Miracle-dz/UniFAR-FTUs huggingface dataset:
An Efficient Feature Selection Algorithm Toward Building Lightweight Intrusion Detection System

Feature selection is one of the most important problems in network security, pattern recognition and data mining areas. For high dimension data, feature selection not only can improve the accuracy and efficiency of classification, but also discover informative subset. This paper proposes a new feature selection algorithm aiming at building lightweight intrusion detection system (IDS) by (1) using a hybrid strategy of genetic algorithm and tabu search (GATS) as search strategy to specify a candidate subset for evaluation; (2) using modified linear Support Vector Machines (SVMs) iterative procedure as wrapper approach to obtain the optimum feature subset. The authors have examined the feasibility of the feature selection algorithm by conducting several experiments on KDD1999 intrusion detection dataset which was categorized as DOS, PROBE, R2L and U2R. The experimental results show that the approach is able not only to speed up the process of selecting important features but also to guarantee high detection rates. Furthermore, the experiments indicate that intrusion detection system with a combination of feature selection algorithm has better performances than that without feature selection algorithm in terms of building time, testing time and detection rates.
Recognition of Kuzushi-ji with Deep Learning Method

Reading ancient documents is one of fundamental works on the study of national literature. However, due to the use of kuzushi-ji (classical cursive handwriting characters) in the ancient documents, it requires a lot of knowledge and labor to read. This paper uses an End-to-End method with attention mechanism to recognize the continuous kuzushi-ji in phrases. Compared with the traditional recognition model with Connectionist Temporal Classification (CTC), our approach can get higher accuracy of recognition. The method can recognize phrases written by kana (47 different characters) with the accuracy 78.92% and recognize phrases containing both kanji (63 different characters) and kana with accuracy 59.80%.
Debriefing Research Games

Background. Debriefing is an intrinsic part of games for learning and proper debriefing can also be beneficial to research games. However, the literature on how to debrief research games is sparse and only provides the professional with an abstract topic guide. Aim. The purpose of this study was to design a framework for the debriefing of research games that are used in ongoing innovation processes. Method. We used the literature on debriefing and experimental research and our experience as game designers to build a framework that tackles the context, substance and method of debriefing research games. Results. Our framework provides three contributions. First, it shows how the context in which a research game is applied sometimes impacts the functionality of the game in negative ways. This can be helped by designing both the game and the debriefing together. Second, we operationalize validity to a greater extent, as this is the core of a good research game. Third, we provide a methodology for debriefing professionals that opens up the black box of the gaming simulation session. Conclusion. The debriefing framework provides a method to collectively assess the validity, reliability and robustness of the causal claims associated with the research conducted.
Investigation of hydrogen evolution on electrodeposited Ni/P coated carbon paper electrode in microbial fuel cell

Microbial electrolytic cell (MEC) is an innovative bioelectrochemical technology for hydrogen production using various organics and even wastewater. The development of a nonprecious metal hydrogen catalyst is critical to MECs. Increasing the stability and activity of such catalyst remains a key challenge. In this study, Ni/P is prepared by electrodeposition on bare carbon paper. The prepared electrode material is used as a cathode electrocatalyst in an MEC. Here, we show that the Ni/P cathode material has excellent hydrogen generation catalytic activity, that is comparable to that of Pt and superior to that of Ni or bare carbon paper. Particularly, the electrode has the best hydrogen evolution ability when the Ni 2+ /PO4 3- ratio is 50:30 and current density is 154.18 A m -2 , which is 12% larger than that of Pt. The cathode hydrogen recovery rate, hydrogen production rate and energy efficiency of the electrode are 74.54%±0.89%, 0.321±0.012 m 3 H 2 m -3 d -1 and 135.11%±5.36%, respectively, which are superior to those of Pt. The Ni/P cathode has good long-term stability. Results of this study, indicate that the Ni/P cathode is an alternative to Pt and have good prospects for MEC hydrogen evolution reaction (HER) cathode.
Segmentation and Classification of Multimodal Imagery

Segmentation and classification are two important computer vision tasks that transform input data into a compact representation that allow fast and efficient analysis. Several challenges exist in generating accurate segmentation or classification results. In a video, for example, objects often change the appearance and are partially occluded, making it difficult to delineate the object from its surroundings. This thesis proposes video segmentation and aerial image classification algorithms to address some of the problems and provide accurate results. We developed a gradient driven three-dimensional segmentation technique that partitions a video into spatiotemporal objects. The algorithm utilizes the local gradient computed at each pixel location together with the global boundary map acquired through deep learning methods to generate initial pixel groups by traversing from low to high gradient regions. A local clustering method is then employed to refine these initial pixel groups. The refined sub-volumes in the homogeneous regions of video are selected as initial seeds and iteratively combined with adjacent groups based on intensity similarities. The volume growth is terminated at the color boundaries of the video. The over-segments obtained from the above steps are then merged hierarchically by a multivariate approach yielding a final segmentation map for each frame. In addition, we also implemented a streaming
Time-Frequency-Domain Copula-Based Granger Causality and Application to Corticomuscular Coupling in Stroke

