Miracle-dz/UniFAR-FTUs huggingface dataset:
Modeling heterogeneous vehicular traffic for intelligent transport system applications. (Modélisation de trafic routier hétérogène pour systèmes de transport intelligents)

This dissertation models and analyzes heterogeneous traffic flow, with a particular focus on mixed traffic flow consisting of cars and two-wheelers. The increase in traffic congestion induces commuters to switch to powered two wheelers (PTWs), i.e. motorcycle, mopeds and scooters, because of their high maneuverability and space efficiency. The growth in number of PTWs, combined with their unique mobility features, results in complex traffic characteristics which are difficult to recreate with the existing modeling approaches. We develop an analytical model that can accurately reproduce the traffic features in a mixed flow of cars and PTWs. The traffic stream is decomposed into two vehicle classes, PTWs and cars. The fundamental properties are derived by employing a porous flow approach. It is assumed that the speed of a vehicle class is dictated by the physical and motion properties of the vehicle class, and the distribution of free spaces on the road. We propose an approximation method to derive the free-space distribution. In order to explore broader aspects of the traffic flow characteristics, notably required by intelligent transport system (ITS) applications, we formulate the model in the Lagrangian and the Eulerian frameworks. Further, we provide a numerical method for the discretization of the mathematical model. We analyze the flow characteristics of mixed PTWs and cars traffic and identify important properties, which give insights for future ITS solutions and traffic policy makers. The applicability of the model for different ITS applications is illustrated. Finally, the developed model is validated using a microsimulation tool.
Exploring the Sensing Capability of Wireless Signals

Wireless communications are ubiquitous nowadays, especially in the new era of Internet of Things (IoT). Most of IoT devices access the Internet via some kind of wireless connections. The major role of wireless signals is a type of communication medium. Besides that, taking advantage of the growing physical layer capabilities of wireless techniques, recent research has demonstrated the possibility of reusing wireless signals for both communication and sensing. The capability of wireless sensing and the ubiquitous availability of wireless signals make it possible to meet the rising demand of pervasive environment perception. Physical layer features including signal attributes and channel state information (CSI) can be used for the purpose of physical world sensing. This dissertation focuses on exploring the sensing capability of wireless signals. The research approach is to first take measurements from physical layer of wireless connections, and then develop various techniques to extract or infer information about the environment from the measurements, like the locations of signal sources, the motion of human body, etc. The research work in this dissertation makes three contributions. We start from wireless signal attributes analysis. Specifically, the cyclostationarity properties of wireless signals are studied. Taking WiFi signals as an example, we propose signal cyclostationarity models induced by WiFi Orthogonal Frequency Division Multiplexing (OFDM) structure including pilots, cyclic prefix, and preambles. The induced cyclic frequencies is then applied to the signal-selective direction estimation problem. Second, based on the analysis of wireless signal attributes, we design and implement a prototype of a single device system, named MobTrack, which can locate indoor interfering radios. The goal of designing MobTrack is to provide a lightweight, handhold system that can locate interfering radios with sub-meter accuracy with as few antennas as possible. With a small antenna array, the cost, complexity as well as size of this device are reduced. MobTrack is the first single device indoor interference localization system without the requirement of multiple pre-deployed access points (AP). Third, channel state information is studied in applications of human motion sensing. We design WiTalk, the first system which is able to do fine-grained motion sensing like leap reading on smartphones using the CSI dynamics generated by human movements. WiTalk proposes a new fine-grained human motion sensing technique with the distinct contextfree feature. To achieve this goal using CSI, WiTalk generates CSI spectrograms using signal processing techniques and extracts features by calculating the contours of the CSI spectrograms. The proposed technique is verified in the application scenario of lip reading, where the fine-grained motion is the mouth movements. Exploring the Sensing Capability of Wireless Signals
Energy Efficient Secure Computation Offloading in NOMA-Based mMTC Networks for IoT

In the era of Internet of Everything, massive connectivity and various demands of latency for Internet of Things (IoT) devices will be supported by the massive machine type communication (mMTC). Nonorthogonal multiple access (NOMA) and mobile edge computing (MEC) have the advantages of improving network capacity, reducing MTC devices’ (MTCDs) latency and enhancing quality of service. Exploiting these benefits, we focus on the energy efficient secure computation offloading in NOMA-based mMTC networks for IoT, where the relay equipped with an MEC server and a passive malicious eavesdropper are presented. We optimize the joint computation and communication resource allocation to maximize the secrecy energy efficiency of computation offloading while guaranteeing the delay requirements of MTCDs. Furthermore, we model the subchannels allocation problem as MTCD-to-subchannel matching. Exploiting difference of convex programming and successive convex approximation, we formulate the Dinkelbach-based SEE optimization algorithm and obtain the closed-form expression of power allocation for MTCDs’ on each subchannel. Based on the communication resources allocation schemes, we propose the Knapsack algorithm to solve the problem of computation resource allocation. Furthermore, we formulate the joint computation and communication resource allocation algorithm for secure computation offloading. Simulation results demonstrate the effectiveness of proposed algorithm for supporting IoT devices energy efficient secure computation offloading.
Attribute Operators for Color Images: Color Harmonization based on Maximal Harmonic Segmentation

