Miracle-dz/UniFAR-FTUs huggingface dataset:
A Taxonomy of Metrics and Tests to Evaluate and Validate Properties of Industrial Intrusion Detection Systems

The integration of Intrusion Detection Systems (IDS) in Industrial Automation Systems (IAS) has gained popularity over the past years. This has occurred due to their ability to detect intrusions at a device and network level. In order for these systems to provide effective and reliable protection, they must possess a set of specific properties. These properties are inherent characteristics that depend on the IDS application field, as different fields provide different deployment conditions. Unfortunately, the evaluation and validation of such properties for IAS has proven challenging, as current contributions often follow evaluation and validation approaches from the IT domain that focus solely on the effectiveness of intrusion detection approaches; hence, neglecting other aspects relevant to the industrial domain. This paper addresses this issue by presenting IDS properties derived from trends and characteristics of IAS; as well as a taxonomy of metrics and tests to evaluate and validate these properties. This taxonomy provides a foundation from which future IDS contributions for IAS can be improved and reinforced by providing an overview of pertinent metrics and tests.
Assessment of virtual thermal manikins for thermal comfort numerical studies. Verification and validation

In turbulent flows as in HVAC applications, the fluctuation of air speed can affect the sensation of thermal comfort. This study is a part of a larger experimental and numerical campaign intended to evaluate the influence of the turbulence intensity at the inlet of air distribution systems on the local draft sensation and thermal discomfort for different ventilation cases using numerical simulations. For five different imposed values of turbulence intensity (0%, 3%, 10%, 30%, 50%) the local heat loss for different body parts was quantified CFD simulations were performed using a realistic model of human body along with an experimental validation with a humanoid thermal manikin. The CFD model was further exploited for several cases to evaluate the influence of turbulence intensity on the comfort indicators and local heat loss. The recorded velocity, turbulence and temperature fields allowed us to estimate the distributions of DR, PPD and PMV indexes which showed no significant changes, but correlations were found for the imposed turbulence intensity and local heat loss on different body parts, indicating the influence on thermal perception.
Mean-Shift Tracking with Adaptive Model Update Mechanism

Considering the issue of model update within the Mean-shift framework, this paper proposes a model update method by using Kalman filters to estimate the object kernel-histogram from the previous and current object models. Filtering residuals are employed to adaptively tune all the parameters of Kalman filters. In addition, Bhattacharyya coefficient is used as a criterion for model update. Therefore, the tracker can avoid over-update. Experimental results show that the method keeps up with the object appearance and scale changes, and is robust under the influence of occlusion and illumination factors, etc.
Triangulation and Segmentation-based Approach for Improving the Accuracy of Polygon Data

Often, same polygon objects are presented in Geoinformational Systems by distinct geometries with random positional discrepancies. It makes difficult to detect correspondences between data layers containing same object or parts of objects. The suggested method allows the user to improve the accuracy of one polygon layer by another more accurate polygon dataset by defining correspondences between polygons and parts of polygon boundaries. Two main techniques are applied: triangulation and segmentation. The triangulation is used to define correspondences between whole polygons by comparing triples of polygons. The segmentation approach is applied for the remaining polygons. Existing approaches do not work well in the case of partial equality of polygon boundaries. The main idea of the segmentation algorithm in this paper is based on defining correspondent segments of polygon boundaries and further replacing polygon boundary segments of the non-accurate layer with segments of the accurate data set; segments without pairs are rectified using ground control points. The resulting data contain parts of the accurate data set polygon boundaries, whereas the remaining elements are rectified according to the replaced boundary segments. From a review implemented by specialists it might be concluded that the results are satisfactory. The developed method could be applied to various types of polygonal datasets with similar scale.
Research on Text Classification Based on LSI and SVM

Text classification is the foundation and crucial problem of text data mining,it is an application based on the technology of natural language processing and machine learning.Feature extraction and categorization algorithm are the most crucial technologies for this problem.This paper proposes that latent semantic indexing(LSI) is used for feature extraction and dimensionality reduction,support vector machine(SVM) is used for text classification.The result shows that compared with the classifier based on vector space model combined SVM and the classifier based on LSI combined K-nearest neighbor(KNN),better performance is acheived.It shows that while the number of categories is small,and the categories are divided distinctly,the method can be used for practical application.
Application of Predicting Composite Foundation Settlement Based on Fuzzy BP Neural Networks

