Miracle-dz/UniFAR-FTUs huggingface dataset:
A Blockchain-Based Load Balancing in Decentralized Hybrid P2P Energy Trading Market in Smart Grid

Local energy generation and peer to peer (P2P) energy trading in the local market can reduce the energy consumption cost, emission of harmful gases (as renewable energy sources are used to generate energy at user’s premises) and increase the smart grid resilience. However, local energy trading with peers can have trust and privacy issues. A centralized system can be used to manage this energy trading but it increases the overall cost of the system and also faces several issues. In this paper, to implement a hybrid P2P energy trading market, a blockchain-based system is proposed. It is fully decentralized and allows the market members to interact with each other and trade energy without involving a third party. Smart contracts play a very important role in the blockchain-based energy trading market. They contain all the necessary rules for energy trading. We have proposed three smart contracts to implement the hybrid electricity trading market. The market members interact with the main smart contract, which requests P2P and prosumer to grid smart contracts for further processing. The main objectives of this paper are to propose a model to implement an efficient hybrid energy trading market while reducing cost and peak to average ratio of electricity.
Algorithms for Optimization [Bookshelf]

Optimization is showcased as an accessible and powerful tool in this attractive and instructive text. Optimization techniques are presented with mathematical motivation, without extensive proofs, and with illustrative Julia code. This text is appropriate for an undergraduate or other introductory course on optimization. It is also suitable for a practicing engineer who wants a broad first taste of the field, along with access to working code. Compared with similar texts, this book generally includes a broader set of topics, including uncertainty propagation, multiobjective optimization, multidisciplinary optimization, Gaussian process surrogate models, and expression optimization. The authors provide the building blocks of optimization algorithms rather than presenting a deep treatment of optimization theory. This extensive use of Julia is the most noticeable innovation of the book. Functions and code are used to illustrate techniques as diverse as automatic differentiation, particle swarm optimization, and Bayesian Monte Carlo methods. The code is presented entirely in the text of the book and duplicated online in Jupyter notebooks. This extensive use of Julia is the most noticeable innovation of the book. Functions and code are used to illustrate techniques as diverse as automatic differentiation, particle swarm optimization, and Bayesian Monte Carlo methods.
Blood Vessel Segmentation using SegNet

Diabetic Retinopathy (DR) is spreading alarmingly in developing countries like India. If not treated early, it may leads to complete blindness. For early treatment, it must be diagnosed early which required complete automation in diagnostic process. The first step of such automatic diagnosis is the blood vessel segmentation. Though there are numerous algorithm mostly in classical methods, are available in litterers, deep neural networks are most promising developed by recent researchers. In this paper, the standard SegNet is used with some modification and simulated with standard publicly available fundus image databases. Simulated result achieves high performance with overall accuracy 0.9298 and precision is 0.80 and its result is much better than the manual way of a skilled ophthalmologist.
Analysis of Artificial Dielectric Material Effect on The Performance of Microstrip Antennas

In this paper artificial dielectric material is used to make microstrip antennas with a resonance frequency of 2.4 GHz. Artificial dielectric materials are produced by the modification of natural dielectrics present in conventional antennas. Modifications were made in 3 ways and antenna parameter changes were observed. The first modification is to vary the thickness of the substrate from 1.6 mm to 11.2 mm. At a thickness of 6.4 m, the results are obtained quite well so that it can be used as a reference for further simulation. The second modification is by inserting a cylindrical conductor on the substrate, with a variation of the cylinder conductor being inserted from 4 to 12. The results obtained are 4 conductors that produce the best parameters of the antenna, including resonant frequency, amplification, VSWR, radiation pattern and S11. The last modification is to vary the diameter of the inserted conductor from 1 mm to 1.5 mm. As with previous simulations, any change in the conductor diameter is observed for the parameters of the antenna. Results from the 1.2 mm diameter conductor are 2.4 GHz resonance frequency, 7.67 dB gain, -46.13 dB S11, 1.03 VSWR and 125 MHz bandwidth. The results of this study show that all material changes made affect all parameters of the antenna, such as decreasing frequency, larger or smaller bandwidth, changes in the direction of the pattern of radiation, VSWR values, and also S11.
Optimal Trajectory for Active Safe Falls in Humanoid Robots

Humanoid robots are being introduced in multiple environments, including houses, health care facilities or factories. Bipedal robots are, however, inherently unstable, and they might fall due to multiple reasons, including internal failures or external perturbations. In these situations, the robot should guarantee as much as possible the integrity of humans in the workspace, of the environment, and of the robot itself. When there is some control authority left on the robot, it can be actively commanded to follow a predefined trajectory that minimizes the consequences of the impact with the ground. This paper presents the computation of an optimal falling trajectory using a telescopic inverted pendulum model, which translates into squatting and stretching motions in the robot to dissipate as much energy as possible. The results show that the prescribed trajectory is effective for maximizing the dissipated energy before the actual impact.
A Modular Approach for Facial Expression Recognition using HSOG