The corticomuscular coupling (CMC) characterization between the motor cortex and muscles during motion control is a valid biomarker of motor system function after stroke, which can improve clinical decision-making. However, traditional CMC analysis is mainly based on the coherence method that can’t determine the coupling direction, whereas Granger Causality (GC) is limited in identifying linear cause–effect relationship. In this paper, a time-frequency domain copula-based GC (copula-GC) method is proposed to assess CMC characteristic. The 32-channel electroencephalogram (EEG) signals over brain scalp and electromyography (EMG) signals from upper limb were recorded during controlling and maintaining steady-state force output for five stroke patients and five healthy controls. Then, the time-frequency copula-GC analysis was applied to evaluate the CMC strength in both directions. Experimental results show that the CMC strength of descending direction is greater than that of ascending direction in the time domain for healthy controls. With the increase of grip strength, the bi-directional CMC strength has an increasing trend. Meanwhile, the bi-directional CMC strength of right hand is larger than that of left hand. In addition, the bi-directional CMC strength of stroke patients is lower than that of healthy controls. In the frequency domain, the strongest CMC is observed at the beta frequency band. Additionally, the CMC strength of descending direction is slightly larger than that of ascending direction in healthy controls, while the CMC strength of descending direction is lower than that of ascending direction in stroke patients. We suggest that the proposed time-frequency domain analysis approach based on copula-GC can effectively detect complex functional coupling between cortical oscillations and muscle activities, and provide a potential quantitative analysis measure for motion control and rehabilitation evaluation.
Investigating the hazards posed by pristine and modified copper nanomaterials using in vitro screens combined with in vivo models

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Robot path planning using Voronoi classifiers

This paper details the development of a minimal set of locally distributed navigation beacons that can provide new waypoints in dense obstacle fields. The 'beacons' provide direction and magnitude inputs for the robot to use for its next waypoint. The beacons are placed in such a manner that all locations within a bounded playing field can reach a goal area in a desired number of steps. This guarantee of total coverage comes only with tuning the magnitudes and directions of each beacon (as well as their position in the field). Key to this approach is the underlying 'color map'. The color map assigns a color to regions of the playing field based on whether the region terminates at the goal ('green'); leaves the playing field and doesn't return ('red'); or doesn’t leave the playing field but does not terminate at the goal (within a fixed number of steps)-also know as 'stagnation' ('yellow'). Changes in the placement of the beacons and their associated parameters result in changes to the color map. A software tool has been developed to allow a user to see the instantaneous changes in the color map when changes are made to the beacons. This paper will also describe how the beacons are related to both Voronoi diagrams and nearest neighbor classifiers-thus generating the final name for the navigation beacons; Voronoi Classifiers. Future work is detailed including the development of color maps for other cost metrics (such as distance traveled, power consumed or terrain trafficabilty) and efforts in developing an algorithm to find the infimum solution (minimize the maximum steps, distance, etc.).
BAIKâ€“ PROGRAMMING LANGUAGE BASED ON INDONESIAN LEXICAL PARSING FOR MULTITIER WEB DEVELOPMENT

Business software development with global team is increasing rapidly and the programming language as development tool takes the important role in the global web development. The real user friendly programming language should be written in local language for programmer who has native language is not in English. This paper presents our design of BAIK (Bahasa Anak Indonesia untuk Komputer)scripting language which syntax is modeled with Bahasa Indonesian for multitier web development. Researcher propose the implementation of Indonesian Parsing Engine and Binary Search Tree structure for memory allocation of variable and compose the language features that support basic Object Oriented Programming, Common Gateway Interface, HTML style manipulation and database connection. Our goal is to build real programming language from simple structure design for web development using Indonesian lexical words. Pengembangan bisnis perangkat lunak dalam tim berskala global meningkat dengan cepat dan bahasa pemrograman berperan penting dalam pengembangan web secara global. Bahasa pemrograman yang benar-benar ramah terhadap pengguna harus ditulis dalam bahasa lokal programmer yang bahasa ibunya bukan Bahasa Inggris. Paper ini menyajikan desain dari bahasa penulisan BAIK (Bahasa Anak Indonesia untuk Komputer), yang sintaksisnya dimodelkan dengan Bahasa Indonesia untuk pengembangan web multitier. Peneliti mengusulkan implementasi dari parsing engine Bahasa Indonesia dan struktur binary search tree untuk alokasi memori terhadap variabel, serta membuat fitur bahasa yang mendukung dasar pemrograman berbasis objek, common gateway interface, manipulasi gaya HTML, dan koneksi basis data. Tujuan penelitian ini adalah untuk menciptakan bahasa pemrograman yang sesungguhnya dan menggunakan desain struktur sederhana untuk pengembangan web dengan menggunakan kata-kata dari Bahasa Indonesia.
Analysis of Heat Transfer in the Case of Non-linear Hyperbolic Conduction Equation Coupled with Radiation or with Thermoelectricity

