Miracle-dz/UniFAR-FTUs huggingface dataset:
Understanding and Mitigating Label Bias in Malware Classification: An Empirical Study

Machine learning techniques are promising for malware classification, but there is a neglected problem of label bias in the annotation process which decreases the performance in practice. To understand the label bias problems and existing solutions, we conduct an empirical study based on two Portable Executable (PE) malware sample datasets (i.e., open-sourced BODMAS with 52,793 samples and a new collected MAIN dataset of 153,811 samples), and 67 anti-virus engines in VirusTotal. We first show the two ways of label bias problems, including chaotic naming rules and annotation inconsistency. Then we present the effects of two solutions (i.e., electing one reputable AV engine and aggregating multiple labels based on majority voting) and find they face the problems of feature preference and engine independence. Finally, we propose some recommendations for improvements and get a 7.79% increase in the F1 score (i.e., from 84.83% to 92.62%). The dataset will be open-source for further study.
Virtual constraints based general design method for dynamical servo control problem for Acrobot

The virtual constraints based general design method is proposed to solve a dynamical servo control problems for the Acrobot system,which forcing it to move along the periodic trajectory passing through the given points.The design of virtual constraint,analysis of system zero dynamics under virtual constraint and periodic property determination of the trajectory are combined to generate the desired periodic orbit function.Based on a Lyapunov methed,smooth feedback controller is proposed to overcome the dependency on the analytic solution of the system zero dynamics which exists in LQR based controller.An example is given and the simulation results show the effectiveness of the proposed general design method.
Improving Knowledge Tracing via Considering Two Types of Actual Differences From Exercises and Prior Knowledge

For offering adaptive learning to learners in intelligent tutoring systems, one of the fundamental tasks is knowledge tracing (KT), which aims to assess learners' learning states and make prediction for future performance. However, there are two crucial issues in deep learning-based KT models. First, the knowledge concepts are used to predict learners' performance instead of exercises. This choice ignores the actual difference among exercises, which leads to the KT model's inability to explore the rich information at the exercise level, thus, weakening the KT model's performance. Second, the actual difference in prior knowledge is neglected. Therefore, in-depth research of these two types of the actual differences offers the possibility to improve the prediction performance of KT further. To this end, we propose a fine-grained knowledge tracing model (FGKT)1 to capture these two types of actual differences. We first obtain the exercise representations by considering the corresponding knowledge concepts and actual difference among exercises. Then, an effective attention mechanism is designed to capture the relevance between assessment exercises and historical interactions to acquire individual prior knowledge. Finally, we evaluate the proposed FGKT model on several available benchmark datasets. The experiment results show that our model surpasses benchmark KT models by up to 7% in prediction performance on the latest ASSISTments dataset.
Distributional spectrum of bacterial wilt of chili incited by Ralstonia solanacearum in Pakistan

: The production of chili is seriously threatened by bacterial wilt incited by Ralstonia solanacearum in Pakistan. As the information about the distribution and prevalence of the disease and the pathogen is scarce, the present study was performed to ascertain the prevalence, occurrence and distribution of R. solanacearum in different regions of chili cultivation. The results showed differences in the incidence and prevalence of bacterial wilt all over the country. Overall, an incidence of 10% and prevalence of 76% of the disease was observed in the country. The highest incidence of bacterial wilt was observed in the province of Sindh whereas it was the lowest in Baluchistan. The prevalence of the disease followed the similar trend in the provinces. As regards agroecological zones, the highest incidence was recorded in Indus delta followed by sandy deserts whereas it was the lowest in western dry mountains. Almost the same tendency was observed regarding prevalence in the eight agroecological zones. In case of districts, the disease incidence was the maximum in Badin while it was the minimum in the district of Loralai. On the other hand, the disease prevalence was the maximum (100%) in the districts of Badin, Mirpurkhas, and Thatta and the minimum (60%) in the districts of Barkhan, Karak, and Loralai. Of the total 114 isolates of R. solanacearum , 81% were confirmed as Biovar 3 whereas the rest 19% isolates were identified as Biovar 4. The findings will help the farmers to design disease management programs accordingly to avert yield losses.
Automatic Test Case Generation for UML Collaboration Diagrams

Abstract A new model-based automatic test case generation for object-oriented systems using UML diagrams has been presented. Real-time application is expressed in the form of Collaboration diagram constructed using Rational Rose software. Collaboration diagram is converted into an intermediate graph form. Prim’s and Dijkstra’s algorithms has been applied to bring all the possible test cases from the graph. Illustrative case study has been presented in order to establish the effectiveness of our approach coupled with mutation analysis.
DISPERSION-DRIVEN MORPHOLOGY OF MECHANICALLY CONFINED POLYMER FILMS