Automatic facial expression recognition is one of the most recently topic in aspect of behaviour analysis and human computer interaction (HCI). Difficulty with facial expression recognition system is to implement generic model. Same facial expression may vary across humans, even this is true for the same person when the expression is displayed in different situations. This paper proposed the local image descriptor that extracts the histogram of second order gradients (HSOG), which capture the local curvatures of differential geometry. The shape index is computed from the curvatures and its different values correspond to different shapes. In case of facial expression recognition using full face images, if any portion of the face image is distorted, it may reflect on the recognition performance. Humans have the capability to recognize faces even by looking at some parts of the face. An attempt has been made to replicate the same on machines by only considering some of the informative regions of the face like eyes, nose, lip and forehead. Facial expression recognition experiments have been performed on some benchmark databases, Better recognition rates were achieved compared to other existing approaches.
Study of nonradiative recombination centers in GaAs:N δ-doped superlattices structures revealed by below-gap excitation light

Nonradiative recombination (NRR) centers in GaAs:N $\delta$-doped superlattices (SLs) grown by molecular beam epitaxy (MBE) has been investigated by two-wavelength excited photoluminescence (TWEPL) method for conduction band scheme. The PL intensity of $E_{-}$ band of the samples with lower nitrogen (N) concentration (0.317% N) initially increases after addition of below-gap excitation (BGE) light over above-gap excitation (AGE) light and then quenches at higher BGE density and energy, while that of GaAs (e-A°) emission of GaAs layers decreases monotonically. For sample with higher N concentration (1.18% N) both the $E _{-}$ band and GaAs (e-A°) emission decreases monotonically with enhancing BGE density and degree of decreasing of PL intensity is higher compared to the low N concentration sample. The quenching of PL intensity indicates the existence of NRR centers in GaAs layers and GaAs:N $\delta$-doped SL region. The recombination models have been proposed for explaining the results from the experiments.
Multicriteria Based Decision Making of DevOps Data Quality Assessment Challenges Using Fuzzy TOPSIS

In current era, DevOps gain much interaction in software industry as it provides the flexible development environment. To meet the continuous development and operations, DevOps mainly focus, to integrate the data from heterogeneous source. While DevOps adoption, the quality assessment of data integrated from heterogeneous environment, is important and challenging at the same time. This study aims to identify the critical factors that could negatively impact the data quality assessment process in DevOps. We have used the systematic literature review (SLR) approach and identify a total of 13 critical challenging factors. The finding of SLR are further validated with industry experts via questionnaire survey. Finally, we have applied the Fuzzy TOPSIS approach to prioritize the investigated challenging factors with respect to their significance of DevOps data quality assessment process. The results show that analyzing data in real time, visualization of data and missing information and other invalid data are the highest ranked challenging factors which need to be addressed on priority basis, to successfully measure the quality of heterogeneous data in DevOps. We believe that the finding of this study will assist the practitioner to consider the most significant factors for measuring the quality of heterogeneous data in DevOps.
User Recruitment for Enhancing Data Inference Accuracy in Sparse Mobile Crowdsensing

Sparse mobile crowdsensing is a practical paradigm for large sensing systems, which recruits a small number of users to sense data from only a few subareas and, then, infers the data of unsensed subareas. In order to provide high-quality sensing services under a budget constraint, we would like to select the most effective users to collect useful sensing data to achieve the highest inference accuracy. However, due to the variable user mobility and complicated data inference, it is really challenging to directly select the best user set which helps the most with data inference. From the user’s side, we can obtain the probabilistic coverage according to the users’ mobilities, while the probabilistic coverage cannot indicate the data inference accuracy directly. From the subarea’s side, we may identify some more useful subareas under the current states (e.g., the previous sensed subareas and the current expected coverage), while these useful subareas may not be covered by the users. Moreover, both the user mobility and data inference introduce a lot of uncertainty, which yields nonmonotonicity and thus nonsubmodularity in the user recruitment problem. Therefore, in this article, we study the user recruitment problem on both the user’s and subarea’s sides and propose a three-step strategy, including user selection, subarea selection, and user–subarea-cross (US-cross) selection. We first select some candidate user sets, which may cover the most subareas under the budget constraint (user selection), then estimate which subareas are more useful on data inference according to the selected candidates (subarea selection), which finally guides us to recruit the best user set (US-cross selection). Extensive experiments on two real-world data sets with four types of sensing tasks verify the effectiveness of our proposed user recruitment algorithms, which can effectively enhance the data inference accuracy under a budget constraint.
A Review on Computer Vision - Scene Classification Techniques