Received: 21 February 2019 Accepted: 3 May 2019 Nonlinear hyperbolic heat conduction problems are analyzed thanks to the CattaneoVernotte model, which takes what happens at very short times into account. First, the case of the coupled conductive-radiative heat transfer in planar and spherical media is considered. The accuracy of the Lattice Boltzmann heat conduction model coupled with an analytical layered spherical harmonics solution of the radiative transport equation is investigated. The effects of different parameters such as scattering albedo on both temperature and conductive heat flux distributions within the medium are studied for steady and transient states. The present predictions agree well with literature benchmarks. It is also shown that the parameters have a significant effect on both temperature profile and hyperbolic sharp wave front. Second, the non-Fourier heat conduction in a thermoelectric thin layer is investigated under several boundary conditions by performing a specific quadrupole method. The expressions of the temperature and the heat flux of the small-scale thermoelectric materials are obtained and the whole matrix formulation is given explicitly. Good agreement is observed between quadrupole temperature predictions and analytical results for the Fourier heat conduction problems.
BLOOD RESPIRATORY FUNCTIONS IN STREPTOZOTOCIN-INDUCED DIABETIC MALE ALBINO RATS TREATED WITH CUCUMIS MELO VAR. FLEXUOSUS LEAVES EXTRACT

Cucumis melo var. flexuosus (L.), snake melon or faqqous, is an ancient plant crop in many different parts of the world. The present study was designed to evaluate the efficacy of faqqous leaves extract on some blood respiratory functions in streptozotocin (STZ)-induced diabetic rats. Thirty five male albino rats were used in the current study and randomly divided into five groups: non-diabetic control group, diabetic control group (received a single intraperitoneal injection of 60 mg STZ/kg body weight), and other three diabetic groups treated orally with different doses of faqqous leaves extract (30, 60 or 120 mg/kg body weight) for 30 consecutive days. Induction of diabetes by STZ caused a significant decrease (P < 0.05) in most venous blood parameters including hemoglobin (Hb) content, hematocrit (Hct) value, oxygen partial pressure (PO2), percentage of O2 saturation (% O2), carbon dioxide partial pressure (PCO2), and the concentrations of bicarbonate (HCO3-), total CO2, and base excess (BE-), as well as the logarithm of hydrogen ion (pH) of the arterial blood. Faqqous leaves extract (especially at 120 mg/kg body weight) modulated significantly almost all these changes in the blood of diabetic rats. Oxygen dissociation curve (ODC) shifted to the left in the diabetic control group compared with the non-diabetic control one; but shifted to the right in the diabetic groups treated withfaqqous leaves extract. In conclusion, dietary supplementation of faqqousleaves extract improved significantly the blood respiratory functions of diabetic rats.
Building Information Modelling Skills in Construction Related Disciplines: A Social Network and Job Advertisement-Based Comparative Analysis

Previous research efforts have attempted to identify the necessary Building Information Modelling (BIM) skills for individuals. However, prior works did not explore those needed for entry-level positions, and may be susceptible to respondents’ bias. This study proposes using methods that do not require individual responses to identify those skills that are related to BIM. Specifically, the authors suggest using publicly available data on social networking profiles. This paper presents the results of the authors’ research on identifying skills that are related to BIM and how they might be used in the industry. This study identifies those skills for graduating students by analysing social networking profiles and entry-level job advertisements. Additionally, the job tasks in those advertisements are analysed to illustrate how the skills might be used. This study performs those analyses to answer the following: (1) How do the skills differ between those that are possessed by individuals related to BIM and those that are listed in entry-level job advertisements that require BIM? And (2) How do the job tasks differ between BIM-specialized and nonBIM-specialized positions from the same entry-level job advertisements? The results suggest that there are discrepancies between the skills identified from the profiles and the advertisements. Additionally, while BIM skills may be a standard expectation in all positions in the future, there are BIM tasks that may disappear when BIM reaches maturity in the industry. Identifying and prioritizing the critical skills based on this type of analysis may enable educators to better prepare students for long-term success.
A Back Propagation Network based MPPT Algorithm for Grid-Tied Wind Energy System with Vienna Rectifier