To probe the effects of mechanical confinement on freely standing polystyrene (PS) films, we have symmetrically capped freely standing PS films by thin, solid layers. Annealing of the trilayer films produces a novel lateral morphology which is driven by the dispersion force between the capping layers. A simple model is presented which describes the scaling behavior of the morphology with the layer thicknesses. The same morphology is observed for freely standing and supported films in eight different systems. We also demonstrate reversibility of the morphology by manipulation of the dispersion force. [S0031-9007(99)08501-4]
Selection and Optimization of Wavelet Base

Different wavelet base has different timefrequency characteristics.Different results will come out while analyzing the same problem with different wavelet base.So there is a question of selection and optimization of wavelet base in the application of wavelet analysis.This paper expounds several important features about the selection of the wavelet base.It tells that we should grasp the features of wavelet base in its application through actual examples.It shows how to choose the suitable wavelet base according to the needs of application.
Forecasting of Satellite Based Carbon-Monoxide Time-Series Data Using a Deep Learning Approach

In last few decades one of the major problems is air pollution which has raised the eyebrows of everyone. Despite all the efforts, it still lies in the category of dangerous. In air pollution there is one of the most hazardous gases named carbon monoxide which is a matter of concern & produced mostly whenever a material burns with a lack of oxygen. This paper presents the forecasting of carbon monoxide with the help of a satellite-based sentinel 5p dataset using earth engine. Further, with the help of the deep learning approach ‘LSTM’, we forecast a time series base result. We have trained and tested the data using a deep-learning model. We have evaluated the potential results by overlapping the original and predicated values and calculating Root-mean-square (RMS) error to validate our approach. The results show that the method of LSTM is very efficient and accurate.
Current Knowledge on Chemosensory-Related Candidate Molecules Potentially Involved in Tick Olfaction via Haller’s Organ

Simple Summary Chemosensation is thought to be the primary sensory mechanism used by ticks to locate their host and partners, as they appear to lack functional eyes or hearing organs, in which their unique olfactory structure, namely Haller’s organ, is considered to play a significant role, although little is known about its molecular biology. This review comprehended and discussed the existing molecular information on candidate chemoreceptors and potential binding proteins preferentially expressed in tick foreleg enriched Haller’s organ, backed by pertinent bioinformatic data. Additionally, tick olfactory-related structural, behavioral, and neurological information was also given, and its limits were highlighted. Despite their expression in the forelegs of ticks, independent functional experiments should confirm the olfactory role of these chemosensory-related candidate molecules. Abstract Ticks are obligatory hematophagous ectoparasites and vectors of many animal and human pathogens. Chemosensation plays a significant role in tick communication with their environment, including seeking out blood meal hosts. Studies on the structure and function of Haller’s organ and its components have improved our understanding regarding tick olfaction and its chemical ecology. Compared with the knowledge on insect olfaction, less is known about the molecular basis of olfaction in ticks. This review focused on the chemosensory-related candidate molecules likely involved in tick olfaction. Members of the ionotropic receptor family and a new class of odorant-binding proteins are now known to be involved in tick olfaction, which appear to differ from that of insects. These candidate molecules are more closely related to those of mites and spiders than to other arthropods. The amino acid sequences of candidate niemann–pick type C2 and microplusin-like proteins in ticks exhibit features indicating their potential role as binding proteins. In the future, more comprehensive pertinent research considering the existing shortcomings will be required to fully understand the molecular basis of tick olfactory chemoreception. This information may contribute to the development of new molecular-based control mechanisms to reduce tick populations and related disease transmission.
A Novel Asynchronous Evolution Opinion Dynamics Model

Persuasion, which is the act of convincing someone to change their idea, attitude, or opinion, happens anywhere and anytime in social life. In this paper, based on the social judgment theory, we propose a novel asynchronous evolution Deffuant-Weisbuch opinion dynamics model by introducing the latitude of non-commitment. In our model, when agents communicate with each other, the evolution of their opinions depend not only on the opinion of themselves and their neighbors but also on the latitude where they locate, thus an opinion evolution rule that close to reality is designed. The model can well explain the persuasion process and captures the assimilation phenomenon in social networks. Simulation results show that the model can finally reach a stable state. Moreover, network topology, initial distribution of agents’ opinions and the range of latitudes of non-commitment all have an impact on the convergence time of opinions.
Coordinated Charging Strategy for Electric Vehicles Based on Customized Dueling Deep Q-Network