In today's era, need for automatic response of machines on certain task has been prevalent. Humans want their life easier and automatic in every possible way. However, those tasks require better understanding by the machine to perform human like tasks. Tasks like classification, detection and localization are on high demand and dominant research area. These tasks fall into a domain called computer vision where computers by analyzing and understanding performs human like tasks. This domain provides the automatic inference by machines to make human life easier. In this paper, we focus on one of the difficult computer vision tasks called scene classification. Scene Classification deals with techniques that make machine intelligent and automated by processing given input say image. As machines are made automatic and intelligent to perform various tasks, Artificial Intelligence and Image processing comes into the picture. We study and analyze various approaches and methods by which such task can be handled easily and accurately. Furthermore, we compare all the approaches and find out the best approach to opt for this task.
HYPNER: A Hybrid Approach for Personalized News Recommendation

A personalised news recommendation system extracts news set from multiple press releases and presents the recommended news to the user. In an effort to build a better recommender system with high accuracy, this paper proposes a personalised news recommendation framework named Hybrid Personalised NEws Recommendation (HYPNER). HYPNER combines both collaborative filtering-based and content-based filtering methods. The proposed framework aims at improving the accuracy of news recommendation by resolving the issues of scalability due to large news corpus, enriching the user’s profile, representing the exact properties and characteristics of news items, and recommending diverse set of news items. Validation experiments showed that HYPNER achieved 81.56% improvement in $F1$ -score and 5.33% in diversity as compared to an existing recommender system, SCENE.
Architecture and Power Converter for Multifrequency Microgrid

This paper focuses on a new class of microgrid called multifrequency microgrid. Multifrequency microgrid uses one common bus where two or more different frequencies are superimposed using superposition theorem and orthogonal power flow theory. With the revolution in power electronic converters presently the formation of the multifrequency microgrid is possible. Multifrequency microgrid has several advantages over AC and DC microgrids. Several new additional features can be implemented which is not possible with conventional AC or DC microgrid. Multifrequency microgrid has been explored very little in the literature. The architecture and converters of multifrequency microgrid are still not well defined. In this paper, a basic architecture of multifrequency microgrid is introduced. A voltage source converter named $DC/AC+DC$ converter is used as building block of the multifrequency microgrid. The operation and control of this converter are discussed in this paper. Simulation of the converter is executed in Matlab Simulink environment and simulation results are presented.
Preparation and Characterization of Nylon 11/CaCu3Ti4O12 (CCTO) Nanocomposites

Polymer-ceramic composites with improved properties find applications in microelectronics and electrical insulation. To obtain enhanced thermal characteristics and dielectric properties, attempts were made to prepare series of nylon 11/CaCu3Ti4O12 (CCTO) nanocomposites, with 0 to 30 weight% CCTO nanoceramics, by solution casting method. These nanocomposites were characterized for X-ray diffraction (XRD), scanning electron microscopy, differential scanning calorimetry, thermo gravimetric analysis and dielectric properties. XRD analysis indicated that the γ-phase of nylon 11 is present in the nylon 11 cast film and in the nylon 11/CCTO nanocomposites as well. Thermal stability of these nanocomposites was much improved than that of nylon 11. The nanocomposite with 20 wt% CCTO exhibited a room temperature dielectric permittivity of about 76 at 10 Hz. These nanocomposites due to their high permittivity can be prospective dielectric materials for capacitors application.
Human Activity Recognition with Wearable Biomedical Sensors in Cyber Physical Systems

Human Activity Recognition has a wide range of applications such as remote patient monitoring, assisting disables and rehabilitation. This paper investigates the use of wearable bio-medical sensors to recognize human activities using supervised learning algorithms in cyber physical systems. We use five bio-medical sensors such as ECG, EMG, Respiration, Force sensitive resistor and a Tri-axial Accelerometer to collect the raw data. All the sensor data is collected in the real-world environment with three human subjects. The received raw data is preprocessed to extract the time domain features. The feature information is used for the training and testing the classifiers. Three classifiers k nearest neighbour (kNN), SVM using the linear kernel and SVM using Gaussian kernel are used for training and testing phases. The kNN classifier provides good accuracy of 99.86 %.
Analysis of Affective Human Motion During Functional Task Performance: An Inverse Optimal Control Approach