Attribute openings and thinnings are morphological connected operators that remove structures from images according to a given criterion. These operators were successfully extended from binary to grayscale images, but such extension to color images is not straightforward. Color attribute operators have been proposed by a combination of color gradients and thresholding decomposition. In this approach, not only structural criteria may be applied, but also criteria based on color features and statistics. This work proposes, in a segmentation framework, a criterion based on color histogram divergence from a harmonic model. This criterion permits a segmentation in maximal harmonic regions. An application indicated that the harmonic segmentation permitted a hue correction that would not cause false colors to appear in regions already harmonic.
Exploring primary school teachers' technological pedagogical content knowledge (TPACK) in online collaborative discourse: An epistemic network analysis

The contextual influences on technological pedagogical content knowledge (TPACK) enactment and the method of TPACK assessment remain to be important research topics. Discourse data of 81 teachers in an online professional learning community were collected and analyzed based on the framework of TPACK. Frequency distribution and time series characteristics of teachers' knowledge domains were analyzed. In addition, epistemic network analysis was used to compare the epistemic network characteristics of teachers in the higher‐score and the lower‐score groups, different age groups, and post and reply groups. Results showed that teachers' knowledge domains enacted in the context of online discourse were mainly pedagogical content knowledge and general pedagogical knowledge. The teachers in the higher‐score group had a rich, organized and flexible knowledge structure of TPACK. Younger teachers had more connections between pedagogical knowledge and pedagogical content knowledge, while senior teachers had more connections between technological knowledge and pedagogical knowledge. The teachers in the reply group had more connections among the different categories of knowledge as compared to the post group. Finally, implications, limitations and future research were discussed. [ABSTRACT FROM AUTHOR]
FoodIE: A Rule-based Named-entity Recognition Method for Food Information Extraction

The application of Natural Language Processing (NLP) methods and resources to biomedical textual data has received growing attention over the past years. Previously organized biomedical NLP-shared tasks (such as, for example, BioNLP Shared Tasks) are related to extracting different biomedical entities (like genes, phenotypes, drugs, diseases, chemical entities) and finding relations between them. However, to the best of our knowledge there are limited NLP methods that can be used for information extraction of entities related to food concepts. For this reason, to extract food entities from unstructured textual data, we propose a rule-based named-entity recognition method for food information extraction, called FoodIE. It is comprised of a small number of rules based on computational linguistics and semantic information that describe the food entities. Experimental results from the evaluation performed using two different datasets showed that very promising results can be achieved. The proposed method achieved 97% precision, 94% recall, and 96% F1 score.
Cenological Measurement of Productive Efficiency

The article shows the big concern that is caused by the problem of spatial economical and industrial bias. The decision of the given problem by classical methods did not bring the required result testifying that such politics is based on wrong or inadequate diagnosis of the bias reason, as well as the lack of qualitative search of benchmarking target state. The authors offer to consider a spatial and industrial bias not only as economically inefficient, but also non-equilibrium result of regulation politics. Thus, the balance can be considered as some economically-effective recombination of subjects of regional economy or branch enterprises. It is shown that the proposed approach does not reflect the system features of real objects (region, branch etc.) to full extent as it uses a mechanical statistical combination theory, without considering the features of considered systems, being oriented to the model which is not achievable in practice. The given downside is offered to eliminate by applying coenosis theory which uses laws of existence and development of complex systems (type “branch”, “region” etc.), relying on numerous researches in various fields of knowledge. The opportunity to calculate and to generate a specified distribution option allows to use it as the starting point of recombination for more accurate definition of potential-efficiency of the structure of a branch.
Simplification of Internet Ossification through Software Defined Network Approach

Software-Defined Networking (SDN) has received a great deal of attention from both academia and industry in recent years. Studies on SDN have brought a number of interesting technical discussions on network architecture design, along with scientific contributions. Researchers, network operators, and vendors are trying to establish new standards and provide guidelines for proper implementation and deployment of such novel approach. It is clear that many of these research efforts have been made in the southbound of the SDN architecture, while the northbound interface still needs improvements. By focusing in the SDN northbound, this paper surveys the body of knowledge and discusses the challenges for developing SDN software. We investigate the existing solutions and identify trends and challenges on programming for SDN
VOLUME CALCULATION OF IRREGULAR OBJECT USING MULTIPLE SOFTWARE PACKAGES