Deep reinforcement learning algorithms have been widely applied in the coordinated charging strategy for electric vehicles (EVs). However, current algorithms make little use of historical load information and cost much training time. We propose a two-stage coordinated charging strategy for EVs that incorporates long short-term memory networks and dueling deep q-network to deal. A representation network is used to extract features from the load sequence. And a two-stage strategy is used to reduce the action space dimension, which will greatly improve the computational cost. Numerical simulations based on practical charging data of a community in Beijing show that the proposed approach reduces load variance by 2.7% compared with the benchmark algorithms.
Optimal Sample Sizes and Go/No-Go Decisions for Phase II/III Development Programs Based on Probability of Success

In the course of late-stage drug development, a phase II/III clinical development program faces a series of sample size planning and decision problems: the simultaneous sample sizes determination (SSD) across phase II and phase III trials at the program planning stage, the go/no-go decisions after phase II data have been observed, and the SSD of phase III trials incorporating phase II data. Conventionally, SSD are carried out on a trial-by-trial basis and independent of one another even though the trials are within the same program; and the go/no-go decisions are made based on the p-values, point estimates, and confidence intervals which may be, at times, ambiguous for decision making. In a combined empirical Bayesian and frequentist framework, this article proposes a probability of success (POS) function to allow an integrated approach to the SSD and go/no-go decision problems for a phase II/III program in which both the phase II and III trials share a common, normally distributed response variable. For the first time, optimal sample sizes can be determined simultaneously for a phase II/III program using the proposed POS function. As the POS function includes the conventional power function as a special case, it enjoys broader applications including SSD of phase II trials and that of phase III trials incorporating phase II data—neither of which can be handled as readily using the conventional power function.
Control of Water Droplets on Super-Hydrophobic Surfaces by Static Electric Field

We investigated the control of water droplets on super-hydrophobic surfaces using a static electric field. The electric field required for droplet motion was decreased as the droplet size or the height from which the droplets were released increased. The Coulomb force of the electric charge provided to the water from the solid surface was the dominant driving force for the movement of the water droplet. In contrast, the contribution of dielectrophoretic force to the movement of the droplet was less than 10% that of the Coulomb force. The electric field required for droplet motion was not strongly influenced by the water conductivity. A film with a low sliding angle required a smaller electric field for droplet motion. Decreases in the sliding resistance had a greater effect than decreases in charge. Our experiments demonstrated that the use of a super-hydrophobic surface is advantageous for the control of water droplets by a small electric field.
Optimum “Eye Location” Problem for Spectral Clustering With Cosine Distance

It has recently been reported that Spectral Clustering gives state-of-the art clustering performance for many real-life benchmark datasets. When building the dissimilarity (distance) matrix for the Laplacian matrix, cosine distance is also reported to give the best performance among the other distance types for various real-life datasets. In this paper, we introduce an Optimum Sphere Location Problem for Spectral Clustering with cosine distance, and our investigations result in novel results, some of which are as follows: $i$ ) The Optimum “Eye Location” Problem (a.k.a. Optimum Sphere Location Problem) is a challenging problem due to the high complexity between the center of the unit sphere and the eigenvalues of the corresponding Laplacian matrix. ii) As compared to the sphere at the origin, choosing the “eye location” at the data mean generally increases the eigenvalues of the Laplacian matrix. iii) As compared to the sphere at the data mean, choosing the “eye location” at an appropriate point on the 2nd or 3rd dominant eigenvector of the data covariance matrix potentially gives better clustering performance for the same spectral clustering algorithm. iv) The proposed method includes the traditional methods, which are locating the unit sphere at the origin and at the data mean, as special cases. Computer simulations confirm the findings.
ucXception: A Framework for Evaluating Dependability of Software Systems

Fault injection is a well-established technique in the research community that consists of emulating faults in order to obtain dependability-related data. Despite its potential, fault injection has been less widely adopted outside of academia, due to the expertise required to effectively conduct fault injection campaigns and to the lack of tools that can be easily adapted to different systems. This paper presents ucXception, an easy-to-install, extendable, open-source framework for orchestrating the entire lifecycle of fault injection campaigns without requiring expert knowledge and using a graphical interface. ucXception supports injection of software and hardware faults using realistic fault models and can be applied to a variety of target systems, including virtualized systems and complex cloud computing deployments. This brings fault injection to modern environments of cloud computing. As a use case, a preliminary analysis on the usage of failure models as a valid alternative to fault models is performed.
Mental Workload Modeling of Time-Critical Tasks in Autonomous Driving Based on a Multi-source Information Fusion Approach

