Miracle-dz/UniFAR-FTUs huggingface dataset:
A PRE-TRAINED DEEP CONVOLUTIONAL NEURAL NETWORK FOR THE DETECTION OF TUNGRO IN RICE PLANTS

This paper presents a computer vision application of transfer learning in the detection of ‘Tungro’ among rice plants, using pre-trained deep convolutional neural networks. An AlexNet network, consisting of 5 convolution layers and 3 fully connected layers of neurons, was customized and fine-tuned to accommodate as inputs, images of rice plants representing two (2) classes: those afflicted with Tungro, and those that are healthy. The fine-tuned network was trained on five hundred twenty (520) images of rice plants, three hundred sixty-eight (368) of which belong to the group without infestation, and one hundred fifty-two (152) are infested with Tungro. Both the training and testing dataset-mages were captured from rice fields around the district and validated by technicians in the field of agriculture. Applying stochastic gradient descent as the learning algorithm, the two-class classifier achieved a very high accuracy of 98.17% at mini batch size of twenty (20) and learning rate of 0.0001.
TOA-Based Passive Localization Constructed Over Factor Graphs: A Unified Framework

Passive localization based on time of arrival (TOA) measurements is investigated, where the transmitted signal is reflected by a passive target and then received at several distributed receivers. After collecting all measurements at receivers, we can determine the target location. The aim of this paper is to provide a unified factor graph-based framework for passive localization in wireless sensor networks based on TOA measurements. Relying on the linearization of range measurements, we construct a Forney-style factor graph model and conceive the corresponding Gaussian message passing algorithm to obtain the target location. It is shown that the factor graph can be readily modified for handling challenging scenarios such as uncertain receiver positions and link failures. Moreover, a distributed localization method based on consensus-aided operation is proposed for a large-scale resource constrained network operating without a fusion center. Furthermore, we derive the Cramér–Rao bound (CRB) to evaluate the performance of the proposed algorithm. Our simulation results verify the efficiency of the proposed unified approach and of its distributed implementation.
Network Resource Management in Infrastructure-as-a-Service Clouds

Cloud Infrastructure-as-a-Service (IaaS) is a form of utility computing which has emerged with the recent innovations in the service computing and data communication technologies. Regardless of the fact that IaaS is attractive for application service providers, satisfying user requests while ensuring cloud operational objectives is a complicated task that raises several resource management challenges. Among these challenges, limited controllability over network services delivered to cloud consumers is prominent in single datacenter cloud environments. In addition, the lack of seamless service migration and optimization, poor infrastructure utilization, and unavailability of efficient fault tolerant techniques are noteworthy challenges in geographically distributed datacenter clouds. Initially in this thesis, a datacenter resource management framework is presented to address the challenge of limited controllability over cloud network traffic. The proposed framework integrates network virtualization functionalities offered by software defined networking (SDN) into cloud ecosystem. To provide rich traffic control features to IaaS consumers, control plane virtualization capabilities offered by SDN have been employed. Secondly, a quality of service (QoS) aware seamless service migration and optimization framework has been proposed in the context of geo-distributed datacenters. Focus has been given to a mobile end-user scenario where frequent cloud service migrations are required to mitigate QoS violations. Finally, an SDN-based dynamic fault restoration scheme and a shared backup-based fault protection scheme have been proposed. The fault restoration has been achieved by introducing QoS-aware reactive and shared risk link group-aware proactive path computation algorithms. Shared backup protection has been achieved by optimizing virtual and backup link embedding through a novel integer linear programming approach. The proposed solutions significantly improve bandwidth utilization in inter-datacenter networks while recovering from substrate link failures.
Study of lexical aspect in the French medical language. Development of a lexical resource

This paper details the development of a linguistic resource designed to improve temporal information extraction systems and to integrate aspectual values. After a brief review of recent works in temporal information extraction for the medical area, we discuss the linguistic notion of aspect and how it got a place in the NLP field. Then, we present our clinical data and describe the five-step approach adopted in this study. Finally, we represent the linguistic resource itself and explain how we elaborated it and which properties were selected for the creation of the tables.
Damage Recovery for Simulated Modular Robots Through Joint Evolution of Morphologies and Controllers

In order to be fully autonomous, robots have to be resilient so that they can recover from damages and operate for a long period of time with no human assistance. To be resilient, existing approaches propose to change the robots’ behavior using a different control system when a hardware fault or damage occurs. These approaches are used for robots which have fixed morphologies. However, we cannot assume which morphology would be optimal for a given problem and which morphology allows resilience. In the present paper, we introduce a new approach that generates resilient artificial modular robots by evolving the robot morphology along with its controller. We used a multi-objective evolutionary algorithm to optimize two objectives at a time, which are the traveled distance of a damage-free robot and the traveled distance of the same robot with damaged parts. The result of preliminary experiments demonstrates that during evaluation, when robots are deliberately faced to motor failures, the evolution process can optimize and generate new morphologies for which the robot’s behavior is less affected by damage. This makes the robot capable to recover its ability to move forward.
An enhanced Genetic Algorithm with an innovative encoding strategy for flexible job-shop scheduling with operation and processing flexibility