This paper presents a boost type Vienna Rectifier with an Elman back propagation neural network algorithm for maximum power point tracking (MPPT) from the wind energy system. The preferred control algorithm deals with non-linear problems with improved conversance precision and reduced learning time. In this system, boost type Vienna Rectifier is employed as a machine side converter for single stage energy conversion of AC to DC with the enhanced output voltage and a grid side converter worked for DC to AC conversion. Vienna Rectifier facilitates power flow with high power density, continuous sinusoidal input current, improved power factor and offers low voltage stress across the switches. The proposed system configuration is designed to meet the load power demand of 1kW Active power with the combined contribution of the Wind energy conversion system and the main grid. The resulting analysis of the Vienna Rectifier with the aforementioned control algorithm is validated through Matlab-Simulink for variable wind speeds.
Large-scale Multi-output Gaussian Processes for Clinical Decision Support

In the scenario of real-time monitoring of hospital patients, high-quality inference of patients’ health status using all information available from clinical covariates and lab tests is essential to enable successful medical interventions and improve patient outcomes. Developing a computational framework that can learn from observational large-scale electronic health records (EHRs) and make accurate real-time predictions is a critical step in achieving this goal. However, existing EHRs pose several challenges for conventional methods. For instance, many covariates are sparsely sampled in time across patients. In addition, there are substantial uncertainties in patient condition and disease progression at any time. These properties make inferring the physiological status of a patient or joint analysis of time series across patients challenging. This dissertation first presents MedGP, a statistical framework that provides accurate real-time predictions of physiological states. MedGP is based on multioutput Gaussian processes, a Bayesian nonparametric model, to capture temporal structures between clinical covariates from noisy and irregularly sampled time series data. MedGP has a number of benefits over current methods. First, alignments of time series across patients are not required. Next, MedGP learns robust and interpretable relationships across covariates through regularization. Lastly, to tackle the computational bottlenecks, an approximate inference approach is derived and shown to achieve orders of speedup. The empirical results show significant improvements over baselines in making online predictions on two real-world medical data sets consist of tens of thousands of patients and millions of observations. Finally, two extended frameworks based on MedGP are explored to enhance clinical decision-making processes, including unifying with reinforcement learning algorithms for action recommendation and designing non-stationary kernels to learn the underlying dynamics of clinical treatments. Both methods demonstrate encouraging results on improving clinical practices and advancing towards precision medicine. iii
Ensemble of clustering algorithms for anomaly intrusion detection system

Maximizing detection accuracy and minimizing the false alarm rate are two major challenges in the design of an anomaly Intrusion Detection System (IDS). These challenges can be handled by designing an ensemble classifier for detecting all classes of attacks. This is because, single classifier technique fails to achieve acceptable false alarm rate and detection accuracy for all classes of attacks. In ensemble classifier, the output of several algorithms used as predictors for a particular problem are combined to improve the detection accuracy and minimize false alarm rate of the overall system. Therefore, this paper has proposed a new ensemble classifier based on clustering method to address the intrusion detection problem in the network. The clustering techniques combined in the proposed ensemble classifier are KM-GSA, KM-PSO and Fuzzy C-Means (FCM). Experimental results showed an improvement in the detection accuracy for all classes of network traffic i.e., Normal, Probe, DoS, U2R and R2L. Hence, this validates the proposed ensemble classifier.
Multi-agent model of ontology-based extraction of physical effects descriptions from natural language text

— Authors developed a representation model of physical effects knowledge based on an ontological approach. Authors proposed a model of multi-agent system for updating a physical effects database, including a model of agents, the model of interaction between agents, the role of agents in interaction. The developed system's efficiency was tested on a special documents corpus. Created multi-agent system can significantly reduce search time and increase efficiency in comparison with software "IOFFE".
Signature-based Extrusion Detection

Today, the comprehensive protection of a company computer network is extremely important. Intrusion detection systems, an important building block of any sound defense infrastructure, usually do not play the role they should: Alerting the administrators in case of a severe security breach. The reason lies in the huge amount of false alarms, which makes the system not practicable. The big challenge is to differentiate between an attempted attack and a successful attack. In this thesis, we investigate if and how the outbound traffic of an infected host can be used to detect intrusions. Based on my findings, we propose and implement a proof-of-concept extension to the open source intrusion detection system Snort. This extension establishes a layer which refines the escalated alerts using behaviour-based correlation.