The supervision tasks of complex dynamic systems are usually characterized by dynamics, suddenness, timeliness and uncertainty, so it is necessary to keep human drivers at an appropriate mental workload (MWL) level to ensure that they make the best decisions, judgments and actions in the dynamic environment of the real world. This paper proposes and validates a quantitative assessment model for studying MWL in time-critical multitasking scenarios. The Multi-Attribute Task Battery II (MATB-II) is used as a multi-task platform in the experiment. The results show that the multiple linear regression model, which comprehensively considers human performance data and eye movement data, has better prediction performance compared with single data alone, and can predict the MWL level of different task scenarios, which provides a reference for switching control authority of human-machine system and alarm design of the system, and has goods application prospects.
Sustainable natural resource management must recognise community diversity

ABSTRACT Deforestation and overexploitation of mangrove forests are affecting the livelihoods of millions of families that rely on their ecosystem services. Understanding local perceptions about the status and threats to mangroves is therefore crucial in addressing this issue. This research aims to enhance understanding of how sociodemographic factors influence resource use and perceptions of environmental changes through a questionnaire survey (n = 592 households) in five locations in Lamu County, home to 62% of Kenya’s mangroves. The results highlight the variability of mangrove use, ecosystem service recognition, and perceptions and drivers of change across locations, which are influenced by sociodemographic factors such as gender, education, and occupation. Although 89% of respondents reported using mangrove products, only 56% were able to identify mangrove ecosystem services, with those without formal education being less likely to recognize them. Interestingly, 50% of respondents perceived an increase in mangrove cover, contrary to research showing mangrove loss in the area over the last decade. Results show that communities are diverse and perceptions vary between groups, suggesting that implementing uniform management measures may be incomplete or ineffective. Awareness campaigns and capacity-building efforts must be tailored to reduce misperceptions about the state of local resources and to address the specific needs and challenges faced by different groups. Recommendations made here are widely applicable to promote more inclusive and sustainable community engagement in the management of natural resources in developing countries worldwide.
Dispersion analysis and engineering of 2D plasmonic waveguides

A full investigation of the low-order guided modes in a two-dimensional (2D) hollow metallic waveguide is performed. The dispersion characteristics of the 2D hollow metallic waveguides are identified and analyzed. Manipulating the dispersion is proposed by either changing the geometrical shapes from rectangular to trapezoidal waveguide or changing the material of the cladding region to TiN. The dispersion analysis of the 2D plasmonic waveguide using TiN is investigated for the first time. The effect of varying the shape parameters on the cutoff in the modes dispersion is studied. The trapezoidal shape waveguide that causes the most significant shift in the cutoff is selected and detailed dispersion analysis of its guided modes is performed. The effect of changing the plasmonic material on the dispersion curve key characteristics is also identified. Finally, the effect of shifting the cutoff on the enhanced transmission phenomena is investigated.
Deformation analysis of reinforced‐core auxetic assemblies by close‐range photogrammetry

In this article, we present an experimental study validating the concept of the reinforced‐core auxetic assemblies. The main distinctive feature of these assemblies is the presence of a reinforcing core in the microstructural units that increases the shear stiffness and thus produces negative Poisson's ratio. We tested a number of prototypes manufactured using the stereolithography and selective laser sintering methods. To measure the deformations, we employed the close‐range photogrammetry method, which enables monitoring of locations of unlimited number of points from digital images. We developed a procedure for multi‐point measurement of planar deformations using a single digital camera. This procedure allows for continuous measurement of deformations without interruption to the loading sequence. Camera self‐calibration is based on multiple images of stable reference points, which enables high accuracy of the measurements even using a consumer grade digital camera. We compare the obtained experimental results with a simplified continuum model. We demonstrate a good agreement between the theoretical predictions and the experimental results.
An Application of the Bootstrap Variance Estimation Method to the Canadian Participation and Activity Limitation Survey

The bootstrap method is increasingly used to estimate the variance of estimates obtained from complex survey designs. This method has been shown to work well for a wide range of estimators, including medians and quantiles, as well as smooth functions based on totals. In addition, the bootstrap can be less computer intensive than the jackknife method for surveys with a very large number of primary sampling units (PSUs). The sampling plan of the Canadian 2001 Participation and Activity Limitations Survey (PALS) is a stratified two-stage design in which PSUs are selected without replacement with probability proportional to size. The survey presents specific challenges to the use of the bootstrap method. For instance, the sampling fraction for PALS is relatively high in many strata, which causes the bootstrap method to overestimate the variance. What is the magnitude of this overestimation? Also, a logistic regression response propensity model is used for the nonresponse adjustment in PALS. Should a logistic regression model be fitted to each bootstrap sample? How does this method compare with maintaining fixed response classes over all bootstrap samples? This paper will address these issues.