Volume calculation is needed in many civil engineering projects. Recently, several software packages are used in volume calculation such as Civil3D, Surfer, and GIS softwares. The main objective of the presented work is to explain how volumes of cut and fill using these software packages can be calculated. Moreover, methods of interpolation using different software packages are investigated. The test site used in this research is located at Ain El Sokhna- Zafarana Road, Red Sea, Egypt with surface area around 126,000m2. Methods of the data gridding and precision of volume calculation based on Civil3D, Surfer, and ArcMap software packages are studied. Distribution and intensity of points are important factors in modeling earth surfaces. Interpolation methods employing different mathematical models are used to obtain 3D models of terrain surfaces. The 3D surface and volume are calculated by using 12 different interpolation methods. The accuracy of the volume calculation depends on the appropriateness of the interpolation method used in creating 3D models. The calculated net volumes by Inverse Distance, Modified Shepard's Method, Nearest Neighbor, Kriging, and Triangulation with Linear Interpolation methods yielded closed results compared with other methods. In conclusion, the accuracy of volume determination of irregular bodies is dependent on the interpolation of modeling and computational method capable of approximating the irregular body and thus represents its actual, real shape. حساب الحجوم هو من احدى العملیات الهامة فی مشاریع الهندسة المدنیة. فی الآونة الأخیرة، یتم استخدام العدید من حزمالبرامج فی حساب الحجم مثل برامج Civil3D و Surfer و GIS . الهدف الرئیسی من البحث الممدم هو دراسة حسابأحجام الحفر والردم باستخدام هذه الحزم من البرامج. وعلاوة على ذلن، یتم دراسة طرق تخلیک النماط التی یتم حسابالحجوم على اساسها باستخدام تلن الحزم من البرامج. یمع مولع الاختبار المستخدم فی هذا البحث فی العین السخنة -طریک الزعفرانة، البحر الأحمر، مصر بمساحة 126,000 متر مربع. ثلاثة من اهم برامج حساب الحجوم استخدمت فیذلن البحث وهی Civil3D, Surfer, and ArcMap8 . تم حساب السطح ثلاثی الابعاد وبالتالی الحجوم باستخدام 12طریمة من طرق interpolation المختلفة. تعتمد دلة حساب الحجوم على طریمة ال interpolation المستخدمة فی إنشاءالنماذج ثلاثیة الأبعاد. ولد کانت النتائج للحجم الکلى المحسوب باستخدام Inverse Distance, Modified Shepard's Method, Nearest Neighbor, Kriging, and Triangulation with linear interpolation نتائج متماربةممارنة بالطرق الأخرى. ونخلص من ذلن ان حساب الحجوم للأجسام غیر المنتظمة تعتمد فی الأساس على طریمة الinterpolation المستخدمة، وبالتالی تمثیل الشکل ثلاثی الابعاد.
HFDSegNet: Holistic and Generalized Finger Dorsal ROI Segmentation Network

The aforementioned works and other analogous studies in finger knuckle images recognition have claimed that the precise detection of true features is difficult from poorly segmented images and the main reason for matching errors. Thus, an accurate segmentation of the region of interest is very crucial to achieve superior recognition results. In this paper, we have proposed a novel holistic and generalized segmentation Network (HFDSegNet) that automatically categorizes the given finger dorsal image obtained from multiple sensory resources into particular class and then extracts three possible ROIs (major knuckle, minor knuckle and nail) accurately. To best of our knowledge, this is the first attempt, an end-to-end trained object detector inspired by Deep Learning technique namely faster R-CNN (Region based Convolutional Neural Network) has been employed to detect and localize the position of finger knuckles and nail, even finger images exhibit blur, occlusion, low contrast etc. The experimental results are examined on two publicly available databases named as Poly-U contact-less FKI data-set, and Poly U FKP database. The proposed network is trained only over 500 randomly selected images per database, demonstrate the outstanding performance of proposed ROI’s segmentation network.
Avoiding Glare by Controlling the Transmittance of Incident Light

In this paper, we introduce a new method for enhancing the visibility of human vision. In particular we propose a method for avoiding glare caused by strong incident light, such as sunlight and headlight of oncoming vehicles, in driving situations. Our method controls the transmittance of incident light pixel by pixel according to the power of the incident light. For computing the transmittance of light efficiently from camera images, we propose a leaning based method utilizing a generative adversarial network (GAN). By using our method, the visibility of drivers can be improved drastically, and objects in dark place become visible even under strong backlight, such as sunlight and headlight of oncoming vehicles.
Automated Incident Response for Industrial Control Systems Leveraging Software-defined Networking