The final settlement deformation of composite foundation is very important for a building's designing and safety.And it will be a very great waste of human and other resource if we get the final settlement deformation by long time observation.As for the random and fuzzy characters of settlement transition,it's practically reasonable to predict composite foundation settlement using settlement datum.This paper proved the possibility of execution and efficiency predicting composite foundation settlement based on fuzzy BP neural networks.To fuzz up the BP neural networks with the premise of preserving the original structure of networks is to directly fuzzy up the sample datum,the result represents the correlative coefficient between sample set and control set,by this,fuzzy BP neural networks study and evaluate the final results.
An Adaptive Development Framework for Web-Based Enterprise Information System

The rapid evolutions of software environment and business requirements place a high demand on the adaptiveness of enterprise information systems (EIS). Over the last few years, more and more EIS adopted the distributed multi-tiered web-based application architecture. Crosscutting concerns and clone codes make the web-based EIS difficult to evolve and maintain. The traditional Model-View-Controller (MVC) model imposes design-level restrictions that give a clean separation between the presentation, functional and control concerns. However it does not modularize the structure crosscutting concerns. In this paper, we propose a novel development model named Meta Data Object (MDO) that modularizes the structure concerns and reduces some specific clone codes. Panther is a new domain-driven web development framework that implements the MDO model. Panther has been used to develop many web-based EIS. Development with Panther benefits from a significant improvement in code reuse, adaptability, and maintainability. Keywords-enterprise information system; web-based application; development framework; crosscuting concerns; maintainability; reusability
Design, Fabrication, and Characterization of a Composite Scaffold for Bone Tissue Engineering

Poly(lactide-co-glycolide) (PLGA) scaffolds have been successfully used in bone tissue engineering, with or without hydroxyapatite (HA) and with a macroporosity given either by simple PLGA sphere packaging and/or by leaching out NaCl. The objective of this work was the optimization of the design parameters for bone tissue engineering scaffolds made by sintering microspheres of PLGA, HA nanocrystals for matrix reinforcement and osteoconduction, and salt crystals for macroporosity and control of matrix pore size. Microsphere fabrication by a single-emulsion and solvent evaporation technique was first optimized to obtain a high yield of PLGA microspheres with a diameter between 80 and 300 μm. The influence of the sintering process and matrix composition on the scaffold structure was then evaluated morphologically and mechanically. Three scaffold types were tested for biocompatibility by culturing with human fibroblasts for up to 14 days. The most important parameters to obtain microspheres with the selected diameter range were the viscosity ratio of the dispersed phase to the continuous phase and the relative volume fraction of the 2 phases. The Young's modulus and the ultimate strength of the sintered matrices ranged between 168–265 MPa and 6–17 MPa, respectively, within the range for trabecular bone. Biocompatibility was demonstrated by fibroblast adhesion, proliferation, and spreading throughout the matrix. This work builds upon previous work of the PLGA/HA sintering technique to give design criteria for fabricating a bone tissue engineered matrix with optimized morphological, functional, and biological properties to fit the requirements of bone replacements.
Online Monitoring of Air Quality at the Postanesthetic Care Unit by Proton-Transfer-Reaction Mass Spectrometry

The subthreshold exposure to trace anesthetic gases is not associated with considerable risk of adverse health effects. Online control of ambient air exchange at the postoperative workplace may help in supervising air quality and lead to cost reduction. A proton-transfer-reaction mass spectrometer system was used for online monitoring of volatile organic compounds, especially anesthetic gases. The mean exposure to sevoflurane and isoflurane at the urological postanesthesia care unit (PACU) was 15.9 and 9.5 parts per billion, respectively. Sevoflurane and isoflurane concentrations at the urological PACU showed a patient turnover-dependent burden during our investigation period. Because modern PACUs have a high ventilation capacity, the 24-h occupational burden by anesthetic gases at the PACU is relatively low. Monitoring and controlling of ambient air by automatic built-in alarm systems would be useful for quality control of the postoperative workplace. Moreover, energy costs of ventilation systems could be reduced by coupling ventilation capacity to the effective exposure.
Development of System Rice Intensification (SRI) Paddy Transplanter