This paper considers the Flexible Job-shop Scheduling Problem with Operation and Processing flexibility (FJSP-OP) with the objective of minimizing the makespan. A Genetic Algorithm based approach is presented to solve the FJSP-OP. For the performance improvement, a new and concise Four-Tuple Scheme (FTS) is proposed for modeling a job with operation and processing flexibility. Then, with the FTS, an enhanced Genetic Algorithm employing a more efficient encoding strategy is developed. The use of this encoding strategy ensures that the classic genetic operators can be adopted to the utmost extent without generating infeasible offspring. Experiments have validated the proposed approach, and the results have shown the effectiveness and high performance of the proposed approach.
A Novel Self-Feedback Intelligent Vision Measure for Fast and Accurate Alignment in Flip-Chip Packaging

A template matching (TM) algorithm has been widely employed in a visual inspection process of wafer-level flip-chip packaging, but the structures of traditional TM algorithms are always direct feedforward, which leads to the difficulty in achieving fast speed and high accuracy at the same time. The motivation of this article is to combine the ability to enable the chip visual measurement running in a fast-speed and high-accuracy manner. First, a novel self-feedback intelligent template matching (SFI-TM) structure is proposed, which can enable the intermediate information in the matching process to be fully utilized. The intelligent speed and resolution regulation rules are combined to construct the SFI-TM algorithm. Then, the reliability and robustness of the SFI-TM algorithm are theoretically analyzed to make sure it works in a stable manner. Finally, a series of practical chip alignment visual inspection experiments, including parameter testing, comparing experiments with the other five proposed visual detection algorithms, and robustness testing, are carried out in detail, respectively. The experimental results indicate that the SFI-TM algorithm can achieve the highest average measurement accuracy (1.08 $\mu$m) with almost the same measurement speed as fast as Tiny YOLOv2, and that it can resist the brightness variation from $-$20 to 30 gray value, the pepper–salt noise with a density of 0.5/pixel, and the Gaussian noise with a large variance of 2.5. In addition, it has good robustness against blurring and distortion of images under the dynamic speed motion processes.
Assessment and Benchmarking of Spatially Enabled RDF Stores for the Next Generation of Spatial Data Infrastructure

Geospatial information is indispensable for various real-world applications and is thus a prominent part of today’s data science landscape. Geospatial data is primarily maintained and disseminated through spatial data infrastructures (SDIs). However, current SDIs are facing challenges in terms of data integration and semantic heterogeneity because of their partially siloed data organization. In this context, linked data provides a promising means to unravel these challenges, and it is seen as one of the key factors moving SDIs toward the next generation. In this study, we investigate the technical environment of the support for geospatial linked data by assessing and benchmarking some popular and well-known spatially enabled RDF stores (RDF4J, GeoSPARQL-Jena, Virtuoso, Stardog, and GraphDB), with a focus on GeoSPARQL compliance and query performance. The tests were performed in two different scenarios. In the first scenario, geospatial data forms a part of a large-scale data infrastructure and is integrated with other types of data. In this scenario, we used ICOS Carbon Portal’s metadata—a real-world Earth Science linked data infrastructure. In the second scenario, we benchmarked the RDF stores in a dedicated SDI environment that contains purely geospatial data, and we used geospatial datasets with both crowd-sourced and authoritative data (the same test data used in a previous benchmark study, the Geographica benchmark). The assessment and benchmarking results demonstrate that the GeoSPARQL compliance of the RDF stores has encouragingly advanced in the last several years. The query performances are generally acceptable, and spatial indexing is imperative when handling a large number of geospatial objects. Nevertheless, query correctness remains a challenge for cross-database interoperability. In conclusion, the results indicate that the spatial capacity of the RDF stores has become increasingly mature, which could benefit the development of future SDIs.
Stereo RGB-D indoor mapping with precise stream fusing strategy