Modern technologies and concepts for Industrial Control Systems (ICS) are evolving towards high flexibility of processes and respectively networks. Such dynamic networks are already functioning well, for example in data centres. This is enabled by application of the Software-defined Networking (SDN) paradigm. For this reason, ICS is currently adopting SDN. The concept of having a centralized view of the network and generating packet forwarding rules to control it enables performing automated responses to network events and classified incidents via SDN. This automation can provide timely and, due to the holistic view of the network, accurate incident response actions. However, availability, safety, real-time and redundancy requirements within the ICS domain restrict the application of such an automated approach. At present, SDN-based incident response (SDN-IR) does not take into consideration these requirements. In this work, we identify possible SND-based response actions to ICS incidents and introduce classification of assets and links. Furthermore, we present a concept for SDN-IR where a predefined rule set restricts the response actions based on the asset’s classification thereby satisfying ICS specific requirements. Subsequently, we describe and evaluate a prototype implementation of this concept, built with the open-source SDN platform OpenDaylight and the SDN protocol OpenFlow.
Dual Mechanical Port Machine Based Hybrid Electric Vehicle Using Reduced Switch Converters

Due to the increased environmental pollution, hybrid vehicles have attracted enormous attention in today’s society. The two most important factors in designing these vehicles are size and weight. For this purpose, some researchers have presented the use of the dual-mechanical-port machine (DMPM) in hybrid electric vehicles (HEVs). This paper presents two modified converter topologies with a reduced number of switching devices for use on DMPM-based HEVs, with the goal of reducing the overall size and weight of the system. Beside the design of the DMPM in the series-parallel HEV structure along with the energy management unit, the conventional back-to-back (BB) converter is replaced with nine-switch (NS) and five-leg (FL) converters. These converters have never been examined for the DMPM-based HEV, and therefore, the objective of this paper is to reveal the operational characteristics and power flow mechanism of this machine using the NS and FL converters. The simulation analysis is carried out using MATLAB/Simulink considering all HEV operational modes. In addition, two proposed and the conventional converters are compared in terms of losses, maximum achievable voltages, required dc-link voltages, the rating of the components, and torque ripple, and finally, a recommendation is made based on the obtained results.
Towards Automated Comprehensive Feature Engineering for Spam Detection

Everyday billions of emails are passed or processed through online servers of which about 59% is spam according to a recent research. Spam emails have increasingly contained viruses or other harmful malware and are a security risk to computer systems. The importance of spam filtering and the security of computer systems has become more essential than ever. The rate of evolution of spam nowadays is so high and hence previously successful spam detection methods are failing to cope. In this paper, we propose a comprehensive and automated feature engineering framework for spam classification. The proposed framework enables first, the development of a large number of features from any email corpus, and second extracting automated features using feature transformation and aggregation primitives. We show that the performance of classification of spam improves between 2% to 28% for almost all conventional machine learning classifiers when using automated feature engineering. As a by product of our comprehensive automated feature engineering, we develop a Python-based open source tool, which incorporates the proposed framework.
Eye Gesture in a Mixed Reality Environment

Using a simple approach, we demonstrate that eye gestures could provide a highly accurate interaction modality in a mixed reality environment. Such interaction has been proposed for desktop and mobile devices. Recently, Gaze gesture has gained a special interest in Human-Computer Interaction and granted new interaction possibilities, particularly for accessibility. We introduce a new approach to investigate how gaze tracking technologies could help people with ALS or other motor impairments to interact with computing devices. In this paper, we propose a touch-free, eye movement based entry mechanism for mixed reality environments that can be used without any prior calibration. We evaluate the usability of the system with 7 participants, describe the implementation of the method and discuss its advantages over traditional input modalities.
An Analytical Review of Lossy Image Compression using n-TV Method

Image compression is widely used in general life for many purpose. Image compression process reduce required storage size of image, to store in digital devices. Digital devices & computational resources have limited communication & storage capabilities hence there is need to compress high quality digital image to overcome this limitation of digital devices & computational resources, for ex. a single high quality image may require 10 to 100 million of bits for representation. If such type of images are in communication with low bandwidth, than there is need to compress each image before communication to use proper bandwidth & for synchronized communication. Hence image compression is an important topic for researcher to improve image compression process by introducing new technique or method in this field. This paper deals with embedding n-TV method before entropy encoding this method make repeated sequence of values before entropy encoding (RLE), to get high compression ratio. This paper gives an analytical review of lossy image compression by using n-TV method at different positions in lossy image compression process. Keywords—image, compression, n-TV, RLE, entropy, encoding.