The System of Rice Intensification (SRI) practices have been developed in order to increase the production and quality of rice. Based on SRI practices the rice seedlings are transplanted at the young ages, 15 days old with just 2 leaves and carefully planting of just one seedling per hill and space the hills optimally widely in a square pattern of 25×25 cm for better usage of water, sunlight, minerals, space, nutrient, weeding and pest management within shallow depth (1-2 cm) in the moist soil condition. The existing method of mechanical transplanting of paddy which planted between 5 and 8 seedlings per stand is claimed to be inefficient to produce higher yield. From the survey that was carried out, most farmers are looking forward to single-planting translators as it is impossible to do it manually. Modifications have to be carried out on the planting claw (kuku kambing) so that it will only catch one seedling at a time, redesign the seedling tray to allow the SRI transplanter to catch one seedling at a time and determine the best soil condition suitable for the SRI practices. No doubt that the new develop transplanter for SRI will be the future machines for the farmers in Malaysia. Evidence have shown that the SRI practices in paddy cultivation has resulted in the increase in yield as well as superior quality paddy because of its shorter crop cycle, less need for seeds and fertilizer, less chaffy grain because of higher percentage grain filling, little or no lodging from wind or rain and higher head rice recovery rate, so more milled rice from a given amount of paddy and reduced labour demand while boosting productivity. The most important factors that influence the performance are soil moisture content, system of raising seedlings in nursery, degree of land labelling and finally the size of planting claw.
Embedded Face Detection and Recognition

The need to increase security in open or public spaces has in turn given rise to the requirement to monitor these spaces and analyse those images on-site and on-time. At this point, the use of smart cameras - of which the popularity has been increasing - is one step ahead. With sensors and Digital Signal Processors (DSPs), smart cameras generate ad hoc results by analysing the numeric images transmitted from the sensor by means of a variety of image-processing algorithms. Since the images are not transmitted to a distance processing unit but rather are processed inside the camera, it does not necessitate high- bandwidth networks or high processor powered systems; it can instantaneously decide on the required access. Nonetheless, on account of restricted memory, processing power and overall power, image processing algorithms need to be developed and optimized for embedded processors. Among these algorithms, one of the most important is for face detection and recognition. A number of face detection and recognition methods have been proposed recently and many of these methods have been tested on general-purpose processors. In smart cameras - which are real-life applications of such methods - the widest use is on DSPs. In the present study, the Viola-Jones face detection method - which was reported to run faster on PCs - was optimized for DSPs; the face recognition method was combined with the developed sub-region and mask-based DCT (Discrete Cosine Transform). As the employed DSP is a fixed-point processor, the processes were performed with integers insofar as it was possible. To enable face recognition, the image was divided into sub- regions and from each sub-region the robust coefficients against disruptive elements - like face expression, illumination, etc. - were selected as the features. The discrimination of the selected features was enhanced via LDA (Linear Discriminant Analysis) and then employed for recognition. Thanks to its operational convenience, codes that were optimized for a DSP received a functional test after the computer simulation. In these functional tests, the face recognition system attained a 97.4% success rate on the most popular face database: the FRGC.
A novel complex neural network model for computing the largest real part of eigenvalues and the corresponding eigenvector of a real matrix

A novel complex neural network modelwasproposed, which can be used to compute the largestreal part of eigenvalues and the corresponding eigenvector of a general real matrixin this work. Because of the smart regulatory factorof the model, the largest real part also can be extracted in the case of all the real parts of eigenvalues less than 0. Meanwhile, the presented paper provides a rigorous mathematical proof for its convergence for a more clear understanding of network dynamic behaviors relating to the computation of the eigenvector and the eigenvalue. Numerical example showsthat the proposed model has good performance for a general real matrix.
A New Method for Mobile Robot Path Planning based on Particle Swarm Optimization algorithm

: Particle swarm optimization (PSO) algorithm has been widely applied to the mobile robot path planning problems, but it still has some shortcomings such as high coding dimension and premature convergence. This paper proposes a new PSO algorithm combined with cubic spline functions. Firstly, several path nodes are set up in the environment, and then use the cubic spline functions to interpolate on the path of the starting point, path nodes and target point, thus obtain a full path which formed by connecting all interpolation points. The particle coding is just the coordinates of the path nodes, which significantly reduces the coding dimension of the particle. In order to further improve the efficiency of this algorithm, a new initialization strategy and penalty function based fitness are designed. The proposed method shows excellent performance in computational experiments in terms of solving quality, stability and speed.
Analysis of low-noise EEG in search of functional gamma band correlates