Abstract. In order to achieve more robust pose tracking and mapping of visual SLAM, the robotics researcher has recently shown a growing interest in utilising multiple camera, which is able to provide more sufficient observations to fulfil the frame registration and map updating tasks. This implies that better pose tracking robustness can be achieved by extending monocular visual SLAM to utilise measurements from multiple cameras. [1] proposed a visual SLAM method using multiple RGB-D cameras, which integrate the observations from multi-camera for camera tracking. However, they ignored the time-drift between the frames obtained by different cameras, which may result at inaccurate positions of observation used for map updating. Besides, loop closure detection was not been implemented. [2] constructed a multiple RGB-D system with three Kinects V2 camera. This work mainly concentrated on the intrinsic and extrinsic calibration and verify the effectiveness of mapping using multiply RGB-D cameras.
Revue générale Comprendre la variabilité de la pression artérielle et de la fréquence cardiaque To understand blood pressure and heart rate variability

Objectives. – To review current data on the heart rate and blood pressure variability. Data sources. – Search through Medline™ databases of articles in french or english. Data selection. – Original articles and case reports were selected according to their quality and main advances. The articles were analysed in order to obtain current data about the methods of study and clinical application of blood pressure and heart rate variability. Data synthesis. – Various regulatory systems in the cardiovascular system play crucial roles in controlling and assuring adequate perfusion of the peripheral tissues. Among them the baroreceptor reflex is the most important regulatory mechanism in the short-term control of the heart rate and blood pressure, and operates through the autonomic nervous system. The gain of the cardiac baroreflex further referred to, as baroreflex sensitivity is an interesting way to study this system. Unfortunately, with our current knowledge, it is not possible to predict the instantaneous output of the baroreceptor in response to instantaneous changes in input within a frequency range of physiological importance. The fast Fourier transform can describe variables as the sum of elementary oscillatory components and it has been established as practical clinical methods for detecting abnormalities in cardiovascular control. A time–frequency distribution provides an indication of how the spectral energy distribution varies with time and it is an interesting tool in non-stationary data. One of the major motivations behind spectral
A functional data analysis approach for characterizing spatial-temporal patterns of landscape disturbance and recovery from remotely sensed data

Contemporary landscape regionalization approaches, frequently used to summarize and visualize complex spatial patterns and disturbance regimes, often do not account for the temporal component which may provide important insight on disturbance, recovery, and change in ecological processes. The objective of this research was to employ novel statistical approaches in functional data analysis to quantify and cluster spatial-temporal patterns of landscape disturbance and recovery in 223 watersheds using a Landsat disturbance time series from 1985 – 2011 in western Alberta, Canada. Cumulative spatial patterns of disturbance, representing the proportion, arrangement, size, and number of disturbances per watershed, were modelled as functions and scores from a functional principal component analysis were clustered using a Gaussian finite mixture model. The resulting eight watershed clusters were mapped with mean functions representing unique temporal trajectories of disturbance and recovery. There was considerable variability in disturbance amplitude among the clusters which increased markedly in the mid-1990’s while remaining low in parks and protected areas. The regionalization highlights unique temporal trajectories of disturbance and recovery driven by anthropogenic and natural disturbances and enables insight regarding how cumulative spatial disturbance patterns evolve through time.
Placement Strategies to Monitor the Inter-Autonomous System Routing Information

Inter-Autonomous System (AS) route monitoring is the process of collecting the inter-AS routing information. This information flows on the Internet in the form of BGP UPDATE messages, and the BGP data are the messages obtained by the monitors. Existing methods of monitor placement rely on the network topology which provides inadequate visibility. This thesis proposes a novel scheme for monitoring the BGP data by selecting monitoring locations based on BGP message flow.
Development and Evaluation of Predictive Model for Fan Air Flow Rate According to Artificial Neural Network Input Variables

A model for predicting the supply air flow rate in the fan, which plays a important role in HVAC system, is to be developed using artificial neural network. A predictive model has been developed to study with the Levenbarg-Marquardt algorithm through 8760 sets of one-hour resolution. The model of three cases was constructed according to the combination of the input variables constituting the input data of the neural network, and the accuracy of each case model was evaluated through statistical approach using Coefficient of Variation of Root Mean Square Error and the best performance model was determined. The input parameters includes flow rate, pressure, fan power consumption, outdoor air temperature, outdoor air humidity, supply air temperature and zone air temperature. The suggested model including seven input data shows the best performance. The results show that the developed model can provide results sufficiently accurate. 주요어 : 예측모델, 데이터기반 모델, 인공신경망, 공기조화기, 송풍기, 풍량
Countering Ransomware Using Anomaly Detection of Endpoint Events