For robots that collaborate alongside and work with humans, there is great interest in improving robot communication abilities to achieve engaging and successful interactions. Successful task collaborations between humans often involve functional motions in which implicit communication signals, such as affect, are embedded. Thus in order to improve a robot's communication capabilities, it is necessary to identify the different motor control strategies that humans employ when generating such implicit signals. This paper details the adaptation of an Inverse Optimal Control (IOC) methodology for this purpose. We hypothesize that IOC allows for the identification of the motion strategies involved in the implicit communication of affective content during the performance of functional movement. To test our hypothesis, a motion capture dataset consisting of upper-body functional movements was collected and annotated by multiple observers through a perceptual user study. Among the different control strategies considered during our analysis, we found that center of mass movement, quantity of motion, Laban space effort and effort were the most relevant when distinguishing motions that convey different affective states.
Explanation of the PV System Survey Sheet

We design the survey format to collect failure data of PV systems for various climate zones. The goal of the survey is to evaluate the possible different impact of the failures for various climate zones and give recommendations for test methods depending on the climate zone. The survey data may be used for further statistical evaluation. To fill in new data into the survey sheet (Fig. 1) you have to load the survey sheet into the excel program and choose the worksheet “PV_system_survey”. It is important to agree with the two notifications for “enable editing” and “enable macros” if they pop up. Scroll to the upper left corner of the worksheet if you are not already there. Click the button “New form” to generate a new input mask. A new mask appears and the focus automatically jumps to the new mask with a name “PV_system_survey (X)” where X stand for a number. Now you can start to input your data. If you want to delete a table you can press the button “Delete form” then the current visible table will be deleted. If you want to duplicate a table, e.g. your next input is very similar to one which is already in the database, then navigate to the table to be copied and click on “Copy form”. This generates a new mask with exact the same data. Now you can edit this form as you like. The System ID is a category that enables the contributor to identify the source of its own data input and avoids double input of the same data. The System ID should not enable other people to identify the source of the data. Furthermore, the System ID can be used to address questions from the TASK13 team to the contributor of the data set. The System ID will be exchanged in the public version of the database with an arbitrary number. If the current input is extracted from a scientific publication, the System ID has to be used to fill in the reference of the paper in the IEEE format style. The source of the data must be specified in the category Source of data. The version number in the upper right corner of the survey excel sheet has to be specified if you have questions to the TASK13 team on the survey excel sheet. In the following we give examples how to fill in the survey format. Please note that only the cells highlighted in green are provided for editing.
AND MALICIOUS ACTIVITY AVOIDANCE FOR MEDICAL DATA DISTRIBUTION IN CLOUD ENVIRONMENT

: With the proliferation and ever-increasing capabilities of wearable devices, there is a significant role to provide medical data distribution in a secure way. For the sake of data security, the collected big data can be encrypted and then stored on a cloud-let such as authorized user, data owner and the doctors can access. In this work, we propose a collaborative model consists of intrusion detection and prevention system functions based on distribute IDS and IPS using NTRU algorithm. Whereas intrusion detection system monitoring system monitor a network for active or imminent security policy violation, intrusion prevention goes a step further to stop any kind of violation from occurring. Initially, we utilize Number Theory Research Unit in the data collection and encrypt the data which is collected by wearable devices. Then, we divide user’s medical data stored in a remote cloud. Finally, security analysis shows that the data sharing through cloud-let scheme is secure and improve data processing ability in cloud-let.
Working Papers / Documents de travail

Although it is widely acknowledged that non-cognitive skills matter for adult outcomes, little is known about the role played by family environment in the formation of these skills. We use a longitudinal survey of children born in the UK in 2000–2001, the Millennium Cohort Study by the Centre for Longitudinal Studies, to estimate the effect of family size on socio-emotional skills, measured by the Strengths and Difficulties Questionnaire. To account for the endogeneity of fertility decisions, we use a well-known instrumental approach that exploits parents’ preference for children’s gender diversity. We show that the birth of a third child negatively affects the socio-emotional skills of the first two children in a persistent manner. However, we show that this negative effect is entirely driven by girls. We provide evidence that this gender effect is partly driven by an unequal response of parents’ time investment in favour of boys and, to a lesser extent, by an unequal demand for household chores. JEL classification: I20, J13, J16
On a preference analysis

The aim of the paper is to provide several quantitative measures concerning preference structure in a group decision making setting. These measures enable to assess group and individual discord, core preferences and outliers, or to find a consensus, where a consensus is defined as a preference with a minimum sum of distances to other preferences. Also, it is shown that a distance of a consensus to a median preference is upper bounded, which might reduce a search for a consensus significantly.
Does Cloud Computing pose a serious security threat or not ?

: The goal of this research paper is to uncover the risks of usage of remote services. General concepts and tasks of cloud computing systems are described. In the main part the threats of cloud computing are revealed along with methods and practical recommendations for avoiding potential pitfalls. Based on the analyzed data, a conclusion is made, stating that ignoring some of the recommendations may present serious security issues in the process of remote systems’ usage.