The electroencephalogram (EEG) has proven to be a useful information source in analysis of brain activity, diagnosis of neurological disorders, and development of brain-computer interfaces (BCI’s). Through numerous studies over the past decades, EEG activity in different frequency bands has been observed to correspond with various mental states. Clinical use of EEG, however, is often limited to frequency ranges below 30 Hz, ignoring potentially informative patterns within the gamma band (30− 100 Hz). Indeed, the gamma band has received greater scrutiny in recent years and is typically known to underlie and be modulated by sensorimotor behaviors and internal cognitive processes. In this study, we have investigated the potential of an ultra-low noise capsule at the LSBB 1 for acquisition of clean EEG signals, with a focus on analysis of high frequencies (gamma band) in search for novel activity patterns. Using a batteryoperated EEG acquisition system, we acquired 64-channel EEG recordings from a few volunteers performing several cognitive, sensory, and motor tasks in both LSBB and a typical research laboratory. Upon analysis of this data using Stockwell Transform, we compared task-specific gamma band energy increases of signals acquired at the two environments, observing more prominent functional EEG changes in LSBB. Moreover, we studied all recordings in both environments to examine statistically significant spatial and spectral correlates of spontaneous EEG pertaining to each of the tasks. To further assess the task-induced changes in the EEG signals, we have also proposed a framework for analyzing gamma band connectivity; i.e. functional patterns of interaction between distinct channels of the EEG. Using this framework, 1Laboratoire Souterrain à Bas Bruit, Rustrel, France
Towards Theory-of-Mind agents using Automated Planning and Dynamic Epistemic Logic

This thesis is part of a growing body of work in what we call epistemic planning. Epistemic planning is situated at the intersection of automated planning and what can broadly be called dynamic logics. Both are part of the much larger field of Artificial Intelligence. Automated Planning has been around since at least the 1970s. It is a diverse collection of methods, models, algorithms and specification languages for giving autonomous agents the ability to come up with plans for proactively achieving goals. Autonomous agents can be understood as independent actors, given a purpose by their designer. Whether they are in a software system, connected to the real world with sensors and actuators, or used as a tool for modelling people, for instance in economics, they need to be able to imagine (or predict) outcomes of actions in order to form plans. The feature that most distinguishes planning from other decision making methods, is that the planner does not know the full system from the beginning. Most of the time it would simply be too big to store in memory! Instead of being given the entire “game”, they use a specification of actions and the initial state to generate only a fraction of the full search space. This means that what an agent can plan for depends crucially on what domains we can describe. This is where logic comes into the picture. For most of its more than 2500 year long history, logic has been mostly interested in the study of valid reasoning. In later years (in the scheme of things), more attention has been given to studying when reasoning fails in humans. Like using calculus to analyse and simulate both when, for instance, a bridge holds and when it collapses, we can use logic to analyse and simulate reasoning both when it is sound and when it isn’t. The subbranch of logic applied in this work is Dynamic Epistemic Logic. The epistemic part concerns the formalisation of knowledge and belief (mainly) in multi-
Simultaneous Kriging-Based Sampling For Optimization And Uncertainty Propagation

Robust analysis and optimization is typically based on repeated calls to a deterministic simulator that aim at propagating uncertainties and finding optimal design variables. Without loss of generality a double set of simulation parameters can be assumed: x are deterministic optimization variables, u are random parameters of known probability density function and f (x, u) is the objective function attached to the simulator. Most robust optimization methods involve two imbricated tasks, the u's uncertainty propagation (e.g., Monte Carlo simulations, reliability index calculation) which is recurcively performed inside optimization iterations on the x's. In practice, f is often calculated through a computationally expensive software. This makes the computational cost one of the principal obstacle to optimization in the presence of uncertainties.
A Web-based Didactic Tool for Teaching of Distributed Consensus

Currently, educational models face the challenge of using information and communication technologies (ICT) to provide their students with the necessary tools and required knowledge in the 21 st Century. The study of distributed systems is increasingly its important due to the popularity of web applications. For this reason, we address in this paper a topic known as the Byzantine Generals problem, which is a topic that has boomed in recent years, with the emergence of Bitcoin and Blockchain. This paper presents a web tool to support the teaching-learning process, which is given between the teacher and the students. The objective of this tool is to familiarize students with notions of consensus algorithms. Using our didactic tool users can visualize the operation of the Byzantine Generals algorithm and improve their knowledge about this consensus case.