GDP, and reduce the risks resulting from dependence on oil revenues.
Individualized Virtual Reality for Increasing Self-Compassion: Evaluation Study

Background Depression and anxiety are common and debilitating mental disorders with severe negative repercussions at both individual and societal levels. Although virtual reality (VR) has emerged as a safe and effective tool for the treatment of anxiety disorders, studies of the therapeutic application of VR to treat depression are more limited. Objective The purpose of this study was to test whether a novel type of individualized VR (iVR) can be used to improve self-compassion and decrease depressive symptoms and to evaluate the usability and acceptability of this approach, as rated by participants. The iVR system was designed and developed based on the feedback obtained from a previous study, with improved appearance and feel of the avatar and enhanced graphical quality. Methods A total of 36 young adult participants were recruited from a university community social media site. Participants were aware that the study was investigating a treatment for depression but were not recruited based on depression diagnosis. Participants were asked to complete 2 iVR sessions, spaced 2 weeks apart. At baseline and upon completion of each iVR session, participants were asked to complete validated measures of self-compassion and depression. Upon completion of both iVR sessions, additional measures were administered to assess participants’ perceptions about the perceived usability and system acceptability of the iVR approach. Results Self-compassion was assessed at the beginning of session 1 (preintervention baseline) and at the end of session 1 (postintervention assessment). Owing to COVID-19 constraints, 36% (13/36) of the participants were unable to complete the follow-up iVR session. Self-compassion was assessed again for the remaining 64% (23/36) of the participants at the end of session 2 (postintervention assessment). Within-group analyses revealed that self-compassion was significantly increased at the end of both session 1 (P=.01) and session 2 (P=.03) relative to baseline. There was also a nonsignificant trend for depressive symptoms to be low at the end of session 2 relative to baseline. Both quantitative and qualitative participant data supported the iVR approach as being acceptable and usable. Conclusions Although these data must be treated as preliminary owing to the small sample size and potential selection bias, the data provide encouraging initial evidence that iVR might be a useful tool to enhance self-compassion and reduce depressive symptoms, highlighting the need for randomized controlled trials in the future.