Ransomware gained a lot of media attention in 2017 because of the many large-scale attacks that occurred which managed to shut down multiple companies, or even hospitals, and resulted in signi cant costs [4, 18]. Because this speci c type of malware appears to be a lucrative business for criminals, it is of even greater importance for cybersecurity vendors to provide the required measures to prevent ransomware infections by implementing an anti-virus (AV) solution which is capable of mitigating a potential threat. However, the AV solutions which are currently implemented are not build to act upon unknown malware. The Next-Generation Anti-virus (NGAV) solution should therefore be able to dynamically analyze system events to classify speci c actions as malware. Although various cybersecurity vendors claim to have a solution, in practice a awless NGAV is yet nowhere to be found. This research focuses on the implementation of a behavioral and probability based model to enhance the current AV solution of Fox-IT with the capability to detect unknown malware processes. The main research question of this research is: Is it possible to accurately classify system processes on a Windows endpoint as ransomware based on its features? A prototype is presented to extract potentially malicious features from Windows processes in order to classify a process as benign or malicious. These features are recommended by various renowned security specialists [2, 3, 14]. A machine learning classi cation algorithm is trained with testing data of both malicious and benign processes in order to make actual predictions for the testing data set. This is a novel approach where both security intelligence is combined with machine learning whilst not performing an in-depth analysis of system calls or machine code. The data samples used as input for classi cation were obtained in collaboration with the Threat Intelligence (TI) department of Fox-IT to provide a sample set representing real-world ransomware for testing. The classi cation algorithm provided exceedingly well results, which is partially due to the fact that the chosen features did show very strong characteristics of ransomware. The algorithm which was used in the nal prototype even managed to classify every single malware process correctly, directly at the root of the processes. With a True Positive Rate (TPR), or recall, of 100%, the algorithm did alert on various benign processes. These processes all contained features and behavior which indeed appeared malicious such as crash reporters which hooked in on a failing process, or installers containing unsigned executables. However, the amount of false positives remained relatively low overall and they could be easily prevented by whitelisting this small set of speci c processes. Vincent (V.A.) van der Eijk, Countering Ransomware Using Anomaly Detection of Endpoint Events . Graduation report to obtain the Bachelor's degree for the education of Computer Engineering, The Hague University of Applied Sciences, 2017.
Exploiting Natural Language Structures in Software Informal Documentation

Communication means, such as issue trackers, mailing lists, Q&A forums, and app reviews, are premier means of collaboration among developers, and between developers and end-users. Analyzing such sources of information is crucial to build recommenders for developers, for example suggesting experts, re-documenting source code, or transforming user feedback in maintenance and evolution strategies for developers. To ease this analysis, in previous work we proposed Development Emails Content Analyzer (DECA), a tool based on Natural Language Parsing that classifies with high precision development emails’ fragments according to their purpose. However, DECA has to be trained through a manual tagging of relevant patterns, which is often effort-intensive, error-prone and requires specific expertise in natural language parsing. In this paper, we first show, with an empirical study, the extent to which producing rules for identifying such patterns requires effort, depending on the nature and complexity of patterns. Then, we propose an approach, named Nlp-based softwarE dOcumentation aNalyzer (NEON), that automatically mines such rules, minimizing the manual effort. We assess the performances of NEON in the analysis and classification of mobile app reviews, developers discussions, and issues. NEON simplifies the patterns identification and rules definition processes, allowing a savings of more than 70 percent of the time otherwise spent on performing such activities manually. Results also show that NEON-generated rules are close to the manually identified ones, achieving comparable recall.
Hardware Trojan Detection Using Changepoint-Based Anomaly Detection Techniques

There has been a growing trend in recent years to outsource various aspects of the semiconductor design and manufacturing flow to different parties spread across the globe. Such outsourcing increases the risk of adversaries adding malicious logic, referred to as hardware Trojans, to the original design. The increased complexity of modern microprocessors increases the difficulty in detecting hardware Trojans at early stages of design and manufacturing. Therefore, there is a need for run-time detection techniques to capture Trojans that escape detection at these stages. In this paper, we introduce a machine learning-based run-time hardware Trojan detection method for microprocessor cores. This approach uses changepoint-based anomaly detection algorithm to detect the activation of Trojans that introduce abnormal patterns in the data streams obtained from performance counters. It does not modify the original microprocessor design to integrate on-chip monitoring sensors. We evaluate our method by detecting the activation of Trojans that cause denial-of-service, the degradation of system performance, and change in functionality of a microprocessor core. Results obtained using the OpenSPARC T1 core and an field-programmable gate array (FPGA) prototyping framework show that the Trojan activation is detected with a true positive rate of above 99% and a false positive rate of 0% for most of the implemented Trojans.
Improved Extensible Authentication Protocol Using Only a Password

Passwords are a popular form of credential for user authentication. EAP-pwd (RFC 5931) is a popular method of performing secure password authenticaiton. EAP-pwd requires a secret element in a finite cyclic group, unfortunately the technique it uses to derive this secret is open to timing and cache attacks. This improved version, EAP-pwd', uses a different technique to derive the secret element which is resistant to timing and cache attacks.